Journal of Experimental B And Agricultural Scien

Journal of Experimental BiologyAnd Agricultural Sciences

Volume 8

Production and Hosting by Horizon Publisher India[HPI](http://www.horizonpublisherindia.in)

Journal of Experimental BiologyAnd Agricultural Sciences

Volume 8 || Issue II || April, 2020

Production and Hosting by Horizon Publisher India[HPI](http://www.horizonpublisherindia.in)

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JEBAS

Journal of Experimental Biology

, 2020

ISSN:2320-8694

Production and Hosting by Horizon Publisher India[HPI]

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Table of contents

ENTOMOPATHOGENIC NEMATODES AS AN ALTERNATIVE BIOLOGICAL CONTROL AGENTS AGAINST INSECT FOES OF CROPS DOI: 10.18006/2020.8(2).71.75

76—83

EXPLOITING MILLETS IN THE SEARCH OF FOOD SECURITY : A MINI REVIEW DOI: 10.18006/2020.8(2).84.89

84—89

STANDARD HETEROSIS ANALYSIS IN MAIZE HYBRIDS UNDER WATER LOGGING CONDITION DOI: 10.18006/2020.8(2).90.97

90—97

ENERGY USE EFFICIENCY AND GREEN HOUSE GAS EMISSIONS FROM INTEGRATED CROP-LIVESTOCK SYSTEMS IN SEMI-ARID ECOSYSTEM OF DECCAN PLATEAU IN SOUTHERN INDIA DOI: 10.18006/2020.8(2).98.110

98—110

CHARACTER ASSOCIATION AND PATH ANALYSIS FOR SEED VIGOR TRAITS IN SESAME (Sesamum indicum L.) DOI: 10.18006/2020.8(2).111.114

111—114

BIPLOT ANALYSIS FOR SPOT BLOTCH AND YIELD TRAIT USING WAMI PANEL OF SPRING WHEAT DOI: 10.18006/2020.8(2).115.124

115—124

STRIPE RUST RESISTANCE IN WHEAT GERMPLASM OF NORTH-WESTERN HIMALAYAN HILLS DOI: 10.18006/2020.8(2).125.133

125—133

TOXIC EFFECTS OF VARIOUS ARSENIC CONCENTRATIONS ON GERMINATION AND SEEDLINGS GROWTH OF WHEAT (Triticum aestivum L.) DOI: 10.18006/2020.8(2).134.139

134—139

AGRO—MORPHOLOGICAL CHARACTERIZATION OF AFRICAN RICE ACCESSIONS (Oryza glaberrima) IN RAINFED AND IRRIGATED CULTURAL CONDITIONS DOI: 10.18006/2020.8(2).140.147

140—147

GENETIC CHARACTERIZATION OF LOCAL RICE (Oryza sativa L.) GENOTYPES AT MORPHOLOGICAL AND MOLECULAR LEVEL USING SSR MARKERS DOI: 10.18006/2020.8(2).148.156

148—156

IDENTIFICATION OF SUPERIOR THREE WAY-CROSS F1S, ITS LINE×TESTER HYBRIDS AND DONORS FOR MAJOR QUANTITATIVE TRAITS IN Lilium×formolongi DOI: 10.18006/2020.8(2).157.165

157—165

LARVICIDAL ACTIVITY OF TWO RUTACEAE SPECIES AGAINST THE VECTORS OF DENGUE AND FILARIAL FEVER DOI: 10.18006/2020.8(2).166.175

166—175

EFFECT OF CRUDE OIL POLLUTION ON SOIL AND AQUATIC BACTERIA AND FUNGI DOI: 10.18006/2020.8(2).176.184

176—184

DIFFERENTIAL RESPONSES OF CERTAIN ETHIOPIAN GROUNDNUT (Arachis hypogaea L.) VARIETIES VARYING IN DROUGHT TOLERANCE, TO TERMINAL DROUGHT STRESS DOI: 10.18006/2020.8(2).185.192

185—192

IMPACT OF FEED SUBSIDY REMOVAL ON THE ECONOMIC SUCCESS OF SMALL RUMINANT FARMING IN NORTHERN BADIA OF JORDAN DOI: 10.18006/2020.8(2).193.200

193—200

Journal of Experimental Biology and Agricultural Sciences

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ISSN No. 2320 – 8694

Journal of Experimental Biology and Agricultural Sciences, April - 2020; Volume – 8(2) page 76 – 83

ENTOMOPATHOGENIC NEMATODES AS AN ALTERNATIVE BIOLOGICAL

CONTROL AGENTS AGAINST INSECT FOES OF CROPS

Amit Ahuja1*, K.Elango2, Rajendra Kumar3, Ajay Singh Sindhu1, Sachin Gangwar1

1Division of Nematology, ICAR-Indian Agriculture Research Institute, New Delhi, India, 110012

2Agricultural Entomology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India, 641 003

3Agricultural Entomology, Swami Keshwanand Rajasthan Agricultural University, Bikaner, Rajasthan, India, 334006

Received – January 25, 2020; Revision – March 18, 2020; Accepted – March 28, 2020 Available Online – April 25, 2020

DOI: http://dx.doi.org/10.18006/2020.8(2).76.83

ABSTRACT

Entomopathogenic nematodes belonging to the genus Steinernema, Heterorhabditis and

Neosteinernema are the natural killers of insects belonging to different orders. These nematodes are

suitable biocontrol agents as they do not possess a threat to the environment and safer to human health.

Commercially entomopathogenic nematodes are exploited against insect pests of various economically

valuable crops. Upon application in the field, these nematodes face many biotic and abiotic stresses

which results in inconsistent efficacy in pest management. Traditionally artificial selection and

hybridization techniques were adopted to improve traits related to penetration and infectivity to insect

host and storage stability in the formulation. Artificially improved traits tend to losses in the external

environment once the selection pressure removed. Genomics assisted breeding provides an alternative

way for stable trait improvements in entomopathogenic nematodes which last for a longer period and

exhibit maximum efficacy in the field against targeted insect pests. Understating their lifecycles and

complex mechanisms of host’s infectivity exhibited by nematode-bacterial partners would further

enhance our knowledge to improve their efficacy against insect pests. In the future, there is a huge scope

of developing stable commercial formulations of entomopathogenic nematodes as a suitable biological

control agent.

* Corresponding author

KEYWORDS

Biological control

Entomopathogenic nematode

Steinernema

Heterorhabditis

Insects pest

E-mail: [email protected] (Amit Ahuja)

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Entomopathogenic Nematodes as an Alternative Biological Control Agents Against Insect Foes of Crops 77

1 Introduction

The vices of erratic and over uses of insecticides are contaminating

the aquatic and terrestrial ecosystem, posing a serious threat to

biodiversity and developing resistance in insects. As an alternative,

the uses of entomopathogenic nematodes are increasing as a

suitable biological control agent against insect pests of

economically valuable crops (Gaugler, 2018). The

entomopathogenic nematodes belong to the genus Heterorhabditis

Poinar (1975), Steinernema Travassos (1927),

and Neosteinernema Nguyen & Smart (1994) (Rhabditida:

Nematoda) can kill a wide variety of insects. Among these three

genus, the former two are highly exploited against insects as a

biocontrol agent. The genus Heterorhabditis is associated with the

bacteria Photorhabdus and genus Steinernema is associated with

the bacteria Xenorhabdus (Leite et al., 2019). These nematodes

perform ambushingor cruising activities (Ruan et al., 2018) to

locate it hosts, and regurgitate its mutualistic bacteria once they

reach inside the insect’s midgut (Labaude & Griffin, 2018). The

bacteria secrets multiple toxins and kill the insect by septicemia

and coverts the host’s tissue content into a nutrient-rich medium

for the growth and development of its own and its nematode

partner (Mbata et al., 2019). These nematodes don’t cause harmful

effects to the environment, not allow resurgence and resistance

development in insects, hence they are a suitable alternative to

synthetic insecticides (Askary et al., 2018). The entomopathogenic

nematodes are globally present in every continent except

Antarctica (Griffin et al., 1990).

Once the infective juveniles reach inside the midgut of insects, it

just takes two to three days to kill and entire lifecycles complete in

between 10-15 days (Li et al., 2019). Various researches have done

in the past also exhibited the suitability of these nematode’s

applications via traditional equipment. These nematodes also found

compatible with the range of pesticides upon application in the

field (Chavan et al., 2018). These nematodes are commercially

produced and exploited against insects in many countries such as

European nations and USA, yet their share in the pest management

market is hardly 1 percent (Smart, 1995). These nematodes are

produced on its hosts via in vivo or in vitro techniques on solid or

liquid culture media. In the table 1, entomopathogenic nematodes

and their targeted hosts are listed. The commercial share in the pest

management market would only increase with the development of

new production methods and improved efficiency (Saleh et al.,

Table 1 Application of Entomopathogenic nematodes against insects pest attacking different crop

Common name Scientific name Crop Efficacious Nematodes

Army worm Spodoptera spp.ss Vegetables Steinernema carpocapsae, S. feltiae, S. riobrave

Banana moth Opogona sachari Ornamentals Heterorhabditis bacteriophora, S. carpocapsae

Banana root borer Cosmopolites sordidus Banana S. carpocapsae, S. feltiae, S. glaseri

Black cutworm Agrotis ipsilon Turf, vegetables S. carpocapsae

Black vine weevil Otiorhynchus sulcatus Berries, ornamentals H. bacteriophora, H. downesi, H. marelata, H. megidis, S.

carpocapsae, S. glaseri

Cat flea Ctenocephalides felis Home yard, turf S. carpocapsae

Citrus root weevil Pachnaeus spp. Citrus, ornamentals S. riobrave, H. bacteriophora

Codling moth Cydia pomonella Pome fruit S. carpocapsae, S. feltiae

Corn earworm Helicoverpa zea Vegetables S. carpocapsae, S. feltiae, S. riobrave

Corn rootworm Diabrotica spp. Vegetables H. bacteriophora, S. carpocapsae

Crane fly Tipula pubera Turf S. carpocapsae

Fungus gnats Lycoriella spp Mushrooms,greenhouse S. feltiae, H. bacteriophora

Large pine weevil Hylobius abiet Forest plantings H. downesi, S. carpocapsae

Leaf miners Liriomyza spp. Vegetables,ornamentals S. carpocapsae, S. feltiae

Mole crickets Scapteriscus spp. Turf S. carpocapsae, S. riobrave, S. carpocapsae

Scarab grubs Holotrichia sp. Turf, ornamentals H. bacteriophora, S. carpocapsae, S. glaseri, S. scarabaei, H.

zealandica

Shore flies Scatella spp. Ornamentals S. carpocapsae, S. feltiae

Sweet potato weevil Cylas formicarius Sweet potato H. bacteriophora, S. carpocapsae, S. feltiae



78 Ahuja et al.

2020). These nematodes face the biotic and abiotic pressures in the

field (Dzięgielewska & Skwiercz, 2018), which hinder its

maximum efficacy. Because there are certain traits like tolerance to

cold and heat, sensation to ultraviolet light, desiccation, and

persistence determines its fitness in the field (Abd-Elgawad, 2019).

Traditionally artificial selection and hybridization methods had

been applied in trait improvement (Lu et al., 2016). But there is a

huge possibility of losing a novel improved trait once the selection

pressure is removed. Nowadays genomic sequence information of

entomopathogenic nematodes assisted with molecular breeding

tool paving a way for the trait improvements (Sumaya, 2018). The

traits improved via molecular methods would last permanently or

for a longer period as compare to artificial selection and

hybridization (Abd-Elgawad, 2019). Once the traits related to

storage stability and host-seeking behavior would be improved,

then the cost of commercial production would reduce to a certain

level. Entomopathogenic nematodes kill effectively the insect

belonging to most thwarting orders like Coleoptera, Lepidoptera,

Hemiptera, Diptera, and Orthoptera (Belien, 2018) etc. There is a

huge scope lying with the uses of these nematodes against insects

in future when the eco-safety and human health is the main

concern. The aim of this review is to highlight the potential of

entomopathogenic nematodes as an effective biological control

agent against the insect-pests of crops. The inclusion of these

nematodes in integrated pest management schemes can negate the

dependence on excess uses of synthetic pesticides.

2 Life cycle of EPNs

Both entomopathogenic nematodes exhibit a similar type of life

cycle and mode of infection with slight differences in their

formation first generation progeny. Steinernema undergoes

continuous two sexual generations with males and females

separately each time (Gauraha et al., 2018). While in

Heterorhabditis, the first generation coverts to hermaphroditic

female (Zioni et al., 1992). The entomopathogenic nematodes

exhibit a step by step process to infect its insect’s host. Firstly they

move freely in the soil to search for its host, followed by infection

and penetration of its host (Alonso et al., 2018). Once inside the

midgut of hosts, the nematode-bacteria exhibit a complex

mechanism to defend host immunity (Elefttherianos et al., 2018).

The nematode regurgitates its mutualistic bacteria inside the host’s

midgut and kills it by producing an array of insect-toxic protein

toxin complexes (Sheets & Aktories 2016) .The mechanism of

killing insect host by entomopathogenic nematode is depicted in

figure 1. Both nematode and bacteria undergo multiplication and

produces new progeny and completes their lifecycles until the host

contents limits to support further reproduction. At these stages,

newly formed infective juveniles emerge out from the empty

cadaver and moves freely in the soil to search for another host

(Sulistyanto et al., 2018). The sequential lifecycle of

entomopathogenic nematodes is depicted in figure-2.

Figure 1 EPNs mechanism to kill insect host




3 EPN is considered as a good biopesticide

Entomopathogenic nematodes can be used as a good biopesticides

and can be use as an alternative of chemical pesticides. The

following are the unique characteristics of EPN

Ability to search (chemoreceptors) the target

insect

Quick kill of the target insect

Broad host range

Easily cultured

Compatibles with many pesticides

Easy delivery system by spraying EPN suspension

or irrigation system

Safe to vertebrates, plants and non-targets and

environmentally safe

Long-term control

Reproductive potential (like pathogens)

Exempt from registration requirement

Figure 2 Lifecycles of entomopathogenic nematodes Heterorhabditis and Steinernema



80 Ahuja et al.

4 The differences between the Heterorhabditis and Steinernema

lifecycles

The entomopathogenic nematodes belongs to genus

Heterorhabditis and Steinernema are proven to manage effectively

insect pests of crops belonging to different orders. The lifecycles

of both the EPNS i.e. Heterorhabditis and Steinernema are utmost

similar with slight differentiation in their mode of entry inside the

host and production of first-generation progeny (Kooliyottil et al.,

2013). The entry inside the insect’s host is facilitated by natural

pores which include the mouth, spiracles or anus. Besides, the

nematode Heterorhabditis can also enter via penetrating the cuticle

of the insect’s host as it contains a mural tooth on the dorsal wall

of the buccal cavity which helps in tearing of cuticle.

5 Stages in the lifecycle

The Lifecycles of both the nematodes include a total of six stages

namely egg stage, followed by four juvenile stages and adult stage.

In the case of both nematodes, the third juvenile stage is the only

stage that resides in the soil. The third juvenile stage is the non-

feeding stage which carries the symbiotic bacteria inside their

anterior part of the gut. The bacteria reside by forming a film

inside the anterior gut in the case of Heterorhabditis nematode and

a pouch-like structure in the case of Steinernema nematode. The

third juvenile stages are also called as Dauer juvenile or infective

juvenile stages (Grewal et al., 2002).

6 The role of nematode partner

In a mutualistic relationship with bacteria, primarily the nematode

partner provide shelter to its bacterial partner. Nematode partner

further acts as a transporting agent or vector for its bacterial

partner, as bacteria can perform its effective role upon reaching

inside the host midgut. Meanwhile, it protects its bacterial from the

host defense processes (Gaugler, 2018).

7 The role of bacterial partner

The bacterial partner exhibits three main functions once inside the

host midgut. Firstly it produces multiple toxin complexes to kill its

host, followed by the production of bio-enzymes to converts host’s

tissue contents into a suitable growth medium for the

multiplication of both the mutualistic partners. At last, bacteria

encode several antibiotics compounds to inhibit the growth of

secondary microbes on dead insect hosts. Additionally, the

bacterial partner safeguards its nematode partner against host

immune responses (Gaugler, 2018).

8 Mass production technology of Entomopathogenic

nematodes

Entomopathogenic nematodes are being mass produced in several

countries of North America, Europe and Asia, on both a small and

large scale, using bioreactors (ShapiroIlan & Gaugler, 2002). They

can be mass produced in two ways: (i) in vivo and (ii) in vitro. In

the case of in vivo, insects serve as the bioreactor, whereas thein

vitro process is carried out in artificial media (Devi & George,

2018).

This is a low technology method with low startup costs (such as a

cottage industry) that involves the production of EPNs by using

live insects, which are highly susceptible and easily available at a

lower cost. The insects used under this method are the larvae of the

greater wax moth, Galleria mellonella, the rice moth, Corcyra

cephalonica, or the mealworm, Tenebrio molitor, which are reared

in the laboratory (Griffin et al., 2005).

Generally, the last instar of G. mellonella is preferred, due to its

high susceptibility, easy availability and high yield of IJs (Rahoo et

al., 2018). The approach is based on two dimensional systems that

rely on nematode production in trays and

shelves (Ehlers & ShapiroIlan, 2005). The method involves four

steps: inoculation, harvest, concentration and decontamination.

8.1 Inoculation

Insects are inoculated with IJs on a tray or dishlined with filter

paper or another substrate conducive to nematode infection, such

as soil or plaster of Paris. The nematode dosage and host density

should be optimized for maximum yield. Too low a dosage of IJs

may result in low host mortality, whereas too high a dosage may

result in failed infections due to competition with secondary

invaders. Approximately 25–200 IJs are sufficient to cause

infection on one insect larva of G. mellonella.

8.2 Harvest

This step is performed by using a technique based on the White

trap, wherein after 2–5 days, the host insects killed by nematodes

are placed above a water reservoir. The nematode produced by this

method is harvested by placing moist filter paper on a concave side

up watch glass surrounded with water in a large Petri dish. The

progeny IJs migrate from the depleted host cadaver into the water

reservoir, where they are trapped and subsequently harvested.

8.3 Concentration

IJs are decanted, transferred to a beaker and then kept in biological

oxygen demand (BOD) incubator at 10–15°C. During the process,

care should be taken that settling for a prolonged period may prove

detrimental to the nematodes, as this often causes a lack in oxygen

content Although this may be accomplished by vacuum filtration

or centrifugation for commercial in vivo operations, the total cost

will be much higher for a centrifuge of sufficient capacity

(ShapiroIlan et al., 2004).




8.4 Decontamination

There is a chance of host material or microbial contamination on

nematodes while migrating away from the cadaver. Therefore, the

nematodes harvested by this method are washed repeatedly. This

can be accomplished by gravity settling (Dutky et al., 1964),

wherein antimicrobial compounds such as streptomycinsulfate,

Hyamine® (methylbenzethonium chloride), merthiolate, NaOCl

and HgCl2 are used (Lunau et al., 1993). These compounds

have not been found to have any detrimental effect on nematodes

during commercial application (ShapiroIlan et al., 2004).

9 Availability and Importance of genomics information of

entomopathogenic nematodes

Entomopathogenic nematodes exhibit a high level of pathogenic

activity against insect pests of crops belonging to different orders.

These nematodes have been commercialized in many countries as

effective biocontrol agents against insect pests. In spite of strong

pathogenic activities against insects, their full potential under

insect-pest management is yet to be realized. These nematodes

suffer setbacks and losses consistency when applied in the field as

they suffer a direct pressure of biotic and abiotic stresses. These

external stresses forces hinder their normal establishment in the

field and fail to adapt to the environment. For commercial level

exploitation of entomopathogenic nematodes, there is a need to

improve its certain specific genetic traits. These traits include host-

seeking abilities, host-penetration, and infectivity, longevity,

persistence, and storage stability (Gaugler, 2018).Traditionally

artificial selection and hybridization tactics were employed to

improve these aforementioned traits, but once the selection

pressure is mitigated, there are abundant chances of losing these

traits. New modern genetics tools assisted with genomic

information provide a choice for improvement of genetic traits. In

recent years, at least entomopathogenic nematodes genomic

sequences have been published and other sequencing projects are

in progress (Yadav et al., 2015). Analysis of genomic sequences

helps in identifying the candidate genes involved in specific trait

regulation. Thus these genes can be isolated and transformed in

commercialized strains of entomopathogenic nematodes for better

efficacy in the field. In the Table 2, the genome size and number of

estimated G-protein coupled receptors (GPCRs) and proteases

information are listed for different entomopathogenic nematodes.

10 Future perspectives

Entomopathogenic nematodes exhibit broad-spectrum control

against insect pests belonging to different orders. Efficacy of these

nematodes have been checked against various thwarting insect

pests like Armyworms, plume moth, cutworms, weevils, shoot

borers, codling moths, leaf miners, mole crickets, shoot flies, etc.

But the results were found inconsistent as certain limiting factors

affect the establishments of these nematodes directly or indirectly.

The selection of a unique strain of EPNs against a target pest is the

major step that decides success or failure in control aspects. None

of the single strain is unique for all the key traits which include

cold tolerance, heat tolerance, and desiccation, high host-seeking

ability, penetration, and infectivity. Previous researches have also

found that a native strain of a particular geographical region

manages insect pest effectively belonging to the same geographical

region. Among these to genus, Heterorhabditis is mostly adopted

to the tropical and subtropical environment

while Steinernema confines its better efficacy in temperate

environments. Genomics insights and breeding methods provide an

alternative way to improvised native strains for specific

aforementioned traits. The development of commercial

formulations and storage stability are the major challenging task,

as these nematodes are live organisms. These nematodes have to

be stabilized in the carrier material to develop a formulation and

also suffer transportation shocks. There is an immediate need to

search for genes responsible for longevity and persistence, as

manipulation of these genes would help in the long term survival

of these nematodes in commercially developed formulations. The

other challenging tasks related to the entomopathogenic nematodes

are the way of application in the field. These nematodes are usually

applied with irrigation water, drenching in soil and spraying but

Table 2 Genome size and number of estimated G-protein coupled receptors (GPCRs) and proteases

S.No. Nematode species Genome size

(Mb)

Estimated

Putative GPCR

Estimated

proteases References

1 Heterorhabditis bacteriophora 77.0 82 19 Bai et al., 2013

2 Steinernema carpocapsae 85.6 604 268 Rougon-Cardoso et al., 2016

3 Steinernema scapterisci 79.4 731 357 Dillman et al., 2015

4 Steinernema feltiae 82.4 883 267 Dillman et al., 2015

5 Steinernema glaseri 92.9 806 248 Dillman et al., 2015

6 Steinernema monticolum 89.3 690 423 Dillman et al.,2015



82 Ahuja et al.

the efficacy does not found the same in all the cases. In the

changing scenario where the uses of synthetic pesticides are

neglected, the scope of utility of entomopathogenic nematodes in

insect pest's management would be promoted. In the coming

future, enormous scientific studies and researches are required to

understand the complex biology of these nematodes to maximize

its utility under integrated pest management schemes.

Conclusion

The entomopathogenic nematodes are very effective against the

insect-pests dwelling in the soil environment as these nematodes

naturally thrive well in soil. These nematodes have the great

potential to be used as a biocontrol agent in crop protection

schemes. But the consistency of these nematodes in the field is the

major challenge as these nematodes face environmental extremes

once applied as formulation against a target insect-pests. In the

past, many commercial formulations have been made and applied

to control the insect-pests of economically valuable crops. But a

broad spectrum utility of these nematodes can be achieved by trait

improvements. Genomics assisted with breeding is an effective

methodology to improve traits related to infectivity, persistence

and storage stability of entomopathogenic nematodes. Genomics

help in the identification of genes and their interaction mechanisms

involved in traits regulation. The novel genes specific for a

particular trait can be isolated from a donor strain and transformed

in native strains of entomopathogenic nematodes for their

maximum efficiency. A better understanding of the complex

mechanism of entomopathogenic nematodes involved in

mutualism with bacterial partner and pathogenicity to insects will

enable us to enhance the utilization of these nematodes for

biological control of insect pests.

Conflict Of Interest

Authors would hereby like to declare that there is no conflict of

interests that could possibly arise.

References

Abd-Elgawad MM (2019) Towards optimization of

entomopathogenic nematodes for more service in the biological

control of insect pests. Egyptian Journal of Biological Pest Control

29: 77. DOI: https://doi.org/10.1186/s41938-019-0181-1.

Alonso V, Nasrolahi S, Dillman AR (2018) Host-specific

activation of entomopathogenic nematode infective

juveniles. Insects 9: 59. doi:10.3390/insects9020059.

Askary TH, Ahmad MJ, Wani AR, Mohiddin S, Sofi MA (2018)

Behavioural Ecology of Entomopathogenic Nematodes,

Steinernema and Heterorhabditis for Insect Biocontrol. Sustainable

Agriculture Reviews 31: 425-441.

Bai X, Adams BJ, Ciche TA, Clifton S, Gaugler R, Kim KS,

Spieth J, Sternberg PW, Wilson RK, Grewal PS (2013) A lover

and a fighter: the genome sequence of an entomopathogenic

nematode Heterorhabditis bacteriophora. PloS One 8.7: e69618.

Belien T (2018) Entomopathogenic nematodes as biocontrol agents

of insect pests in orchards. CAB Rev 13: 1-11.

Chavan SN, Somasekhar N, Katti G (2018) Compatibility of

entomopathogenic nematode Heterorhabditis indica (Nematoda:

Heterorhabditidae) with agrochemicals used in the rice

ecosystem. Journal of Entomology and Zoology Studies 6 : 527-

532.

Devi G, George J (2018) Formulation of Insecticidal

Nematode. Annual Research & Review in Biology 7: 1-10.

Dillman AR, Macchietto M, Porter CF, Rogers A, Williams B,

Antoshechkin I, Lee MM, Goodwin Z, Lu X, Lewis EE, Goodrich-

Blair H, Stock SP, Adams BJ, Sternberg PW, Ali Mortazavi A

(2015) Comparative genomics of Steinernema reveals deeply

conserved gene regulatory networks. Genome Biology 16: 200.

https://doi.org/10.1186/s13059-015-0746-6.

Dutky SR, Thompson JV, Cantwell GE (1964) A technique for the

mass propagation of the DD-136 nematode. Journal of Insect

Pathology. 6:417–422.

Dzięgielewska M, Skwiercz A (2018) The influence of selected

abiotic factors on the occurrence of entomopathogenic nematodes

(Steinernematidae, HeterorHabditidae) in soil. Polish Journal of

Soil Science 51 :11-15.

Ehlers RU, Shapiro-Ilan DI (2005) Mass production of

entomopathogenic nematodes as biocontrol Agents. CAB

International 5:65–78.

Eleftherianos I, Yadav S, Kenney E, Cooper D, Ozakman,Y,

Patrnogic J (2018) Role of endosymbionts in insect–parasitic

nematode interactions. Trends in Parasitology 34: 430-444.

Gaugler R (2018) Entomopathogenic nematodes in biological

control. CRC press publication.

Gauraha R, Ganguli J, Deole S (2018) Entomopathogenic

nematodes and their efficiency in different host. Journal of Plant

Development Sciences 10 : 367-373.

Grewal PS, Wang X, Taylor RAJ (2002) Dauer juvenile longevity

and stress tolerance in natural populations of entomopathogenic




nematodes: is there a relationship? International Journal for

Parasitology32 : 717-725.

Griffin CT, Boemare NE, Lewis EE (2005) Biology and Behavior.

In: Grewal PS, Ehlers RU, Shapiro-IIan DI (Eds.) Nematodes as

Biocontrol agents, CAB international, Wallingford, UK, Pp. 47-64.

Griffin CT, Downes MJ, Block W (1990) Tests of Antarctic soils

for insect parasitic nematodes. Antarctic Science 2 : 221-222.

Kooliyottil R, Upadhyay D, Inman F, Mandjiny S, Holmes L

(2013) A comparative analysis of entomoparasitic nematodes

Heterorhabditis bacteriophora and Steinernema carpocapsae. Open

Journal of Animal Sciences 3: 324-326.

Labaude S, Griffin CT (2018) Transmission success of

entomopathogenic nematodes used in pest control. Insects 9: 72.

Leite LG, de Almeida JEM, Chacon-Orozco JG, Delgado CY (2019)

Entomopathogenic Nematodes. In: Natural Enemies of Insect Pests

in Neotropical Agroecosystems. Springer Cham pp: 213-221.

Li C, Zhou X, Lewis EE, Yu Y, Wang C (2019) Study on host-seeking

behavior and chemotaxis of entomopathogenic nematodes using Pluronic

F-127 gel. Journal of Invertebrate Pathology 161: 54-60.

Lu D, Baiocchi T, Dillman AR (2016) Genomics of

entomopathogenic nematodes and implications for pest

control. Trends in Parasitology32: 588-598.

Lunau S, Stoessel S, Schmidt-Peisker AJ, Ehlers RU (1993)

Establishment of monoxenic inocula for scaling up in vitro cultures

of the entomopathogenic nematodes Steinernema spp. and

Heterorhabditis spp. Nematologica 39: 385-399.

Mbata GN, Shapiro-Ilan DI, Alborn HT, Strand MR (2019)

Preferential infectivity of entomopathogenic nematodes in an

envenomed host. International journal for Parasitology 49: 737-745.

Nguyen KB, Smart GC (1994) Neosteinernemalongicurvicauda n.

gen., n. sp. (Rhabditida: Steinernematidae), a Parasite of the

Termite Reticuldermesflavipes (Koller). Journal of

Nematology 26: 162.

Poinar GO (1975) Description and biology of a new insect parasitic

rhabditoid, Heterorhabditis bacteriophora n. gen., n. sp.(Rhabditida;

Heterorhabditidae n. fam.). Nematologica 21: 463-470.

Rahoo AM, Mukhtar T, Abro SI, Bughio BA, Rahoo RK (2018)

Comparing the productivity of five entomopathogenic nematodes

in Galleria mellonella. Pakistan Journal of Zoology 50:2

Rougon-Cardoso A, Flores-Ponce M, Ramos-Aboites HE,

Martínez-Guerrero CE, Hao YJ, Cunha L, Chavarría-Hernández N

(2016) The genome, transcriptome, and proteome of the nematode

Steinernema carpocapsae: evolutionary signatures of a pathogenic

lifestyle. Scientific Reports 6:37-53.

Ruan WB, Shapiro-Ilan D, Lewis EE, Kaplan F, Alborn H, Gu

XH, Schliekelman, P (2018) Movement patterns in

entomopathogenic nematodes: Continuous vs. temporal. Journal of

Invertebrate Pathology 151: 137-143.

Saleh MM, Metwally HM, Abonaem M (2020)

Commercialization of Biopesticides Based on Entomopathogenic

Nematodes. In: Cottage Industry of Biocontrol Agents and Their

Applications, Springer Cham Pp: 253-275.

Shapiro-Ilan DI, Gaugler R (2002) Production technology for

entomopathogenic nematodes and their bacterial symbionts. Journal

of Industrial Microbiology and Biotechnology 28: 137–146.

Shapiro-Ilan DI, Gaugler R, Lewis EE (2004) In vivo production

of entomopathogenic nematodes. International Journal of

Nematology 14:13–18.

Sheets J, Aktories K (2016) Insecticidal toxin complexes from

Photorhabdus luminescens. In: The Molecular Biology of

Photorhabdus Bacteria, Springer Cham Pp: 3-23

Smart GC (1995) Entomopathogenic nematodes for the biological

control of insects. Journal of Nematology 27:529.

Sulistyanto D, Ehlers RU, Simamora BH (2018) Bioinsecticide

Entomopathogenic Nematodes as Biological Control Agent for

Sustainable Agriculture. Journal of Tropical Agricultural

Science 41:12-20.

Sumaya NHN (2018) Genetic improvement of oxidative stress

tolerance and longevity of the entomopathogenic nematode

Heterorhabditis bacteriophora. Doctoral thesis submitted to the

university of Christian-Albrechts Universität Kiel.

Travassos L (1927) Sobre O genera Oxystomatium.

BoletimBiologico (Sau Paulo) 5: 20–21

Yadav S, Sharma HK, Siddiqui AU, Sharma SK (2015) In vitro

mass production of Steinernema carpocapsae on different artificial

media. Indian Journal of Nematology 45: 123–124.

Zioni S, Glazer I, Segal D (1992) Life cycle and reproductive

potential of the nematode Heterorhabditis bacteriophora strain

HP88. Journal of Nematology 24: 352.



ISSN No. 2320 – 8694


EXPLOITING MILLETS IN THE SEARCH OF FOOD SECURITY : A MINI REVIEW

Inderpreet Dhaliwal1, Prashant Kaushik*,2,3

1Department of Plant Breeding and Genetics, Punjab Agricultural University, 141004 Ludhiana, India

2Instituto de Conservación y Mejora de la Agrodiversidad Valenciana, UniversitatPolitècnica de València, 46022 Valencia, Spain

3Nagano University, 1088 Komaki, Ueda, 386-0031 Nagano, Japan

Received – January 05, 2020; Revision – April 01, 2020; Accepted – April 11, 2020 Available Online – April 25, 2020


ABSTRACT

Climate change is negatively influencing agricultural production, and there is an urgent need for a

rational and cost-effective technique like crop diversification to develop resilience into agrarian

systems. For diversifying against the monoculture of conventional staples, the proposed crops shall

have essential nutritional advantages and also higher income perks for the farmers. Millets are the

better options for the crop diversification. In India, millets are traditionally cultivated from pre-

historic occasions. Millets because of their higher resistance against biotic and abiotic stresses, they

are sustainable towards the climate. Nutritionally, millets are gluten-free and are with a micro-

nutrients profile better than of conventional cereals like rice and wheat. But, millets have faced lots

of neglect within the Indian subcontinent because the population is obtaining much more conscious

from the challenges of food security and climate change. New methods for millet processing are

essential to revert the dietary habits in favour of millet-based diets along with more economical

initiatives for the farmers taking up the millet cultivation. In this review article, author have

discussed the three millets namely foxtail millet, proso millet and finger millet with the hope of

popularizing their cultivation in the Indian subcontinent. We hope that the information provided in

this review will help in the better understanding of the minor millets.


KEYWORDS

Finger millet

Food security

Foxtail millet

Millets

Proso millet

E-mail: [email protected] (Prashant Kaushik)


















Exploiting Millets in the Search of Food Security:A Mini Review 85

1 Introduction

The world population is continuously rising; this step rise causes a

lot of pressure in terms of food security and deciding the means to

feed the rising population (Dyson, 1996; Roy et al., 2006).

Tackling the hidden hunger as a result of the deficiencies of macro

and micronutrients is a notable challenge. However, a number of

approaches, such as crop biofortification and yield improvement,

were tried to overcome the hunger issue. But this problem still

persists (Sharma et al., 2019; Saini et al., 2020;). Furthermore, the

challenges imposed by climate change and global warming are also

increasing. Climate change will affect the world population and

agriculture productivity by threatening the overall ecosystem

(DeFries et al., 2019; Kellogg, 2019). Also, the agriculture sector

is among the primary producers of greenhouse gases like methane.

Cereal crop production is contributing to a significant amount of

global warming; additionally, cereals are deficient in important

micronutrients (Soares et al., 2019). The cereal crops like wheat,

rice and maize have a very high global warming potential (releases

around 4 tons CO2 eq/ha) whereas the carbon footprints of minor

millets are far less (Singh et al., 2019; Adegbeye et al., 2020).

Moreover, millets cultivation is recommended to reduce the world

carbon footprint along with sustaining the food production (Jaiswal

& Agrawal, 2020).

Millets cultivation is vital for developing countries like India.

Besides, more than 90% of the global millets produce comes from

the developing world (Taylor, 2019). Pearl millet is the most

widely grown millet. In contrast, millets like foxtail millet, finger

millet, little millet, barnyard millet, proso millet, and Kodo millet

are also cultivated in India but to a lesser extent (Alavi et al.,

2019). The cultivation of millet is undergoing from as far as 5000

year ago for Little millet (Panicum sumatrense) in South Asia and

Kodo millet (Paspalumscrobiculatum) was cultivated for 3700

years before present (Tadele, 2016). India is the leading millet

producing country followed by Niger and China. Efforts are being

executed to increase the demand of millets in India (the Smart

Food campaign) and the world, especially because they provide

cheap and high nutrient options like high fibre content,

magnesium, calcium, iron, potassium, phosphorus and Niacin

(Vitamin B3)(Rao et al., 2018) (Table 1). Millets are gluten-free

and are rich sources of protein and also do not get destroyed easily,

thus providing food security. Most of the millets grown in India are

of short duration, taking 3-4 months from sowing to harvesting. In

the metros and cities, these crops are sold at a premium (Saleh et

al., 2013).

Millets are C4 plants they can work out photosynthesis more

efficiently. Millets are rich in nutrients and are also gluten-free can

be consumed by the people who are allergic to cereals (Thakur &

Tiwari, 2019). Moreover, millets are easily digestible and possess

numerous health benefits like anticancer, antidiabetic and

anticholesterol. Furthermore, millets-based diet is recommended

for the patients facing diabetes and even for patients with chronic

diseases like cancer (Kam et al., 2016). Several mineral elements

like iron, zinc and copper, etc. which are essential for human

health and well-being are present in high quantities in the millets

(Stein, 2010). Moreover, finger millet is known to possess ten

times higher content of calcium than rice or wheat. Early maturing

varieties of millets can be a good alternative for sustaining crop

production under irrigated and also under the stress conditions

(Council, 1996). Millets have a better storability than most of the

cereals (Taylor & Emmambux, 2008). In this review, author have

gathered information regarding the millets in a hope to improve the

cultivation of millets in the Indian subcontinent.

2 Foxtail Millet

Foxtail millet (Setaria italica), also known as dwarf Setaria is

an annual millet which is also the second-most cultivated

species of millet next to pearl millet (Figure 1). It is

extensively cultivated in the Indian subcontinent. Earliest

record of about foxtail millet is from 8700 BC (Onziga, 2015).

Similarly, it is also widely cultivated in India. The eco-friendly

crop foxtail millet due to its health benefits, excellent yield

potential, tolerance to biotic and abiotic stresses is gaining

popularity in the Indian subcontinent (Jia et al., 2007).

Moreover, in recent years’ foxtail millet has been tagged as a

model plant because of its short life cycle, optimum seed

Table1 Nutrition status of Minor Millets vis-à-vis Cereals

(Compiled from a study published by National Institute of Nutrition, Hyderabad) (Rao et al., 2018).

Nutritional

content in 100 gms of dry grain

Protein

(gms)

Carbohydrates

(gms)

Fat

(gms)

Minerals

(gms)

Fiber

(gms)

Calcium

(mgs)

Phosphorus

(mgs)

Iron

(mgs)

Energy

(Kcal)

Thiamin

(mgs)

Niacin

(mgs)

Finger millet 7.3 72.0 1.3 2.7 3.6 344 283 3.9 336 0.42 1.1

Prosomillet 12.5 70.4 1.1 1.9 5.2 80 206 2.9 354 0.41 4.5

Foxtailmillet 12.3 60.2 4.3 4.0 6.7 31 290 2.8 351 0.5 3.2

Rice (paddy) 6.8 78.2 0.5 0.6 1.0 33 160 1.8 362 0.41 4.3

Wheat 11.8 71.2 1.5 1.5 2.0 30 306 3.5 348 0.41 5.1



86 Dhaliwal & Kaushik

production, and small genome size (around 400 Mb)

(Zhang et al., 2012). Also, foxtail millet is a C4 plant

and is used to provide valuable information regarding

the C4 photosynthesis (Doust et al., 2009).

Botanically, foxtail millet produces leafy stems with

height upto 200 cm. Foxtail produces a seed head with

a hairy panicle of 5-30 cm long. The seeds are small

approximately 2mm in diameter with a seed colour

that varies from greenish to whitish. In the Indian

subcontinent, its cultivation stretches in arid and semi-

arid regions. It is planted in late spring and usually has

a crop duration of 65-70 days for hay production and

75-90 days as a grain crop. The grain yield typically

varies between 800–900 kg/ha(Austin, 2006).

3 Proso Millet

Proso millet (Panicum miliaceum) was first

domesticated before 10,000 BCE in Northern China

(Sakamoto, 1987). Whereas, the weedy wild relatives

of proso millet are distributed throughout central Asia.

It cultivated in South East Asia, middle east, Europe

and also in the United States (Lu et al., 2009). Proso

millet takes upto 60 days from the seed to grain

production (Figure 2). It is a drought-tolerant millet.

Porso millet is cultivated as an allotetraploid resulted

from the wide hybridization of the two ancestors.

Porso millet has been recently sequenced, and it has a

genome size of 920 Mb (Zou et al., 2019). Proso millet

is well customized to plateau and the regions with high

elevation. Additionally, proso millet production can be

sustained under unirrigated conditions. Whereas,

under temperature that is high as well as with drought

conditions, proso millet prevents its vegetative growth

(Habiyaremye et al., 2017). Botanically, proso millet

is an upright grass with a height of approximately 1.5

m tall and is cultivated as an annual. Proso millet

produces tillers and possesses a shallow root system.

The stem is cylindrical with simple alternate and hairy

leaves. The inflorescence of proso millet is a panicle.

The grains are ovoid, up to 3 mm × 2 mm, and are

usually white coloured. Proso millet have anti

cholesterol properties. The determination of the

harvesting time for proso millet grains is not easy as

the grains mature at different times (Gomeshe, 2016).

4 Finger Millet

Finger millet (Eleusine coracana) also known as kodo,

is a millet that is cultivated annually in arid and semi-

arid regions of the world (Chandra et al., 2016). Finger

millet is a self-pollinating tetraploid plant (Figure 3). It

Figure 1 Plants of foxtail millet with their inflorescence (Rao et al., 2018).

Figure 2 Plants of proso millet with their inflorescence(Rao et al., 2018).




is believed to be the native of Ethiopian highlands. Finger millet

can be cultivated in the high altitudes of over 2000 m furthermore,

has a high degree of drought tolerance (Chandrashekar, 2010). The

optimal growth temperature for finger millet production is about

27 °C, with a minimum temperature of 18 °C. Finger millet can be

cultivated from 500 to about 2400 m above mean sea level. It is

also highly tolerant to soil salinity. But finger millet is sensitive to

waterlogging. Finger millet is also tolerant of acidic soils (pH 5),

and alkaline soils (pH 8.2). This crop produces tillers with erect

and light green coloured stems around 1.7 m in height (Prasad &

Staggenborg, 2010), smooth leaves which are hairy along the

margins. The inflorescence is comprised of fingers with a cluster

of 3–26, it has dense spikelets (Thapa & Tamang, 2004). Finger

millet crop doesn’t mature uniformly, and therefore its maturity is

taken up when the earhead on the main shoot as well as 50%

earheads on the crop turn brown. The later process like cutting,

drying, threshing and cleaning is carried out in the same order.

Finger millet produces grains of size1–2 mm diameter, with colour

usually from the light brown or dark brown(Brar et al., 2019; Udeh

et al., 2018).

Conclusions and Future Roadmap

Climate change will negatively impact agricultural production, but

a rational and cost-effective method to build resilience into

agrarian systems is the implementation of crop diversification.

This would enable farmers to increase their crop portfolio so that

they are not dependent on a single crop to generate their income.

Besides, depleting water resources and smaller landholdings calls

for the judicious utilisation of these resources for the sustainability

of agricultural growth. Diversifying from the monoculture of

traditional staples shall have critical nutritional benefits as well as

augment farmers’ income in developing countries. In India, these

are considered to have been cultivated since pre-historic times.

Although millets are gluten-free with a micro-nutrients profile

much better than rice and wheat, millets have faced a lot of neglect

in the Indian subcontinent as the population is getting more aware

of the challenges of food security and climate change the growers

and even projects are now favouring them are also getting funding.

The points that favour the millets cultivationisthetolerance to biotic

and abiotic conditions, and their ability to delay climate change.

Previously, in the last several decade's Indian farmers have ignored

the millets for rice, wheat, oilseeds and pulses. As compared to the

5000 litres of water requirement for one-kilogram rice, there is

250-300 litres of water required for the production of the

equivalent amount of millets. New ways of food processing are

necessary to revert the dietary habits in favour of millet-based

diets. There should be more economical initiatives for the farmers

taking up the millet cultivation. Likewise, advertising strategy is

actually required for targetting the growers facing the problems in

marketing their products based on minor millets.

Conflict of Interest

The authors declare no conflict of interest

References

Adegbeye MJ, Reddy PRK, Obaisi AI, Elghandour M, Oyebamiji

KJ, Salem AZM, Morakinyo-Fasipe OT, Cipriano-Salazar M,

Camacho-Díaz LM (2020) Sustainable agriculture options for

production, greenhouse gasses and pollution alleviation, and

nutrient recycling in emerging and transitional nations-An

overview. Journal of Cleaner Production 242:1-21.

Alavi S, Mazumdar SD, Taylor JR (2019) Modern Convenient

Sorghum and Millet Food, Beverage and Animal Feed Products,

and Their Technologies. in: Sorghum and Millets.Taylor RNJ,

Duodu GK Elsevier, Pp. 293–329.

Austin DF(2006) Fox-tail millets (Setaria: Poaceae)—abandoned

food in two hemispheres. Economic Botany 60:143–158.

Brar NS, Saini DK, Kaushik P, Chauhan J, Kamboj NK (2019)

Directing for Higher Seed Production in Vegetables. Agronomy1:

13-28. https://doi.org/10.5772/intechopen.90646.

Chandra D, Chandra S, Arora P, Sharma AK (2016) Finger millet

(Eleusine coracana L.) Gaertn: A power house of health benefiting

nutrients, a review. Food Science and Human Wellness 5: 24-35.

https://doi.org/10.1016/j.fshw.2016.05.004.

Figure 3 Plants of finger millet with their inflorescence(Rao et al., 2018).



88 Dhaliwal & Kaushik

Chandrashekar A(2010)Finger Millet: Eleusinecoracana.In: Taylor

SL (Ed.), Advances in Food and Nutrition Research. Academic

Press, Pp. 215–262. https://doi.org/10.1016/S1043-4526(10)59006-5

Council NR(1996) Lost crops of Africa: volume I: grains. National

Academies Press.

DeFries RS, Edenhofer O, Halliday AN, Heal GM, Lenton T,

Puma M, Rising J, Rockström J, Ruane A, Schellnhuber HJ (2019)

The missing economic risks in assessments of climate change

impacts. Available on

http://www.lse.ac.uk/GranthamInstitute/publication/the-missing-

economic-risks-in-assessments-of-climate-change-impacts/ access

on 17 October, 2019.

Doust AN, Kellogg EA, Devos KM, Bennetzen JL (2009) Foxtail

millet: a sequence-driven grass model system. Plant Physiology

149: 137–141.

Dyson T(1996) Population and food: global trends and future

prospects. Routledge.

Gomeshe SS (2016) Proso millet, Panicum miliaceum (L.): Genetic

improvement and research needs. In: Patil JV (Ed.) Millets and

Sorghum: Biology and Genetic Improvement, John Wiley & Sons

Ltd Pp.150–179. DOI: https://doi.org/10.1002/9781119130765.ch5.

Habiyaremye C, Matanguihan JB, D’Alpoim Guedes J, Ganjyal

GM, Whiteman MR, Kidwell KK, Murphy KM(2017) Proso Millet

(Panicum miliaceum L.) and Its Potential for Cultivation in the

Pacific Northwest, U.S.: A Review. Frontiers in Plant Science 7:1-

11. https://doi.org/10.3389/fpls.2016.01961

Jaiswal B, Agrawal M, (2020) Carbon Footprints of Agriculture

Sector, in: Carbon Footprints. Springer, pp. 81–99. DOI:

10.1007/978-981-13-7916-1_4.

Jia XP, Shi YS, SongYC, Wang GY, Wang TY, Li

Y(2007)Development of EST-SSR in foxtail millet (Setariaitalica).

Genetic Resources and Crop Evolution 54: 233–236.

Kam J, Puranik S, Yadav R, Manwaring HR, Pierre S, Srivastava

RK, Yadav RS(2016) Dietary interventions for type 2 diabetes:

How millet comes to help. Frontiers in Plant Science 7: 1-9.

https://doi.org/10.3389/fpls.2016.01454.

Kellogg WW(2019) Climate change and society: consequences of

increasing atmospheric carbon dioxide. Routledge. 85-101.Available

on https://www.routledge.com/Climate-Change-And-Society-

Consequences-Of-Increasing-Atmospheric-

Carbon/Kellogg/p/book/9780367018870 Access on 17 October, 2019.

Lu H, Zhang J, Liu K, Wu N, Li Y, Zhou K, Ye M, ZhangT, Zhang

H, Yang X (2009) Earliest domestication of common millet

(Panicum miliaceum) in East Asia extended to 10,000 years ago.

Proceedings of the National Academy of Sciences 106: 7367–7372.

Onziga DI (2015) Characterizing the genetic diversity of finger

millet in Uganda. PhD Thesis submitted to the Makerere

University, Kampala, Uganda Pp. 55-91.

Prasad PVV, Staggenborg S (2010) Growth and Production of

Sorghum and Millets. Encyclopedia of Life Support Systems,

Publisher: EOLSS Publishers, Oxford, U.K. Pp.3-9.

Rao BD, Bhat BV, Tonapi VA (2018)Nutricereals for Nutritional

Security. Director, Indian Institute of Millets, Research,

Hyderabad, Pp. 78:86. Available on

http://www.millets.res.in/technologies/Bulletin-Millets_chapke.pdf

access on 17 October, 2019.

Roy RN, FinckA, Blair GJ, Tandon HLS(2006) Plant nutrition for

food security. A guide for integrated nutrient management 16: 368.

Saini DK, Devi P, Kaushik P (2020) Advances in Genomic

Interventions for Wheat Biofortification: A Review. Agronomy 10:

62-75. https://doi.org/10.3390/agronomy10010062.

Sakamoto S(1987) Origin and dispersal of common millet and

foxtail millet. Japan Agricultural Research Quarterly 21: 84–89.

Saleh AS, Zhang Q, Chen J, Shen Q (2013) Millet grains: nutritional

quality, processing, and potential health benefits. Comprehensive

reviews in food science and food safety 12: 281–295.

Sharma V, Saini DK, Kumar A, Kaushik P (2019) A Review of

Important QTLs for Biofortification Traits in Rice. Preprints 12:1-

16. DOI: 10.20944/preprints201912.0158.v1

Singh H, Sethi S, Kaushik P, Fulford A(2019) Grafting vegetables

for mitigating environmental stresses under climate change: a

review. Journal of Water and Climate Change 29:1-14.

Soares JC, Santos CS, Carvalho SM, Pintado MM, Vasconcelos MW

(2019)Preserving the nutritional quality of crop plants under a

changing climate: importance and strategies. Plant and Soil 443: 1–26.

Stein AJ (2010) Global impacts of human mineral malnutrition.

Plant and soil 335:133–154.

Tadele Z (2016) Drought Adaptation in Millets. In: Shanker A,

Shanker C (Eds.) Abiotic and Biotic Stress in Plants - Recent

Advances and Future Perspectives, Intechopen Publication.

https://doi.org/10.5772/61929.

Taylor JR (2019) Sorghum and Millets: Taxonomy, History,

Distribution, and Production. in: Sorghum and Millets.

Elsevier, Pp. 1–21.




Taylor JR, Emmambux MN (2008) Gluten-free foods and

beverages from millets, in: Gluten-Free Cereal Products and

Beverages. Elsevier, pp. 119–V.

Thakur M, Tiwari P (2019) Millets: The undtapped and

underutilized nutritional functional foods. Plant Archives

19:875–883.

Thapa S, Tamang JP(2004) Product characterization of kodo ko

jaanr: fermented finger millet beverage of the Himalayas. Food

microbiology 21: 617–622.doi: 10.1016/j.fm.2004.01.004.

Udeh HO, Duodu KG, Jideani AI(2018) Effect of malting period

on physicochemical properties, minerals, and phytic acid of finger

millet (Eleusinecoracana) flour varieties. Food Science &

Nutrition 6: 1858–1869.https://doi.org/10.1002/fsn3.696.

Zhang G, Liu X, Quan Z, Cheng S, Xu X, Pan S, Xie M, Zeng P,

Yue Z, Wang W (2012) Genome sequence of foxtail millet

(Setariaitalica) provides insights into grass evolution and biofuel

potential. Nature biotechnology 30: 549-554.doi: 10.1038/nbt.2195.

Zou C, Li L, Miki D, Li D, Tang Q, Xiao L, Rajput S, Deng P,

Peng L, Jia W, Huang R, Zhang M, Sun Y, Hu J, Fu X, Schnable

PS, Chang Y, Li F, ZhangH, Feng B, Zhu X, Liu R, SchnableJC,

Zhu JK, Zhang H (2019) The genome of broomcorn millet. Nature

Communications 10:1–11. https://doi.org/10.1038/s41467-019-

08409-5.



ISSN No. 2320 – 8694


STANDARD HETEROSIS ANALYSIS IN MAIZE HYBRIDS UNDER WATER

LOGGING CONDITION

Gayatri Kumawat*, Jai Prakash Shahi, Munnesh Kumar, Ashok Singamsetti, Manish Kumar

Choudhary, Kumari Shikha

Department of Genetics and Plant breeding, Institute of Agriculture Science, Banaras Hindu University, Varanasi-221005, India

Received – February 18, 2020; Revision – March 03, 2020; Accepted –March 26, 2020

Available Online – April 25, 2020


ABSTRACT

Maize is one of the important food and forage crops with abundant natural diversity. Determination of

heterosis in CIMMYT maize hybrids under water logging condition is necessary for their commercial

exploitation. The synthetics and composites have contributed to maize production in India in the initial

stages of maize improvement programme, of late, hybrids are playing a vital role due to their high

yielding potential. Breeding of water logging tolerant maize varieties will likely boosts maize

production beyond the present level. Data derived from current study were complied to determine

standard heterosis and identify high yielding hybrids. Among the tested 55 maize hybrids, the maize

hybrids, namely, ZH17506, ZH17496 and VH11128 produced high heterosis which indicating that these

hybrids are available for commercial cultivation. Maize hybrids that perform better than the checks

could be used for release as hybrid variety after re-evaluation in multi-location trials.


KEYWORDS

Maize

Water logging condition

Standard heterosis and hybrids

E-mail: [email protected] (Gayatri Kumawat)


















Standard Heterosis Analysis in Maize Hybrids under Water Logging Condition 91

1 Introduction

Maize (Zea mays L.) is one of the most important field crops

cultivated in India to ensure food security. Maize contributes the

greatest share of production and consumption together with other

major cereal crops, such as wheat, rice and Sorghum. Among the

cereal crops, maize ranks third in area coverage and total annual

production and productivity in India (Economic Survey 2015-16,

MoA and FW, GOI). Globally maize covers an area of 178 million

hectares with a production of 978.1 million tonnes (USDA, 2014-

15) and Indian maize occupies an area of 9.2 million hectares with

a production of 22.99 million tonnes (Indiastat.com, 2014-15) and

productivity of 2583 kg ha-1. Initially synthetic and composite

have contributed to maize production in India but now heterosis

breeding play a key role due to their high yielding potential.

Generally, heterosis is an important trait used by breeders to

evaluate the performance of offspring in relation to their parents. It

estimates the enhanced performance of hybrids as compared to

their parents. Often, the superiority of F1 is estimated over the

average of the two parents, or the mid parent or standard check

(Shushay, 2014). Heterosis played an important role in maize

breeding and selection of heterosis is dependent on level of

dominance and differences in gene frequency. The manifestation

of heterosis depends on genetic divergence of the two parental

varieties (Hallauer & Miranda, 1988). Heterosis is manifested as

an increase in vigor, size, growth rate, yield or other

characteristics. But in some cases, the hybrid may be inferior to the

weaker parent, which is also considered as heterosis. That means

heterosis can be positive or negative (Ram Reddy et al., 2015;

Shah et al., 2016). The interpretation of heterosis depends on the

nature of trait under study and the way it is measured.

The low grain yield can be attributed to a number of constraints

which include biotic stress and abiotic stress. Unlike wetland crops,

maize plants do not have a gaseous exchange system between above-

ground plant parts and inundated roots under water lodging

conditions. Therefore, excess soil moisture will results in anoxic soil

condition for maize crop. Therefore, breeding of water logging

tolerant maize varieties will likely boosts maize production beyond

the present level (Kaur et al., 2019). Progress in different discipline

of plant breeding for increased resistance for biotic and abiotic stress

depends predominantly on the extent of heterosis present in

germplasm. So the present investigation conducted to exploit

standard heterosis to select best water logging resistance hybrid.

2 Materials and Methods

2.1 Estimation of mean performance and heterosis

This experimental study was carried out during crop season Kharif

2017 in alpha lattice design with two replications at the Agriculture

Research Farm of Banaras Hindu University, Varanasi, UP, India. The

experiment genotypes comprised of 55 maize genotypes in which two

standard checks (900MG from Monsanto and P3502 from Pioneer)

and 53 maize hybrids were obtained from CIMMYT (International

Maize and Wheat Improvement Center, Mexico) germplasm under the

project- ―Climate Resilient Maize for Asia (CRMA)‖. Each hybrid was

planted in a single row of 3 meters in length with a spacing of row to

row 60cm and plant to plant 25 cm. Water logging stress was imposed

at V6-V7 growth stage/ knee height stage of crop growth (35 days after

sowing). Draining out of excess water was done on seventh day (Zaidi

et al., 2016). The crop was raised as per the recommended agronomic

package of practices. The observations were recorded for fifteen

characters viz. pre harvest data like-number of nodes bearing brace

roots, number of surface roots, days to 50 percent anthesis, days to 50

percent silking, plant height (cm), ear height (cm) and post harvest data

like-ears per plot, grain weight (t/h), plant population, ear length (cm),

ear diameter (cm), number of kernel rows per ear, number of kernels

per row, 100 seed weight (g) and yield per plant (g). The statistical

analysis of data based on the mean value of recorded observations on

five random plant basis was done.

Standard heterosis was estimated for grain yield per plant as deviation

of F1 hybrid from the check included in the trial. It is expressed as

percentage superiority over standard check. Standard heterosis was

calculated for those traits that showed statistically significant

differences among genotypes as suggested by Falconer & Mackay

(1996). These were computed as percentage increase or decrease of the

cross performances over best standard check as follows.

Standard heterosis (SH) = F1− Check

Check ˟100

Where, F1 = mean value of F1 ; SH = mean value over replication

of the local commercial check

2.2 Test of significance

The significance of heterosis was tested by using‗t‘ test as suggested by

Snedecor & Cochran (1989) and Paschal & Wilcox (1975).

SE (d) = 2MSE

r

t = F1− standard Check

SE (d)

Where, SE (d) is standard error of the difference; MSE is error mean

square and r is number of replications and calculated t value was

compared against the tabulated t-value at degree of freedom for error.

3 Results and Discussion

3.1 Mean performance of genotypes

The mean performances of the genotypes (the 53 hybrid progenies

and two standard checks) across site are given in Table 1. Yield



92 Kumawat et al.



94 Kumawat et al.

per plant was recorded with a range of 12.00 g (ZH15564) to

100.00 g (ZH15506). Among the tested hybrids, ZH15506

yielded higher than 900MG check (67.3g). The presence of

crosses having mean values better than the standard checks

indicate the possibility of obtaining good hybrid (s) for future

use in breeding program or for commercial use general mean was

observed 63.9g. Number of surface roots ranged from 5

(ZH17501, ZH17508) to 19.5 (ZH138303) with general mean

11.34 surface root per plant. Number of nodes bearing brace

roots were recorded with a class from 1.50 (ZH15559) to 4.10

(ZH15560) with an average mean of 2.75.

The number of days to 50 % anthesis and days to 50% silking

classified from 56.00 (ZH138260) to 68.50for (ZH15565) and

57.40 (ZH15547) to 68.5 (ZH15565) coupled with a general mean

of 61.07 and 62.30 days respectively among 55 maize genotypes

studied. Most of the crosses showed longest number of days to

anthesis and silking. This shows that it might be because of late

crosses in these genotypes. Late maturing hybrids are important in

the breeding programs for development of high yielding hybrids in

areas that receive sufficient rain fall (Hosana et al., 2015). Further

evaluation and recommendation of this group of materials should

be based on agro-ecological suitability.

At maturity, plant height and ear height mean value of different

genotypes arranged from 77.50 cm (ZH17507) to 147.50 cm

(ZH17499, P3502) and from 20.00cm (ZH17507) to 77.50 cm

(P3502) with an average mean of 116.8 cm and 51.86 cm

respectively. In line with these finding, Hosana et al. (2015)

reported higher grain yield from taller plants; this could be

attributed to high photosynthetic products accumulation during

long period for grain filling.

Plant population ranged from 7.00 (ZH15564) to 18.00

(ZH138312) with an average mean of 13.60. Ear per plots ranged

from 6.00 (ZH17497) to 21.00(ZH15563) with an average mean

of 13.66. Grain weight (weighing the total ears in a plot and later

converted in to tones per hectors) was varied from 0.24

(ZH15564) to 2.30 ton per hectare (ZH17496). Ear length and

ear diameter was recorded with a range of ranged from 8.90 cm

(ZH15561) to 15.60 cm (ZH17232) and 2.70 cm (ZH15564) to

6.90 cm (ZH17498) with an average mean of 12.12cm and 3.90

cm respectively. The number of kernel rows per ear ranged from

7.00 to 16.20 with the mean 13.20. The lowest rows were found

in ZH15564 whereas the highest was found in ZH15553. The

number of kernels per row for 55 maize genotypes varied from

10.70 (ZH15504) to 27.50 (ZH17506) with a general mean

19.01. Wait for 100 grains for all 55 genotypes ranged from

18.27 g in ZH15558 to 48.15g in ZH17494 genotype. The

observed mean was 25.5 g. The genotypes showing high mean

value for post harvest yield attributing traits under water logging

condition can be exploited further for high soil moisture

resistance genotype development (Zaidi et al., 2010; Shushay,

2014;).

3.2 Standard heterosis

The estimates of standard heterosis over the standard check

900MG were computed for grain yield and yield related traits and

presented in Table 2. As the mean value of yield per plant higher

for 900MG check than P3502 check, so 900MG check exploit as

standard check in this experiment. Three hybrids showed positive

and significant heterosis to 900 MG standard check for grain yield

per plant. Standard heterosis for grain yield per plant ranged from -

82.17 (ZH 15564) to 49.48% (ZH17506). ZH17496 and VH11128

(47.25%) exhibited positive standard heterosis to the standard

check. Positive heterosis for this trait indicates increased yield

advantage over the existing standard check. Maize hybrids that

perform better than the checks could be used for release as hybrid

variety after verification.

Standard heterosis for days to 50% anthesis and silking ranged

from -9.68 to 10.48% and -4.88 to 11.38% to 900MG

respectively. Three hybrids showed positive and significant

heterosis for these traits to check indicating that these hybrids

were late maturing as compared to the checks. Heterosis in the

negative direction for these traits indicates earliness of the

crosses over the standard checks so negative heterosis is

desirable for them. Significant negative heterosis for days to

tasseling was observed in ZH15546, ZH138260, ZH15558,

ZH17502 and ZH17504 crosses. Earlier Amiruzzaman et al.

(2013) and Bello & Olawuyi (2015) reported negative heterosis

for these traits in maize.

Standard heterosis for surface and brace roots showed three

and two genotypes positively significant respectively. The

positive heterosis in these traits indicates desirable hybrids

with more water logging resistance. Positively significant

genotypes for plant height and ear height indicated desirable

hybrids with more photosynthetic efficiency. Similarly post

harvest data (yield data) indicates viz- ear length, ear diameter,

number of rows per ear, number of kernels per row and 100

seed weight showed positive significant heterosis for those

hybrids which can be used for commercialization of hybrids

(Singh & Jha 2007; Ulaganathan et al., 2015).

Conclusion

Three hybrids (ZH17506, ZH17496 and VH11128) showed

positive and significant heterosis to 900 MG standard check for

grain yield per plant. Maize hybrids that perform better than the

checks could be used for release as hybrid variety after re-

evaluation in multi-location trials.



96 Kumawat et al.




Acknowledgements

We would like to express our sincere appreciation to the CIMMYT

Hyderabad and Institute of Agriculture Sciences Banaras Hindu

University.




References

Amiruzzaman M, Islam MA, Hasan L, Kadir M, Rohman MM

(2013) Heterosis and combining ability in a diallel among elite

inbred lines of maize (Zea mays L.). Emirates Journal of Food and

Agriculture 25: 132-137.

Bello OB, Olawuyi OJ (2015) Gene action, heterosis, correlation

and regression estimates in developing hybrid cultivars in

maize. Tropical Agriculture 92: 102-117.

Economic Survey (2016). Economic Survey 2015-16 Retrieved

from idiabudget.nic.in/es2015-16/ estat1.pdf access on 18

September, 2019.

Falconer DS, Mackay TFC (1996) Introduction to Quantitative

Genetics, 4th edn. Longman, London.

Hallauer AR, Miranda JB (1988) Quantitative genetics in maize

breeding. Iowa State Univ. Press, Ames. Quantitative genetics in

maize breeding. 2nd ed. Iowa State Univ. Press, Ames.

Hosana GC, Alamerew S, Tadesse B, Menamo T (2015) Test cross

performance and combining ability of maize (Zea mays L.) inbred

lines at Bako, Western Ethiopia. Global Journal (USA) 15: 24.

Indiastat Agriculture production (2014) Indiastat Agriculture

production 201315 available on http://www.indiastat.com access

on 18 September, 2019.

Kaur G, Zurweller B, Motavalli PP, Nelson KA (2019) Screening

Corn Hybrids for Soil Waterlogging Tolerance at an Early Growth

Stage. Agriculture 9: 33.

Paschal EH, Wilcox JR (1975) Heterosis and combining ability in

exotic soybean germplasm. Crop Science 15: 344-349.

Ram Reddy V, Farzana zabeen, Sudarshan MR (2015) Heterosis

studies in diallel crosses of maize for yield and yield attributing

traits in maize over locations. International Journal of Agricultural,

Environment and Biotechnology 8: 271-283.

Shah RT, Prasad K, Kumar P (2016) Maize—A potential source of

human nutrition and health: A review. Cogent Food and

Agriculture 2: 1166995.

Shushay WA (2014) Standard heterosis of maize (Zea mays L.)

inbred lines for grain yield and yield related traits in central Rift

Valley of Ethiopia. Journal of Biology, Agriculture and Healthcare

4: 31-37.

Singh SP, Jha PB (2007) Heterotic potential of winter maize

hybrids for yield attributing traits under variable moisture

condition. Journal of Applied Bioscience 33: 1-7.

Snedecor, GW, Cochran, WG (1989) Statistical Methods. 8th

ed.

Lowa State University press. Ames. IA.

Ulaganathan V, Vinoth R, Baghyalakshmi K, Chimili SR,

Gurusamy A (2015) Standard heterosis for grain yield and other

agronomic characters in maize (Zea mays L.) under normal and

moisture stress conditions. An International Quarterly Journal of

Life Science 10: 1251-1253.

USDA (2014) World Agricultural Production. 2013-2014.

Available on www.usda.gov access on 19 September, 2019.

Zaidi PH, Maniselvan P, Srivastava A, Yadav P, Singh RP (2010)

Genetic analysis of water-logging tolerance in tropical maize (Zea

mays L.). Maydica 55: 17-26.

Zaidi PH, Vinayan MT, Seetharam K (2016) Phenotyping for

abiotic stress tolerance in maize: Waterlogging stress. A field

manual. CIMMYT: Hyderabad, India.



ISSN No. 2320 – 8694


ENERGY USE EFFICIENCY AND GREEN HOUSE GAS EMISSIONS FROM

INTEGRATED CROP-LIVESTOCK SYSTEMS IN SEMI-ARID ECOSYSTEM OF

DECCAN PLATEAU IN SOUTHERN INDIA

Md. Latheef Pasha1, G Kiran Reddy2,*, S Sridevi3, M Govardhan4, Md. Ali Baba5, B. Rani6

1Senior Scientist (Agronomy), AICRP-IFS, Hyderabad – 500030

2Scientist (Soil Science), AICRP-IFS, Hyderabad – 500030

3Principal, Agricultural Polytechnic, Tornala

4Principle Scientist & Head, AICRP-IFS, Hyderabad – 500030

5Scientist (Agricultural Economics), AICERP-IFS, Hyderabad – 500030

6SRF, AICRP-IFS, Hyderabad – 500030

Received – January 19, 2020; Revision –March 20, 2020; Accepted – March 28, 2020 Available Online – April 25, 2020


ABSTRACT

Integrated crop-livestock system is the default in vogue farming system followed in semi arid Deccan

plateau in Southern India. Energy flow and environmental impact plays an anchor role for the sustainability

of any farming system. The objective of the present study is to know the energy use and greenhouse gas

(GHG) emission in the integrated crop-livestock systems in study area. Primarily data was collected from

36 farmers by bench mark survey questionnaire. The study area includes 54.53 ha of cultivated land, 177

dairy cattle, 466 sheep and 129 poultry birds. Total input energy required for crop production and livestock

management was 1598441.0 and 6168311.9 MJ respectively, output energy generated was 9063909.8 and

408331.0 MJ respectively. Even livestock enterprise have shown negative energy balance (-5759980.9 MJ),

overall system has shown positive energy balance of 1705487.9 MJ as crop enterprise offset the ill energy

efficiency of livestock enterprise. Rice emits highest amount of CO2eq (3099.4 kg CO2eq ha-1

) among crops

in study, around 50% is contributed by submergence (continuous flooding). Total GHG emission from the

study area was 532215.3 kg CO2eq. Out of which, 26.1% and 73.9% of the emissions were emitted by crop

(138637.3 kg CO2eq) and livestock enterprise (393578.0 kg CO2eq). Both the cases crop enterprise has

greater advantage over the livestock enterprise. The key policy implication from the current study was

integrated crop-livestock system will sustain in long run, as less energy use and higher GHG emissions of

livestock enterprise will be nullified by the crop enterprise.


KEYWORDS

Integrated crop-livestock

system

Greenhouse gas (GHG)

emission

Energy use

Sustainability

Input energy

Positive energy balance

E-mail: [email protected] (G Kiran Reddy)


















Energy use efficiency and green house gas emissions from integrated crop-livestock systems 99

1 Introduction

Agriculture is the base for livelihood of 66% of the Indian

population and it contributes 20% of national gross domestic

products. It is the source for national food security (ICAR, 2015).

In 1970’s, with advent of green revolution in India, one side it

increased the food grain production. It directly or indirectly

increased the livestock population, as the energy availability

increased. Other side fertilizer and other external inputs usage

increased manifold resulted in deterioration of soil health, thereby

paradism shift in energy use and GHG’s (green house emissions)

emissions over a period. Agriculture requires large energy and

potent source of GHG as stated by Hoffman et al. (2018).

Nearly 85% of the Indian farmers fall under the category of small

and marginal farmers with acreage less than 1 ha. Initial

investment capacity of the Indian farmers is very low. Investment

should not impair the crop productivity. So, the cost of cultivation

should be kept low. In addition to economic analysis, energy

analysis which indicates the inputs to be minimized and energy use

has to be increased. Farming uses the energy in different capacities

e.g. machinery, human power, seed, diesel, animal power,

irrigation etc. An agricultural production system is to be efficient,

if it produces more output (output energy) with minimum input

energy (especially non reneweable inputs). This makes the

agricultural production system viable in environmental and

economic terms (Sefeedpari et al., 2012). It is very useful to

analyse crops in terms of energy. This should be done without

impairing the yield of the crops.

Livestock and land utilization are the critical factors for the GHG

emissions. Farming contributes around 10-12% of the GHG

globally (IPCC, 2007). The share in India context is a bit higher

side at 18% (INCCA, 2010). Farming is in third line after energy

and industry sector in GHG emissions in India. In Indian

agriculture system, farmers cultivate versatile crops and maintain

livestock for the livelihood and nutrition security to family. Mixed

crop-livestock farming is age old practice, which provides food for

more than half of the world’s population (Ghahramani & Bowran,

2018). So, such farming systems environmental impact assessment

is also an important factor. Most part of GHG from farming in

India is contributed by the livestock (Steinfeld et al., 2006). From

global food security point of view livestock plays a very critical

role, as it is meeting the 17% of global energy and 33% of protein

requirement (Rosegrant et al., 2002). Animal protein demand is

increasing day by day globally in general and semi arid deccan

plateau in Southern India in particular. Increased per capita income

is also playing a pivotal role for the increasing consumption and

demand for animal products in this part of world. In addition,

increase in animal population also has other benefits like, provides

organic matter, which is wealth for poor farmers etc.

In regarding the Deccan plateau in Southern India, livestock – crop

integration system is a principle farming pattern adapted by bulk of

the cultivators. India is also one of the leading producers of rice

and milk. Wide variety of crops like rice, maize, groundnut, cotton,

sorghum, vegetables etc. are grown. More or less all farm families

will maintain livestock (either dairy, sheep, poultry etc.).

Highly scarce information is available regarding the impact of

different farming systems on energy use and GHG emissions in the

Deccan plateau of Southern India. An attempt was made to

evaluate the energy sequestered in integrated crop-livestock

farming system, the energy sequestration of different crops,

livestocks and GHG emission from livestock and crop

management in Southern Deccan plateau region of India.


2.1 Description of study area

The survey was conducted under All India Coordinated Research

Project on Integrated Farming System, as part of On Farm

Research in Medak district of Telangana State in Southern India.

The area of surveyed includes two blocks of Medak district i.e.

Yeldurthy and Toopran covering three villages in each block.

Three representative villages from each block i.e. six villages for

each district were selected randomly. From each village six farm

households were chosen at a random keeping in mind that at least

four households should represent each farming system. Yeldurthy

and Toopran blocks were the typical blocks of Medak districts,

which are located 100 kms east of capital city Hyderabad

(Telangana State). Study area is situated in the central part of

Telangana State. Its coordinates are 17o 27’ 0” – 18

o 19’ N latitude,

17o 27’ 0” – 18

o 19’ E longitude 442m. Study area comes under

Southern plateau and hill zone agroclimatic zone of India. The

mean annual rainfall was 861 mm, the mean temperature of the

study area was 26.8○C. Climatic condition of the region is tropical.

Before commencement of the study, the strong research

methodology was developed to investigate the targeted farmers

via. statistical and scientific methods through prepared

questionnaire. Overall 36 farmers were surveyed in the year 2017-

18 for a period of one year from June, 2017 to May, 2018. Farmers

in the study area grow crops like rice, maize, fodder sorghum,

tomato, okra, cotton groundnut there acreages are 37.74, 14.04,

0.25, 0.9, 0.3, 1.0, 0.2 ha respectively. With regard to livestock,

total of 177 dairy cattle, 466 sheep and 129 poultry were

maintained by 36 farm families in total. All the farmers were

following integrated livestock and crop farming system in different

proportions. Geographic and meteorological features of the

surveyed villages were similar to other villages, where the

integrated livestock-crop farming systems were practiced in this

part of world.



100 Latheef Pasha et al.

2.2 Collection of data

The data regards to the complete production of crops and live

stocks were assessed from the targeted farmers by detail interview

with individual farmer with the help of bench mark survey

questionnaire in the study area. General information of farmer such

as holding size, farm land details, farm machinery and equipment,

crop wise input used like seed, fertilizers, human labour, bullock

labour, chemical, oils used. Production of various farm out puts

(rice, maize, tomato and fodder etc) and disposal of produce

livestock details like number of animals, fodder, concentrates,

mineral mixture, livestock products were reported in detail to

calculate the energy balance and GHG emissions.

2.3 Calculations of energy balance and GHG emissions

Various inputs were used for the production of crops and

maintenance of livestock, outputs that were generated by using the

inputs and their energy equivalents are presented in table 1

(ubiquitous environmental sources of energy i.e. radiation, wind

etc. was not taken into account). Energy equivalents present in the

table 1 were used to calculate the input and output energy values

i.e., the input energy which was utilized to produce the output

energy. Input energy classified into direct (labour, fuel and

electricity) and indirect energy (fertilizers, chemicals, machinery,

irrigation, manures and seed), renewable (labour, organic manures

and seed) and non renewable energy (fuel, fertilizers, chemicals,

machinery and water). Output energy like the energy embodied in

the crops, livestock products and byproducts were considered.

Different values generated from the input and output energy were

used to calculate the energy use efficiency, energy productivity,

specific energy, net energy, land use efficiency, non renewable

energy ratio, direct energy, indirect energy, renewable energy and

non renewable energy (standard formulas are given in table 2).

Land use efficiency is the amount of energy generated in a given

unit of land. The ratio between total output and input energy is the

energy use efficiency. Net energy is the difference between the

total output energy generated minus total input energy supplied to

produce the crop. Energy productivity is the quantity of crop

produced by the supply of given amount of energy. Non renewable

energy ratio is the same as energy use efficiency except it

considers only non renewable energy used for the crop production.

GHG emissions were in principle calculated with default

emission factors, as cited by many authors. In present study

different factors that contribute to the GHG emissions were

machinery, diesel, N, P, K chemical fertilizers, FYM, electricity,

chemicals, rice under submergence, production of milk, FYM,

mutton, eggs and poultry. Default factors per unit for the above

said factors were used to calculate the GHG emissions (Table 3).

The present study accounts only for farm management (within

the farm gate) do not account for outside the farm gate.

Calculation was done up to the farm gate only.


3.1 Energy use analysis

The data collected from the surveyed area covering 36 farmers

with total acreage of 54.43 ha under crops (occupied by rice,

maize, fodder sorghum, tomato, okra, cotton and groundnut),

177 dairy cattle, 466 sheep and 129 poultry birds; these data

helps in characterizing the energy use by 36 farmers of the

study area in crops and livestock. Input quantities used for

production of crops are presented in table 4, input and output

energies for crop production are presented in table 5. In

production of crops, input energy per unit area crop production

varied considerably among crops. The input energies for the

production of rice, maize, fodder sorghum, tomato, okra,

cotton, groundnut were 1171215.0, 347730.5, 6096.8, 24959.7,

9561.7, 32629.3, 7247.5 MJ respectively. Total input energy

for the crop production in the study area was 1598441.0 MJ.

Input energies required to calculate for individual crops per ha

to produce the output were 31007.2, 24767.1, 24387.3,

27733.0, 31872.3, 32629.3, 36237.7 MJ for rice, maize, fodder

sorghum, tomato, okra, cotton, groundnut respectively. Highest

input energy required for the production of groundnut while the

least one was observed in the fodder sorghum per ha area.

The output energy i.e. economic and byproducts energy varies

with crops, presented in table 5 (in lower panal). The variation

in yield depends on the genetic potential of crop and

management practices. The output energy calculated for both

economic and byproducts, the values for rice, maize, fodder

sorghum, tomato, okra, cotton, groundnut were 6122882.0,

2702090.0, 59850.0, 58634.8, 23458.0, 75725.0, 21270.0 MJ

respectively. Mean values of the output energy in MJ ha-1

(table 5) were calculated as rice (162288.5), maize (192456.6),

fodder sorghum (239400), Tomato (65149.8), okra (78193.3),

cotton (75725.0) and groundnut (106350). Sartori et al. (2005)

observed in the maize conservation farming, it requires input

energy of 46900 MJ ha-1

and it generated 161980.0 MJ ha-1

output energy. The overall output energy for the study area for

crop enterprise was 9063909.8 MJ. Output energies produced

by the crops were higher than the energy consumed for

production (Table 5). The crop enterprise has produced a

positive energy of 7465468.8 MJ compared to consumed

energy in the study area. Similarly, Tsatsarellis (1991)

calculated the energy use in cotton and reported that cotton

crop in total sequestrated energy of 82600.0 MJ ha-1

.




Table 1 Energy coefficients of inputs and outputs in integrated crop-livestock production

Input

Item Unit Energy equivalent Referance

Human labour h 1.96 Mobtaker et al., 2012

Bullock labour h 10.10 Chandrakar et al., 2013

Diesel L 56.31 Barber, 2004

Machinery h 62.70 Rafiee et al., 2010

Seeds

a. Rice kg 14.70 Mohammadi et al. 2014

b. Maize kg 14.70 Mohammadi et al. 2014

c. Fodder sorghum kg 14.70 Mohammadi et al. 2014

d. Tomato kg 0.96 Gopalan et al., 2012

e. Okra kg 1.46 Gopalan et al., 2012

f. Cotton kg 18.00 Larson & Fangmeir, 1978

g. Groundnut kg 23.73 Gopalan et al., 2012

N kg 60.60 Akcaoz et al. 2009

P kg 11.10 Akcaoz et al. 2009

K kg 6.70 Ozkan et al. 2004

FYM kg 0.30 Devasenapathy et al., 2009

Electricity kWh 12.00 Tsatsarellis, 1991

Herbicide kg 102.00 Chaudhary et al., 2009

Pesticide kg 120.00 Rahman & Barmon, 2012

Insecticide kg 58.00 Tabar et al., 2010

Dry fodder kg 12.50 Mohammadi et al. 2014

Concentrates kg 11.71 Petal, 2012

Green fodder kg 8.37 Petal, 2012

Output

Rice kg 14.70 Mohammadi et al. 2014

Rice straw kg 12.50 Mohammadi et al. 2014

Maize kg 14.70 Mohammadi et al. 2014

Maize stover kg 12.50 Mohammadi et al. 2014

Fodder Sorghum kg 13.30 Krishnamoorthy et al.1995

Tomato kg 0.96 Gopalan et al., 2012

Tomato stover kg 13.00 Mondal, 2010

Okra kg 1.46 Gopalan et al., 2012

Okra stover kg 13.00 Mondal, 2010

Cotton kg 15.50 Larson & Fangmeir, 1978

Cotton stalk kg 18.2 Ozturk & Bascetincelik, 2006

Groundnut kg 23.73 Gopalan et al., 2012

Groundnut stover kg 17.58 Koopmans & Koppejan, 1997

Milk L 4.9 Gopalan et al., 2012

FYM kg 0.3 Devasenapathy et al., 2009

Mutton kg 4.94 Gopalan et al., 2012

Eggs kg 7.24 Gopalan et al., 2012

Poultry kg 21.75 Cao & Adeola, 2015




Table 2 Standard formulas used for calculation of different indices of energy use

Parameter Formula Reference

Energy use efficiency Output energy (MJ ha-1) / Input energy (MJ ha-1) Paramesh et al. (2018)

Energy productivity Crop yields (kg ha-1) / Input energy (MJ ha-1) Mohammadi et al. (2010)

Specific energy Input energy (MJ ha-1) / Output (t ha-1) Paramesh et al. (2018)

Net energy gain Output energy (MJ ha-1) - Input energy (MJ ha-1) Mohammadi et al. (2010)

Land use efficiency Output energy (MJ) / Total land (ha) Vanloon et al. (2005)

Non renewable energy

ratio Output energy (MJ ha-1) / Non-renewable energy input (MJ ha-1) Vanloon et al. (2005)

Direct energy Labour+fuel+electricity (MJ ha-1) Mohammadi et al. (2014)

Indirect energy Chemical fertilizers+

pesticides+insecticides+herbicides+machinery+manure+seed(MJ ha-1) Mohammadi et al. (2014)

Renewable energy Labour+FYM+seed (MJ ha-1) Mohammadi et al. (2014)

Non renewable energy Machinery+diesel+electricity+chemical

fertilizers+pesticides+insecticides+herbicides(MJ ha-1) Mohammadi et al. (2014)

Table 3 GHG emission coefficients of integrated crop-livestock system

Item Unit GHG emission equivalent Referance

Machinery kg CO2eq MJ-1 0.071 Komleh et al., 2013

Diesel kg CO2eq L-1 2.76 Moghimi et al., 2014

N kg CO2eq kg-1 1.3 Lal, 2004

P kg CO2eq kg-1 0.2 Lal, 2004

K kg CO2eq kg-1 0.15 Lal, 2004

FYM kg CO2eq kg-1 0.126 Komleh et al., 2013

Electricity kg CO2eq kWh-1 0.8 Nguyen & Hermansen., 2012

Herbicide kg CO2eq kg-1 3.9 Soni et al., 2013

Pesticide kg CO2eq kg-1 5.1 Soni et al., 2013

Insecticide kg CO2eq kg-1 6.3 Lal, 2004

Paddy 1.1 kg CH4/ha/day× 25 kg CO2eq 1.1 IPCC, 2006

Milk kg CO2eq kg-1 3.4 Gerber et al., 2013

Mutton kg CO2eq kg-1 23.8 Gerber et al., 2013

Eggs kg CO2eq kg-1 4.2 Gerber et al., 2013

Poultry kg CO2eq kg-1 6.6 Gerber et al., 2013

Table 4 Rate of inputs applied in Crop production

Units Rice Maize Sorghum Tomato Okra Cotton Groundnut

Area ha. 37.74 14.04 0.25 0.90 0.30 1.0 0.20

Inputs

Human labour h 38696.0 12048.0 120.0 2160.0 1200.0 920.0 320.0

Bullock labour h 1622.4 760.0 0.0 128.0 8.0 160.0 24.0

Diesel L 3077.5 1035.0 2.5 12.5 20.0 40.0 5.0

Machinery h 615.5 207.0 0.5 2.5 4.0 8.0 1.0

Seed kg 2499.0 347.0 30.0 10.0 3.0 3.0 40.0

N kg 6667.5 2864.0 45.0 220.0 32.0 317.0 64.0

P kg 4053.0 1582.0 10.0 112.0 23.0 219.0 46.0

K kg 2683.0 790.0 0.0 12.0 0.0 45.0 0.0

FYM kg 165500.0 76000.0 1500.0 500.0 3000.0 10000.0 0.0

Electricity kWh 25000.0 1355.0 100.0 220.0 150.0 50.0 50.0

Herbicide kg 72.5 20.7 1.5 5.0 4.0 4.5 0.5

Pesticide kg 30.5 14.0 3.2 3.2 2.5 2.2 0.2

Insecticide kg 28.2 14.0 3.7 3.7 2.5 2.2 0.2




Table 5 Input and output energies for crop production

Units Rice Maize Sorghum Tomato Okra Cotton Groundnut

Area ha 37.74 14.04 0.25 0.90 0.30 1.00 0.20

Input energy

Human labour h 75844.7 23614.1 235.2 4233.6 2352.0 1803.2 627.2

Bullock labour h 16386.2 7676.0 0.0 1292.8 80.8 1616.0 242.4

Diesel L 173294.0 58280.8 140.8 703.9 1126.2 2252.4 281.5

Machinery h 38591.8 12978.9 31.3 94.0 250.8 501.6 62.7

Seed kg 36735.3 5100.9 441.0 9.6 4.4 54.0 949.2

N kg 404050.5 173558.4 2727.0 13332.0 1939.2 19210.2 3878.4

P kg 44988.3 17560.2 111.0 1243.2 255.3 2430.9 510.6

K kg 17976.1 5293.0 0.0 80.4 0.0 301.5 0.0

FYM kg 49650.0 22800.0 450.0 150.0 900.0 3000.0 0.0

Electricity kWh 300000.0 16260.0 1200.0 2640.0 1800.0 600.0 600.0

Herbicide kg 7400.1 2116.5 153.0 510.0 408.0 459.0 51.0

Pesticide kg 3660.0 1680.0 390.0 390.0 300.0 270.0 30.0

Insecticide kg 1638.5 812.0 217.5 217.5 145.0 130.5 14.5

Total 1170215.0 347730.8 6096.8 24959.7 9561.7 32629.3 7247.5

per ha. 31007.3 24767.1 24387.3 27733.0 31872.3 32629.3 36237.7

Output energy

Economic part kg 3275557.0 1483965.0 4684.8 4088.0 30225.0 14238.0

Straw/fodder kg 2847325.0 1218125.0 59850.0 53950.0 19370.0 45500.0 7032.0

Total 6122882.0 2702090.0 59850.0 58634.8 23458.0 75725.0 21270.0

per ha. 162238.5 192456.6 239400.0 65149.8 78193.3 75725.0 106350.0

Table 6 Input energy and output energy of livestock enterprise in integrated crop-livestock system

Input

Buffalos

Units Quantity Energy

Dry fodder kg 289635.0 3620438.0

Concentrates kg 63879.0 748023.1

Human labour hr 54900.0 107604.0

Green fodder kg 102332.0 856314.2

Energy 5332379.0

Sheep

Dry fodder kg 57890.0 723625.0



Green fodder kg 1000.0 8368.0

Energy 821823.2

Poultry



Energy 14109.7

Total input energy of livestock 6168311.9

Output

Milk lit 57632.0 282396.8

FYM kg 239800.0 71940.0

Mutton kg 10252.0 50644.9

Eggs kg 18.0 130.3

Poultry kg 148.0 3219.0

Total output energy of livestock 408331.0




Input and output energies for the livestock enterprises are

presented in table 6. Input energy required for the maintenance of

dairy cattle, sheep, poultry were 5332379.0, 821823.2, 14109.7 MJ

respectively. Total amount of energy input required was 6168311.9

MJ for livestock enterprise in the study area. The output energy

was very low in livestock enterprise i.e. milk (282396.8 MJ), FYM

(71940.0 MJ), mutton (50644.9 MJ), eggs (130.3 MJ) and poultry

(3219.0 MJ). Total output energy for the livestock enterprise was

408331.0 MJ. The livestock enterprise has produced negative

energy use of -5759980.9 MJ compared to consumed energy. The

livestock enterprise was the most energy intensive component and

was great consumer of the energy due to large use of feed

ingredients and it is very labor intensive component compared with

the crop enterprise.

Table 7 presents the energy indices for crops. The results of study

revealed that energy use efficiency of overall crop enterprise was

4.70. It indicates that output energy 4.70 times to the input energy

in crops in the study area. Helander & Delin, (2004) reported that

energy efficiency of integrated system is more than conventional

system. With regards to individual crops, it was highest in fodder

sorghum (9.82) and least in cotton (2.32). Lewandowski &

Schmidt (2006) stated that increase in chemical N fertilizer

application decreases the energy efficiency. The energy

productivity of the crops was calculated to be 0.26 kg MJ-1

, which

means 0.26 kg output is produced per MJ energy consumption. In

the present study energy productivity is highest for fodder sorghum

(0.74 kg MJ-1

) and least for cotton (0.06 kg MJ-1

). Specific energy

is the amount of energy in MJ required to produce the 1 kg

economic yield. Crop enterprises have the mean specific energy of

6.77 MJ kg-1

. It means 6.77 MJ is required to produce 1 kg

economic product. Fodder sorghum requires only 1.35 MJ and

cotton requires 16.73 MJ to produce a kg of economic produce.

Crop enterprise consumes 65.7% indirect energy and 77.7% of non

renewable energy these findings are in line with Talukder et al.

(2019). They reported that in rice production consumes substantial

amount of non renewable energy i.e. 68 – 84% of total input

energy. Crop enterprise have land use efficiency of 131359.0 MJ

ha-1

, it means system produces 131359.0 MJ output energy per ha

area. Net gain of energy per ha area for crop enterprise was

101554.2 MJ. Deike et al. (2008) reported that high values of

output energy results in greater net energy gain. Crop enterprise

has the non renewable energy ratio of 5.89.

Table 8 presents the various energy indices of livestock enterprises.

The energy use efficiency of dairy cattle, sheep, poultry were 0.066,

0.061, 0.237 respectively in the study area. Energy productivity of

the livestock enterprises were dairy cattle (0.011), sheep (0.012) and

poultry (0.012). Among the livestock enterprise, dairy cattle requires

92.52 MJ per liter of milk, sheep requires 80.16 MJ to produce one

Table 7 Energy indices in crop production

Units Rice Maize Fodder

Sorghum

Tomato Okra Cotton Groundnut Avg. of

crops

EUE 5.23 7.77 9.82 2.35 2.45 2.32 2.93 4.70

Energy productivity kg MJ-1 0.19 0.29 0.74 0.20 0.29 0.06 0.08 0.26

Specific Energy MJ kg-1 5.25 3.44 1.35 5.11 3.41 16.73 12.08 6.77

Net Energy MJ ha-1 131231.2 167689.4 215012.7 37416.7 46321.1 43095.7 70112.2 101554.2

Land use efficiency MJ ha-1 162238.5 192456.5 239400.0 65149.8 78193.3 75725.0 106350.0 131359.0

Non renewable energy ratio

6.17 9.36 12.04 3.04 3.77 2.90 3.92 5.89

Direct energy MJ ha-1 14984.7 7537.8 6303.9 9855.8 17863.3 6271.6 8755.7 10224.7

(34.3%)

Indirect energy MJ ha-1 16022.5 17229.3 18083.4 17877.2 14008.9 26357.7 27482.0 19580.2 (65.7%)

Reneweable energy MJ ha-1 4732.8 4215.9 4504.8 6317.8 11123.9 6473.2 9094.0 6637.5

(22.3%)

Non renewable energy MJ ha-1 26274.5 20551.3 19882.5 21415.2 20748.3 26156.1 27143.7 23167.4 (77.7%)

Table 8 Energy indices in livestock enterprise

EUE Energy productivity Specific Energy Net Energy

Buffalos 0.066 0.011 92.52 -4978042.0

Goat 0.061 0.012 80.16 -771178.0

Poultry 0.237 0.012 86.03 -10760.4




kg of mutton and poultry requires 86.03 MJ to produce one kg of

poultry meat. All the three enterprises in the livestock have a

negative energy gain. Pahlavan et al. (2011) stated that low energy

efficiency in system is due to higher input energy.

Table 5 and 6 presents the results accounts for the energy

performance of study area i.e. crop production and livestock

respectively. Table 6 shows clearly that livestock enterprises

consumes substantially higher energy and in return produces the

very little output energy, which can potentially cause a serious

impact on sustainability in long run. In this regard Moore (2010)

stated that, increase the energy productivity of the system to attain

the sustainability of production. It was quiet opposite in case of the

crop enterprise, it consumes little energy and produces much

higher output energy. As the farmers of the study area practices

integrated livestock and crops, the total input energy for the study

area (all 36 farmers) was 7766752.9 MJ and output energy was

9472240.8 MJ. When the total system is considered, it is

environmentally sustainable in long run, as system as positive

energy of 1705487.9 MJ. This is made possible by the energy use

saving in the crop enterprise; totally offset the livestock negative

energy use. Livestock enterprise is a good revenue generation

sector to farmers, it is recommended to go for integrated livestock-

crops for the energy sustainability. Malcolm et al. (2015), also

reported integration helps in lowering the energy use. According to

Moraine et al. (2017) integration of crop-livestock farming systems

promises a greater sustainability.

3.2 Greenhouse gas emissions

The data collected from the surveyed area, covering 36 farmers,

where integrated livestock-crops were followed and converted into

kg CO2eq by using the emission coefficients for crops and live

stocks. GHG emissions regarding the crop production was

presented in table 9. GHG emissions were highest in rice crop

(116969.6 kg CO2eq) it occupies an area of 37.74 ha of land, with

mean of 3099.4 kg CO2eq ha-1

. Mohammadi et al. (2014) also

reported that compared to all crops under study rice has produced

highest GHG emissions. Maize occupies an area of 14.04 ha and

had potential to emit GHG of 17929.9 kg CO2eq with an average of

1277.1 kg CO2eq ha-1

, followed by cotton (1916.8 kg CO2eq), okra

(643.8 kg CO2eq), tomato (643.6 kg CO2eq), fodder sorghum (382.5

kg CO2eq) and groundnut (151.0 kg CO2eq). Total GHG emissions

from the crops were 138637.3 kg CO2eq in total of 54.43 ha. If we

consider GHG emissions for specific area of ha it ranges between

715.4 kg CO2eq ha-1

(tomato) to 3099.4 kg CO2eq ha-1

(rice). Similar

study was conducted by Tongwane et al. (2016) noticed that

tomato crop management has produced 1650 kg CO2eq ha-1

. Bos et

al. (2014) reported that GHG emission in crop production ranged

from 45 kg CO2eq Mg-1

(sugar beet) to 520 kg CO2eq Mg-1

(pea).

Figure 1 presents the contribution of different parameters to GHG

emissions for 1 ha crop production and figure was quite helpful to

Table 9 Amount of GHG emissions from crop and livestock enterprises

GHG (kg CO2eq) in surveyed area GHG (kg CO2eq)/ha

Crops

Rice 116969.6 3099.4

Maize 17929.9 1277.1

Fodder sorghum 382.5 1529.9

Tomato 643.6 715.1

Okra 643.8 2145.9

Cotton 1916.8 1916.8

Groundnut 151.1 755.4

Crops total 138637.3

Livestock

Milk 195948.8

FYM 30214.8

Mutton 166362.0

Eggs 75.6

Poultry 976.8

Livestock total 393578.0

Overall total 532215.3




assess the role of different parameters for GHG emissions in crop

production. For rice production, submergence of crop was the most

important source for GHG emissions followed by electricity and

FYM application. Similar findings were summarized by Liu et al.

(2010); Nayak et al. (2015) and Xu et al. (2017). Rice crop under

submergence (continuous flooding) generates CH4 emissions

because of reduced conditions in soil. While in cotton, okra, fodder

sorghum, maize, the application of FYM was among the most

important contributor of GHG emission. In the study, after FYM,

electricity and N fertilizers play a pivotal role in GHG emissions.

Sarauskis et al. (2019) also reported that FYM fertilization resulted

in increase in GHG emissions.

As the input energy was very high to produce a specific quantity

of livestock products and was expected that GHG emissions from

the livestock will be higher than the crop production. The results

of the GHG emission was summarized in the table 9 (in lower

panal). In the study 177 dairy cattle and bullocks has produced

57637 lit of milk, 6740 kg of meat, 239800 kg of FYM. The CO2

emission coefficient to produce a liter of milk, kg of meat and

FYM were 3.4, 36.8 and 0.126 kg CO2eq respectively. The dairy

cattle produces 195948.8, 30214.8 kg CO2eq for production of

milk, FYM respectively. In total produces 226163.6 kg CO2eq

from the dairy cattle components. Similar findings were reported

by Mariantonietla et al. (2017) in Italy, these researchers stated that

production of milk was one of the important factors for GHG

emissions in agriculture. GHG emissions associated with sheep

were presented in table 9. To produce a kg of mutton the CO2

emission coefficient was 23.8 kg CO2eq. In the study year sheep

weight was 10252 kg. The potential GHG emission from sheep

components was 166362.0 kg CO2eq. The CO2 emission

coefficient for eggs and poultry meat was 4.2 and 6.6 kg CO2eq

kg-1

. The production of eggs and poultry meat in study was 18.0

kg and 148.0 kg respectively. So, the poultry component can

produce the GHG emissions of 1052.4 kg CO2eq. Total GHG

emissions from the livestock enterprises were 641610.0 kg

CO2eq. Whole study area GHG emission was 780247.3 kg CO2eq.

Vetter et al. (2017) reported that GHG emission was highest for

rice and livestock products. The plant protein to animal protein

conversion was inefficient in livestock, this was the utmost

important point for the high GHG emissions from livestock as

reported by Ripple et al. (2014). Li et al. (2017) concluded that

integrated livestock-crop systems can reduce the net GHG

emissions by 10.15% compared to two separate systems. Saltona

et al. (2014) and Buller et al. (2015) conducted a case study in

Pantanal savanna highland, Brazil regarding integrated crop-

livestock systems and summarized that system can improve soil

fertility and mitigate GHG emissions helps towards a more

sustainable agriculture in long term for Brazilian cerrado.

Figure 1 Contribution of different parameters in the GHG emissions of 1 ha crop production in study area

0% 20% 40% 60% 80% 100%

Rice

Maize

Fodder Jowar

Tomato

Bhendi

Cotton

GroundnutMechinary

Diesel

N fertilizers

P fertilizers

K fertilizers

FYM

Electricity

Herbicide

Pesticide

Insecticide

Paddy submergence




Conclusion

The principal aim of the current study was to assess the energy use

and GHG emissions from integrated crop-livestock systems in

semi arid Deccan plateau of Southern India and their sustainability

in long run. Policy makers are very keen at popularizing the

integrated crop-livestock system, assessing energy dynamics and

environmental impact of the system helps in its sustainability in

future. The information regarding inputs and outputs were

collected from 36 farmers, integrated crop-livestock was evaluated

in terms of energy use and GHG emissions. Among enterprises,

crop enterprise in the system was highly efficient in energy while

livestock enterprise was very highly inefficient. The results

indicate that crop enterprise consumed 1598441.0 MJ of energy

and generated 9063909.8 MJ. Crop enterprise has a positive

energy balance of 7465468.8 MJ. Energy use efficiency of 4.7

and it consumed indirect (65.7%), non renewable energy

(77.7%) greater than direct (34.3%) and renewable energy

(22.3%). Regards to livestock enterprise, it consumed

6168311.9 MJ of energy and produced 408331.0 MJ. This

enterprise has a negative energy balance of -5759980.9 MJ.

Overall, the net energy balance of integrated crop-livestock

system was 1705487.9. It implies, negative energy balance in

livestock enterprise is neutralized by positive energy balance of

the crop enterprises. On a whole integrated crop-livestock

system can sustain in long run due to positive energy balance

the system. Livestock enterprise alone sustainability is big

issue in future.

Comparison between energy input and emitted CO2 in the study

area showed that there was a direct relationship between energy

input and CO2 emissions. In GHG emission analysis in crop

enterprise emissions ranged between 3099.4 kg CO2eq ha-1

(rice) to 715.4 kg CO2eq ha-1

(tomato). Among the crops rice

has emitted greater kg CO2eq ha-1

per specific area and 50% of

emissions were caused by submergence of rice crop. Total

GHG emissions form crop enterprise was 138637.3 kg CO2eq.

Livestock enterprise production system emitted 641610.0 kg

CO2eq, it was much higher compared to crop enterprises in

study area. To whole system has produced 780247.3 kg CO2eq.

Livestock production represents the one of the prime source of

income for small and marginal farmers and was the protein

supplement in this part of India. This study clearly insight that

integrating livestock with crop production is best possible option to

increase the energy use and to reduce GHG emissions that helps in

environmental sustainability. Hope, all the farmers will convert to

integrated crop-livestock and policy makers should encourage the

system, so that farming will be sustained both in energy use and

environmental impact.

Conflict of interest

Not have any conflict of interest with any one of co authors and

others.

References

Akcaoz H, Ozcatalbas O, Kizilay H (2009) Analysis of energy use

for pomegranate production in Turkey. Journal of Food,

Agriculture and Environment 7: 475-80.

Barber A (2004) Seven Case Study Farms: Total Energy & Carbon

Indicators for New Zealand Arable & Outdoor Vegetable

Production. AgriLINK New Zealand Ltd, Pukekohe. Available on

http://www.agrilink.co.nz/Files/Arable_Vegetable_Energy_Use_M

ain_Report.pdf access on 21 October, 2019.

Bos JFFP, Haan JD, Sukkel W, Schils RLM (2014) Energy use and

greenhouse gas emissions in organic and conventional farming

systems in the Netherlands. NJAS-Wageningen Journal of Life

Sciences 68: 61-70. DOI:

http://dx.doi.org/10.1016/j.njas.2013.12.003.

Buller LS, Bergier I, Ortega E, Moraes A, Bayma-Silva G, Zanetti

MR (2015) Soil improvement and mitigation of greenhouse gas

emissions for integrated crop–livestock systems: Case study

assessment in the Pantanal savanna highland, Brazil. Agricultural

Systems 137: 206-219. DOI:

http://dx.doi.org/10.1016/j.agsy.2014.11.004.

Cao MH, Adeola O (2015) Energy value of poultry by product

meal and animal-vegetable oil blend for broiler chickens by the

regression method. Poultry Science 95: 268–275. DOI:

http://dx.doi.org/10.3382/ps/pev317.

Chandrakar SK, Soni DL, Yadav DK, Sahu LK (2013)

Experimental study on animal powered mechanical device for

home lighting system. International Journal of Environmental

Engineering and Management 4: 471-482.

Chaudhary VP, Gangwar B, Pandey DK, Gangwar KS (2009)

Energy auditing of diversified rice- wheat cropping systems in

Indo-gangetic plains. Energy 34: 1091-1096. DOI:

https://doi.org/10.1016/j.energy.2009.04.017.

Deike S, Pallutt B, Christen O (2008) Investigations on the energy

efficiency of organic and integrated farming with specific

emphasis on pesticide use intensity. European Journal of

Agronomy 28: 461-470. doi:10.1016/j.eja.2007.11.009

Devasenapathy P, Senthilkumar G, Shanmugam PM (2009)

Energy management in crop production. Indian Journal of

Agronomy 54: 80-89.




Gerber PJ, Steinfeld H, Henderson B, Mottet A, Opio C, Dijkman

J, Falcucci A, Tempio G (2013) Tackling climate change through

livestock-A global assessment of emissions and mitigation

opportunities. Food and Agriculture Organization of the United

Nations (FAO), Rome.

Ghahramani A, Bowran D (2018) Transformative and systemic

climate change adaptations in mixed crop livestock farming

systems. Agricultural Systems 164: 236–251. DOI:

https://doi.org/10.1016/j.agsy.2018.04.011.

Gopalan C, Sastri BVR, Balasubramanian SC (2012) Nutritive

value of Indian foods. National institute of nutrition, ICMR,

Hyderabad.

Helander CA, Delin K (2004) Evaluation of farming systems

according to valuation indices developed within a European

network on integrated and ecological arable farming systems.

European Journal of Agronomy 21: 53-67. doi:10.1016/S1161-

0301(03)00089-3.

Hoffman E, Cavigelli MA, Camargo G, Ryan M, Ackroyd VJ,

Richard TL, Mirsky S (2018) Energy use and greenhouse gas

emissions in organic and conventional grain crop production:

Accounting for nutrient inflows. Agricultural systems 162: 89-96.

https://doi.org/10.1016/j.agsy.2018.01.021.

ICAR (2015) VISION 2020—Indian Council of Agricultural

Research. ICAR, New Delhi, India.

INCCA (2010) India : Greenhouse Gas Emissions 2007. Indian

Network for Climate Change Assessment, Ministry of

Environment and Forests, Government of India.

IPCC (2006) Guidelines for national greenhouse gas inventories.

In: Eggleston HS, Buendia L, Miwa K, Ngara T, Tanabe K (Eds),

Prepared by the national greenhouse gas inventories programme.

2006. Japan: IGES available on

http://www.ipccnggip.iges.or.jp/public/2006gl/index.htm access on

20 October,2019.

Intergovernmental Panel on Climate Change - IPCC (2007)

Climate Change 2007: Impacts, Adaptation and Vulnerability.

Summary for policy makers. Available on

http://www.ipcc.cg/SPM13apr07.pdf access on 20 October,2019.

Komleh SHP, Omid M, Heidari MD (2013) On the study of energy

use and GHG (greenhouse gas) emissions in greenhouse cucumber

production in Yazd province. Energy 59: 63-71. DOI:


Koopmans A, Koppejan J (1997) Agricultural and forest residues-

generation, utilization and availability. In: Regional consultation

on modern applications of biomassenergy 1997 held at,

Kualampur, Malaysia: FAO on 6–10January1997.

Krishnamoorthy U, Solled H, Steingass H, Menke KH (1995)

Energy and protein evaluation of tropical feed stuffs for whole

tract and ruminal digestion by chemical analyses and rumen

inoculum studies in vitro. Animal Feed Science and Technology

52: 177-188.

Lal R (2004) Carbon emission from farm operations. Environment

International 30: 981-990. DOI:

https://doi.org/10.1016/j.envint.2004.03.005.

Larson DL, Fangmeir DD (1978) Energy in irrigated crop

production. Transactions of the American Society of Agricultural

Engineers 21:1075-1080.

Lewandowski I, Schmidt U (2006) Nitrogen, energy and land use

efficiency of miscanthus, reed canary grass and triticale as

determined by the boundary line approach. Agriculture, Ecosystem

& Environment 112: 335-346.

Li Z, Sui P, Wang X, Yang X, Long P, Cui J, Yan L, Chen Y

(2017) Comparison of net GHG emissions between separated

system and crop-swine integrated system in the North China Plain.

Journal of Cleaner Production 149: 653-664. DOI:

http://dx.doi.org/10.1016/j.jclepro.2017.02.113.

Liu S, Qin Y, Zou J, Liu Q (2010) Effects of water regime during

rice-growing season on annual direct N2O emission in a paddy

rice-winter wheat rotation system in southeast China. Science of

the Total Environment 408: 906-913.

Malcolm GM, Camargo GGT, Ishler VA, Richard TL, Karsten HD

(2015) Energy and greenhouse gas analysis of northeast US dairy

cropping systems. Agricultural, Ecosystem & Environment 199:

407-417.

Mariantonietta F, Alessia S, Francesco C, Giustina P (2017) GHG

and Cattle Farming: Co-Assessing the Emissions and Economic

Performances in Italy. Journal of Cleaner Production 172: 3704-

3712. doi: 10.1016/j.jclepro.2017.07.167.

Mobtaker HG, Akram A, Keyhani A, Mohammadi A (2012)

Optimization of energy required for alfalfa production using data

envelopment analysis approach. Energy Sustainable Development

16: 242-248. DOI: https://doi.org/10.1016/j.esd.2012.02.001.

Moghimi MR, Pooya M, Mohammadi A (2014) Study on energy

balance, energy forms and greenhouse gas emission for wheat

production in Gorve city, Kordestan province of Iran. European

Journal of Experimental Biology 4: 234-239.




Mohammadi A, Rafiee S, Jafari A, Keyhani A, Avval SHM,

Nonhebel S (2014) Energy use efficiency and greenhouse gas

emissions of farming systems in North Iran. Renewable and

Sustainable Energy Reviews 30: 724-733.

Mohammadi A, Rafiee S, Mohtasebi SS, Rafiee H (2010) Energy

inputs-yield relationship and cost analysis of kiwifruit production

in Iran. Renewable Energy 35: 1071-1075.

Mondal AH (2010) Implications of renewable energy technologies

in the Bangladesh power sector: long-term planning strategies.

ZEF. Ph D Thesis. ISBN: 978-3-940124-25-8

Moore SR (2010) Energy efficiency in small-scale bio intensive

organic onion production in Pennsylvania, USA. Renewable

Agriculture and Food System 25: 181-188.

Moraine M, Melaca P, Ryschawya J, Durua M, Theronda O (2017)

A participatory method for the design and integrated assessment of

crop-livestock systems in farmer’s groups. Ecological Indicators

72: 340–351.

Nayak D, Saetnan E, Cheng K, Wang W, Koslowski F, Cheng Y,

Zhu WY, Wang J, Liu J, Moran D, Yan X, Cardenas L, Newbold J,

Pan G, Lu Y, Smith P (2015) Management opportunities to

mitigate greenhouse gas emissions from Chinese agriculture.

Agricultural Ecosystems and Environment 209: 108-124.

Nguyen TLT, Hermansen JE (2012) System expansion for

handling co-products in LCA of sugarcane bio-energy systems:

GHG consequences of using molasses for ethanol production.

Applied Energy 89: 254-261.

Ozkan B, Akcaoz H, Fert C (2004) Energy input-output analysis in

Turkish agriculture. Renewable Energy 29: 39-51.

Ozturk HH, Bascetincelik A (2006) Energy exploitation of

agricultural biomass potential in Turkey. Energy Exploration &

Explotation 24: 313-330.

Pahlavan R, Omid M, Akram A (2011) Energy use efficiency in

greenhouse tomato production in Iran. Energy 36: 6714-6719.

DOI: https://doi.org/10.1016/j.energy.2011. 10.038.

Paramesh V, Arunachalam V, Nikkhah A, Das B, Ghnimi S (2018)

Optimization of energy consumption and environmental impacts of

arecanut production through coupled data envelopment analysis

and life cycle assessment. Journal of Cleaner Production 203: 1-11.

DOI: https://doi.org/10.1016/j.jclepro.2018.08.263.

Petal A (2012) Nutritive value of commonly available feeds and

fodders in India. Animal nutrition group, National Dairy

Development Board, Anand.

Rafiee S, Avval SHM, Mohammadi A (2010) Modelling and

sensitivity analysis of energy inputs for apple production in Iran.

Energy 35: 3301-3306. DOI:


Rahman S, Barmon BK (2012) Energy productivity and efficiency

of the ‘gher’ (prawn-fish-rice) farming system in Bangladesh.

Energy 43: 293-300. DOI:

https://doi:10.1016/j.energy.2012.04.027.

Ripple WJ, Smith P, Haberl H, Montzka SA, McAlpine C,

Boucher DH (2014) Ruminants, climate change and climate policy.

Nature Climate Change 4: 2-5.

Rosegrant MW, Cai X, Cline SA (2002) Global water outlook to

2025: Averting and impending crisis. International Water

Management Institute (IWMI), 2020 Vision for Food, Agriculture,

and the Environment, International Food Policy Research Institute

(IFPRI). Washington, D.C, Colombo, Sri Lanka.

Saltona JC, Mercantea FM, Tomazi M, Zanatta JA, Concenc G,

Silva WM, Retorea M (2014) Integrated crop-livestock system in

tropical Brazil: Toward a sustainable production system.

Agriculture, Ecosystems and Environment 190: 70-79. DOI:

http://dx.doi.org/10.1016/j.agee.2013.09.023.

Sarauskis E, Masilionyte L, Juknevicius D, Buragiene S,

Kriauciuniene Z (2019) Energy use efficiency, GHG emissions and

cost-effectiveness of organic and sustainable fertilization. Energy

172: 1151-1160.

Sartori L, Basso B, Bertocco M, Oliviero G (2005). Energy Use

and Economic Evaluation of a Three Year Crop Rotation for

Conservation and Organic Farming in NE Italy. Biosystems

Engineering 91: 245–256.

Sefeedpari P, Rafiee S, Komleh SHP, Ghahderijani M (2012) A

source-wise andoperation-wise energy use analysis for corn silage

production, a case study of Tehranprovince, Iran. International

Journal of Sustainable Built Environment 1: 158-166.

Soni P, Taewichit C, Salokhe VM (2013) Energy consumption and

CO2 emissions in rainfed agricultural production systems of

Northeast Thailand. Agricultural systems 116: 25-36.

Steinfeld H, Gerber P, Wassenaar T, Castel V, Rosales M, Haan

CD (2006) Livestock’s Long Shadow: Environmental Issues and

Options. Food and Agriculture Organization of the United Nations

(FAO).

Tabar BI, Keyhani A, Rafiee S (2010) Energy balance in Iran’s

agronomy (1990-2006). Renewable Sustainable Energy Reviews

14: 849-855.




Talukdera B, Vanloonb GW, Hipel KW (2019) Energy efficiency

of agricultural systems in the southwest coastal zone of

Bangladesh. Ecological Indicators 98: 641-648. DOI:

https://doi.org/10.1016/j.ecolind.2018.11.030.

Tongwane M, Mdlambuzi T, Moeletsi M, Tsubo M, Mliswa V,

Grootboom L (2016) Greenhouse gas emissions from different

crop production and management practices in South Africa.

Environmental Development 19: 23-35. DOI:

http://dx.doi.org/10.1016/j.envdev.2016.06.004.

Tsatsarellis CA (1991) Energy requirements for cotton production

in Central Greece. Journal of Agricultural Engineering Research.

50: 239-246.

Vanloon GW, Patil SG, Hugar LB (2005) Agricultural

Sustainability: Strategies for Assessment. SAGE.

Vetter SH, Sapkota TB, Hillier J, Stirling CM, Macdiarmid JI,

Aleksandrowicz ., Green R, Joy EJM, Dangour AD, Smith P

(2017) Greenhouse gas emissions from agricultural food

production to supply Indian diets: Implications for climate change

mitigation. Agriculture, ecosystems and environment. 237: 234-

241. DOI: http://dx.doi.org/10.1016/j.agee.2016.12.024.

Xu G, Liu X, Wang Q, Yu X, Hang Y (2017) Integrated rice-duck

farming mitigates the global warming potential in rice season.

Science of the Total Environment 575: 58-66. DOI:

http://dx.doi.org/10.1016/j.scitotenv.2016.09.233.



ISSN No. 2320 – 8694


CHARACTER ASSOCIATION AND PATH ANALYSIS FOR SEED VIGOR TRAITS

IN SESAME (Sesamum indicum L.)

Manjeet, Mahavir, Anu, Vivek K. Singh*, P.K. Verma

Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar, Haryana (India)-125 004

Received – February 05, 2020; Revision – March 06, 2020; Accepted – March 28, 2020 Available Online – April 25, 2020


ABSTRACT

The present investigation was conducted using 24 genotypes of sesame for correlation and path analysis

among seven seed quality characters which revealed that standard germination showed a significant and

positive correlation with shoot length, root length, seedling length, seed vigor index-I and seed vigor

index-II, which indicated that by increasing these attributes, standard germination will be increased, thus

these parameters are suggestive of good plant stand and ultimately seed yield. Path coefficient analysis

suggested that seed vigor index-I, shoot length and seedling length are major components of standard

germination which could increase the seed yield.


KEYWORDS

Sesame

Genotype

Correlation

Path analysis

E-mail: [email protected] (Vivek K. Singh)


















Character Association and Path Analysis for Seed Vigor Traits in Sesame (Sesamum indicum L.) 112

1 Introduction

Crop yields and quality would be greatly affected without a steady

supply of high-quality seed (Douglas, 2019). Seed is a basic input in

agriculture and quality seed plays an important role in the sustainable

agronomic and horticultural crops production (Shubha et al., 2017).

Seed quality describes the potential performance of a seed lot.

Trueness to variety is depended on the various important aspects of

seed quality among these some common are, the presence of inert

matter, other crops seeds, noxious weed seed, freedom from disease

and insect infestations (Bishaw et al., 2007). High-quality seed lots

should meet minimum standards for each of these characteristics.

Achieving and maintaining high seed quality is the prerequisite for

any seed certification programs (Gebeyehu et al., 2019). Germination

potential and vigor are at their highest potential when the seed

reaches physiological maturity. Due to high seed moisture, most

crops are not ready to be harvested at that time (Rao et al., 2017).

Seed vigor is the potential of seeds for rapid and uniform emergence

and development of normal seedlings under various environmental

conditions (Marcos Filho, 2015). Although vigor testing is not

required for labeling of seed, many seed producers use vigor tests as

a quality control to ensure that the seed produced is of high quality.

Seed vigor is influenced by many factors, such as maturity level at

harvest, age of the seed, mechanical injuries, disease infection,

storage environment and genetic constitutions of the seeds (Khare &

Bhale, 2016). Correlation coefficient analysis is very important in

plant breeding experiment because it computes the degree of genetic

and non-genetic association between two or more traits and to help in

concurrent selection when more than one trait is desired (Laidig et

al., 2017). Although, correlation coefficient analysis among the

different traits does not considers the cause and effect relationships

between dependent and independent characters. Path analysis

provides a measure of relative significance of each independent

variable to prediction of changes in the dependent one. A path

coefficient is a standardized partial regression coefficient which

measures the direct effect of one trait upon other and allows the

partition of correlation coefficient into direct and indirect effects

(Yakubu, 2010; Mecha et al., 2017). Path coefficients show direct

influence of independent variable upon dependent variable (Jiang et

al., 2017). In plant breeding programme, path coefficient analysis has

been used by geneticist and crop breeders to aid in identifying traits

that are useful as selection criteria to improve seed germination and

seed vigor traits (Sunday et al., 2007; Rahman et al., 2012).

Therefore, the present study was planned to study the association

between different seed quality traits which influences the seed vigor

and germination per cent.

2 Materials and methods

The experimental material comprised of 24 diverse genotypes of

sesame procured from Oilseeds Section, Department of Genetics

and Plant Breeding, CCS Haryana Agricultural University, Hisar.

Seed quality parameters viz., standard germination (%), shoot

length (cm), root length (cm), seedling length (cm), seedling dry

weight (mg), seed vigor index-I & seed vigor index-II were

estimated and recorded in the seed quality testing laboratory of the

Department of Seed Science and Technology from the harvested

seeds of selected plants. For the estimation of standard germination

(%), 3 replications with 100 seeds per replication for each

genotype were placed on sufficiently moistened rolled germination

papers in petri dishes (top of the paper method of standard

germination testing) at 25°C temperature with 90-95% relative

humidity in the seed germinator. Final count for germination was

recorded on 6th

day (as per International Seed Testing Association,

2009). At the time of final seedling evaluation, seeds were

classified as normal seedling, abnormal seedling, fresh un-

germinated seeds and dead seed. Normal seedlings including fresh

un-germinated seeds were expressed as per cent germination.

Shoot length (cm) and root length (cm) were estimated using 30

seedlings selected randomly from the normal seedlings in each

replication for all genotypes at the time of final count of standard

germination and average shoot and root length for each genotype

were measured. Seedling length (cm) was calculated using the

same seedlings which used for calculating shoot and root lengths

for all the genotypes and average seedling length was measured as

Seedling length = Root length + Shoot length. Seedling dry weight

(mg) was measured using the same 30 normal seedlings that were

taken for measuring the root and shoot length and these seedlings

were kept further in hot air oven at 80°C for 24 hours and average

dry weight per seedling was recorded in milligram. From the

observations of standard germination test, the seed vigor index-I

and seed vigor index-II were calculated according to the method

suggested by Abdul-Baki & Anderson (1973) using the following

formulae viz., Seed vigor index-I = Standard Germination (%) X

Seedling length (cm) and Seed vigor index-II = Standard

Germination (%) X Seedling dry weight (mg). The correlation

coefficients among all possible character combinations at

phenotypic ‘r (p)’ and genotypic ‘r (g)’ level were estimated by

employing the formulae given by Al- Jibouri et al. (1958). The

path coefficient analysis was performed as per the formula given

by Wright (1921) and adopted by Dewey & Lu (1959).


Genotypic correlation coefficients have higher magnitude than

their corresponding phenotypic correlation coefficients, which

revealing a good amount of strong inherent association between

different attributes. Similar findings were also reported by Garg et

al. (2017) in green gram. Standard germination (%) showed

significantly positive correlation at both genotypic and phenotypic

level with shoot length (cm), root length (cm), seedling length

(cm), seed vigor index-I and seed vigor index-II. These parameters

were indicative of good plant stand in the field and ultimately seed



113 Manjeet et al.

yield. Similar results for one or more characters were reported by

Singh et al. (2000) in wheat, Garg et al. (2017) in mungbean,

Yadav et al. (2011) in barley and Jan & Kashyap (2019) in rice.

Both the seed vigor index-I and seed vigor index-II showed

significantly positive correction at both genotypic and phenotypic

level with shoot length (cm), root length (cm), seedling length

(cm), seedling dry weight (mg) and standard germination (%), and

also showed significantly positive correlation with each other at

both genotypic and phenotypic level (Table 1). Similar results for

one or more of these characters were also reported in barley

(Yadav et al., 2011). Usually, traits that exerted positive direct

effect as well as significantly positive correlation coefficient with

standard germination (%) and seed vigor indices were known to

affect seed vigor and germination in the favourable direction and

need much consideration during the process of selection. The

highest positive direct effect on standard germination (%) was

exerted by seed vigor index-I (3.080) followed by shoot length

(0.872). The positive direct effect on standard germination (%) was

also exhibited by seedling length (0.265) while the characters viz.,

root length (cm), seed vigor index-II and seedling dry weight (mg)

had negative direct effect on standard germination (%) with the

values of -1.602, -0.148 and -0.057, respectively (Table 2). The

residual effect (-0.01721) indicate that the component characters

under study were responsible for about 98% of variability in

standard germination.

Conclusion

So far from the combined results of correlation coefficient and path

coefficient analysis, it may be concluded that seed vigor index-I,

shoot length and seedling length are major components of standard

germination which can be taken into consideration to improve

plant population through increasing germination per cent,

ultimately resulting in higher seed yield.




Table 1 Phenotypic (above diagonal) and genotypic (below diagonal) correlation coefficients among seed quality traits in sesame

Seed quality

traits SL RL SdL SdW VG 1 VG 2 SG

SL 1.000 0.347** 0.667** 0.324** 0.614** 0.413** 0.306**

RL 0.815** 1.000 0.930** 0.387** 0.848** 0.524** 0.427**

SdL 0.891** 0.989** 1.000 0.434** 0.913** 0.578** 0.459**

SdW 0.171 0.603** 0.515** 1.000 0.371** 0.844** 0.157

VG 1 0.924** 0.928** 0.960** 0.527** 1.000 0.703** 0.775**

VG 2 0.596** 0.751** 0.739** 0.861** 0.835** 1.000 0.659**

SG 0.807** 0.663** 0.724** 0.462** 0.891** 0.849** 1.000

* Significant at P = 0.05 Level; ** Significant at P = 0.01 Level

Table 2 Path coefficient analysis for seed quality traits in sesame for standard germination

Seed Quality traits SL RL SdL SdW VG 1 VG 2

Shoot length (cm) 0.872 -0.710 -0.777 -0.149 -0.805 -0.519

Root length (cm) -1.306 -1.602 -1.584 -0.966 -1.486 -1.203

Seedling length (cm) 0.236 0.262 0.265 0.137 0.255 0.196

Seedling dry weight (mg) -0.010 -0.034 -0.029 -0.057 -0.030 -0.049

Seedling vigour index I 1.103 2.857 2.957 1.623 3.080 2.572

Seedling vigour index II -0.088 -0.111 -0.109 -0.127 -0.124 -0.148

Genotypic correlation with SG 0.807** 0.663** 0.724** 0.462** 0.891** 0.849**

Residual effect = -0.01721; * Significant at P = 0.05 Level ** Significant at P = 0.01 Level; SG = Standard germination

(%), SL = Shoot length (cm), RL = Root length (cm), SdL = Seedling length (cm), SdW = Seedling dry weight (mg), VG 1 = Seed vigor index-I,

VG 2 = Seed vigor index-II



Character Association and Path Analysis for Seed Vigor Traits in Sesame (Sesamum indicum L.) 114

References

Abdul-Baki AA, Anderson JD (1973) Vigor determination in

soybean seed by multiple criteria 1. Crop Science 13: 630-633.

Al-Jibouri HA, Miller PA, Robinson HF (1958) Genetic and

environmental variances and co-variances in an upland cotton

cross of interspecific origin. Agronomy Journal 50: 633-636.

Bishaw Z, Niane AA, Gan Y (2007) Quality seed production.

In: Springer, Dordrecht, Lentil, Pp. 349-383.

Dewey DI, Lu KH (1959) A correlation and path-coefficient

analysis of components of crested wheatgrass seed production.

Agronomy Journal 51: 515-518.

Douglas JE (2019) Successful seed programs: A planning and

management guide. CRC Press.

Garg GK, Verma PK, Kesh H, Kumar A (2017) Genetic

variability, character association and genetic divergence for seed

quality traits in mungbean [Vigna radiata (L.) Wilczek]. Indian

Journal of Agriculture Research 51: 521-528.

Gebeyehu S, Kangile J, Mwakatobe E (2019) Assessment of seed

quality along the rice seed value chain in Tanzania. Development

in Practice 29: 854-866.

ISTA (2009) International rules for seed testing. Seed Science and

Technology 27: 1-334.

Jan N, Kashyap SC (2019) Correlation and path analysis in rice

(Oryza sativa L.) for seed and seed vigor traits. Journal of

Pharmacognosy & Phytochemistry 8: 222-226.

Jiang S, Zhou F, Yang Q, Yang L (2017) Correlation and path

coefficient analyses of the morphological characteristics and body

weight of Penaeus monodon (Crustacea, Decapoda,

Penaeidae). Insights in Aquaculture and Biotechnology 1: 2.

Khare D, Bhale MS (2016) Seed technology. Scientific Publishers,

Jodhpur, India.

Laidig F, Piepho HP, Rentel D, Drobek T, Meyer U (2017)

Breeding progress, genotypic and environmental variation and

correlation of quality traits in malting barley in German official

variety trials between 1983 and 2015. Theoretical and Applied

Genetics 130: 2411-2429.

Marcos Filho J (2015) Seed vigor testing: an overview of the past,

present and future perspective. Scientia Agricola 72: 363-374.

Mecha B, Alamerew S, Assefa A, Assefa E, Dutamo D (2017)

Correlation and path coefficient studies of yield and yield

associated traits in bread wheat (Triticum aestivum L.)

genotypes. Advances in Plants & Agriculture Research 6: 00226.

Rahman MM, Syed MA, Adil M, Ahmad H, Rashid MM (2012)

Genetic variability, correlation and path coefficient analysis of

some physiological traits of transplanted Aman rice (Oryza sativa

L.). Middle East Journal of Scientific Research 11: 563-566.

Rao NK, Dulloo ME and Engels JM (2017) A review of factors

that influence the production of quality seed for long-term

conservation in genebanks. Genetic Resources and Crop

Evolution 64: 1061-1074.

Shubha K, Mukherjee A, Kumari M, Tiwari K, Meena VS (2017)

Bio-stimulants: an approach towards the sustainable vegetable

production. In: agriculturally important microbes for sustainable

agriculture, Springer, Singapore, Pp. 259-277.

Singh AK, Prakash V, Sastry EVD (2000) Effect of salinity stress

on seed germination and seeding growth of wheat. Agricultural

Science Digest 20: 96-98.

Sunday OF, Ayodele AM, Babatunde KO and Oluwole AM (2007)

Genotypic and phenotypic variability for seed vigour traits and

seed yield in West African rice (Oryza sativa L.)

genotypes. Journal of American Science 3: 34-41.

Wright S (1921) Correlation and causation. Journal of Agricultural

Research 20: 557-585.

Yadav INDAL, Soni SK, Jain R, Vimal SC, Yadav VK (2011)

Character Association and Path Analysis of Vigor Index with some

Important Seed Vigor Contributing Traits in Barley (H. vulgare

L.). Environment and Ecology 29: 1866-1870.

Yakubu A (2010) Path coefficient and path Analysis of body

weight and biometric traits of Yankasa lambs. Slovak Journal of

Animal Science 43: 17-25.



ISSN No. 2320 – 8694


BIPLOT ANALYSIS FOR SPOT BLOTCH AND YIELD TRAIT USING WAMI

PANEL OF SPRING WHEAT

Ram Narayan Ahirwar1, Vinod Kumar Mishra1*, Dwijesh Chandra Mishra2, Neeraj Budhlakoti2,

Shweta Singh3, Ramesh Chand4

1Department of Genetics and Plant Breeding, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi-221 005, India

2ICAR- Indian Agricultural Statistical Research Institute, New Delhi-110012, India

3Department of Botany, Institute of Science, Banaras Hindu University, Varanasi-221 005, India

4Department of Mycology and Plant Pathology, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi-221 005, India

Received – February 17, 2020; Revision – April 03, 2020; Accepted – April 19, 2020 Available Online – April 25, 2020


ABSTRACT

Wheat (Triticum aestivum L.) is a staple food worldwide. Spot Blotch (SB) has been a major disease for

cultivation of wheat in Eastern Gangatic Plain (EGP). The goal of this study was to evaluate genetically

diverse, advanced elite lines of wheat association mapping initiative (WAMI) population to identify useful

genetic diversity and other related traits for spot blotch resistance present in WAMI panel for wheat

improvement. A panel of 289 elite lines of WAMI population were assessed for spot blotch resistance, days

to heading and plot yield for three consecutive years (2012-13, 2013-14 & 2014-15). The significant

differences among genotype, year and genotype × year for area under disease progress curve (AUDPC),

days to heading and plot yield were exhibited. The negative correlation between days to heading and spot

blotch AUDPC was observed whereas, AUDPC and days to heading was significantly and negatively

correlated with plot yield. Based on GGE biplot, the first two principal components explained 100%, 100%

and 84.5% of the total variation for AUDPC, days to heading and plot yield respectively. Germplasms

9122, 9049 and 9239 were showed most resistant under study. The highest mean value for plot yield was

observed for the panel 9061, 9070, 9203, 9216, 9118 and 9227 which were found relatively more stable for

grain yield. These genotypes could be used in wheat improvement programmes for developing superior

genotypes for yield and spot blotch resistance contributing to food security in south Asia.


KEYWORDS

Wheat

Spot blotch

AUDPC

Eastern Gangetic Plains and

GGE biplot

E-mail: [email protected] (Vinod Kumar Mishra)


















Biplot Analysis for Spot Blotch and Yield Trait Using WAMI Panel of Spring Wheat 116

1 Introduction

Wheat is most important and widely cultivated cereal crop

throughout the world and remains a vital source for human food

(FAOSTAT, 2016), contributes substantially to human daily

calories and food security (Braun et al., 2010). Gradually and

consistently wheat demand has been always increasing in the

developing world. The world population expected to increase over

9 billion by 2050, the demand of wheat consumption will be

increase by 60% compared with 2010. To fulfill this demand,

global annual yield of wheat must increase from the current 1% per

year (2001–2010) to 1.6% per year (2011–2050) (Giraldo et al.,

2019). Biotic and abiotic stresses are major constraints leading to

more impacts in productivity of wheat. Among biotic stresses, spot

blotch has emerged as major challenge in the eastern parts of India;

some reports indicated that this disease also spread into the cooler

traditional rice-wheat production areas (Chand et al., 2003).

Spot blotch is a fungal disease caused by pathogen Bipolaris

sorokiniana and it is considered as most important and destructive

disease in the region having warm and humid climate of the world,

restraining wheat production in the areas such as eastern India,

South East and Latin America, the tarai of Nepal, China and Africa

(Raemakers 1988; Saari 1998). Due to wide spread losses, this

disease is considered as the most significant disease of wheat in

north-eastern plain zone (NEPZ) of India encompassing eastern

Utter Pradesh, Bihar, West Bengal and Orissa (Saari 1998; Joshi et

al.,2007b). Due to losses caused by this disease, wheat breeders

rank this disease as number one disease in NEPZ of India (Saari,

1998; Joshi & Chand, 2002). Disease severity is affected by crop

management, soil fertility, planting density, the development stage

of the plant and the weather conditions experienced by the host

during later part of life cycle (Joshi et al., 2007b). Particularly in

grain filling stage, this disease becomes more severe (Joshi &

Chand, 2002) and in Indian subcontinent, the crop losses have been

reported to be in range of 15-25% (Dubin & Van Ginkel, 1991),

but level of losses can be much higher in individual fields. Reports

from several countries indicated that average yield loss due to spot

blotch is estimated to be 15–20% but can reach 40–70% in

susceptible genotypes (Fernandez et al., 2014).

Many effort has been done to control spot blotch disease but only

integrated approach is considered most effective component (Joshi

& Chand, 2002; Duveiller, 2003). However, in some instances,

only resistant varieties prove to be controlling this disease. Wheat

variety that possess high yield, early maturity along with resistance

to spot blotch disease could be best in NEPZ. However, a negative

correlation between spot blotch susceptibility and early maturity

was observed by many researchers (Dubin et al., 1998; Shrestha et

al., 1998). The primary focus of disease control relies on genetic

resistance that involves race non-specific mechanism. Use of

resistant cultivars is the best way to control this disease (Bai &

Shaner, 1994; Chaurasia et al., 1999). Although in recent years,

significant effort has been made by wheat breeding programs

targeting Indian gangetic plains, most cultivars grown in the

subcontinent still harbor relatively low level of resistance to foliar

blight (Joshi et al.,2004b).

Keeping in view of increasing threat of spot blotch disease and

more heat stress to the wheat crop, there is necessity to identify

and develop genetically improved germplasm incorporating

earliness, tolerance and resistance to spot blotch for EGP of south

Asia. In this study, an attempt has been made to identify the

germplasms belong to south Asia which are tolerant to spot blotch

disease by analyzing the genotype x environment interaction in

WAMI panel of wheat.

2 Materials and methods

The experimental materials comprised of 289 genetically diverse,

high-yielding, advanced elite lines of wheat association mapping

initiative (WAMI) population of wheat obtained from Matthew

Reynolds, CIMMYT Mexico.

2.1 Experimental design and trait evaluation

The WAMI population was evaluated at agricultural research farm

of Banaras Hindu University, Varanasi, India (25˚18' N lat., 83˚03'

E long. and 75 m amsl.) for three consecutive years i.e., 2012-13,

2013-14 and 2014-15. The mean temperatures (November-April)

during 2012-13, 2013-14 and 2014-15 was 26.12˚C, 25.41˚C and

23.21˚C respectively and mean annual rainfall was 737.7, 932.7

and 1009.4mm respectively. The experiment field was followed by

rice-wheat cropping system and conducted under irrigated

conditions. The sowing of crop was done from 1th to 10

th December

during three consecutive rabi season. The experiment was laid out

in alpha lattice design with two replications and each genotype was

sown in two rows of two meters long plot under irrigated

conditions maintaining row-to-row distance 20 cm and plant to-

plant 5 cm. The genotypes were allocated randomly to each

replication using Fisher and Yates Random Table (Panse &

Sukhatme, 1985). Recommended fertilizer dose (120 kg N: 60 kg

P2O5: 40 kg K2O in per ha) were applied in field. Full amount of

K2O and P2O5 were supplied as single dose at sowing, while

Nitrogen was given in three split doses: 60 kg per ha at sowing, 30

kg at the time of the first irrigation (21 days after sowing–DAS)

and remaining 30 kg at the time of the second irrigation (45 DAS).

Data was recorded for spot blotch severity, days to heading, plot

yield (grain yield of 50 spikes) and 1000 grain weight.

2.2 Inoculation of pathogen and disease assessment

For creating artificial epiphytotic conditions, the most aggressive

isolate of B. sorokiniana (NABM MAT1; NCBIJN128877, BHU,

Varanasi, India) obtained from the department of mycology and



117 Ahirwar et al.

plant pathology, Banaras Hindu University, Varanasi (Joshi &

Chand, 2002) was used. The isolate was purified, multiplied on

potato dextrose agar (PDA) medium and the mass culture was

produced on sorghum grains. A spore suspension adjusted to 104

spores per ml of water using haemocytometer and applied during

the evening hours as per the method of Chaurasia et al. (1999). The

field was irrigated immediately after inoculation to ensure spore

germination and disease development. Frequent irrigation were

given to provoke environmental conditions facilitate to spot blotch.

For area under disease progress curve (AUDPC), disease severity

(%) was recorded on each germplasm line of artificially inoculated

plots at three different growth stages (GS) viz., GS 63 (beginning

of anthesis to half complete), GS 69 (anthesis complete) and GS 77

(late milking) by using Saari & Prescott (1975) double digit (00 to

99) method. The first digit (D1) indicates vertical disease progress

on the plant and the second digit (D2) indicates severity measured

in diseased leaf area. Disease severity was then used to estimate

the AUDPC by following formula given by Shaner & Finney

(1977).

𝐴𝑈𝐷𝑃𝐶 = 𝑌𝑖 + 𝑌𝑖+1 /2 × 𝑡𝑖+1 − 𝑡𝑖

𝑛−1

𝑖=0

Where, 𝑌𝑖 =disease severity at time 𝑡𝑖 , 𝑡𝑖+1 − 𝑡𝑖 = Time (days)

between two disease scores, 𝑛 = number of dates at

which spot blotch was recorded.

2.3 Statistical analysis

Analysis of variance (ANOVA) for the traits was performed using

proc GLM procedure of SAS version 9.2 statistical software to

determine the contribution of genotypes, year and their

interactions. The association study among traits was estimated by

using the Proc CORR procedure in SAS in terms of Pearson‟s

correlation coefficients.

GGE biplot GUI package of R software was used to estimate the

genotype × year interaction, to test the most discriminating years,

which-won-where/what pattern, identify most stable lines over

years using first two principal components (PC1 and PC2) derived

from principal component (PC) analysis of environment-centered

data (Yan, 2001).

3 Results

The analysis of variance of 289 WAMI panels tested over 3 years

at Varanasi, India showed significant differences among genotype,

year and genotype × year for AUDPC and, days to heading and

plot yield (Table 1). A negative and non-significant phenotypic

correlation was observed between AUDPC and days to heading

whereas, AUDPC was significantly and negatively correlated with

plot yield and while days to heading revealed significant negative

associated with plot yield (Table 2).

Out of three years, highest mean of relative humidity was recorded

in the year 2014-15 at BHU, Varanasi. The highest mean value for

AUDPC was observed for the genotypes during the year 2014-15

as shown in GGE biplot (Figure 2A & B). It is evident that the

high humidity during booting to milking stage of wheat provokes

spot botch (Figure 1B). As the minimum temperature increases

during night resulted more chances of disease occurrence

(Table 3). It was observed that humidity is more important than the

temperature for spot blotch development whereas, resistant

genotype under study shows independent of years. Hence, the

selection of these lines will be beneficial for the farmers.

Table 1 Pooled variance components due to genotypes, year and G×Y interaction for 3 quantitative traits in 289 WAMI panels of wheat

tested in three years at BHU, Varanasi, India

Source d.f. Mean sum of squares of 3 years

AUDPC Days to heading Plot yield

Genotype 288 102858.93* 11.25* 444.45*

Year 2 549899.51* 4072.36* 1709.12*

Replication 1 10776.34 122.56* 231.31*

Genotype × Year 576 18904.76* 3.50* 118.03*

Error 866 3319.37 1.18 34.39

*Significant at the p-value (P<0.01) probability level.

Table 2 Correlation among AUDPC, days to heading (DH)and plot yield (PY).

AUDPC DH

DH -0.01

PY -0.21* -0.07*

* Significant at P < 0.05.




3.1 GGE biplot results

The first principal component (PC1) accounted for 75.13% of total

variation and the second principal component (PC2) accounted for

24.87%. Cumulatively; these two principal components explained

approximately 100% variation for AUDPC (Figure 2A, B and C).

The polygon is formed by connecting the markers of the WAMI

panel that are further away from the biplot origin, such that all

other panels come within the polygon (Figure 2A).The polygon

also contains number of lines perpendicular to each side of the

polygon. These perpendicular lines divide the biplot into sectors.

The vertex panel in each sector represents the highest AUDPC

panel in the year that falls within that particular sector (Yan

& Tinker, 2005; Yan et al., 2010). The figure 1A has nine sectors

Figure 1 (A) Mean temperature (B) Relative humidity (%) during wheat growing period over three year at BHU, Varanasi.

Table 3 Monthly mean minimum temperature (Tmin. in °C) and Relative humidity (RH %) at the test center in three years

at Banaras Hindu University, Varanasi

Month Year 2012-13 Year 2013-14 Year 2014-15

T max. T min. RH T max. T min. RH T max. T min. RH

Nov. 26.74 11.53 70.16 26.61 11.71 66.78 27.16 11.46 63.63

Dec. 21.99 10.35 66.16 23.81 11.65 67.66 21.14 9.4 73.02

Jan. 20.8 7.75 66.73 19.54 11.34 79.19 16.58 10.31 83.06

Feb. 24.43 12.3 72.52 22.33 12.5 71.46 17.91 13.51 69.88

Mar. 31.28 16.67 53.11 29.68 16.5 61.82 29.57 16.95 61.32

Apr. 37.79 20.36 38.93 36.77 20 34.47 34.62 20.39 52.27

Overall

mean 26.12 12.58 62.23 25.41 13.61 66.09 23.21 13.21 69.35



119 Ahirwar et al.

with vertex panels 9296, 9177 9235, 9094, 9105, 9148, 9040, 9046,

9147 and 9104. The vertex panels 9296, 9177 and 9235 fell in the

sector of year 2013 indicating that these panels were gave highest

AUDPC for that particular year. Panels 9105, 9094 and 9148 were the

highest AUDPC panels in the year 2014 while panels 9040, 9046,

9147 and 9104 were the highest AUDPC panels in the year 2015.

Panels that fell within the polygon were less responsive than the vertex

panels. The three years fell into three different sectors. This pattern

implies that the target years may consist of three different

environments and that different panel should be selected and utilized

for each. Using the mean vs. stability GGE biplot of AUDPC for the

289 WAMI line as shown in Figure 2B, the vertical line separates lines

with below-average means from those with above-average means. The

mean AUDPC is estimated by the projections of their markers on the

average-tester axis. The stability of the panels is determined by their

projection onto the middle horizontal line. The greater the absolute

length of the projection of a panel, that panel indicates less stable. In

present study, the highest mean value for AUDPC was observed for

(A) sWhich Won Where/What (B) Mean vs. Stability

(C) Ranking Genotypes

Figure 2 (A) „Which-won-where/what view of genotype × year biplot (B) The mean vs. stability view of genotype x year interaction effect

(C) Ranking of genotype for AUDPC over the years.




the panels 9104 followed by 9158, 9012, 9132, 9014 and 9176 that

were consistently more susceptible by being longest vector on the right

side of the origin of the biplot on the performance line. On the other

hand, panels 9122, 9049 and 9239 were consistently most resistant by

being longest vector on the left side of the origin of the biplot on the

performance line. The performance of lines 9040, 9288, 9235 and 9204

were highly variable over the years, but the resistant panels (9122,

9239 and 9049) were more stable over the years.

The first principal component (PC1) accounted for 57.44% of total

variation and the second principal component (PC2) also accounted for

42.56% for days to heading (Figure 3A). The vertex genotypes were

9175, 9228, 9131, 9130, 9001, 9147, 9112, 9180 and 9193. These

genotypes had the longest distance from the origin of the biplot and

showed the late or early headed genotypes over the years under study.

Genotypes 9180, 9193, 9112, 9137 and 9227 had the late heading by

being longest distance from the origin of the biplot on the right side of

the performance line, while genotypes 9131, 9195, 9130 and 9045 had

the earliest days to heading. The performance of lines 9198, 9133,

9216 and 9235 were unstable for days to heading by being greater the

absolute length of the projection of a panel in either side of horizontal

line (Figure 3B). The ideal genotype showed both highest mean and

stable thus using ideal genotype as the center, concentric circles were

drawn to help visualize the distance between each genotype and the

ideal genotype (Figure 3C). On the basis of relative position of ideal

(A) Which Won Where/What (B) Mean vs. Stability


Figure 3 (A) “Which won where/what” of genotype × year biplot

(B) The mean vs. stability view of genotype × year interaction effect (C) Ranking of genotype based on days to heading the year.



121 Ahirwar et al.

genotype, the ranking of genotype are done. Therefore, genotypes

placed closer to the ideal genotype are more desirable than others. Thus

genotype 9195, 9131 and 9045 were regarded as ideal genotype and

more favorable than all the other genotypes. The other genotypes were

distantly placed from ideal genotypes declared unfavorable one.

The first & second principal component explained 64.3% and 20.2%

variation respectively. In total, both principal components explained

84.5% of the total variation for the plot yield (Figure 4A, B and C).

In this polygon, perpendicular lines are drawn from the biplot origin

gave 10 sectors with vertex genotypes 9036, 9170, 9131, 9104, 9070,

9061, 9203 and 9255 of the polygon (Figure 4A). The property of

biplot polygon view is that each vertex genotype has higher yield

than the other genotypes that fall in the related sector (Yan, 2002).

GGE biplot were used for graphically visualization of performance

and stability of genotypes (Figure 4B). The highest mean value for

grain yield was observed for genotypes 9061, 9070, 9203, 9216,

9118 and 9227. Genotypes with highest mean value could be

(A) Which Won Where/What (B) Mean vs. Stability


Figure 4 (A) “Which won where/what” view of genotype × year biplot (B) The mean vs. stability view of genotype x year interaction effect

(C) Ranking of genotype based on plot yield over the year.




selected, whereas the rest were discarded. The ideal genotypes

identified were 9070 followed 9216, 9227, 9118, 9183 and 9183 for

plot yield (Figure 4C).

4 Discussion and Conclusion

Spot blotch caused by a fungus B. sorokiniana is an important

disease in wheat production. It has become more devastating

disease in the warmer areas of the world particularly the Indian

subcontinent characterized by prevalent warm and humid

conditions (Aggarwal et al., 2010). Further, the wide spread

use of conservation tillage practices may also be favorable for

spot blotch incidence in the south east Asia (Duveiller &

Sharma, 2009). It has been reported that this pathogen spread

into the cooler traditional rice-wheat production areas (Chand

et al., 2003). The extensively adopted rice-wheat cropping

system facilitates suitable environment for the survival and

multiplication of foliar blight pathogens in south Asia. It is

generally believed that the level of resistance in high-yielding

wheat genotypes is still unsatisfactory and needs to be

improved significantly in warmer humid regions of south Asia

(Sharma & Duveiller, 2006; Joshi et al., 2007a). Consequently,

an integrated approach, with host resistance as a major

component, is generally considered best for controlling the

disease (Hetzler et al., 1991; Mehta et al., 1992). The

resistance to spot blotch in prominent cultivars has been slow

due to quantitative nature of resistance which has a large

influence of environment (Joshi et al., 2004a; Kumar et al.,

2009). This is one of the reasons why selection of spot blotch

resistant plants in breeding programmes becomes complicated.

The agricultural research farm of Banaras Hindu University,

Varanasi testing center used in this study fall under EGP

having characteristics of warm-humid weather particularly at

reproductive stage of the crop which provokes spot blotch

disease.

Result of current study indicate significant differences among

genotype, year and genotype × year for AUDPC percent, days

to heading and plot yield exhibit considerable phenotypic

variations. The negative correlation among earliness and spot

blotch susceptibility was found (Dubin et al., 1998; Mahto,

2001; Rosyara et al., 2009) while AUDPC and days to heading

was significantly and negatively correlated with plot yield. The

similar results of Saxesena et al. (2017) also showed by

developing recombinant inbred line population from cross of

two parents YS#58 × YS#24 for spot blotch resistance. The

value of plant genetic resources are realized only when each

and every germplasm line is characterized for relevant traits to

reveal new gene combinations for their use in crop

improvement (Upadhyaya et al., 2010). Further, an association

study on agro-morphological traits with spot blotch resistance

will provide a roadmap in making selection strategy.

According to Heidari et al., 2016, GGE biplot facilitated visual

comparison and informative methods to detect genotypes

stability and in the preferential genotypes recommendations. This

approach provides an easy and comprehensive analysis of

genotype by years‟ interaction such that not only it allows

effective evaluation of genotypes but also a comprehensive

understanding of the target environment (year) (Yan & Tinker,

2005). Genotypes showed variation for spot blotch score and

morpho-agronomic traits. Current study identified superior lines

for specific traits with resistance to spot blotch disease that are

likely to serve as new potential sources of variation in wheat

improvement programme. For AUDPC, the first two principal

components explained 100% (PC1=75.13% and PC2=24.87%) of

the total variation of the year-centered data implying complex

interaction between genotypes and year. The stability analysis of

wheat genotypes is explained by performance line passing

through the origin of the biplot showed that the germplasm 9122,

9049 and 9239 was farthest to the left of the biplot origin and

near to performance line indicating lower AUDPC mean. The

more stable a genotype will be more near to the performance line

as reported earlier by Yan et al., 2000 hence, these lines could be

considered stable for spot blotch resistance across the years.

Identification of genotypes with stable resistance for disease is

an important component that ensures selection of appropriate

sources of resistance for breeding programmes (Sharma et al.,

2012; Singh et al., 2015). Using the ideal environment (year) as

the centre, concentric circles were drawn to help visualize the

distance between each environment and the ideal environment

(Yan et al., 2000; Yan & Rajcan, 2002).

The variation in plot yield was mainly affected by genotype and

genotype × year interactions. The PC1 vs PC2 of genotypes and

environments (years) explained 84.5% of the total variance.

When a genotype is near to performance line and environment on

the right side of origin of biplot, this indicates that the genotype

is specifically adapted to that environment (Kumar et al., 2016).

The highest mean value for plot yield was observed for the panel

9061, 9070, 9203 and 9216 which were found relatively more

stable while 2014 was most discriminating year for plot yield. In

breeding, an ideal genotype is regarded as the one that has the

highest mean yield and is stable across environments (Yan &

Kang, 2003). Results of this study implied that genotypes

possessing good spot blotch resistance, high yield potential and

adaptation under spot blotch conducive environment could be

used in wheat improvement programs for developing superior

genotypes for yield and spot blotch resistance contributing to

food security in south Asia.

Acknowledgements

Authors gratefully acknowledge Matthew P. Reynolds, CIMMYT,

Mexico, for providing WAMI population.



123 Ahirwar et al.


There is no conflict of interest to declare.

References

Aggarwal R, Singh VB, Shukla R, Gurjar MS, Gupta S, Sharma

TR. (2010) URP-based DNA fingerprinting of Bipolaris

sorokiniana isolates causing spot blotch of wheat. Journal of

Phytopathology 158: 210–216.

Bai G, Shaner G (1994) Scab of Wheat: prospects for control.

Plant Disease 78: 760-766.

Braun HJ, Atlin G, Payne T (2010) Multi-location testing as a tool to

identify plant response to global climate change. In: Reynolds M.P, ed,

Climate change and crop production. CABI, London, UK. pp. 115-138.

Chand R, Pandey SP, Singh HV, Kumar S, Joshi AK (2003)

Variability and its probable cause in the natural populations of spot

blotch pathogen Bipolaris sorokinianaof wheat (T. aestivum L.) in

India. Journal of Plant Diseases and Protection 110: 27-35.

Chaurasia S, Joshi AK, Dhari R, Chand R (1999) Resistance to

foliar blight of wheat; a search. Genetic Resources and Crop

Evolution 46: 469-475.

Dubin HJ, Arun B, Begum SN, Bhatta M, Dhari R, Goel LB, Joshi

AK, Khanna BM, Malaker PK, Pokhrel DR, Rahman MM, Saha

NK, Shaheed MA, Sharma RC, Singh AK, Singh RM, Singh RV,

Vergas M, Verma PC (1998) Results of the south Asia regional

Helminthosporium leaf blight and yield experiment, 1993–94. In:

Duveiller E, Dubin HJ, Reeves J, McNab A (Eds.),

Helminthosporium Blights of Wheat: Spot Blotch and Tan Spot,

CIMMYT, Mexico, D.F. Pp. 182–187.

Dubin HJ, Van Ginkel M (1991) The status of wheat diseases and

disease research inwarmer areas. In: Saunders DA (Ed.), Wheat for the

Nontraditional Warmer Areas. CIMMYT, Mexico, D.F, pp. 125-145.

Duveiller E (2003) Controlling foliar blight of wheat in rice-wheat

system of Asia. Plant Disease 88: 52-56.

Duveiller E, Sharma RC (2009) Genetic improvement and crop

management strategies to minimize yield losses in warm

nontraditional wheat growing areas due to spot blotch pathogen

Cochliobolus sativus. Journal of Plant Pathology157: 521-534.

FAOSTAT (2016) FAOSTAT. Food Agric. Organ. United Nations.

Fernandez MR, Fox SL, Hucl P, Singh AK (2014) Leaf spotting

reaction of spring common durum and spelt wheat and Kamut

under organic management in western. Canadian Journal of Plant

Science 94: 929-935.

Giraldo P, Benavente E, Manzano-Agugliaro F, Gimenez E (2019)

Worldwide Research Trends on Wheat and Barley: A Bibliometric

Comparative Analysis. Agronomy 9: 352.

Heidari Shadi, Azizinezhad Reza, Haghparast Reza (2016) Yield

stability analysis in advanced durum wheat genotypes by using

AMMI and GGE biplot models under diverse environment. Indian

Journal of Genetics 76 : 274-283.

Hetzler J, Eyal Z, Mehta YR, Campos LA (1991) Interaction

between spot blotch (Cochliobolus sativus) and wheat cultivars. In:

Saunders DA, Hettel G, eds. Wheat for Nontraditional Warm

Areas. Mexico, DF: CIMMYT, 146-64.

Joshi AK, Chand R (2002) Variation and inheritance of leaf angle,

and its association with spot blotch (Bipolaris sorokiniana)

severity in wheat (Triticum aestivum). Euphytica 124: 283-291.

Joshi AK, Chand R, Kumar S, Singh RP (2004b) Leaf tip necrosis:

a phenotypic marker associated with resistance to spot blotch

disease in wheat. Crop Science 44:792–796.

Joshi AK, Ferrara O, Crossa G, Singh J, Alvarado J, Bhatta G,

Duveiller MR, Sharma E, Pandit RC, Das SY, Chand R (2007a)

Associations of environments in South Asia based on spot blotch

disease of wheat caused by Cochliobolus sativus. Crop Science 47:

1071-1081.

Joshi AK, Kumar S, Chand R, Ortiz-Ferrara G (2004a) Inheritance

of resistance to spot blotch caused by Bipolaris sorokiniana in

spring wheat. Plant Breeding 123: 213-219.

Joshi AK, Mishra B, Chatrath R, Ortiz-Ferrara G, Singh RP

(2007b) Wheat improvement in India: present status, emerging

challenges and future prospects. Euphytica 157: 431-446.

Kumar U, Joshi AK, Kumar S, Chand R, Roder MS (2009)

Mapping of resistance to spot blotch disease caused by Bipolaris

sorokiniana in spring wheat. Theoretical and Applied Genetics

118: 783-792.

Kumar V, Kharub AS, Verma RPS, Verma A (2016) AMMI, GGE

biplot and regression analysis to comprehend the G x E interaction

in multi environment barley trials. Indian Journal of Genetics 76 :

202-204.

Mahto BN (2001) Effect of Helminthosporium leaf blight on yield

components and determination of resistance in selected wheat

varieties. Annals of Agricultural Sciences 22: 177-181.

Mehta YR, Riede CR, Campos LAC, Cooli MM (1992) Integrated

management of major wheat diseases in Brazil: an example for

southern cone region of Latin America. Crop Protection 11: 517-524.




Panse VG, Sukhatme PV (1985) Statistical method for agricultural

workers, 4th edition, New Delhi.

Raemaekers RH (1988) Bipoloris sorokiniana disease complex on

wheat and source of resistance in Zambia. In: Klatt AR (Eds.)

wheat production constraints in tropical environment, Mexico.

D.F. CIMMYT. 175-185.

Rosyara UR, Khadka K, Subedi S, Sharma RC, Duveiller E (2009)

Field resistance to spot blotch is not associated with undesirable

physio-morphological traits in three spring wheat populations.

Journal of Plant Pathology 91 : 113-122.

Saari EE (1998) Leaf blight diseases and associated soil borne

fungal pathogens of wheat in north and south East Asia. In:

Duveiller E, Dubin HJ, Reeves J, Mc Nab A (Eds)

Helminthosporium Blights of wheat: Spot Blotch and Tan Spot

CIMMYT, Mexico, D.F. Pp. 37-51.

Saari EE, Prescott JM (1975) A scale for appraising the foliar

intensity of wheat diseases. Plant Disease Report 59: 377- 380.

Saxesenaa RR, Mishra VK, Chand R, Chowdhury A, Kumar B,

Prateek Madha, Joshi AK (2017) Pooling together spot blotch

resistance, high yield with earliness in wheat for eastern Gangetic

Plains of South Asia. Field Crops Research 214: 291-300.

Shaner G, Finney R (1977) The effect of nitrogen fertilization on

the expression of slow mildewing in knox wheat. Phytopathology

67: 1051-1056.

Sharma M, Babu TK, Gaur PM, Ghosh R, Rameshwar T, Chaudhary

RG, Upadhyay JP, Gupta O, Saxena DR, Kaur L, Dubey SC,

Anandani VP, Harer PN, Pande S (2012) Identification and multi-

environment validation of resistance to Fusarium oxysporum f. sp.

ciceris in chickpea. Field Crops Research 135: 82-88.

Sharma RC, Duveiller E (2006) Spot blotch continues to cause

substantial grain yield reductions under resource-limited farming

conditions. Journal of Phytopathology 154: 482-488.

Shrestha KK, Timila RD, Mahto BN, Bimb HP (1998) Disease

incidence and disease loss due to foliar blight of wheat in Nepal.

In: Duveiller E, Dubin HJ, Reeves J, McNab A (Eds.),

Helminthosporium Blights of Wheat: Spot Blotch and Tan Spot.

CIMMYT, Mexico, D.F, pp. 67-72.

Singh PK, Zhang Y, Xinyao H, Singh RP, Chand R, Mishra VK,

Malaker PK, Reza MA, Rahman MM, Islam R, Chowdhury AK,

Bhattacharya PM, Kalappanavar I K, Crossa J, Joshi AK (2015)

Development and characterization of the 4th CSISA-spot blotch

nursery of bread wheat. European Journal of Plant Pathology 143:

595-605.

Upadhyaya D, Sharma S, Ramulu B, Bhattacharjee R, Gowda

CLL, Reddy VGG, Singh S (2010) Variation for qualitative and

quantitative traits and identification of trait-specific sources in new

sorghum germplasm. Crop and Pasture Science 61: 609-618

Yan W (2001) GGE biplot: a windows application for graphical

analysis of multi-environment trial data and other types of two-

way data. Agronomy Journal 93: 1111-1118.

Yan W, (2002) Singular-value partitioning in biplot analysis of

multi environment trial data. Agronomy 94: 90-996.

Yan W, Fregeau-Reid J, Pageau D, Martin R, Mitchell-Fetch J,

Etienne M, Sparry E (2010) Identifying essential test locations for

oat breeding in Eastern Canada. Crop Science 50: 505-515.

Yan W, Hunt LA, Sheng QL, Szlavnics Z (2000) Cultivar

evaluation and mega environment investigation based on the GGE

biplot. Crop Science 40: 597-605.

Yan W, Kang MS (2003) GGE biplot analysis: A graphical tool for

breeders, geneticists, and agronomists (pp. 63-88). Boca Raton,

FL: CRC Press.

Yan W, Rajcan I (2002) Biplot analysis of test sites and trait

relations of soybean in Ontario. Crop Science 42: 11-20.

Yan W, Tinker NA (2005) An integrated biplot analysis system for

displaying, interpreting, and exploring genotype-by environment

interactions. Crop Science 45: 1004-1016.

k Journal of Experimental Biology and Agricultural Sciences


ISSN No. 2320 – 8694


STRIPE RUST RESISTANCE IN WHEAT GERMPLASM OF NORTH-WESTERN HIMALAYAN HILLS

B.R. Raghu1*, O.P. Gangwar2, S.C. Bhardwaj2, K.K. Mishra1

1Vivekananda Parvatiya Krishi Anusandhan Sansthan (VPKAS), Indian Council of Agricultural Research (ICAR), Almora, India. 2Indian Institute of Wheat and Barley Research (IIW&BR), Indian Council of Agricultural Research (ICAR), Shimla, India.

Received – March 09, 2020; Revision – April 11, 2020; Accepted – April 19, 2020 Available Online – April 25, 2020


ABSTRACT

Stripe rust of wheat is one of the major biotic stresses occurring in cooler regions of wheat production

worldwide. In the present study, about 464 wheat germplasm collected from cooler areas of northern

hills, India, were characterized for stripe rust resistance both at seedling and adult-plant stages. Initially,

these genotypes were evaluated in the field at two locations, Hawalbagh and Dalang-Maidan during

three continuous cropping seasons. Under field conditions, 22 genotypes showed a consistent expression

of high field resistance (ACI<5.0), 349 expressed partial field resistance (ACI 5.0-60.0), and 93 were

highly susceptible (ACI>60.0). In next season, the high field resistant genotypes were subjected to both

seedling and an adult-plant stage resistance test against four virulent Pst pathotypes (78S84, 46S119,

110S119 and 238S119) under glasshouse conditions at ICAR-IIWBR, Shimla and Hawalbagh,

Uttarakhand, respectively. Among 22 high resistance genotypes, 14 genotypes possessed all-stage

resistance against all 4 Pst pathotypes and 4 genotypes were resistant to only two pathotypes (78S84 and

46S119) both at seedling and adult plant stages. Further, 349 partially field resistant genotypes were

evaluated for adult-plant stage resistance under artificially inoculated conditions, out of which 18

genotypes were found to have adult-plant stage resistance. Seedling resistance reported in the current

study is effective against newly emerged Pst pathotypes 110S119 and 238S119 and previously reported

predominant pathotypes 78S84 and 46S119 in India. The stripe rust resistance genotypes identified in

the current study may serve as potential donors of stripe rust resistance to wheat breeding programmes

in India and elsewhere.


KEYWORDS

Germplasm

Pathotypes

Resistance

Stripe-rust

Wheat

E-mail: [email protected], [email protected] (Dr Raghu B R)

Peer review under responsibility of Journal of Experimental Biology and Agricultural Sciences.

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Stripe rust resistance in wheat germplasm of North-western Himalayan Hills 126

1 Introduction

Yellow or stripe rust of wheat, caused by Puccinia striiformis

Westend. f. sp. tritici Erikss. (Pst) is one of the most

economically important fungal disease of global spread (Chen et

al., 2014). In India, stripe rust is a major biotic stress observed in

cooler areas of the country and causing considerable yield loss

during epidemic years (Hodson, 2011; Sharma & Saharan, 2011;

Prashar et al., 2015; Khan et al., 2016). Under conducive weather

conditions, stripe rust can cause yield loss up to 100%, but

usually damage remains in the range of 10-70% depending upon

crop stage, disease severity, and susceptibility of cultivar

(Gangwar et al., 2019). Yield losses in wheat due to stripe rust

are usually ascribed to reduction of kernel numbers per spike,

shriveled grains, and reduced test weight due to decreased

photosynthesis (Gangwar et al., 2019). Emergence of new

pathotypes and subsequent breakdown of resistance is a major

challenge in stripe rust management. In 1996, pathotype 46S119

which was virulent on widely used Pst resistance gene i. e. Yr9

was reported, followed by 78S84 having combined virulence on

Yr9 and Yr27 was identified in 2001 (Prashar et al., 2007;

Bhardwaj et al., 2019). Both pathotypes have predominantly

distributed and responsible for most of the stripe rust epidemics

in major-wheat growing regions of India (Prashar et al., 2015).

Besides, 3 new Pst pathotypes viz., 110S119, 238S119 and

110S84 with additional virulence on Riebesel47/51,

SuwonXOmar, Yr11, Yr14, and Yr24 were reported during 2013-

14 (Gangwar et al., 2016; Gangwar et al., 2019). Thus, search for

resistance to new Pst pathotypes in addition to existing virulent

pathotypes is need of the hour.

Although, multiple applications of fungicides can save the crop

from stripe rust, but development and deployment of resistant

varieties is the most efficient and environmentally sustainable

means of reducing the loss due to stripe rust (Chen, 2005). In

India, as an emergent tool for managing wheat rusts under high

disease incidence, the fungicides propiconazole 25% EC (tilt),

tebuconazole 25% EC (folicur) and triadimefon 25% EC

(Bayleton) are usually recommended at the rate of 0.1 percent

(Bhardwaj et al., 2016). But use of resistant cultivars has

remained preferable, economically viable, ecologically safe

and effective method for managing wheat rusts epidemics. A

rigorous screening of germplasm against the available

population of rust pathogens leads to the identification of rust

resistance sources. A dynamic rust resistance breeding

programme should always think ahead of the pathogen through

identification of sources of resistance to any new pathotype in

addition to the prevalent ones. Current study related to

identification of seedling and adult-plant resistance sources

among the wheat germplasm collected from Northern hills are

reported in the present communication.


2.1 Wheat genotypes

The plant materials consisted of 464 germplasm accessions of

cultivated bread wheat collected from Northern hills, India.

2.2 Stripe rust pathogen

Four highly virulent pathotypes (pts.) of wheat stripe rust

(Puccinia striformis f. sp. Tritici) namely, 78S84, 46S119,

110S119 and 238S119 were used in the present study.

2.3 Experimental setup

The schematic diagram of experimental layout and flow of

germplasm in different years and locations is presented in Figure 1.

2.4 Evaluation of wheat germplasm to stripe rust under

natural field conditions

Screening of 464 wheat germplasm to stripe rust was carried out at

two locations, Hawalbagh (29036’N, 79040’E; 1250 m asl),

Uttarakhand and Dalang Maidan (32021’N, 77014’E; 2347 m asl),

Himachal Pradesh, India. These locations are considered as hot

spots for stripe rust development. At these locations, stripe rust

appears every year in severe form on susceptible varieties. At

Hawalbagh, the wheat germplasm was evaluated in three regular

cropping seasons (November to May) during 2013-2016. Whereas,

at Dalang Maidan, the germplasm were evaluated in off-seasons

(May to August) for two years (2014 and 2015).

At both locations, the experiment was laid out in an augmented

design consisting of 16 blocks. In each block, susceptible check

(Agra Local) was repeated after every 29 test genotypes. Seeds of

each genotype were sown by dibbling in two rows with 30 cm

inter-row spacing and100 cm row length. The stripe rust infector’s,

a homogeneous mixture of highly susceptible wheat lines such as

NI5439, Avocet and Agra Local were used as stripe rust spreader

in the field. Infector rows were settled surround the experimental

plot to increase inocula load.

The infection types (IT) and disease severity data were recorded

using modified Cobb scale (Peterson et al., 1948) from boot to

milk stages. The infection types were recorded as 0= no visible

infection (immune); R= necrotic areas with or without uredia

(resistant); MR= necrotic areas with small uredia (moderately

resistant); MS=medium size uredia with no necrosis but some

chlorosis (moderately susceptible); S= large sized uredia with no

necrosis and chlorosis (susceptible); X= variable sized uredia with

necrosis or chlorosis and fully susceptible (intermediate). Stripe

rust severity was assessed as percentage of leaf area infected using

modified Cobb scale. The response value of 0.0, 0.2, 0.4, 0.8 and

1.0 were assigned for 0, R, MR, MS and S, respectively.

Journal of Experimental Biology and Agriculturhttp://www.jebas.org

127

The coefficient of infection (CI) of each genotype was

calculated by multiplying response value with the severity of

infection (Pathan & Park, 2006). The average coefficient of

infection (ACI) of a germplasm was determined by dividing the

sum of CI values with total number of seasons (Stubbs

1986). In the current study, sum of CI values was divided by

five to derive ACI value of each genotype. Based on CI and

ACI values, the germplasm was classified into different

categories of rust resistance. Genotypes with CI/ACI values of

0, <5, <20, 21-40, 41-60 and >60 were considered as immune,

highly resistant, high partial resistant, medium partial resistant,

low partial resistant and susceptible, respectively (Sajid

al., 2009; Raghu et al., 2018).

2.5 Seedling resistance test (SRT)

Highly field resistant genotypes with CI/ACI values of 0

(n=22) were evaluated at seedling-stage against four

pathotypes namely, 78S84, 46S119, 110S119 and 238S119

under glasshouse conditions. SRT was conducted at ICAR

Indian Institute of Wheat and Barley Research (IIWBR),

Shimla, India, during 2016-17 cropping season. Aluminum

trays containing sterilized mixture of fine loam and farmyard

manure (3:1) were used for raising seedlings of genotypes.

Each tray was sufficient to accommodate 18 genotypes along

Figure 1 Schematic diagram of experimental layout and flow of germplasm.


coefficient of infection (CI) of each genotype was

calculated by multiplying response value with the severity of

infection (Pathan & Park, 2006). The average coefficient of

infection (ACI) of a germplasm was determined by dividing the

total number of seasons (Stubbs et al.,

1986). In the current study, sum of CI values was divided by

five to derive ACI value of each genotype. Based on CI and

ACI values, the germplasm was classified into different

s with CI/ACI values of

60 and >60 were considered as immune,

highly resistant, high partial resistant, medium partial resistant,

low partial resistant and susceptible, respectively (Sajid-Ali et

Highly field resistant genotypes with CI/ACI values of 0-5

stage against four Pst

78S84, 46S119, 110S119 and 238S119

under glasshouse conditions. SRT was conducted at ICAR-

Indian Institute of Wheat and Barley Research (IIWBR),

17 cropping season. Aluminum

trays containing sterilized mixture of fine loam and farmyard

manure (3:1) were used for raising seedlings of genotypes.

to accommodate 18 genotypes along

with a susceptible check. For each genotype, 5

sown in hills. One-week old seedlings were inoculated 10 mg

spores of an individual Pst pathotype suspended in 1 ml light

grade mineral oil (Soltrol 170; Chevron

Asia Pvt. Ltd., Singapore). Inoculated seedlings were incubated

in dew chambers at 16±2°C for 48 hours. Subsequently,

seedlings were transferred to glasshouse benches and incubated

at 18±2°C with 60-75% relative humidity, illuminated at

approximately 15,000 lux for 12 hours. The infection types

(IT) were recorded at 16 days post-inoculation using standard

procedure (Nayar et al., 1997).

2.6 Adult-plant stage resistance (APSR)

Partial field resistant genotypes along with highly field res

genotypes with CI/ACI values ranges between 0

were tested for adult-plant stage resistance to stripe rust at

Hawalbagh, ICAR-VPKAS, Almora, Uttarakhand, India, during

2016-17. The experiment was laid out in

In each block, susceptible check Agra local was repeated after

every 53 test genotypes. Seeds were sown in two

with 30-cm row to row distance during second fortnight of

November 2016. Single row of infector was repeatedly pl

after every entry. Additionally, the field was surrounded by

infector rows to increase inocula load.

Figure 1 Schematic diagram of experimental layout and flow of germplasm.

Raghu et al.

with a susceptible check. For each genotype, 5-6 seeds were

week old seedlings were inoculated 10 mg

pathotype suspended in 1 ml light

grade mineral oil (Soltrol 170; Chevron Phillips Chemicals

Asia Pvt. Ltd., Singapore). Inoculated seedlings were incubated

in dew chambers at 16±2°C for 48 hours. Subsequently,

seedlings were transferred to glasshouse benches and incubated

75% relative humidity, illuminated at

approximately 15,000 lux for 12 hours. The infection types

inoculation using standard

Partial field resistant genotypes along with highly field resistant

genotypes with CI/ACI values ranges between 0-60 (n=371),

plant stage resistance to stripe rust at

VPKAS, Almora, Uttarakhand, India, during

17. The experiment was laid out in an augmented design.

In each block, susceptible check Agra local was repeated after

53 test genotypes. Seeds were sown in two-meter rows

cm row to row distance during second fortnight of

November 2016. Single row of infector was repeatedly planted

after every entry. Additionally, the field was surrounded by



From late January to early March, stripe rust epidemic was created artificially by inoculating infectors with a mixture of four Pst pathotypes. The Infector rows were first syringe inoculated with the mixed inocula of pathotypes followed by repeated sprays. Irrigations were carried out as required to maintain sufficient humidity for better rust infection. Disease severity and infection types were recorded five times at 15 days interval from boot to milk stage. The procedure for recording infection types and disease severity, and calculating coefficient of infection (CI) was same as described in natural field test. Area under the disease progress curve (AUDPC) was calculated using CI values (Gyawali et al., 2018).


The CI values of wheat genotypes were subjected to ANOVA using PROC GLM of SAS (SAS Institute, 1988) statistical software package. The AUDPC of wheat genotypes was differentiated by Fisher’s least

significant difference (LSD) (p=0.05) based on standard error of mean difference of repeated check Agral Local. The cut-off of resistance and susceptible genotype was determined by significant t test of susceptible checks and test genotypes at p<0.05(AUDPC=8.2) and LSD0.05

(AUDPC=375.67). Therefore, genotypes with rust severity lower than the cut-off AUDPC 383.9 were considered resistance.

3 Results

3.1 Stripe rust resistance under natural field conditions

The ANOVA for terminal stripe rust severity of 464 wheat

germplasm evaluated under natural field conditions is given in the

Table 1. Whereas, grouping of genotypes into different resistant

and susceptible categories based on CI and ACI values are

summarized in Table 2. The results of current study indicated that

22 (4.8%), 349 (75.2%) and 93 (20%) genotypes were highly

Table 1 Mean square (MS) values of terminal stripe rust severity in natural field test at Hawalbagh and Dalang-Maidan among464 wheat germplasm

Source

Hawalbagh1 (Winter season)

Dalang Maidan2 (Rainy season)

Pooled**

2013-14 2014-15 2015-16 2014 2015

Df MS df MS df MS df MS df MS df MS

Blk 15 53.3* 15 74.7** 15 53.3* 15 53.1* 15 52.5** 15 53.3*

Tests 463 637.8** 463 767.5** 463 702.2** 463 840.7** 463 766.1** 463 587.7**

Error 15 10.0 15 11.5 15 10.0 15 9.9 15 5.83 15 10.0

CV 9.84 10.39 7.42 6.76 5.58 8.00

1based on co-efficient of infection (CI) values; 2based on average co-efficient of infection (ACI) values; significant at *p=0.05 and **p=0.01 levels of probability.

Table 2 Stripe rust reaction of 464 wheat germplasm under natural field conditions

Disease Reactions

Hawalbagh* (Winter season)

Dalang Maidan* (Rainy season)

Pooled**

2013-14 2014-15 2015-16 2014 2015

Highly Resistant

Immunea 19 (4.0) 20 (4.3) 14 (3.0) 12 (2.6) 15 (3.2) 5 (1.1)

Resistantb 40 (8.7) 31 (6.7) 20 (4.3) 28 (6.0) 16 (3.5) 17 (3.7)

(a+b) 59 (12.7) 51 (11.0) 34 (7.3) 40 (8.6) 31 (6.7) 22 (4.8)

Partial resistant

Highcc 219 (47.2) 230 (49.6) 177 (38.1) 149 (32.1) 188 (40.5) 163 (35.1)

Mediumd 76 (16.4) 79 (17.0) 110 (23.7) 104 (22.4) 93 (20.1) 109 (23.5)

Lowe 65 (14.0) 44 (9.5) 55 (11.9) 119 (25.7) 53 (11.4) 77 (16.6)

(c+d+e) 360 (77.6) 353 (76.1) 342 (73.7) 372 (80.2) 334 (72.0) 349 (75.2)

Susceptiblef 45 (9.7) 60(12.9) 88 (19.0) 52 (11.2) 99 (21.3) 93 (20.0)

Total 464 464 464 464 464 464

*based on Co-efficient of Infection (C.I) values; **based on Average Co-efficient of Infection (A.C.I) values; Disease classification based on specified range of CI/ACI values as aCI/ACI values =0; bCI/ACI values<5.0; cCI/ACI values upto 20.0; dCI/ACI values =21-40; eCI/ACI values =41-60; fCI/ACI values>60; Values in parenthesis indicated percentages.


129 Raghu et al.

resistant, partially resistant and susceptible across locations and

seasons, respectively (Table 2). Of these 349 field partially

resistant genotypes, 160 (34.5%), 104 (22.4%) and 85 (18.3%)

genotypes showed high, medium and low degree of field partial

resistance, respectively (Table 3). Location and season wise

breakup of stripe rust resistance among 464 wheat germplasm

under natural field conditions is shown in Figure 2. The susceptible

check Agra Local showed stripe rust of 80S-100S with the ACI

values of 96.0 under natural field conditions. Besides, the rust

severity range of 80S-100S was observed in the infector’s rows

indicating sufficient Pst inocula load in the test locations.

3.2 Seedling resistance test (SRT)

The results of SRT of 22 highly field resistant genotypes are

presented in Table 3. Out of 22 resistant genotypes tested, 14

genotypes namely, IC0138525, IC0469441, IC0469503, VHC-

6161, IC0469464, IC0469440, VHC-6211, VHC-6162, VHC(BD)-79,

Table 3 Seedling resistance test of 22 highly field resistance wheat genotypes against four Pst pathotypes under glasshouse conditions

Genotypes Natural field testa Seedling resistance testb

HS ACI 110S119 238S119 46S119 78S84

Group1c

IC0138525 0 0.00 0; 0; 0; 0;

IC0469441 0 0.00 0; 0; 0; 0;

IC0469503 0 0.00 0; 0; 0; 0;

VHC-6161 0 0.00 0; 0; 0; 0;

IC0469464 0 0.00 0; 0; 0; 0;

IC0469440 10MS 1.60 0; 0; 0; 0;

VHC-6211 10S 3.00 0; 0; 0; 0;

VHC-6162 5S 1.80 ; 0; ; 0;

VHC(BD)-79 5S 2.16 ; 0; 0; 0;

VHC-6202 10MS 2.00 0; 0; 0; 0;

IC0316103 10MR 0.88 0; 0; 0; 0;

VHC-6265 10MS 1.96 0; 0; 0; 0;

IC0281542 10MS 2.68 ; 0; ; 0;

VHC-6209 10MS 2.40 0; 0; ; 0;

Group2d

IC0138518 5S 1.30 3+ 3+ 0; 0;

IC0138524 10S 3.60 3+ 33+ ; 0;

Group3e

IC0138520 10MS 1.76 3+ 3+ 3 ;

VHC-6290 10S 2.84 3+ 3+ 3+ 0;

Group4f

IC0469478 10MS 2.60 3+ 3+ 3+ 3

VHC-6294 5S 1.08 3+ 3+ 3+ 3

VHC-6372 10MS 2.80 3+ 3+ 3+ 3

IC0138516 10MS 1.91 3+ 3+ 3+ 3+

Agra Local 100S 96.00 3++ 3++ 3++ 3++

astripe rust resistance under natural field conditions during 2013-14 to 2015-16 at Hawalbagh, Uttarakand and Dalang Maidan, Himachal Pradesh (HP), India; bseedling resistance test (SRT) against four Pst pathotypes at ICAR-IIWBR, Shimla, H.P., India during winter,2016-17; cwheat germplasm showing seedling resistance to all 4 Pst pathotypes; dwheat germplasm showing seedling resistance to only existing pathotypes (46S119 and 78S84); ewheat germplasm showing seedling resistance to existing pathotype 78S84; fwheat germplasm susceptible to all 4 Pst pathotypes;

Journal of Experimental Biology and Agriculturhttp://www.jebas.org

Stripe rust resistance in wheat germplasm of North

Figure 2 Location-cum-season wise breakup of

Figure 3 Least square (LS) mean of area under the disease progress curve (AUDPC) of 372 wheat genotypes including susceptibl(Agra local) to stripe rust (Puccinia striiformisless than Cut-off AUDPC value 383.9 considered adult

VHC-6202, IC0316103, VHC-6265, IC0281542 and VHC

showed seedling resistance against all 4 Pst

78S84, 110S119 and 238S119). Further, two genotypes,

IC0138518 and IC0138524 showed resistance at seedling stage

against two Pst pathotypes, 46S119 and 78S84, and susceptible to

110S119 and 238S119. Another two genotypes, IC0138520 and

VHC-6290 showed resistance to 78S84 and susceptible to 46S119,

238S119 and 110S119. Remaining 4 genotypes namely,

IC0469478, VHC-6294, VHC-6372 and IC0138516 found

susceptible to all 4 Pst pathotypes.


Stripe rust resistance in wheat germplasm of North-western Himalayan Hills

season wise breakup of stripe rust resistance in 464 wheat germplasm under natural field conditions. Wand R-Rainy season.

Figure 3 Least square (LS) mean of area under the disease progress curve (AUDPC) of 372 wheat genotypes including susceptiblPuccinia striiformis f. sp. tritici) during winter, 2016-17 at Hawalbagh, Uttarakhand, India [The genotypes with

off AUDPC value 383.9 considered adult-plant resistance. Region-1: genotypes with adult-plant resistance (genotypes with adult-plant susceptible (n=336)].

6265, IC0281542 and VHC-6209

pathotypes (46S119,

78S84, 110S119 and 238S119). Further, two genotypes,

138524 showed resistance at seedling stage

pathotypes, 46S119 and 78S84, and susceptible to

110S119 and 238S119. Another two genotypes, IC0138520 and

6290 showed resistance to 78S84 and susceptible to 46S119,

ing 4 genotypes namely,

6372 and IC0138516 found

3.3 Adult-plant stage resistance

The adult-plant stage resistance test of 371 genotypes (genotypes

showed highly and partial resistance under natural field conditions)

along with susceptible check Agra local is summarized in the

figure 3. The cut-off AUDPC value 383.9 was determined by

of susceptible checks and test genotypes at 0.05 probability

[(AUDPC = 8.2 (p < .05) plus LSD0.05

375.67]. The genotypes with lesser than AUDPC 375.67 were

considered resistant to stripe rust at adult-

130

natural field conditions. W-winter season

Figure 3 Least square (LS) mean of area under the disease progress curve (AUDPC) of 372 wheat genotypes including susceptible check

17 at Hawalbagh, Uttarakhand, India [The genotypes with plant resistance (n=36). Region-2:

plant stage resistance test of 371 genotypes (genotypes

showed highly and partial resistance under natural field conditions)

along with susceptible check Agra local is summarized in the

off AUDPC value 383.9 was determined by t test

of susceptible checks and test genotypes at 0.05 probability

0.05 which was AUDPC =

375.67]. The genotypes with lesser than AUDPC 375.67 were

-plant stage. Out of 371


131 Raghu et al.

genotypes tested, 36 showed APSR (Region-1 of figure 3) and

remaining 336 genotypes along with susceptible check Agra local

were found susceptible to APSR (Region-2 of figure 3). These 36

genotypes with APSR are also included 18 genotypes with

seedling resistance against 4 Pst pathotypes indicating all stage

resistance (ASR) in those genotypes (Table 3). Whereas, remaining

18 genotypes showed only APSR to Pst. However, the susceptible

check Agra Local with corresponding AUDPC value of 3064.0

was placed in the region-2 indicating high susceptibility to stripe

rust (Figure 3).

4 Discussion

Shift in the pathotypic virulence and emergence of new Pst races are

the major reasons for frequent outbreak of stripe rust in wheat

(Prashar et al., 2015; Gangwar et al., 2016). This leads to frequent

breakdown of known resistance and posing major challenges to rust

resistance breeding programme (Prashar et al., 2007). Thus, it

necessitates the identification and introgression of new sources of

stripe rust resistance (Joshi et al., 2011). Kumar et al. (2016)

evaluated entire collections of cultivated wheat germplasm (19,460)

for rusts resistance and identified 45 new sources of resistance.

Raghu et al. (2018) reported a seedling and adult plant resistance to

stripe rust in Uttarakhand wheat landraces. It suggests that wheat

germplasm from NW hills region possessing stripe rust resistance.

Current study identified 22 genotypes (4.8%) showing high degree of

stripe rust resistance over 5 seasons at two locations under natural

field conditions (Table 2). In which five genotypes were found

immune (ACI=0) and seventeen highly resistant (ACI</=5.0) (Table

3). Of them, Fourteen genotypes namely, IC0138525, IC0469441,

IC0469503, VHC-6161, IC0469464, IC0469440, VHC-6211, VHC-

6162, VHC(BD)-79, VHC-6202, IC0316103, VHC-6265,

IC0281542 and VHC-6209 showed resistance both at seedling and

adult-plant stages . This indicated that, these genotypes possessed

broad-spectrum resistance. Kumar et al. (2016) opined that

genotypes with consistent expression of high degree of resistance

across hot spots were expected to show resistance to multiple races.

Raghu et al. (2018) identified a wheat landrace VHC-6285 collected

from hilly areas of Uttarakhand showing seedling and adult-plant

resistance to stripe rust at two hotspots. Recently, a genetic stock

HS-628 was identified for resistance to all prevailing Pst pathotypes

at seedling stage (Pal et al., 2018).

Besides, 4 genotypes (IC0138518, IC0138524, IC0138520 and

VHC-6290) were showed susceptibility to 2 new pathotypes

(110S119 and 238S119) but found resistant to earlier reported

pathotypes 46S119 and 78S84 at seedling stage (Table 3). This is a

clear example of genotypes carrying both seedling resistance and

adult plant resistance (APR). Badoni et al. (2017) reported genotypes

carrying APR genes Lr34/Yr18/pm38 in combination with seedling

resistance genes Yr5, Yr10 and Yr15 in wheat. Based on pathological

studies, several studies reported similar kind of findings previously

(Maccaferri et al., 2015; Bulli et al., 2016; Kumar et al., 2016;

Gyawali et al., 2018; Verma et al., 2018; Raghu et al., 2018). The

seedling resistance expressed early in seedling-stage and it will

remain effective at all post-seedling stages of plants (Chen, 2005;

Lagudah, 2010). Often, it is governed by major genes result in a

hypersensitive response associated with high levels of resistance

(Maccaferri et al., 2015). So far, 76 officially named stripe rust

resistant genes (Yr1 to Yr76) and 42 with temporary designations

have been reported in wheat (McIntosh et al., 2016). Most of the

reported Yr genes confer all-stage resistance (seedling resistance). In

the current study, 18 genotypes showed seedling resistance, were

also tested for adult-plant stage resistance against mixture of 4 Pst

pathotypes (Table 3 and 4).

Further, SRT and APSR tests in the current study revealed that,

genotype IC0469478 possess only adult-plant stage resistance to

stripe rusts (Table 3). In addition, 17 more new sources of adult-

plant stage resistance to stripe rust were identified in this study

(IC0279883, IC0281544, IC0281570, IC0282865, IC0282866,

IC0310124, IC0310127, IC0313151, IC0316085, IC0316086,

IC0316088, IC0316093, IC0356468, IC0393111, IC0398292

IC0398297 and IC0398310). Earlier, several new sources of adult-

plant stage resistance to stripe rust were reported both in barley

(Gyawali et al., 2018; Verma et al., 2018) and in wheat (Ali et al.,

2008; Maccaferri et al., 2015; Saleem et al., 2015; Bulli et al.,

2016; Badoni et al., 2017; Kumar et al., 2016; Raghu et al., 2018).

Unlike seedling resistance, adult-plant resistance (APR) typically

expressed at adult-plant stages (post-seedling), is characterized by

various degree of resistance (quantitative or partial resistance) and

often shows race non-specific resistance (Lagudah, 2011).

Although, varieties with seedling resistance are more attractive to

the farmers, but quickly become susceptible due to emergence of

new virulent pathotypes (Kolmer et al., 2009; Hubbard et al.,

2015). Therefore, emphasis must be given for deployment of both

seedling and adult plant resistance as a long term disease

management strategies for stripe rust (Hulbert & Pumphrey, 2014).

In the current study, we identified 18 novel sources of all-stage

resistance and 18 adult-plant stage resistance among 464

germplasm of cultivated wheat of Northern hills, India. The

present findings would prove to be a useful source for developing

potential stripe rust resistant varieties. These genotypes could serve

as potent donor for creating new utilizable variability in wheat

against stripe rust under wheat improvement programme of India.

Acknowledgement

The authors convey their gratitude to different scientists and

technical staff involved in wheat exploration trips and germplasm

maintenance, the technical staffs of wheat program, ICAR-VPKAS

and to the ICAR, New Delhi for providing financial support.






References

Ali S, Shah SJA, Maqbool K (2008) Field-Based Assessment of

Partial Resistance to Yellow Rust in Wheat Germplasm. Journal of

Agriculture & Rural Development 6(1): 99-106.

Badoni S, Chaudhary, R, Shekhar R, Badoni S, Ahmad E,

Gangwar RP, Tiwari KN, Rawat RS, Deepshikha, Jaiswal JP

(2017) Unveiling sources of stripe rust resistance in diverse wheat

(Triticum Aestivum L.) germplasm using narrow down

methodology: a proof of concept. Journal of Crop Science and

Biotechnology 20: 393-403.

Bhardwaj SC, Prasad P, Gangwar OP, Khan H, Kumar S (2016)

Wheat rust research - then and now. Indian Journal of Agricultural

Science 86: 1231 – 1244.

Bhardwaj SC, Singh GP, Gangwar OP, Prasad P, Kumar S (2019)

Status of Wheat Rust Research and Progress in Rust Management-

Indian Context. Agronomy 9: 892-905.

Bulli P, Zhang J, Chao S, Chen X, Pumphrey M (2016) Genetic

architecture of resistance to stripe rust in a global winter wheat

germplasm collection. G3 Genes Genomes Genetics 5: 2237-2253.

Chen W, Wellings C, Chen X, Kang Z, Liu T (2014) Wheat stripe

(yellow) rust caused by Puccinia striiformis f. sp. tritici.

Molecular Plant Pathology 15(5):433–446.

Chen XM (2005) Epidemiology and control of stripe rust

(Puccinia striiformis f. sp tritici) on wheat. Canadian Journal of

plant Pathology 27: 314–337.

Gangwar OP, Kumar S, Bhardwaj SC, Kashyap PL, Prasad P

(2019) Characterization of three new Yr9-virulences and

identification of sources of resistance among recently developed

Indian bread wheat germplasm. Journal of Plant Pathology 101:

955-963.

Gangwar OP, Subodh K, Pramod P, Bhardwaj SC, Hanif K,

Verma H (2016) Virulence pattern and emergence of new

pathotypes in Puccinia striiformis tritici during 2011-15 in India.

Indian Phytopathology 69: 178-185.

Gyawali S, Singh RP, Kumar S, Bhardwaj SC, Gangwar OP,

Selvakumar R, Shekhawat PS, Rehman S, Sharma-Poudyal D

(2018) Seedling and adult-plant stage resistance of a world

collection of barley genotypes to stripe rust Journal of

Phytopathology 166: 18-27.

Hodson DP (2011) Shifting boundaries: challenges for rust

monitoring. Euphytica 179: 93–104.

Hubbard A, Lewis CM, Yoshida K, Ramirez-Gonzalez RH, de

Vallavieille-Pope C, Thomas J, Kamoun S, Bayles R, Uauy C,

Saunders DGO (2015) Field pathogenomics reveals the emergence

of a diverse wheat yellow rust population. Genome Biology 16: 23.

Hulbert SH, Pumphrey MO (2014) A time for more booms and

fewer busts? Unraveling cereal–rust interactions. Molecular Plant-

Microbe Interactions 27: 207–214.

Joshi AK, Azab M, Mosaad M, Moselhy M, Osmanzai M,

Gelalcha S ( 2011) Delivering rust resistant wheat to farmers: A

step towards increased food security. Euphytica 179: 187-196.

Khan H, Gangwar OP, Prasad P, Bhardwaj SC (2016) Mehtaensis

Newsletter 36: (1) 1-2.

Kolmer J, Chen X, Jin Y (2009) Diseases which challenge global

wheat production-The wheat rusts. In: Carver BE (Ed) Wheat,

Science and Trade, Wiley-Blackwell, Ames, IA Pp. 89–124.

Kumar S, Sunil A, Tyagi R K, Jagdish K, Vikas V K, Sherry R J,

Kalyani S, Radhamani J, Parimalan R, Sivaswamy M, Sandhya T,

Mamata Y, Jyotisna K, Deepali, Sandeep S, Indoo B, Madhu M,

Bains NS, Chowdhury AK, Saha BC, Bhattacharya PM, Jyoti K,

Singh M C, Gangwar O P, Prasad P, Bharadwaj S C, Robin G,

Sharma JB, Sandeep, Kumar G M, Saharan MS, Manas B, Anirban

R, Prasad T V, Sharma R K, Dutta M, Indu S, Bansal KC (2016)

Evaluation of 19460 wheat accessions conserved in the Indain

national Genebank to identify new sources of resistance to rust and

spot blotch diseases. pLoS ONE, 12: 167-702.

Lagudah ES (2010) Molecular genetics of race non-specific rust

resistance in wheat.. In: Borlaug Global Rust Initiative,

Proceeding of Technical Workshop held on May 30-31, 2010 at St.

Petersburg, Russia, Pp. 183-196.

Lagudah, ES (2011) Molecular genetics of race non-specific rust

resistance in wheat. Euphytica 179: 81–91.

Maccaferri MJ, Zhang PB, Abate Z, Chao S, Cantu D, Bossolini E,

Chen X, Pumphrey M, Dubcovsky J (2015) A genome wide

association study of resistance to stripe rust (Puccinia striiformis f.

sp. tritici) in a worldwide collection of hexaploid spring wheat

(Triticum aestivum L.). G3 Genes Genomes Genetics 5: 449–465.

McIntosh RA, Dubcovsky J, Rogers WJ, Morris C, Appels R,

Xia XC (2016) Catalogue of Gene Symbols for Wheat.

In: Grain Genes. Available on

http://www.shigen.nig.ac.jp/wheat/komugi/genes/symbolClass

List.jsp. Accessed June 2017.


133 Raghu et al.

Nayar SK, Prashar M, Bhardwaj SC (1997) Manual of current

techniques in wheat rust. Research bulletin 2, Regional Station,

Flowerdale, Shimla, Himachal Pradesh, India: Directorate of

Wheat Research Pp. 1–32.

Pal D, Bhardwaj SC, Hanif K, Patial M (2018) HS628-a potential

genetic stock for resistance to new virulent pathotypes of black,

brown and yellow rusts of wheat (Triticum aestivum L.). Wheat

and Barley Research 10: 132-135.

Pathan AK, Park RF (2006) Evaluation of seedling and adult

plant resistance to leaf rust in European wheat cultivars.

Euphytica 149: 327-342.

Peterson RF, Champbell AB, Hannah AE (1948) A diagrammatic

scale for estimating rust intensity of leaves and stem of cereals.

Canadian Journal of Research 26: 496-500.

Prashar M, Bhardwaj SC, Jain SK, Datta D (2007) Pathotypic

evolution in Puccinia striiformis in India during 1995-2004.

Australian Journal of Agricultural Research 58: 602-604.

Prashar M, Bhardwaj SC, Jain SK, Gangwar OP (2015) Virulence

diversity in Puccinia striiformis f. sp. tritci causing yellow rust on

wheat (Triticum aestivum) in India. Indian Journal of

Phytopathology 68: 129-133.

Raghu BR, Gangwar OP, Bhardwaj SC, Jain SK (2018) seedling

and adult plant resistance to stripe rust in Uttarakhand landraces of

wheat. Applied Biological Research 20: 291-301.

Sajid Ali, Jawad S, Shah A, Rahman H (2009) Multi-location

variability in Pakistan for partial resistance in wheat to Puccinia

striiformis f. sp. Tritici. Phytopathologia Mediterranea 48: 269-279.

Saleem K, Arshad HMI, Shokat S, Atta BM (2015). Appraisal of

wheat germplasm for adult plant resistance against stripe rust.

Journal of Plant Protection Research 55: 405-414.

SAS Institute (1988). Users guide: Statistics, 6th edn. Cary,

NC: SAS Inst.

Sharma I, Saharan MS (2011) Status of wheat disease in India with a

special reference to stripe rust. Research in Plant Diseases 26: 156.

Stubbs RW, Prescott JM, Saari EE, Dubin HJ (1986) Cereal

Diseases Methodology Manual. CIMMYT, Mexico.

Verma RPS, Selvakumar R, Gangwar OP, Shekhawat PS, Bhardwaj

SC, Rehman S, Sharma-Poudyal D, Gyawali S (2018) Identification of

additional sources of resistance to Puccinia striiformis f. sp. hordei

(PSH) in a collection of barley genotypes adapted to the high input

condition. Journal of Phytopathology 1: 1-10.



ISSN No. 2320 – 8694


TOXIC EFFECTS OF VARIOUS ARSENIC CONCENTRATIONS ON GERMINATION

AND SEEDLINGS GROWTH OF WHEAT (Triticum aestivum L.)

Rakesh Sil Sarma*, Pravin Prakash, Savita Jangde

Department of Plant Physiology, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, India

Received – February 27, 2020; Revision – April 09, 2020; Accepted – April 17, 2020



ABSTRACT

The present study was conducted to assess the effect of different arsenic concentrations on various

germination and growth parameters of wheat. For this, wheat seeds of variety HUW-234 were expose

with five arsenic (AsV) concentrations viz., 50 µM, 100 µM, 150 µM, 200 µM and 250 µM, while

treatment without AsV considered as control (AsV 0). Various growth parameters such as germination

percentage, germination index, shoot length, root length, seedling vigour index, dry matter has been

recorded at 3 days of germination. Results of study revealed that the germination percentage,

germination index, Shoot length, root length, dry matter and SVI significantly reduced at the increasing

the arsenic (AsV) concentrations. Results of study suggested that arsenic have harmful effects on seed

germination and establishment in wheat crops which restricts plant growth and development.


KEYWORDS

Wheat

Arsenic

Germination

Seeds

Growth

E-mail: [email protected] (Rakesh Sil Sarma)


















Toxic effects of various arsenic concentrations on germination and seedlings growth of wheat 135

1 Introduction

Wheat (Triticum aestivum L.) is one of the significant crops, grown as

second most important staple food crop of the world. Wheat

accounting nearly 30% of global cereal production and covers an area

of 218.5 million hectares with an average productivity of 3.26 tonnes

ha-1

(FAOSTAT, 2019). In Indian, wheat production stood at record

99.70 million tonnes in 2017-18 cropping year while it crossed 100

million tonnes in the year 2018-19 (Ministry of agriculture, and farmer

welfare, 2018-19). Among major wheat growing countries, China

(134.34 million tonnes) ranked first in the wheat production, followed

by India (98.51 milliontonnes), Russia (85.86 milliontonnes), and the

United States (47.37 million tonnes) (FAOSTAT, 2019). In India,

Uttar Pradesh ranked first in wheat production and productivity. The

state produces up to 300.01 lakh tonnes of wheat followed by Punjab

(164.72 lakh tonnes), Haryana (116.30 lakh tonnes), Madhya Pradesh

(76.27 lakh tonnes), and Rajasthan (72.14 lakh tonnes) (Ministry of

agriculture and farmer welfare 2018-19). World Bank estimates the

demand for wheat in developing countries will increase 60% by 2050.

(FAOSTAT, 2019). Wheat growers around the world need to increase

their productivity for betterment of upcoming food demand and

overwhelming increasing global populations.

More than 200 types of minerals was reported in the Earth’s crust, it’s

maximum percentage (60%) are found in the form of arsenate, 20% are

in the form of sulphides and rest 20% are mainly found at the form of

arsenides, sodium silicates, metal oxides (Onishi.1969). Arsenic

mainly exist in several oxidation states such as As(-III), As(0), As(III),

or As(V) (Panda et al.,2010). Plants can uptake arsenic mainly in

inorganic form with the help of several transporter proteins (Neidhardt

et al., 2015). The principle driving force for arsenic uptake in plant

roots is a concentration gradient between source and sink. According to

Lei et al. (2012) that As (V) uses various Pi channels for its entry into

the plant root cell. Arsenic contaminations disturb physio-chemical

properties of soils and leads to severe loss of crop yields (Rahman et

al., 2007). Arsenic exists in the environment in two forms mainly

inorganic and organic, with arsenate [As(V)] and arsenite [As(III)]

being the most prevalent inorganic and most toxic forms of arsenic

(Duxbury et al., 2003). Arsenate being a phosphate analogy interferes

with phosphate metabolism (phosphorylation and ATP synthesis) in

plants while As(III) binds to sulfhydryl groups of proteins affecting

their structures and/or catalytic functions (Zhao et al., 2010). Several

studies report that arsenic toxicities in plants increased production of

reactive oxygen species (ROS) that leads to membrane damage,

leakage, non-specific oxidation of proteins and membrane lipids and

also causes DNA injury (Srivastava et al., 2011). The mechanism of

As(III) allows As(III) to act as a cross-linking agent by binding up to

three monothiol molecules, and disbalance functions of GSH and

alternatively destroyed several antioxidant systems like SOD, APX,

Glutathione, GPX etc in plant metabolic system (Kitchin & Wallace,

2006). Inappropriate arsenic concentrations can be harmful to wheat

seedling at early developmental stages. Seed germination and seedling

growth were not much affected at low concentration but at higher

concentrations it inhibit both seed germination and seedling growth (5-

20 mg/kg soil). Physiological and biochemical activities of wheat

seedlings were also changed under arsenic stress. Seed germination,

biomass, root length and shoot height decreased, and as accumulation

increased on early seedlings of several wheat varieties as

concentrations increased (Liu et al., 2005). Wheat growers around the

world need to increase their productivity, while the prices of wheat and

other cereal grains decrease. In addition, with the expectation that

prices for fertilizers and chemicals will continue to rise in the future,

wheat producers must substantially improve their production efficiency

to stay competitive. Arsenic in wheat grains was mainly found as

inorganic form. Inorganic arsenic, a class I carcinogen, is more toxicant

than other metalloids. So, it will result in health risk for wheat

consumers. So understanding the mechanisms of arsenic metabolism in

wheat is essential to fulfil the demand of future growing populations.

Current study was conducted to access the effect of arsenic

concentration on seed germination and seedling growth.


In the present study, 6 different concentrations of arsenic (AsV)

viz., 0, 50, 100, 150, 200 and 250 µM was used to access the

effect of As on wheat seed germination and seedling growth.

Each treatment contained 5 healthy and uniform sized seed in

each petriplates. In experiment, three petri plates per

combination will be arranged in randomized block design and

experiments were repeated three times in a BOD chamber. Data

were recorded each after 3 days intervals of experimental setup.

Thereafter, germination percentage, germination index, shoot

length, root length, dry matter and seedling vigour index was

observed and analyzed.

2.1 Germination percentage (%)

Numbers of germinated seeds were recorded after 48 hours of seed

germination. Germination percentage was calculated as per the

Association of official seed analysis (1983).

Germination percentage = (No of seed germinated/ total

number of seed sown) × 100.

2.2 Germination Index (GI):

Germination index of wheat seed were calculated by the formula

given by Ranal & Santana (2006).

GI= (4×N1) + (3×N2) + · · · +

Where N1, N2 ...N4 is the number of germinated seeds on the first,

second and subsequent days until 3rd day and the multipliers (e.g.

4, 3 ...etc.) are weights given to the days of the germination

2.3 Seedling vigor index (SVI):

Seedling vigor index was measured in 7 day old seedlings and it was

calculated by the formula given by Goodi, & sharifzadeh, (2006).

SVI= germination % × Dry wt. of 7 days old seedling.



136 Sarma et al.

2.4 Root and shoot length

The maximum root and shoot length was measured with the help

of scale at 3rd

days of germinations.

2.5 Total dry matter

Seedlings were excised and placed into a oven (NSW-142) at 105oC for 5

minutes, this was followed by 65oC till the getting constant weight. Dry

weight is taken at electrical balance (ADGR-200) at 3 days.


Germination percentage is the most important traits which revealed

germination capacity of plant. The value of GI indicates both the

germination percentage and germination speed. Likewise, shoot

length represents the vertical growth of the plants, root length is

the important seedling parameters indicates the better

establishment of seedlings and dry matter measure the production

photosynthates in relation to productivity. Whereas, SVI is the

important parameter indicates the seedling establishment and

growth. The observations recorded for GP, GI, shoot length, root

length, dry matter and SVI under different concentrations of

arsenate (AsV). ) has been represented in figure 1-4. Presented

data showed that the germination percentage, GI, Shoot length,

root length, dry matter and SVI significantly decreased as

compared to control (no AsV) with the increasing concentration

of arsenic (50, 100, 150, 200 µM, 250µM). The highest

germination percentage (84.3%), germination index (487.3), SVI

Figure 1 Effect of Arsenic on Germination percentage in wheat seeds

Figure 2 Effect of Arsenic on germination index and seedling vigour index in wheat seeds

0

10

20

30

40

50

60

70

80

90

100

0 µM 50 µM 100 µM 150 µM 200 µM 250 µM

Ger

min

ati

on

(%

)

Treatment

0

100

200

300

400

500

600

700

800

900

1000

0 µM 50 µM 100 µM 150 µM 200 µM 250 µM

Germination Index Seedling vigour index

Treatment




(810.6), shoot length (5.27 cm), root length (4.02 cm) and dry matter

(0.126g) were recorded in control, whereas, the least germination

percentage (32.6%) germination index (251.7), SVI (372.3), shoot

length (2.40 cm), root length (2.02 cm) and dry matter (0.041 g)

were recorded in highest concentration of arsenic (250 µM).

Results of study revealed that comparatively higher concentrations

of arsenic have higher toxicities, it might be because of arsenic

entry in to plants cells which can destroys thiol (SH) groups of

enzymes that inhibits biochemical pathways of cells and disturb

physiological activities (Abbas et al., 2018). In Brassica juncea

and Arabidopsis thaliana crops arsenic mainly present as AsIII in

tissues which form complexes with thiol compounds such as

glutathione (GSH) and phytochelatins (PCs) (Castillo-Michel et

al., 2011). In another study when plants were exposed to arsenic,

reactive oxygen species (ROS) produced which induces lipid

peroxidation and causes plant wilting (Farooq et al., 2016). Similar

study was found that, plant species cultivated on AsV

contaminated sites can readily adapt to suppression of P trans-

porters (Meharg & Hartley-Whitaker, 2002). Arsenic can severely

inhibit plant growth by arresting cell division expansion and

significant dry matter accumulation, as well as disturbing plant

Figure 3 Effect of Arsenic on Shoot and Root length of wheat seedling

Figure 4 Effect of Arsenic on total dry matter of wheat seedlings

0

1

2

3

4

5

6

7

0 µM 50 µM 100 µM 150 µM 200 µM 250 µM

Cen

tim

etr

e

Treatment

Shoot Length Root Length

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0 µM 50 µM 100 µM 150 µM 200 µM 250 µM

Dry

ma

tter (

g/s

eed

lin

g)

Treatments



138 Sarma et al.

reproductive capacit and reducing crop production ( Garg &

Singla, 2011). Further, it was observed that seedlings of Cicer

arietinum (Malik et al., 2011) and Oryza sativa (Vromman et al.,

2013) showed stunted growth of roots, shoots and also suppress the

number of leaves, leaf area, and fresh and dry mass of plants (Nath

et al., 2014).

Conclusion

In the presented study, effect of Arsenic (AsV) on wheat

germination and seedling growth evaluated. The increasing

concentration of arsenic has severe negative effects in terms of

demodulate seed germination, overall roots, shoots and leaves

length, and on the dry biomass. It was observed that concentrations

of arsenic (AsV,250 µM) showed maximum decreasing effects in

all parameters. These findings showed that harmful effect of

arsenic toxicities in agricultural crops, and its effects on seed

germination and overall yield in wheat crops. Further research is

required under both lab and field conditions, to use several

remediation techniques for practical applications of agriculture for

better crop productions.




References

Abbas G, Murtaza B, Bibi I, Shahid M, Niazi NK, Khan MI,

Amjad M, Hussain M, Natasha (2018) Arsenic uptake, toxicity,

detoxification, and speciation in plants: physiological,

biochemical and molecular aspects. International Journal of

Environmental Research 15: 59-70.

Association of Official Seed Analysts (1983) Seed Vigor Testing

Handbook. 1st Edition, AOSA, East Lasing, 88.

Castillo-Michel H, Hernandez-Viezcas J, Dokken KM, Marcus

MA, Peralta- Videa JR, Gardea-Torresdey JL (2011) Localization

and speciation of arsenic in soil and desert plant Parkinsonia

florida using mu XRF and mu XANES. Environment Science And

Technology 45: 7848–7850.

Duxbury JM, Mayer AB, Lauren JG, Hassan N (2003) Food chain

aspects of arsenic contamination in Bangladesh: Effects on quality

and productivity of rice, Journal of Environmental Science and

Health, Part A, 38: 1–69.

FAOSTAT (2019) Part 3: Feeding the world. In: FAO Statistical

Yearbook 2019: World Food and Agriculture. Food and

Agriculture Organization of the United Nations, Rome, Italy, pp.

123–199. [Accessed January 04, 2019].

Farooq MA, Gill RA, Ali B, Wang J, Islam F, Ali S, Zhou WJ

(2016) Subcellular distribution, modulation of antioxidant and

stress-related genes response to arsenic in Brassica napus L.

Ecotoxicology 25: 350–366.

Garg N, Singla P (2011) Arsenic toxicity in crop plants:

physiological effects and tolerance mechanisms. Environmental

Chemistry Letter 9: 303–321.

Goodi M, Sharifzadeh F (2006) Evaluation effect of hydropriming

in barley difference cultivars. Biaban Desert Journal 11: 99-109.

Kitchin KT, Wallace K (2006) Arsenite binding to synthetic

peptides: The effect of increasing length between two cysteines.

Journal of Biochemical and Molecular Toxicology 20: 35–38.

Lei M, Wan XM, Huang ZC, Chen TB, Li XW, Liu YR (2012)

First evidence on different transportation modes of arsenic and

phosphorus in arsenic hyperaccumulator Pteris vittata.

Environmental Pollution 161: 1–7.

Liu XM, Zhang FR, Wang R, Wang Y (2005) Effects of nano-

ferric oxide on the growth and nutrients absorption of peanut. Plant

Nutrition and Fertility Science 11: 14-18.

Malik JA, Goel S, Sandhir R, Nayyar H (2011) Uptake and

distribution of arsenic in chickpea: Effects on seed yield and seed

composition. Communications in Soil Science and Plant Analysis

42: 1728–1738.

Meharg AA, Hartley-Whitaker J (2002) Arsenic uptake and

metabolism in arsenic resistant and nonresistant plant species. New

Phytology 154: 29–43.

Ministry of agriculture and farmer welfare Report (2018-19)

Government of India,Annual reports.

Nath S, Panda P, Mishra S, Dey M, Choudhury S, Sahoo L, Panda

SK (2014) Arsenic stress in rice: Redox consequences and regulation

by iron. Plant Physiology and Biochemistry 80: 203–210.

Neidhardt H, Kramar U, Tang X, Guo H, Norra S (2015) Arsenic

accumulation in the roots of Helianthus annuus and Zea mays by

irrigation with arsenic-rich groundwater: Insights from synchrotron

X-ray fluorescence imaging. Geochemistry 75: 261–270.

Onishi H (1969) Arsenic. In Wedepohl KH (Ed.) Handbook of

Geochemistry; Springer: Berlin, Germany,

Panda S, Upadhyay R, Nath S (2010) Arsenic stress in plants.

Journal of Agronomy Crop Sciences 196: 161–174.

Rahman MA, Hasegawa H, Rahman MM, Miah MAM, Tasmin A

(2007) Arsenic accumulation in rice (Oryza sativa L.): Human




exposure through food chain. Ecotoxicology and Environmental

safety 69: 317–324.

Ranal MA, Santana DG (2006) How and why to measure the

germination process? Brazil journal of Botany 29: 1-11.

Srivastava S, Suprasanna P, D’Souza SF (2011) Redox state and

energetic equilibrium determine the magnitude of stress in Hydrilla

verticillata upon exposure to arsenate. Protoplasma 48:805–815.

Vromman D, Lutts S, Lefèvre I, Somer L, De Vreese O, Šlejkovec

Z, Quinet M (2013) Effects of simultaneous arsenic and iron

toxicities on rice (Oryza sativa L.) development, yield-related

parameters and As and Fe accumulation in relation to As

speciation in the grains. Plant and Soil 371: 199–217.

Zhao FJ, McGrath SP, Meharg AA (2010) Arsenic as a food chain

contaminant: mechanisms of plant uptake and metabolism and

mitigation strategies. Plant Biology 61:535–559.



ISSN No. 2320 – 8694


AGRO-MORPHOLOGICAL CHARACTERIZATION OF AFRICAN RICE ACCESSIONS

(Oryza glaberrima) IN RAINFED AND IRRIGATED CULTURAL CONDITIONS

Claude GNACADJA*,1,2, Balbine FAGLA AMOUSSOU2, Saïdou SALL6, Baboucar MANNEH5,

Benjamin TOULOU3, Florentin AMETONOU5, Jean MOREIRA3, Paulin AZOKPOTA1,2, Moussa SIE4

1Laboratory of Molecular Biology and Food Formulation; Nutrition, Food and Technologies Sciences School, Faculty of Agricultural Sciences, Abomey Calavi University, Benin

2Laboratory of Food Sciences; Nutrition, Food and Technologies Sciences School, Faculty of Agricultural Sciences, Abomey Calavi University, Benin

3Africa Rice Center (Africa Rice), Benin

4Africa Rice Center (Africa Rice), Madagascar

5Africa Rice Center (Africa Rice), Senegal

6Training and Research Unit in Agricultural Sciences, Aquaculture and Food Technologies, GASTON BERGER University, Saint-Louis, Senegal

Received – June 24, 2019; Revision – September 14, 2019; Accepted – April 14, 2020 Available Online – April 25, 2020


ABSTRACT

African rice is used for its resistance to diseases and stresses, to improve Asian species. Unfortunately,

its culture is greatly neglected in favor of Asian varieties and because of this only many African rice

varieties is literally on the way of extinction. Due to its potential, it is important to preserve this

agricultural resource. This work aims primarily at doing agro-morphological characterization of African

rice accessions in the perspective to identify the useful varieties of Oryza glaberrima for future

exploitation. This study was carried out at the Africa Rice Center sites in Republic of Benin and

Senegal, for this 235 African rice accessions including two controls (CG14, NERICA4) have been

observed during rainfed (Benin) and irrigated (Senegal) culture using “Augmented design in randomized

complete block” method. Result of study revealed that all accessions completed their life cycle; among

the tested accessions, 22 were reported as O. sativa and interspecific accessions while reset 213

accessions were phenotypically identified as O.glaberrima. The discriminate variables of PCA (based

on the 9 main parameters) of 213 O.glaberrima accessions, leaded to a dendrogram with two clusters:

cluster I (114 accessions) is characterized by the precocity of their cycle: it can be used as an important


KEYWORDS

Wheat

Arsenic

Germination

Seeds

Growth

E-mail: [email protected] (Claude GNACADJA)


















Agro-morphological characterization of African rice accessions 141

1 Introduction

The rice (Oryza spp) is a basic food that provides 27% of the

energy intake in population of third world countries (FAO, 2004).

It is an important agricultural product for food in West

Africa, where 40% of needs are insured by the international market

(Mendez del Viflar & Bauer, 2013). Two species Oryza sativa

(Asian rice) and Oryza glaberrima Steud (African rice) are mainly

cultivated in Africa (Anonymous, 1991; Anonymous, 2002). The

African species is composed of several varieties used for a long

time in varietal improvement systems because of their potential for

resistance to diseases and drought (Africa Rice, 2010).

The introduction and rapid adoption of Asian rice varieties has,

on one hand, led to an increase in rice production resulting in a

positive impact on producer’s income (Obilana & Okumu,

2005 ), and on the other hand, abandonment of African rice.

However, the success of Asian and improved varieties does not

fully satisfy breeder’s ambitions and the expectations of

producers and consumers. There is still an unfavorable

difference between improvement varieties and their parent O.

glaberrima either in the form of resistance, tolerance to

cultural constraints (biotic and abiotic), or organoleptic and

nutritional quality (Futakuchi & Sié, 2009; Africa Rice, 2010).

These observations challenges breeders to improve the abilities

of native varieties (interspecific) being created and tested under

various growing conditions (rainfed, lowland, irrigated, etc.) so

that they can better adapted, more productive and accepted by

consumers. The use of parents such as the O. glaberrima was

therefore indispensable, since they constitute the very basis of

the creation of interspecific varieties.

Domestication, gene flow and natural introgression are interspecies

phenomena that can affect over time the diversity of rice species.

In West Africa, due to its abandonment, very few studies have

focused on characterization of the African rice in varied growing

conditions. This study would make it possible to better evaluate,

with a wide range of agro-morphological indicators, the agronomic

potentials of the African rice. This work, in context of a research

project about the valorization of African rice, aims mainly to

identify the successful accessions of African rice useful for varietal

improvement systems, through a hierarchical classification based

on agronomic descriptors. The results will not only complete the

list of characterized African rice varieties, but also ensure the use

of its diversity for the development of new rice varieties.

2 Materiel and Methods

2.1 Test sites

Two sites namely “the Africa Rice station of Cotonou (Benin)” for

rainfed test (between October and January) and “the Africa Rice

station of Sahel (Ndiaye, Saint Louis, Senegal)”, for irrigated test

(between September and December) were exploited for testing.

2.2 Materials

It consists of 235 accessions of African rice collected in the

villages of Danyi in Togo (in August and December 2008) and

two controls (CG 14, NERICA4) having all been subjected to the

germination test.

2.3 Experimental Setup

The setup used for the implementation of the two tests is built

according to the method "Augmented Design in Randomized

Complete Block" described by Nokoe (2001). This test has been

used because of the high number of accessions while the quantity

of seed is very limited (about 5g). The method consists in

designing a device in which only the controls are repeated in each

block (Sharma, 1988), to be used for the estimation of the

experimental error and the block effect. 24 blocks of 10 entries and

two witnesses, totaling 12 entries per block were designed. Each

elementary plot has a density of 42 plants 20cm apart between the

lines and on the lines. The elementary plots (1.68 m2 of area) are

separated by a path of 30cm.

2.4 Agronomic Practices

2.4.1 For rainfed crops (Benin)

Sowing was carried out in a line by a direct method at the rate of 2

seeds per plant, with a separation (after two weeks) at one plant.

Only basic fertilizer NPK (10-18-18) is applied at the rate of 100

kg / ha at seeding. Irrigation was carried out with a watering

system and weeding was carried out manually.

2.4.2 For irrigated crops (Senegal)

The seeds were pre-germinated at artificial incubator at 30°C for

48 hours. Seedlings were transfer in to pots in greenhouse

conditions. Nine (09) days after sowing, the pots were removed

from the greenhouse and exposed to natural conditions for

trait for variety selection. Cluster II (99 accessions including CG14 and NERICA4) is characterized by

the strong production yield. These results allow us to have a collection of O. glaberrima accessions in

order to study not only its genetic diversity but also to evaluate more potential of these varieties for

exploiting their genetic and nutritional advantage for the development of new varieties.



142 Claude et al.

acclimatization; this transplanting was carried out in a line.

Selective Herbicide, LONDAX 60% (Bensulfuron-Methyl

100 g/kg) was applied for weed control with a recommended

application rate of 100g/ha (Africa Rice 2010). This application is

accompanied by manual weeding. Three successive applications of

fertilizer NP2O5KO2 (150-60-60) were carried out for the treatment

of the tests. For irrigation, draining system has been developed to

allow the management and renewal of water whose level is

controlled according to the evolutionary stage of the plants.

2.5 Agro-morphological parameters observed

Observations are related to agro-morphological characteristics of

varieties and some characteristics of post-harvest grain. Overall 36

quantitative and qualitative parameters were measured according

to the SES (Standard Evaluation System) and the International

Rice Research Institute (IRRI) descriptors. However, nine mains

parameters viz tillering at 30 days after Sowing (Till30), tillering at

60 days after Sowing (Till60), height 60 days after Sowing

(Hgt60), maturity height (Mat.Hgt), flowering date (Flw.Date),

maturity date (Mat.Date), number of fertile panicles (NFP), weight

of 1000 grains (Wgt.1000g) and yield have been studied in detail.

2.6 Statistical treatment

Since only the witnesses were repeated the effect of the blocks was

first evaluated and the adjustment of the averages of the accessions

was then realized. For the different parameters, the level of

variability and degree of heritability within the collection were

studied to ensure homogeneity and absence of the effect of the

environmental factor. A simple descriptive analysis was realized

with the R software, followed by significance and correlation test

of a Genotype-Environment (GXE). Principal Component Analysis

(PCA), Hierarchical Ascending Classification (HAC) and study of

variance of the different groups were realized using the adjusted

averages of the common parameters evaluated at the two sites.

3 Results

The results of germination test of the accessions showed on

average of 80%. Within the tested collection, it was observed in

the two sites, twenty-two (22) accessions within agro-

morphological characteristics widely distinct from others, with a

mixture of specific traits (of O. sativa or interspecific).

3.1 Presentation of Agronomic Characteristics

The heritability coefficient varies between 0.86 and 0.99 for

rainfed conditions and between 0.84 and 0.99 for the irrigated

conditions. The coefficient of variability varies between 0.021 and

1.9. The correlation Genotype X Environment of parameters

showed a p-value above 0.05 for the yield. The adjusted average of

the data of the two sites made it possible to extract the descriptive

values from the agronomic parameters (Table 1) and to make a

Principal Component Analysis (PCA).

3.2 Identified Phenotypic Groups

The individuals factor map (Figure 1) obtained from PCA showed

the distribution of accessions based on their similarity. The two

axes of distribution map represent more than 50% of the

information in the collection. The selection of the characteristics

parameters was made from their value contribution, which informs

on the quality of their representation and their contribution to the

distribution.

These results made it possible to make the hierarchical

classification represented by the dendrogram (Figure 2), this

appeared two groups (clusters) phenotypes. Table 2 shows the

different values-tests for the variables of each cluster. The

fundamental difference between the accessions is related to the

variables as: Till30 (4.10), Till60 (16.62), Hgt60 (83.15cm), Mat.Hgt

(81.07cm), Flw.Date (≈ 48 days), Mat.Date (≈ 80 days), NFP

(19.76), and yield (497.30g/m2). This grouping made it possible

Table 1 Descriptive values of the agronomic characteristics of the accessions studied.

variables Moy Low Max Standard deviation

Till30 4.10 1.94 9.10 0.16

Till60 16.62 9.51 26.07 1.97

Hgt60 (cm) 83.15 69.21 100.64 3.25

Mat.Hgt (cm) 81.07 66.16 99.83 1.42

Flw.Date (days) 47.20 41.30 59.10 2.65

Mat.Date (days) 79.80 74.35 90.05 5.47

NFP 19.76 12,10 29.45 5.09

Wgt.1000g (g) 26.61 20.41 36.15 1.98

Yield (g/cm2) 497.30 297.57 702.25 60.97




Factor map

Figure 1 Accessions distribution map

Figure 2 Hierarchical classification of accessions according to their agronomic characteristics



144 Claude et al.

to identify 114 O.glaberrima accessions for the cluster1, and 99

O.glaberrima accessions for the cluster 2, this included two

controls CG14 and NERICA4.

4 Discussion

4.1 Agro-morphological characteristics

All accessions have finished their growth cycle: which gave

information on their good germination and vegetative

vigor. Values expressing heritability (≈1) explain a very low

impact of environmental factors. These observations suggested that

phenotypic characters come from genotypic expression. In

addition, the analysis of variance revealed a significant effect of

the genotype on the studied variables. According to Okeno (2001)

and Pliura et al. (2014) genotypic variation is an important factor

for the improvement of variability and selection of new varieties.

Absence of Genotype X Environment effects on yield revealed that

site variation does not seem to affect the productivity. These

results are in agreement with the findings of Gueye et al. (2016)

those who reported similar results on inter and intraspecific rice

lines by crossing Genotypes X Season factors. Indeed, one might

consider this absence of Genotype X Environment interaction for

yield as an interesting result; this provides information on the

potential of varieties especially with respect to their stability and

their ability to give good performance in various conditions.

According to Vitaa et al. (2010) modern varieties are characterized

by reducing G X E interaction and increasing stability.

The evolution of the data in relation to the number of tillers and

height shows a normal growth of the plants. In fact, 30 days after

sowing, the average numbers are equal to that of the control CG14,

thus indicating a good development of the plants with the

minimum cultivation conditions imposed which is a very important

characteristic of O. glaberrima (Anonymous, 1991; Anonymous,

2002; Aboa et al., 2004; Vido, 2011). The observed variability may

depend on other factors related to genotypic traits. In the same

order of data, the average number of tillers 60 days after sowing is

greater than 16. Based on the criteria of IRRI (2002) & Sanni et al.

(2009b) it is realized that this parameter has strongly contributed to

the number of fertile panicles of accessions thus indicating for a

normal progression of the maturity phase of the plants (Sanni et al.,

2009b; IRRI, 2002). Indeed, the number of tillers produced by a

variety is related to the stage of plant development (Sanni

et al., 2009a), which is strictly related to the variety

(Nguetta et al., 2006). In relation to the number of fertile panicles,

this parameter is related to the tillering attitude of the plants (Wang

et al., 2007), but it does not necessarily determine the yield,

because it is important to note that the yield is not directly

correlated with the vegetative development of the plant (Guéi

et al., 2005), a very strong tillering (in tuft) at the vegetative phase

could give way to competition and produce a small number of

fertile panicles. In addition, resistant varieties, when threatened

with severe attacks (midge for example), emit new tillers that are

not all productive (Nwilene et al., 2006). However, a low attitude

to tillering presented by some varieties is linked to the low

nitrogen supply or perhaps the combined effect, in ground, of the

possible deficiency of phosphorus and percentage of acidity

(Wopereis et al., 2009; Dewa, 2013). The variability observed in

these two studies is certainly not only to the conditions of the test

sites but also depend on the genotypic characters of the accessions.

Considering the dates of flowering and maturity, all accessions had a

mean of 47 days for the cycle of sowing-flowering 50% that is

relatively early compared to NERICA4 (65 days on average). This

value also reflects an early cycle of sowing-maturity 80% (79 days),

shorter than this same control (92 days on average). These observations

explain a good ability to withstand the cold stress observed generally in

December (after flowering in the Sahelian conditions). These results

are contradictory to the findings of Wopereis et al. (2009) those who

reported that cold is an abiotic constraint that delays the growth of rice

and increases the duration of its cycle. Varieties used in current study

are revealed as early cycle varieties (very interesting aspect for any

producer or breeder). Indeed, Bezançon & Diallo (2006) reported that

African rice is a species whose life cycle is early and varies between 90

and 100 days. Research conducted by other author has shown that

typical rainfed varieties of rice mature between 150 and 170 days and

improved varieties mature between 120 and 140 days after sowing

(Monty et al., 1997). Given these considerations,

the accessions characterized in this study outdo the boundaries and are

very interesting from the point of view of life cycle and probably offer

a good prospect of study. For the characterized collection, the mean

yield evaluated is 497 g/m2 (in both sites), and is substantially equal to

the yield of the control CG14; while more than 70% of accessions have

a higher yield than that of NERICA4. These results are in agreement

Table 2 Agro-morphological parameters characteristic of clusters

Cluster 1

Cluster 2

variables v.test

variables v.test

NFP 4.79

Hgt60 8.59

Flw.Date 3.73

Mat.Hgt 8.01

Till30 2.82 Yield 7.76

Mat.Date -4.17

Till60 7.09

Till60 -7.09

NFP 4.79

Yield -7.76 Mat.Date 4.17

Mat.Hgt -8.01

Flw.Date 3.73

Hgt60 -8.59

TIll30 2.82




with the assertions of Linares (2002) who estimates the productivity of

African rice varieties (O. glaberrima) and reported lower yield. These

observations explain a relative control of chattering, lodging incidence

and pests; these factors being controlled by fillets, bird hunters and

border plants during agronomic test. In fact, lodging incidence and

chattering are the factors of the low yield potential of African rice

(Futakuchi & Sié, 2009). Similar results were observed by Africa Rice

(2010) and Aboa et al. (2004), those who suggested that water lodging

and chattering are the major limiting factors for O.

glaberrima varieties. The exsertion panicle also contributes to the

reduction of damage. Indeed, a good exsertion of the panicle is a

characteristic of weak attack of blast on the neck of the panicle and a

good maturation of the spikelets (Jacquot, 1974). This character

obviously favored the absence of damage due to blast and maturation

of grain that improved yields. This study revealed that there are

still accessions of African rice that can have strong yield potential.

4.2 Phenotypic groups identified

The classification of the accessions showed two groups (cluster1

and cluster2). The fundamental difference was reported between

the studied accessions. Studied nine characteristics are well

discriminating the selected African rice collection. Previous

studies conducted on agronomic characteristics revealed that

height of plant and tillering ability are essential characteristics

which can discriminate rice populations (Ogunbayo

et al., 2007; Ojo et al., 2009; Moukoumbi et al., 2011). Current

study identified two clusters, among these, cluster I have 114

accessions while cluster II have 99 accession of African rice, this

cluster also have two controls CG14 and NERICA4. The values-

test (v-test) obtained made it possible to distinguish the variables

that strongly characterize the accessions of each

cluster. Indeed, cluster 1 groups more than half of the accessions,

and the two clusters are strongly characterized by tillering 60

days after sowing (Till60), height of plant (at 60 days after

sowing and at maturity) and yield. For these variables, the

accessions of cluster 1 have their averages relatively lower than

the average of the entire collection; on the other hand, the

accessions of cluster 2 are characterized by their average values

above the average of the entire collection. Concerning the

variable « precocious of the cycle », which is an interesting and

exploitable trait for the varietal selection, the accessions of the

cluster 1 are revealed very early, with sowing-flowering and

sowing-maturity cycles very short compared to the average of the

collection. Results of current study are contradictory to the

findings of Montcho et al. (2017) those who reported that O.

sativa has a shorter life cycle than O. glaberrima. According to

Takeshi (2007), the vegetative cycle is an important factor that

can be used as a control parameter for climatic factors and

pests. The cycle time of a rice variety is strongly related to its

photoperiod sensitivity and depends mainly on the duration of its

basic vegetative phase (Dingkuhn & Asch, 1999).

Conclusion

The results of this study reveal that the collection is composed

of O. glaberrima accessions grouping according to agronomic

performance in two clusters. The characteristics of the hierarchical

groups show that these varieties present an appreciable agronomic

performance with the capacity to develop under several constraints

associated with several ecosystems such as the rainfed and the irrigated

cultural condition. Globally, the accessions of cluster 1 seem to be the

most interesting, owing to their good performance in different

conditions (sites and seasons) and their precocious cycle. It is favorable

materials for further study because these genotypes have good

environmental adaptation, higher productivity, high and stable yields.

This collection constitutes a reservoir of interesting genes, which

explains its use in variety improvement programs. These results give

an opening on the study of the nutritional values in order to reveal over

assets for the valorization of the African rice O. glaberrima.

Acknowledgment

Authors are thankful to competent authorities of Africa Rice,

HAAGRIM Project which is funded by the European Union


There is no conflict of interest for the publication of this article.

References

Aboa K, Tamaka K, Dantsey-Barry H (2004) Multiplication and

characterization of rice ecotypes. Activity Report, Lomé, ITRA, Pp.3.

AfricaRice (2010) New Breeding Directions at AfricaRice:

Beyond NERICA. Cotonou, Benin: Pp.24.

Anonymous (1991) Memento of the agronomist. Paris, Ministry of

Cooperation and Development, Pp.687-716 (1635 p).

Anonymous (2002) Memento de l'agrronome, Ministry of Foreign

Affairs - CIRAD -GRET; Pp.799 - 811 (1687 p).

Bezançon G, Diallo S (2006) Oryza glaberrima Steud. [Internet]

Protabase sheet. In: Brink M, Belay G (Eds). PROTA (Plant

Resources of Tropical Africa/Plant Resources of Tropical Africa),

Wageningen, the Netherlands, available on

http://database.prota.org/recherche.htm. Access on June 2014.

Dewa KMK (2013) Agro-morphological characterization of

African rice accessions (Oryza glaberrima steud.) Collected on the

Danyi plateau in Togo. Memory of DEA School of Agronomy

University of Lome Order no.: 023 / SAGE / 13.

Dingkuhn M, Asch F (1999) Phenological responses of Oryza

sativa, O. glaberrima and inter-specific rice cultivars are described

in West Africa. Euphytica 110: 109-126.



146 Claude et al.

FAO (2004) Food and Agriculture Organization of the United

Nations. International Year of Rice. Rice is life.

Futakuchi K, Sie M (2009) Better Exploitation of African Rice

(Oryza glaberrima Steud) in Varietal Development for

Resource-Poor Farmers in West and Central

Africa. Agricultural Journal 4: 96 - 102.

Guei GR, Sanni KA, Abamu FS, Fawole I (2005) Genetic diversity

of rice (Oryza sativa L.) Landraces from Ivory Coast. African

Agronomy 5: 17-28.

Gueye T, Saliou N, Abdoulaye D (2016) Evaluation of intra and

inter specific rice lines in irrigated conditions in

Senegal. International Journal of Biological and Chemical

Sciences 10 : 232-241.

IRRI (2002) Standard Evaluation System (SES), International Rice

Research Institute, Pp.65.

Jacquot M (1974) Gene systems controlling a short plant size for

varietal improvement of rainfed rice. Tropical Agronomy,

vol. XXIX, Pp.911-6.

Linares OF (2002) African rice (Oryza glaberrima): History and

future potential. Proceedings of the National Academy of

Sciences, USA, 99:16360-16365.

Mendez Del Villar P, Bauer JM (2013) Rice in West Africa:

dynamics, policies and perspectives. Cahier d’ Agriculture 22:

336-44. doi: 10.1684 / a9r.2013.0657.

Montcho D, Gbenou P, Missihoun AA, Futakuchi K, Ahanhanzo

C, Agbangla C (2017) Comparative study of two rice cultivars

(Oryza glaberrima and O. sativa) under different cultural

conditions. Journal of Experimental Biology and Agricultural

Sciences 5: 45-53.

Monty PJ, Dingkhun M, Aluko G, Mande S (1997)

"Interspecific Oryza sativa L. x O. glaberrima steud, Progenies

in upland rice improvement". Euphytica 92: 237-246.

Moukoumbi YD, Sié M, Vodouhe R, N'dri B, Toulou B,

Ogunbayo SA, Ahanchede A (2011) Assessing phenotypic

diversity of interspecific rice varieties using agromorphological

characterization. Journal of Plant Breeding and Crop Science 3:

74-86.

Nguetta ASP, Guéi RG, Diatta S (2006) Contribution to the

identification of successful varieties of rainfed rice (Oryza sp.)

in the subequatorial region of Congo Brazzaville. Africa

Science 02: 352-364.

Nokoe S (2001) Augmented block Design (ABD): The choice for

large scale farmer participatory on-farm trials, Available on

http://www.unu.edu/enu/plec access on August 2014.

Nwilene FE, Nwanze KF, Okhidievbie O (2006) African Rice Gall

Midge: Biology, Ecology and Control. Field Guide and Technical

Manual. Africa Rice Center WARDA, Pp.20.

Obilana AB, Okumu BN (2005) Evaluation Study Report for

Interspecific Hybridization between African and Asian Rice

Species. African rice center (WARDA) Cotonou, Benin; Available

on http // www.warda.org / publications / UNDP% 20 Report /

UNDP% 20 Report. Pdf access on September 2016

Ogunbayo SA, Ojo DK, Popoola AR, Ariyo OJ, Sié M

(2007) Genetic comparison of landrace rice accessions by

morphological and technical RAPDs. Asian Journal of Plant

Sciences 6: 653-666.

Ojo DK, Ogunbayo SA, Sanni AK, Guei RG (2009) The

Determination of Diversity and Relationships of the Sorrows

(Oryza sativa L ) accessions by comparative analysis of

morphological and RAPD data Pp.12.

Okeno JA (2001) Genotypic variation in morphological traits of

barley as affected by nitrogen supply. In: Horst WJ (Ed.) Nutrition

-Food Security and Sustainability of Agro-Ecosystems. Kluwer

Academic Publishers, Pp.64-65.

Pliura A, Suchockas V, Sarsekova D, Gudynaita V

(2014) Genotypic variation and heritability of growth and adaptive

traits, and adaptation of young poplar hybrids at northern margins

of natural distribution of Populusnigra in Europe. Biomass and

Bioenergy, 70: 513-529.

Sanni KA, Ariyo OJ, Ojo DK, Gregorio G, Somado EA, Sanchez I,

Sie M, Futakuchi K, Ogunbayo SA, Guei RG, Wopereis MCS

(2009b) Additives and Multiplicative Interactions Analysis of

Grain Yield Performances in Rice Genotypes across

Environments. Asian Journal of Plant Science 8: 48-53.

Sanni KA, Ojo DK, Adebisi MA, Somado EA, Ariyo OJ, Sie M,

Akintayo I, Tia DD, Ogunbayo SA, Cisse B, Sikirou M, Adekoya

MA (2009a) Potential of interspecific NERICA Rice

varieties (Oryza glaberrima x Oryza sativa). International Journal

of Botany 5: 112-115.

Sharma JR (1998) Statistical and Biometrical Techniques in Plant

Breeding. New Age International (P) Limited Publishers, New

Delhi, Pp. 432 .

Takeshi I (2007) Adaptation of flowering time by natural and

artificial selection in Arabiodopsis and rice. Journal of

Experimental Botany 58:3097-3097.




Vido AA (2011) African rice (Oryza glaberrima Steudel), an

aspect of the rural history of the Gold Coast or the extension of the

area of "rice civilization" (XVII-XVIII centuries). Ivorian Journal

of History 19:41 - 60.

Vitaa PD, Mastrangeloa AM, Matteua L, Mazzucotellib E, Virzì N,

Palumboc M, Stortod ML, Rizzab F, Cattivelli L (2010) Genetic

improvement effects on yield stability in durum wheat genotypes

grown in Italy. Field Crops Research, 119: 68-77.

Wang F, Cheng F, Zhang GP (2007) Difference in Grain Yield and

Quality among Rice Tillers in Genotypes Differing in Tillering

Capacity. Rice Science 14: 135-140. DOI:

http://dx.doi.org/10.1016/S1672- 6308(07)60019-5.

Wopereis MCS, Defoer T, Idinoba P, Diack S Dugué MJ (2009)

Curriculum for Participatory Learning and Action Research

(PLAR) for Integrated Rice Management (IRM) in Inland Valleys

of Sub-Saharan Africa. Technical Manual African Rice, Pp.128.



ISSN No. 2320 – 8694


GENETIC CHARACTERIZATION OF LOCAL RICE (Oryza sativa L.) GENOTYPES

AT MORPHOLOGICAL AND MOLECULAR LEVEL USING SSR MARKERS

Pooja Pathak, S. K. Singh, Mounika Korada*, Sonali Habde, D. K. Singh, Amrutlal Khaire,

Prasanta Kumar Majhi

Department of Genetics and Plant Breeding, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi-221005, India.



ABSTRACT

Current study was conducted to estimate the genetic diversity of 29 local rice cultivars including 3

checks at both morphological and molecular level during Kharif 2017 in an augmented design.

Significant results obtained from ANOVA of 29 genotypes for 16 quantitative traits; Mahalanobis’ D2

grouped the total genotypes into 6 clusters. Highest inter-cluster distance was found between clusters III

and VI indicating the genotypes in these clusters are most diverse. The SSR banding pattern revealed a

total of 65 alleles from 21 polymorphic markers across 29 rice genotypes with an average of 3.09

alleles. The polymorphism information content (PIC) values ranged from 0.701 (RM 277) to 0.346

(RM237) with a mean value of 0.571 showing the marker RM277 as best based on the above study. The

dendrogram analysis revealed all the 29 genotypes were grouped into two main clusters i.e. cluster I and

cluster II with dissimilarity coefficient 0.36. Both the clusters were further divided into two groups each

of which are further divide into two sub-groups each. Based on the genetic distances and the

dissimilarity coefficient obtained from both morphological and molecular analysis, genotypes like

Bahubali, Golden 105, Pusa 1121, HUR-1301, RK-2 Lal kasturi and Pan 815 can be selected and used

as parents due to their greater diversity. Knowledge of genetic diversity available within a population at

both morphological and molecular level helps the breeder to formulate a successful hybridization

programme and gain good results.


KEYWORDS

Genetic Diversity

Mahalanobis

Molecular markers

Rice

SSR

E-mail: [email protected] (Mounika Korada)


















Genetic Characterization of Local Rice Genotypes 149

1 Introduction

Rice (Oryza sativa L.) is an annual grass of the family Poaceae

(graminae) with chromosome number 2n=24. Rice is being consumed

by more than half of the world’s population and serves as a major

source of carbohydrates. Having more amounts of carbohydrate, it

provides instant energy, and is a staple food that is consumed by the

majority of India’s population. Rice grain contains about 75-80%

starch, 12% water and 7% protein (Hossain et al., 2015). It is also an

important source of vitamins like thiamine, riboflavin and niacin and

minerals like magnesium, phosphorus and calcium. It is enormously

diverse both in the way it is cultivated and its usage by the humans.

Having the smallest genome of all cultivated cereals, being diploid and

self pollinating, it is the most extensively studied species among

cereals (Pooja et al., 2019). Among very few options for increasing

yield potential of rice, improvement of the genetic potential of the crop

cultivars is one of the best options. Rice has endowed with rich natural

genetic diversity and there is tremendous scope to exploit diversity for

improvement of the genetic base.

To encounter the existing food requirements, genetic diversity is a

natural source for rice breeding (Reig-Valiente et al., 2016). The higher

level of genetic variation present in a population, the more valuable

resource, is used for enlarging the genetic base of the breeding program

(Nachimuthu et al., 2015). This would help in development of

transgressive segregants which will perform better than both the

parents. In addition, Haritha et al., 2016 postulated that genetic

diversity obtained precious information for both basic studies and

practical applications.

Mahalanobis’s D2 statistic is a powerful tool in measuring the degree of

divergence between biological populations at genetic level and

provides a quantitative measure of association between geographic and

genetic diversity based on generalized distance. For the assessment of

genetic diversity, microsatellite or simple sequence repeat (SSR)

markers are considered as most amenable, as they are multi- allelic in

nature, highly informative, highly reproducible, have co-dominant

inheritance and provide extensive genomic coverage (McCouch et al.,

2002). SSR markers are able to detect great level of allelic diversity

and they have been extensively used to identify genetic variation

among rice subspecies. Keeping this in view, the present experiment

was conducted using 29 local rice genotypes to characterize the

morphological and molecular genetic diversity.


2.1 Plant material:

A total of twenty nine local rice genotypes including 3 checks were

grown in an augmented block design with three blocks having repeated

checks during Kharif 2017. The list of genotypes used in this

experiment is presented in table 1.

2.2 Morphological observations:

Data was recorded on sixteen quantitative traits viz. days to first

flowering, days to 50% flowering, tillers per plant, panicle length, plant

height (cm), fertile spikelets, sterile spikelets, total grains per panicle,

kernel length(cm), kernel breadth(cm), L/B ratio,1000 grain weight(g),

plot yield(kg/ha), grain yield (kg/ha), biomass(kg/ha) and Harvest

Index. The data for days to first flowering, days to 50% flowering and

plot yield was collected on plot basis whereas for other yield and yield

attributing traits, data was recorded on five randomly selected plants of

each genotype in the respective block.

2.3 Molecular analysis:

DNA was extracted following CTAB extraction method according to

Doyle & Doyle, (1987) with few modifications and the DNA quality

was estimated using Biophotometer plus. 21 simple sequence repeat

(SSR) markers were used for molecular analysis. The details of SSRs

used are listed in table 2 along with the forward and reverse sequence.

Table 1 List of rice (Oryza sativa L.) genotypes used in the present study

1.Kalanamak-3119 11. Red long 21. Sambha Red

2.RK-2 Lal Kasturi 12. Vishnu Bhog black 22. HUR-97PB-1-S

3. Damni 13.Lal Basmati 23.Govind Bhog

4.Golden G.R.32 White 14. Golden 105 24.Kalanamak-11

5.Vijay 15. Tulsi Manjari 25. Red Mahsuri

6.BHULC-13 16.Kudrat 5 26. HUR-1301

7. Divya 17. NDR 118 27. HUR-3022

8. Bahubali 18.RK-8 Gold 28. BPT5204

9. Pan 815 19.Kudrat 5-17 29.Pusa 1121

10.Red Basmati 20. BHULC-5



150 Pathak et al.

Polymerase chain reaction (PCR) was performed using a thermal

cycler (SureCycler 8800) in vitro to amplify a specific segment of the

total genomic DNA to a billion fold (Mullis et al., 1986). The amplified

DNA products generated through SSR primers were resolved through

electrophoresis in 2.5 per cent agarose gel prepared in TAE buffer and

the gels were visualized under a UV light source in a gel

documentation system (Gel DocTM XR+, BIO-RAD, USA). The

images of amplified products were used for the evaluation of genetic

diversity between the genotypes. A binary data matrix was utilized to

generate genetic similarity data among the 29 lines of rice genotypes.


The quantitative trait mean values computed based on data on five

randomly tagged plants in each genotype and checks were used for

statistical analysis. Analysis of variance was performed to partition

the total variation among the genotypes and check entries into

sources attributable to ‘genotypes+ checks entries, ‘genotype check

entries’ and ‘genotype vs. check entries’ following Augmented

design (Federer, 1956) WINDOSTAT version 9.0. Adjusted trait

mean of each of the genotype accession was estimated (Federer,

1956) and the same were used for all subsequent statistical analysis.

In the present study, Mahalanobis’ generalized distance as

described by Rao (1952) was used to estimate the genetic

divergence. Data Analysis for the SSRs used in the present study

was done using the software NTSYSpc version 2.02 (Rohlf, 1998).

The binary data matrix generated by polymorphic SSR markers

were subjected to further analysis using NTSYS-pc version 2.11W

(Rohlf, 1997). The SIMQUAL programme was used to calculate

Table 2 Details of the SSR primers used in the present study along with No. of Alleles and PIC value obtained

S.No. Marker name

Chromosome no.

Forward/ Reverse

Sequence Ta No. of alleles obtained

PIC value obtained

1 RM431 1 Forward

Reverse

TCCTGCGAACTGAAGAGTTC

AGAGCAAAACCCTGGTTCAC

55

55 3 0.567

2 RM433 1 Forward Reverse

TGCGCTGAACTAAACACAGC AGACAAACCTGGCCATTCAC

52 52

3 0.585

3 RM5 1 Forward

Reverse

TGCAACTTCTAGCTGCTCGA

GCATCCGATCTTGATGGG

52

51 3 0.588


TGGAGTTTGAGAGGAGGG CTTGTTGCATGGTGCCATGT

50 52

3 0.591

5 RM11943 1 Forward

Reverse

CTTGTTCGAGGACGAAGATAGGG

CCAGTTTACCAGGGTCGAAACC

57

57 3 0.527


CAAATCCCGACTGCTGTCC TGGGAAGAGAGCACTACAGC

53 54

2 0.346

7 RM514 3 Forward

Reverse

AGATTCATCTCCCATTCCCC

CACGAGCATATTATAGTGG

52

50 4 0.693


ACTTGAGACGATCGGACACC TCACCCATGGATGTTGTCAG

54 52

3 0.532

9 RM60 3 Forward

Reverse

AGTCCCATGTTCCACTTCCG

ATGGCTACTGCCTGTACTAC

54

52 3 0.586


GTTCAGTGTTCAGTGCCACC CACGAGCATATTACTAGTGG

54 52

3 0.527

11 RM510 6 Forward

Reverse

AACCGGATTAGTTTCTCGCC

TGAGGACGACGAGCAGATTC

52

54 3 0.501


AACAACCCACCACCTGTCTC AGAAGGAAAAGGGCTCGATC

54 52

3 0.563

13 RM11 7 Forward

Reverse

TCTCCTCTTCCCCCGATC

ATAGCGGGCGAGGCTTAG

53

53 4 0.652

14 RM44 8 Forward

Reverse

ACGGCAATCCGAACAACC

TCGGGAAAACCTACCCTACC

53

54 3 0.580

15 RM284 8 Forward

Reverse

ATCTCTGATACTCCATCCATCC

CCTGTACGTTGATCCGAAGC

53

54 3 0.574

16 RM215 9 Forward

Reverse

CAAAATGGAGCAGCAAGAGC

TGAGCACCTCCTTCTCTGTAG

52

54 3 0.559

17 RM474 10 Forward

Reverse

AAGATGTACGGGTGGCATTC

TATGAGCTGGTGAGCAATGG

52

52 3 0.576

18 RM171 10 Forward

Reverse

AACGCGAGGACACGTACTTAC

ACGAGATACGTACGCCTTTG

54

52 3 0.576

19 RM536 11 Forward

Reverse

TCTCTCCTCTTGTTTGGCTC

ACACACCAACACGACCACAC

52

54 3 0.589

20 RM552 11 Forward

Reverse

CGCAGTTGTGGATTTCAGTG

TGCTCAACGTTTGACTGTCC

52

52 3 0.585

21 RM277 12 Forward

Reverse

CAAATCCCGACTGCTGTCC

TGGGAAGAGAGAGCACTACAGC

51

52 4 0.701




the Jackard’s dissimilarity coefficient. To measure the in

formativeness of the markers, the polymorphism information

content (PIC) for each SSR marker was calculated according to the

formula suggested by (Weir, 1996) and it is written as

PIC=1-(Pi2)

Where, ‘i’ is the total number of alleles detected for each SSR

marker, ‘Pi’ is the frequency of the ith

plus allele in the set of 29

genotypes studied. PIC value is used to estimate the discriminatory

power of the SSR marker.


The result of ANOVA revealed significant difference among the

genotypes with respect to all the characters under study and this

indicates that there is an inherent genetic difference among the

genotypes for all the traits studied.

3.1 Diversity analysis of morphological data

Mahalanobis’ D2 analysis grouped the total genotypes into 6

clusters (table 3) on the basis of inter-cluster genetic distances.

Clustering pattern indicated that 17 out of 29 genotypes belong to

cluster I indicating their close relationship among themselves as

compared to others. Genotypes grouped in one cluster are less

diverse than the genotypes located in a different cluster. Further, 8

genotypes belong to the cluster II and 1 genotype each in cluster

III, IV, V, & VI. The intra and inter cluster distances i.e. D2 values

are presented in table 4 and the diagrammatic representation of

clusters with inter and intra cluster D2 values have been presented

in figure 1. Highest intra-cluster distance was observed in the

cluster I (22.431) with 17 genotypes followed by cluster II

(11.657) with 8 genotypes. Highest inter-cluster distance was

found between clusters III and VI (99.21), indicating that the

hybridization between the genotypes of these clusters would yield

desirable segregates with the accumulation of favorable genes in

the segregating generations. This is followed by cluster I and VI

(63.13) and cluster III and IV (61.74). The smallest inter-cluster

distance (22.44) was observed between cluster I and II followed by

cluster II and V (23.37). Similar results were found by Awasthi et

al. (2005), Rajesh et al. (2010) and Devi et al., (2019). Information

about genetically diverse genotypes would help in selection of

parents while planning for a hybridization programme which

would yield useful segregants. However, while selecting parents

for hybridization programme their yield potential should not be

overlooked (Ramya & Kumar 2008).

Table 3 Grouping of twenty nine genotype of rice into six clusters by Tocher method

S.No. Cluster Name Number of Genotypes Name of genotypes

1. Cluster I

17

Vijay, Lal Basmati, Red Basmati, Divya, BPT5204, Kudrat 5,

RK-2 Lal kasturi, Vishnu bhog, black Damni, Pusa 1121, Red long, Kalanamak 3119, Kudrat 5-17,

BHULC-13, HUR-3022, HUR 1301, BHULC-5

2 Cluster II 8 RK-8 Gold, HUR 97 PB-1-S, Red Mahsuri, Tulsi manjari, NDR 118, Sambha

red, Govind bhog, Kalanamak-11

3 Cluster III 1 Bahubali

4 Cluster IV 1 Golden GR-32 white

5 Cluster V 1 Pan 815

6 Cluster VI 1 Golden 105

Table 4 Average inter and intra cluster distance (D2 values) among six clusters of twenty nine genotypes of rice by Tocher Method.

Cluster

I Cluster

II Cluster

III Cluster

IV Cluster

V Cluster

VI

Cluster I 22.431 22.445 43.537 40.379 38.969 63.138,

Cluster II 11.657 41.122 28.715 23.371 42.910

Cluster III 0 61.741 57.887 99.217

Cluster IV 0 24.592 43.000

Cluster V 0 34.659

Cluster VI 0



152 Pathak et al.

Based on the results of Mahalanobis D2 analysis, the genotypes

grouped in cluster III are having maximum genetic distance with

the genotypes placed in the cluster VI. This indicates that crosses

between the genotypes present in these two clusters are expected to

be much heterotic. It should be kept in mind that, along with the

genetic distance, per se yield and yield contributing characters are

to be taken into consideration while selecting the genotypes. In this

respect, genotypes like Bahubali and Golden 105 are having

highest inter cluster distance and high per se yield can be

considered suitable for hybridization programme.

3.2 Diversity analysis of molecular data:

Molecular diversity analysis with the help of markers is based on

the naturally occurring polymorphism of the individuals which is

not affected by the environment. In the present study, all the 29

genotypes were analysed with 21 SSR markers and the results

obtained are explained in the following subheads.

3.2.1 Scoring of SSR band and PIC value:

The presence or absence of each band in all genotype was scored

manually by binary data matrix with 1 and 0 for presence and

absence respectively. Level of polymorphism in rice genotypes

was evaluated by calculating allelic number and PIC values for

each of twenty one polymorphic SSR markers. 21 polymorphic

markers run across 29 rice genotypes obtained a total of 65 alleles

with an average of 3 alleles. Among the 21 SSRs, 1 marker

produced 2 alleles, 3 markers have produced 4 alleles each and 17

markers have produced 3 alleles each. Similar results were shown

by Pachauri et al. (2013) and Anh et al. (2018).

The PIC (Polymorphic information content) was assessed for each

locus to evaluate the information of each marker and this PIC

value is an evidence of allele diversity and frequency among

genotypes and its values ranged from 0.701 (RM 277) followed by

0.693 (RM514) to 0.346 (RM237), with a mean value of 0.571.

Hossain et al. (2012) reported similar range of PIC value (0.239 to

0.765) with an average of 0.508. The PIC values obtained for all

the markers are shown in table 2. In another study conducted by

Singh et al. (2015), PIC values ranged from 0.265 to 0.65 with an

average of 0.47. Similar results were further obtained by Roy et al.

(2015), Krupa et al. (2017) and Anh et al. (2018). Higher PIC

value of a locus detected higher alleles value. From this data, it is

shown that, RM 277 was found to be the most appropriate marker

among the rice genotypes owing to the highest PIC value of 0.701

can be employed to enlarge the genetic foundation of the current

genotypes (Anupam et al., 2017). The other markers with PIC

value above 0.5 also play a significant role in studying the genetic

divergence of the rice genotypes (Anh et al., 2018) Gel pictures

obtained from analysis of 29 genotypes with some of the SSR

markers is shown in figure 2 (figure 2a to 2c).

3.2.2 Dendrogram analysis

A dendrogram (figure 3) based on Jackard’s dissimilarity

coefficient was constructed using UPGMA (Unweighted Pair

Group Method with Arithmetic Averages) method and the 29 rice

genotype were grouped into two main clusters i.e. cluster I and

cluster II with dissimilarity coefficient 0.36. Cluster I was further

divided into two groups IA and IB with dissimilarity coefficient

0.46. Cluster IA was further divided into two sub- groups IA-1 and

IA-2 with dissimilarity coefficient 0.56. Cluster IA-1 contains 6

genotypes and IA-2 contains 3 genotypes. Cluster IB further

divided into IB-1 and IB-2 with dissimilarity coefficient 0.58.

Cluster IB-1 consists of 8 genotypes and I B-2 contains 2

genotypes. Cluster II was divided into two main sub-groups II-A

and II-B with dissimilarity coefficient 0.50. Cluster IIA had 6 and

II B had 4 genotypes. Grouping of the clusters along with the name

of genotypes included in each cluster is shown in table 5. Similar

results were obtained by Sonkar et al., 2016, Vengadessan et al.,

2016, Anh et al., 2018 and Pooja et al., 2019.

3.2.3 Jackard dissimilarity coefficient

To determine the level of relatedness among the genotypes the

dissimilarity coefficient was used. The dissimilarity coefficient

varies from zero to one, closer to one shows a higher dissimilarity,

whereas, closer to zero shows a higher similarity. The average

dissimilarity ranged from 0.6536 to 0.7262. The total average of

dissimilarity coefficient of all the 29 genotypes is 0.714. The

dissimilarity coefficient varied from the largest value 0.88 between

the cultivar Pusa 1121 and HUR-1301 followed by RK-2 Lal

kasturi and RK-8 Gold (0.86) and pan 815 and RK-8 Gold (0.86)

Tocher Method

Mahalnobis Euclidean Distance (Not to the Scale)

Figure 1 Cluster diagram obtained from Mahalanobis D2 analysis

using Tocher method




which shows high dissimilarity between them showing that they

are highly dissimilar with each other. The lowest value (0.48) was

found between Red long and HUR-97 PB 1-S followed by 0.50

between Vishnu bhog black and Pusa 1121. Similar results were

obtained by Pooja et al., 2019.

Finally, according to the dendrogram and Jackard dissimilarity

coefficient values, the most diverse cultivars among the 29 local

rice genotypes studied are Pusa 1121 and HUR-1301 followed by

RK-2 Lal kasturi and RK-8 Gold, Pan815 and RK-8 Gold as they

showed greater genetic distances which means a greater chance of

getting useful segregants when the above said genotypes used in a

hybridization programme and having lower relatedness making

them genetically diverse which is of prime importance for any

breeding programme. Similar results were obtained and explained

by Ahn et al., 2018 and Pooja et al., 2019.

Figure 2a Gel image showing banding profile obtained by RM 334.

Figure 2b Gel image showing banding profile obtained by RM 227.

Figure 2c Gel image showing banding profile obtained by RM 11.

Figure 2 Gel images of 29 local rice genotypes analysed using SSR markers. Lane 1-29 represented the rice genotypes as listed in table 1 and L is

the Ladder

1 2 3 4 5 6 7 8 9 L 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

1 2 3 4 5 6 7 8 9 L 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

1 2 3 4 5 6 7 8 9 L 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29



154 Pathak et al.

Figure 3 Dendrogram based on Jackard’s dissimilarity coefficient using UPGMA




Conclusion

Study of genetic diversity at both morphological and molecular level

gives a reliable information regarding the genotypes included in the

study. From the above study, It is observed that both morphological

and molecular variations exists among the 29 local rice genotypes

evaluated and differences in the grouping of the genotypes in

different clusters was observed. Considering all the aspects, the

genotypes, Bahubali, Golden 105, Pusa 1121, HUR-1301, RK-2 Lal

kasturi and Pan 815 have the advantage of both greater genetic

distance from each other and have higher per se yield. Hence, these

genotypes can be used by the breeders for planning a successful

hybridization programme and for creation of more variability in rice.

Acknowledgement

The authors are highly thankful for the support given by Molecular

drought laboratory, Dept of genetics and Plant Breeding for

providing the genotypes used in the study and Niche area lab,

Central Laboratory, Institute of Agricultural sciences, BHU for

valuable support in the conduct of molecular analysis.

References

Anh T, Thi T, Khanh TD, Dat TD, Xuan TD (2018) Identification

of phenotypic variation and genetic diversity in rice (Oryza sativa

L.) mutants. Agriculture 8(2):30.

Anupam A, Imam J, Quatadah SM, Siddaiah A, Das SP, Variar M,

Mandal NP (2017) Genetic diversity analysis of rice germplasm in

Tripura State of Northeast India using drought and blast linked

markers. Rice Science 24(1):10-20.

Awasthi LP, Misra CH, Pandey VK (2005) Genetic divergence in

Indian aromatic rice. Crop Research 30: 199-201.

Devi M, Jyothula DP, Krishnaveni B, Rao VS (2019) Genetic

Divergence Studies in Rice (Oryza sativa L.) Hyrids for Yield, Yield

Component Traits and Quality Parameters. International Journal of

Current Microbiology and Applied Sciences 8: 1577-1583.

Doyle JJ, Doyle JL (1987) Isolation of plant DNA from fresh leaf

tissue. Focus 12: 13-15.

Federer WT (1956) Augmented (or Hoonuiaku) designs. Hawaiian

Planters' Record 55:191-208.

Haritha G, Sudhakar T, Chandra D, Ram T, Divya B, Sarla N

(2016) Informative ISSR markers help identify genetically distinct

accessions of Oryza rufipogon in yield improvement. Rice Science

23(5): 225-241.

Hossain MM, Islam MM, Hossain H, Ali MS, Jamie A, Komamine

A, Prodhan SH (2012) Genetic diversity analysis of aromatic

landraces of rice (Oryza sativa L.) by microsatellite markers.

Genes, Genomes and Genomics 6(1):42-47.

Hossain SM, Haque J, Rahman (2015) Genetic variability,

correlation and path coefficient analysis of morphological traits in

some extinct local Aman rice (Oryza sativa L.). Journal of Rice

Research 34(1): 158.

Krupa KN, Shashidhar HE, Ningaraj D, Mahendra R, Vijaykumara

SHV (2017) Molecular Marker Based Genetic Diversity Analysis in

Rice Genotypes (Oryza sativa L.) using SSR Markers. International

Journal of Pure and Applied Biosciences 5 (2): 668-674.

McCouch SR, Teytelman L, Xu Y, Lobos KB, Clare K, Walton M,

Fu B, Maghirang R, Li Z, Xing Y, Zhang Q, Kono I, Yano M,

Fjellstrom R, DeClerck G, Schneider D, Cartinhour S, Ware D,

Stein L (2002) Development and mapping of 2240 new SSR

markers for rice (Oryza sativa L.) (supplement). DNA Research

9(6): 199–207.

Mullis K, Faloona F, Scharf S, Saiki R, Horn G, Erlich H (1986)

Specific enzymatic amplification of DNA in vitro: the polymerase

Table 5 Grouping of Twenty Rice Genotypes into different cluters based on Jackard IJ coefficient

S. No Cluster Name Number of genotypes Name of genotypes

1.

I A-1 6

Kalanamak 3119, Golden 105,

GR.32, White Vijay, BHULC-5,

NDR 118

2 I A-2 3 Red long, HUR-97, PB-1-S, HUR-3022

3 I B-1 8

Damni, BHULC13, Lal Basmati, RK-8 Gold,

Bahubali, Red Basmati, Divya, Red Mahsuri

4 I B-2 2 Vishnu bhog black, PUSA 1121

5

II-A 6

RK-2 Lal Kasturi, BPT5204,

Pan 815, Tulsi manjari, Kalanamak 11,

Sambha red

6 II-B 4

Kudrat 5, Govind bhog Kudrat-5-17,

HUR 1301



156 Pathak et al.

chain reaction. In Cold Spring Harbour Symposia on Quantitative

Biology 51: 263-73.

Nachimuthu VV, Muthurajan R, Duraialaguraja S, Sivakami R,

Pandian BA, Ponniah G, Gunasekaran K, Swaminathan M, Suji

KK, Sabariappan R (2015) Analysis of population structure and

genetic diversity in rice germplasm using SSR markers: an

initiative towards association mapping of agronomic traits in Oryza

sativa. Rice 18(1):30.

Pachauri V, Taneja N, Vikram P, Singh NN, Singh S (2013)

Molecular and morphological characterization of Indian farmers

rice varieties (Oryza sativa L.). Australian Journal of Crop

Sciences 7 : 923-932.

Pooja S, Singh SK, Sonali H, Singh DK, Prudhvi RV, Amrutlal K

(2019) Studies on genetic diversity in rice (Oryza sativa L.) using

SSR markers. Journal of Pharmacognosy and Phytochemistry 8 :

922-928.

Rajesh T, Paramsivam, Thirumeni S (2010) Evaluated genetic

divergence of land races of rice. Electronic Journal of Plant

Breeding 1(2): 199-204.

Ramya K, Kumar S (2008) Genetic divergence in rice. Crop

Improvement 35 : 119-121.

Rao RC (1952) Advanced statistical methods in biometric

research. New York: John Wiley and Sons, 390p.

Reig-Valiente JL, Viruel J, Sales E, Marqués L, Terol J, Gut M,

Derdak S, Talón M, Domingo C (2016) Genetic diversity and

population structure of rice varieties cultivated in temperate

regions. Rice 9 :58.

Rohlf FJ (1997) NTSYS-pc Version. 2.02i Numerical Taxonomy

and Multivariate Analysis System. Applied Biostatistics Inc.,

Exeter Software, Setauket, New York.

Rohlf FJ (1998) NTSYS-pc: Numerical Taxonomy and

Multivariate Analysis System, Version 2.02i, Exeter Software,

New York.

Roy S, Banerjee A, Mawkhlieng B, Misra AK, Pattanayak A,

Harish GD, Singh SK, Ngachan SV, Bansal KC (2015) Genetic

diversity and population structure in aromatic and quality rice

(Oryza sativa L.) landraces from North-Eastern India. PloS one

10(6): e0129607.

Singh AK, Singh PK, Arya M, Singh NK, Singh US (2015)

Molecular screening of Blast resistance genes in rice using SSR

markers. The Plant Pathology Journal 31(1):12.

Sonkar S, Singh SK, Vennela PR, Singh DK (2016) Molecular

marker based genetic diversity analysis in rice (Oryza sativa L.)

using SSR markers. International Journal of Agriculture,

Environment and Biotechnology 9(1):45-51.

Vengadessan V, Ramapriya S, Selvarajeswari S (2016) Morpho-

molecular diversity analysis of traditional and improved cultivars

in rice. International Journal of Multidisciplinary Education and

Research 1(4): 59-65.

Weir BS (1996) Genetic data analysis II: Methods for discrete

population genetic data. Sinauer Associates Inc, Sunderland,

MA, USA.

148-156_Galley Proof_14_Volume_8_Issue_2_JEBAS2020000505.docx



ISSN No. 2320 – 8694


IDENTIFICATION OF SUPERIOR THREE WAY-CROSS F1S, ITS LINE×TESTER

HYBRIDS AND DONORS FOR MAJOR QUANTITATIVE

TRAITS IN Lilium×formolongi

Rameshwar Rai1, Amarsanaa Badarch1, Jong-Hwa Kim1, 2*

1Department of Horticulture, Kangwon National University, Chuncheon 24341, Korea

2Oriental Bio-herb Research Institute, Kangwon National University, Chuncheon 24341, Korea



ABSTRACT

This experiment was carried out to identify superior parents [three way-cross F1s and donors

(cultivars/breeding line)], and their Line×Tester hybrids (hereafter L×T hybrids) for major quantitative

traits in Lilium×formolongi. The experiment was designed in line×tester mating design taking 5 three

way-cross F1s as lines, 3CVs/breeding lines as testers and their 15 L×T hybrids in a Randomized

Complete Block Design (RCBD) with 3 replications. The analysis of variance (ANOVA) for L×T

analysis revealed a highly significant difference for all the studied quantitative traits revealing the

presence of genetic variability among the genotypes under investigation. The estimated GCA effects

revealed three way-cross F1s; (Stu× W) × AF1-6 (L2) and (AF×Ad) × HU-3(L1) found to be superior as

they possessed significant GCA effects for 7 and 4 quantitative traits respectively. Likewise, Julius-19

(T1) and 12-1-2(T3) can be used as effective donors as both of them possessed significant GCA effects

for 6 quantitative traits. The L×T hybrids, [57-7 (Aug×AugE)×BT] ×12-1-2 i.e. L4×T3 and [60.1

(AF×Ad) × Gel] × Julius-19 i.e.L5×T1 has possessed significant SCA effects for 5 traits and [(Stu×

W) × AF1-6]×Julius-19 i.e.L2×T1, [(Stu× W) × AF1-6]×WT-5 i.e.L2×T2 and [(Stu× W) × AugE-

2]×WT-5 i.e.L3×T2 had revealed significant SCA effects for 4 traits. These hybrids can be considered

as promising hybrids. The estimation of gene action divulged that bud length and the attitude of the

floral axis shown the prevailing of additive gene action while the rest of the traits had predominantly

exhibited non-additive gene action.


KEYWORDS

Three way-cross F1s

L×T hybrids

ANOVA for L×T analysis

Randomized Complete Block

Design (RCBD)

Quantitative traits

Lilium×formolongi

E-mail: [email protected], [email protected] (Jong-Hwa Kim)

ORCID: https://orcid.org/0000-0002-9300-6465


















Identification of superior three way-cross F1s for major quantitative traits in Lilium×formolongi 158

1 Introduction

Lily (Lilium L., 2n=2x=24) is one of the most important

ornamental bulbous crops which belongs to the family Liliaceae,

includes 110 species (McRae,1998), 7 sections and more than

10000 documented cultivars (Mathews, 2007; Bakhshaie et

al.,2016) which increases year by year. It is a perennial herb that

possesses scaly bulb, unbranched stem, and smooth/pubescent,

usually bright green, sometimes tinged purple or brown and

generally covered with leaves. The native Lilium species are

spread over the Northern Hemisphere and centered mainly in

Asia, North America, and Europe (Van Tuyl & Arens, 2011).

Lilies are economically most important bulbous crops mostly

used for cut flower, potted plants, some species used as edible

bulbs or medicinal use in Eastern Asia (Bakhshaie et al., 2016).

According to Lucidos et al. (2013) due to its multi aspects viz.

diversity of flower color, flower shape, long and multi flowering

stem and having long post-harvest shelf life; lilies are highly

demanded for its cut flowers in recent international flower trade.

The Lilium×formolongi hort is an interspecific hybrid obtained

through crossing of two Lilium species i.e. L. formosanum and L.

longiflorum.It is popular in East Asia (especially Korea, China and

Japan) as commercial cut flower in flower market (Ho et al., 2006;

Grassotti & Gimelli, 2011; Rai et al., 2018).

The single cross F1s lack population buffering and possess only

individual buffering so it was therefore apprehended that single

cross may not perform as stable as double cross and three way-

cross hybrids (Allard & Bradshaw, 1964). As three way-cross

have both populations and individual buffering; three way-cross

hybrids are intermediate between single and double cross hybrids

concerning uniformity, yield, stability and the relative simplicity

of selecting and testing (Weatherspoon,1970;

Schnell,1975). Keeping this point in mind, current study used

three way-cross hybrids as mother lines.

The Line×Tester mating design is one of the helpful tools

available to the breeder for quantitative plant breeding analysis.

Basically it is the extension of top cross where only one tester

used while more than one tester are used in L×T mating design.

(Nduwumuremyi et al., 2013). It involves hybridization between

lines (f) and wide based testers (m) in one to one fashion for

generating f×m=fm hybrids (Sharma, 2006). Furthermore, it

provides GCA of both lines and testers and SCA of each cross

(Sharma, 2006). At last it is very useful for estimating various

types of gene actions important for the expression of quantitative

traits (Rashid et al., 2007).

The Lilium×formolongi is used for cut flower production mainly

as seed propagation, seeds used to sown during December-

February (upon the availability of labor) usually flowered during

July-August (Goo et al., 2003; Xuan et al., 2005). As populations

derived from the seed propagation are not uniform in plant

height, flowering time and flower shape (Roh, 2002) and the

stability of novel cut flower traits is a major concern for

commercial-scale production (Ho et al., 2006) in this experiment

clonal lines (scaled) of parental material was used to maintain

the heterozygous genotypes for the seed production of a

homogenous population. In this way identification of appropriate

mother lines i.e. three way-cross F1s and its proper donor (tester)

and their promising L×T hybrids for major quantitative traits are

the major concern of this experiment.


2.1. Plant material, generation of crosses and field experiment

layout

The series of experiments plant material preparation, generation of

crosses according to Line×Tester mating design and the final step

of experiment were carried out in the backside school field, main

gate field and experimental farm of KNU at Chuncheon,

Kangwon-Do, South Korea respectively during the season of 2016

to 2018. The seeds of three-way cross F1s and donor cultivar and

breeding lines were obtained from KNU, Department of

Horticulture, Floricultural breeding lab in 2016 winter for the

preparation of seedlings. During the summer season of 2016 based

on morphological observation of major growth and flowering traits

lines of five promising three-way cross F1s and three clonal lines

of broad-based donor cultivar and breeding, lines have been

selected for the purpose to be used as lines and testers respectively.

The details of parental materials (Lines and testers) are given in

Table 1. In the succeeding year, 15 L×T hybrids have been

prepared to hybridize those 5 three-way cross F1s and three donors

in the Line×Tester method. The seedlings of altogether 23

genotypes (5lines, 3testers, and 15 L×T hybrids) has been prepared

during the January to April in 2018 following the method as

described by Rai et al. (2018) inside plastic house and have been

transplanted in the main field in the last week of April. The field

experiment has been carried out in RCBD plot design with

maintaining 3 replications. The land preparation, fertilizers and

insecticides application, as well as other intercultural operations,

has been carried out following the Rai et al. (2019).

2.2 Measurement of quantitative traits

The measurement of major quantitative traits has been done

during the main flowering season i.e. from last week of June to

the first week of August; taking samples of 12 reliable plants

from each replication. The generation of crosses preparation

procedure and measurement of morphological traits have been

adopted from Rai et al. (2018).



159 Rai et al.


The preparation of all recorded data of all traits has been done

using MS-Excel-2013 and TNAUSTAT statistical package. The

ANOVA for L×T combining ability analysis was carried out

following the method suggested by Kempthorne (1957).

Furthermore, estimation of GCA effects, SCA effects as well the

standard errors for testing the significance of GCA and SCA

effects, the estimation of components of genetic variance, the

proportional contribution of lines, testers and line×tester

interactions to the total variance and mean performance for all

traits of lines, testers, and L×T hybrids were performed adopting

the software of TNAUSTAT statistical packages (Manivannan,

2014). Likewise, based on the overall GCA effects of their

involved parents, the ranking of the best specific combiner has

been arranged for the particular traits according to the method

outlined by Arunachalam & Bandyopadhyay (1979).

3 Results

3.1The mean performance of studied traits

The performance of all 23 genotypes (5lines, 3testers, and 15 L×T

hybrids) for all studied traits have been measured and estimated

individually and presented as the group-wise overall mean

performance of lines, testers and L×T hybrids (Figure 1). The

mean performance of testers demonstrated higher for plant height,

stem diameter, number of leaves, leaf length, leaf width, number of

flowers, days to flowering (lower number means earlier flowering

and taken as positive way) and attitude of the floral axis as

compared to the mean performance of lines. That is why testers

have selected for the improvement of those respective traits for

lines. On the other hand, better performance for floral diameter and

attitude of the floral axis has been observed in L×T hybrids than

lines.

3.2 The ANOVA for L×T analysis

The mean sum square of all genotypes for all studied quantitative

traits for L×T hybrids (crosses), lines and testers are revealed

highly significant. Furthermore, mean sum square of L×T

interaction sources of variance has demonstrated that all most traits

studied besides bud length and attitude for floral axis were highly

significant(Table 2) In this way the ANOVA for L×T analysis

indicated significant genetic variability among the genotypes of

lines, testers and L×T hybrids for studied traits.

3.3. Gene action and contribution of line, tester, and line×tester

interaction

According to the estimated gene action as shown in Table 4; eight

studied traits possessed non-additive gene action while rest of the

traits viz. bud length and the attitude of the floral axis has

demonstrated the prevailing of additive gene action.

As shown in Table 3, the range of contribution (in percentage) of

lines found to be highest for the attitude of the floral axis

(75.90%)while it is appeared to be lowest (27.03%) for stem

diameter. Likewise in case of donors (Testers), the highest

contribution appeared for leaf width (47.50%) and lowest for number

of leaves (6.24%). In other hand, the maximum contribution of L×T

interaction has appeared for stem diameter (43.35%) and the

minimum contribution has appeared for the attitude of the floral axis

(2.66%). At last, in case of leaf length, the number of flowers and

days to flowering, the contribution of lines, testers and L×T

interaction has found almost same margin (Table 3).

Table 1 The list of parental materials (Lines and testes) and special remarks of the traits

S.N. Genotypes Source Remarks

(a)Lines

L1 (AF×Ad) × HU- 3 KNU, Floricultural breeding lab. PHT Intermediate to tall, floral axis upward facing

L2 (Stu× W) × AF1-6 KNU, Floricultural breeding lab. Taller ,floral axis upward facing, strong but late flowering

L3 (Stu× W) × AugE-2 KNU, Floricultural breeding lab. PHT Intermediate, up directional floral axis, early flowering

L4 57-7(Aug×AugE)×BT KNU, Floricultural breeding lab. PHT Intermediate to tall, up directional floral axis.& strong

L5 60.1(AF×Ad)×Gel KNU, Floricultural breeding lab. PHT Intermediate, early flowering ,strong and up directional floral axis

(B) Testers

T1 Julius-19 KNU, Floricultural breeding lab. Intermediate, very early flowering & up facing floral axis

T2 WT-5 KNU, Floricultural breeding lab. PHT Intermediate to tall, early flowering & near up floral axis

T3 12-1-2 KNU, Floricultural breeding lab. Late flowering ,up facing floral axis ,number of flower and thick stem

L1=Line 1………L5=Line 5, T1=Tester 1……T3=Tester 3, KNU=Kangwon National University, PHT=Plant height




Figure 1 Comparative mean performance of Parents (Lines and Testers) and L×T hybrids for studied traits

PHT=Plant height, STD=Stem diameter, NOL=Number of leaves, LL=Leaf length, LW=Leaf width, NOF=Number of flower, DTF=Days to flowering, FLD=Diameter of the flower, BL=Bud length, AFA=Attitude of the floral axis

Table 2 ANOVA for L×T analysis

Sources of

variation d.f.

The mean sum squares of studied quantitative traits

PHT STD NOL LL LW NOF DTF FLD BL AFA

Rep. 2 2.13ns 0.10ns 2.37ns 0.44ns 0.00ns 0.02ns 9.43ns 0.52ns 0.47* 2.27ns

Cross 14 164.73** 1.20** 92.84** 4.19** 0.43** 0.49** 68.37** 2.82** 1.33** 174.75**

Lines 4 389.72** 1.14** 239.00** 5.86** 0.51** 0.52** 77.84** 4.87** 2.53** 464.24**

Tester 2 158.59** 2.48** 40.56** 8.96** 1.44** 1.08** 169.92** 1.26* 3.64** 262.20**

L×T 8 53.78** 0.910** 32.82** 2.17** 0.14** 0.32** 38.24** 2.18** 0.16ns 8.15ns

Error 28 1.23 0.07 2.44 0.19 0.01 0.01 2.93 0.23 0.10 13.57

**and * significant at 1%and5% level of significance respectively.ns denotes non-significant.

Table 3 Proportional contribution of Lines, Testers and Line×Tester interaction

Parameters PHT STD NOL LL LW NOF DTF FLD BL AFA

Contribution of Lines 67.59 27.03 73.55 39.91 34.16 30.75 32.53 49.29 54.30 75.90

Contribution of Testers 13.75 29.62 6.24 30.50 47.50 31.86 35.50 6.38 39.00 21.43

Contribution of LXT 18.66 43.35 20.21 29.59 18.04 37.40 31.97 44.32 6.71 2.66

Table 4 Estimation of genetic component for 10 studied quantitative traits

Parameters PHT STD NOL LL LW NOF DTF FLD BL AFA

σ2A(VA=4σ2GCA)F=1 7.8453 0.0205 4.2434 0.1431 0.0208 0.0119 2.1300 0.0448 0.0833 11.7803

σ2D(VD=4σ2SCA)F=1 17.5168 0.2786 10.1305 0.6602 0.0406 0.0999 11.7720 0.6526 0.0195 -1.8065

72.81

7.00

38.95

9.012.21 2.64

106.31

49.60

16.03

49.75

82.77

7.47

52.25

10.35

2.34 3.80

99.27

49.16

14.92

65.4264.28

6.48

31.76

9.212.39 2.23

108.84

49.95

15.80

58.08

-20.00

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

PHT(cm) STD(mm) NOL(no.) LL(cm) LW(cm) NOF(no.) DTF(days) FLD(mm.) BL(cm) AFA(°)

Lines Testers L×T hybrids



161 Rai et al.

3.4, General combining ability (GCA) effects

The estimated GCA effects of both female (lines) and male

(testers) parents (Table 5.1and 5.2 respectively) illustrated that

none of the parents showed highly significant for all studied

quantitative traits. Out of 5 lines, the performance of L2 i.e. (Stu×

W) × AF1-6 for studied quantitative traits has found outstanding

with highly significant GCA effects in positive direction for seven

traits while rest of the traits i.e. stem diameter, the number of

leaves and the number of flowers has found non-significant.

Likewise,(L1) i.e. (AF×Ad) × HU sel.3 has shown highly

significant GCA effects for plant height, leaf length, leaf width and

the number of flowers while it’s GCA effects for the remaining

traits found to be either non-significant or significant in negative

direction. In another hand, line3 (L3) has demonstrated negatively

significant GCA effects for eight traits.

Likewise, in the case of male parents (testers), T1 (Julius-19) has

represented significant GCA effects for 6 traits besides non-

significant GCA effects for plant height and stem diameter and

significant GCA effects in negative direction for number of leaves

and the number of flowers.

While T3 (12-1-2) L3 had possessed significant GCA effects for 6

traits, non-significant GCA effects for flower diameter and

significant GCA effects in negative direction for leaf length, days to

flowering and bud length. At last T2 (WT-5) had shown all studied

traits either non-significant or significant in negative direction.

3.5. The specific combining ability (SCA) effects and ranking

of the L×T hybrids

The SCA is very important to determine whether particular crosses

are superior or not. Moreover, SCA effects comprised both

dominance and epistasis gene effects. The estimated SCA effects

demonstrated that none of the L×T hybrids showed significant

SCA effects for all studied quantitative traits (Table 6). Out of 15

L×T hybrids so far employed for studied quantitative traits in this

experiment, on the basis of significant SCA effects in positive

direction around half of them are found good performer. The L×T

hybrids, L4×T3has possessed significant SCA effects in positive

direction for plant height, stem diameter, number of leaves, leaf

length and days to flowering while rest 5 of the traits are found

non-significant. Another L×T hybrid that had possessed significant

SCA effects for 5 traits is L5×T1. It had represented significant

SCA effects for plant height, stem diameter, leaf width, days to

flowering and flower diameter while it’s SCA effects for rest of the

different five studied traits has demonstrated non-significant.

Likewise, other groups of L×T hybrids, L2×T1, L2×T2 and

L3×T2 had possessed significant SCA effects for 4 studied

quantitative traits and had possessed non-significant SCA effects

for rest of the studied traits and The L×T hybrids (L3×T3) had

Table 5.1 GCA effects of lines and testers for studied traits

Parents

(Lines)

GCA with SE


L1(gi) 4.30** 0.56** 6.84** -0.09ns 0.03ns 0.37** 0.24ns -0.61** -0.37** 1.51ns

L2 6.49** -0.11ns -4.18** 0.44** 0.31** 0.00ns -2.87** 0.98** 0.61** 11.92**

L3 -7.98** -0.25* -2.93** 0.72** 0.07ns -0.23** -2.29** -0.85** -0.70** -5.58**

L4 3.33** 0.12ns 4.22** -1.34** -0.35** 0.06ns 4.57** 0.24ns 0.33** -3.91**

L5 -6.14** -0.32** -3.95** 0.27ns 0.08* -0.20** 0.35** 0.23ns 0.13ns -3.94**

SE 0.3704 0.0911 0.5204 0.1464 0.0396 0.0443 0.5709 0.1606 0.1044 1.2278

Table 5.2 GCA effects of lines and testers for studied traits

Parents

(Testers)

GCA with SE


T1(gj) 055ns -0.03ns -1.89** 0.64** 0.07* -0.21** -3.88** 0.33* 0.28** 2.42*

T2 -3.49** -0.39** 0.82ns 0.22ns -0.34** -0.09* 2.05** -0.15ns 0.29** -4.83**

T3 2.94** 0.42** 1.07* -0.86** 0.27** 0.30** 1.84** -0.18ns -0.57** 2.41*

SE 0.2869 0.0706 0.4031 0.1134 0.0307 0.0343 0.4422 0.1244 0.0809 0.9510

**and * significant at 1%and5% level of significance respectively. ns denotes non-significant.




shown significant SCA effect for stem diameter, leaf width and

number of flower.

According to the method of Arunachalam & Bandyopadhyay

(1979), the ranking of the different L×T hybrids has been prepared

on the basis of traits and presented in Table 7.

4 Discussion

In this study, the mean performance of Lines (three way-cross F1s),

Testers (donor CVs/breeding lines) and their L×T hybrids

demonstrated significant differences for the studied quantitative

traits (Rai et al., 2018; Rai et al., 2019). Montazeri et al. (2014) in

Rice and Kumari et al. (2015) in Pea had also been found

significant variation among the studied traits of lines, testers and

L×T hybrids. Furthermore, the main source of variation among the

studied genotypes is their genetic background. Since the time of

flowering is a major concern for the Lily grower in Korea (Kang et

al., 2013) to earn the hard cash due to off-season supply of cut

flower in the market and the breeding trend of making interspecific

hybridization (Younis et al., 2014), the single cross F1s and thereby

three-way cross F1s had been prepared including at least one

genotype from L. longiflorum and two genotypes had included

from Lilium×formolongi. Besides, donors (testers) has also chosen

purposefully keeping in mind their quantitative traits (Table 1), the

genetic makeup of three way-cross F1s; one clonal line

of Lilium×formolongi (i.e.Julius-19), one clonal line of L.

longiflorum CV White Tower (i.e.WT-5) and one clonal line of L.

longiflorum breeding line 12-2-2 to introduce the earliness to the

L×T hybrids so that they can use for forcing inside the greenhouse

for off-season supply to the flower market. On the other hand, the

ANOVA for L×T analysis showed significantly different for all

studied quantitative traits among the genotypes under investigation

that means prevailing genotypic variation among them indicated

the rationality of conducting this experiment. Genetic variability is

the key instrument for any breeder to execute any breeding and

improvement scheme for particular quantitative traits. Though

morphological observation is one of the basic criteria for the

making decision about selection and improvement of particular

quantitative traits but also have to consider other genetic analysis.

The study of L×T combining ability analysis represents another

important insight is related to GCA and SCA effects which

comprises the relative measure of additive and non-additive gene

action which involved in the inheritance of particular quantitative

traits. Out of the 10 studied quantitative traits, only bud length and

the attitude of the floral axis demonstrated the prevailing of the

additive type of gene action. It opens the possibility of further

backcross breeding for these traits choosing L×T hybrids;

(L4×T3), (L5×T1), (L2×T1), (L2×T2) and (L3×T2) mother line

Table 6 SCA effects of L×T hybrids for studied traits

L×T hybrids(Cross) PHT STD NOL LL LW NOF DTF FLD BL AFA

L1×T1 -4.70** -0.26ns -1.61ns -0.73** -0.16* -0.19* -0.76** -0.07ns -0.01ns 0.49ns

L1×T2 1.24ns -0.03ns 0.01ns 0.76** 0.18* -0.21* 1.97ns -0.05ns 0.01ns -1.01ns

L1×T3 3.47** 0.29ns 1.59ns -0.03ns -0.02ns 0.40** 1.79ns 0.12ns 0.00ns 051ns

L2×T1 1.37** 0.36* 3.08** -0.03ns -0.11ns 0.28** 4.02** 0.15ns -0.02ns 1.33ns

L2×T2 5.21** 0.23ns 2.80** -0.00ns 0.14ns 0.16* -2.75** -0.43ns -0.24ns -2.67ns

L2×T3 -6.59** -0.60** -5.88** 0.04ns -0.03ns -0.44** -1.27ns 0.28ns 0.26ns 1.34ns

L3×T1 0.68ns -0.45** -0.61ns 0.26ns -0.03ns -0.09ns 0.61ns -0.82** -0.14ns -1.17ns

L3×T2 -1.21ns -0.22ns -0.49ns 0.75** -0.18* -0.11ns -2.16* 0.76* 0.41* 2.33ns

L3×T3 0.52ns 0.67** 1.09ns -1.01** 0.21** 0.20* 1.55ns 0.06ns -0.26ns -1.16ns

L4×T1 -0.46ns -0.17ns -0.55ns 0.19ns 0.08ns 0.12ns 2.91** -0.70* 0.09ns -0.34ns

L4×T2 -2.59** -0.18ns -3.36** -1.28** -0.20** -0.20* -1.59ns 0.08ns -0.12ns 0.66ns

L4×T3 3.05** 0.35* 3.92** 1.09** 0.12ns 0.08ns -1.31ns 0.62* 0.04ns -0.32ns

L5×T1 3.11** 0.52** -0.32ns 0.31ns 0.22** -0.12ns -3.77** 1.44** 0.09ns -0.31ns

L5×T2 -2.65** 0.20ns 1.04ns -0.23ns 0.06ns 0.36** 4.53** -0.35ns -0.06ns 0.69ns

L5×T3 -0.45ns -0.72** -0.72ns -0.09ns -0.28** -0.24** -0.76ns -1.09** -0.03ns -0.38ns

SE 0.6416 0.1578 0.9013 0.2535 0.0687 0.0767 0.9888 0.2781 0.1808 2.1266




163 Rai et al.

and T1 (i.e.Julius-19) as the donor (since GCA effects of T1 shows

significant in the positive direction for both of these traits). In

contrast with the findings of Song et al., 2004; Rai et al., 2018 and

Rai et al.,2019 plant height, stem diameter, the number of leaves,

leaf length, the number of flowers, days to flowering and flower

diameter represented prevalence of non-additive gene action

thereby indicated the importance of heterosis breeding followed by

selection for these quantitative traits. The contrast gene action

Table 7 Ranking of superior specific combiner for particular quantitative traits.

Traits L×T hybrids SCA effect GCA effect Mean

Performance Combination

Female Male

1.PHT

(cm)

L1×T3 3.47** 4.30** 2.94** 75.00 H×H

L2×T1 1..37** 6.49** 0.55 ns 72.70 H×H

L2×T2 5.21** 6.49** -3.49** 72.50 H×L

L4×T3 3.05** 3.33** 2.94** 73.60 L×H

L5×T1 3.11** -6.14** 0.55 ns 61.80 L×H

2. STD

(mm.)

L2×T1 0.36* -0.11ns -0.03ns 6.71 H×H

L3×T3 0.67** -0.25* 0.42** 7.32 L×H

L4×T3 0.35* 0.12ns 0.42** 7.37 L×H

L5×T1 0.52** -0.32** -0.03ns 6.66 L×H

3.NOL

(no.)

L2×T1 3.08** -4.18** -1.89** 28.77 H×H

L2×T2 2.80** -4.18** 0.82ns 31.20 H×L

L4×T3 3.92** 4.22** 1.07* 40.97 L×H

4.LL

(cm.)

L1×T2 0.76** -0.09ns 0.22ns 10.10 H×L

L3×T2 0.75** 0.72** 0.22ns 10.90 L×L

L4×T3 1.09** -1.34** -0.86** 8.10 L×H

5.LW

(cm.)

L1×T2 0.18* 0.03ns -0.34** 2.27 H×L

L3×T3 0.21** -0.07ns 0.27** 2.80 L×H

L5×T1 0.22** 0.08* 0.07* 2.77 L×H

6.NOF (no.)

L1×T3 0.40** 0.37** 0.30** 3.30 H×H

L2×T1 0.28** 0.00ns -0.21** 2.30 H×H

L2×T2 0.16* 0.00ns -0.09* 2.30 H×L

L3×T3 0.20* -0.23** 0.30** 2.50 L×H

7.DTF

(days)

L2×T2 -2.75** -2.87** 2.05** 105.27 H×L

L3×T2 -2.16* -2.29** 2.05** 106.43 L×L

L5×T1 -3.77** 0.35ns -3.88** 101.53 L×H

8.FLD

L3×T2 0.76* -0.85** -0.15ns 49.72 L×L

L4×T3 0.62* 0.24ns -0.18ns 50.64 L×H

L5×T1 1.44** 0.23ns 0.33* 51.96 L×H

9.BL L3×T2 0.41* -0.70** 0.29** 15.80 L×L


D.F. =Degree of freedom, GCA=General combining ability, SCA=Specific combining ability, SE=Standard error, L×T=Line×Tester, H×H=High×High, H×L=High×Low, L×H=Low×High, L×L=Low×Low,

PHT=Plant height, STD=Stem diameter, NOL=Number of leaves, LL=Leaf length, LW=Leaf width, NOF=Number of flower, DTF=Days to

flowering, FLD=Diameter of the flower, BL=Bud length, AFA=Attitude of the floral axis




estimation for particular quantitative traits is normal primarily that

depends upon the pedigree of the selected lines and testers.

Furthermore, Song et al. (2004) have employed Single cross F1s in

their diallel analysis while Rai et al. (2018) and Rai et al. (2019)

have used Single cross F1s and Double-cross F1s as mother lines in

their lines×tester analysis.

The most important insight for the selection of particular cross

combinations (hybrids) depending upon the choice of particular

traits is their SCA effects. The SCA effects represent both the

dominance and epispastic effects of a hybrid. In this experiment

out of 15 L×T hybrids none of the hybrids demonstrated

significant value in a positive direction for all studied quantitative

traits. Though SCA effects reflect that L×T hybrids ( L4×T3),

(L5×T1), (L2×T1), (L2×T2), (L3×T2) and (L3×T3) can be used

for the further selection process. The previous results of

Narasimhamurthy & Gowda, (2013) in Tomato, Kumari et al.

(2015) in Pea, Rai et al. (2018) and Rai et al. (2019) in Lilium×

formolongi has been so far reported that the high specific combiner

is the outcomes of the all three types of combination of general

combiner parents viz. high×high, low×low and high ×low that

means not always comes only from the high×high general

combiner parents. There are another evidence that according to the

Sharma et al. (2013), in the majority of the cases, those crosses

exhibiting high SCA effects in a positive direction, were found to

have both or one of the parent as good general combiner for the

studied traits exhibiting non-additive gene action in the genetic

control. Those parents having high GCA effects in positive

direction but sometimes produced low SCA effect is the clear

evidence of the lack of complementation of the parental genes.

Likewise those parents having low GCA effects produced hybrids

with high SCA effects is the clear evidence of complementary gene

action for some traits (Kumari et al., 2015).

Conclusions

The significant variability prevailing in the lines (three way-cross

F1s), testers (donor CVs/breeding line) and their L×T hybrids for

studied quantitative traits has provided the significance of this

experiment. The three way-cross F1s; (Stu× W) × AF1-6 (L2) and

(AF×Ad) × HU-3(L1) can be used as mother lines for seed

production inside the plastic house. For this; Julius-19(T1) and 12-

1-2(T3) can be used as effective donors. Furthermore, L×T

hybrids; (L4×T3), (L5×T1), (L2×T1), (L2×T2) and (L3×T2) can

be used as promising hybrids and some lines of this genotypes can

select and either register as a new cultivar or can be employed for

further breeding program.

Acknowledgements

This work was carried out with a grant of a Golden Seed Project,

the Ministry of Agriculture, Food and Rural Affairs, Republic of

Korea (Project No.213007-05-4-SBN10), a grant from the

Germplasm Reservation Center Program, Rural Development

Administration, Republic of Korea (Project PJ015202012020)


All the authors declare that there is no conflict of interest about the

contents of the article.

References

Allard RW, Bradshaw AD (1964) Implications of genotype-

environmental interactions in applied plant breeding. Crop Science

4:503-508.

Arunachalam V, Bandyopadyya A (1979) Are "multiple cross

multiple pollen hybrids "an answer for productive population in

Brassica compestris var. brown sarson ?1. Methods for studying

'mueromphs'. Theoretical and Applied Genetics 54:203-207.

Bakhshaie M, Khosravi S, Azad P, Bagheri H , Van Tuyl JM

(2016) Biotechnological advances in Lilium. Plant Cell Reports

35:1799-1826.

Goo DH, Lim JH, Rhee HK, Cho HR, Kim YJ, Chung HY, Kim

KW (2003) Lily Culture.No.133 Rural Development

Administration, Suwon, Korea (in Korean).

Grassotti A, Gimelli F (2011) Bulb and cut flower production in the

genus Lilium: current status and the future. Acta Horticulture 900.

Ho CW, Jian WT, Lai HC (2006) Plant regeneration via somatic

embryogenesis from suspension cell cultures of Lilium ×

formolongi Hort.using a bioreactor system. In vitro Cellular and

Developmental Biology-Plant 42:240-246.

Kang YI, Joung HY, Goo DH, Choi YJ, Choi MP, An HR, Ko JY,

Choi KJ, Lee KH, Hong KW (2013) A Survey on Cut Flower

Cultivar Trends and Horticultural Status of Lilies (Lilium Hybrids)

in South Korea. Hort Technology 23:629-634.

Kempthorne O (1957) An Introduction to Genetic Statistics. Johan

wiley and sons Inc. New York, U.S.A.

Kumari J, Dikshit HK, Singh B, Singh D (2015) Combining ability

and character association of agronomic and biochemical traits in

pea (Pisum sativum L.). Scientia Horticulturae 181:26-33.

Lucidos JGB, Kwang R, Younis A, Kim CK, Hwang YJ, Son BG,

Lim KB (2013) Different day and night temperatures responses in

Lilium hansonii in relation to growth and flower development.

Horticulture, Environment, and Biotechnology 54:405-41.

Manivannan N (2014) TNAUSTAT-Statistical Package. Retrieved

from https://sites.google/site/tnaustat access on 19December, 2019.



165 Rai et al.

Matthews V (2007) The International Lily Register and Checklist.

RHS London, Fourth Edition.

McRae E (1998) Lily species. In: Lilies Timber Press,Portland,

Ore. USA, pp.319-330.

Montazeri Z, Jelodar NB, Bagheri N (2014) Genetic Dissection of

Some Important Agronomic Traits in Rice Using Line × Tester

Method. International Journal of Advanced Biological and

Biomedical Research 2:181-191.

Narasimhamurthy YK, Gowda PHR (2013) Line x Tester analysis

in Tomato (Solanum lycopersicum L.): Identification of superior

parents for fruit quality and yield-attributing traits. International

Journal of Plant Breeding 7:50-54.

Nduwumuremyi A,Tongoona P, Habimana S (2013) Mating

designs: helpful tool for quantitative plant breeding analysis.

Journal of Plant Breeding and Genetics 1:117-129.

Rai R, Shrestha J, Kim JH (2018). Combining Ability and Gene

Action Analysis of Quantitative Traits in Lilium× formolongi.

Journal of Agricultural, Life and Environmental Sciences

30:131-143.

Rai R, Shrestha J, Kim JH (2019) Line xTester analysis in Liliumx

formolongi: Identification of superior parents for growth and

flowering traits. SAARC Journal of Agriculture 17:175-187.

Rashid M, Cheema AA, Ashraf M (2007) Line x tester analysis in

basmati rice. Pakistan Journal of Botany 39: 2035-2042.

Roh MS (2002) New Production Technologies of Lilium -

Prospects and Limitations. Korean Journal of Horticultural Science

and Technology 20:174-190.

Schnell FW (1975) Type of variety and average performance on

hybrid maize. Zeitschrift fuer Pflanzenzuechtung 74:177-188.

Sharma BB, Sharma VK, Dhakar MK, Punetha S (2013)

Combining ability and gene action studies for horticultural traits in

garden pea: A review. African Journal of Agricultural Research

8:4718-4725.

Sharma JR (2006) Statistical and Biometrical techniques in Pland

breeding (First ed.) New Age International ,New Delhi, India.

Song CY, Park SC, Lee JS, Kim YA, Kim JH (2004) Correlation

and combining ability of plant growth and flowering in F1 hybrids

by diallel cross in Lilium formolongi and L. formosanum. Korean

Journal of Horticultural Science and Technology 22:107-113.

Van Tuyl JM, Arens P (2011) Lilium:Breeding History of the

modern cultivar assortment. Acta Horticulturae 900:223-230.

Weatherspoon JH (1970) Comparative Yields of Single, Three-

Way, and Double Crosses of Maize. Crop science 10:157-159.

Xuan Y, Davaasuren YE, Kyung HY, Kim JH (2005) Recovery

of quantitative cut flower characters in backcross generation of

Lilium formolongi. Korean Journal of Flower Research Society

13:268-272.

Younis A, Hwang YJ, Lim KB (2014) Classical vs. modern

genetic and breeding approaches for lily (Lilium) crop

improvement: a review. Flower Research Journal 22:39-47.



ISSN No. 2320 – 8694


LARVICIDAL ACTIVITY OF TWO RUTACEAE SPECIES AGAINST THE VECTORS

OF DENGUE AND FILARIAL FEVER

Grace Marin1, Subramanian Arivoli2, Samuel Tennyson3*

1Department of Zoology, Scott Christian College, Nagercoil 629 003, Tamil Nadu, India

2Department of Zoology, Thiruvalluvar University, Vellore 632 115, Tamil Nadu, India

3Department of Zoology, Madras Christian College, Chennai 600 059, Tamil Nadu, India

Received – January 22, 2020; Revision – March 12, 2020; Accepted – April 03, 2020 Available Online – April 25, 2020


ABSTRACT

Mosquito control strategies have been primarily dependent on the use of synthetic chemical insecticides

but its long-term stability and its tendency to bioaccumulate have fostered many environmental and

human health concerns resulting in increase of resistance to chemical insecticides and rebounding

vectorial capacity by mosquitoes. Botanical insecticides serve as suitable alternatives to synthetic ones

as they are relatively effective, and are safe to environment, human, and animal life. In the present

study, Citrus sinensis and Murraya koenigii leaf powders were tested separately on the third instar

larvae of Aedes aegypti and Culex quinquefasciatus at concentrations of 0.2, 0.4, 0.6, 0.8 and 1.0%.

Larval mortality was assessed after 24, 48, 72 and 96 hours and their respective LC50 values for C.

sinensis were 0.69, 0.54, 0.48 and 0.36% for A. aegypti; and 0.61, 0.53, 0.44 and 0.34% for C.

quinquefasciatus. For, M. koenigii, it was 1.05, 0.73, 0.38 and 0.24%; and 0.54, 0.50, 0.32 and 0.22%

for C. sinensis respectively. Amongst tested two botanicals, C. sinensis was found to be more active

against C. quinquefasciatus in dose and time dependent manner and the impact of phytochemicals on

expression of C. quinquefasciatus protein analysed by SDS-PAGE revealed that the phytochemicals

from C. sinensis suppressed the expression of certain proteins present in C. quinquefasciatus. Hence,

this study confirms and recommends that use of C. sinensis and M. koenigii safe and eco-friendly and

could use as an alternative to synthetic pesticides in vector control.


KEYWORDS

Citrus sinensis

Murraya koenigii

Aedes aegypti

Culex quinquefasciatus

Larvicidal

E-mail: [email protected] (Samuel Tennyson)


















Larvicidal activity of two Rutaceae species against the vectors of dengue and filarial fever 167

1 Introduction

Insect transmitted disease remains a major source of illness and

death worldwide of which vector-borne diseases are responsible

for 17% of the global burden of parasitic and infectious diseases

(WHO, 2014). Mosquitoes are tremendous public health pests

because of their predominance as marketers of potentially deadly

pathogens of human beings and the annoyance of skin reactions

caused by their bites. Most of the mosquito control programmes

target the larval stage in their breeding sites with larvicides

(Elimam et al., 2009). Most of the mosquito control techniques

have depended on the use of artificial chemical insecticides

(Hemingway et al., 2006). However, the unfriendly effect of most

of these synthetic chemical insecticides leads the insect pest

managers of the world to comb for alternative ways of countering

this disease causing insect (Ileke & Ogungbite, 2015). Also, the

long-term stability of many of these chemical insecticides and their

tendency to bioaccumulate in non-target organisms have fostered

many environmental and human health concerns such as the threats

faced due to resulting in increase of resistance to chemical

insecticides and rebounding vectorial capacity by mosquitoes

(Senthilkumar et al., 2008). Botanical insecticides may serve as

suitable alternatives to synthetic ones in future, as they are

relatively effective, and safe to environment, human, and animal

life (Pitasawat et al., 2007; Borah et al., 2010). Research from all

over the world have documented the effect of various

phytochemical compounds against a wide range of mosquito

species (Samuel et al., 2016; Pathak et al., 2018; Huang et al.,

2019; Kaushik et al., 2019; Samuel et al., 2019; Nathan, 2020).

The Rutaceae family comprises of 150 genera and 1,600 species of

trees, shrubs, and climbers distributed throughout the temperate and

tropical regions of the world (Pollio et al., 2008). The important

genera of this family are Citrus, Fortunella, Murraya, Ptelea, Ruta

and Zanthoxylum (Siddique et al., 2012). Most of the Rutaceae plants

are aromatic whose leaves, fruits or cotyledon in seeds contain a

complex mixture of volatile aroma compounds (Aziz et al., 2010);

which are used in perfumery, gastronomy and traditional medicine.

In addition, various researchers have reported the presence of various

secondary metabolites viz., alkaloids, coumarins, flavonoids,

limonoids, and volatile oils from family Rutaceae, these metabolites

are associated to different biological activities such as antimicrobial

(Ali et al., 2008), antidiarrhoeal (Mandal et al., 2010),

anticholinesterasic (Cardoso-Lopes et al., 2010), antileishmanial

(Andres et al., 2011), antiprotozoal (Severino et al., 2009),

antioxidant (Wansi et al., 2006), and mosquito larvicidal (Rajkumar

& Jebanesan, 2008; Arivoli & Samuel, 2011; Arivoli et al., 2015)

properties. Consequently, this study was conducted to determine the

larvicidal efficacy of C. sinensis and M. koenigii leaves as an

alternative to synthetic insecticides for the management of A. aegypti

and C. quinquefasciatus.


2.1 Plant collection and preparation of phytopowders

Mature and healthy leaves of C. sinensis and M. koenigii were

collected from Nagercoil, Kanyakumari district, Tamil Nadu, India

and identified taxonomically and confirmed at the Department of

Botany and Research Centre, Nagercoil, Kanyakumari district,

Tamil Nadu, India. In the laboratory, dechlorinated water was used

to wash the leaves and thereafter these leaves were shade dried.

Leaves of each plant (1Kg) was then powdered by an electric

blender and was stored in air tight sterilized amber coloured bottles

for bioassay.

2.2 Phytochemical screening

The active phytochemical compounds in C. sinensis and M.

Koenigii leaf powders were qualitatively determined using

methods described by Harborne (1998) for alkaloids, glycosides,

steroids, phenolics and carbohydrates, Van Burden & Robinson

(1981) for tannins and proteins, Obadoni & Ochuko (2001) for

saponins, Boham & Kocipai (1974) for flavonoids, and Okwu &

Okwu (2004) for vitamins.

2.3 Fourier Transform Infrared Spectroscopy (FTIR)

FTIR is the most powerful tool for identifying functional groups

present in plant extracts. Dried methanol extract powder (10mg) of

each plant was encapsulated in 100mg of KBr pellet, in order to

prepare translucent sample discs. The powdered samples were

loaded in FTIR spectroscope (Shimadzu, Japan), with a scan range

from 400 to 4000cm1 (Visveshwari et al., 2017).

2.4 Sodium Dodecyl sulphate – PolyAcrylamide Gel Electrophoresis

(SDS - PAGE) analysis

SDS-PAGE is the most widely used analytical method to resolve

separate components of a protein mixture. It is almost obligatory to

assess the purity of a protein through an electrophoretic method.

SDS-PAGE simultaneously exploits differences in molecular size

to resolve proteins differing by as little as 1% in their

electrophoretic mobility through the gel matrix. The technique is

also a powerful tool for estimating the molecular weights of

proteins. The molecular weight of the protein was estimated using

a high molecular weight protein calibration kit (Merck, Bangalore,

India) as markers. The molecular mass markers (expressed in Da)

used were myosin (205,000), β-galactosidase (11,600),

phosphorylase b (97,400), bovine serum albumin (66,000),

ovalbumin (43,000), carbonic anhydrase (29,000), soyabean

trypsin inhibitor (20,100) and lysozyme (14,300).

Total protein was extracted by using acetone- TCA (Trichloro Acetic

Acid) precipitation technique of Damerval et al. (1986) and the



168 Marin et al.

estimation of protein was executed according to the methodology of

Lowry et al. (1951). Every sample (0.5g) with a buffer (2mL)

containing Tris (hydroxymethyl) aminomethane (Tris)-Glycine (pH

8.3) (50mM), sucrose (0.5M), EDTA (50mM), potassium chloride

(0.1M), PMSF (2mM) and 0.1% (v/v) 2-mercaptoethanol was

homogenized at 4°C in a chilled pestle and mortar. The homogenate

was centrifuged at 14000 rpm for 10 minutes in a refrigerated

centrifuge. The concentration of protein in supernatant samples were

assessed according to the technique of Bradford (1976) and gels

prepared as per the protocol adopted by Laemmli (1970). A

separating gel (12.0%) was used for resolving the polypeptides

which comprised of Tris- HCl (375mM), pH 8.8, 0.1% (w/v) SDS,

0.05% (w/v) ammonium persulfate and 0.4µLmL-1

TEMED. For a

stacking gel (4%) used to concentrate (stack) the polypeptides, it

comprised of Tris-HCl (125mM), pH 6.8, 0.1% (w/v) SDS, 0.05%

(w/v) ammonium persulfate and 0.5µLmL-1

TEMED. The

electrophoresis running buffer was made of Tris (25mM), glycine

(192mM), SDS (0.1%) with pH 8.3, and was accomplished for 4

hours at 35mA. The gels were stained for two hours with Coomassie

Brilliant Blue R-250 (Sigma) (0.25%) in 50% (v/v) methanol and

(v/v) acetic acid (10%) and then destained with methanol and acetic

acid until a clear background was obtained.

2.5 Vector mosquitoes

Insecticide free A. aegypti eggs and C. quinquefasciatus egg rafts

were obtained from Entomology Research Institute, Loyola

College, Chennai, Tamil Nadu, India. Cyclic generations of the

above mentioned vector mosquitoes were maintained separately in

two feet mosquito cages with a mean room temperature of 27±2°C

and a relative humidity of 70-80% inside an insectary and the

adults were fed on 10% glucose solution in water. Ovitraps were

placed inside the mosquito cages for the female mosquitoes to

oviposit eggs and the laid eggs were then transferred to the larval

rearing chamber and were maintained in enamel larval trays. The

larvae fed with larval food (dog biscuits and yeast in the ratio 3:1)

on becoming pupae were transferred to plastic bowls kept inside

another mosquito cage for emergence of adults.

2.6 Larvicidal bioassay

According to the guidelines of World Health Organization (WHO,

2005) with minor modifications, bioassays were performed on

twenty five F1 generation of laboratory colonized third instar

larvae of the above mentioned vector species at test concentrations

of (w/v) 0.2, 0.4, 0.6, 0.8 and 1.0% by introducing them into glass

beakers (250mL) containing 200mL of distilled water and test

concentration. One per cent stock solution for each plant powder

was prepared by dissolving 1g of each leaf powder in 100mL

distilled water, from which the above mentioned concentrations

were arrived. Third instar larvae formed the choice as the research

sample, compared to first and second instar since they possessed a

larger body size and are more adaptive to the environment; while

fourth instar transforms to a pupa in approximately 48 hours

(Marin et al., 2020). Control (distilled water) was run

simultaneously and maintained separately. Bioassays were

performed with five replicates for each concentration per trial with

a total of three trials. Larval mortality was observed 24, 48, 72 and

96 hours after treatment and moribund larvae were recorded dead

when they displayed no signs of movement when probed by a

needle at their respiratory siphon.


Data was subjected to statistical analysis with significance set at

95% confidence in IBM SPSS Statistics v22 (SPSS, 2010). Per

cent larval mortality was calculated and corrections for control

mortality (5-20%) if required was carried according to Abbott’s

formula (Abbott, 1925) and then larval mortality were subjected to

probit analysis. Analysis of variance (ANOVA) of larval mortality

was performed to measure differences between treated bioassays

and controls and at which doses in particular and the differences

were considered significant at P≤0.001 level.

1 – n in T after treatment x 100

n in C after treatment

Where, n is the number of larvae, T: treated and C: control.

3 Results

The qualitative phytochemical analysis of C. sinensis and M.

koenigii leaves are presented in Table 1. The FTIR spectrum of C.

sinensis showed the presence of alcohol, alkenes, aromatic amines,

phenyl, ether, methylene, 1° and 2° amines and aliphatic chloro

compound. The major band was observed at 3308cm-1

due to O-H

stretching vibrations of alcohol group (Figure 1). M. koenigii

Table 1 Phytochemical composition of Citrus sinensis and Murraya koenigii leaves

Plant species Phytocompounds

Alkaloids Carbohydrates Flavonoids Glycosides Phenols Proteins Saponins Steroids Tannins

Citrus sinensis + + + + + + + + + + + + + + + + + + + + + + +++++ + + + + + + + ++

Murraya koenigii + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + + +

+++++ Above 80%; ++++ 50-75%; +++ Below 50%; ++ Below 25%; and – Nil




indicated the presence of alcohol, alkenes, aromatic

amines, phenyl, ether, methylene, cyclic ether and vinyl

groups. The major band was observed at 3398.2cm-1

due

to O-H stretching vibrations of alcohol group (Figure 2).

No larval mortality was observed in control. The

percentage mortality of A. aegypti and C.

quinquefasciatus larva treated with the various

concentrations of C. sinensis after 24, 48, 72 and 96

hours are presented in Figure 3 and 4 and its LC50 values

were reported 0.69, 0.54, 0.48 and 0.36%; and 0.61,

0.53, 0.44 and 0.34% respectively. One Way ANOVA,

comparing treated and control group, with a significance

level established at P<0.001 showed that C. sinensis

concentrations significantly influenced the mortality of

larvae; F value 82.52 for A. aegypti and 59.46 for C.

quinquefasciatus. In the case of M. koenigii, the

percentage mortality of A. aegypti and C.

quinquefasciatus after 24, 48, 72 and 96 hours are

presented in Figure 5 and 6 and its LC50 values are 1.05,

0.73, 0.38 and 0.24%; and 0.54, 0.50, 0.32 and 0.22%

respectively. One Way ANOVA, comparing treated and

control group, with a significance level established at

P<0.001 showed that M. koenigii concentrations

significantly influenced the mortality of larvae, F value

30.27 for A. aegypti and 21.81 for C. quinquefasciatus.

C. sinensis was found to be more effective against third

instar larvae of C. quinquefasciatus. Hence, these

mosquito larvae treated with C. sinensis leaf powder

were further investigated to reveal the impact of

phytochemicals on their proteins. SDS-PAGE analysis

revealed that the phytochemicals from C. sinensis

suppressed the expression of certain proteins present in

C. quinquefasciatus (Figure 7).

Figure 1 FTIR analysis of Citrus sinensis leaf

Figure 2 FTIR analysis of Murrayakoenigii leaf

Figure 3 Per cent larval mortality of Aedes aegypti by Citrus sinensis



170 Marin et al.

Figure 4 Per cent larval mortality of Culex quinquefasciatus by Citrus sinensis

Figure 5 Per cent larval mortality of Aedes aegypti by Murraya koenigii




4 Discussion

Mosquito control is vital and is still in a state of evolution,

dependent upon synthetic organic insecticides, many of which

have been removed from the arsenal of weapons (Das et al., 2007)

and botanicals replaced as the new weapons. Vector control is

preferably performed at the larval stage, due to its larger

vulnerability (Zoubiri & Baaliouamer, 2014). The activities of

botanicals are often attributed to the complex mixture of their

phytochemical active compounds. Plant families, viz., Asteraceae,

Boraginaceae, Fabaceae, Piperaceae and Rutaceae (Garcez et al.,

2013) are highlighted as producers of compounds with larvicidal

activity. Results of current study revealed that both plants are rich

sources of bioactive compounds and have great potential of

eradicating mosquito larvae (Ghosh et al., 2012; Samuel &

William, 2014; Pathak et al., 2018; Kaushik et al., 2019; Nathan,

2020). Among these two, C. sinensis was found more efficient

against C. quinquefasciatus in dose and time dependent manner. C.

sinensis also has an intense larvicidal activity against Anopheles.

labranchiae (El-Akhal et al., 2015) and A. aegypti (Galvao et al.,

2015) besides acting as a potent fumigant against mosquitoes

(Ezeonu et al., 2001). Similarly, Sattar et al. (2016) also reported

the larvicidal property of C. sinensis leaf powder against C.

quinquefasciatus. Also, the results of the present study are in

accordance with Bilal et al. (2012) who have reported that the leaf

extract of C. sinensis exhibited 97% mortality against A. albopictus

larva. Similarly, Murugan et al. (2012) studied the effect of ethanol

peel extract of C. sinensis against Anopheles stephensi, A. aegypti

and C. quinquefasciatus larvae and reported their LC50 values

1 2 3

Figure 7 SDS PAGE analysis of proteins from Culex quinquefasciatus

Lane 1:Molecular weight marker; Lane 2:Control larva; and

Lane 3:Treated larva

Figure 6 Per cent larval mortality of Culex quinquefasciatus by Murraya koenigii



172 Marin et al.

291.69, 342.45, and 385.32ppm respectively. Warikoo et al. (2012)

also testified the activity of hexane extract of C. sinensis leaf

extract against A. aegypti larvae and reported 446.84ppm LC50

value. George (2019) stated that the aqueous peel extract of C.

sinensis caused 100% larval mortality in Anopheles species at

2.0g/mL for 24 hours exposure. The results of the present study

also validates with the report of Arivoli & Samuel (2011) who

investigated the larvicidal effect of different solvent extracts of M.

koenigii leaves against A. aegypti, An. stephensi and C.

quinquefasciatus larvae and confirmed the presence of various

active substances. Arivoli et al. (2015) reported that the hexane

fraction of M. koenigii leaves produced 100, 99.2 and 97.6% larval

mortality rate at 100ppm after 24 hours against A. aegypti, C.

quinquefasciatus and An. stephensi respectively.

Mortality of larvae is related to the phytochemical constituents

present in the leaves of C. sinensis and M. koenigii. Active

ingredients in botanical derivatives owning mosquitocidal properties

due to the presence of alkaloids, flavonoids, steroids, tannins,

terpenes and terpenoids (Scherer et al., 2010; Farooq et al., 2014)

directly spasm the nervous system, disturb the mid-gut epithelium

and secondarily distress the gastric caeca and malpighian tubules in

mosquito larvae (Rey et al., 1999). Further, they act as mitochondrial

poison (Mann & Kaufman, 2012), and work by means of networking

and intermingling with the larvae cuticle membrane, and ultimately

disarranging the membrane which would be the utmost probable

reason for larval death (Hostettmann & Marston, 1995). The

larvicidal activity of C. sinensis might be due to the high quantity of

alkaloids, saponins and tannins. Kumar et al. (2012) reported the

presence of alkaloids, flavonoids and terpenoids in the petroleum

ether leaf extracts of C. sinensis which caused mortality in A. aegypti

larvae. Musau et al. (2016) suggested that alkaloids act as

anticholinesterases that bind to acetylcholine enzymes and disrupts

the membrane integrity, impair microtubules functioning and could

cause impairment in digestive system by inhibiting hydrolytic

enzyme. Further, these alkaloids slow down larval movement by

interfering nerve impulse transmission (Mansour et al., 1998;

Armadhani, 2014). Phytochemical saponins are toxic as they disrupt

the oxygen supply to larvae and disrupts the larvae before it could

pupate (Bagavan et al., 2008; Chapagain et al., 2008). Tannins work

as a stomach poison and can interfere with the larvae digestion

process by binding with proteins in the digestive system (Boudko et

al., 2011). On the other hand, M. koenigii extract is also a rich source

of alkaloids, saponins and flavonoids and larvicidal properties shown

in current study might be due to the presence of these chemicals.

Results of current study are in agreement with the findings of Sukari

et al. (2013) who reported the larvicidal property of M. koenigii

phytocompounds against A. aegypti. According to Palanikumar et al.

(2017) flavonoids available in M. koenigii inhibits the larval

respiration and disrupts the transport of electrons as they enter

through the respiratory siphon and are forwarded to trachea

throughout the body and attacks the central nerve ganglion which

gets disturbed, which leads to paralysis of nerve cells and eventually

death of mosquito larvae (Adnyani & Sudarmadja, 2016). Further,

this plant is rich in coumarins, acridine alkaloids and carbazole

alkaloids (Ito, 2000). Apart from the specific role of certain

compound, synergistic effect of closely related compounds in plants

could also contribute to death of mosquito larvae (Isman, 1997).

SDS-PAGE analysis of proteins from third instar of C.

quinquefasciatus revealed the absence of some of the proteins

when treated with C. sinensis. In the control sample, 13 protein

bands were clearly visible after CBB staining; however in the

treated mosquito sample only nine proteins were visualized. This is

mainly due to suppression or inhibition of protein synthesis by

phytochemicals. These phytochemicals enter into the body of

mosquito larvae through digestive tract and by diffusion. The

reduction in protein level in the treated mosquito larvae has been

reported previously in Culex larvae by the action of Artemisia

annua extract which caused a ruptured and degenerated body wall

of larval tissues (Sharma et al., 2006). Senthilkumar et al. (2009)

reported that protein levels in An. stephensi larvae treated with

Annona squamosa, Artemisia annua, Centella asiatica,

Cymbopogan citratus, Eucalyptus globulus, Justicia gendarussa

and Myristica fragrans extracts were reduced and resumed that it

was the result of interference of the phytochemicals with normal

protein synthesis mechanism. In general, protein is the essential

element of the animal body as well as insect structure. Its higher

amount in their body indicates larger body mass, which establishes

higher reproductive success in insects, improved competitive

ability (Warren et al., 2006), and disease vulnerability and stress

resistance (Lee et al., 2008; de Souza Wuillda, 2019).

Conclusion

In conclusion, the present study established the larvicidal activity

of the two Rutaceae species against A. aegypti and C.

quinquefasciatus. Botanicals are one of the best alternatives to

their synthetic counterpart and this study confirms and

recommends that C. sinensis and M. koenigii are safe and eco-

friendly alternative of synthetic pesticides in vector control.

However, the mechanism of action and the structure activity

relationship for larvicidal activity remain unclear. Therefore,

understanding the mechanism of action of secondary metabolites

with larvicidal action can help in reducing the resistance of

insecticides and aid the production of analogs with more

pronounced activity with specific or multiple sites of action.







References

Abbott WS (1925) A method of computing the effectiveness of an

insecticide. Journal of Economic Entomology 18: 265-267.

Adnyani IGAP, Sudarmadja IM (2016) Pengaruh Konsentrasi

Ekstrak Etanol Daun Pepaya (Carica papaya L) terhadap

Kematian Larva Nyamuk Aedes aegypti. E-Jurnal Medika 5: 1-5.

Ali MS, Fatima S, Pervez MK (2008) Haplotin: A new

furanoquinoline from Haplophyllum acutifolium (Rutaceae).

Journal of the Chemical Society of Pakistan 30: 775-779.

Andres CCA, Barrera CED, Falla GDS, Murcia DG, Suarez CLE

(2011) Seco-limonoids and quinoline alkaloids from Raputia

heptaphylla and their antileishmanial activity. Chemical and

Pharmaceutical Bulletin 59: 855-859.

Arivoli S, Raveen R, Samuel T (2015) Larvicidal activity of

Murraya koenigii (L.) Spreng (Rutaceae) hexane leaf extract

isolated fractions against Aedes aegypti Linnaeus, Anopheles

stephensi Liston and Culex quinquefasciatus Say (Diptera:

Culicidae). Journal of Mosquito Research 5: 1-8.

Arivoli S, Samuel T (2011) Studies on the mosquitocidal activity

of Murraya koenigii (L.) Spreng (Rutaceae) leaf extracts against

Aedes aegypti, Anopheles stephensi and Culex quinquefasciatus

(Diptera: Culicidae). Asian Journal of Experimental Biological

Science 2: 721-730.

Armadhani (2014) Keefektifan Ekstrak Etanol Daun Petai Cina

(Leucaenaglauca, Benth) sebagai Larvasida Alamiterhadap

Kematian Larva Nyamuk Ae. aegypti Instar III. NaskahPublikasi.

Semarang: Universitas Negeri Semarang.

Aziz SSSA, Sukari MA, Kitajima M, Aimi N, Ahpandi NJ (2010)

Coumarins from Murraya paniculata (Rutaceae). Malaysian

Journal of Analytical Sciences 14: 1-5.

Bagavan A, Rahuman AA, Kamaraj C, Geetha K (2008) Larvicidal

activity of saponins from Achyranthes aspera against Aedes

aegypti and Culex quinquefasciatus (Diptera: Culicidae).

Parasitology Research 103: 223-229.

Bilal H, Akram W, Ali-Hassan S (2012) Larvicidal activity of

Citrus limonoids against Aedes albopictus larvae. Journal of

Arthropod-Borne Diseases 6: 104-111.

Boham A Kocipai AC (1974) Flavonoids and condensed tannins

from leaves of Hawaiia vaccinum vaticulatum and V. calyscinium.

Pacific Science 48: 458-463.

Borah R, Kalita MC, Kar A, Talukdar AK (2010) Medicinal plants

and their applications. African Journal of Biotechnology 9: 25-27.

Boudko DY, Moroz LL, Harvey WR, Linser PJ (2011)

Alkalinization by chloride bicarbonate pathway in larval mosquito

midgut. Proceedings of the National Academy of Sciences of the

United States of America 26: 15355-15359.

Bradford MM (1976) A rapid and sensitive method for the quantitation

of microgram quantities of protein utilizing the principle of protein-dye

binding. Analytical Biochemistry 72: 248-254.

Cardoso-Lopes EM, Maier JA, Da Silva MR, Regasini LO, Simote

SY, Lopes NP, Pirani JR, Bolzni VS, Young MCM (2010) Alkaloids

from stems of Esenbeckia leiocarpa Engl. (Rutaceae) as potential

treatment for Alzheimer disease. Molecules 15: 9205-9213.

Chapagain B, Saharan V, Wiesman Z (2008) Larvicidal activity of

saponins from Balanites aegyptiaca callus against Aedes aegypti

mosquito. Bioresource Technology 99: 1165-1168.

Damerval C, De Vienne C, Zivy M, Thiellement H (1986) The

technical improvements in two-dimensional electrophoresis

increase the level of genetic variation detected in wheat seedling

proteins. Electrophoresis 7: 52-54.

Das NG, Goswami D, Rabha B (2007) Preliminary evaluation of

mosquito larvicidal efficacy of plant extracts. Journal of Vector

Borne Disease 44: 145-148.

de Souza Wuillda ACJ, Martins RCC, Costa FN (2019) Larvicidal

activity of secondary plant metabolites in Aedes aegypti control:

An overview of the previous 6 years. Natural Product

Communications 1: 1-11.

El-Akhal F, Lalamia AEO, Guemmouh R (2015) Larvicidal

activity of essential oils of Citrus sinensis and Citrus aurantium

(Rutaceae) cultivated in Morocco against the malaria vector

Anopheles labranchiae (Diptera: Culicidae). Asian Pacific Journal

of Tropical Disease 5: 458-462.

Elimam AM, Elmalik KH, Ali FS (2009) Efficacy of leaves extract

of Calotropis procera Ait. (Asclepiadaceae) in controlling

Anopheles arabiensis and Culex quinquefasciatus mosquitoes.

Saudi Journal of Biological Sciences 16: 95-100.

Ezeonu FC, Chidume GI, Udedi SC (2001) Insecticidal properties of

volatile extracts of orange peels. Bioresource Technology 76: 273-274.

Farooq U, Waseem B, Muzaffar R, Tripathi J, Tharani M, Sharma

MA (2014) Comparative study of phytochemical investigation of

Xanthium strumarium medicinal plant. International Journal of

Research in Pharmacy and Chemistry 4: 96-100.

Galvão JG, Silva VF, Ferreira SG, França FRM, Santos DA,

Freitas LS, Alves PB, Araújo AAS, Cavalcanti SCH, Nunes RS

(2015) Cyclodextrin inclusion complexes containing Citrus

sinensis (L.) Osbeck essential oil: An alternative to control Aedes

aegypti larvae. Thermochimica Acta 608: 14-19.

Garcez WS, Garcez FR, Silva L, Sarmento UC (2013) Substâncias

de origem vegetal com atividadelarvicida contra Aedes aegypti.

Revista Virtual de Química 5: 363-393.



174 Marin et al.

George UI (2019) Environmental control of malaria: Can Citrus

sinensis peel be a potent larvicide for household vector control?

GSC Biological and Pharmaceutical Sciences 09(03): 085-090.

Ghosh A, Chowdhury N, Chandra G (2012) Plant extracts as

potential mosquito larvicides. Indian Journal of Medical Research

135(5): 581-598.

Harborne JB (1998) Phytochemical methods, a guide to modern

techniques of plant analysis, 3rd

Edition. Chapman and Hill Ltd.,

London, 279.

Hemingway J, Beaty BJ, Rowland M, Scott TW, Sharp BL (2006)

The innovative vector control consortium: Improved control of

mosquito-borne diseases. Trends in Parasitology 22: 308-312.

Hostettmann K, Marston A (1995) Saponins (Chemistry and

Pharmacology of Natural Products). University Press, Cambridge, 132.

Huang HT, Lin CC, Kuo TC, Chen SJ, Huang RN (2019)

Phytochemical composition and larvicidal activity of essential oils

from herbal plants. Planta 250: 59-68.

Ileke KD, Ogungbite OC (2015) Alstonia boonei De Wild oil extract in

the management of mosquito (Anopheles gambiae), a vector of malaria

disease. Journal of Coastal Life Medicine 3: 557-563.

Isman MB (1997) Neem and other botanical insecticides: Barriers

to commercialization. Phytoparasitica 25: 339-344.

Ito C (2000) Studies on medicinal resources of rutaceous plants

and development to pharmaceutical chemistry. Natural Medicine

54: 117-122.

Kaushik S, Sharma NR, Thomas TG, Sharma AK, Bansal A (2019)

Indigenous plants and their larvicidal potential against Indian

mosquito vectors: A review. Journal of Communicable Diseases

51: 59-72.

Kumar S, Ray A, Kaur J, Samal R, Wahab N, Warikoo R (2012)

Efficacy of the ethanolic leaf extracts of citrus plant, Citrus sinensis as

larvicidal and irritant agent against dengue vector Aedes aegypti L.

Asian Pacific Journal of Tropical Biomedicine 3: 152-155.

Laemmli UK (1970) Cleavage of structural proteins during the

assembly of the head of bacteriophage T4. Nature 227: 680-685.

Lee KP, Simpson SJ, Wilson K (2008) Dietary protein-quality

influences melanization and immune function in an insect.

Functional Ecology 22: 1052-1061.

Lowry OH, Rosebrough NJ, Farr AL, Randall RJ (1951) Protein

measurement with the Folin phenol reagent. Journal of Biological

Chemistry 193: 265-275.

Mandal S, Nayak A, Kar M, Banerjee SK, Das A, Upadhyay SN,

Singh RK, Banerji A, Banerji J (2010) Antidiarrhoeal activity of

carbazole alkaloids from Murraya koenigii Spreng (Rutaceae)

seeds. Fitoterapia 81: 72-74.

Mann RS, Kaufman PE (2012) Natural product pesticides: their

development, delivery and use against insect vectors. Mini-

Reviews in Organic Chemistry 9: 185-202.

Mansour SA, Messeha SS, Mohamed SM (1998) Botanical

biocides: Mosquitocidal activity of certain Nigella sativa

constituents. Journal of Union Arab Biology 10: 45-63.

Marin G, Arivoli S, Samuel T (2020) Synergistic larvicidal action

of Citrus limon (L.) Osbeck (Rutaceae) and Bacillus thuringiensis

Berliner 1915 (Bacillaceae) against the dengue vector Aedes

aegypti Linnaeus 1762 (Diptera: Culicidae). GSC Biological and

Pharmaceutical Sciences 10: 25-33.

Murugan K, Kumar PM, Kovendan K, Amerasan D, Subrmaniam

J, Hwang JS (2012) Larvicidal, pupicidal, repellent and adulticidal

activity of Citrus sinensis orange peel extract against Anopheles

stephensi, Aedes aegypti and Culex quinquefasciatus (Diptera:

Culicidae). Parasitology Research 111: 1757-1769.

Musau JK, Mbaria JM, Nguta JM, Mathiu M, Kiama SG (2016)

Phytochemical composition and larvicidal properties of plants used

for mosquito control in Kwale County, Kenya. International

Journal of Mosquito Research 3: 12-17.

Nathan SS (2020) A review of resistance mechanisms of synthetic

insecticides and botanicals, phytochemicals, and essential oils as

alternative larvicidal agents against mosquitoes. Frontiers in

Physiology 10: 1591.

Obadoni BO Ochuko PO (2001) Studies and comparative efficacy

of the crude extracts of some homostatic plants in Edo and Delta

states of Nigeria. Global Journal of Pure and Applied Science 8b:

203-208.

Okwu DE, Okwu ME (2004) Phytochemicals and vitamins content

of indigenous plant species of south eastern Nigeria. Journal of

Sustainable Agriculture and Environment 6: 30-37.

Palanikumar M, Pravin Y, Navaneethan M, Mahendren S, Mohanraj

RS, Dhanakkodi B (2017) Callistemon citrinus (Myrtaceae)

methanolic leaf extract: a potent mosquitocidal agent for controlling

dengue vector mosquito Aedes aegypti (Diptera: Culicidae). Journal of

Entomology and Zoology Studies 5: 1051-1059.

Pathak M, Kumar R, Singh MK, Maurya O, Bharati KA (2018) A

review on traditional larvicidal plants. Mintage Journal of

Pharmaceutical & Medical Sciences 7: 1-9




Pitasawat B, Champakaew D, Choochote W, Jitpakdi A, Chaithong

U, Kanjanapothi D, Rattanachanpichai E, Tippawangkosol P,

Riyong D, Tuetun B, Chaiyasit D (2007) Aromatic plant-derived

essential oil: an alternative larvicide for mosquito control.

Fitoterapia 78: 205-210.

Pollio A, De Natale A, Appetiti E, Aliotta G, Touwaide A (2008)

Continuity and change in the Mediterranean medical tradition:

Ruta spp. (Rutaceae) in Hippocratic medicine and present

practices. Journal of Ethnopharmacology 116: 469-482.

Rajkumar S, Jebanesan A (2008) Bioactivity of flavonoid

compounds from Poncirus trifoliata L. (Family: Rutaceae) against

the dengue vector, Aedes aegypti L. (Diptera: Culicidae).

Parasitology Research 104: 19-25.

Rey D, Pautou MP, Meyran JC (1999) Histopathological effects of

tannic acid on the midgut epithelium of some aquatic Diptera

larvae. Journal of Invertebrate Pathology 73: 173-181.

Samuel T, Arivoli S, William J (2016) Phytonanopesticides for

vector control: A review. Journal of Applied Zoological

Researches 27: 1-11.

Samuel T, Kalarani A, Vinodha V, Arivoli S, Selvakumar S, Meeran

M, Syedalifathima A (2019) Repellent property of plants against

mosquitoes in field conditions by traditional method. International

Journal of Zoology and Applied Biosciences 4: 258-263.

Samuel T, William SJ (2014) Potentiality of botanicals in

sustainable control of mosquitoes (Diptera: Culicidae). In:

Achieving Sustainable Development: Our Vision and Mission, Ed.

William, SJ, Loyola College, Chennai, Tamil Nadu, India 204-227.

Sattar M, Iqbal MN, Ashraf A, Ali S, Shahzad MI, Alam S, Ali T,

Sheikh R (2016) Larvicidal efficacy of Citrus sinensis extracts

against Culex quinquefasciatus. PSM Microbiology 1: 56-61.

Scherer R, Wagner R, Meireles MAA, Godoy HT, Duarte MCT,

Filho JT (2010) Biological activity and chemical composition of

hydrodistilled and supercritical extracts of Xanthium strumarium

L. leaves. Journal of Essential Oil Research 22: 424-429.

Senthilkumar A, Kannathasan K, Venkatesalu V (2008) Chemical

constituents and larvicidal property of the essential oil of Blumea

mollis (D. Don) Merr. against Culex quinquefasciatus. Parasitology

Research 103: 959-962.

Senthilkumar N, Varma P, Gurusubramanian G (2009) Larvicidal and

adulticidal activities of some medicinal plants against the malarial vector,

Anopheles stephensi Liston. Parasitology Research 104: 237-244.

Severino VG, Cazal CM, Forim MR, da Silva MF, Rodrigues-Filho E,

Fernandes JB, Vieira PC (2009) Isolation of secondary metabolites from

Hortia oreadica (Rutaceae) leaves through high-speed counter-current

chromatography. Journal of Chromatography A 1216: 4275-4281.

Sharma P, Mohan L, Srivastava CN (2006) Phytoextract-induced

developmental deformities in malaria vector. Bioresource

Technology 97: 1599-1604.

Siddique S, Javed S, Nawaz S, Perveen Z, Khan RA, Khanum R,

Shahzad K (2012) Volatile components and antimicrobial activity

of Citrus sinensis var. mosammi leaves oil. Journal of Medicinal

Plants Research 6: 2184-2187.

SPSS (2010) IBM SPSS Statistics for Windows, Version 22.0.

Armonk, NY: IBM Corp.

Sukari MA, Noor HSM, Bakar NHA, Ee GCL, Ismail IS, Rahmani

M, Abdul AB (2013) Larvicidal carbazole alkaloids from Murraya

koenigii against dengue fever mosquito Aedes aegypti Linnaeus.

Asian Journal of Chemistry 25: 7719-7721.

Van Burden TP, Robinson WC (1981) Formation of complexes

between protein and tannin acid. Journal of Agricultural Food

Chemistry 1: 77.

Visveshwari M, Subbaiyan B, Thangapandian V (2017)

Phytochemical analysis, antibacterial activity, FTIR and GCMS

analysis of Ceropegia juncea Roxb. International Journal of

Pharmacognosy and Phytochemical Research 9: 914-920.

Wansi JD, Wandji J, Meva'a ML, Waffo KAF, Ranjit R, Khan SN,

Asma A, Iqbal CM, Lallemand MC, Tillequin F, Tanee FZ (2006)

Alpha-glucosidase inhibitory and antioxidant acridone alkaloids

from the stem bark of Oriciopsis glaberrima Engl. (Rutaceae).

Chemical and Pharmaceutical Bulletin 54(3): 292-296.

Warikoo R, Ray A, Sandhu JK, Samal S, Wahab N, Kumar S

(2012) Larvicidal and irritant activities of hexane leaf extracts of

Citrus sinensis against dengue vector Aedes aegypti L. Asian

Pacific Journal of Tropical Biomedicine 2: 152-155.

Warren M, McGeoch MA, Nicolson SW, Chown SL (2006) Body

size patterns in Drosophila inhabiting a mesocosm: interactive

effects of spatial variation in temperature and abundance.

Oecologia 149: 245-255.

WHO (2005) Guidelines for laboratory and field testing of mosquito

larvicides. WHO, Geneva, WHO/CDS/WHOPES/GCDPP/13.

WHO (2014) A global brief on vector-borne diseases.

WHO/DCO/WHD/2014.1

Zoubiri S, Baaliouamer A (2014) Potentiality of plants as

source of insecticide principles. Journal of Saudi Chemical

Society 18: 925-938.



ISSN No. 2320 – 8694


EFFECT OF CRUDE OIL POLLUTION ON SOIL AND AQUATIC

BACTERIA AND FUNGI

Eze Chibuzor Nwadibe*, Eze Emmanuel Aniebonam, Okobo Uchenna Jude

Department of Microbiology, University of Nigeria, Nsukka

Received – February 25, 2020; Revision – March 31, 2020; Accepted – April 14, 2020



ABSTRACT

Crude oil and its derivatives are among the most potent contaminants of the environment, affecting both

the biotic and abiotic components of the ecosystem. The present study was undertaken to evaluate the

effects of crude oil contamination on terrestrial and aquatic microorganisms. Eight different

concentrations of crude oil (Bonny light) were used to contaminate soil and water samples obtained

from pristine environments. Both the control and polluted samples were organized in triplicates and the

studies carried out by plate count procedures using nutrient agar and sabouraud dextrose agar for

bacteria and fungi respectively. Effect of the crude oil on bacterial and fungal counts was significantly

(P<0.05) inhibitory and dose-dependent with 15.0% and 20.0% levels of pollution having the highest

impact on the microbial counts. In the control soil samples, bacterial numbers varied between 2.32x109

to 2.80x109

cfu/g while their numbers varied between 2.00x108 to 2.77x10

9 cfu/g in the test samples.

For the fungi, numbers varied from 1.02x107 to 1.39x10

7 cfu/g in the control soil while it was reported

1.60x105 to 1.18x10

7 cfu/g for the test samples. Results showed that both bacteria and fungi were

significantly affected by crude oil contamination, among tested microorganisms marine microorganisms

demonstrated some tolerance against crude oil contamination.


KEYWORDS

Crude oil

Pollution

Ecosystem

Microorganisms

Soil

Water

E-mail: [email protected] (Eze Chibuzor Nwadibe)


















Effect of Crude Oil Pollution on Soil and Aquatic Bacteria and Fungi 177

1 Introduction

Since the discovery of petroleum as an energy source it has

remained the mainstay of many national economies. In spite of

this crude oil and its products have been shown to be toxic to

living organisms as well as arable lands (Eze et al. 2013; Xue et

al., 2015; Wang et al., 2018; Abdullah & Peramaiyan, 2019). Oil

spill incidents in Nigeria are major environmental issues

especially in the oil-producing Niger Delta region. Nigeria has

had over 4000 oil spills ranging from minor spills of a few

hundred barrels to over half a million barrels in a single incident.

Releases of petroleum into the environment occur naturally from

seeps as well as from human sources. These spillages have

caused much destruction of flora, fauna and arable lands in

Nigerian environment (Ekpo & Udofia, 2008). On the whole,

natural and human sources introduce about 380 million gallons

of oil into the marine environment annually (National Research

Council, 2002). About 55% from this arises from human sources

via petroleum production and transportation while the remainder

comes from natural seeps.

Present day technology is inadequate to handle such large spills.

However, techniques employed include mechanical containment

with booms and removal using suction equipment and sorbents,

chemical treatment with detergents, and physical removal

(National Oceanic and Atmospheric Administration, 1992).

Natural processes account for the removal of a large percentage

of petroleum spills from the environment. Natural removal of

petroleum from water takes place through evaporation, photo-

oxidation, microbial degradation and utilization (Yan et al.,

2018; Xingjian et al., 2018; Xinxin et al., 2019). Some

microorganisms involved in crude oil degradation, detoxication

and bioremediation of polluted environments. Notwithstanding,

petroleum hydrocarbons have been shown to have deleterious

effects on microorganisms through reduction of cell membrane

permeability due to its hydrophobicity leading to reduced water

and nutrient absorption (Pezeshki et al., 2000) as well as oxygen

exchange between soil and the atmosphere (Adedokun & Ataga,

2007). Even though general reports abound on the negative

effects of crude oil on living organisms and arable land, there is

paucity of research work on its effects on microorganisms

specifically. This research work was undertaken to evaluate the

relative impact of crude oil on bacterial and fungal populations

by checking its effects on their numbers, soil microbial

respiration and phospholipids content.


Bonny light crude oil, uncontaminated sandy loam soil, marine and

freshwater samples are the materials which were used in current

study. The crude oil was supplied by the Nigerian National

Petroleum Corporation (NNPC), Port Harcourt, Rivers State,

Nigeria. Sandy loam soil was obtained from Botany Garden,

University of Nigeria.

2.1 Evaluation of the Impact of Crude Oil on Microbial

Numbers in Soil

Non-petroleum contaminated sandy loam soil was air dried, sieved

and measured in 0.5kg portions into twenty-seven plastic buckets

(13cm x 12cm). The buckets were arranged in triplicates and each

triplicate set apart from the control was contaminated with one of

the following concentrations of crude oil i.e. 0.5%, 1.0%, 2.0%,

2.5%, 5.0%, 10.0%, 15.0% or 20.0%v/w. After crude oil addition

the soil in each bucket was thoroughly mixed. Microbiological

assay was done with 1.0g of soil from each bucket every week for

eight weeks. The population of viable microbial cells (bacteria and

fungi) in each soil sample was determined by the spread plating

technique as described by Wistreich (1997) using nutrient agar and

sabouraud dextrose agar for bacterial and fungal cultivations

respectively.

2.2 Evaluation of the Impact of Crude Oil on Microbial

Numbers in Water

Marine and freshwater samples were each measured in 200ml

volumes into twenty-seven 500ml flasks grouped in triplicates.

Each triplicate set excluding the controls was contaminated with

one of the following concentrations of crude oil viz., 0.5%, 1.0%,

2.0%, 2.5%, 5.0%, 10.0%, 15.0% or 20.0% v/v. Uncontaminated

marine and fresh water were used as control samples.

Microbiological assay was carried out with 1.0ml of water from

each flask every week for eight weeks. The spread plating

technique was also used for the determination of microbial

numbers and the media were nutrient agar and sabouraud dextrose

agar for bacteria and fungi respectively.

2.3 Determination of the Effects of Crude Oil on Soil Microbial

Respiration

This was carried out by the method of Isermeyer (1952)

which

quantified the level of carbon di oxide (CO2) evolved from the soil

microbes. Fifty grams of each soil sample was weighed in

duplicate into beakers placed inside jars with air-tight covers. A 25

ml volume of 0.05M NaOH was introduced into each jar and the

jars were instantly sealed with rubber rings. Controls for both

contaminated and uncontaminated soil samples consisted of three

jars, each containing 0.05M NaOH without soil. All jars were

incubated at 25o C for 3 days.

Following incubation, the beakers were brought out and their

external surfaces washed with CO2-free water. Subsequently,

5ml of 0.5M barium chloride solution was introduced into each

jar and few drops of phenolphthalein indicator were also added.

This was followed by the addition of few drops of hydrochloric



178 Eze et al.

acid (0.05M) with continuous stirring until the colour changed

from red to colourless. The rate of microbial respiration in soil

was calculated with the following formula devised by

Isermeyer (1952):

CO2 (mg) / SW/t = (Vo – V) x 1.1

DWT

Where SW is the amount of soil dry weight in grams, T is the

incubation time in hours, Vo is the volume of HCl used for blank

titration (average value) in milliliters. V is the volume of HCl used

for the soil sample (average value), DWT is the dry weight of 1g

moist soil and 1.1 is the conversion factor (1ml 0.05M NaOH

equals 1.1 mg CO2).

2.4 Determination of Microbial Phospholipid Phosphate (PLP)

This was done by the method of Frostegard et al. (1991) with some

modification. Glass containers used for estimation of PLP were

washed with methanol and 15% HNO3; this was followed by the

rinsing twice with tap water and thrice with deionized water.

Phospholipids were extracted using a chloroform : methanol :

citrate buffer solvent in a ratio of 1 : 2 : 0.8v/v/v. The citrate buffer

was made up of 0.015M citric acid and 0.15M trisodium citrate at a

ratio of 5.9 : 4.1v/v to give a pH of 4.0. A 1.0g wet weight of soil

was put in a Mc Cartney bottle containing 11.65ml of the

extraction solvent. The ratio of chloroform to soil was 3:1 as

prescribed by Frostegard et al. (1991). Two hours later, 3.1ml of

citrate buffer and 3.1ml of chloroform were added with

intermittent shaking for another hour to enhance extraction. The

solutions were left overnight to dissociate into two partitions. A

6.4ml volume of the lower chloroform partition was removed using

a syringe and put into a vial, taking care not to include any soil

particles. An aliquot (0.1-1.0ml) was transferred to a 5ml bijou

bottle and dried with nitrogen.

Digestion and phospholipid assay were carried out using the

method of Findlay et al. (1989). Lipid extracts and glycerol

phosphate standards were treated with 1.8ml of acidified potassium

persulphate (K2S3O8) solution (5g to 100ml of 0.35N H2S04) for

24h at 95oC. The mixture was still hot, this was followed by the

addition of 0.4ml of ammonium molybdate solution and allowed to

stay for 10 min before the introduction of 1.8ml of malachite green

solution. Optical absorbance at 610nm was read after 30min.

Distilled water was used for the zero.


Data analysis was carried out using a two-way analysis of variance

(ANOVA) and the difference done by comparing tests with

P<0.05.

3 Results

3.1 Effects of Crude Oil on the Colony Counts of Soil and

Aquatic Bacteria and Fungi

Effects of different crude oil concentrations on microbial numbers

in soil and water are presented in Figures 1 and 2. The pristine soil

Figure 1 Effects of varying levels of crude oil on total colony count of soil bacteria

0

0.5

1

1.5

2

2.5

3

3.5

0 1 2 3 4 5 6 7 8

Bacte

rial

Nu

mb

er/

gra

m o

f S

oil

(x10

9 cfu

)

Sampling weeks

Fig.7: Effects of varying levels of crude oil on total colony count of soil bacteria

0%

0.50%1%

2%

2.50%5%

10%

15%

20%




sample (0% pollution) experienced increase in bacterial numbers from

the first week to the eight week. At low concentrations (0.5-2%) the

crude oil did not have significant negative effects on the cell number of

the soil bacteria. The growth curves (Figure 1) at lower concentrations

(0.5-2%) was slower for first week, after which they increased

progressively till the eighth week. So, when it compared to the control

(0%), there is no significant difference between them. On the contrary,

crude oil at high levels (2.5%-20% v/w) had a significant (P<0.05)

negative effect on bacterial cell numbers. There was a sharp reduction

in bacterial populations at 2.5% crude oil level from the first week to

the fourth week. Afterwards a gradual increase in population was noted

from the fifth week to eighth week.

The fungi seemed to be more prone to the toxic effects of crude oil.

This is evidenced by the progressive decrease in fungal numbers

with time at the different concentrations of crude oil (Figure 2).

Maximum inhibition in fungal growth was reported at 15 and 20%

(v/w) concentration of crude oil.

Figures 3 and 4 showed the effects of crude oil on the cell numbers

of marine and freshwater bacteria. Like soil bacteria, reduction in

the cell numbers of these bacteria was reported at the first week,

this was followed by the gradual increase in bacterial numbers with

the exposure time increased (Figure 3 & 4). Freshwater

bacteria were more susceptible to crude oil toxicity than their marine

Figure 2 Effects of varying levels of crude oil on total colony count of soil fungi

Figure 3 Effects of crude oil on the total colony count of marine bacteria

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 1 2 3 4 5 6 7 8

Fu

ng

al N

um

ber/

gra

m o

f S

oil

(x10

7cfu

)

Sampling Weeks

Fig. 8: Effects of varying levels of crude oil on total colony count of

soil fungi

0%

0.50%

1%

2%

2.50%

5%

10%

15%

20%

0

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8

Bacte

rial

Nu

mb

er/

ml

(x10

7cfu

)

Sampling Weeks

Fig. 4: Effects of Crude oil on the Total Colony Count of Marine Bacteria

0%

0.50%

1%

2%

2.50%

5%

10%

15%

20%



180 Eze et al.

counterparts as evidenced by the higher

population recorded in marine than

freshwater bacteria. Contrary to the bacteria,

exposure time of the fungi to crude oil did

not enhance their population (Figures 5 and

6). The same trend also occurred in the fungi

(Figure 5, 6). In all the aquatic bacteria and

fungi the toxic effects of crude oil were

highest at 15% and 20% pollution levels.

3.2 Impact of Crude Oil on Microbial

Respiration and Phospholipid Phosphate

(PLP)

Results of these analyses are shown in

Figures 7 to 9. Microbial respiration was

evaluated from the quantity of Carbon di

oxide emitted from the soil over a specific

period. According to the results, high

concentrations of crude oil (15% and 20%)

significantly (P<0.05) decreased CO2

emission (Figure 7) as well as phospholipid

phosphate in the soil samples. The length of

time microorganisms were exposed to the

crude oil (especially low to moderate levels)

increased the levels of CO2 and PLP in the

soil (Figure 7 and 8). Mean maximum levels

of CO2 and PLP in the test samples occurred

at 0.5% and 1.0% crude oil contaminations.

This trend was also observed with the PLP

and maximum levels also occurred at 0.5%

and 1.0% crude oil pollution levels. There

was a positive correlation (Pearson’s

Correlation Model) between soil CO2 and

PLP (correlation coefficient= 0.74).

4 Discussion

Result of current study clearly indicated that

crude oil contamination negatively affected the

microbial population, in this manner results of

current study are in agreement with the

findings of previous researchers (Boethling &

Alexander, 1979; Long et al., 1995), this is

only manifest at high oil concentrations (Ma et

al., 2015). Results of this study show low

levels of crude oil (0.5- 2.0%) did not have any

negative effect on microbial population. This

might be because at low levels

hydrocarbonoclastic microorganisms very

easily metabolize crude oil as carbon source

for their growth (Adedokun & Ataga, 2007).

Figure 4 Effects of crude oil on the total colony count of fresh water bacteria

Figure 5 Effects of crude oil on the total colony count of marine fungi

Figure 6 Effects of crude oil on the total colony count of fresh water fungi

0

20

40

60

80

100

120

0 1 2 3 4 5 6 7 8B

acte

ria

l N

um

ber/m

l (x

10

7cfu

)Sampling Weeks

Fig.11: Effects of crude oil on the total colony count of fresh water

bacteria

0%

0.50%

1%

2%

2.50%

5%

10%

15%

20%

0

1

2

3

4

5

6

7

8

9

10

0 1 2 3 4 5 6 7 8

Fu

ng

al N

um

bers/m

l (x

10

6cfu

)

Sampling Weeks

Fig.12: Effects of crude oil on the total colony count of marine fungi

0%

0.50%

1%

2%

2.50%

5%

10%

15%

20%

0

2

4

6

8

10

12

0 1 2 3 4 5 6 7 8

Fu

ng

al N

um

bers

/ml

(x10

5cfu

)

Sampling Weeks

Fig. 13: Effects of crude oil on the total colony count of fresh water

fungi

0%

0.50%

1%

2%

2.50%

5%

10%

15%

20%




Figure 7 Effects of Crude oil on soil respiration

Figure 8 Effects of varying levels of crude oil on soil microbial phospholipids (nm/g of soil)



182 Eze et al.

The sharp decline observed in soil bacterial numbers from the first

week up to the fourth week and subsequent gradual rise in

population from the fifth to the eighth week might be the result of

a decrease in the non-hydrocarbonoclastic bacterial population

caused by the oil pollutant (Ebueli et al. 2005). The heterotrophic

microorganisms (non-hydrocarbonoclastic) usually outnumber

hydrocarbon degraders in unpolluted habitats and any introduction

of a hydrocarbon pollutant will seriously reduce their numbers. On

the contrary, the degradation facilities of hydrocarbonoclastic

microorganisms are activated in the presence of hydrocarbon

contaminants leading to utilization of the substrate and an increase

in their population (Aniruddha & Hermen 2010; Das & Chandran,

2011; Owabor et al. 2011).

Both the aquatic and soil microorganisms had similar sensitivity

patterns to crude oil toxicity. In general, the water bacteria

demonstrated higher adaptive capacity to the oil than water fungi.

This might be because of important role of bacteria in crude oil

biodegradation than fungi. Further bacteria are more flexible as a

result of the presence of more adaptation features. Furthermore,

marine microorganisms more resisted to crude oil toxicity than

their freshwater counterparts. This might be result of innate traits

of the organisms (Chen et al., 2017) or the development of

adaptive characteristics occasioned by pre-exposure of marine

organisms to petroleum pollutants (Amanchukwu et al. 1989).

These factors plays a significant role in pre-adaptation of marine

organisms to hydrocarbons because of oil spills on the high seas

caused by oil tanker accidents, oil leakages from motorized

seafaring vessels and other offshore incidents that introduce

petroleum into the sea.

The crude oil also had dose-dependent effects on soil microbial

respiration and phosphoslipids content. Crude oil at high

concentrations significantly (P<0.05) decreased the amounts of

CO2 and PLP in the soil samples. This is because of its lethal

effects on microbial cells. Phospholipids are a constituent of all

cell membranes and their levels are always proportional to the

bacterial biomass and they disappear soon after the death of the

cell (Peterson et al. 1991). Therefore it follows that whatever

affects microbial cell number will invariably affect the

phospholipid phosphate level. According to Frostegard et al.

(1991), phospholipid levels are used to measure total microbial

biomass, activity and metabolic status.

Conclusion

The toxic effects of crude oil on microorganisms generally are

more perceptible at high levels of the oil. On the other hand very

low levels of crude oil enhance the growth of some groups of

microorganisms. Additionally, marine microorganisms exhibit a

higher tolerance to crude oil than their freshwater counterparts.

More research work is needed to unravel and fully elucidate the

Figure 9 Relationship between microbial phospholipid and carbon (IV) oxide evolution from crude oil-contaminated soil samples.




reasons for the discrepancy between the reactions of marine and

freshwater microorganisms to crude oil pollution.

Acknowledgement

The authors wish to acknowledge the support given by the

Department of Microbiology, University of Nigeria, Nsukka

through the provision of laboratory space and other facilities used

for the work.




References

Abdullah MA, Peramaiyan R (2019) Petroleum hydrocarbon and

living organisms. In: Muharrem I, Olcay KI (Eds.), Hydrocarbon

Pollution and Effects on the Environment. IntechOpen.

Doi:10.5772/intechopen.86948.

Adedokun OM, Ataga AE (2007) Effects of amendments and

bioaugmentation of soil polluted with crude oil, automotive

gasoline oil and spent engine oil on the growth of cowpea(Vigna

unguiculata). Scientific Research and Essays 2: 147-149.

Amanchukwu SC, Obafemi A, Okpokwasili GC (1989)

Hydrocarbon degradation and utilization by a palm wine yeast

isolate. FEMS Microbiology Letters 57: 151-154.

Aniruddha S, Hermen S (2010) Enhanced biodegradation of oil

products by some microbl isolates supplemented with heavy

metals. International Journal of Botany 6: 441-448.

Boethling RS, Alexander M (1979) Effect of concentration of

organic chemicals on their biodegradation by natural microbial

communities. Applied and Environmental Microbiology 37: 1211-

1216.

Chen W, Li J, Sun X, Min J, Hu X (2017) High efficiency

degradation of alkanes and crude oil by salt-tolerant bacterium

Dietzia species CN-3. International Biodeterioration and

Biodegradation 118: 110-118. Doi: 10.1016/j.ibiod.2017.01.029.

Das N, Chandran P (2011) Microbial degradation of petroleum

hydrocarbon contaminants. Biotechnological Research

International 2011. Article ID 941810, 13 Pages.

http://dx.doi.org/10.4061/2011/941810.

Ebuehi OAT, Abibo IB, Shekwolo PD, Sigismund KI, Adoki A,

Okoro IC (2005) Remediation of crude oil contaminated soil by

enhanced natural attenuation technique. Journal of Applied Science

and Environmental Management 9: 103-106.

Ekpo MA, Udofia US (2008) Rate of biodegradation of crude oil

by microorganisms isolated from sludge environment. African

Journal of Biotechnology 3: 4495-4499.

Eze CN, Maduka JN, Ogbonna JC, Eze EA (2013) Effects of

Bonny light crude oil contamination on the germination, shoot

growth and rhizobacterial flora of Vigna unguiculata and Arachis

hypogea grown in sandy loam soil. Scientific Research and Essays

8: 99-107.

Findlay RH, King GM, Watling L (1989) Efficacy of

phospholipids analysis in determining microbial biomass in

sediments. Applied Environmental Microbiology 55: 2888-2893.

Frostegard A, Tunlid A, Bath E (1991) Microbial biomass

measured as total lipid phosphate in soils of different organic

content. Journal of Microbiological Methods 13 : 151-163.

Isermeyer H (1952) Eine eifache Methode zur Bestimmung der

Bodenatmung und dem Karbonate im Boden. Zeitschrift für

Pflanzenernährung und Bodenkunde 56 : 26-38.

Long SC, Aelion CM, Dobbins DC, Ptaender FK (1995) A

comparison of microbial community characteristics among

petroleum-contaminated and uncontaminated sub-surface soil

samples. Journal of Microbial Ecology 30: 297-307.

Ma YL, Lu W, Wan LL, Luo L (2015) Elucidation of fluoranthene

degradative characteristics in a newly isolated Achromobacter

xylosoxidans DN OO2. Applied Biochemistry and Biotechnology

175: 1294-1305. Doi:10.1007/s12010-014-1347-7

National Oceanic and Atmospheric Administration (1992) Oil Spill

Case Histories, 1967-1991: Summaries of Significant US and

International spills. NOAA Report NO HMRAD 92-111.

National Research Council (2002) Oil in the sea 111: Inputs, Fates

and Effects. Washington D C: National Academy Press.

Owabor CN, Onwuemene OC, Enaburekhan I (2011)

Bioremediation of polycyclic aromatic hydrocarbon contaminated

aqueous soil matrix: Effect of co-contamination. Journal of

Applied Science and Environmental Management 15: 583-588.

Petersen SO, Henriksen K, Blackburu TH, King GM (1991) A

comparison of phospholipids and chloroform fumigation analyses

for biomass in soil, potentials and limitations. FEMS Microbial

Ecology 85: 257-268.

Pezeshki SR, Hester MW, Lin Q, Nyman JA (2000) The effects of

oil spill and clean-up on dominant US Gulf coast marsh

macrophytes: A review. Environmental Pollution 108: 129-139.



184 Eze et al.

Wistreich GA (1997) Microbiology Laboratory: Fundamentals and

Applications. Prentice-Hall Inc. New Jersey, USA. Pp. 144-186,

194-259.

Wang Y, Liang J, Wang J, Gao S (2018) Combining stable carbon

isotope analysis and petroleum fingerprinting to evaluate

petroleum contamination in the Yanchang oilfield located on loess

plateau in China. Environmental Science and Pollution Research

25: 2830-2841.

Xingjian X, Wenming L, Shuhua T, Wei W, Qige Q, Pan J, Xinmei G,

Fengjiao, Haiyan L, Hongwen Y (2018). Petroleum hydrocarbon-

degrading bacteria for the remediation of oil pollution under aerobic

conditions : A perspective analysis. Frontiers in Microbiology 03

December 2018|https://doi.org/10.3389/fmicb.2018.02885.

Xinxin L, Ville AO, Kari S, Aki S (2019) Meat and bone

meal as a novel biostimulation agent in hydrocarbon

contaminated soils. Chemosphere 225: 574-578:

Doi:10.1016/j.chemosphere.2019.03.053.

Xue J, Yu Y, Bai Y, Wang L,Wu Y (2015) Marine oil-degrading

microorganisms and biodegradation process of petroleum

hydrocarbon in marine environments: a review. Current

Microbiology 71 : 220-228.

Yan S, Anna-Lea R, Martin R, Aki S (2018) Requirement of

ecological replication with independent parallel analysis of each

replicate plot to support soil remediation. International

Biodeterioration and Biodegradation 133: 133-141.

Doi:10.1016/j.ibiod.2018.06.006.



ISSN No. 2320 – 8694


DIFFERENTIAL RESPONSES OF CERTAIN ETHIOPIAN GROUNDNUT

(Arachis hypogaea L.) VARIETIES VARYING IN DROUGHT TOLERANCE,

TO TERMINAL DROUGHT STRESS

P.R. Jeyaramraja1,*, Woldesenbet Fantahun2

1Department of Biology, College of Natural Sciences, Arba Minch University, Post Box No. 21, Arba Minch, Gamo Zone, Federal Democratic Republic of Ethiopia.

2Ethiopian Biotechnology Institute, Addis Ababa, Federal Democratic Republic of Ethiopia.

Received – February 05, 2020; Revision – March 21, 2020; Accepted – April 15, 2020



ABSTRACT

Responses of drought tolerant (DT) and drought susceptible (DS) Ethiopian groundnut varieties to

terminal drought stress were compared to determine the traits behind drought tolerance. Drought (D)

induced from 91 to 105 days after sowing (DAS) reduced leaf RWC (relative water content, %), leaf

area (cm2 plant

-1), Chl (chlorophyll, mg g

-1 fresh weight), RDM (root dry mass, g plant

-1), ADM (above-

ground dry mass, g plant-1

), TDM (total dry mass, g plant-1

) and plant height (cm); however, D increased

SLM (specific leaf mass, g m-2

). High leaf RWC in DS types reinstated the hypothesis that capacity to

save leaf water is not a method of drought tolerance. Although there were insignificant Chl differences

between DT and DS types, dry matter accumulation (RDM and ADM) was higher in DT types, which is

attributed to higher SLM in DT types. SLM had significant positive relationship with RDM. An increase

in plant height without increase in leaf area explains drought susceptibility in DS types. Resumption of

irrigation on 106 DAS resulted in an increase in leaf RWC; however, this accompanied no resurrection

response in terms of studied physiological and growth parameters and thus, it was not possible to restore

pod yield after D impaired groundnut growth. Certain parameters were higher in DT types, positively

correlated with DRI (drought response index) and primarily decided by the genotype; such parameters

were concluded to be the traits behind drought tolerance.


KEYWORDS

Drought susceptibility

Drought tolerance

Peanut

Specific leaf mass

Water stress

E-mail: [email protected] (P.R. Jeyaramraja)


















Groundnut responses to terminal drought stress 186

1 Introduction

Groundnut (Arachis hypogaea L.) is a significant crop plant

utilized to produce foodstuff and cooking oil. Being a rain-fed crop

(Nagaveni & Khan, 2019), it generally undergoes D under field

conditions that brings about reductions in crop yield. Besides, D is

a predisposing factor for aflatoxin production in groundnut

(Waliyar et al., 2003). D is a devastating physical stress and more

challenging to the attempts of breeders (Tuberosa & Salvi, 2006).

Breeding drought tolerance in groundnuts cultivars would reduce

yield losses due to D in rain-fed growing areas (Kakeeto et al.,

2020). Breeding works to develop drought tolerance have been

hampered in the past by its quantitative genetic base and by low

understanding of the physiological base of productivity under D

(Passioura, 2002). Hence, improvement of the current knowledge

of reactions of crops to D and the methods contained in drought

tolerance have turn out to be main goals of research and

investment, with the crucial objective of producing crops with

better WUE and reduced yield loss due to D (Somerville &

Briscoe, 2001; Zhang et al., 2004).

Plants notice and react quickly to little changes in water status

through a series of physiological, cellular, and molecular

events (Chaves et al., 2009). D can activate a range of plant

reactions, which comprise reduction in leaf RWC and water

potential (Lawlor & Cornic, 2002), decrease in stomatal

aperture and net photosynthetic rate. Photosynthesis is an

important metabolism which is altered by D via reduced

diffusion of CO2 to chloroplast and metabolic limitations

(Pinheiro & Chaves, 2011). Important assessments on crop

reactions at large (Yordanov et al., 2000), and groundnut

especially (Reddy et al., 2003), to D give additional knowledge

on physiological features accompanying D. Nevertheless, there

is even now no complete standard system for assessing drought

tolerance, particularly since the physiological method is not all

the time sufficient for selection on account of negative

relationships between physiological attributes contained in

drought adaptation (Turner et al., 2001). As a result, crop

development plans have been unable to completely use current

physiological data.

Responses of model plants namely, Arabidopsis and

Craterostigma to D have been broadly studied (Yamaguchi-

Shinozaki et al., 1995; Shinozaki & Yamaguchi-Shinozaki,

1996). Evaluation of the regulatory mechanisms of carbon

assimilation in peanut due to forthcoming alterations in

climate, including D, is inadequate (Clifford et al., 2000). Even

though remarkable developments have been achieved regarding

the type of actions happening in crops exposed to D,

explanation of the metabolic regulation is even now absent

(Rolland et al., 2006; Shinozaki & Yamaguchi-Shinozaki,

2007). Jeyaramraja & Thushara (2013) postulated a series of

physiological reactions in peanut exposed to D. However,

holistic studies in groundnut on the relationships among

physiological, growth and yield parameters and their changes

due to terminal D under natural field conditions are limited. As

groundnut is mostly grown as rain-fed crop, it faces D during

terminal stages of crop growth (Hampannavar & Khan, 2019),

which results in reductions in yield and quality. Hence, it is

essential to produce crops that bear D. Knowledge of drought

tolerance mechanisms in legumes innately adjusted to D, such

as groundnut, is essential in identifying markers for drought

tolerance which in turn, can aid in plant breeding programmes.

Nevertheless, little literature is available in groundnut on the

physiological and growth parameters especially during the

terminal stages of crop growth which is prone to D under field

conditions i.e., from 90 to 110 DAS. Hence, the present study

has been taken up.

Bacharou Falke et al. (2019) stated that investigation of

groundnut genotypes response to drought stress could

contribute to improving drought tolerance and productivity.

Hence, in the present investigation, responses of DT and DS

groundnut varieties during terminal stages of growth (90 to 110

DAS) was studied. This study was planned to investigate the

consequence of D on certain physiological and growth

parameters in Ethiopian groundnut varieties differing in

drought tolerance, because it is assumed to be helpful in

identifying traits behind tolerance/susceptibility to D.


The experiment was conducted inside “Natural Beauty

Initiative Centre”, Main Campus, Arba Minch University,

Ethiopia during April to September 2014.

2.1 Plant Material

Pods of six groundnut varieties were collected from Werer

Agricultural Research Centre during April 2014. Pod and seed

characteristics of these varieties were published (Jeyaramraja

& Fantahun, 2014). These varieties were categorized into DT

(ROBA, Werer 962, NC-4x) and DS (FAYO, Tole 2, Werer

964) types based on DRI values (Table 4) obtained from two

growing seasons (Jeyaramraja & Fantahun, 2016).

2.2 Experimental Design

The experimental design was Randomized Block Design

(RBD) with three replications. There were 6 experimental plots

for each variety (3 for C and 3 for D) and since totally 6

varieties were used in this study; there were totally 36 plots (18

for C and 18 for D). A buffer zone of 2 m was left between C

and D plots to avoid diffusion of water through soil layers

during D induction period from C to D plots.



187 Jeyaramraja & Fantahun

2.3 Experimental Layout

After ploughing the field three times into a fine tilth, experimental

layout was made. Each plot had 4 numbers of 5 m ridges. Distance

between two adjacent ridges was 60 cm. Inter-row spacing

between plants was 30 cm while intra-row spacing between plants

was 10 cm. Each plot was surrounded by bunds. Each plot had a

length of 5 m and width of 3 m. Hence, the size of one plot was 15

m2. The size of total experimental layout was 600 m

2.

2.4 Cultivation practices of groundnut

The soil was loamy. Sowing was done manually on both sides of

ridges at ~4 cm depth. Sound, mature and good quality kernels

were only selected for sowing. Kernels were subjected to treatment

with Mancozeb (4g kg-1

kernels) to guard the juvenile seedlings

from root-rot and collar-rot infection. Compost (2 t ha-1

) was

applied as basal dressing and incorporated well into the soil.

Carbaryl 10 per cent DP was applied in soil at the time of seeding

against ants/earwigs/termites.

2.5 Induction of D under field conditions

When groundnut is grown under field conditions as rain-fed crop, it

does not experience D at all developmental stages. Jogloy et al.

(1996) reported that groundnut generally experiences D during

pegging and pod development and then may have sufficient quantity

of water. This would cause severe decrease in yield, and the degree

of decrease would rely on groundnut varieties (Kambiranda et al.,

2011). Hence in the present study, D was induced by withholding

irrigation in the field 91 DAS for a period of 15 days, i.e., up to 105

DAS, so that, simulated D could mimic the effects of naturally

occurring D in the field. Rainout shelter which can cover the whole

D plots was kept ready from 91 DAS to 105 DAS, so that, in the

event of rainfall, the D plots could be covered immediately.

2.6 Irrigation Schedule

On the day of sowing seeds and 1 DAS, the field was irrigated.

Then, irrigation was usually given once a week (i.e., 8 DAS, 15

DAS, 22 DAS and so on) based on soil water measured at four

places (2 in C plots; 2 in D plots) with the help of a tensiometer.

When water potential of top soil reaches -0.25 to -0.50 bars,

irrigation was provided. Irrigation was skipped if rainfall

replenished soil water. For C plots, irrigation continued until a

week before harvest maturity of each variety. For D plots, the

irrigation was skipped two times i.e., at 92 DAS and 99 DAS to

induce D. It has to be noted that there was no rainfall in the field

from 91 DAS to 105 DAS i.e., during the D induction period and

hence, rainout shelter was not used to protect the D plots from rain.

From 106 DAS, water supply was resumed for D plots and was

continued until a week before harvest maturity of each variety.

2.7 Physiological and growth parameters

From each variety that is under C (or) D conditions, one plant was

chosen randomly from each plot for analysis of below-given

parameters which were measured 5 times (i.e., 90 DAS, 95 DAS,

100 DAS, 105 DAS & 110 DAS).

2.7.1 Plant water status

RWC was measured in the third leaf from the top of the main shoot

as said by Clavel et al. (2006) employing the equation: RWC =

[(FM – DM)/(TM – DM)] × 100, where FM was fresh leaf mass,

TM was turgid mass after 4-h rehydration of the leaf in distilled

water at room temperature under dark conditions, and DM was dry

mass after drying at 85°C for 24 h.

2.7.2 Pigment estimation

Chl was determined spectrophotometrically as per the method of

Sadasivam & Manickam (1996) in the second leaf from the top of

main shoot.

2.7.3 Growth parameters

RDM, ADM & TDM were determined in line with Clavel et al.

(2005). Plants were cautiously isolated from the soil and the roots

were cleaned with water. Afterwards, RDM and ADM were

determined after drying at 80°C for 2 days. TDM is the sum of

ADM and RDM. Leaf area was determined by a non-destructive

method employing allometric model (Kathirvelan & Kalaiselvan,

2007). In the third leaf from the top of main shoot, SLM was

obtained (Clavel et al., 2005), which is the ratio of leaf dry mass

per unit leaf area. Plant height was determined with a ruler from

the ground to top of the main axis.


Three factor ANOVA was carried out for the analysis of

physiological and growth parameters wherein Factor A is varieties

(6), Factor B is treatments (2 - C and D) and Factor C is DAS (5 –

90 DAS, 95 DAS, 100 DAS, 105 DAS and 110 DAS). Critical

difference (CD) values were computed at 0.05 and 0.01 levels to

find out whether statistically significant differences existed within

varieties, treatments, and/ DAS. Interactions between the factors

were also studied. Correlation coefficients among various

physiological and growth parameters were studied to find out

relationships among them.


To compare the effects of C and D on moisture content of groundnut

leaves, RWC was used in the present investigation because it is

thought to be a helpful integrator of crop water balance than leaf

water potential (Wright & Nageswara Rao, 1994). Significant




variations were found among the groundnut varieties in terms of

studied physiological and growth parameters except leaf area plant-1

and Chl (Table 1). The fact that DS types had high leaf RWC in this

work is of interest. DT Phaseolus vulgaris frequently open their

stomata under harsh D (Costa França et al., 2000). DS cultivar had at

all times high RWC mainly up to 35 days of D on two-week-old

groundnut seedlings (Clavel et al., 2005). Above quoted earlier

works and the present study reconfirmed the hypothesis proposed by

Jeyaramraja & Thushara (2013) that capacity to save water in

groundnut leaf is not a method of drought tolerance. Vadez (2014)

also said that water saving mechanisms should not be considered as

mechanisms of drought tolerance.

Although leaf RWC was higher in DS types (Table 1), Chl was the

same in both DT and DS types. Kicheva et al. (1994) stated that

reduction in rate of photosynthesis could happen owing to

reduction in Chl under harsh D. Hence, same contents of Chl

observed in this work must lead to same rates of photosynthesis in

DT and DS types and so, same rates of dry matter production.

However, RDM and ADM were higher in DT types. The reason

for higher dry matter production in DT types is attributed to higher

SLM values in DT types. It is imperative to note here that the DT

types had 1.23-fold more SLM as compared to DS types.

Like leaf RWC, plant height was also higher in DS types. Leaf RWC

showed positive correlation with plant height irrespective of DAS (r

= 0.38, p<0.05, n = 36). It must be noted here that leaf area did not

vary between DT and DS types (Table 1); hence, just an increase in

plant height without increase in leaf area would not help DS types to

be productive, which thus resulted in insignificant correlation

between these traits with r value of -0.067. Reduction in plant height

with almost unchanged total biomass accumulation but with

increased grain yield/harvest index was achieved in certain breeding

experiments (Cattivelli et al., 1994; Slafer et al., 1994).

Drought is significantly (p<0.01) reduced leaf RWC, leaf area plant-

1, Chl, RDM, ADM, TDM and plant height (Table 1). SLM was

significantly (p<0.01) increased due to D. The RWC reduction in

plants under D could be related with reduction in crop vigour (Lopez

et al., 2002; Halder & Burrage, 2003). Stomatal regulation of water

loss has been identified (Chaves, 1991) as an initial reaction for

conditioning the leaf water status of field crops, but it rigorously

reduces carbon uptake and biomass production and hence, there was

reduction in RDM, ADM and TDM in the present study.

Although DS types had significantly high leaf RWC than DT types

under C conditions, the leaf RWC of DT types was almost equal to

that of DS types under D conditions (Table 1). As compared to C, D

caused 4.8 % reduction in leaf RWC in DS types whereas in DT

types, it caused only 3 % reduction. Both under C and D conditions,

DT types had significantly higher RDM than DS types. Higher RDM

values in DT types may be attributed to deeper root system for soil

moisture capture which is a successful method of attaining

reproductive success under D (Kirkegaard et al., 2007). High dry

matter production in DT cultivars under field conditions is caused by

more capability to keep up transpiration, which is aided by deep

roots (Blum, 2009). In a study in groundnut profuse root system in

the deep soil was related to better yield under D and it was deduced

that greater, lengthy and denser roots at deep soil was accountable

for more water absorption (Jongrungklang et al., 2012). Although

insignificant difference was found between DT and DS types

regarding ADM and TDM under C conditions, DT types maintained

significantly higher ADM and TDM than DS types under D

conditions. D caused just 9.6 % ADM reduction in DT types while in

DS types, it caused a tremendous reduction of 19.7 % (Table 1).

Because the plants in C and D plots were approaching maturity and

in addition, the plants in D plots suffer D from 91 DAS, leaf RWC

was found to decline significantly and progressively from 90 to

105 DAS (Table 2). However, due to resumption of irrigation on

106 DAS, leaf RWC could significantly (p<0.05) increase on 110

DAS in D plots. Although significant decrease in leaf area plant-1

was observed from 90 DAS, the decrease was irregular (Table 2).

Besides, due to resumption of irrigation on 106 DAS, leaf area

plant-1

could not increase significantly on 110 DAS in D plots. Chl

was also found to decrease significantly from 90 to 105 DAS;

however, Chl could not increase significantly on 110 DAS in D

plots. RDM, ADM and TDM were on the decreasing trend

significantly from 90 to 105 DAS. No resurrection response was

found on 110 DAS in terms of RDM, ADM and TDM in D plots.

Although statistically insignificant, SLM was found to increase

from 90 DAS. Plant height had increased significantly from 90 to

105 DAS and there was statistically insignificant increase in plant

height on 110 DAS in D plots.

As D was induced from 91 DAS, there were no significant

differences between C and D plots on 90 DAS with respect to the

studied physiological and growth parameters (Table 2). Leaf RWC

was found to decrease both in C and D plots as the DAS increased.

Reduction of leaf RWC in C plots was since the groundnut

varieties were approaching maturity while in D plots, the reduction

in leaf RWC was due to both reasons namely, D and approaching

maturity. It is imperative to note here that the reduction of leaf

RWC in D plots was more pronounced than in C plots until 105

DAS. Due to resumption of irrigation on 106 DAS, the leaf RWC

could significantly revive on 110 DAS in D plots. RDM, ADM and

TDM did not change significantly in C plots as DAS increased;

however, these parameters decreased significantly in D plots until

105 DAS. No resurrection response in terms of RDM, ADM and

TDM was observed on 110 DAS in D plots due to resumption of

irrigation on 106 DAS. Plant height significantly increased in C

plots with an increase in DAS; however, in D plots, there was no

such increase in plant height (Table 2).




Proportion of variation due to stress treatments is more with regard

to leaf RWC, leaf area plant-1

, Chl, RDM, ADM and TDM (Table 3).

On the other hand, proportion of variation due to variety is more

followed by stress treatments and DAS regarding SLM and plant

height. SLM is not developmentally controlled; but environmentally

(by D) and, genetically controlled. SLM is an index of mass and

activity of the mesophyll under unit area of leaf (Jun & Imai, 1999).

Hence, higher values of SLM in DT types observed in this work

might lead to an increase in leaf thickness (mesophyll mass and

activity) and thereby, an increase in photosynthetic activity and dry

matter production. It is imperative to note here that SLM had strong

positive relationship with RDM (r = 0.612, p<0.001, n=36) based on

Table 1 Effect of drought stress on physiological and growth parameters of certain Ethiopian groundnut varieties

Variety Leaf RWC

(%)

Leaf area

(cm2 plant-1)

Chl

(mg g-1 FW)

RDM

(g plant-1)

ADM

(g plant-1)

TDM

(g plant-1)

SLM

(g m-2)

Plant height

(cm)

C D C D C D C D C D C D C D C D

Drought Tolerant

ROBA 80.3 76.5 229.9 218.3 2.57 2.52 3.22 3.22 19.61 17.53 22.83 20.74 67.3 69.1 37.4 33.9

Werer 962 79.5 78.4 223.9 210.9 2.75 2.56 3.48 3.26 19.07 17.30 22.55 20.56 65.1 66.3 36.1 33.3

NC-4x 80.2 77.8 208.2 219.4 2.67 2.46 3.43 3.18 18.81 17.12 22.25 20.30 62.5 64.4 47.9 46.3

Mean (DT) 80.0 77.6 220.7 216.2 2.67 2.51 3.38 3.22 19.16 17.32 22.54 20.54 64.9 66.6 40.5 37.8

Drought Susceptible

FAYO 82.4 77.1 236.3 229.3 2.67 2.46 3.27 3.03 19.35 15.51 22.62 18.54 61.5 62.4 45.5 43.7

Tole 2 82.2 78.3 220.2 209.6 2.59 2.36 3.27 3.02 19.62 16.25 22.89 19.27 52.7 54.2 49.2 47.2

Werer 964 79.8 77.2 214.0 217.4 2.54 2.40 3.01 2.82 18.41 14.35 21.42 17.17 44.5 46.7 44.7 40.5

Mean (DS) 81.4 77.5 223.5 218.7 2.60 2.41 3.18 2.96 19.13 15.37 22.31 18.33 52.9 54.4 46.4 43.8

Statistical significance

CD 5 %

between varieties 0.73 NS NS 0.09 0.37 0.38 2.1 1.2

between treatments 0.42 11.6 0.08 0.05 0.21 0.22 1.2 0.7

variety X treatment 1.03 NS NS 0.12 0.52 0.54 NS NS

Values are irrespective of DAS ie., averages of five various days after sowing (90, 95, 100, 105 & 110 DAS)

Table 2 Effect of drought stress on physiological and growth parameters in groundnut at various days after sowing

DAS Leaf RWC

(%)

Leaf area

(cm2 plant-1)

Chl

(mg g-1 FW)

RDM

(g plant-1)

ADM

(g plant-1)

TDM

(g plant-1)

SLM

(g m-2)

Plant height

(cm)

C D C D C D C D C D C D C D C D

90 DAS 82.2 81.9 257.5 257.9 2.72 2.73 3.28 3.30 19.37 19.64 22.64 22.93 58.4 59.3 40.0 39.9

95 DAS 81.8 79.2 218.0 172.4 2.63 2.43 3.28 3.18 17.95 17.39 21.23 20.57 58.9 59.4 41.5 40.8

100 DAS 80.5 76.6 216.1 229.7 2.64 2.43 3.30 3.04 19.17 16.69 22.47 19.73 58.8 60.6 43.5 41.2

105 DAS 80.0 73.9 216.4 207.4 2.58 2.35 3.28 2.94 19.61 14.07 22.89 17.01 59.2 61.8 45.5 41.0

110 DAS 79.2 76.0 202.4 219.9 2.59 2.37 3.26 2.99 19.63 13.92 22.89 16.91 59.3 61.5 46.8 41.2

Statistical

significance

CD 5 %

between DAS 0.67 18.3 0.12 0.08 0.33 0.35 NS 1.1

treatment X

DAS 0.94 25.9 NS 0.11 0.47 0.49 NS 1.6

Values are irrespective of varieties ie., averages of six varieties.




data irrespective of DAS. From the significant interactions between

variety and stress treatments (Table 3), it can be inferred that there is

genotype-specific D response in terms of leaf RWC, RDM, ADM

and TDM. From the significant interactions between variety and

DAS, it can be concluded that each variety responds differently to

various DAS in terms of ADM and TDM (due to differences in days

to maturity). Significant interactions between stress treatments and

DAS were also noted, which suggest that D has different effects at

various DAS in terms of leaf RWC, leaf area plant-1

, RDM, ADM,

TDM and plant height. Low total transpiration to control water loss,

chlorophyll content, and root length density revealed drought

tolerance associated traits for pod production in groundnut,

according to Bacharou Falke et al. (2019). Savita et al. (2019)

reported that RWC at 75 DAS, SLM at 45 DAS and SPAD

chlorophyll meter reading showed significant positive association

with pod yield.

Conclusions

DT types registered high RDM, ADM, TDM and SLM;

nevertheless, these traits could not be the traits of drought

tolerance because except SLM, traits like RDM, ADM and TDM

had insignificant relationships with DRI values obtained in a

previous study on the same groundnut varieties. However, SLM

showed significant (p<0.01) positive relationship with DRI. In

addition, only SLM is decided primarily by the genotype/variety

and hence, it is genetically controlled. This strengthens the

hypothesis that drought tolerance is conferred by a mixture of

many different traits at the genetic level and hence, SLM must be a

marker for drought tolerance in groundnut.

In addition, we found that the ratios of SLM/RWC, SLM/RDM,

SLM/ADM, SLM/Plant height, ADM/RWC had significant

positive relationships with DRI and so, these ratios can be used as

traits to identify drought tolerant varieties of groundnut in plant

breeding/improvement programmes. DS types had significantly

higher leaf RWC and plant height that are insignificantly related to

DRI with r values of -0.451 and -0.628 respectively. Since only

plant height is primarily decided by the genotype/variety, it may be

a marker for drought susceptibility.

Table 3 Mean squares in the three factor analysis of variance for physiological and growth parameters measured

on certain groundnut varieties of Ethiopia

Source of variation d.f. Mean squares

RWC (%) Leaf area

(cm2plant-1)

Chl mg g-1

FW

RDM

(g plant-1)

ADM

(g plant-1)

TDM

(g plant-1)

SLM

g m-2

Plant height

(cm)

Factor A (variety) 5 14.68** 1087.71 0.14 0.75** 17.55** 24.34** 2191.89** 983.78**

Factor B (treatments) 1 459.26** 12384.08** 1.33** 1.64** 353.58** 403.35** 116.22** 314.94**

Factor C (DAS) 4 161.53** 6842.38** 0.38** 0.20** 43.96** 49.50** 20.42 92.74**

Interaction (A X B) 5 15.55** 1234.13 0.04 0.07* 8.73** 9.13** 1.39 8.13

Interaction (A X C) 20 1.18 1371.43 0.01 0.01 2.28** 2.29** 2.13 1.34

Interaction (B X C) 4 40.05** 10799.80** 0.10 0.20** 68.52** 75.34** 7.41 51.31**

Interaction (A X B X C) 20 0.64 1288.13 0.01 0.01 1.45** 1.56** 0.93 0.75

Error 118 2.09 1575.23 0.07 0.03 0.52 0.57 16.41 5.73

CV (%)

1.83 18.50 10.40 5.31 4.07 3.60 6.79 5.68

* significance at p<0.05

** significance at p<0.01

Three factors were used in the data analysis. Factor A is varieties (6 namely Roba, Werer 962, NC -4x, Fayo, Tole 2 and Werer 964). Factor B is

treatments (2 namely C and D). Factor C is days after sowing (5 namely 90 DAS, 95 DAS, 100 DAS, 105 DAS and 110 DAS).

Table 4 Linear regression analysis giving the relationships of DRI values obtained in a previous study on the same groundnut varieties (Jeyaramraja

& Fantahun, 2016) with few studied parameters in the present study.

Relationships are given by the formula y = a + bx. *p<0.05; **p<0.01. y - dependent variable (DRI); x - independent variable (Traits of drought

tolerance); R - correlation coefficient; a, b - constants.

X R R2 a b

SLM 0.917** 0.841 16.92 62.34

SLM/RWC 0.935** 0.875 0.195 0.815

SLM/RDM 0.937** 0.879 8.373 15.03

SLM/ADM 0.884* 0.782 1.541 2.643

SLM/Plant height 0.888* 0.789 -0.338 2.610

ADM/RWC 0.875* 0.765 0.177 0.068




Abbreviations

ADM – aboveground dry mass; C – control, irrigated plants; CD –

critical difference; Chl – chlorophyll; D – drought; DAS – days

after sowing; DRI – drought response index; DS – drought-

susceptible; DT – drought-tolerant; RDM – root dry mass; RWC –

leaf relative water content; SLM – specific leaf mass; TDM – total

dry mass

Acknowledgements

Financial support for this whole study by Arba Minch University

(GOV /AMU /TH14 /CNS /BIOL /01 /06) is gratefully

acknowledged. The authors thank Werer Agricultural Research

Centre for providing the groundnut seeds.


Both the authors declare that there is no conflict of interest.

References

Bacharou Falke A, Hamidou F, Halilou O, Harou A (2019)

Assessment of groundnut elite lines under drought conditions and

selection of tolerance associated traits. Advances in Agriculture

Article ID 3034278, DOI: https://doi.org/10.1155/2019/3034278.

Blum A (2009) Effective use of water (EUW) and not water-use

efficiency (WUE) is the target of crop yield improvement under

drought stress. Field Crops Research 112: 119-123.

Cattivelli L, Delogu G, Terzi V, Stanca AM (1994) Progress in

barley breeding. In: Slafer GA (Ed). Genetic Improvement of Field

Crops, New York: Marcel Dekker Inc Pp. 95-181.

Chaves MM (1991) Effects of water deficits on C assimilation.

Journal of Experimental Botany 42: 1-16.

Chaves MM, Flexas J, Pinheiro C (2009) Photosynthesis under

drought and salt stress: regulation mechanisms from whole plant to

cell. Annals of Botany 103: 551-560.

Clavel D, Diouf C, Khalfaoui JL, Braconnier S (2006) Genotypes

variations in fluorescence parameters among closely related

groundnut (Arachis hypogaea L.) lines and their potential for

drought screening programs. Field Crops Research 96: 296-306.

Clavel D, Drame NK, Roy-Macauley H, Braconnier S, Laffray D

(2005) Analysis of early responses to drought associated with field

drought adaptation in four Sahelian groundnut (Arachis hypogaea

L.) cultivars. Environmental and Experimental Botany 54: 219-230.

Clifford SC, Stronach IM, Black CR, Singleton-Jones PR, Azam-

Ali SN, Crout NMJ (2000) Effects of elevated CO2, drought and

temperature on the water relations and gas exchange of groundnut

(Arachis hypogaea) stands grown in controlled environment

glasshouses. Physiologia Plantarum 110: 78-88.

Costa Franca MG, Pham Thi AT, Pimentel C, Pereyra Rossiello

RO, Zuily-Fodil Y, Laffray D (2000) Differences in growth and

water relation among Phaseolus vulgaris cultivars in response to

induced drought stress. Environmental and Experimental Botany

43: 227–337.

Halder KP, Burrage SW (2003) Drought stress effects on water

relations of rice grown in nutrient film technique. Pakistan Journal

of Biological Sciences 6: 441-444.

Hampannavar MR, Khan H (2019) Association study of

morphological and physiological traits with yield in groundnut

genotypes under terminal drought condition. International Journal

of Current Microbiology and Applied Sciences 8: 668-678.

Jeyaramraja PR, Fantahun W (2014) Characterization of yield

Components in certain groundnut (Arachis hypogaea L.) varieties

of Ethiopia. Journal of Experimental Biology and Agricultural

Sciences 2: 592–596.

Jeyaramraja PR, Fantahun W (2016) Influence of drought at pod-

filling stage on yield and yield components of groundnut (Arachis

hypogaea L.) varieties of Ethiopia. Tropical Agriculture (Trinidad)

93: 235-244.

Jeyaramraja PR, Thushara SS (2013) Sequence of physiological

responses in groundnut (Arachis hypogaea L.) subjected to soil

moisture deficit. Photosynthetica 51: 395-403.

Jogloy S, Patanothai A, Toomsan S, Isleib TG (1996) Breeding

peanut to fit into Thai cropping systems. In Proceedings of the

Peanut Collaborative Research Support Program-International

Research Symposium and Workshop, Two Jima Quality Inn,

Arlington, Virginia, USA. 25-31 March 1996. Pp. 353-362.

Jongrungklang N, Toomsan B, Vorasoot N, Jogloy S, Boote KJ,

Hoogenboom G, Patanothai A (2012) Classification of root

distribution patterns and their contributions to yield in peanut

genotypes under mid-season drought stress. Field Crops Research

127: 181–190.

Jun H, Imai K (1999) Cultivar differences of photosynthesis and

respiration in peanut leaves. Bulletin of the Faculty of Agriculture,

Meiji University 119: 21-25.

Kakeeto R, Melis R, Biruma M, Sibiya J (2020) Gene action

governing the inheritance of drought tolerance and selected

agronomic traits in Ugandan groundnut (Arachis hypogaea L.)

lines under drought environment. Euphytica 216: 1.




Kambiranda DM, Vasanthaiah HKN, Katam R, Ananga A, Basha SM,

Naik K (2011) Impact of Drought Stress on Peanut (Arachis hypogaea

L.) Productivity and Food Safety. In Vasanthaiah HKN, Kambiranda

DM (Eds). Plants and Environment, InTech Pp. 249-272.

Kathirvelan P, Kalaiselvan P (2007) Groundnut (Arachis hypogaea

L.) leaf area estimation using allometric model. Research Journal

of Agriculture and Biological Sciences 3: 59-61.

Kicheva ML, Tsonev TD, Popova LP (1994) Stomatal and

nonstomatal limitation of photosynthesis in two wheat cultivars

subjected to water stress. Photosynthetica 30: 107- 116.

Kirkegaard JA, Lilley JM, Howe GN, Graham JM (2007) Impact

of subsoil water use on wheat yield. Australian Journal of

Agricultural Research 58: 303–315.

Lawlor DW, Cornic G (2002) Photosynthetic carbon assimilation

and associated metabolism in relation to water deficits in higher

plants. Plant, Cell & Environment 25: 275-294.

Lopez CML, Takahashi H, Yamazaki S (2002) Plant water relations of

Kidney bean plants treated with NaCl and foliarly applied glycine

betaine. Journal of Agronomy and Crop Science 188: 73-80.

Nagaveni K, Khan H (2019) Genetic variability studies in terminal

drought tolerant groundnut (Arachis hypogaea L.). Journal of

Pharmacognosy and Phytochemistry 8: 747-750.

Passioura JB (2002) Environmental biology and crop

improvement. Functional Plant Biology 29: 537-546.

Pinheiro C, Chaves MM (2011) Photosynthesis and drought: can

we make metabolic connections from available data? Journal of

Experimental Botany 62: 869-882.

Reddy TY, Reddy VR, Anbumozhi V (2003) Physiological responses

of groundnut (Arachis hypogea L.) to drought stress and its

amelioration: a critical review. Plant Growth Regulation 41: 75-88.

Rolland F, Baena-Gonzalez E, Sheen J (2006) Sugar sensing and

signaling in plants: conserved and novel mechanisms. Annual

Review of Plant Biology 57: 675-709.

Sadasivam S, Manickam A (1996) Biochemical Methods: 2nd

Edition. New Age Int Limited Publishers, Tamil Nadu Agricultural

University, Coimbatore.

Savita SK, Kenchangoudar PV, Mamata SK, Rajashree B, Nadaf

HL, Deshpande SK (2019) Studies on variability and genetic

association of drought and yield related traits in advance breeding

lines of groundnut (Arachis hypogaea L.). Journal of

Pharmacognosy and Phytochemistry 8: 2145-2148.

Shinozaki K, Yamaguchi-Shinozaki K (1996) Molecular responses

to drought and cold stress. Current Opinion in Biotechnology 7:

161-167.

Shinozaki K, Yamaguchi-Shinozaki K (2007) Gene networks

involved in drought stress response and tolerance. Journal of

Experimental Botany 58: 221-227.

Slafer GA, Satorre EH, Andrade H (1994) Increases in grain yield

in bread wheat from breeding and associated physiological

changes. In Slafer GA (Ed). Genetic Improvement of Field Crops,

Marcel Dekker Inc., New York Pp. 1-67.

Somerville C, Briscoe J (2001) Genetic engineering and water.

Science 292: 2217.

Tuberosa R, Salvi S (2006) Genomics-based approaches to

improve drought tolerance of crops. Trends in Plant Science 11:

405-412.

Turner NC, Wright GC, Siddique KHM (2001) Adaptation of grain

legume (pulses) to water-limited environments. Advances in

Agronomy 71: 193-231.

Vadez V (2014) Root hydraulics:The forgotten side of roots in

drought adaptation. Field Crops Research 165: 15–24.

Waliyar F, Reddy SV, Subramaniam K, Reddy TY, Krama D,

Craufurd PQ, Wheeler TR (2003) Importance of mycotoxins in

food and feed in India. Aspects of Applied Biology 68: 1–8.

Wright GC, Nageswara Rao RC (1994) Groundnut Water

Relations. In Smartt JT (Ed). The Groundnut Crop, Springer,

Dordrecht Pp. 281- 335.

Yamaguchi-Shinozaki K, Urao T, Shinozaki K (1995) Regulation

of genes that are induced by drought stress in Arabidopsis

thaliana. Journal of Plant Research 108: 127-136.

Yordanov I, Velikova V, Tsonev T (2000) Plant Responses to

drought, acclimation, and stress tolerance. Photosynthetica 38:

171-186.

Zhang JZ, Creelman RA, Zhu JK (2004) From laboratory to field.

Using information from Arabidopsis to engineer salt, cold, and

drought tolerance in crops. Plant Physiology 135: 615-621.



ISSN No. 2320 – 8694


IMPACT OF FEED SUBSIDY REMOVAL ON THE ECONOMIC SUCCESS OF

SMALL RUMINANT FARMING IN NORTHERN BADIA OF JORDAN

Ja’far Mansur Al-Khaza’leh*

Faculty of Agricultural Technology, Al-Balqa Applied University, P.O. Box 19117, Al-Salt, Jordan

Received – February 12, 2020; Revision – March 15, 2020; Accepted – March 28, 2020



ABSTRACT

The small ruminant sector contributes substantially to food security and the livelihoods of farmers in

Jordan. Subsidy on feed for the livestock sector is considered to be an important policy for maintaining

feed prices low, thus improving the livelihood of the households. This study aimed to evaluate the effect

of feed subsidy removal on the economic success of small ruminant production in two production

systems in northern Badia of Jordan. Data was collected by questionnaire survey administered to 120

small ruminant farmers. Gross margin (GM1 and GM2) and net benefit (NB) were used to measure the

economic success of small ruminant production. GM1 constitutes cash revenue of sheep and goats,

while GM2 additionally includes in-kind benefits (meat and milk consumption). NB comprises cash

revenue, in-kind benefits and intangible benefits (insurance and finance). The feed price was

significantly affected by feed subsidy removal (p<0.05). Feed subsidy removal negatively (p<0.05)

affected all the parameters of the economic success of small ruminant production. GM1, GM2 and NB

per farm with subsidized feed prices were significantly (p<0.05) higher than farms without subsidized

feed prices in both production systems. A 36 % of farms with feed subsidy had a negative NB compared

to 54% of farms without feed subsidy. Findings of current study revealed that under conditions of forage

shortage in rangelands and high feed prices, feed subsidy removal has a negative impact on the

parameters of the economic success of small ruminant production and can threaten the income and

livelihoods of farmers. Keeping subsidies on the feed aimed at livestock keepers is a good policy to

alleviate the detrimental impact on households.


KEYWORDS

In-kind benefits

Intangible benefits

Gross margin

Net benefit

Economic success

E-mail: [email protected] (Ja’far Mansur Al-Khaza’leh)


















Impact of feed subsidy removal on the economic success of small ruminant farming in northern Badia of Jordan 194

1 Introduction

In Jordan, sheep and goats play a considerable role in the

economy, food security and livelihoods of farmers of rural areas

(Al-Atiyat & Tabbaa, 2009; Al-Atiyat, 2014). Small ruminants are

well adapted to the arid and semi-arid conditions of the country

representing the highest proportion of livestock biomass, with an

estimated population of about 3,063,120 head of sheep and

772,670 head of goats in 2017 (Department of Statistics, 2017).

Small ruminants are raised under different production systems

(Abu-Zanat et al., 2005; Alrousan, 2009). Two major production

systems which used in the country are: transhumant production

system (pastoral) which is the prevalent production system and the

sedentary production system (agro-pastoral), which is practiced in

the villages and peri-urban parts of the country.

A sufficient provision of feed is necessary for livestock to ensure

their production and health status. The scarcity of water resources,

shortage of rangeland and feed are major constraints for small

ruminant production in Jordan (Al-Karaki & Al-Momani 2011; Al-

Khaza’leh et al., 2015a). In Jordan, pastures with low nutritional

quality and poor biomass are inadequate for feeding small ruminant

animals (Obeidat et al., 2014). Therefore, animals are supplemented

with concentrates which are a shortage and costly in order to meet

their nutrient needs (Alshdaifat & Obeidat, 2019). Moreover, the

total yield of rained and irrigated barley production as a major

concentrates in Jordan was 394,277 tons in 2016 (Ministry of

Agriculture, 2016) which is not enough to feed livestock. Therefore,

the Jordanian government depends on importing feedstuff from other

countries, which in turn has a significant impact on Jordan's GDP

(Ministry of Industry Trade and Supply, 2018).

Price subsidy on feed is a prevalent policy in many developing

countries like Jordan. The price of feed in Jordan for the livestock

sector had been subsidized by the government for many years.

Nevertheless, the subsidy on feed is one of the most existing volatile

agro-economic policies in Jordan impacting its budgetary. The feed

subsidy is an important instrument for the livelihood of farmers.

Taking into account that feed price in Jordan is the greatest variable

expenses in the sheep and goat industry, stopping feed subsidy could

have a considerable effect on livestock production, incomes,

expenses, profitability, the economic performance of small ruminants

and thus on the sustainability of sheep and goat farming. The

economic consequences of feed subsidy removal on sheep and goat

farming were not adequately investigated in Jordan and hence there

is a need for in-depth analysis including cost-benefits to determine

the impact of subsidy removal on sustaining small ruminant

production. In the light of this background, the objectives of the

present study were to evaluate the economic performance of small

ruminants and to analyze the impact of feed subsidy lifting on the

economic success of small ruminant production in two production

systems of Jordan.


2.1 The study area and production systems

The study was carried out in the northern Badia of Jordan east of

the Mafarq governorate. The term "Badia" refers to desert or arid

region, dwelled by Bedouins or Badu. The northern Badia

constituted 36% of the total area of Jordanian Badia (km2=71,474

which constitutes 80% of the total area of Jordan). The study area

is characterized by transhumant production system (pastoral, the

herders practice two major patterns of seasonal mobility: toward

east ‘‘al tashreeg’’ to benefit from grazing on the vegetation during

late winter and early spring and back toward west ‘‘al taghreeb’’

after that) and sedentary production system (agro-pastoral). The

northern Badia has arid climate and it has been affected frequently

by drought cycles, receiving a total annual rainfall of about 116

mm. Therefore, water and pasture biomass availability in the study

area are low.

Sheep and goats are the predominant animal species representing the

major small ruminant production systems in the northern Badia.

According to secondary data obtained from the study region's

agricultural offices in 2017, the livestock population in the study area

is estimated to be about 366,940 head of sheep, 61,210 head of goats,

3,020 head of cattle, 1,130 head of camels. The same secondary data

showed that in 2017, the total population of sheep and goats keepers

was 3,150. For the same year, the small ruminant population in the

study area constituted 12% sheep and 7.9% goats of the total

population of sheep and goats in Jordan, respectively.

2.2 Sampling and data collection

For this study, a list of all small ruminant keepers and their

livestock holdings in each village within administrative unit of

the study area was obtained from local officials of the Ministry

of Agriculture. Farmers, who kept at least 10 adult animals in the

flocks, were identified from the list of farmers and systematic

random sampling at the third name was used to select 120

farmers for the interview by the assistance of key informant. In

general, 5 farmers were replaced by the next one during

conducting the actual questionnaire survey. Accordingly, a

sample of 62 farms was assigned for transhumant production

system and 58 farms for sedentary production system. Data were

collected between December 2017 and March 2018 using a

structured questionnaire format. Suitability of questionnaire

survey to the farmers in the study area regarding the language

and the logical flow was assured before conducting the actual

questionnaire survey.



195 Al-Khaza’leh

.

The questionnaires captured information regarding socio-economic

variables, livestock holdings, animal productivity, input and output

parameters and annual production costs and revenues generation in

the last 12 months. In addition to the survey method, secondary

data on the number of small ruminants and small ruminant keepers,

water and feed prices were obtained from water and agricultural

offices of the study area. Moreover, climate data including amount

of rainfall and temperature in the study area were acquired from

Meteorological Department of Jordan.

2.3 Economic performance of small ruminant production

The major variable costs (VC) from small ruminant production

comprised purchased feedstuff cost, stubble grazing cost, water cost,

veterinary cost, hired labor cost and transportation cost. Stubble

grazing cost included fees for rented land for grazing. Hired labor

cost included the wage payment given for either the continuous or

seasonal herders. Transportation costs comprised costs for water,

feed and animal transport. The annual costs of housing and

machinery were not considered in the study due to depreciation and

simplicity of material used in animal housing.Veterinary costs

included expenses on medications and vaccinations.

In the present study, the cash revenues (CR) from small ruminant

production included the sale of animals, milk and dairy products. For

home consumption, the unit price of each product was estimated

based on the farm gate price. In-kind benefits (IK) comprised the

values of meat and milk consumption. The monetary values of

manure, wool and hair were negligible and not considered in the

calculations because most of farmers in the study area did not market

or use them. Intangible socio-economic benefits of goat production

included financial (F) and insurance (I).The financial benefit of flock

per household was valued as follows: (number of animals owned X

respective market price of animals) X interest rate of the finance

(3.6%, based on Central Bank of Jordan monthly reviews

2016/2017). The insurance value of flock per household was

estimated as follows: (average monetary values of the flock per

household) X the insurance factor (3.6%, based on Central Bank of

Jordan monthly reviews 2016/2017).

The economic parameters (in JD farm−1

year−1

) were valued by

using the following equations:

GM1 = CR – VC (1)

GM2 = (CR+IK) – VC (2)

NB = (CR+ IK+F+I) – (VC) (3)

Where: GM is the gross margin, NB is the net benefit. Other

acronyms are previously defined. All economic parameters are

given in JD (Jordanian Dinar) whereby 1 JD equivalent to 1.4 US

dollars in the year 2017.

2.4 The policy of feed subsidy

The government allocates the subsidy in the rate of 10 to 15 kg per

head per month for sheep and goat owners who have a

certificate/fodder card (indicates the share of subsidized feed based

on the livestock holdings). All the farmers interviewed had the

fodder card and received the benefit from fodder subsidies. The

value of subsidy was calculated as the difference between the

actual selling price of feed to farmers and the international market

price. In the present study, the subsidized price for barley and

wheat bran was 175 JD/ ton and 77 JD/ ton, respectively, while the

unsubsidized market price for barley and wheat bran used for

calculations was 235 JD/ ton and 140 JD/ ton, respectively, in

2017. Thus, the percentage of the subsidized price for barley and

wheat bran was 25% and 45%, respectively, below the

international market price. The average percentage difference of

barley and wheat bran was added for unsubsidized feed and used

for calculation.


Households' socio-economic characteristics and flock ownership

were descriptively summarized by their frequencies, means and

percentages. Chi-square test for proportions of categorical

variables and the t-test for continuous variables were applied after

checking the normal distribution of residuals to compare the two

production systems. The variable costs, revenues and economic

efficiency parameters were not normally distributed. Hence, the

Wilcoxon-Mann-Whitney test (Bergmann et al., 2000) was

employed to detect significant differences in the mean values of

variables between subsidized and unsubsidized feed and among

transhumant and sedentary systems. The analyses were performed

using SAS version 9.3 (SAS Institute, 2012). For providing overall

ranking of purposes of keeping small ruminants, indices were

calculated and ranks were based on the first five choices of priority

characteristics in order of importance by respondent (i.e. 5=

highest importance, 1= lowest importance) and were calculated as

: index = sum of [5 for rank 1+ 4 for rank 2+3 for rank 3+ 2 for

rank 4 + 1 for rank 5 for specific purpose] divided by the sum [5

for rank 1+ 4 for rank 2+3 for rank 3+ 2 for rank 4 + 1 for rank 5]

for all purposes of keeping small ruminants.

3 Results

3.1 Household characteristics

Small ruminant owners indicated that all households were male-

headed, with average age of 49 years, average household size of 10

members, and average experience of 36 years in livestock

husbandry. The majority of the respondents (77.6%) in the

sedentary system depend on family labor for herding and caring

animals while the transhumant systems (45.2%) had more either




continuous or seasonal labor. The illiteracy rate among the

household heads was higher in the transhumant (51.6%), while the

sedentary system had more educated household heads (75.9%).

The majority of farmers kept mixed species of sheep and goats,

and sheep was the most dominant livestock species owned by the

majority of farmers. The local Awassi breed is the major and

abundant sheep breeds in the study area and few farmers keep

Naemi and Najdi sheep breeds. For goats, the major goat breeds

prevailing in the study area were northern desert goat followed by

Mountain Black and crossbreds. The proportion of households kept

sheep was higher in the transhumant than in the sedentary system

(100% vs 81%). The present study also recorded a significantly

higher (P <0.05) number of sheep in the transhumant compared to

the sedentary system (468 vs. 170 head), but a marginal difference

(P= 0.0861) was observed for goat flock size (74 vs. 45 head)

However, the overall small ruminants flock sizes were

significantly higher in transhumant than in the sedentary (P<0.05).

3.2 The benefit of keeping small ruminants

The reasons for keeping small ruminants are shown in Figure 1.

Though most households kept sheep and goats for multiple

purposes, the highest benefit from keeping of sheep and goats was

cash generating followed by savings, milk production, meat

production for home consumption, and manure.

The estimated monetary values of small ruminant benefits to the

households in each production system are presented in Table 1. The

highest benefit from keeping sheep and goats in the surveyed

households was from live sales of animals, followed by meat

consumption, insurance, finance, and milk consumption. Economic

benefits from milk consumption were the lowest in the two

production systems. Small ruminants slaughtered and meat

consumed by households or used for social events or sacrificed

during religious festivities. As a livelihood strategy of households to

increase cash liquidity of sheep lambs and goat kids of less than one

year were sold most often, followed by mature males while mature

females had a lower share of sales. The average annual off-take rate

for live sale of small ruminants was significantly (P<0.0001) higher

in the transhumant system than the sedentary system.

3.3 The cost of keeping small ruminants

Costs of small ruminant keeping among the production systems are

presented in Table 2. The total variable costs among production

systems varied significantly (P<0.05). A significantly (P<0.05)

higher feed costs in the transhumant than in the sedentary systems

were observed and the feed costs represented the largest share of

the total variable costs in the transhumant (74.8%) and

sedentary(75.1%) systems. However, the difference among

systems in the costs of rented land for grazing was not significant

(P>0.05). Water costs were not significantly different among

production systems and accounted for the lowest share (1.2% vs

2.2%) of the total variable costs in the transhumant and sedentary

systems, respectively. The other variable costs, namely veterinary,

transportation and hired labor were significantly (P<0.05) higher in

the transhumant than in the sedentary systems.

Figure 1 Reasons for keeping small ruminants and the ranking of the

importance of these reasons (Index based on rank weights of the

choices of priority characteristics i.e. 5= highest importance).

0.000.050.100.150.200.250.300.35

Ran

k In

de

x

Goat Sheep

Table 1 Estimated small ruminant benefits from live sales, meat and milk consumption, and intangible functions to the households in two

production systems of Jordan in the year 2017.

Benefits Production systems

Transhumant(n=62) Sedentary(n=58)

Value (JD) % of total Value (JD) % of total

Live sales 2,060,500 71.4 700,950 69.5

Milk 146,771 5.1 48,588 4.8

Meat 206,150 7.1 105,300 10.4

Financial 212,292 7.4 68,220 6.8

Insurance 260,520 9.0 85,922 8.5

Total 2,886,233 100 1,008,980 100

JD = Local currency (1JD = 1.4 USD in 2017)



197 Al-Khaza’leh

.

3.4 Economic efficiency of small ruminant keeping

The economic parameters per flock and head among production

systems are presented in Table 3. All the economic parameters

evaluated in the present study were similar among systems,

except for GM1 per head where it was significantly (P<0.05)

higher in the transhumant compared to the sedentary system. In

the transhumant system, 58% and 47% of farmers had negative

values of GM1 and GM2 per head compared to 62% and 45%

of farmersin the sedentary system,respectively.Thirty-one

percent and 33 % of the farmers in the transhumant and

sedentary systems, respectively, had a negative NB per head.

3.5 Feed subsidy removal

The average of the unsubsidized feed price per farm (n=120) was

significantly (p<0.05) higher than the subsidized feed price (n=120).

The overall median and mean of the subsidized feed price were

10,200 JD and 18,049 JD, respectively while the overall median and

mean of the unsubsidized feed price were 13,770 JD and 24,366 JD,

respectively. If the feed subsidy was removed, the share of feed cost

would increase from 75% to 80% of the total variable costs.

The values of feed variable costs per farm with subsidy retained

and without subsidy in two production systems are presented in

Table 4.The average of the subsidized and unsubsidized feed price

Table 2 Variable costs of small ruminant rearing in two production systems of Jordan in the year 2017

Parameters (JD) Production systems

Transhumant(n=62) Sedentary(n=58) P-value

Mean Median Mean Median

Feed costs 26,343.2 15,000.0a 9,183.1 4,800.0b <.0001

Grazing costs 1,881.0 0.0a 795.3 97.5a 0.8586

Water costs 428.8 37.5a 266.2 120.0a 0.4009

Veterinary costs 530.9 240.0a 259.4 120.0b 0.0001

Transportation costs 3,401.5 2,400.0a 1,102.8 420.0b <.0001

Hired labor cots 2,614.8 0.0a 625.3 0.0b 0.0002

Total variable costs 35,200.1 19,610.0a 12,232.1 6,180.0b <.0001

JD = Local currency ([1JD = 1.4 USD] in 2017); Pairs of variables per flock in the transhumant and sedentary systems with different

superscripts are statistically significant.

Table 3 Economic efficiency of small ruminants rearing in two production systems of Jordan in the year 2017

Parameters (JD) Production systems

Transhumant(n=62) Sedentary(n=58) P-value


Total variable costs/head 62.7 61.5a 58.8 53. 0a 0.2383

GM1/flock 401.0 -1,845.0a 691.0 -902.5a 0.6031

GM1/head -2.6 -6.0a -14.3 -12.0b 0.0483

GM2/flock 3,726.0 70.0a 2,506.5 101.0a 0.9686

GM2/head 4.9 0.0a -2.5 2.5a 0.2134

NB/flock 11,352.1 3,151.5a 5,164.1 931.5a 0.1910

NB/head 18.7 14.0a 10.4 15.5a 0.1629

GM = gross margin, NB = net benefit, JD = Local currency ([1JD = 1.4 USD] in 2012.); Pairs of variables per flock/head in the transhumant

and sedentary systems with different superscripts are statistically significant

Table 4 Purchased feed cost per farm with subsidy retained and without the subsidy of the different production systems in the year 2017

Variable

Production system

Transhumant (n=62) Sedentary (n=58)

Mean (JD) Median Mean (JD) Median

Subsidized 26,343.2 15,000.0a 9,183.1 4,800.0b

Unsubsidized 35,563.3 20,250.0a 12,397.2 6,480.0b

JD = Jordanian Dinar (1JD ≈1.4 USD in 2017); Medians with different superscripts in the rows differ significantly (p<0.05)




per farm in the transhumant system was significantly (p<0.05)

higher than subsidized and unsubsidizedfeed price in the sedentary

system, respectively.

3.6 Feed subsidy removal and economic efficiency of small

ruminant keeping

The analysis showed that all economic efficiency parameters per

farm with feed subsidy retained and without feed subsidy were

significantly different (Table 5). The percentage of farms with

subsidized feed prices that had more often a negative GM1

(GM1>0) is apparently lower (60%) compared to 76% of farms

with unsubsidized feed prices. Regarding the GM2 parameter, 47%

of farms with subsidized feed had a negative GM2 (GM2>0)

compared to 68% of farms with unsubsidized feed prices. GM1

and GM2 per farm with subsidized feed prices were significantly

higher than farms with unsubsidized feed prices (p<0.05).

The cost-benefit ratio per farm with subsidized feed prices was

significantly (p<0.05) higher than farms with unsubsidized feed

prices. Thirty-sixpercent of the farms with subsidized feed prices had

a negative NB (NB>0) compared to 54% of farms within subsidized

feed prices. NB per farm of subsidized feed prices was significantly

(p<0.05) higher than those of farmers of unsubsidized feed prices.

4 Discussion

Nutrition in livestock production systems is essential and

economically crucial. In Jordanian small ruminant farming,

nutrition is critical because the share of feed cost constituted the

major part of total variable costs impacting productivity and

decreasing profitability (Obeidat & Shdaifat, 2013; Al-Khaza’leh

et al., 2015b; Obeidat, 2018). In 2017, feed shared the biggest

(94%) portion of the total physical input values used in small

ruminants production in Jordan (Department of Statistics, 2017)

The higher of the subsidized and unsubsidized feed cost observed

in the transhumant system than those in the sedentary system in the

present study was probably due to the differences in average flock

size per farm between systems (483 in transhumant vs 172 in

sedentary). The lower of the subsidized feed cost compared to

unsubsidized feed cost within the production systems indicating

the large gap between the actual subsidized selling price of feed

and the free unsubsidized market price. A previous analysis study

(Kristjanson & Tyner, 1992) showed that the effects of subsidy

removal would be much larger on livestock when the feed cost

forms the biggest portion of the total costs.

In this study, although the Jordanian government provides farmers

feed subsidy, the feed cost accounted for the biggest share (75%)

of the total variable costs, the averages of benefit-cost ratio in both

systems of farms without feed subsidy seemed to be not

economically acceptable. The economic success parameters (GM1,

GM2, and NB) of farms without feed subsidy in the transhumant

and sedentary systems would be more negatively affected

compared to subsidized farms. GM1, GM2 and NB values of small

ruminant production were more negative for most farmers with

unsubsidized feed prices compared to those with subsidized feed

prices in the present study, which was mainly due to high variable

costs in general and feed cost in particular.The higher economic

efficiency in terms of GM1 per head observed in the transhumant

system is mainly due to better animal market accessibility and

subsequently higher selling prices of animals in this system as

compared to the sedentary system. Moreover, keeping large flock

size with high off-take rate in the transhumant system compared to

sedentary did hardly influence GM1 per flock, probably due to

high variable costs to maintain larger flock sizes. However, there

were farms that remain profitable even without subsidy, this could

be ascribed to adopt technical strategies and managerial choices

such as keeping high productive and fertile sheep or goat breeds,

keeping large flock size, improving production level, feed self-

sufficiency and adequate market accessibility.

The economic efficiency reported in this study would probably be

increased in two production systems by reducing feed price,

retaining feed subsidy, keeping larger flock size and increasing off-

take rate. Owning larger flock size increases the total cash

revenues, utilization of more products from animals, in-kind

benefits and intangible socio-economic benefits of small ruminants

Table 5Economic efficiency of small ruminantsper farm with feed subsidy retained and without feed subsidy in the year 2017

Feed

Parameter (JD) Subsidized (n=120) Unsubsidized (n=120)


GM1 541.2 -1,242.0a -5,776.0 -3,902.5b

GM2 3,136.6 95.0a -3,180.6 -2,422.5b

NB 8,361.2 1,963.0a 2,044.0 -311.5b

GM = gross margin, NB = net benefit, Pairs of variables of economic parameters with feed subsidy retained and without subsidy with

different superscripts differ significantly (p<0.05); JD = Jordanian Dinar (1JD ≈1.4 USD in 2017)



199 Al-Khaza’leh

.

and in turn the economic success and food security of households.

If feed subsidy is fully removed, the negative economic

implications are expected to be aggravated in the future in both

production systems especially during seasons when the vegetation

on the rangeland at minimum. A previous study (Abu Zanat &

Tabbaa, 2004) showed that drought and removal feed subsidies

negatively impacted the small ruminant production. In 1996, the

Jordanian government suddenly removed the subsidy on livestock

feed, causing the price of feed to jump from 85 USD to around 130

USD per ton (Al-Tabini et al., 2012). Increasing feed price is

usually opposed by farmers and should be usually evaluated by the

government prior to an increase in feed pricing. However, the

government sometimes reduce the percentage of subsidized price

for feed and justifies that to the manipulation in having actual

livestock holdings by farmers (having fake holdings) e.g. due to

the exporting the livestock to neighbour countries, slaughtering the

animals during the religious festival (Eid Aldha), normal occasions

and mortalities during the year.

Different coping strategies have been suggested by farmers to

reduce risks related tothe problem of feed subsidy removal. These

include reducing the quantity of feed supplied, using low quality

fodders, changing ration composition or reducing the flock size. In

1996, when the Jordanian government removed the subsidy on

livestock feed, the livestock owners in the Badia region adapted to

that situation by reducing the flock size by selling some animals to

increase cash liquidity in order to feed the rest of the flock which

in turn the family income affected adversely (Al-Tabini et al.,

2012). Similarly, Jetter (2008) reported that, as a consequence of

feed subsidy removal, many farmers in the Northern Badia of

Jordan sold off their whole flock or reduced their flock sizes to

feed the rest of the flocks. The reduction in the number of livestock

was closely linked to feeding subsidy removal. During the period

of time from 1996 to 1999, the goat and sheep population

apparently decreased as a consequence of feed subsidy lifting and

drought then started to rise after retaining the subsidy. This caused

a further drop in the market price of animals due to the oversupply

in the market and a substantial rise in production costs, and

consequently a decrease in the profitability of livestock production.

Moreover, due to high feed cost as a result of feed subsidy removal

and shortage of rangeland, farmers reduced the share of feed

assigned for each head, which had a negative impact on health,

reproductive efficiency and productivity of livestock (Jetter, 2008).

Furthermore, nutritional qualities and composition of animal feed

rations could also be affected. The ingredients used in producing a

well-balanced and high energy ration will be replaced with other

low-quality ingredients, and in turn, lower feed conversion

efficiency. It is most profitable for farmers to continue to offer a

feed with low-cost ingredients which have a negative impact on

livestock health and production due to malnutrition.

Conclusions

This study revealed that under prevailing conditions of forage and

rangeland shortage in Jordan, the impact of fully feed subsidy

elimination for the livestock sector would aggravate the problem

due to fact that feed cost constitutes 75% of the total variable costs

and dynamic increase in the market prices of feeds. All the

parameters of the economic efficiency of small ruminant

production were found to be different between farms with subsidy

retained and without feed subsidy among production systems.

Hence, removing the subsidies is likely to exacerbate the

inefficiency in the small ruminant production by accentuating feed

inadequacy. However, the variation in feed purchasing and the

consequent variation in animal productivity after the variation of

feed market price should be considered in analysis of farm

efficiency. Technical strategies and managerial choices such as

keeping high productive sheep or goat breeds, increasing flock

size, improving production level, better market accessibility and

feed self-sufficiency are required to sustain small ruminant

production without feed subsidy.

Acknowledgments

Funding the research (107039/2017) by Deanship of Scientific

Research at Al-Balqa Applied University is greatly appreciated.

All contributions provided by the northern Badia people in data

collection are gratefully acknowledged.


The authors declare that they have no conflict of interest.

References

Abu-Zanat MM, Migdady HA, Tabbaa MJ (2005) Production

systems of small ruminants in middle Badia of Jordan.Dirasat:

Agricultural Sciences 32:205-214.

Abu Zanat MMW, Tabbaa M (2004) Effect of drought on feed

resources and performance ofsmall ruminants in the Northern

Badia of Jordan. Dirasat: Agricultural Sciences32:347-354.

Al-Atiyat RM (2014) Role of small-scale dairy sector in food

security and poverty alleviation. Food, Agriculture and

Environment12:427-433.

Al-Atiyat RM, Tabbaa MJ (2009) Role of livestock in poverty

alleviation and food security: A review study. In: Proceeding

of Jordan Society of Scientific Research, Amman, Jordan.

Al-Karaki GN, Al-Momani N (2011) Evaluation of Some Barley

Cultivars for Green Fodder Production and Water Use Efficiency

under Hydroponic Conditions. Jordan Journal of Agricultural

Sciences 7:448-457.




Al-Khaza’leh J, Reiber C, Al Baqain R, Valle Zárate A (2015a)

Drinking water sources, availability, quality, access and utilization

for goats in the Karak Governorate, Jordan. Tropical Animal

Health and Production 47:163-169.

Al-Khaza’leh J, Reiber C, Al Baqain R, Valle Zárate A (2015b) A

comparative economic analysis of goat production systems in Jordan

with an emphasis on water use. Livestock Research for Rural

Development 27 (5), http://www.lrrd.org/lrrd27/5/khaz27081.html,

accessed on 01 February 2020.

Alrousan L (2009) Goat production in Jordan. Proc. the 24th Ann.

Goat Field Day, Langston Uni., Oklahoma, USA, pp. 33-40.

Alshdaifat SN, Obeidat BS (2019) The impact of feeding corn

dried distillers grains with solubles on milk yield and composition

in lactating Awassi ewes and digestibility and N partitioning in

Awassi ewe lambs. Italian Journal of Animal Science 18:522-529.

Al-Tabini R, Al-Khalidi K, Al-Shudiefat M (2012)Livestock,

medicinal plants and rangeland viability in Jordan’s Badia, through

the lens of traditional and local knowledge. Pastoralism: Research,

Policy and Practice 2: 1-16.

Bergmann R, Ludbrook J, Spooren WPJM (2000) Different

outcomes of the Wilcoxon-Mann-Whitney test from different

statistics packages. The American Statistician 54: 72-77.

Central Bank of Jordan (2017) Publication and statistics, statistical

database, interest rate structure for 2016/2017. Amman. Jordan.

Department of Statistics (2017) Statistics by sector, agriculture,

surveys, livestock for 2016-2017.http://www.dos.gov.jo, accessed

on 01 March 2019.

Jetter C (2008) An assessment of subsidy removal effects on and

future sustainability for livestocksector of in the Northern

Jordanian Badi. Independent Study Project (ISP) Collection. Paper

7.2019.https://digitalcollections.sit.edu/isp_collection/7/, accessed

on 01 October.

Kristjanson P, Tyner WE (1992) Impacts of feed ingredient

subsidy removal and concurrent trade liberalization in Tunisia.

Agricultural Economics 7: 331-339.

Ministry of Agriculture (2016) Statistical annual report 2016,

Amman, Jordan.

Ministry of Industry Trade and Supply (2018) Annual report 2018,

Amman, Jordan.

Obeidat BS (2018) Influence of corn-dried distiller’s grain with

solubles on growth performance and blood metabolites of Awassi

lambs offered a concentrate diet. Italian Journal of Animal

Science17:636-642.

Obeidat BS, Awawdeh MS, Kridli RT, Al-Tamimi HJ, Ballou MA,

Obeidat MD, Abu Ishmais MA, Al-Lataifeh FA, Subih HS (2014)

Feeding corn silage improves nursing performance of Awassi ewes

when used as a source of forage. Animal Feed Science and

Technology 192:24-28.

Obeidat B, Shdaifat M (2013) Partial substitution of barley grain

with ProsopisJuliflora pods in lactating Awassi ewes’ diets: effect

on intake, digestibility, and nursing performance. Small Ruminant

Research 111:50-55.

SAS Institute (2012) Base SAS® 9.3 Procedures Guide: Statistical

Procedures, Second Edition. SAS Institute Inc, Cary, NC, USA.

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