Symbiotic Nitrogen Fixation in Common Bean - University of ...

Symbiotic Nitrogen Fixation in Common Bean

By

Mehdi Farid

A Thesis

presented to

The University of Guelph

In partial fulfilment of requirements

for the degree of

Doctor of Philosophy

in

Plant Agriculture

Guelph, Ontario, Canada

© Mehdi Farid, October, 2015

ABSTRACT

SYMBIOTIC NITROGEN FIXATION IN COMMON BEAN

Mehdi Farid Advisors:

University of Guelph, 2015 Dr. Ali Navabi

Dr. K. Peter Pauls

Despite its inherent symbiotic N2-fixing (SNF) ability, the common bean (Phaseolus

vulgaris L.), compared to other legumes, is generally known as a weak N2-fixer. The objectives

of this thesis were to examine the genetic variation, environmental and genotype by

environmental effects, and response to selection for SNF and related traits, as well as to identify

genomic regions and candidate genes underlying SNF in common bean. Significant variation

was found among bean genotypes for percentage of nitrogen derived from atmosphere (%Ndfa),

a measure of SNF, and related traits. In studies of a recombinant inbred line (RIL) population,

derived from a cross of high- and low-SNF genotypes, while SNF-dependent and fertilizer-

dependent N management strategies did not affect the overall yield, genotypes responded

differentially to N management across environments. Among the RILs, 6% maintained a stable

yield, independent of N management strategies. Estimates of heritability and genetic gain were

higher for %Ndfa and related traits in optimum moisture environments. Genotypes with longer

vegetative growth had higher SNF ability. Quantitative trait loci (QTL) analysis detected 42

QTL for %Ndfa and related traits. In spite of the significant Genotype by environment

interactions, a QTL was detected on Pv08 that was significantly associated with %Ndfa across

environments, accounting for up to 17% of variation in %Ndfa. Another QTL was identified on

Pv07, associated with %Ndfa only in the dry environments, which accounted for 14% of the

phenotypic variation. This QTL was in close linkage with a QTL for δ13

C, a measure of water

use efficiency. Close genetic association between %Ndfa and δ13

C QTL, either in the dry or

optimum rainfall conditions, conformed to the positive association between SNF and water use

efficiency, which emphasizes the importance of nodulation early in the growing season for

efficient SNF in common bean. Three potential candidate genes were detected in the %Ndfa

QTL region on Pv08, with potential roles in SNF.

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ACKNOWLEDGEMENTS

First and foremost, I would like to offer my sincerest gratitude to my supervisor, Dr. Ali

Navabi, for his support and supervision of this work. My thanks also extend to Dr. K. Peter

Pauls, my co-advisor, for his care and guidance. I acknowledge the extensive and critical review

of this work provided by members of the advisory committee, Dr. Hugh J. Earl, Dr. Istvan

Rajcan and Dr. John Lauzon. I also acknowledge Dr. Dave Hume for helpful guidance.

I wish to gratefully acknowledge the technical assistance of Tom Smith, Alberto Aguilera,

Terry Rupert, Peter Smith, Clarence Gilbertson, Benjamin Ellert, Jan Brazolot, Godfrey Chu,

Dietmar Scholz, Donna Hancock, BaiLing Zhang, Anastasia Chechulina, Katie Keenan,

Melanie Wolters, Allie Core, Katie Caldecott, Josh Good, Melanie Wolters-Inksetter, Melinda

Drummond, Sarah McClymont, Kristina Dydensborg, Emily Lynn, Arad Moghaddasi, Raja

Khanal, Weilong Xie, Andrew Burtt, Mohammad Erfatpour, and YanZhou Qi. I also appreciate

Jim Hoare and Mike H. Peppard for all their supports.

The financial support of the project provided by Agriculture and Agri-Food Canada, the

Ontario Bean Growers, the Ministry of Research and Innovation and the Agriculture Adaptation

Council of Canada are kindly acknowledged.

Last, but not the least, I thank my wife and daughter for supporting me through all my

studies and for providing a home, in which to complete my thesis write up.

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TABLE OF CONTENTS

CHAPTER 1 General Introduction

1.1 General introduction………………………………………………………………………….2

1.2 Hypotheses ............................................................................................................................... 4

1.3 Research Objectives ................................................................................................................. 5

CHAPTER 2 Literature Review

2.1 Nitrogen (N) …………………………………………………………………………………7

2.2 Biological nitrogen fixation……………………………………………………..……………9

2.2.1 Free-living N2 fixing organisms…………………………………………………………..10

2.2.1.1 Aerobic, Azotobacter…………………………………………………..………………..11

2.2.1.2 Anaerobic, Clostridium and Klebsiella…………………………………………………12

2.2.2 Associative symbiosis relationship…………………………………………………….…13

2.2.2.1 Cyanobacteria…………………………………………………………………………...13

2.2.2.2 Azospirillum……………………………………………………………………...……..16

2.2.3 Symbiotic N2 fixation (SNF) ……………………………………………………………..17

2.2.3.1 Frankia………………………………………………………………………………..…17

2.2.3.2 Rhizobium………………………………………………………………………………19

2.3 BNF process………………………………………………………………………….……..20

2.4 SNF regulation……………………………………………………………….……………..22

2.4.1. Physiology regulation of BNF………………………………………………..…………..22

2.4.2 Host-plant regulation of SNF……………………………………………………………..24

2.4.3 Genetic Regulation of SNF………………………………………………………...……..28

2.4.3.1 Genes controlling SNF and nodule formation…………………………………...…...…28

2.4.3.2 Genes controlling infection and nodule development………………………...…...……32

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2.4.4 Environmental regulation of SNF……………………………………………...…………33

2.4.4.1 Soil moisture stress…………………………………………………………..………….33

2.4.4.2 Heat stress…………………………………………………………………………...…..34

2.4.4.3 Salinity stress……………………………………………………………………………35

2.4.4.4 Soil pH stress…………………………………………………………………..………..36

2.4.4.5 Macronutrients stress………………………………………………………..…………..38

2.4.4.5.1 Nitrogen………………………………………………………………………...……..38

2.4.4.5.2 Phosphorus………………………………………………………………...……...…..40

2.4.4.5.3 Potassium,Calcium and Sulfur………………………….……………………...……..41

2.4.4.6 Micronutrients…………………………………………………………………………..41

2.4.4.6.1 Boron………………………………………………………………………………….41

2.4.4.6 .2 Copper………………………………………………………………………………..42

2.4.4.6.3 Iron………………………………………………………………………………..…..42

2.4.4.6.4 Molybdenum…………………………………………………...……………………..43

2.4.4.6.5 Nickel………………………………………………………………………..………..43

2.4.4.6.6 Cobalt………………………………………………………………..………………..44

2.5 SNF estimation methods…………………………………………………………..………..44

2.5.1 N difference technique………………………………………………………………...….45

2.5.2 Acetylene reduction assay (ARA) …………………………………………..……………45

2.5.3 Relative ureide-abundance technique…………………………………………………..…46

2.5.4 Nitrogen-15 (15

N) isotope techniques…………………………………………………..…47

2.5.4.1 Total 15

N balance technique………………………………………………………….…48

2.5.4.2 15

N -enrichment isotope-dilution (ID) technique……………………………………….48

2.5.4.3 15

N natural abundance (NA) technique…………………………………...………..…...50

vii

2.6 SNF in common bean………………………………………………………………...……..52

2.6.1 Possibility of SNF improvement in common bean……………………………….………53

2.6.2 Relationships of SNF with agronomic traits……………………………………...………54

2.6.3 Molecular Tools in Common Bean……………………………………………….………56

2.6.4 SNP markers applications in common bean………………………………………………59

2.6.5 Quantitative trait loci (QTL) associated with SNF traits…………………………………60

CHAPTER 3 N2 Fixation Ability of Different Dry Bean Genotypes

3.1 ABSTRACT…………………………………………………………………………….…..66

3.2. INTRODUCTION…………………………………………………………………...……..67

3.3. MATERIALS AND METHODS…………………………………………………………..70

3.3.1 Plant materials…………………………………………………………………...………..70

3.3.2 Seedling nodulation assay………………………………………………………………...70

3.3.3 Greenhouse nodulation assay……………………………………………………………..72

3.3.4 Field N2 fixation assays…………………………………………………………………..73

3.3.5 Data collection …………………………………………………………………………...74

3.3.6 Statistical Analysis …………………………………………………………………….…76

3.4 RESULTS…………………………………………………………………………….……..78

3.4.1 Seedling Nodulation assays…………………………………………………………….....78

3.4.2 Greenhouse assays………………………………………………………………….……..78

3.4.3 Field assays………………………………………………………………………………..79

3.4.4 Seed yield…………………………………………………………………………..……..80

3.4.5 Nitrogen derived from atmospheric air (%Ndfa)……………………………………..…..80

3.4.6 Nitrogen Fixed per Unit Area……………………………………………………………..81

3.4.7 Seed nitrogen percentage………………………………………………………………….82

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3.4.8 Nodule weight and number…………………………………………………………...…..82

3.4.9 Carbon Discrimination (δ13

C)……………..…………………………………………..….82

3.4.10 Genotype by Trait Biplot and Trait Relationships……………………………………....83

3.5 DISCUSSION……………………………………………………………………………....84

CHAPTER 4 Yield Stability of Dry Bean Genotypes across Nitrogen Fixation

dependent and Fertilizer-dependent Management Systems

4.1 ABSTRACT………………………………………………………………………………..94

4.2 INTRODUCTION…………………………………………………………………………100

4.3 MATERIAL AND METHODS…………………………………………………………...102

4.3.1 Plant materials…………………………………………………………………………...102

4.3.2 Field trials………………………………………………………………………………..103

4.3.3 Nitrogen management treatments…………………………………………………….....104

4.3.4 Data collection……………………………………………………………………….….105

4.3.5 Statistical analysis…………………………………………………………………….....106

4.4 RESULTS………………………………………………………………………………….109

4.4.1 Mixed model analysis…………………………………………………………………....109

4.4.2 Multiplicative models of G by E interaction…………………………………………….109

4.4.3 Stability analysis………………………………………………………………………....111

4.5 DISCUSSION…………………………………………………………………………..…112

CHAPTER 5 Response to Selection for Improved Nitrogen Fixation in

Common Bean

5.1 ABSTRACT…………………………………………………………………………….…124

5.2 INTRODUCTION…………………………………………………………………………125

5.3 MATERIALS AND METHODS……………………………………………….…………127

ix

5.3.1 Plant Materials …………………………………………………………………………..127

5.3.2 Field trials…………………………………………………………………………….…128

5.3.3 Data Collection………………………………………………………………………….129

5.3.4 Statistical Analysis………………………………………………………………………131

5.4 RESULTS………………………………………………………………………………….135

5.5 DISCUSSION……………………………………………………………………………..137

CHAPTER 6 Quantitative Trait Loci for Symbiotic Nitrogen Fixation and Related

Traits in Common Bean

6.1 ABSTRACT……………………………………………………………………………….150

6.2 INTRODUCTION………………………………………………………………………....151

6.3 MATERIALS AND METHODS……………………………………………………...…..155

6.3.1 Plant materials…………………………………………………………………………...155

6.3.2 Growth-room assay…………………………………………………………………... ...155

6.3.3 Field assay…………………………………………………………………………….…156

6.3.4 Statistical Analysis……………………………………………………………………....159

6.3.5 Genotyping………………………………………………………………………………160

6.3.6 Linkage mapping and QTL analysis………………………………………………….…161

6.3.7 Candidate Gene Identification………………………………………………………..….162

6.4 RESULTS……………………………………………………………………………….…162

6.4.1 Phenotypic data analysis……………………………………………………………..….162

6.4.2 Linkage map……………………………………………………………………………..162

6.4.3 QTL mapping……………………………………………………………………………163

6.4.3.1 Chlorophyll concentration (SPAD)………………………………………………...….163

6.4.3.2 Nodule traits…………………………………………………………………………...163

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6.4.3.3 Flowering date…………………………………………………………………………164

6.4.3.4 Maturity………………………………………………………………………………..164

6.4.3.5 Common bacterial blight….…………………………………………………………...164

6.4.3.6 Carbon isotope discrimination…………………………………………………………165

6.4.3.7 Seed protein content…………………………………………………………………...165

6.4.3.8 Seed yield…………………………………………………………………………..….165

6.4.3.9 Nitrogen derived from atmosphere…………………………………………………….166

6.4.4 Repeatable and overlapping %Ndfa QTL…………………………………………….…167

6.4.5 %Ndfa Candidate genes search…………………………………………….…………....168

6.5 Discussion…………………………………………………………………………………170

6.5.1 Phenotypic data analysis………………………………………………………………...170

6.5.2 QTL study………………………………………………………………………………..170

6.5.3 Potential candidate genes for %Ndfa………………………….…………………………175

CHAPTER 7 General Conclusions and Future Directions

7.1 General Conclusions……………………………………………………………………….188

7.2 Future Directions………………………………………………………………………......192

LITERATURE CITED………………………………………………………………………194

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LIST OF TABLES

Table 3.1 Gene-pool origin, commercial market class, growth habit,

and specific characteristics of dry bean genotypes.....................………..………………......…88

Table 3.2 Soil properties of trial environments………………………………………………...89

Table 3.3 Nodule number and SPAD reading of bean genotypes in a

growth pouch assay…………………………………………………………………………… 90

Table 3.4 Comparison of nitrogen fixation related traits per plant

.among 12 bean genotypes…………………………….…………………………………...…...91

Table 3.5 Maximum, minimum, and mean temperature, total monthly

precipitation data………………………………………..………………………..….……..….92

Table 3.6 Comparing dry bean genotypes for seed yield and maturity………….……………..93

Table 3.7 Comparison among 12 dry bean genotypes for nitrogen

fixation traits in different field trials ……………………..………………………………..…..94

Table 3.8 Comparison of 12 bean genotypes for nitrogen fixation

related traits across field trials environments ………………………………………….......…..96

Table 4.1 Soil properties of trial environments …………..………………………….……….116

Table 4.2 Maximum, minimum, and mean temperature, total monthly

precipitation data……………………………………………………………..…….…...…….117

Table 4.3 Maximum, minimum and mean and 95% confidence

interval of lsmeans of SNF related traits of 140 recombinant inbred

lines population and under different n management systems…………………...…………....118

Table 4.4 F-test of fixed effect of entry and variance component

estimates (S2) and their standard error (Se) of random effects in

the mixed-model analysis in multiple environments……………………………………………………………...…..119

Table 5.1 F-test of the fixed effect of genotype and variance

component estimates (S2) and their standard error (Se) of random

effects in the mixed-model analysis under N management systems……..........…...…………143


estimates (S2) and their standard error (Se) of random effects in the

mixed-model analysis under SNF-dependent environments………...……………...….……..144

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Table 5.3 Minimum (Min), maximum (Max), mean, phenotypic

variance (σ2

p), genetic variance (σ2

g), heritability (h2) and its standard

error (Se), observed genetic advance (GAo) and expected genetic

advance (GAe)…………..…………………………………………………………………….145

Table 5.4 Phenotypic (rp) and genetic (rg) correlations……………………………………….146



mixed-model analysis in multiple locations field assays…..……………...…………...……..178



mixed-model analysis in growth room assay……..…………………………...…………...…179

Table 6.3 Quantitative trait loci (QTL) analysis for RIL population

derived from Sanilac × Mist cross…………………………….………………………………180

Table 6.4 Candidate genes detected for %Ndfa in a 40 bp in the genomic

region of significant and repeatable %Ndfa QTL……………………………….…....……....182

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LIST OF FIGURES

Figure 3.1 Genotype by Trait (GT) biplot …..……………………………………..………….97

Figure 4.1 Diagram of dendrogram generated from SHMM analysis for

grouping eight environments, under two different nitrogen (N) managements………………120

Figure 4.2 GGE biplot resulting from site regression analysis of average yield of

140 recombinant inbred lines (RIL) and two parental lines…………………………..……...121

Figure 4.3 Eberhart and Rusell yield stability model……………………………………..…..122

Figure 5.1 SNF relative efficiency index % (SNFRI) of 140 mapping population

and their parents tested across multiple location-years………………………….……………147

Figure 5.2 Genotype by Trait (GT) biplot…………………………………………..…….…..148

Figure 6.1 Maximum, minimum, mean (vertical solid line) and 95% confidence

interval (dashed lines) of least square means for SNF and related traits………………..…....183

Figure 6.2 Genetic linkage maps, QTL and corresponding marker(s)..………………………184

xiv

ABBREVIATIONS

BNF, Biological nitrogen fixation

δ13

C, Carbon discrimination

E, Environment effect

G, Genotype effect

G × E, Genotype by environment interaction effect

%Ndfa, Nitrogen derived from atmosphere

Nfix, Nitrogen fixed per unit area

N, Nitrogen

Ny, Nitrogen yield

PC, Principal component

QTL, Quantitative trait loci

SY, Seed yield

SNF, Symbiotic nitrogen fixation

WUE, Water use efficiency

1

CHAPTER 1

General Introduction

2

1.1 GENERAL INTRODUCTION

Among the five cultivated species of the genus Phaseolus, including P. acutifolius A. Gray

(tepary bean), P. coccineus L. (scarlet runner bean), P. lunatus L. (lima, butter or madagascar

bean), P. polyanthus Greenman (year-long bean), and P. vulgaris L. (common bean), the latter is

economically the most important one (Debouck, 1999). While common bean, in terms of its

global harvested area, is the third most economically-important grain legume after soybean

(Glycine max (L.) Merr.), and peanut (Arachis hypogaea L.), in terms of its role in direct human

consumption, is the most important grain legume (Broughton et al., 2003). Common beans are

generally harvested as dry beans, harvested as dried and matured seeds, shell beans, harvested at

physiological maturity before seeds are dry and green pods.

Dry bean balances the daily dietary needs of over 500 million people, mainly in Latin

America (Broughton et al., 2003). It supplies people with dietary protein (around 20% of seed

weight), fiber (Díaz-Batalla et al., 2006), folate (Hefni et al., 2010), minerals such as calcium,

zinc, copper, potassium, and iron (Sathe et al., 1984; Ribeiro et al., 2012), and thiamine and

riboflavin (Słupski, 2012).

The common bean belongs to Phaseoleae tribe, Fabaceae (formerly Leguminosae) family

and Papilionoideae subfamily. This herbaceous annual plant has determinate or indeterminate

growth habit. Archaeological and genetic evidence (Gepts, 1998) suggest that common bean

evolved and was domesticated in two distinct centers of origin in the Middle American and

Andean regions. More recently, molecular evidence identified the Oaxaca valley, in

3

Mesoamerica, and southern Bolivia and northern Argentina, in South America, as the centers of

domestication of common bean (Bitocchi et al., 2013).

Mendel (1870) explored the important principles of genetics in peas. Thereafter, beans

were used for illustration of nature of the inheritance of certain quantitative traits such as seed

weight by Johannsen (1911). All species of the genus Phaseolus are diploid and most have 22

chromosomes (2n=2x=22). P. vulgaris with a 625 Mbp of haploid genome size has one of the

smallest genomes among the legume family (Broughton et al., 2003).

From about 29.05×106 ha of planted area, globally, production of dry beans is about

22.8×106 t, annually (FAOSTAT; 2015). Brazil, Mexico, China, and the USA are the largest dry

bean producers. In Canada, dry bean is an economically important crop. In 2014, a total of

278,000 t of dry bean was harvested from 126,000 ha in Canada, with an average yield of 2.27 t

ha-1

(Statistics Canada, 2014). Canada imported about 80,000 t and exported 363,000 t of dry

bean in 2013 with an average price of CND$ 765-795 t-1

(Statistics Canada, 2014). Considering

the world population projected for the year 2050, a 30% growth in common bean production is

required to meet the global demand (Porch et al., 2013).

Inorganic nitrogen fertilizers are recommended to be applied in common bean farms at a

rate of 10 to 100 kg per hectare, depending on the region, for economical harvests (e.g.,

OMAFRA, 2009). Even though common bean is inherently capable of establishing a symbiotic

relationship with Rhizobium leguminosarum bv. Phaseoli, through which it can fix atmospheric

nitrogen, compared with other legumes it is generally considered as one of the poorest fixers of

N2 (Graham, 1981; Piha and Munns, 1987; Isoi and Yoshida, 1991; Santamaria et al., 1997;

4

Martínez-Romero 2003). Both the total amount of nitrogen derived from the atmosphere per unit

area and the proportion of the plant N from the atmosphere in commonly-grown cultivars have

often been reported to be insufficient for production of economical yields (Bliss 1993). Bliss

(1993), however, argued that reports of poor N fixation of common beans have often been based

on the analysis of only a few genotypes and with unsuitable N2 measurement techniques. A

number of studies, on the other hand, have reported genetic diversity for symbiotic nitrogen

fixation (SNF) in common bean (Graham and Halliday 1976; Graham and Rosas 1977;

Kumarasinghe et al., 1992).

Identifying bean genotypes with high N2 fixation ability across a wide range of

environments could be a key finding, as an environmentally-friendly and low-cost strategy

towards the improvement of bean germplasm productivity with elevated N2 fixation ability and

to establish low-input cropping systems involving common bean. In addition, identification of

genomic regions, and ultimately gene(s), conditioning SNF in common bean can be useful for

breeding programs targeting improved nitrogen fixation.

1.2 HYPOTHESIS

The overarching hypothesis of the studies reported in this thesis was that if genetic

variation exists for SNF potential in the gene pools of common bean, then it should be feasible to

improve the SNF potential of dry beans in high yielding common bean genotypes. The following

predictions were made, examined, and reported in different research chapters:

1. Genetic variation exists for SNF ability.

5

2. Genotype by envioronemt effects influence the SNF ability of bean genotypes.

3. Ndfa and related traits have heritability greater than zero.

4. Phenotypic selection for N2 fixation traits will result in genetic improvement.

5. There are genomic regions in the vicinity of SNP markers with a consistently

significant effect on Ndfa and related traits in dry bean.

6. There are candidate gene(s) in the genomic regions of the QTL with significant

effect on Ndfa.

1.3 RESEARCH OBJECTIVES

1. To evaluate the potential differences in the common bean gene pools in terms of

SNF and its related traits and identify parental lines with contrasting %Ndfa for genetic

studies.

2. To establish selection criteria for SNF improvement.

3. To investigate genomic region(s) associated with SNF and related traits using single

nucleotide polymorphism (SNP).

4. To identify the potential candidate gene(s) conditioning SNF in common bean.

6

CHAPTER 2

Literature Review

7

2 LIRETATURE REVIEW

2.1 Nitrogen (N)

Although nitrogen gas (N2) constitutes near 80% of the Earth’s atmosphere, living

organisms can die of nitrogen deficiency (Döbereiner, 1997). Nitrogen is a vital element in all

organisms. It is needed in plant cells to manufacture amino acids, proteins, nucleic acids,

membrane lipids and other nitrogen-containing compounds. Therefore, meeting the N needs of a

crop is always an important consideration in agronomic practices. Generally, the nitrogen must

be supplied as NO3- and NH4

+ to the roots, since unlike most organisms except some prokaryotic

cells, plants cannot directly assimilate atmospheric N2 (Lam et al., 1996; Franche et al., 2009).

Fritz Haber (1868-1934) synthesized NH3 in 1905 from its elements in a laboratory with a

much more effective process. Four years later, Carl Bosch (1874-1940) in Germany’s largest

chemical company, BASF, scaled up Haber’s synthetic process of NH3 production to produce

inexpensive nitrogenous fertilizers. Hereafter, this process is referred to as the Haber-Bosch

process for the synthesis of ammonia (cited by Stoltzenberg, 2014).

By the 1960s, the introduction of semi-dwarf cereals, with increased harvest indices,

permitted increased N fertilizer application rates, which resulted in large gains in the productivity

of cereal crops. Increasing crop yields promoted the use of additional N fertilizer applications in

agricultural lands across the world. A FAO report in 2008 showed that the production of

inorganic nitrogen fertilizers for 2007 was around 130 Tg of N. The report also predicted further

increases in N fertilizer production and use. Today, China, as the world’s most populated

8

country, is the largest producer and consumer of synthetic N (FAO, 2012). Globally, total N

fertilizer nutrient demand has been projected to be 117 million tonnes in 2016 (Canfield et al.,

2010). It is estimated that approximately 140 Tg of N is annually incorporated into the soil

through inorganic N fertilization (Canfield et al., 2010).

The Haber-Bosch process is a costly N fertilizer production technique, because of the very

high energy demand at the chemical reduction step of N2 to NH3 (Bruinjin, 2015). Moreover,

plants generally only take up 30-40% of the N applied to the soil (Raun and Johnson, 1999).

Nitrification of N in the soil, leaching of nitrate into the ground water (London, 2005), and

emission of nitrous oxide through de-nitrification, which can contribute to depletion of ozone

(Moiser, 2002), are major environmental concerns associated with application of N fertilizers to

agricultural lands. Additionally, the process of N fertilizer manufacturing is an important source

of CO2 emission into the atmosphere, contributing to greenhouse and global warming effects

(Razon, 2014).

On the other hand, atmospheric N2 can be transformed into available forms for plants

(either NH4+ or NO3

-) through other processes. These could be through either abiotic fixation or

biological fixation, BNF, (Timothy, 1999). Mosier (2002) estimated that 20 million tons of

available forms of N for plants can originate from abiotic fixation i.e., atmospheric phenomena

such as lighting. Because of high economic and environmental costs of the Haber-Bosch process,

more attention has recently been directed towards BNF as an environmentally-friendly,

sustainable, and inexpensive approach towards supplying N demands of crop production

(Ferguson et al., 2010).

9

2.2 Biological nitrogen fixation

Even though the beneficial effect of legumes intercropped with cereals has been known

since ancient times, it was not until the late 19th

century that the involvement of soil bacteria

associated with the plant root surface (rhizosphere) in BNF was recognized by Hiltner,

1901(Rovira, 1991). BNF has tremendous environmental importance and is known to contribute

to the sustainability of agricultural systems, and is a critical part of the nitrogen cycle. BNF has

an essential role in the marine nitrogen cycle and the sequestration capacity of the marine living

organisms as well (Capone, 2001).

Galloway et al. (1995) indicated that the annual N contribution of BNF is globally

approximated 200 and 300 Tg of fixed N2 in terrestrial and marine systems, respectively. In other

estimations, Peter et al. (2002) projected that about 175 billion metric tons of atmospheric N2 per

year, around 70% of all N2 fixation on the earth, is fixed by some micro-organisms, including:

autotrophs, and heterotrophs. More recently Mosier (2002) and Herridge et al. (2008) estimated

that the annual fixation of N2 through BNF in the terrestrial and marine systems may exceed 100

Tg and120 Tg, respectively. Rubio and Ludden (2008) estimated that BNF accounts for about

65% of the total nitrogen fixed, globally.

N2 fixing organisms are able to improve soil fertility as well as plant nutrient and water

uptake through the production of chemicals such as hormones and siderophores (iron carriers),

(Badawi et al., 2011). For a long time, only a limited number of bacterial species were known to

be as N2-fixers (Postgate, 1981). However, more recently more micro-organisms have been

identified that have N2 fixing ability (Young, 1992); However, BNF can only be carried out by

10

prokaryotes, namely archaea (Lobo and Zinder, 1992; Leigh, 2000) and bacteria, with a majority

of the organisms being bacteria (Dean and Jacobson, 1992; Young, 1992; Fischer, 1994). These

prokaryotes are generally called diazotrophs (Figueiredo et al., 2013). Archaea were the first

diazotrophs that were discovered (Leigh, 2000) and are mainly restricted to the genus

Methanosarcina (Lobo and Zinder, 1992; Leigh, 2000), which includes two species i.e.,

Methanosarcina barkeri (Murray et al., 1984) and M. thermolithotrophicus (Belay et al., 1984).

Atmospheric N2 fixing bacteria can be divided into three major categories (Timothy 1999)

on the basis of their life forms, including:

(i) the free-living heterotrophic bacteria, characteristically associated with soil or detritus,

non-living particulate organic material, or autotrophic bacteria;

(ii) the bacteria that form associative relationships with fungi (lichens) or plants, and

(iii) those that establish symbiotic associations with vascular plants.

2.2.1 Free-living N2-fixing organisms

These bacteria are also called soil plant growth-promoting bacteria, rhizosphere, rhizoplane

or phylosphere bacteria. They are beneficial to plants under some conditions, since they are able

to fix atmospheric N2, solubilize phosphorus (P) and iron (Fe) and induce the production of plant

hormones. These bacteria live in soil, media or water and release N to the rhizosphere after

death. It is estimated that around 6 kg N2 ha-1

is annually fixed by these organisms (Van Kessel

11

and Hartley, 2000). Heterotrophic free-living N-fixing bacteria are divided into three subgroups,

including: aerobics, obligatory and facultative, and anaerobics.

2.2.1.1 Aerobic, Azotobacter

Azotobacter is an obligatory aerobic, N2-fixing, Gram-negative bacterium, though

Azotobacter, as a heterotrophic diazotroph can survive in low oxygen conditions. The high

sensitivity of its nitrogenase to O2 has resulted in the development of a dual mechanism to

decrease the O2 concentration at the active site of the nitrogenase (Yates, 1970). These two

mechanisms are:

(i) temporary inactivation of nitrogenase by a quick, symmetrical, change within the

nitrogenase complex structure so that the oxygen-sensitive sites are protected against

expose to O2, and

(ii) extreme increases in respiration to eliminate excess O2 at the N fixing site.

In Azotobacter species, molybdenum-iron nitrogenase or vanadium-iron nitrogenase can be

the N fixing enzyme (Robson et al., 1986; Narula et al., 2000). The energy requirement for N

fixation is obtained from carbon sources in the soil or water. Azotobacter can generally fix 10

mg N/g of carbohydrates under field conditions (Kanungo et al., 1997), which helps the survival

of the soil microbial community. It also brings back the N level into old fields through

production of plentiful amount of exopolysaccharides (Gauri et al., 2012), through protecting

nitrogenase against high oxygen concentration (Dalton and Postgate, 1968, cited by: Gauri et al.,

12

2012) and also participates in interaction between plants and N-fixing bacteria (Mandal et al.,

2008). The ecological distribution of Azotobacter is mainly a function of soil and environmental

conditions (González et al., 2005). Azotobacter is widely used as a bio-fertilizer and is

considered as a key player in developing sustainable agricultural systems (Bhardwaj et al., 2014),

especially in rice, Oryza sativa L., (Kannaiyan et al., 1980; Rüttimann et al., 2003). It is reported

that Azotobacter can also enhance the growth and yield in other cereals, e.g., in wheat, Triticum

aestivum L., in the presence of 50% inorganic N fertilizer (Kader et al., 2002; Milošević et al.,

2012; Salantur et al., 2006) and in maize, Zea mays L., especially in the absence of chemical

fertilizers (Baral and Adhikari, 2013).

2.2.1.2 Anaerobic, Clostridium and Klebsiella

Clostridium pasteurianum was the first free-living N-fixing organism to be isolated

(Winogradsky, 1895 cited by: Chen et al., 2001). A few species of the genus Clostridium have

been reported as N-fixing anaerobic microorganisms (McCoy et al., 1928; Sjolander and McCoy,

1937; Bodily, 1938; all cited by Rosenblum and Wilson, 1949). The ability of clostridia to fix N

was first reported by Rosenblum and Wilson (1949). It was later reported that clostridia could fix

atmospheric N in sugarcane fields under anaerobic conditions (Ruschel et al., 1975). Clostridium

is primarily a heterotrophic N-fixing bacterium (Brenzonik and Harper 1969; Brooks et al., 1971;

Keirn, 1971). Stewart (1966) correlated the rate of Clostridium N fixation with the presence of

photosynthetic bacteria in a marine ecosystem. Klebsiella is a wide-spread bacterium, often

present in surface water, but not in chlorinated water. Due to some illnesses it causes, Klebsiella

pneumoniae in water is a concern. Klebsiella pneumoniae has been shown to be a facultative

aerobic diazotroph (Young, 1992) as well.

13

Prior to 1970, the genetics of nitrogen fixation in diazotrophs was not well understood. In

the early 1970’s, Klebsiella pneumoniae became the model organism for genetic analysis of

nitrogen fixation in diazotrophs (Dixon, 1984) and helped scientists to discover the complex

inheritance of BNF.

2.2.2 Associative symbiosis relationship

Frequent isolation of bacteria from surface-sterilized plant roots explored a new class of N

fixing endophytes called associative symbionts, i.e. cyanobacteria and Azospirillum (Döbereiner,

1997; Reinhold-Hurek and Hurek, 1998). Associative symbionts are free living organisms that

assimilate atmospheric N using a particulate oxygen-sensitive nitrogenase in association with

some plants (Kelly, 1969). They include a wide range of N fixing species that colonize the root

surface of non-leguminous plants without formation of unique structures such as nodules

(Franche et al., 2009).

2.2.2.1 Cyanobacteria

Cyanobacteria are the largest and most diverse group of Gram-negative prokaryotes,

distributed in aquatic and terrestrial environments. They are found in relationships with large

number of vascular and non-vascular plants, fungi and algae (Meeks and Elhai, 2002). They are

classified into the domain of bacteria, into five sections based on morphology and reproductive

system. These five subgroups are: Chroococcales, Chamaesiphonales, Pleurocapsales,

Nostocales (including the families Oscillatonaceae, Nostocaceae, and Rivulanaceae), and

Stigonematales (Rippka et al. 1979). The first two subgroups are unicellular or colonial and the

14

latter three are non-differentiated filamentous, heterocystic filamentous, and branched

filamentous heterocystic, respectively. These prokaryotic cellular organisms are characterized by

the presence of extensive thylakoid membranes containing photosynthetic pigments (Fogg et al.,

1973). Under nitrogen limiting conditions, many filamentous and unicellular cyanobacteria are

able to fix atmospheric N through the formation of nitrogen-fixing cells called heterocysts, which

contain nitrogenase enzymes (Fogg et al., 1973), either in aquatic or in terrestrial ecosystems

(Fay, 1981; Bergman et al. 1996; Rai et al. 2000).

These microorganisms, as the first oxygenic photosynthetic creatures, appeared on the

earth more than 2.5 billion years ago (Rippka et al., 1979; Fay, 1992). They are commonly

termed thermophilic blue-green algae (Miyamoto et al., 1979) and acquire their vitality through

photosynthesis (Stewart, 1980), the same as vascular plants (Ho and Krogman, 1982). Water is

the final electron source for photosynthesis in these blue or green algae (Ho and Krogman,

1982), Electrons are transferred between two types of photosystems, photosystem I and

photosystem II, during light reactions of photosynthesis (Glazer, 1987). In some strains, slow

chemo-heterotrophy has been reported as a source of energy in weak light to dark conditions

(Smith, 1982). In phosynthetic cyanobacteria the phycobiliproteins (phycocyanin,

allophycocyanin, and phycoerythrin), are the source of their color and bind the major pigments

responsible for light interception (Glazer, 1987).

Heterocysts convert N2 to available forms of N and in filamentous cyanobacteria

heterocysts fix atmospheric N2 in return for carbohydrates from photosynthetic cells (Franche et

al., 2009). Cyanobacteria, utilizing a similar exchange, can form associative relationships with

vascular and non-vascular plants belonging to the bryophytes (such as liverworts and hornworts),

15

as well as with algae, fungi and many marine eukaryotes (Bergman et al. 1996; Rai et al. 2000).

The rate of N fixation in a plant-cyanobacteria association is approximately 4 to 35 times higher

than in free-living cyanobacteria (Adams 2002). N fixation occurs in unicellular and filamentous

species. However, only heterocystous Nostocales (Nostoc and Anabaena genera) can have

associations with plants, e.g., Azolla.

Azolla is a free floating water fern which is abundant in fresh water in tropical, subtropical,

and warm-temperate habitats (Yanni et al., 1992; Bindhu 2013). It is also known as mosquito

fern, duckweed fern or fairy moss (Yanni et al., 1992). The association between cyanobacteria

and Azolla is the only plant-prokaryote symbiosis that persists through all of the reproductive

growth phase of the host plant (Lumpkin and Plucknett, 1980; Nierzwicki-Bauer, 1990; Lechno-

Yossef and Nierzwicki-Bauer, 2002).

Cyanobacteria, through participation in an obligatory symbiotic relationship with Azolla,

create an endosymbiont called Anabaena azollae (Bergman et al. 1996; Rai et al. 2000; Bindhu

2013). This relationship starts with the establishment and growth of cyanobacteria inside a

distinct leaf cavity at the base of the dorsal lobe of Azolla leaves, during the vegetative growth

stage of the plant. Azolla leaves can also be a host for other bacteria with unknown functions

(Franche et al., 2009).

Decayed Anabaenazollae can release nutrients into water (Marwaha et al., 1992). N is

released from the cyanobiont mostly in the form of NH4+. Due to their high N content, Azolla

species, including Azolla pinnata R. Br., A. caroliniana Willd., and A. filiculoides Lam., have

been used as green manure in the wetland rice paddies in Northern Vietnam and China (Yanni et

16

al., 1992). Uma and Kannaiyan (1995) illustrated that the presence of cyanobacteria in paddies

can help remediate high saline paddies by reducing the need for urea application, and prevent

from soil erosion. Moreover, Mandal et al. (1999) suggested that N-fixing cyanobacteria can

improve plant growth and yield through the production of growth-promoting substances, e.g.,

gibberellins, cytokinins, auxins, abscisic acids, vitamins, antibiotics and amino acids. Azolla, as a

biofertilizer promotes plant growth in plants such as rice (Hove et al., 1996) and tomato (Ismail,

2015). Prasanna et al. (2015) reported positive impacts of cyanobaterial inoculants on soil

fertility, plant barrier catalyst activity, Zn content, and yields in maize. It can also suppress

different plant-parasitic nematodes such as Pratylenchus penetrans (Walker, 1969), Heterodera

glycines (Barker et al., 1971) Tylenchorhynchus vulgaris (Pate et al., 1989; Ismail, 2015) and

Meloidogyne incognita (Abadir and El-Hamawi, 1995) and weeds in paddies.

2.2.2.2 Azospirillum

Bacteria of the genus Azospirillum are associative nitrogen fixers, widespread in the soil of

tropical, subtropical and temperate areas. These microaerophilic organisms grow in close

associations with the roots of different wild and cultivated plants (Doroshenko et al., 2007;

Rawia et al., 2009) such as cereals. They penetrate into the root system of the plants and grow

intracellularly (Santi et al., 2013; Saikia et al., 2007). Beneficial effects of these bacteria have

been reported on yield and growth of different vascular plant species, including: Setaria italic

(Okon et al., 1983), rice (Nayak et al., 1986; Murty et al., 1988), wheat, soybean (Bashan et al.,

1990), foxtail millet (Rao and Charyulu, 2005), and maize (Saikia et al., 2007).

17

2.2.3 Symbiotic N2 fixation (SNF)

Peoples (2009) estimated that nearly 17% of annual BNF is derived from legume-rhizobia

symbiosis or symbiotic N2 fixation (SNF, Figueiredo et al., 2013). SNF is not only a source of N

for the plant but also helps it cope with different environmental stresses (Bashan and de-Bashan,

2005; Franche et al., 2009). The ability to symbiotically fix atmospheric N by microorganisms

nodules of vascular plants can be found in two unrelated bacteria, namely: rhizobia (Alpha-

proteobacterium), in association with legumes (Sy et al., 2001) and Frankia (an actinobacteria)

associated with a wide range of plants from eight families (Huss-Danell, 1997; Vessey et al.,

2004).

2.2.3.1 Frankia

Frankia, a Gram-positive filamentous actinobacterium, is a member of the family

Frankiaceae in the order of Actinomycetales (Normand et al., 1996). Frankia is able to fix

atmospheric N2 in small growths, called nodules, on the roots of some dicotyledonous plants

(Susamma et al., 2002). The colonized plants are called actinorhizal plants. Since 1928, the

symbiotic associations between N2-fixing Frankia and more than 200 tree species, from 24

genera of 8 families of angiosperms, have been studied (Schwencke and Carú, 2001) and to date

Frankia are the only actinobacteria that have been identified with the capability of nodulating on

actinorhizal plant roots (Clawson et al., 2004).

Definitive evidence of a symbiotic association between a Frankia strain and a plant came

from the successful culture of the bacteria from root nodules of Comptonia peregrina by

18

Callaham et al. (1978). This allowed the bacterium to be studied outside of its host and showed

that it forms slow growing hyphal colonies with the capacity to develop into two distinctive

structures, including vesicles and spores. The vesicles and spores have different roles in Frankia-

actinorhizal plant associations. The vesicles are sites of N fixation and spores are storage places

for the reproductive structures of sporangia (Lechevalier, 1994).

Under low levels of soil N, Frankia could improve soil fertility of agro-ecosystems

through fixing atmospheric N2 by symbiotic relationship with actinorhizal plants (Wheeler and

Miller, 1990; Myrold and Huss-Danell, 2003). Actinorhizal symbioses are spread all over the

world and their overall rates of N2 fixation are generally at the same level as Rhizobium-legume

symbiosis (Torrey, 1978). For instance, Frankia- actinorhizal plants in birch tree canopies can

annually fix at the range of 20 to 300 kg N ha-1

based on canopy age and density, and

environmental conditions (Wheeler and Miller, 1990). Hurd et al. (2001) indicated that 85-100%

of total foliar N in a spotted birch wood could be attributed to N fixation through the symbiotic

relationship between Frankia and birch.

In Frankia- actinorhizal plant symbiotic relationships, Frankia meets its energy needs by

acquiring carbohydrates from the host plant (Reddell et al., 1986; Smolander et al., 1988).

Advances in the molecular genetic of Frankia have been very small compared to the

understanding of rhizobia because of the slow growth rate of filamentous hyphae (Lavire and

Cournoyer 2003; Normand and Mullin 2008). Lois et al. (1999) and Joel et al. (2002) indicated

that the nif gene controlled N-fixation in Frankia.

19

2.2.3.2 Rhizobium

The first pure cultures of rhizobia were obtained at the end of the nineteenth century

(Callaham et al. 1978). Rhizobia have the ability to establish symbiotic relationships with a large

number of the 18,000 species of the Leguminosae (Fabaceae) family by inducing the

development of particular organs, called nodules. Rhozobia are Gram negative, free living

organisms in the soil able to convert atmospheric N2 to NH3 only in an endo-symbiotic life

association with the [lateral] roots of legumes (Cooper, 2004). These bacteria belong to five

major genera, including: Rhizobium, Bradyrhizobium, Azorhizobium, Mesorhizobium, and

Sinorhizobium (Rogel et al., 2011). While some rhizobium species, like the β-rhizobia have

narrow host ranges, some others, like γ-rhizobia, can establish symbiotic relationships with a

broad range of hosts (Dwivedi et al., 2015). For example, Burkholderia phymatum from the

genus Burkholderia effectively nodulate several important legumes, including common bean

(Gyaneshwar et al., 2011). Host-specific symbiovars, as a group of bacterial strains

distinguishable from other strains of the same species on the basis of physiological or

biochemical characters (Dwivedi et al., 2015), have been reported for all of the rhizobium genera

in different legumes, e.g. in soybean (Glycine max) Bradyrhizobium japanicum and Rhizobium

fredii; in alfalfa (Medicago sativa) R. meliloti; in sweet clovers (Melilotus sp.) R. trifolii; in peas

(Pisum sativum), R. leguminosarum; Sesbania sp. (Sesbania rostrata), Azarhizobium

caulinodans (stem nodulating rhizobia); in common bean (Phaseolus vulgaris), R. phaseoli

(Peoples et al. 1989 cited by Kumar and Rao, 2012), R. gallicum, R. giardinii, R.

leguminosarum, R. etli; in faba bean (Vicia faba), R. fabae; in common vetch (Vicia sativa), R.

leguminosarum and R. pisi (Rogel et al., 2011).

20

In a rhizobia-legume symbiotic relationship, atmospheric N fixation is carried out by the

bacteria within the nodules and the NH3 produced is absorbed by the plant. In return the bacteria

receive carbohydrate from the host plant. Legumes in association with rhizobia are often thought

to be the most important nitrogen-fixing living system, as they may absorb up to 90% of their N

from the atmosphere and play an important role in cropping systems as a renewable source of N

through fixation of 20-22 Tg atmospheric N per year (Herridge et al., 2008). Most legumes in a

symbiotic relationship with rhizobia can obtain around 200 to 300 kg N fixed ha-1

(Peoples et al.,

1995; Van Kessel and Hartley, 2000) though Unkovich and Pate (2000) reported a value of 165

to 450 kg N ha-1

y-1

for grain legumes with the maximum rate of 600 kg N fixed ha-1

y-1

for

temperate clover pastures. Large differences were, however, noted in the proportion of

atmospheric N2 fixed by the grain legume crops, e.g., 75% of the total nitrogen in plant was

derived from SNF by faba bean; 62–94% by soybean, groundnut, pea, and lentil; 54–58% by

cowpea, chickpea, and pigeon-pea; and up to 39% by common bean (Dwivedi et al., 2015). The

amount of atmospheric N fixed through rhizobium-legume symbiosis mostly depends on host

species and cultivars, micro-symbiont strain, and environmental conditions (Peoples et al., 1995;

Van Kessel and Hartley 2000; Unkovich and Pate, 2000). This relationship is not only beneficial

to the legume and bacteria, but also it is also able to enhance the growth and productivity of

other plants following them in the cropping system (Yanni and El-Fattah, 1999).

2.3 BNF process

During the BNF process, diazotrophs catalyse the conversion of atmospheric N2 to NH3

using a nitrogen fixing enzyme, called nitrogenase (Santi et al., 2013). This enzyme was

extracted from a wide range of diazotrophic species between the late 1960s and early 1970s

21

(Dixon, 1984). This complex enzyme is composed of two proteins, dinitrogenase and nitrogenase

reductase (Bottomley and Myrold, 2007). The most common form of nitrogenase consists of a

large heterotetrameric molybdenum and iron-containing iron-molybdenum cofactor, FeMo-co.

The smaller component of nitrogenase is the iron (Fe) protein. This dimeric protein is an ATP-

dependent electron donor to the larger heterotetrameric FeMo protein. Molybdenum depletion, in

some diazotrophic bacteria such as Azotobacter and several photosynthetic nitrogen fixers

including some cyanobacteria, induces alternative nitrogenases, containing vanadium-iron, V-

nitrogenase, or iron-only nitrogenases, Fe-Fe nitrogenase (Eady, 1996; Rubio and Ludden,

2005).

The N-fixation process can be summarized by the following equation:

N2 + 16 ATP + 8 e- + 8 H

+ → 2 NH3 + H2 + 16 ADP + 16 pi [1]

The process is a very high energy-consuming process. For reduction of one atmospheric

N2 molecule to two molecules of NH3, a total number of 16 molecules of adenosine triphosphate

(ATP) and eight electrons are needed [equation 1]. In the process 6 electrons and 16 ATPs are

used to reduce N2 into 2 molecules of NH3. The last 2 electrons are used to reduce 2 molecules of

H to H2 (Bottomley and Myrold, 2007). To activate of the nitrogenase for N2 fixation the Fe

component of the reductase enzyme must be reduced, which involves electron donors, e.g.

ferredoxin and flavodoxin. Then, ATP-dependent transfer of electrons occurs from the Fe protein

to the active site of the MoFe protein, containing a Pi cluster and is the site of N reduction

(Hageman and Burris, 1978, cited by: Dixon and Kahn, 2004).

22

2.4 SNF regulation

2.4.1. Physiological regulation of BNF

During the late 1960s to early 1970s, nitrogenase was extracted from a wide range of N-

fixing organisms. Afterwards, considerable physiological limits on diazotrophy due to the nature

of this enzyme were identified by scientists. In particular, it was found that nitrogenase is an

extremely O2-sensitive protein and is subject to be denatured by it (Yates, 1970). Indeed,

diazotrophs do respire to meet the high energy demand of BNF, but because free O2 has an

inhibitory effect on nitrogenase, diazotrophs have developed different anatomical and

physiological mechanisms to protect the nitrogenase enzyme from O2 damage (Denison et al.,

1992).

In some cyanobacteria specialised cells, heterocysts are developed that can use direct light

energy to drive the BNF process, without evolving O2 (Buikema and Haselkorn, 1993).

Combining respiration with thick cell walls is another mechanism to protect nitrogenase. N-

fixing azotobacters are able to grow and fix N2 at the highest dissolved O2 concentrations, about

230 μM O2 (Hill et al., 1972). In fact, azotobacters, through an extreme increase in their

respiration rate, eliminate the excess inner O2, subsequently protecting nitrogenase from

irreversible denaturation. This mechanism is called ‘respiratory protection’ (Yates, 1970). Oleze

(2000), however, believed that ‘respiratory protection’ of nitrogenase is less operational than

generally supposed. He suggested that “alternative factors such as ATP supply and reducing

equivalents” are more important than the respiratory protection mechanism in azotobacters. In

some N2-fixing, such as non-heterocystous cyanobacteria, N2 fixation can occur in darkness or

23

dim light in the absence of O2 (Fay, 1992). Another protection mechanism for nitrogenase

against O2 is a quick change in the symmetrical shape of the nitrogenase complex. This

temporary deformation can take the oxygen-sensitive sites of the enzyme out of O2 rich

environments (Einsle et al., 2002). In the legume-bacteria symbiosis, the nodule cortex is an

oxygen diffusion barrier (Appleby, 1984).

Not only is BNF a very high energy-demanding process, but also the catalysing enzyme,

nitrogenase, has a relatively slow turnover time. Therefore, large quantities of nitrogenase (10-

20% of the total protein in the cell) need to be synthesized by diazotrphs (Thornely and Lowe,

1985, cited by: Dixon and Kahn, 2004) to make the process efficient. In addition, many

functional nitrogenase proteins are denatured by O2 at a high rate. Turnover of these denatured

proteins demands an additional energy cost (Dixon, 2004). To prevent a negative influence of the

reduction in O2 on the ATP supply for BNF, a high affinity terminal oxidase protein, such as

NODULIN leghaemoglobin, facilitates oxygen diffusion at low free O2 concentrations (Appleby,

1984).

NH3 is the primary product of N2 fixation [equation 1], but at high concentrations it can be

toxic to living cells (Darwin, 1882, cited by: Schenk and Wehrmann, 1979). Glutamine

synthetase (GS) and glutamate synthase (GOGAT) are two highly efficient enzymes that prevent

NH3 from accumulating in N2-fixing bacteria and compromising more energy production.

Increases in nitrogenase, GS and GOGAT activities have been reported during nodule

development in legumes (Egli et al., 1989; Reynolds et al., 1982). In most diazotrophs, the

enzyme combination of GS and GOGAT carry out the reactions shown in equations 2 and 3:

24

In plant-rhizobium combinations the amount of photo-assimilate received by bacteria could

limit the rate of SNF carried out by bacteroids (Ben Salah et al., 2009). However, Kaschuk et al.

(2009) believed that SNF can stimulate photosynthesis and legumes are not suffering from

carbon assimilate limitation under symbiotic conditions. In a recent study on the role of nitrogen

and photo-assimilate on SNF in cowpea, Rodrigues et al. (2013) showed that SNF rate of

soybean plants co-inoculated with Bradyrhizobium species and plant growth-promoting bacteria

(either Paenibacillus durus or Paenibacillus graminis) was not significantly associated with

carbohydrate assimilation size, but was weakly associated with soluble acid invertase activity in

nodules at the beginning of senescence.

2.4.2 Host-plant regulation of SNF

Plant-rhizobia symbiosis is initiated by the invasion of plant roots or stem cortex by

rhizobia, followed by formation of specialized organs calls nodules. Nitrogen fixation only

happens after the establishment of the symbiotic state (Cooper, 2004). If this state is not

achieved, the bacteria will act as plant parasites (Franche, 2009).

A complex molecular dialogue between nitrogen-fixing rhizobium and the legume plant is

the first step in the invasion of plant by the bacteria (Dénarié et al., 1993, cited by: Franche,

2009; Perret et al., 2000). Legumes release some of their assimilated carbon through their roots

(Nguyen, 2003, cited by: Gage, 2009). These assimilates can promote rhizobial movement

towards the plant and act as inducer chemicals (Choudhury, et al., 2004). The most effective of

i [2] GS

Ketoglutarate + Glutamine + NADH 2 Glutamine + NADox [3] GOGAT

Glutamate + NH3 + ATP Glutamine + ADP + P

i

25

these inducers belong to the group of flavonoids (Schlaman et al., 1991). There are other

molecules, such as the betaines (e.g., stachydrine and trigonelline) and the aldonic acids (e.g.,

erythronic acid and tetronic acid) with inducing activity on some rhizobial species at higher

concentrations than flavonoids (Gagnon and Ibrahim, 1998). Rhizobial factors which contribute

to the specificity of the interaction include Nod factors and surface polysaccharides (Fauvart and

Michiels, 2008). Nod factors consist of an acylated chitin oligomeric backbone with various

substitutions at the non-reducing-terminal and/or nonterminal residues (D’Haeze and Holsters,

2002).

Flavonoids act as chemical signals and determine the compatibility of the plant host for

the bacterium through a type of LysR-type transcriptional factor (Peck et al., 2006) called NodD

protein, (Gage, 2009) or Lipo-chitooligosaccharidic nodulation (Nod) factors (D’Haeze and

Holsters, 2002). Nod factor is secreted by rhizobia in response to plant root exudates (Yang et

al., 1994). The induction of Nod factor production is specific for the structure of the flavonoid.

Taxonomically diverse bacteria produce different polyunsaturated acyl chain substituents

(Suominen et al. 2001; Yang et al. 1994). These features result in very specific symbiotic

relationships.

Flavanoids and Nod factor are important in host specificity too. While some plant species

can be infected by different symbionts from different bacterial genera, other species just accept a

very narrow range of bacteria. Host specificity is determined by several factors. From the

bacterial side, the main signalling molecules are Nod factors, surface polysaccharides and

secreted proteins. Either the composition of plant root secretion or the Nod factor construction

determines host specificity to the special symbionts (Perret et al., 2000).

26

As mentioned above, establishing the symbiotic state depends on the interaction between

the bacteria and plant host, created through a “molecular dialogue”. The first secreted rhizobial

protein with a role in symbiosis is NodO (a flavonoid) detected in Rhizobium leguminosarum bv.

viciae (De Maagd et al., 1989). Russo et al. (2006) also detected eight secreted rhizobial-

adhering proteins (Rap) in total, including: NodO, PlyA, PlyB, agglutinins (RapA1, RapA2, and

RapC), rhicadhesin and bacterial lectins. The last one is believed to be common to all rhizobia

(Smith et al., 1992; Ausmees et al., 2001). Later, Krehenbrink and Downie (2008) identified six

additional proteins in R. leguminosarum bv. viciae strain 3841 that function as a metalloprotease,

a glycosyl hydrolase, cadherins and a nucleoside diphosphate kinase. Calsymin is another

secretion observed in R. etli (Xi et al., 2000).

Bacteria, after recognizing the chemical signal released by the plant, will invade the plant

root hairs and infect them. Thereafter, the root hair begins to curl, which is a response to

penetration of rhizobium into the root hair, and the bacteria begins to multiply, form an infection

thread and initiate the formation of a nodule. The bacteria then convert to bacteroids, which can

convert atmospheric N2 into ammonia in return for receiving assimilates from the plant host. The

symbiotic relationship modifies and the metabolism and physiology of both plant host and

bacteria (Spaink, 2000).

As indicated before, plant-rhizobium combinations can result in nodule formation

containing parasitic rhizobia. Host and bacteria relationship can also be determined by the level

of N in the soil. High levels of soil N can induce bacteria to act as plant parasites. Also, different

bacterial variants can be ineffective (non-fixing) or effective (fixing nitrogen). It is also true for

different genotypes of a plant with specific bacterial strains. Plants cannot differentiate between

27

non-fixing and fixing strains of a bacterium and in a mixed inoculant treatment they accept both

strain types (Tas et al. 1996). Some bacterium species have a wide range of strains and genetic

variants (Johnston et al. 1978; Laguerre et al. 1993; Young et al. 2006). Rhizobium

leguminosarum can be exemplified as a bacterial species with a wide range of biovars due to

gene exchange. Among these, leguminosarum biovar viciae typically nodulates peas, Pisum sp.,

and vetches, Vicia sp., biovar phaseoli nodulates beans, Phaseolus sp., and biovar trifolii

nodulates clovers, Trifolium sp., (Mutch and Young, 2004).

In addition, there are bacterial strains with the ability to establish symbiotic relationships

with an enormous range of plant hosts. For instance, Sinorhizobium sp. strain NGR 234 can form

nodules on 112 plant genera. The flexibility of NodD protein induced by a variety of root

extracts and secretion of nodulation outer proteins (Nops), with a role in nodulation efficiency

and in some cases host specificity, gives this ability to this kind of bacteria (Freiberg et al., 1997;

Pueppke and Broughton, 1999; Marie et al., 2003; Ausmees et al., 2004; Cooper, 2007).

Rhizobia can produce four main types of surface polysaccharides. These can participate in

different stages of the symbiotic relationship establishment, including: root colonization, host

recognition, infection thread formation and nodule invasion (Mathis et al., 2005). They are

comprised of extracellular polysaccharides (EPS), K polysaccharides (K- antigens, capsular

polysaccharides or KPS) and cyclic glucans. EPS is essential for the development of fully

functioning nodules (Mathis et al., 2005). Rhizobia use the exact same secretion pathways and

some of the proteins utilized by pathogenic micro-organisms (Fauvart and Michiels, 2008).

https://en.wikipedia.org/wiki/Genetics

28

2.4.3 Genetic Regulation of SNF

Besides the high ATP cost of reducing N2 to NH3, many genes and their products are

necessary for synthesis of a completely functional N2-fixing enzyme system. A large number of

bacterial genes that play roles in the formation of nodules on leguminous plants have been

identified, including more than 65 nodulation genes in the rhizobia. Each strain can carry one or

more of these genes. Several investigators have examined the possible functions of the common

genes involved in nodulation process.

2.4.3.1 Genes controlling SNF and nodule formation

The most important genes controlling SNF include three clusters of host plant genes and

bacterial genes. The host plant genes are called nod genes while the rhizobial genes that

condition SNF include nif and fix genes. The bacterial genes, that are collectively involved in the

synthesis of nitrogenase and the catalytic process of N2 fixation, are called nif genes. The fix

genes are accessory genes and play roles in the function and regulation of nitrogenase in micro-

aerobic and aerobic diazotrophic bacteria (Dixon and Khan, 2004).

The nif genes were discovered in the bacterium Klebsiella pneumoniae. The nif genes in

this bacterium are located on a very compact region of the genome, 24kb in size, containing

approximately 20 genes (Arnold et al., 1988). The nif genes, which have structural and functional

regulation roles in N2 fixation, are present in both free living and symbiotic bacteria

(Choudhurry, et al., 2004). The structural nif genes from taxonomically diverse microbes are

almost identical and act in similar ways to encode nitrogenase (Ruvkin and Ausubel, 1980;

29

Swain and Abhijita, 2013). In fact, two different groups of the nif genes are responsible for

coding the nitrogenase polypeptides. The first group includes nifD, nifK and nifH, of which the

first two control expression of structural Mo-nitrogenase polypeptides. Dinitrogenase is a

complex protein consisting of α and β subunits. The α subunit is the product of the nifD gene

while β is coded by the nifK gene (Beringer and Hirsch, 1984). The nifH gene codes for the

structural Fe protein component (Roberts et al., 1978). The second group of nif genes, (nifB,

nifQ, nifE, nifN, nifX, nifU, nifS, nifV, and nifY) have roles in the full assembly of nitrogenase.

From this group nifB, nifQ, nifE, nifN, nifX, nifU, nifS, nifV, nifY in addition to nifH from the first

group are involved in the synthesis of FeMoCo. Moreover, the nifS and nifU code for the

assembly of iron sulfur (Fe-S) clusters and nifW and nifZ code for maturation of the nitrogenase

components (Zheng et al., 1998; Rubio and Ludden, 2008). In addition to these genes, three more

genes were detected in Klebsiella, including nifF and nifJ, which are required for electron

transport to nitrogenase, and nifLA genes, which conditions expression of the nif cluster (Dixon

and Kahn 2004). It is now known that nifH, nifD, nifK, nifY, nifB, nifQ, nifE, nifN, nifX, nifU,

nifS, nifV, nifW, and nifZ are necessary for nitrogenase synthesis in all diazotrophs, although the

other genes are required for in vivo nitrogenase activity. For example, these genes code for

electron transport chain components (such as flavodoxin, ferredoxin), molybdenum uptake and

homeostasis, and oxygen protection and regulation (including respiratory chains adapted to low

oxygen conditions) which are all required for nitrogen fixation (Fischer 1994; Dixon and Kahn

2004; Pedrosa and Elmerich, 2007).

SNF is an energetically expensive process, so bacteria do not usually fix N2 in the presence

of mineral N sources. As a result, the synthesis of both Mo and the Mo-independent nitrogenases

are strictly regulated, at the transcriptional level, by the availability of inorganic N in the soil. A

30

number of mechanisms have been identified whereby nitrogenase activity is inhibited, and nif

gene expression is down-regulated, in response to any increase in levels of NO3- , NH4

+, and/or

amino acid N in the environment (Merrick, 1993). In the legume-Rhizobium symbiosis the root-

nodule cortex affects nif gene expression because transcriptional regulation of the nif gene in

diazotrophs is tightly controlled in response to the external oxygen concentration (Thornely and

Lowe, 1985, cited by: Dixon and Kahn, 2004).

The term "fix gene" is used for bacterial genes that are essential for nitrogen fixation but do

not have corresponding homologs in K. pneumoniae. The fix genes are, in fact, accessory genes

needed for development and metabolism of bacteroids. They are necessary for the function and

regulation of nitrogenase in micro-aerobic and aerobic diazotrophic bacteria. SNF requires the

coordinated function of the fix genes with nif genes (Fischer, 1994).

The nod genes responsible for the biosynthesis of chitin backbone are nodA, nodB and

nodC. The other nod gene, named nodD, activates the transcription of other genes, including the

nod genes (Martínez et al., 1990). The induction of Nod factor production is specific for the

structure of the flavonoid. The specificity of this process has been shown to be mediated by the

nodD genes in all rhizobial species (Spaink et al., 1987). Multiple isoforms of the nodD genes

have been found in several rhizobial strains. This multiplication of the nodD genes is probably

related to interactions of bacterial Nod factors with multiple hosts that secrete different

flavonoids (Demont et al., 1994, cited by: Spaink, 2000). The nod gene’s regulatory effect on

Nod factor production might also happen at the post-transcriptional level (Olsthoorn et al., 2000).

Olsthoorn et al. (2000) reported that the activity of the Nod factor was strongly temperature

dependent. The nod gene products are required for the early steps in nodule formation. Actually,

31

the nod genes are switched off at later stages of the symbiosis and at higher temperatures

(Schlaman et al., 1991).

There are additional genes involved in nodulation, other than those in the three main

categories (nod, nif and fix) (Shamseldin, 2013). For instance, the hsn gene is responsible for

host specific nodulation in Rhizobium leguminosarum sv. trifolii (Rolfe et al., 1985, cited by:

Shamseldin, 2013). Genotypic specific nodulation gene (nolA) was identified by Loh et al.

(1999), which controls nodulation of specific soybean genotypes by Bradyrhizobium japonicum.

The hydrogen gas that is evolved during the nitrogen fixation process [equation 1] can be

recycled through the activity of an H2-uptake (Hup) system synthesized by certain strains of

rhizobia (Brito et l., 1997), which is under control of hydrogenase structural (hup) gene (Soom et

al., 1993). Other genes reported with roles in nodulation including: gln, acds, dct, nfe, bacA, tfx

and moc. The gln (glutamine synthase) gene conditions antioxidant defenses involving NO

(nitric oxide) around the plant roots (Spaink, 2000). Genotype-specific infection and nodulation

can be also controlled by some other genes. Rj2 and Rfg1, encoding a class of plant resistance

(R) proteins called a Toll-interleukin receptor/nucleotide binding site/leucine-rich repeat (TIR-

NBS-LRR), was detected by Yang et al. (2010). Therefore, it is possible that there are similar

recognition mechanisms for legume-rhizobial and legume-pathogenic bacteria interactions Yang

et al. (2010).

The gene acds, detected by Ma et al. (2003), promotes nodule formation in pea (Finan et

al., 1983). The nfe gene controls nodule formation efficiency in Rhizobium meliloti (Sanjuan and

Olivares, 1989). The bacA has a critical role in bacteroid development during the symbiosis stage

in Rhizobium meliloti (Glazebrook et al., 1993). In different strains of R. etli, which nodulates

32

common bean roots, a gene was detected by Robleto et al. (1998) called tfx (Trifolitoxin), that

affects the competitiveness of the R. etli strains. A bacterial stress gene, moc, (Buts et al., 2005)

was identified with a role in nodule stabilization in the bacteroids of nodulated legumes. Sánchez

et al. (2013) detected two genes with important roles in nodule regulation, including the nap

gene encoding a periplasmic nitrate reductase and nos, encoding a N2O reductase.

2.4.3.2 Genes controlling infection and nodule development

The genes involved in the infection and nodule development process in several plants can

be categorized into two main groups, of early and late nodulin genes. The genes enod2, enod12

and enod40 are the early and leghemoglobin and uricase are examples of late nodulin genes.

While the early nodulin genes encode proteins responsible for the “molecular dialogue” during

the infection process, late nodulin genes encode proteins that play roles in the transformation of

bacterial into bacteroids and their settlement inside the nodules (Van De Sande et al., 1997).

In two different studies on R. meliloti, Ariel et al. (2012) and González-Rizzo et al. (2006)

detected knOx and Cre1 genes, involved in cytokinin function, as essential in nodule

development. In another study done by Suzaki et al. (2013), a gene named LHK1encoded the

lotus histidine kinase which had a role in nodule initiation and primordium development. A gene

called MtNIN was highlighted by Marsh et al. (2007) in the legume model of Medicago

truncatula with downstream effects on the early nod factor (NF) signaling in sequential and

spatial formation of root nodules. Another gene, CEP1, a peptide-encoding gene, noted by Imin

et al. (2013), controls lateral root and nodule numbers in Medicago truncatula. ACC gene affects

Rhizobium symbiosis and nodule senescence through coding for aminocyclopropane 1-

33

carboxylase (Nukui et al., 2006) and macronutrient degradation and remobilization (Xi et al.,

2013).

2.4.4 Environmental regulation of SNF

In addition to host genotype and rhizobia strain effects and their interaction, environmental

conditions, can affect SNF (Zahran, 1999; Hungria and Kaschuk, 2014). Most importantly,

factors that directly influence legume growth, such as soil moisture, nutrient availability,

pathogens and pests, crop husbandry practices (e.g., tillage, soil, nutrient and water management

and the use of crop protection practices) and natural resource management can limit SNF, either

through limiting the presence of effective rhizobia in the soil (Zahran, 1999; Giller, 2001), by

enhancing competition for soil minerals (Peoples et al., 2009; Weisany et al., 2013) or limiting

carbon supply to the SNF.

2.4.4.1 Soil moisture stress

SNF is an extremely drought-sensitive process. The evidence suggests that drought stress

could restrict SNF in legumes such as alfalfa, Medicago sativa (Abdel-Wahab and Zahran,

1983), pea and soybean (Gil-Quintana et al., 2013), broad bean, Vicia faba (Guerin et al., 1990),

common bean (Serraj and Sinclair, 1998) and the model legume Medicago truncatula (Gil-

Quintana et al., 2013). Water scarcity could affect N-fixing legume crops through oxygen

limitation, through an increase in nodular oxygen diffusion resistance (Durand et al., 1987), and

carbon shortage (Serraj and Sinclair, 1998). However, it does not have any impact on existence

and survival of rhizobia (Waldon et al., 1989). Moreover, Marino et al. (2007) in a partial

34

drought experiment where the roots were split, with half of the root system irrigated at field

capacity and the other half maintained with limited available water, observed that plant carbon

metabolism, protein synthesis, amino acid metabolism, and cell growth were the most affected

plant characteristics under drought stress in nodulted soybean plants (Gil-Quintana et al., 2013).

In spite of reports showing an inhibitory effect of drought stress on SNF, legume species showed

significant genetic variation in their ability to fix N2 under drought. For example, genotypes with

N2 fixation tolerance to water deficit were detected in soybean (Sinclair et al., 2000; Chen et al.,

2007; Devi and Sinclair, 2013), common bean (Serraj and Sinclair, 1998), and peanut (Dinh et

al., 2013).

2.4.4.2 Heat stress

Heat stress is a major limitation for SNF by legumes, not only in semiarid and arid regions,

but also in tropic of areas. Heat stress adversely affects rhizobium effectiveness and reduces host

legume and symbiont growth and development (Hungria and Kaschuk, 2014). High soil

temperatures strongly limit rhizobium populations and SNF in legumes, due to a delay in

nodulation or its restriction to the subsurface regions of the soil profile (Graham, 1992). The

critical temperature for SNF varies (from 30 to 42 oC) and is a function of the legume and

rhizobium strain. Graham (1992) indicated that for most rhizobium strains, 28 to 31°C is the

optimum temperature range of the soil with a threshold of 37°C, as many strains are not able to

grow after this temperature.

Inhibitory effects of high temperature were reported in some crops. For example, Piha and

Munns (1987) indicated a significant decline in nodule size of bean plants due to high

35

temperature. They reported that nodules were smaller at 35°C and subsequently low specific

nitrogenase activity was recorded. Moreover, Hernandez-Armenta et al. (1989) indicated that

transferring the nodulated plants from a daily temperature of 26 to 35°C significantly decreased

SNF.

However, there are some reports that legumes (Hungria and Franco, 1993) and rhizobium

strains (Graham, 1992) have a capacity to adapt to high temperature stress to maintain SNF at an

effective rate. Synthesis of heat-shock proteins has been reported as one of the most notable

mechanisms of heat tolerance in heat-tolerant bean genotypes and rhizobium strains (Graham,

1992; Michiels et al., 1994).

Heat stress and drought stress often occur together and synchronously making it difficult to

separate their effects (Sinclair and Serraj, 1995; Zahran, 1999). Many researchers have found

high performance rhizobia strains in symbiosis with legumes under both moisture deficiency and

heat stress (Hunt et al., 1981; Osa-Afiana and Alexander, 1982; Busse and Bottomley, 1989). So

far, many high-SNF legume landraces and cultivars have been identified under drought or high

temperature (Rai and Prasad, 1983; Venkateswarlu et al., 1983; Keck et al., 1984; Devi et al.,

2010).

2.4.4.3 Salinity stress

Soil salinization is also a major restriction for SNF and growth in legumes (Munns, 2005).

Salinity generally affects photosynthesis, and nitrogen and carbon metabolism in legumes

(Balibrea et al., 2003). High soil salinity can be lethal to legumes and rhizobia (Serraj and Adu-

36

Gyamfi, 2004). However, there are some reports showing genetic diversity of these organisms

for salt tolerance. Among different legumes some genotypes have been detected with high SNF

under salinity stress. For example, Tajini et al. (2012) reported SNF tolerance in two common

bean genotypes to moderate salinity stress. These genotypes could maintain adequate leaf area

and numerous active nodules under salt stress. There is some evidence of genetic diversity in

tolerance of SNF in other legumes to salinity, e.g., in alfalfa (Bernstein and Ogata, 1966), in

chickpea (Rao et al., 2002), and in broad bean (Cordovilla et al., 1995). However, Cordovilla et

al. (1995) believed that legume and rhizobial response to salinity depends on many factors such

as climatic conditions.

So far, many Rhizobium strains have been introduced to improve yielding ability of N-

fixing legumes under salinity stress (Sharma et al., 2013). However, there are some reports

indicating that the legume-Rhizobium symbioses and nodule formation in legumes are more

sensitive to salt stress than free-living rhizobia (El-Shinnawi, 1989; Zahran, 1991). Therefore,

inoculation of legumes with salt-tolerant bacteria could improve the SNF under salinity stress,

but it may not be enough to restore levels to non-stressed states (Craig et al., 1991). It seems that

tolerance of host plant is more important in this case for SNF improvement under saline

conditions.

2.4.4.4 Soil pH stress

A common problem caused by low pH in legumes, especially lower than 5, is a significant

decline in nodulation, even where a viable Rhizobium population is present in the field (Graham,

1992). Nodulation of legumes appears to be more sensitive to low pH than is plant growth

37

(Graham, 1992). Most leguminous plants require a neutral or slightly acidic soil for growth,

especially in SNF-dependent environments (Bordeleau and Prevost, 1994). Unsuitable soil

acidity could affect both rhizobia and the plant host. It could reduce SNF by limiting the

population of rhizobia in the soil and reducing nodulation (Graham et al., 1982; Ibekwe et al.,

1997). However, both legumes and rhizobia have varied responses to the soil pH. For example,

leguminous species exhibit a very wide range of acidity tolerance from extremely sensitive to

relatively high tolerance, e.g., alfalfa (M. sativa) and Lotus tenuis, respectively (Correa et al.,

1997). Highly tolerant rhizobium strains, usually, but not always, perform better in low pH soil

conditions in the field (Graham et al., 1994). Slow growing bacteria, Bradyrhizobium strains, are

generally more acid tolerant than fast growing ones though some strains of the fast-growing

rhizobia, e.g., R. loti and R. tropici (Graham et al.,1994) are acid tolerant. Acid tolerant bacteria

slow growing show less fluctuations of their cytoplasmic pH under different soil acidity (Chen et

al., 1993). R. leguminosarum bv. phaseoli is known as an acid-tolerant strain. This bacterium

increases its cytoplasmic potassium and glutamate levels in response to soil acidity, as reported

by Aarons and Graham (1991). Other acidity tolerance mechanisms reported in legumes include:

proton exclusion and extrusion (Chen et al., 1993), accumulation of cellular polyamines

(Fujihara and Yoneyama, 1993), synthesis of acid shock proteins (Hickey and Hirshfield, 1990),

and an acid tolerant composition and structure of the outer cell membrane (Graham et al., 1994).

Genetic studies of rhizobial tolerance to acidity revealed at least two loci conditioning this

trait (Chen et al., 1993). The host cultivar-rhizobial strain interaction is also important in SNF

response to soil acidity (Vergas and Graham, 1988). Vargas and Graham (1988) examined the

host plant-rhizobial interaction in common beans with Rhizobium strains under acidic conditions.

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC98982/#B134


38

They reported that nodule occupancy of each strain was significantly influenced by the effect of

host cultivar, ratio of inoculation, and soil pH.

Caetano-Anolles et al. (1989) and Vergas and Graham (1988) indicated that an early stage

of nodulation process, when the rhizobia are attached to the root hairs, is the sensitive period of

nodualtion to low pH. Soil acidity impacts the survival and persistence of rhizobia and it could

be the main reason for nodulation failure (Graham et al., 1982). There are also some indirect

effects of acidity on SNF. In acidic soils, with pH of <5.0, metal sensitivities (to common metals

activity such as aluminum (Al) may be high. Decreasing the soil pH and increasing the

availability of Al inhibits legume nodulation (Bordeleau and Prevost, 1994). Both rhizobia and

legume species respond differently to high Al levels, with some plants being significantly and

more strongly affected by the metal than are the rhizobia (Graham, 1992). Taylor et al. (1991)

believed that improvement of Al tolerance is achieved by improving the plant, rather than the

rhizobia. Toxicity effects of manganese (Mn) under acidic soil on SNF are also well known. For

instance, this toxicity was reported as one of the main reason for low SNF in beans (Döbereiner,

1966).

2.4.4.5 Macronutrients stress

2.4.4.5.1 Nitrogen

SNF is energetically expensive for the host plants; therefore, bacteria do not usually fix N2

in the presence of available forms of inorganic N. The inhibitory effect of nitrate on SNF of

legume plants has been known for a long time (Minchin et al., 1987; Serrano and Chamber,


39

1990; Arrese-Igor et al., 1997; Chamber-Pérez et al., 1997). Generally, inorganic N in the soil

reduces both nodulation and N2 fixation levels of legumes (Streeter, 1988, cited by Arrese-lgor et

al., 1997; Fujikake et al., 2003). Although there are a few reports of positive effects of low

nitrate concentrations (less than 5 mM) on N2 fixation in legume species such as soybean

(Streeter, 1982; Gremaud and Harper, 1989; Gulden and Vessey, 1997), nitrogen fertilization

negatively affects nodulation in common bean and the usually-recommended rates of 40-60 kg N

ha-1

suppress SNF in this plant (Graham, 1981).

Mineral N usually accumulates in soils over the fallow period, before planting the annual

legume in a typical annual cropping system. At the beginning and during the lag phase of plant

growth, SNF level is expected to be low but it is anticipated to gradually increase when the plant

enters its exponential phase of growth if itis not limited by poorly effective bacteria or

inadequate nodulation (Pate and Layzell, 1990). Indeed, the quick growth rate of plants reduces

the seasonal mineral N (Unkovich et al., 1997) through increased demand for N (Pate and

Layzell, 1990). Therefore, the plant photosynthetic rate would determine the amount of N2 fixed,

only when the symbiotic machinery is operating effectively.

It is also known that there are various mechanisms by which nif gene expression is down-

regulated and nitrogenase activity is inhibited by increases in available N for the plant (Merrick,

1993). Mineral N can impact SNF during root infection by affecting nitrogenase activity and/or

the ratio of the nodule dry mass to the whole plant mass (Streeter, 1988, cited by Arrese-lgor et

al., 1997).

40

It is known that nitrogenase activity mainly relies on the concentration of free O2 inside the

nodules (Layzell and Hunt, 1990), which is controlled by the plant host (Minchin, 1997). The

host plant can regulate the available free O2 inside, due to the presence of leghemoglobin and the

diffusion resistance (Robertson et al., 1984). It is known that available nitrate in the soil may

directly or indirectly influence the proficiency of gas diffusion resistance (Arrese-Igor et al.,

1997).

2.4.4.5.2 Phosphorus

Phosphorus (P) is an essential element in various molecular and biochemical plant

processes, including construction of high energy compounds such as ATP and NADPH for

energy acquisition, storage and consumption (Epstein and Bloom, 2005). SNF is an expensive

process in terms of energy consumption and the availability of P determines the efficacy of any

energy-generating metabolic process (Plaxton, 2004). SNF is, therefore, a high P demanding

process and any P deficiency can supress legume SNF (Schulze et al., 1999). P deficiency also

causes a severe limitation on nodulation. The P content of nodules is usually greater than the

roots and shoots, particularly in environments with P shortages (Adu-Gyamfi et al., 1985). These

results indicate that nodules are stronger sinks for P than are the other parts of plant (Hart, 1989)

and suggest that a P shortage can have a bigger impact on nodulation than on plant growth

(Singleton et al., 1985; Leung and Bottomley, 1987; Saxena and Rewari, 1991).

Rhizobial and plant diversity for P requirements have been reported. For instance, Beck

and Munns (1985) indicated that slow-growing rhizobial strains were more tolerant to low levels

of P than the fast growing ones. Pereira and Bliss (1987) reported genetic diversity in common

41

bean for the response to P deficiency. Nodules usually compete for P with the vegetative sinks

such as shoots and roots in legumes (Jakobsen, 1985).

2.4.4.5.3 Potassium, Calcium and Sulfur

The availability of other macronutrients can also affect SNF. Potassium, K, is an essential

element for some rhizobia (Vincent, 1977). Incorporating calcium (Ca) into acidic soils can

eliminate the inhibitory effect of acidity on SNF, through increases in soil pH, as reported in

common bean (Buerkert, 1990) and alfalfa (Pijnenberg and Lie, 1990). SNF limitations have also

been reported in T. subterraneum (Banath et al., 1966), soybean (Blevins et al. 1977), and alfalfa

(Miller and Sirois, 1983) due to calcium deficiencies. Calcium deficiencies affect all stages of

the nodulation process, including root hair infection, attachment of rhizobia to the root hairs,

(Smith et al., 1992), nodulation and nodule development (Alva et al., 1991). In legumes,

limitation of sulfur (S) can also reduce N2 fixation by affecting nodule development and function

(Scherer, 2008).

2.4.4.6 Micronutrients

2.4.4.6.1 Boron

Boron (B) deficiency can cause significant declines in SNF (Yamagishi and Yamamoto,

1994). Bellaloui and Mengistu (2015) reported that foliar B resulted in a higher nitrogen fixation

in soybean plants. Bonilla et al. (1997) indicated that B deficiency impacts SNF through a

decrease in the number of nodules, when the existing nodules show structural aberrations.

42

2.4.4.6 .2 Copper

Copper (Cu) has been reported as an important micro-element in SNF in some legumes

(Sekliga, 1998). For instance, a need for Cu has been demonstrated for several rhizobial strains,

particularly R. leguminosarum bv. phaseoli (Johnston et al., 1978) and some legumes, such as

subterranean clover, white clover and Lupin (Cited by: Sekliga, 1998). Cu also plays a role in the

nitrogenase proteins encoded by nif genes in R. leguminosarum bv. phaseoli (Johnston et al.,

1978; Ruvkin and Ausubel, 1980). Different sensitivities of N-fixing grain legumes to Cu have

been reported (Sekliga, 1998).

2.4.4.6.3 Iron

Poor nodulation caused by Fe deficiency has been reported in chickpea (Rai et al., 1982),

lentil (Rai et al., 1984) and French bean (Hemantarajan and Garg, 1986). Indeed, Fe deficiency

affects both nodule initiation and formation, which results in a significant decline in the number

of nodules and plant biomass (Tang et al., 1990). Tang et al. (1990) indicated that Fe

requirements for nodule formation are greater than for host plant growth. It is also believed that

Fe deficiency directly restricts nodule function, because a reduction in nitrogenase activity has

been observed in Fe-limited nodules (O’Hara et al., 1988). This view is consistent with the fact

that Fe is also a key structural element of leghaemoglobin, nitrogenase, nitrogenase reductase,

FeMoCo cofactor, cytochromes and other electron donors (Delgado et al., 1998).

Although Fe deficiency did not significantly affect shoot growth, it severely depressed

nodule mass and particularly leghemoglobin content, number of bacteroids and nitrogenase

43

activity, compared with those plants five days after a foliar spray of Fe in peanut (O’Hara et al.,

1988). In contrast to peanut, in lupin (Lupinus angustifolius) Fe is not re-translocated into the

nodules after a foliar spray, and directs iron supply at the infection sites on the roots was required

for effective nodulation (Tang et al., 1990). In laboratory conditions, the lack of Fe has dramatic

effects on nodule development. In lupin and peanut, nodule development is much more

susceptible to a shortage of Fe than are other plant characteristics related to shoot and root

growth (O’Hara et al., 1988).

2.4.4.6.4 Molybdenum

It is well known that N2-fixation is much more sensitive to a Mo deficiency than is host

plant growth (Parker and Harris, 1977). This is attributed the role of Mo as a cofactor in the Mo–

Fe nitogenase protein. It contains two atoms of Mo and organizes the active site of the Mo-

containing nitrogenase protein in N2-fixing organisms (Allen et al., 1999). Mo provides the site

of N2 reduction to NH3 during N2 fixation (Smith, 1977, cited by: Brodrick and Giller, 1991).

Franco and Day (1980) reported that in the soil acidity range of 5.3-6.0, Mo application

significantly increased N2 fixation. At low Mo levels, in large seeded legumes, such as Andean

common bean, it is translocated to the nodules from the planted seed to support SNF (Brodrick

and Giller, 1991).

2.4.4.6.5 Nickel

Nickel (Ni) has both inhibitory and promoting effects on SNF at high and low

concentrations in soil, respectively. Singh and Rao (1997) reported that Ni at a low concentration

44

(1 μg mL-1

) promoted SNF through an increase in nodule number and nitrogenase activity.

However, Ni at high concentrations (>10 μg mL-1

) had inhibitory effects on SNF (Singh and

Rao, 1997). Buerkert (1990) and Dalton et al. (1985) reported that in many rhizobial bacteria Ni

is a key element in the hydrogenase activity during SNF as well.

2.4.4.6.6 Cobalt

Cobalt (Co) is an essential element for SNF because of its importance in rhizobial growth

(Cowles et al., 1969) and its role as a cofactor of cobalamin (Vitamin B6) involved in N2 fixation

and nodule growth (Licht et al., 1996; Jordan and Reichard, 1998). Cobalt is also required as a

part of a bacterial enzyme complex. A promoting effect of Co applications on N-fixing legumes

has been observed (Robson et al., 1979).

2.5 SNF estimation methods

An accurate method for measuring N2 fixing ability of legumes should be able to measure

the amount of N2 fixed by the crop. Bliss (1993) believed that distinguishing fixing from non-

fixing genotypes, while plants are growing in the field, is not possible so he recommended

measuring the SNF only on plants growing in an N-free medium under controlled environments.

Unkovich et al. (2008) mentioned that assessing the total amount of N accumulated during the

plant growing season simply gives the quantity of N2 derived from atmosphere through N

fixation in an N free medium. This opinion came from the fact that the early N2 fixation

assessment methods were based on plant N yield (La Rau and Peterson, 1981), which means that

soil N plant uptake was ignored in these methods (Herridge and damson, 1995). Thereafter, a

45

range of methods for field assessment of N2 fixation were developed. Most of the techniques to

estimate field N2 fixation in annual legume crops require computation of two assessments,

namely: the percentage of plant N derived from atmosphere (%Ndfa) and plant N yield, (Ny).

The most popular methods used to estimate N fixation are described below.

2.5.1 N difference technique

The N difference method is a simple and inexpensive technique which only relies on total

N analyses. It has a long history of utilization since its origin in the 1960s. In this technique, the

N2 fixation magnitude is estimated as the Ny difference value, between a reference plant (a non

N2 fixing crop) and a N2 fixing legume growing in a low N soil. The reference plant Ny is used as

an indicator of soil N absorbed by the N fixing legume. The accuracy of this method is a function

of the similarity of the root morphology between the reference plant and N fixing legume

(Herridge et al., 1995). Greater similarity makes it a reliable technique with the same accuracy as

other complicated methods if the reference and N fixing legume crop, growing in a low available

N soil, have large difference in their Ny (Boddey et al. 1984)

2.5.2 Acetylene reduction assay (ARA)

The reduction of acetylene (C2H2) to ethylene (C2H4) is also catalysed by nitrogenase

during SNF. This reaction creates a unique opportunity to estimate nitrogen reduction indirectly,

with the acetylene reduction assay (ARA). In this technique, the concentration of C2H2 and C2H4

is measured using the highly sensitive gas chromatographic procedures on tissues undergoing

SNF. The rate of C2H2 production gives an estimate of SNF (Vessey, 1994). ARA is known as a

46

simple, quick and inexpensive method (Hardy et al., 1973). However, since the 1980s it has been

broadly criticized for its low accuracy in field studies (Boddey et al., 1984). These criticisms at

first arose from the difficulties in root sampling. Nodules could not easily be recovered and

physical damage as well as their exposure to acetylene impacted their activity. Moreover, some

other disadvantages have been mentioned, including the complexity of applying this method in

the field. Calibration of the assay for each and every SNF evaluation must be accurately done. In

addition, significant extrapolation of the assay results in time need to be done because of the

short assay times that are typically used (Unkovich et al., 2008). This method is also very

environmentally sensitive, due to high sensitivity of ARA to soil moisture conditions (Huang et

al., 1975).

2.5.3 Relative ureide-abundance technique

In members of the Fabaceae family, N fixed through SNF in nodules can be transported

towards the shoots in two different forms (Schubert, 1986). Legumes, originating from temperate

regions, such as species of Phaseoleae and Desmodieae within the Papilionoideae subfamily,

mainly transport N in forms of ureides (allantoin and allantoic acid) from their nodules (Sprent

2001). These legumes are categorized in the ureide group. In the rest of the legume species N is

exported from the nodules to the shoots in the form of amides, either asparagine or glutamine so

they are labeled as belonging to the amide group (Atkins 1991, Pate 1989).

In the late 1970’s, nodules were discovered to be the central place of ureide synthesis

(Herridge et al., 1990). The ureides are exported to the upper parts of the plant through the xylem

along with NO3-. A relative ureide-abundance technique was developed, based on measuring of

http://jxb.oxfordjournals.org/content/57/1/5.full#ref-37

47

the ratio of ‘ureide’ to ‘ureide + NO3-’ to estimate %Ndfa at the time of measurement (Peoples,

2009). It is recommended making a few (at least four) samplings (destructive harvests) of the

xylem sap to estimate the ratio of ‘ureide’ to ‘ureide + NO3-’ during plant development (Peoples,

2009). It is possible to measure the total %Ndfa through the plant development cycle. Many

studies were later conducted to estimate %Ndfa using this method (McClure and Israel, 1979)

and later Peoples and Herridge (1990) listed more than 25 tropical species in which the xylem

sap ‘ureide’ to ‘ureide + NO3-’ ratios were substantially correlated to estimated %Ndfa.

Advantage of this technique include that it does is not involve a technically demanding

procedure (Herridge et al., 1990), and does not require high-priced or complicated equipment to

perform (Unkovich, 2008). Moreover, it can provide a good estimate of %Ndfa without any

need to evaluate Ny (Peoples et al, 2009). However, because of two main drawbacks, the ureide

method is not known as a reliable way to estimate %Ndfa. First, “each field measurement reflects

N2 fixation by the crop at, or shortly before, the time of the assay” (Unkovich, 2000). As Aveline

et al. (1995) caution, it is very risky to estimate the whole growth period %Ndfa of the plant

based only on a single measurement. Moreover, Alamillo (2010) illustrated that during water

deficit conditions the accumulation of ureides in ureidic plant tissues comes not only from

synthesis of ureides in nodules, but it can also be from plant response to drought stress.

2.5.4 Nitrogen-15 (15

N) isotope techniques

Nitrogen has two naturally stable isotopic forms 14

N and 15

N, the first one of which, the

lighter isotope, is more abundant than the second one in nature. The concentration of 15

N in

atmospheric N2 is at the constant rate of 0.3663 atoms% (Högberg, 1979). Screening of the

48

difference between the 15

N concentration in the air and that of soil N, which is mostly 15

N, in the

N extracted from the plant, is the basis of three major 15

N isotope techniques described below.

2.5.4.1 Total 15

N balance technique

The total 15

N balance technique is based on exposing the whole soil/plant system to 15

N-

enriched air. The plant systems, including N-fixing legumes and non-fixing reference plants,

uptake soil N with the same isotopic composition. In this way, any difference between 15

N

utilization by the N-fixing legume and the reference plant can provide an estimation of %Ndfa

(Eskew et al., 1981). The total 15

N-balance technique requires very accurate analysis of 15

N in

both soil and plant at the planting date, and after harvest the quantification of total N in the

system is analysed again. During this quantification, N losses from the system must be

minimized and measured by completely controlling the isolated environment for light intensity,

temperature, CO2 and O2 pressures, etc. In spite of the fact that it is applicable to long term field

studies, the best results are obtained from indoor tests (Lima et al., 1987). This method is rather

expensive in terms of instruments and 15

N -labeled materials. This technique is, therefore,

practically impossible to apply in the field.

2.5.4.2 15

N -enrichment isotope-dilution (ID) technique

This method was the most commonly used approach for %Ndfa estimation in the 1970s

and 1980s (Peoples et al, 2009). For the ID method, the soil is artificially enriched with 15

N by

adding 15

N-labled fertiliser (e.g., ammonium sulphate, urea and/or calcium nitrate). To determine

the possible range of 15

N enrichment, it is recommended that several non-fixing reference plants

49

be sampled and slow-release labeled fertiliser is applied to prevent any leaching of the plant-

available 15

N during the enrichment process (Watanabe et al., 1990; McNeill et al., 1998). Many

protocols have been introduced for applying the ID technique, all based on the same principles

(Chalk, 1985). 15

N enriched fertilizer will be absorbed by both the N-fixing legume and

reference plant; the 15

N concentration of these two groups of plants will be different because the

N-fixing legume can dilute it by absorbing 14

N from atmospheric N2 (99.6337%), but it would be

constant in the reference plant. To determine the dilution value of the 15

N enriched fertiliser, both

N-fixing and reference plant materials are analysed for their 15

N content, following the harvest.

%Ndfa can be then estimated by the 15

N content of the N-fixing legume relative to that of the

non-N2-fixing plant, as the representative of 15

N enriched soil pool (Unkovich and Pate, 2000).

Danso et al. (1993) reported that a complication associated with the ID technique is the

difficulty of establishing the space and time of the 15

N labelling procedure. This is related to the

fact that the efficiency of N uptake by plants ranges from approximately 5%-90% (Peoples et al.

1995) and that N uptake pattern of plants are divergent Danso et al. (1993). Also, the

incorporation 15

N labeled fertilizer into the soil makes two soil inorganic N pools, including the

15N enriched pool and natural abundance pool (Peoples et al., 2009). A significant portion of

available N in the soil enrichment using 15

N labeled fertilizer comes from contributions of the

native inorganic N soil, unlabeled pool (Peoples et al., 2009). All these make it hard to get a

reliable estimate of the plant’s enriched mineral N (Watanabe et al., 1990; McNeill et al., 1998).

To alleviate this problem, the ID procedure was modified by Fried and Broeshart in 1975.They

suggested applying different rates of 15

N labeled fertilizer to the fixing and non-N2-fixing,

reference, plants according to differences in their corresponding orders for N. However, Chalk

(1996) indicated that Fried and Broesharts’ modification could not noticeably improve this

50

methodology. Labeling the soil N by 15

N labeled fertilizer is necessity in both, modified or

original technique. The other problem with this method is negative effect of the 15

N fertilizer

addition on legume nodulation. Even if it does not have any impact on nodulation, even under

conditions where 15

N fertiliser addition does not affect nodulation, nodule activity and

subsequently SNF may still be weakened. Therefore, some researchers believe that there are

numerous circumstances where assessments of N2 fixation, utilizing the ID technique, are

mistaken (Danso et al., 1993).

2.5.4.3 15

N natural abundance (NA) technique

The invention of gas chromatographs equipped with mass spectrometers, GC-MS (Gohlke

and McLafferty, 1993) encouraged scientists to develop an alternative technique to the ID

method. The method known as the 15

N natural abundance (Peoples et al., 2009) is a widely used

technique. The ratio of the two natural stable isotopic forms of N2, 15

N:14

N, can be measured by

mass spectrometry. The natural 15

N abundance is therefore conventionally estimated as

δ15

Nvalues, where

(𝛿15𝑁 =(15𝑁/𝛿14𝑁𝑠𝑎𝑚𝑝𝑙𝑒) − (15𝑁/𝛿14𝑁𝑎𝑡𝑚𝑜𝑠𝑝ℎ𝑒𝑟𝑒)

(15𝑁/𝛿14𝑁𝑎𝑡𝑚𝑜𝑠𝑝ℎ𝑒𝑟𝑒) × 100

15N values are expressed as parts per thousand or per mil (‰) with respect to the atmospheric N2

gas (Peterson and Fry, 1987).

51

Today, the 15

N natural abundance technique is one of the most easily applied methods in

field trials to evaluate the SNF activity of legume plants (Delwiche et al. 1979; Virginia and

Delwiche 1982; Shearer and Kohl 1986; Högberg 1997; Pauferro et al., 2010). The proportion of

15N atoms in the atmospheric N2 is always constant, and around 0.3663 atom %

15N (Högber,

1979) while the majority of N2 transformed in the soil is in the 15

N isotopic form of N.

This method relies on the different natural abundance of 15

N in soil N and atmospheric N.

The 15

N abundance in a non-N- fixing (reference) plant, which is all derived from the soil is

larger than that of a N fixing plant, which derives some of its N from atmospheric N through

SNF (Shearer and Kohl, 1986). Then the proportion of N in the legume plant derived from the

air (%Ndfa) can be quantified by interpreting this difference between the N-fixing and reference

plant. A critical assumption for this technique is that the N-fixing and non N-fixing plant have

the same ability to take up and accumulate N from the soil at the same 15

N abundance. Boddey et

al. (2000) suggested measuring 15

N abundance in several reference plants to define a possible

range of this value in woody plants. However in agricultural systems, minor variations in 15

N

natural abundance have been found among different non-N2-fixing reference plants (Unkovich et

al., 2000).

In the case of annual field crops, %Ndfa can be calculated with the equation presented by

Shearer and Kohl (1986):

%𝑁𝑑𝑓𝑎 =(𝛿15𝑁𝑟𝑒𝑓. 𝑝𝑙𝑎𝑛𝑡−𝛿15𝑁𝑁 𝑓𝑖𝑥𝑖𝑛𝑔 𝑝𝑙𝑎𝑛𝑡)

(𝛿15𝑁𝑟𝑒𝑓. 𝑝𝑙𝑎𝑛𝑡 − B)

[1]

52

where δ15

Nref. plant is the abundance of δ15

N in the non-fixing reference plant, δ15

Nfixing plant

is the 15

N abundance of the N-fixing legume and B is the δ 15

N of the legume obtaining its entire

N from N2 fixation in N-free medium. The B value has been shown to vary between legume

species, varieties of the same species, and different rhizobium strains (Boddey et al., 2000). It is

usually extremely difficult to recover all roots and nodules in field studies, but 15

N abundance of

the N derived from the air can be determined in the seed as described by Okito et al. (2004).

2.6 SNF in common bean

Common bean ranks low compared to the other grain legumes in terms of SNF. It was

claimed that SNF in common bean contributed less than 100 kg N ha-1

y-1

, which was 40 to 50%

of the total N found in bean plants at maturity (Westermann, 1981). However, substantial

genotypic variation for SNF has been reported in common bean germplasm. Hardarson et al.

(1983) compared 20 bean genotypes for SNF, using the 15

N isotope dilution method. They

reported significant SNF diversity among the genotypes with average and highest values of 35

and 70 %Ndfa, respectively. Rennie and Kemp (1983) reported that climbing beans were about

six times stronger than bush type beans in SNF. Afterwards, many other researchers recognized

genotypic variability for SNF in this species (Rennie and Kemp, 1983; Pereira et al., 1989; St.

Clair and Bliss 1991; Miranda and Bliss, 1991; Bliss, 1993; Hardarson et al., 1993; Kipe-Nolt,

1993, Peña-Cabriales and Castellanos, 1993; Tsai et al., 1993; Devi et al., 2013). In addition,

genetic diversity for nodulation-related traits was reported by some researchers, e.g., Graham and

Rosas (1977) and Park and Buttery (1989).

53

2.6.1 SNF improvement in common bean

Genetic variation in germplasm provides the raw material for crop improvement programs.

The findings mentioned in section 2.6 can, therefore, suggest that it is feasible to improve the

genetic potential of SNF in common bean. Bliss (1993) believed that common bean lines,

infected with effective rhizobia, could produce seed yields of 1-2 t ha-1

in field conditions

without any major yield limiting factors. In the 1980s and 1990s, plant breeders were

concentrating on combining high SNF efficiency into the elite common bean genotypes.

Selection for high SNF genotypes has produced breeding lines able to fix high levels of

atmospheric N2 (St. Clair et al., 1988; Miranda and Bliss, 1991; St. Clair and Bliss, 1991).

Bliss (1993) believed that an appropriate selection criterion to separate beans with superior

SNF ability from those that are inferior for SNF, combined with genetically diverse plant

materials segregating for SNF (or surrogate traits) and the use of a breeding method that provides

maximum breeding advance, are the main prerequisites for SNF breeding. Based on those

recommendations, field trials should be carried out without applications of large amounts of N

fertilizers.

In accordance with its genetic complexity, the genetic improvement of SNF is not an easy

task. A few reports (e.g., Pimratch et al., 2004; Sikinarum et al., 2007) suggest that the trait has

low to moderate heritability in legumes. It is, therefore, anticipated that there are strong

environmental and genotype × environmental effects on the SNF trait. Therefore, greater

selection efficiency can be achieved if selection is based on family mean rather than individual

values as the unit of selection (Barrona et al., 1999).

54

Moreover, direct selection of high SNF genotypes is difficult. SNF estimation in the field

is laborious and expensive, especially when the 15

N-isotope method (Shearer and Kohl, 1986) is

used. Most bean breeding programs do not routinely select for superior SNF per se. They select

on the basis of some easily measured agronomic and plant characteristics to determine

superiority for SNF and to develop an understanding of the inheritance of the trait (Pedalino et

al., 1992; Kipe-Nolt et al., 1993; Peña-Cabriales and Castellanos, 1993).

2.6.2 Relationships of SNF with agronomic traits

The presence of variation for SNF in the germplasm collection and the existence of

moderate to high associations between SNF and agronomic traits mean that nitrogen fixation per

se may be improved by introgressing positive alleles into locally adapted grain legume cultivars.

Even agronomic traits associated with SNF and grain yield could be used as surrogate traits to

improve high yielding varieties with improved SNF. However, the interaction of genotypes with

rhizobium (strain variability and effectiveness), and environment (location × year effect) could

complicate this breeding task. Therefore, this issue must be considered in any breeding effort for

high SNF.

So far, a few studies have been conducted to examine if there is any indicator or surrogate

trait for SNF improvement. In previous studies, a negative association between nitrogen fixation

and δ13

C, an indicator of water use efficiency in plants, has been reported (Knight et al., 1993;

Kumaarasinghe et al., 1992). However, there has been some discussion about the stability of this

association in different environmental conditions. Rennie and Kemp (1983) reported that

55

indeterminate and climbing beans were stronger N2 fixers than the bush type beans, due to longer

N fixation period, resulting from delayed flowering times and longer times to maturity.

The SNF process starts with the attraction of N-fixing bacteria by legumes. There is some

discussion in the literature whether the same infection mechanism is utilized by N-fixing bacteria

to establish nodules and bacterial pathogens to establish a disease state. In common bean,

breeders have over the years selected genotypes of beans that are resistant against to bacterial

diseases. Among the bacterial diseases of common bean, common bacterial blight (CBB), caused

by Xanthomonas axonopodis pv. phaseoli (Smith), has been a major disease of common bean, in

North America and elsewhere, for which bean breeders have introgressed resistance through

inter-specific crosses to close relatives (e.g., P. acutifolius) (Thomas and Waines, 1984; Scott

and Michaels, 1992). It is still unknown whether or not selection for resistance against the

bacterial pathogen causing CBB, over the years, has reduced the ability of the bean varieties to

establish symbiotic relationship with the beneficial bacteria.

A high and positive correlation of leaf color score (SPAD) with SNF suggests that this trait

as an indicator of SNF effectiveness in groundnut (Dinh et al., 2013) and soybean (Gwata et al.,

2004). Since SNF is adversely affected by low levels of P in the rhizosphere (Vance, 2001;

Schulze et al., 2006; Jemo et al., 2010) genetic differences for SNF under P deficiency may be

related to differences in P-uptake efficiency (Pereira and Bliss, 1987), which could be considered

as an indicator of SNF in low input environments. More studies are needed, involving diverse

germplasm and breeding populations to elucidate such relationships prior to exploiting them as

indices in breeding for high SNF in agriculturally important grain legume crops.

56

2.6.3 Molecular Tools in Common Bean

Narrow genetic diversity is the main restriction to achieving high rates of genetic gain in

breeding programs for many crops (Tanksley and McCouch 1997). It is likely that this is true for

common bean as well (Buso et al., 2006). Hence, detailed knowledge of the genetic diversity of

the available germplasm is the first prerequisite to improve the traits. Nowadays, DNA sequence

polymorphism marker technologies can provide enough knowledge of the genetic diversity in

breeding germplasm and also address the difficulty of direct selection for high SNF as well.

Some grain legumes have sufficient genomic data to identify genetic markers associated with

SNF. These valuable alleles could be transferred into the elite genotypes later. However,

compared with human and economic resources allocated to the research of other commercial

crops, efforts to develop molecular tools for the genetic analysis and breeding of common bean

are still limited.

In common bean, expressed sequence tags (ESTs), fragments of mRNA sequences derived

through single sequencing reactions performed on randomly selected clones from cDNA

libraries, are the source of gene-based markers, e.g., insertion-deletions (Indels) or single-

nucleotide polymorphisms (SNPs) (Cortés et al., 2011). For genetic studies the ideal molecular

marker should be generated in a high-throughput and cost-effective manner. However because of

the nature of these kinds of markers, the detection of polymorphisms is difficult. Instead,

development of intron-spanning EST-SNP markers has been developed as a valuable resource

for genetic studies (Cortés et al., 2011).

57

The search for molecular markers to increase the efficiency of selection is essential in plant

breeding programs. Among molecular markers, simple sequence repeats (SSRs) and single

nucleotide polymorphism (SNPs) are highly desirable, since they can be used for in-depth

genetic analysis (Cortés et al., 2011). SSR markers are highly polymorphic and evenly

distributed across the genome, co-dominant, and generate accurate and reproducible data (Yan et

al. 2010). However, these are not always low cost due to labor and time consumption (Brondani

et al., 1998; Ritschel et al., 2004). Single nucleotide polymorphism (SNP) markers exhibit high

levels of genetic diversity and low cost genotyping together with the potential for high

throughput (Cortés et al., 2011).

Single nucleotide polymorphisms (SNPs) are the most abundant class of polymorphic sites

in any genome, which are well distributed across the genome. They have become a powerful tool

in genetic mapping, association studies, diversity analysis and positional cloning (Rafalski, 2002;

Hyten et al., 2010a; Yan et al., 2010). SNPs are usually bi-allelic, therefore less polymorphic

than SSRs. However, this limitation is compensated for by the ability to use more markers and to

build SNP haplotypes (Rafalski, 2002). In common bean, SNP markers have mostly been used to

perform linkage map construction and synteny analysis (Galeano et al. 2009; McConnell et al.

2010). To date, gene-based markers have been used to develop transcript maps in chickpea

(Gujaria et al., 2011) and soybean (Choi et al., 2007). QTL analysis in cowpea, Vigna

unguiculata L. (Muchero et al., 2011), association mapping in Medicago truncatula (Friesen et

al, 2010) and synteny analysis in common bean (Galeano et al., 2009; McConnell et al., 2010)

have been reported as well.

58

In common bean, EST libraries of Negro Jamapa 8, a Middle American, and G19833, an

Andean, genotype were used to establish the first consolidated resource of SNP markers

(Ramirez et al., 2005). Ramírez et al. (2005) sequenced 21,026 ESTs from various cDNA

libraries (nitrogen-fixing root nodules, phosphorus-deficient roots, developing pods, and leaves)

derived from the Negro Jamapa 81 genotype and leaves from G19833 genotype. Approximately

10,000 of these identified ESTs were classified into 2,226 contigs and 7,969 singletons. It was a

wonderful resource of SNP markers for genetic studies in common bean. At the same time

Melotto et al. (2005) constructed three cDNA libraries from the Middle American breeding line

SEL1308 derived from 19-day old trifoliate leaves, 10-day old shoots, and 13-day old shoots

(inoculated with Colletotrichum lindemuthianum). Graham et al. (2006) integrated the complete

EST data set created by Ramírez et al. (2005) and Melotto et al. (2005) and provided 20,578

ESTs. Buso et al. (2006) constructed a genomic library enriched for AG/TC repeat sequences for

this species. They initially developed 20 microsatellite markers, 10 of which were characterized

using a panel of 85 representative accessions of the bean gene bank. Tian et al. (2007)

constructed a cDNA library of the Middle American genotype G19833 to identify genes

involved in response to phosphorous starvation derived from 6-day old seedlings from the

genotype. Later, Gaitan et al. (2008) identified a limited set of 25 SNPs from sequence fragments

obtained from DOR364, another Middle American genotype, and G19833. Galeano et al. (2009)

developed single-strand conformation polymorphism (SSCP) markers based on these ESTs and

named these BSNP (bean SNP) markers. Thibivilliers et al. (2009) detected 6,202 new common

bean ESTs by using a cDNA library assembled from the common bean rust resistant-cultivar

‘Early Gallatin’.

59

McConnell et al. (2010) generated gene-based SNP markers from the Andean genotype

JaloEEP558 and the Middle American genotype BAT93. Hyten et al. (2010b) discovered a total

of 3,487 SNPs of which 2,795 contained sufficient flanking genomic sequence for SNP assay

development. Furthermore, they designed a GoldenGate assay which contained 1,050 SNPs.

Kalavacharla et al (2011) obtained a substantial (31,664) transcriptome dataset for common

bean. Cortés et al. (2011) developed 94 SNP at 84 gene-based and 10 non-genic loci in a

diversity panel of 70 (41 Middle American and 29 Andean) genotypes. Galeno et al. (2012)

developed a total of 313 new gene-based markers. They placed a total of 53 new marker loci on

an integrated molecular map in the DOR364 × G19833 recombinant inbred line (RIL)

population. They also constructed the new map by merging the linkage maps of the BAT93 ×

JALO EEP558 and DOR364 × BAT477 populations. They succeeded to map a total of 1,060

markers, with a total map length of 2,041 cM across 11 linkage groups.

The first reference genome for common bean was introduced by Schmutz et al. (2014)

based on the assembly of 473 Mb of the 587-Mb genome and genetically anchoring 98% of this

sequence into 11 chromosome-scale pseudomolecules. The researchers compared this reference

genome with the soybean genome to identify changes in soybean resulting from polyploidy.

They also confirmed two independent origins of domestication for common bean, based on gene

pools of Andean high throughput

2.6.4 Applications SNP markers in common bean research

Genotyping technologies such as Single Nucleotide Polymorphism (SNP) can be used to

study the association of DNA markers with phenotypic data in germplasm collections or other

60

plant populations (Beebe et al., 2013).Using these technologies quantitative trait loci (QTL)

associated with desirable phenotypes (i.e., higher SNF ability, and seed yield) can be identified.

SNPs can be used in breeding and genetics programs for construction of linkage maps, genetic

diversity analyses, or marker-phenotype association studies, and ultimately for marker-assisted

selection (MAS) (Cortésetal.,2011). More than 30 approaches for SNP detection have been

proposed and applied in different plant species (Gupta et al., 2008). SNP detection in common

bean has been applied using Single Base Extension (SBE) (Gaitán-Solís et al.,2008), Expressed

Sequence Tags (EST) (Galeano et al., 2012), NGS (Hyten et al., 2010b) and competitive allele-

specific PCR (KASP) assays (Cortésetal.,2011; Gorettietal.,2014). SNP information from

common bean has previously been used to build linkage maps and to conduct synteny analysis

(Galeanoetal.,2009, 2012; Shi et al., 2011; Yuste-Lisbona et al., 2012). SNPs have been used for

the identification of QTL associated with SNF ability and related traits in a RIL population of an

inter-genepool cross (Ramaekers, et al., 2013) and in a genome-wide association analysis of SNF

in common bean (Kamfwa et al., 2015).

2.6.5 Quantitative trait loci (QTL) associated with SNF traits

Knowledge of the inheritance of SNF, not only in common bean, but also in other

economically important legumes, is very limited. Studies of SNF (Del Rosario et al., 1997),

nitrogenase activity (Nigam et al., 1985; Miller et al., 1986; Phillips et al., 1989) and nodulation

related traits (Isleib et al., 1980; Bhapkar and Deshmukh, 1982; Nigam et al., 1985; Miller et al.,

1986; Phillips et al., 1989; Del Rosario et al., 1997; Franco et al., 2001; Phudenpa et al., 2006)

suggest that these traits are controlled by additive or non-additive genes, with some evidence of

61

epistatic interactions. All studies suggest that SNF has a complex inheritance with involvement

of multiple genes.

Understanding the genetic basis of SNF by identifying quantitative trait loci (QTL) for

SNF-associated traits, therefore, advances the proficiency of breeding efforts to improve it.

However, few quantitative trait loci (QTL) and candidate genes underlying QTL associated with

SNF have been identified in model legumes and soybean. Most of these QTL are for nodulation-

related traits. For instance, eight and seven QTL in a RIL soybean population associated with

nodule number (NN) and nodule size, respectively, were detected by Hwang et al. (2014). Three

and one QTL for NN and ratio of nodule dry weight to nodule number (NDW/NN), respectively,

were reported by Santos et al. (2013) in soybean. In another study, Nicolás et al. (2006)

identified two QTL explaining up to 15 % of the diversity of NN and NDW in a soybean RIL

population. Bourion et al. (2010) reported nine QTL for NN and four genomic regions for NDW

were determined in a RIL population of pea.

To date, only six QTL studies on SNF traits in common bean have been published

(Nodari et al. 1993; Tsai et al. 1998; Souza et al. 2000; Franco et al., 2001; Ramaekers et al.;

Kamfwa et al., 2015). The majority of these studies identified quantitative trait loci (QTL) with

the focus on just SNF related traits, particularly nodulation traits. The first QTL study on NN

trait in common bean was done by Nodari et al. (1993), who reported four genomic regions for

NN in a RIL population derived from a cross between a Middle American genotype, BAT93

(low nodulating; moderately common bacterial blight, CBB, resistant), and the Andean

genotype, Jalo EEP558 (high nodulating; susceptible to CBB). One of these four genomic

regions influenced both NN and CBB resistance/susceptibility (Nodari et al., 1993). Later, Tasi

62

et al. (1998) screened the same RIL population, but at two different soil N levels. They

confirmed previous findings by Noradi et al. (1993) only in the low N level. Tsai et al. (1993)

reported three other QTL for NN in high levels of soil N. In 2000, Souza et al. constructed 12

linkage groups for common bean and identified 15 markers associated with NN on seven linkage

groups and seven QTL for NN in the same RIL population as screened by Noradi et al. (1993)

and Tsai et al. (1998). Souza et al. (2000) indicated that NN and CBB resistance in common bean

have four overlapping QTL. The stability of these QTL was claimed to be a function of the

adaptability of genotypes to diverse soil fertility (Souza et al., 2000). These three groups of

researchers did not construct similar numbers of linkage groups for QTL analysis of the traits.

There have been a few genetic studies of SNF traits in legumes, especially in soybean. For

instance, two QTL associated with plant acetylene reduction activity (ARA) were reported by

Tanya et al. (2005) in soybean. In addition, a QTL for ARA was reported by Tominaga et al.

(2012) with a pleiotropic effect on SNF and nodule weight in the model legume, Lotus

japonicas. ARA is very highly sensitive to soil moisture conditions (e.g., Huang et al., 1975).

Therefore, these findings should be examined in different environmental conditions. To address

the disadvantage of ARA method, it has been suggested that a more efficient method of SNF

measurement in the field, such as the natural 15

N abundance (Shearer and Kohl, 1986) be used.

Despite the availability of the more reliable natural 15

N abundance method for SNF

measurement, there have only been two studies of quantitative trait loci (QTL) associated with

SNF in common bean (Ramaekers et al., 2013; Kamfwa et al., 2015). Ramaekers et al. (2013)

conducted a QTL analysis of SNF and related traits in a bi-parental recombinant inbred line

(RIL) population derived from a cross between a climber, Middle American, genotype and a

bush, Andean, genotype. They constructed 11 linkage groups and identified two QTL accounting

63

for up to 21% of phenotypic variance in nitrogen content (N%) in a greenhouse. One of the QTL

overlapped with a QTL reported for total N content in a one year-location field trial. In another

greenhouse experiment, they detected two QTL, accounting for up to 18% of total variation of

total plant N fixed, on linkage groups 1 and 4, while in the field trial in only one site one QTL

for percentage of nitrogen derived from atmosphere (%Ndfa) at harvest was identified on linkage

group 4. Recently, Kamfwa et al. (2015) in an Andean diversity panel, including 237 common

bean genotypes, detected 11 SNPs, five on Pv03 and six on Pv09 for %Ndfa.

Ramaekers et al. (2013) detected two candidate genes, for an auxin-responsive

transcription factor and AP2/ERF-domain-containing transcription factor, underlying these QTL.

These candidate genes are associated with differences in growth between climbing and bush

beans and possibly yield and N accumulation and total amount of symbiotic nitrogen fixed,

respectively. Kamfwa et al. (2015) reported one gene, encoding leucine-rich repeat receptor-like

protein kinases (LRRRLK), as a potential candidate gene for %Ndfa. Both research groups had

only one location-year data for their field studies.

With the exception of a genome-wide mapping population of the Andean gene pool by

Kamfwa et al. (2015), all genetic studies of either SNF or its related traits in dry bean have been

conducted using bi-parental recombinant inbred line (RIL) populations originating from crosses

between two different common bean gene pools, Middle American and Andean. This was done

to take advantage of greater genetic diversity due to lack of SNF genetic diversity within each

gene pool. Recent findings reveal that there are molecular markers closely associated with SNF

which could potentially provide useful tools to increase the efficiency of selection for high SNF

64

in breeding programs. However, the stability of these QTL over different environments must be

considered before using them in applied crop breeding (Dwivedi et al., 2007).

65

CHAPTER 3

N2 Fixation Ability of Different Dry Bean Genotypes1

1 A version of this chapter has been published at Farid, M., and A. Navabi. 2015. N2 fixation

ability of different dry bean genotypes. Can. J. Plant Sci. In press. doi:10.4141/CJPS-2015-

084.

66

3.1 ABSTRACT

Common bean (Phaseolus vulgaris L.) is generally known as a weak N2 fixer compared

to other legumes. The availability of genetic variation for N2 fixation potential of common bean

supports the idea that symbiotic nitrogen fixation (SNF) in common bean can be improved

through breeding. To assess the potential N2 fixation of selected common bean genotypes, twelve

bean varieties including 3 Andean and 9 Middle American were screened for SNF ability and

related traits in controlled environments and field trials in Ontario, Canada. A non-nodulating

mutant, R99, was used as the reference plant to estimate percentage of nitrogen derived from the

atmosphere (%Ndfa) through the natural 15

N abundance method. Significant variation was found

among the bean genotypes for %Ndfa and its related traits. Environmental and genotype by

environmental effects significantly influenced %Ndfa and its related traits. The three Andean

bean genotypes were superior to Middle American genotypes for nodulation ability, while the

Middle American genotypes were generally stronger nitrogen fixers in optimum soil moisture

conditions. In general, nitrogen fixation was found to be significantly associated with seed yield

and carbon isotope discrimination, an indicator of water use efficiency.

67

3.2. INTRODUCTION

Common bean (Phaseolus vulgaris L.), with in excess of 23 million tonnes of global

production in 2012 (FAOSTAT, 2013), is an important pulse crop in the world. As a source of

protein, fiber, and minerals (Singh, 1999), common bean is a staple food for over 500 million

people in Latin America and Africa (Fageria et al., 2011). Genetic and archaeological studies

indicate that domestication of P. vulgaris occurred in two distinct centres of domestication in the

Middle America and Andes (Koenig and Gepts, 1989) resulting in two highly differentiated gene

pools (Gepts, 1998). More recent studies of domestication and the genetic diversity of P.

vulgaris within the two gene pools, using molecular evidence, identified the Oaxaca valley in the

Middle Americas and southern Bolivia and northern Argentina in South America as the centres

of domestication of common bean (Bitocchi et al., 2013).

Since the advent of nitrogen fertilizers in the 1800’s (Slack, 1973), chemical fertilizers

have become a major source of nitrogen supplements in agricultural lands across the world.

However, plants cannot uptake all the nitrogen applied to the soil in the form of nitrogen

fertilizers (Raun and Johnson, 1999). Therefore, the amount of nitrogen that is not absorbed by

the plants poses a serious management and often environmental concern in these cropping

systems (London, 2005). Symbiotic nitrogen fixation (SNF), the process of converting the

abundantly-available gaseous N2 to biologically-useful nitrogen through symbiotic relationships

between plant species and soil microbes, on the other hand, is known as an important

environmentally-sustainable source of nitrogen in the soil (Triboi and Triboi-Blondel, 2014).

Biological nitrogen fixation potentially contributes to reduced application of N fertilizer on

agricultural lands throughout the world. While free-living nitrogen-fixing organisms, such as

68

blue-green algae, may deposit less than 5.6 kg of nitrogen per ha per year (Van Kessel and

Hartley, 2000), nitrogen fixation through the symbiotic relationship between the members of the

leguminous family of plants and the soil rhizobial bacteria is known to supply in the range of 28

to 83 kg N ha-1

yr-1

in a natural ecosystem (Van Kessel and Hartley, 2000), and up to 400-500 kg

N ha-1

yr-1

in cropping systems involving nitrogen-fixing legumes (Van Kessel and Hartley,

2000).

Despite common bean’s inherent N2-fixing ability, the actual SNF of dry beans is often

reported to be lower than other legumes (Martínez and Roméro, 2003). Therefore, application of

N fertilizers in bean fields is recommended to achieve higher yields. However, it has been

reported that the climbing and indeterminate cultivars consistently have higher nodulation and

SNF abilities, compared with most bush type cultivars. These greater abilities are attributed to

the relatively longer period of fixation during the growth cycle in climbing type cultivars

(Graham, 1981; Piha and Munns, 1987). Miranda and Bliss (1991) reported that selection for

high levels of SNF, especially when performed in low fertility soils, might result in genetic gains

in common bean breeding populations. Bliss (1993) also discussed that the level of N2 fixation

can vary significantly among bean genotypes and argued that reports of insufficient levels of N2

fixation in common bean were often based on observations with only a few genotypes and

conducted with unsuitable N2 fixation measurement assays.

In addition to genetic background, environmental factors can greatly influence the SNF

ability of legumes. Carbon isotope discrimination (δ13

C), an indicator of water use efficiency

(Farquhar et al. 1989), was found to be negatively correlated with SNF (Kumarasinghe et al.,

1992), which indicated a positive correlation between drought tolerance and SNF in soybean.

69

Devi et al. (2013) also illustrated significant differences among twelve selected bean genotypes

(10 Middle American and 2 Andean genotypes) for SNF under different soil water conditions.

Other than water shortage, deficiencies of phosphorus (P), potassium (K), and sulfur (S) have

also been reported as environmental SNF-limiting factors, which may influence number and

weight of nodules (Divitoa and Sadras, 2014). Direct impacts of P, K and (or) S deficiencies on

nodules could be due to their influences on physiological and metabolic processes in nodules

(Divitoa and Sadras, 2014), though different responses of common bean genotypes to P

deficiency has been reported (Namayanja et al., 2014). The other important environmental factor

affecting SNF is salinity. In salt-sensitive species, like common bean (Faghire et al., 2011), salt

stress can suppress crop productivity due to reduction in plant growth and the capacity of SNF

(Delgado et al., 1993), because of weaker symbiotic association between plants and rhizobia

(Georgiev and Atkins, 1993).

Breeding for improved SNF can potentially improve legume crops that are normally

dependent on N fertilizers for high yields and promote the development of cropping systems that

are less dependent on N chemical fertilizers. The availability of superior genotypes with higher

N2-fixation ability supports the idea that SNF in common bean can be improved through

breeding efforts. The objective of this study was, therefore, to assess the potential genetic

diversity in selected North American common bean genotypes, from different market classes of

the two common bean gene pools, for their SNF ability and related traits across different

environmental conditions.

70

3.3. MATERIALS AND METHODS

3.3.1 Plant materials

Twelve common bean genotypes, selected based on their different morphological traits

from the two Phaseolus gene pools, Andean and Middle American, were included in this study

(Table 3.1). The non-nodulating mutant (R99; Park and Buttery, 1997) and its wild type

genotype, ‘OAC Rico’, were included as non nodulating and nodulating checks, respectively,

while R99 was also used as reference genotype for estimating N fixation via the 15

N natural

abundance approach (Shearer and Kohl, 1986). The determinate-bush navy bean genotype

‘Sanilac’ was included as a low-nodulating genotype based on previous studies (Bliss, 1993).

The upright indeterminate navy bean cultivars ‘Mist’ and OAC 09-3 were included as late

maturity navy beans, while the semi-determinate upright ‘AC Compass’ (Park and Rupert, 2000)

and indeterminate bush ‘OAC Thunder’ (Michaels and Smith, 1999) were included as early

maturity genotypes. SXB 415 is a carioca bean selected from our previous low-N studies (Smith

et al. 2012), because of its stable high yield across N treatments and environments. From the

Andean gene pool, the determinate upright bush light-red kidney bean ‘Chinook 2000’ was

selected for its strong root system (Román-Avilés et al. 2004), while ‘Majesty’ dark-red kidney

was selected for its upright indeterminate growth habit (unpublished).

3.3.2 Seedling nodulation assay

Seedling nodulation assays were conducted between June and September 2012 in a growth

room at the University of Guelph (Guelph, Ontario) set at 25/18 °C day/night temperature with

71

16 h photoperiod regime under overhead tubular florescent and incandescent lamps (1:1)

delivering approximately 300 μmol m-2

s-1

flux of photosynthetically active radiation (PAR).

Seeds were surface sterilized in a 7 % sodium hypochlorite solution for 20 min followed by

washing them twice with germ-free distilled water. The seeds were germinated in petri dishes,

thoroughly washed and sterilized in a hot air oven at 80oC for 36 h, on Whatman filter papers,

sterilized at 80oC for 24 h, in a laboratory at room temperature as described by Taffouo et al.

(2009). Next, 24 uniform seeds of each bean genotype were placed on two Petri dishes, 12 seeds

each, at a uniform distance. The Petri dishes were randomly arranged in a glass box to avoid the

loss of moisture through evaporation. When the radical emerged (> 5 mm), twelve seedlings

were inoculated by soaking in a solution of a commercially-available peat-based inoculum

(Rhizobium leguminosarum bv. phaseoli; Becker Underwood, Saskatoon, Saskatchewan,

Canada) at a 0.1% concentration, and two seedlings were placed in each growth pouch (Mega

International, St. Paul, MN, United States) following the method explained by Peters and Crist-

Estes (1989). Growth pouches were kept in a water container, containing distilled water. A

nutrient solution, containing a mixture of H3PO4 (18.7 g L-1

), KHCO3 (37.5 g L-1

), MgSO4-7H2

(3 g L-1

), and 3 g L-1

of chelated micronutrient (Plant Products Co. Ltd., 314 Orenda Road,

Brampton, ON, Canada), was added as a 1% solution when unifoliate leaves were fully

expanded. The position of the seedling root tip was marked on the growth pouch at the

beginning of the experiment. The trial was arranged in a randomized complete block design

(RCBD) with 6 replicates planted sequentially with a 1-week interval between replicates, to

spread the workload. Each experimental unit consisted of two seedlings of each genotype (tow

observations per experimental unit). Two weeks after transferring the seedlings to the growth

pouches, number of nodules above the root tip marks was counted and chlorophyll content of the

72

unifoliates leaves was measured using a SPAD 502 chlorophyll meter (Spectrum Technologies,

3600 Thayer Court, Aurora, IL 60504).

3.3.3 Greenhouse nodulation assay

This assay was conducted in 2.5 L plastic pots with drainage holes, filled with an N-free

medium MVP turface (Profile Products, LLC 750 Lake Cook Rd, Suite 440 Buffalo Grove, IL

60089) with a pH of 6.7, after fertigation. Four seeds of each genotype were planted in each pot,

following inoculation with 1 mL of a 1% slurry of the commercial peat-based inoculum of

Rhizobium leguminosarum bv. phaseoli (Becker Underwood, Saskatoon, Saskatchewan, Canada)

in a greenhouse at the University of Guelph. Approximately one week after planting, seedlings

were thinned to one per pot. Pots were arranged in a randomized complete block design with six

replicates, planted sequentially with a 5-d interval between replicates between February and

November 2011. Greenhouse conditions were day/night temperature settings of 25/18°C with

16-h photoperiod. Day length was extended with overhead high pressure sodium and metal

halide lamps delivering an additional flux of photosynthetically active radiation (PAR) of

approximately 300 μmol m-2

s-1

set at the top of the pots during the experiment. Plants were kept

water replete at all times by using daily subsurface drip irrigation system through two vinyl

tubes for each pot, equipped with one 12.5-cm slotted drip spike. Fertilizer was added to

irrigation water as an N-free solution containing H3PO4 (18.7 g L-1

), KHCO3 (37.5 g L-1

),

MgSO4-7H2 (3 g L-1

), 3 g L-1

chelated micronutrient mix (Plant Products Co. Ltd., Brampton,

Ontario, Canada), which was added in a 1% solution per pot, once a week after the first trifoliate

was fully expanded. The experiment was terminated 42 days after planting at the onset of

flowering. Roots were thoroughly washed and number of mature (red) and immature (pale)

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73

nodules were determined using WinRHIZO (Regent Instruments Canada Inc., 2009) following

the manufacturer’s instructions. Root, shoot, and nodules were later dried in a forced air oven at

60 oC for about 48 h and weighed to measure their dry weight.

3.3.4 Field N2 fixation assays

Field trials were grown in four location-years (environments) during 2011 and 2012.

Locations in 2011 were farmers’ fields in the Grey-brown luvisols soil zone near Belwood

(43o40’16”N, -80

o11’34”W, elevation 430 AMSL) and near Rockwood (43

o39’56”N, -80

o9’54”W, elevation 353 AMSL) and in 2012 one field site at the University of Guelph Elora

Research Station near Elora (43°38'27.8"N 80°24'20.4"W, elevation 379 AMSL) and the other

field site at a farmer’s field near Belwood (43o40’16”N, -80

o11’34”W, elevation 430 AMSL),

all in Ontario, Canada. All trial locations were chosen based on their low N content, less than 5

ppm nitrate content for all locations according to soil analysis prior to planting (Table 3.2), as

well was having had no dry bean history for at least 10 years. Field sites were fertilized with 200

kg ha-1

of 0-20-20 of N-P-K fertilizers and sprayed, pre-planting with S-metolachlor (Syngenta

Crop Protection Canada, Inc, Guelph, Ontario), Imazethapyr (BASF Canada, Mississauga,

Ontario ) and Trifluralin (Dow AgroSciences Canada Inc., Calgary, Alberta) at the rates of 2.3 L

ha-1

, 200 mL ha-1

and 1.5 L ha-1

, respectively. The experimental design in each environment was

a randomized complete block design (RCBD) with 4 replicates. Each experimental unit

consisted of a 4-row, 6 m long plots with 36 cm row-spacing, planted the second week of June

of each year. Three hundred and fifty seeds of each genotype were inoculated, immediately prior

to planting, with a peat based culture of rhizobia, at the rate of 2.5 g of Rhizobium

74

leguminosarum bv. phaseoli in a commercial product (Becker Underwood, Saskatoon,

Saskatchewan, Canada) per kg of seed to give approximately 5×105 bacteria/seed.

3.3.5 Data collection

Greenness of the plants (leaf chlorophyll concentration) was measured twice using SPAD

502 meter (Monje and Bugbee 1992) at the first trifoliate stage and at the first flower stage on

the youngest leaves, with three readings from each of the three leaflets on four plants per plot. At

the first flowering stage, three randomly chosen plants per plot were dug out carefully; the roots

were washed to remove adhering soil fractions and then transferred to the laboratory for nodule

count. Nodules were then dried in a forced air oven at 60oC for about 48 h and weighed to

estimate 100 nodule dry weight. Nodule weight and number were only measured in 2011

environments and were not measured in 2012 trials due to labor constraints. Days to

physiological maturity was recorded as the number of days from planting to the day when 50%

of pods turned yellow. At harvest maturity (18 -20 % moisture content), all plants in each plot

were harvested using a Wintersteiger plot combine (Wintersteiger AG, Ried im Innkreis, Upper

Austria, Austria) with a Classic Seed Gauge weighing system by HarvestMaster and plot seed

weight and moisture content were recorded. A sample of seeds was dried in a forced air oven at

60oC for about 48 h. From each experimental unit, five to six mg of ground seed sample was

weighed into a tin capsule (8×5 mm). The capsule was then closed, compressed and placed in

96-well micro plates. Samples were analyzed at the Agriculture and Agri-Food Canada,

Lethbridge Research Centre (Lethbridge, Alberta, Canada) to determine δ15

N, total N and δ13

C,

known to be negatively associated with water use efficiency (WUE; Farquhar et al., 1989),

75

using gas chromatography-mass spectrometry (GC-MS) following methods explained by

Shearer and Kohl (1993).

The non-nodulating mutant, R99 (Park and Buttery, 1997), was used as reference genotype

in N fixation estimates. Percent nitrogen derived from the atmosphere (%Ndfa) was calculated

for each experimental unit following the equation given by Shearer and Kohl (1986) as:



where δ15

Nref. plant is the rate of δ15

N in the reference genotype (R99), δ15

Nfixing plant is the 15

N

abundance of the dry bean genotype and B is the δ 15

N of the legume grown obtaining its entire

N from N2 fixation in an N-free medium. The B-value was obtained, as proposed by Peoples et

al. (2002), by taking the average of δ15

N measurements of a total of 20 randomly selected bean

genotypes including 11 genotypes studied here and 9 recombinant inbred lines from a cross

between Sanilac, low SNF genotype, and Mist, high SNF genotype, grown in a greenhouse

(Appendix I). Total nitroigen fixed per unitarea by each genotype in each experimental unit was

estimated following the equation

𝑁𝑓𝑖𝑥 = 𝑁𝑌 × 𝑁𝑑𝑓𝑎%

where NY represents the nitrogen yield, estimated with the following equation:

𝑁𝑌 = 𝑆𝑌 × 𝑁%

76

where SY is seed yield and N% is the percentage of seed nitrogen in the sample.

3.3.6 Statistical Analysis

Single environment and multi-environment data were subjected to an analysis of variance

in mixed model using the PROC MIXED procedure in SAS (version 9.3, SAS Institute, Cary,

NC, USA, 2012). For single environment analyses, the effects of replication and genotype were

considered random and fixed, respectively. In multi-environment analyses, the effects of year,

location within year, replication within year × location, and the interaction effects with year were

considered random, while the effect of genotype was considered fixed. The COVTEST statement

was used to perform a Z test for the significance of the variances of the random effects, while the

fixed effect test of significance was performed using an F-test with type III sums of squares.

Least squared means (LSmeans) were computed for fixed effects using the LSMEANS

statement. Standard errors of LSmeans were estimated and pair-wise tests of significant

differences were performed using the PDIFF statement. Genotype by environment interaction

effect was significant for the majority of N fixation and agronomic traits. Also for the majority of

SNF related traits the error variances of individual locations were not homogeneous (χ 2=491;

P<0.0001 in Bartlett’s test of homogeneity). Transformation of the raw data did not help to

achieve homogeneous error variances and therefore, we chose to present the results of test

environments separately, in addition to pooled data.

The PROC CORR procedure of SAS was used to compute Pearson’s and Spearman’s

correlation coefficients between pairs of traits. Furthermore, the Genotype × Trait (GT) biplot

analysis (Yan and Rajcan, 2002) was implemented for %Ndfa, seed yield and carbon isotope

77

discrimination (δ 13

C) to study trait associations in environments, based on the following

formula, which standardizes the data to remove the units of each measured trait:

𝛼𝑖𝑗 − 𝛽𝑗

𝜎𝑗= ∑ 𝛾𝑛𝛿𝑖𝑛

∗ 𝜌𝑗𝑛∗

2

𝑛=1

+ 휀𝑖𝑗 = ∑ 𝛿𝑖𝑛∗

2

𝑛=1

𝜌𝑗𝑛∗ + 휀𝑖𝑗

where 𝛼𝑖𝑗 is the value of the ith genotype for the jth trait, 𝛽𝑗 is the mean value of all

genotypes for the jth

trait, 𝜎𝑗 is the standard deviation of the jth

trait, 𝛾𝑛 is the singular value for

principle component n (PCn), 𝛿𝑖𝑛 is the PCn score for the ith

genotype, 𝜌𝑗𝑛 is the PCn score for

the jth

trait, and 휀𝑖𝑗 is the residual associated with genotype i in trait j. Genotype × Trait biplots

were constructed by plotting PC1 scores against PC2 scores for each genotype and each trait

(Yan and Rajcan, 2002). In GT biplots, if a line is drawn that passes through the origin of the

biplot and the marker of each trait, then perpendicular lines drawn from the markers of genotypes

to the first line, the order in which the genotype perpendiculars cross the first line indicates the

ranking of genotypes for that trait. Moreover, if the biplot explains a sufficient amount of the

total variation, then the cosine of the angle between the vectors of the two traits (or genotypes)

approximates the correlation coefficient between the two, while the distance of the markers of

traits or genotypes from the origin of the bilpot represents the amount of variation for that trait or

genotype.

78

3.4 RESULTS

3.4.1 Seedling Nodulation assays

Two-week old bean seedlings (growth stage V1) of different bean genotypes in growth

pouch assays were significantly different for nodule numbers (P < 0.0001). In accordance with

the nodulation assays, the Andean genotypes, on average, had 37% more (P = 0.05) nodules than

the Middle American genotypes two weeks after emergence (Table 3.3). The SPAD chlorophyll

readings in these assays were not significantly different among genotypes.

3.4.2 Greenhouse assays

In greenhouse, adult-plant nodulation, assays the effect of genotype was significant for all

nitrogen-fixation related traits. The Andean genotypes, on average, had 55% higher nodule dry

weight and 35 % higher nodule numbers than the Middle American genotypes (P < 0.0001).

However, the Middle American genotypes had significantly higher number of red nodules (P >

0.05) and the ratio of red nodules to total number of nodules (P > 0.01) than the Andean

genotypes. Among the Middle American genotypes, ‘Mist’ with 0.23 g/plant and 1094

nodules/plant had the highest nodule weight and number. The lowest nodule weight per plant

was observed for SXB 415 (0.04 g/plant), ‘OAC Rico’ (0.04 g/plant) and ‘Sanilac’ (0.03 g/plant)

(Table 3.4). The Andean genotype ‘Chinook 2000’ among all genotypes had the highest number

of nodules per plant (1497) followed by the Andean variety, ‘Majesty’, and the Middle American

variety ‘Mist’ with 1228 and 1094 nodules per plant, respectively.

79

While the Andean genotypes’ shoot dry weight, on average, was about twice that of the

Middle American genotypes (P < 0.05), their root dry weight was not significantly different

(Table 3.4). The Andean genotypes ‘Majesty’ and ‘Chinook 2000’ with 10.33 and 8.56 g/plant,

respectively, followed by the Middle American genotype ‘Mist’ with 6.65 g/plant, had the

highest shoot dry weight in greenhouse assays. ‘Mist’ with 1.44 g/plant of root dry weight had

significantly higher root dry weight than the other Andean or Middle American genotypes.

Among the Middle American genotypes, ‘OAC Rico’ had noticeably lower root dry weight (0.06

g/plant), even less than the non-nodulating genotype, ‘R99’. Even though ‘OAC Rico’ had the

lowest root dry weight, it had the highest ratio of red nodules to total number of nodules at

flowering stage. Total nodule weight was significantly correlated with root dry weight (r = 0.72,

P < 0.01), shoot dry weight (r = 0.82, P< 0.01) and total plant biomass (r = 0.78, P < 0.01).

Significant correlation was observed between total number of nodules and shoot dry weight (r =

0.89, P < 0.0001) However, no significant association was observed between nodule number and

root dry weight. Moreover, there was no significant correlation between number of red nodules

and total number of nodules, while the correlation between number of pale nodules and the total

number of nodules was highly significant (Spearman’s rank correlation r = 0.89; P < 0.0002).

3.4.3 Field assays

Based on the climatic data (Table 3.5) obtained from the weather stations closest to the

field sites, Rockwood in 2011, with 473 mm of rain from May to September, higher than the 30

year average, was considered an optimum soil moisture environment, while both locations in

2012 with less than the 30-year average (Belwood with 349 mm and Elora with 292 mm

precipitation during the same period) were considered drought-stressed environments. In the

80

field trials, the effect of genotype for all traits, with a few exceptions, was significant in all

environments (Tables 3.6 and 3.7) with no significant environmental effect for the traits (Table

3.8). Although the genotype × environment interaction was always significant for all traits, the

main effect of environment was not significant (Table 3.8).

3.4.4 Seed yield

Overall seed yield of bean genotypes showed 5% yield difference between optimum

moisture environments, Rockwood and Belwood in 2011, and dry environments, Belwood and

Elora in 2012. This difference was generally larger for Andean genotypes, 7% than that of

Middle American genotypes, 4% (Table 3.6). On average, the nodulating Andean and Middle

American genotypes yielded 15 % and 20 % higher than the non-nodulating genotype R99,

respectively. The Middle American genotype ‘Mist’, which was one of the highest nodulating

genotypes in the growth room nodulation assays produced up to 51 %, higher yield than the non-

nodulating R99, which was the lowest yielding genotype, in different location-years. Middle

American genotypes generally yielded about 8% more than the Andean genotypes (P = 0.03),

significant genotype × environment interaction was observed (Table 3.8).

3.4.5 Percentage of Nitrogen derived from atmospheric air (%Ndfa)

Although there was generally no significant difference between Andean and Middle

American genotypes across all environments (Table 3.8), significant diversity for nitrogen

derived from atmospheric air (%Ndfa) among genotypes (P < 0.0001) with a range of 9% to 78%

in an optimum environment (Belwood in 2011) was observed. ‘Belwood 2012’ and ‘Belwood

81

2011; with an average %Ndfa of 12% and 54 % had the lowest and highest %Ndfa, among all

environments. The difference between the Andean and Middle American genotypes, in general,

was significant only in Belwood 2011, with Middle American genotypes deriving about 5%

more of their N from atmosphere. A significant genotype × environment interaction for %Ndfa

(P < 0.0001) indicated that the genotypes responded differently to environments. Among the

Andean genotypes, ‘Red Rider’ cranberry bean was the lowest nitrogen fixing genotype in every

environment. ‘Mist’ was consistently one of the highest nitrogen fixing genotypes in all

environments (Table 3.6).

3.4.6 Nitrogen Fixed per Unit Area

There was a significant difference among genotypes for nitrogen fixed per unit area in all

location-years in the field (Table 3.7). In Belwood 2011, the environment with the highest

rainfall, the Middle American genotypes had more N fixed per unit area, about 34% higher than

the Andean genotypes (P = 0.0002). However there was no significant difference between these

two gene pools in the other environments or across all environments (Table 3.8). Among the

Middle American genotypes with extreme value for this parameter, ‘Mist’ fixed more nitrogen

per unit area than all other genotypes in all environments except Elora in 2012 (Table 3.7).

‘Sanilac’ in Belwood 2011 and ‘AC Compass’ in other environments were the weakest nitrogen

fixing genotypes (Table 3.7).

82

3.4.7 Seed nitrogen percentage

Seed nitrogen percentage (N%) was different among genotypes (P < 0.0001) in all

environments except Elora 2012, which was the driest environment. In the optimum soil

moisture environments, Belwood and Rockwood in 2011, the Andean genotypes had 7% and

15% lower N%, respectively than the non-nodulating mutant R99. However, in the dry

environments of Belwood and Elora 2012 they had about 5% and 7% higher N% than R99,

respectively (Table 3.7). Across all environments, the Middle American genotypes generally had

34% higher percentage of N than the non-nodulating genotype R99.

3.4.8 Nodule weight and number

Significant differences were only detected among genotypes in Belwood, but not in

Rockwood. ‘Chinook 2000’ and ‘Majesty’, both Andean genotypes, had noticeably higher

number of nodules than the other genotypes. SXB 415 and ‘OAC Rico’, Middle American

genotypes, produced the least number of nodules (48 and 56, respectively).

3.4.9 Carbon Discrimination (δ13

C)

Significant variation (P < 0.0001) was observed among genotypes for δ13

C. Moreover,

there was significant genotype × environment interaction for this trait (P < 0.0001) (Table 3.8).

Middle American genotypes in general had significantly lower δ13

C, i.e., higher WUE, values

than the Andean genotypes across all environments (Table 3.8). This was the case in every

83

environment except Belwood 2012, which received the highest rainfall among the test sites

(Table 3.7).

3.4.10 Genotype by Trait Biplot and Trait Relationships

The first two PCs of the genotype by trait biplot, constructed using the standardized values

of the selected traits for all genotypes in four environments explained a total of 62.44 % (49.39

% PC1 and 13.05% PC2) of the total variation (Figure 3.1).

The GT biplot indicated close association between measurements of % in Rockwood 2011

and Belwood 2012, (r = 0.85, P < 0.001). Similarly the %Ndfa measurements of Belwood 2011

and Elora 2012 were closely associated, (r = 0.74, P < 0.01). The %Ndfa and seed yield of each

environment, except Belwood 2011, were closely associated. %Ndfa was associated with yield in

Rockwood 2011 (r = 0.59, P < 0.05), Belwood 2012 (r = 0.71, P < 0.01), and Elora 2012 (r =

0.62, P < 0.05). In the same way, %Ndfa in Belwood 2012 was found closely associated with

yield in Belwood 2012 (r = 0.68, P < 0.05) and Elora 2012 (r = 0.65, P < 0.05). Overall, very

close association (r = 0.81, P = 0.03) was found between %Ndfa and seed yield across all

environments.

Carbon discrimination (δ13

C) in all environments, except for Elora 2012, was negatively

associated with %Ndfa (r = -0.62, P = 0.04; r = -0.42, P =0.2; r = -0.55, P = 0.05) in Belwood

2011, Rockwood 2001 and Belwood 2012, respectively) and seed yield (r = -0.42, P = 0.20, r = -

0.47, P = 0.05; r = -0.66, P = 0.03 in Belwood 2011, Rockwood 2001 and Belwood 2012,

respectively) as illustrated by their vectors in opposite directions in the biplot (Figure 3.1). The

84

association between %Ndfa and N% in the seed, even though observed for some specific

environment e.g., Belwood 2011, was not repeated across all environments.

In the GT biplot (Figure 3.1) genotype number 2 (Mist) can be identified as a genotype,

that ranked in the top two for yield and with a high %Ndfa, consistently in all four environments.

In Belwood 2012, genotype number 11 (Zorro) and in Belwood 2011, genotype number 9 (SXB

415) had high %Ndfa, but their high fixation was not repeated across all environments (Figure

3.1). In terms of %Ndfa, three Andean genotypes, number 3, 4 and 8 (Chinook 2000, Majesty

and Red Rider) and one of the Middle American genotypes, Sanilac (number 10) had the lowest

%Ndfa in all environments (Figure 3.1).

3.5 DISCUSSION

Results of this study, both in the greenhouse assays and in the field trials, highlighted the

relative advantage of the Andean genotypes over the Middle American genotypes in terms of

nodule number and weight. These findings are in agreement with the previous report by Franco

et al. (2002). Although genotypes of the two gene pools, examined in this study, were not found

to be different for root dry weight, the Andean genotypes had significantly higher shoot dry

weight than the Middle American genotypes. Namayanja et al. (2014) linked the relative

advantage of the Andean genotypes in terms of nodulation ability to their higher

photosynthetically-active plant tissue, which can support more and heavier nodules at the

flowering stage. Our results support this hypothesis considering the observed significant

correlation between shoot dry weight in the adult plant nodulation assay with nodule number in

the same assay as well as in the field trials. However, other contrasting findings reported

85

recently by Akter et al. (2014), in which the Middle American genotypes had higher nodulation

ability than the Andean genotypes, may suggest that the relative advantage of the two gene pools

in terms of nodulation ability may be genotype-specific and should not be generalized.

It is known that leghemoglobin, a protein complex with a high affinity to oxygen that

reduces the concentration of free oxygen at nitrogenase activity site (Appleby, 1984), causes the

red color of fully developed nodules (Downie, 2005). The results of this study showed that the

Middle American genotypes possessed significantly higher numbers of red nodules than the

Andean ones in a greenhouse assay. We also found a strong correlation between shoot dry

weight and numbers of pale nodules in Andean genotypes.

Generally, Middle American genotypes yielded more than the Andean genotypes in the

field, which is in accordance with previous reports (e.g., Sexton et al., 1994). The yield

advantage of the Middle American genotypes can, at least in part, be attributed to their

indeterminate growth habit, later maturity and higher %Ndfa. This is in accordance with a

significant correlation between seed yield and %Ndfa. Moreover, on average, the Middle

American genotypes had lower δ13

C than the Andean genotypes and therefore are considered

more water use efficient than the Andean genotypes studied here. This could also explain the

smaller average yield difference between dry and optimum environments for the Middle

American genotypes compared with the Andean genotypes. The observed positive correlation

between root dry weight and seed yield across field environments, when seed yield was

negatively associated with δ 13

C, may also explain the higher water use efficiency through a

stronger root system in high yielding genotypes under mild water stress. Other factors, such as

86

indeterminate growth habit and time to maturity are no doubt other contributors to greater yield

of the Middle American genotypes.

In accordance with previous reports, we report here that environmental and genotype by

environmental variation significantly influence N fixation in common beans. Substantial

differences for overall nitrogen fixation ability of dry beans were observed between

environments with optimum soil moisture and dry environments. Among all environments, the

highest (55.6%) and lowest (11.48%) %Ndfa were observed in Belwood 2011 with maximum

(602 mm) and Elora 2012 with the least (292 mm) amount of rainfall over the growing season.

The same trend was observed for N fixed per unit area. These results suggested that soil moisture

could be one of the major environmental factors influencing the SNF ability of beans in our

study. This is consistent with previous reports of the negative impact of water shortage on SNF

in common bean (Devi et al., 2013; Sangakkara et al., 1996), soybean (Sprent, 2006; Djekoun

and Planchon, 1991), faba bean, lupin, and peanut (Sinclair and Serraj 1995) . The reduction of

N fixation under dry environments has been attributed to induced nodule senescence and

declining in nitrogenase activity (Becana et a1. 1986), which cause decline in crop productivity.

Differential reduction in N fixation ability of bean genotypes under drought conditions has been

reported by Devi et al. (2013) and Serraj and Sinclair (1998) in common bean and Devi et al.

(2010) in peanut. In accordance with the findings of Farquhar et al. (1989), a negative

association between seed yield and δ13

C was observed. Moreover, the negative association

between %Ndfa and δ13

C, observed here, implies that the bean genotypes with higher water use

efficiency (or drought stress tolerance) were more efficient in terms of SNF and productivity.

This report confirmed the finding by Kumaarasinghe et al. (1992), who illustrated a negative

correlation between δ13

C and %Ndfa. Knight et al. (1993), on the other hand, suggested that the

87

negative association between these two traits is observed only under drought stress conditions.

The other environmental factors known to significantly affect nitrogen fixation are P deficiency

(Vadez et al., 1996), cation exchange capacity (CEC) and Na concentration (Faghire et al. 2013),

for which differential genotypic response is known to occur.

Strong genotype by environment interaction observed here for %Ndfa and seed yield were

associated with crossover interaction and significant genotypic rank change. In accordance with

significant genotype by environment interaction for %Ndfa, two environmental groups could be

identified. Each group included environments with closer association in terms of genotypic

ranking. The first group comprised of Belwood 2011, with optimum soil moisture condition, and

Elora 2012, with low soil moisture and high available P condition, formed the first group.

Rockwood 2011, optimum moisture, and Belwood 2012, dry with low available soil P were also

grouped together.

Although Chaverra and Graham (1992) reported positive correlation between early growth

stage nodulation and nitrogen fixation under phosphorous deficient conditions in a greenhouse

study, we could not find any correlation between nodule numbers in the seedling nodulation

assays in the growth room and SNF in field assays. This may require further investigation to

develop seedling assays that are more similar to field conditions.

88

Entry Gene pool Market class

Growth

habit Description

Sanilac Middle American White-Navy Type Iz Bush, poor nitrogen fixing Navy (Bliss, 1993)

Mist Middle American White-Navy Type IIy Upright, late maturity, high yielding, with P. acutifolius introgression

OAC 09-3 Middle American White-Navy Type II Upright, late maturity, high yielding, with P. acutifolius introgression

OAC Thunder Middle American White-Navy Type I Bush, strong root system (unpublished), early-mid maturity (Michaels and Smith, 1999)

Zorro Middle American Black Type II Upright, high-yielding, midseason-maturing (Kelly et al. 2009)

SXB 415 Middle American Carioca Type II Upright, stable yield under N limited environments (unpublished) (CIAT, 2009)

AC Compass Middle American White-Navy Type I Upright, high yielding (Park and Rupert, 2000)

OAC Rico Middle American White-Navy Type II Bush, wild type genotypes used in developing nodulating mutants (Beversdortf, 1984)

Chinook 2000 Andean Light Red Kidney Type I Upright, strong root system (Kelly et al., 1999)

Red Rider Andean Cranberry Type I Upright, high yielding (Park et al. 2009)

Majesty Andean Dark Red Kidney Type II Upright, dark red kidney (Park and Agriculture and Agri-Food Canada, 2006)

R99 (Reference) Middle American White-Navy Type I Bush, non-nodulating mutant (Park and Buttery, 1997)

Table 3.1 Gene-pool origin, commercial market class, growth habit, and specific characteristics of genotypes tested for N fixation-related traits in the

greenhouse and at four locations in south-west Ontario, Canada.

z Determinate

y Indeterminate

89

0-15 (cm) 15-45 (cm) 0-15 (cm) 15-45 (cm) 0-15 (cm) 15-45 (cm) 0-15 (cm) 15-45 (cm)

Organic matter (%) 3 1.5 3.5 1.4 4.9 3 4.4 2.3

P (bicarb, ppm) 7 19 5 4 5 4 82 23

P (Bray-P1, ppm) 16 25 12 5 6 5 219 39

K (ppm) 28 20 48 34 47 37 111 60

Mg (ppm) 190 145 235 190 315 265 365 335

Ca (ppm) 1330 970 2890 3040 2080 2040 2740 3520

Na (ppm) 8 3 60 50 2 1 13 14

Al (ppm) 850 1117 604 603 808 900 675 587

Nitrate (ppm) 4 3 3 2 3 2 5 4

pH 7.1 6.9 7.9 8.1 7.5 7.7 7.6 7.9

CECz (meq/100g) 9.2 7.3 16.8 17.1 13.2 12.5 17.1 20.6

Table 3.2 Soil properties of trial environments presented for samples taken from 0-15 and 15-45 cm soil depth, Ontario, Canada.

Soil Property

z Cation exchange capacity

Elora

2012Rockwood 2011

Belwood

2011

Belwood

2012

90

Nodule number

(Plant-1

)SPAD

Large-Seeded Andean

Chinook 2000 54 35.8

Majesty 59 35.1

Red Rider 32 35.0

Small-seeded Middle American

AC Compass 32 33.4

Mist 19 32.1

OAC 09-3 20 34.3

OAC Rico 24 35.6

OAC Thunder 27 37.7

SXB415 18 32.5

Sanilac 23 34.5

Non-nodulating reference

R99 0 19.3

Genotype < 0.0001 nsz

Andean vs. Middle American 0.05 ns

Test mean 36 35

LSD 95% 11.17 16.09z Non-significant

Table 3.3 Nodule number and SPAD reading of 11 dry bean genotypes in a growth pouch assay in a growth

room, Guelph, Ontario, Canada.

F-test

91

Nodule dwt.z

(g)

Pale/Total

nodule

number

(Plant-1)

Pale nodule

number

(Plant-1)

Red nodule

number

(Plant-1)

Red/total

nodule number

(Plant-1)

Root dwt.

(g)

Shoot dwt.

(g)

Total dwt.

(g)

Nodule

number

(Plant-1)

Chinook 2000 0.11 0.90 1344 153 0.10 0.78 8.56 9.68 1497

Majesty 0.26 0.91 1121 107 0.09 0.94 10.33 11.27 1228

Red Rider 0.27 0.93 759 60 0.07 0.86 6.48 7.36 819

AC Compass 0.06 0.71 513 211 0.29 0.72 4.4 6.22 724

Mist 0.23 0.83 912 182 0.17 1.44 6.65 8.27 1094

OAC 09-3 0.22 0.87 939 137 0.13 0.61 6.23 6.84 1076

OAC Rico 0.04 0.56 416 331 0.44 0.06 2.18 2.94 747

OAC Thunder 0.07 0.80 515 132 0.20 0.36 3.77 3.4 647

SXB 415 0.04 0.84 437 81 0.16 0.26 4.65 6 518

Sanilac 0.03 0.79 610 160 0.21 0.62 3.74 4.06 770

Zorro 0.07 0.93 501 38 0.07 0.55 3.52 4.09 539

R99 0 0 0 0 0 0.37 0.3 0.15 0

< 0.0001 < 0.0001 0.0006 0.0003 < 0.0001 0.0003 < 0.0001 < 0.0001 < 0.0001

< 0.0001 < 0.0001 0.004 0.03 0.004 ns 0.03 ns < 0.0001

0.13 0.80 734 145 0.20 0.65 5.50 6.26 878

0.059 0.243 70.5 13.7 0.137 0.316 1.982 2.803 229.3z Dry weight

Andean vs. Middle American

Test mean

LSD 95%

F-test

Table 3.4 Comparison of nitrogen fixation related traits per plant among 12 bean genotypes in a greenhouse in 2011, Guelph, Ontario, Canada.

Large-Seeded Andean



Genotype

92

Table 3.5 Maximum, minimum, and mean temperature, total monthly precipitation at Rockwood, Belwood (two different fields), and Elora, Ontario, Canada for the 2011-2012 growing seasons.

Rockwood (2011)

May June July August September

Max Temperature (°C) 17.6 22.4 28 25.2 20.5

Min Temperature (°C) 8.8 12.5 16.5 14.6 11.3

Mean Temperature (°C) 13.2 17.4 22.2 19.9 15.9

Total Precipitation (mm) 119.8 112 37.6 125.1 78.6

Belwood (2011)


Max Temperature (°C) 17.5 22.3 28.2 25.2 20.2

Min Temperature (°C) 8.1 11.9 15.3 14 10.8


Total Precipitation (mm) 191.8 142.4 27.8 145.6 94.6

Belwood (2012)



Min Temperature (°C) 8.3 12.6 15.5 13.1 8.4

Mean Temperature (°C) 15 18.4 22.1 19 14.1

Total Precipitation (mm) 25.9 97.2 44 52 130

Elora (2012)



Min Temperature (°C) 7.5 12.2 14 11.8 7.3


Total Precipitation (mm) 28.2 64.6 30.4 62.6 106.2

93

Belwood Rockwood Belwood Elora Belwood Rockwood Belwood Elora

Large-Seeded Andean

Chinook 2000 1.11 1.64 1.25 0.69 121 120 115 114

Majesty 1.28 1.62 1.40 1.43 121 116 115 110

Red Rider 1.68 _ 1.67 1.95 122 123 116 116


AC Compass 1.81 1.17 1.32 0.77 113 114 107 109

Mist 2.05 2.18 2.21 2.36 122 119 116 113

OAC 09-3 1.85 1.13 1.23 1.54 121 120 114 114

OAC Rico 1.50 1.30 0.97 1.35 116 115 110 109

OAC Thunder 1.07 1.53 1.56 1.42 113 108 107 102

SXB 415 1.52 1.58 1.34 1.03 123 120 117 114

Sanilac 1.45 1.55 1.16 1.25 119 115 113 109

Zorro 2.00 1.82 2.20 2.76 121 114 115 108

Non-nodulating check

R99 1.05 1.46 1.07 1.17 120 121 114 115

Genotype <.0001 ns <.0001 0.005 <.0001 <.0001 <.0000 <.0001

Andean vs. Middle American 0.02 ns 0.006 0.04 0.0006 ns 0.005 <.0001

Test mean 1.58 1.55 1.50 1.43 119 117 113 111

LSD 95% 0.766 0.417 0.208 0.684 2.3 2.5 2.9 2.3_ Missing observation

Table 3.6 Comparing dry bean genotypes for seed yield and maturity in the field trails in Ontario, Canada, 2011-2012.

F-test

20122011

Maturity

(days)

2012

Seed yield

(tha-1)

2011

94

Belwood Rockwood Belwood Elora Belwood Rockwood Belwood Elora Belwood Rockwood Belwood Elora

Large-Seeded Andean

Chinook 2000 40 64 45 30 57.4 30.9 10.5 14.7 21 18.1 4.7 10

Majesty 48 56 50 57 46.6 24.5 15 11.6 18.1 14.9 7.5 11.5

Red Rider 59 _ 59 79 50.3 _ 13.2 8.1 28.3 _ 8.2 7.9


AC Compass 74 47 51 35 64.5 16.3 5.2 7.7 55.7 7.5 2.7 7

Mist 84 91 85 98 78.5 47.6 17.9 19.6 69.7 41.5 15.3 24.3

OAC 09-3 85 55 46 65 55.3 21.8 12 17.4 41.3 11.9 6.5 18.5

OAC Rico 58 59 38 58 61.5 21.5 8.6 19.2 34.8 13.7 3.2 18.5

OAC Thunder 49 57 62 59 69.3 13.6 9.9 8.4 34 7 6.3 7

SXB 415 64 65 62 43 53.9 15.1 11.7 20.2 34.5 11.1 7.2 17.5

Sanilac 82 76 43 54 9.3 24.4 13.6 12.9 8.9 19.3 5.9 12

Zorro 59 67 82 108 52.6 37.2 17.2 18.1 27.6 27.8 14.2 26


R99 42 63 36 45 0 0 0 0 0 0 0 0

Genotype 0.0002 ns <.0001 0.02 <.0001 0.05 <.0001 <.0001 <.0001 0.02 <.0001 <.0001

Andean vs. Middle American 0.0008 ns <.0001 0.03 0.002 ns ns ns 0.0002 ns ns ns

Test mean 64 64 57 59 54 25 12 14 34 17 7 14

LSD 95% 37.2 17.6 7.8 31.4 23.52 17.64 4.00 8.31 31.36 13.72 3.10 8.41z Nitrogen derived from atmosphere_ Missing observation

F-test

N fixed per unit area (kg ha-1)

201220112012 201220112011

Nitrogen yield (kg ha-1) Ndfa z (%)

Table 3.7 Comparison among 12 dry bean genotypes for nitrogen fixation traits in field trials in Ontario, Canada, 2011-2012.

95

Belwood Rockwood Belwood Elora Belwood Rockwood Belwood Elora

Large-Seeded Andean

Chinook 2000 3.8 3.9 3.6 4.3 -26.37 -26.20 -26.36 -24.42

Majesty 3.6 3.5 3.6 4.0 -26.21 -26.09 -26.19 -24.49

Red Rider 3.5 _ 3.6 4.1 -26.38 _ -26.74 -25.03


AC Compass 4.2 4.1 3.9 4.5 -26.59 -26.26 -26.60 -25.29

Mist 4.1 4.2 3.9 4.1 -26.72 -26.30 -27.02 -25.08

OAC 09-3 4.5 5 3.8 4.2 -26.19 -25.93 -26.34 -24.64

OAC Rico 3.9 4.5 3.9 4.3 -26.54 -25.83 -26.33 -24.77

OAC Thunder 4.5 3.9 4 4.1 -25.74 -25.78 -26.23 -24.58

SXB 415 4.2 4.2 4.7 4.2 -26.49 -26.30 -26.46 -24.89

Sanilac 4 4.2 3.7 4.3 -25.65 -26.91 -26.88 -25.46

Zorro 4.1 4 3.7 3.9 -26.06 -26.70 -27.03 -25.49


R99 3.9 4.4 3.4 3.8 -26.82 -25.78 -26.30 -24.58

Genotype 0.03 0.02 <.0001 ns 0.05 <0.0001 0.003 0.002

Andean vs. Middle American 0.01 0.002 0.001 ns ns ns ns 0.01

Test mean 4.05 4.14 3.79 4.13 -26.31 -26.19 -26.55 -24.89

LSD 95% 0.43 0.45 0.29 0.45 0.650 0.196 0.314 0.431

Table 3.7 Continued...

Carbon discrimination (δ13C)

20122011

F-test

Seed nitrogen (%)

20122011

96

Seed yield

(tha-1

)

Maturity

(days)

Nitrogen yield

(kgha-1

)

Ndfa z

(%)

N fixed

(kgha-1

)

Seed nitrogen

(%)

Nodule

number

(Plant-1)

100 Nodule

wdt.y

(g)

Carbon

discrimination

(δ13C)

Large-Seeded Andean

Chinook 2000 1.50 120 59.72 26.52 13.81 3.96 170 0.10 -25.66

Majesty 1.75 118 65.67 22.28 12.40 3.69 154 0.09 -25.59

Red Rider 2.09 122 88.88 23.03 18.31 3.78 101 0.07 -25.70


AC Compass 1.50 114 63.27 19.65 14.64 4.21 56 0.06 -26.05

Mist 2.90 121 118.18 40.25 49.16 4.09 109 0.08 -26.07

OAC 09-3 1.74 120 75.87 24.07 17.91 4.44 128 0.08 -25.61

OAC Rico 1.63 116 69.16 25.26 17.09 4.25 116 0.11 -25.68

OAC Thunder 1.76 110 70.91 20.60 11.45 4.1 107 0.07 -25.45

SXB 415 1.68 122 72.19 22.44 16.70 4.27 48 0.10 -25.89

Sanilac 1.88 117 78.39 16.27 13.75 4.1 105 0.04 -26.04

Zorro 2.44 118 95.88 30.04 24.81 3.94 82 0.06 -26.21


R99 1.56 120 57.26 0 0 3.95 0 0 -25.64

Environment ns ns ns ns ns ns ns ns ns

Genotype < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.002 ns < 0.0001

Andean vs. Middle American 0.01 ns 0.02 ns ns ns ns ns 0.005

Genotype x Environment 0.0003 0.008 0.001 < 0.0001 < 0.0001 0.04 0.06 0.02 < 0.0001

Test mean 1.92 118 78.01 25.15 19.09 4.08 107 0.077 -25.83

LSD 95% 0.307 1.7 2.332 8.428 8.549 0.221 51.1 0.0529 0.210

Table 3.8 Comparison of 12 bean genotypes for nitrogen fixation related traits in the field across four environments, in Ontario, Canada, 2011-2012.

F-test

z Nitrogen derived from atmospherey Dry weight

97

Fig. 3.1 Genotype by Trait (GT) biplot of seed yield (GY), nitrogen derived from atmosphere (%Ndfa) and carbon discrimination (δ13C) of 11 bean genotypes tested in four environments Belwood 2011 (B11), Rockwood 2011 (R11), Belwood 2012 (B12), and Elora 2012 (E12) in Ontario, Canada. The non-nodulating genotype R99 was not included in the GT biplot analysis.

98

CHAPTER 4

Yield Stability of Dry Bean Genotypes across Nitrogen Fixation-dependent and Fertilizer-

dependent Management Systems2

2 A version of this chapter has been published at Farid, M., H. J. Earl, and A. Navabi. 2015.

Yield stability of dry bean genotypes across nitrogen fixation-dependent and fertilizer-dependent

management systems. Crop Sci. In press.

99

4.1 ABSTRACT

Despite the inherent N2-fixing ability of legumes, the actual symbiotic N2 fixation (SNF) of dry

bean (Phaseolus vulgaris L.) compared to other legumes is relatively low. Accordingly,

application of inorganic nitrogen (N) in bean fields has often been recommended to maximize

economical yield. The genetic diversity for SNF in common bean may provide the opportunity to

develop bean genotypes with stable yield across N fertilizer-dependent and SNF-dependent

production practices. A population of 140 recombinant inbred lines (RILs) of a cross between

high and low N fixing genotypes, Mist and Sanilac, respectively, was evaluated under two

different N management conditions, SNF- and N fertilizer-dependent, across multiple

environments. While N management did not significantly affect the overall yield, genotypes

responded differentially to SNF-dependent and N fertilizer-dependent environments. Among the

RILs with higher than average yield, the stability analysis identified 6% as generally adapted to

all environments, regardless of N management. The study highlights the opportunity to select

bean genotypes that maintain their yielding ability under SNF-dependent management systems.

100

4.2 INTRODUCTION

Common bean (Phaseolus vulgaris L.) accounts for around 50% of the grain legumes

consumed globally (Broughton et al., 2003). As a major source of protein in many parts of the

world, common bean provides an excellent source of low-fat carbohydrates (Díaz-Batalla et al.,

2006), fiber (Díaz-Batalla et al., 2006), folates (Hefni et al., 2010), minerals such as calcium,

zinc, copper, potassium, iron and phosphorous (Ribeiro et al., 2012), and thiamine and riboflavin

(Słupski, 2012).

Nitrogen management is an important aspect of any cropping system (Dalton and

Krammer, 2006) strongly affecting the overall productivity of the system. Application of

inorganic nitrogen (N) fertilizers in common bean fields is often recommended, due to the

generally poor symbiotic N fixation ability of common bean as compared with other members of

the Fabaceae family (Martínez and Roméro, 2003). However, variation for N fixation potential

has been reported among bean genotypes (Graham, 1981; Miranda and Bliss, 1991; Bliss, 1993;

Farid et al., 2015). It has been shown that SNF in beans and other legumes might be suppressed

in the presence of inorganic N, even at very low rates (Da Silva et al., 1993), as well in response

to other environmental factors such as extreme pH, low soil moisture, low phosphorus

availability, low soil temperature, and salinity. This causes SNF potential of bean genotypes to

be highly environment-dependent, and therefore inconsistent, representing an N-management

challenge. To achieve higher yields, application of N fertilizers may be recommended instead of

relying on SNF, which is generally more environment-dependent than relying on N fertilizer

application.

101

We hypothesize that bean genotypes respond differentially to SNF-dependent and N

fertilizer-dependent environments, and that they differ in the stability of their performance across

N management environments. Such genetic variation would permit selection for improved

performance in both N fertilizer-dependent and (or) SNF-dependent environments. We use

genotype by environment (G×E) interaction analysis models to test our hypotheses. Previous

G×E studies (Khalifa et al., 2013; Mekbib, 2003) in beans have not considered the stability of

genotypic performance in SNF-dependent vs. N-fertilizer application environments.

Dealing with G×E is often more complicated when it includes genotypic rank change,

generally known as cross-over interaction (COI; Gail and Simon, 1985). In the presence of COI-

type G×E, genotype correlation across different environments will not be significant (Yang and

Baker, 1991). Two linear-bilinear models, the shifted multiplicative model (SHMM) (Seyedsadr

and Cornelius, 1992) and the sites regression model (SREG) (Cornelius and Seyedsadr, 1997),

are suitable for dealing with G×E associated with COI. The SHMM has been used for studying

G×E and clustering environments (or genotypes) into groups with reduced rank change (Crossa

and Cornelius, 1997). The SREG model in G×E analysis of multi-location data has been applied

in association with a GGE (genotype and genotype-environment interaction) biplot for

interpretation of G×E associated with COI (Yan et al., 2000).

Among different stability analysis approaches, the linear regression method, first

proposed by Finlay and Wilkinson (1963) is the most commonly used method, in which the

coefficient of regression (bi) of genotypic yield over environmental yield is computed as a

stability parameter. Eberhart and Russell (1966) further proposed a linear regression stability

analysis model, in which deviation from linear regression, in addition to regression coefficient, is

102

used as the stability parameter. More recently, Piepho (1999) proposed the methods to estimate

Finlay and Wilkinson’s, and Eberhart and Russell's stability parameters in a “unifying mixed

model framework”, where regression-based stability parameters (denoted λi and σ2

fi) are

estimated as functions of variance components of a specific mixed model using the variance-

covariance structures (Piepho and Van Eeuwijk, 2002). Piepho (1999) provided SAS code for

computing the stability parameters using mixed model analysis, in which genotype and

environment are considered fixed and REPEATED factors, respectively.

This research was designed to examine the G×E interaction patterns of a recombinant

inbred line (RIL) population in a diverse set of environments, in which N management, either

through application of N fertilizers or through SNF, contributes to environmental variation. The

objectives of the study were to examine the magnitude of genotype by nitrogen within

environment interaction of bean genotypes in N management environments including those with

minimal inorganic N with rhizobial inoculation (SNF-dependent environments) and with

conventional inorganic nitrogen fertilizing systems (N fertilizer-dependent environments), and to

study the stability performance of the bean genotypes across theses environments.

4.3 MATERIAL AND METHODS


A total of 144 bean genotypes, including a population of 140 F4:5 RILs of a cross between

two navy bean varieties ‘Mist' and ‘Sanilac’, the two parental lines, the non-nodulating mutant,

R99 (Park and Buttery, 1988), and its wild type, OAC Rico (Beversdorf, 1984) were included in

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this study. The parental lines of the RIL population were chosen because of their difference in

SNF ability. Mist is an indeterminate upright navy bean, which was found to be a high SNF

genotype in our previous work (Farid and navabi, 2015), while Sanilac (Anderson et al., 1960) is

a determinant bush type bean, reported to be a low SNF genotype (Bliss, 1993; Farid and

Navabi, 2015).

4.3.2 Field trials

Field trials were conducted in a total of five location-years during 2011 to 2013. These

included one location in 2011in a farmer’s field near Rockwood (43o39’56”N, 80

o9’54”W, 353

m elev.), two locations in 2012; one in a farmer’s field near Belwood (43o40’16”N, 80

o11’34”W, 430 m elev.) and one at the University of Guelph Elora Research Station near Elora

(43°38'27.8"N 80°24'20.4"W, 379 m elev.), and two locations in 2013; one in a farmer’s field

near Belwood (43o40’16”N, 80

o11’34”W, 430 m elev.) and one at the Elora Research Station,

all in the Grey-Brown luvisols soil zone in Ontario, Canada. The test at the Elora Research

station in 2013 was not harvested due to severe flooding, which caused severe non-uniformity

across the test.

All test-sites were chosen based on their low N availability, based on soil analysis

performed before planting (Table 4.1) and no dry bean history of cultivation for at least 10 years.

Rockwood 2011 and Belwood 2013 with total precipitation of 473 mm and 579 mm,

respectively, received more than average rainfall, while Belwood 2012 and Elora 2012 with

total precipitation of 349 mm and 299 mm, respectively, received less than average precipitation

during the growing season from May to September (Table 4.2). All test sites had very low

104

inorganic nitrogen levels in the soil root zone (0-45 cm) though they were very diverse for other

soil properties especially phosphorous content (Table 4.1). Field sites were sprayed, pre-

planting, with S-metolachlor (Syngenta Crop Protection Canada, Inc, Guelph, Ontario),

Imazethapyr (BASF Canada, Mississauga, Ontario ) and Trifluralin (Dow AgroSciences Canada

Inc., Calgary, Alberta) at the rates of 2.3 L ha-1

, 200 mL ha-1

and 1.5 L ha-1

, respectively. The

experimental design was a 12 ×12 unbalanced square lattice design (Cochran and Cox, 1950)

including 144 entries when each incomplete block of 12 included 12 genotypes. Each

experimental unit consisted of a plot with 4 rows, 2.7 m long with 36-cm row spacing. Planting

was done using an experimental plot drill (Fabro Enterprise Ltd., Swift Current, Saskatchewan,

Canada) with 350 seeds per plot.

4.3.3 Nitrogen management treatments

Two sets of the lattice design, each with two replications, were planted side by side in each

test site in each year. Each set was subjected to a different nitrogen management strategy. One

set did not receive any N amendment, but the seed was inoculated, immediately prior to

planting, with a peat based culture of rhizobia, at the rate of 2.5 g of Rhizobium leguminosarum

bv. phaseoli in a commercial product (Becker Underwood, Saskatoon, Saskatchewan, Canada)

per kg of seed to give approximately 5×105 bacteria/seed. This set represented the SNF-

dependent management. The other set received 100 kg ha-1

N (N100), which represented

fertilizer-dependent management.

All experimental plots received 200 kg ha-1

of 0-20-20 of N-P-K fertilizers to supply plots

with 40 kg each of P2O5 and K2O ha-1

, while the N100 (100 kg N ha-1

) lattice was additionally

105

fertilized with 294 kg ha-1

of 34-0-0 of N-P-K fertilizer. Fertilizer application was performed at

the time of planting. Location-years are referred to as environments and combinations of

environments and N management treatments are referred to as N-environments. The N-

environments are henceforth identified with abbreviations, in which the first letter identifies the

location (R, E, and B for Rockwood, Elora, and Belwood, respectively), followed by a two-digit

number identifying the year, and the subscripts Rh and N for the rhizobia-inoculated (SNF-

dependent) and the N-amended (fertilizer-dependent) treatments, respectively. For example the

abbreviation R12Rh identifies the SNF-dependent N-environment in Rockwood 2012, while B11N

identifies the fertilizer-dependent N-environment in Belwood.

4.3.4 Data collection

As a measure of leaf chlorophyll concentration per unit area, leaf greenness was measured

using SPAD 502 Chlorophyll Meter (Minolta) at the first flowering stage on the second from

top-most fully-expanded leaf between 9:00 am and 5:00 pm. Four observations were recorded

for each leaflet on three plants in each experimental unit. Days to flowering and physiological

maturity were recorded as the number of days from planting until 50% of plants had at least one

open flower, and to the day when 50% of the pods turned yellow, respectively. At harvest

maturity (18 -20% seed moisture content), all plants in each plot were harvested using a

Wintersteiger plot combine (Wintersteiger AG, Ried im Innkreis, Upper Austria, Austria) with a

Classic Seed-Gauge weighing system by Harvest-Master (Juniper Systems Inc., Utah, USA) and

plot seed weight and moisture content were recorded. Seed weight was then adjusted to 18%

moisture.

106

4.3.5 Statistical analysis

The main and interaction effects of genotype (G), environment (location by year

combinations; E), and nitrogen management nested within environment [N(E)] were studied.

Data were subjected first to single environment and then multi-environment analysis of variance

in a mixed model analysis using the PROC MIXED procedure in SAS version 9.3; (SAS

Institute, Cary, NC, USA). In the single environment analyses, the effects of Nitrogen (N),

genotype (G) and genotype by N interaction (G×N) were considered fixed, while the effects of

Block within N [Blk(N)], incomplete block within block by N [iblk(N × Blk)] were considered

random. In the multi-environment analyses, the effect of G was considered fixed and the effects

of E, N(E), Blk(E × N), Iblk(Blk×E×N), and G × N(E) were considered random effects. The

COVTEST statement was used to compute the standard error values and to test the significance

of the components of variance for random effects. Tests of significance for the fixed effects were

performed using F-tests of type III sums of squares. Least squared means (lsmeans), their

standard errors and 5% confidence limits were computed for the fixed effects using the

LSMEANS statement in SAS.

A shifted multiplicative model (Seyedsadr and Cornelius, 1992) with a cluster analysis was

performed using SAS code provided and described by Crossa et al. (1993) using the lsmean

values of each genotype in each N management treatment in each environment. The SHMM

model with multiplicative terms is given by the model presented by Seyedsadr and Cornelius

(1992) as:

107

𝑦𝑖𝑗 = 𝛼 + ∑ 𝛽𝑘 . 𝛾𝑖𝑘 . 𝛿𝑗𝑘

𝑡

𝑘=1

+ ℰ𝑖𝑗

where 𝑦𝑖𝑗 is the seed yield of the 𝑖th genotype in the 𝑗th

N-environment (location×year×N

level); 𝛼 is the shift parameter; 𝛽𝑘 is the particular value (1-t) for the axis 𝑘; 𝛾𝑖1 and 𝛿𝑗1 are the

"primary effects" of the 𝑖th genotype and the 𝑗th

N-environment, respectively, 𝛾𝑖2 and 𝛿𝑗2 are

their "secondary effects", etc; ℰ𝑖𝑗 is a random error. This was done to identify sub-sets of

environments with reduced frequency of COI. The frequency of COI in each branch of the

dendrogram (subgroups of environments) was estimated using a modification of the Azzalini-

Cox’ test (Azzalini and Cox, 1984) using SAS code kindly provided by Dr. Rong-Cai Yang

(University of Alberta).

In addition, the genotypic lsmean values of the RILs in the N-environments were subjected

to the site regression model (SREG) analysis with two principle components (PC). The SREG

model presented by Cornelius and Seyedsadr (1997) is:

𝑋𝑖𝑗 = x̅𝑜𝑗 + 𝜆1 Ę1Ƞ1 + 𝜆2 Ę2Ƞ2 + ℰ𝑖𝑗

where, 𝑋𝑖𝑗 is the lsmean (seed yield) of the 𝑖th genotype in the 𝑗th

N-environment; x̅𝑜𝑗 is the

average of genotypes lsmeans in the 𝑗th N-environment; 𝜆1 is the singular value for the first

principal component (PC1); Ę1Ƞ1are the scores for Genotype i and Location j on PC1,

respectively; 𝜆2 is the singular value for the second principal component (PC2); Ę2Ƞ2are the

scores for 𝑖th genotype in the 𝑗th

N-environment on PC2, respectively.A biplot was then

108

constructed by plotting the first principal component (PC1) scores of the genotypes and N-

environments against the scores for the second principal component (PC2). The association

between any two N-environments in the biplot is approximated by the cosine of the angle

between their vectors, while the size of the vector represents the amount of relative genotypic

variation in the N-environment (Yan and Kang, 2003). The SAS codes used for the SREG

analysis were kindly provided by Dr. Jose Crossa of CIMMYT Biometrics Group.

Stability analysis was performed using the lsmeans of seed yield of the RILs and parental

lines in 4 environments, each with two N-management treatments, using linear regression of the

genotypic yield over environmental yield. A modification of the method proposed by Eberhart

and Russell (1966) was used, in which the regression coefficient (λi) in addition to the variance

of deviation from regression (σ2

fi) are used as stability parameters. The analysis was performed in

the mixed model framework following Piepho (1999). This mixed model analysis included two

factors, genotype and N-environment, which are considered fixed and the REPEATED factors,

respectively. Regression coefficient (λi), deviation variance (σ2

fi) and their standard error (Se)

values were computed using the variance-covariance structures by insertion of TYPE=FA(1) in

the REPEATED statement of the MIXED model procedure in SAS as proposed by Piepho

(1999). Genotypes with a λi close to the average, within two confidence limit (P = 0.05)

intervals, were grouped as genotypes with average stability while those with lower or higher λi

values were identified as genotypes with above and below average stability, respectively.

Genotypes with above-average seed yield across all environments which had λi close to the

population average and lower σ2

fi were classified as adapted to all environments.

109

4.4 RESULTS

4.4.1 Mixed model analysis

For the single-environment analyses (not shown), considerable genotype variation for yield

(P< 0.0001) and the other traits (P= 0.01 to < 0.0001) was observed in all the environments.

Although the G×N interaction was always significant (P < 0.0001), the main effect of N

management on yield was not significant in all environments. In the optimum precipitation

environments, R11 and B13, the effect of N management was not significant, whereas in the low

precipitation environments, B12 and E12, the fertilizer-dependent treatment yielded significantly

higher (11% and 8%, respectively) than the SNF-dependent treatment (Table 4.3). In the pooled

analysis over environments, the effect of genotype was highly significant (P < 0.0001) for all

traits, except days to maturity. However, neither E nor N(E) were significant for any trait, while

the effect of G×N(E) was significant for seed yield (P<0.0001), days to flowering (P< 0.001) and

maturity ( P < 0.001) (Table 4.4). The high SNF variety, Mist, had significantly higher yield,

SPAD reading and later flowering and maturity time than the low-SNF variety, Sanilac, in every

environment.

4.4.2 Multiplicative models of G by E interaction

Considering the presence of the highly significant G×N(E) effect on seed yield (Table 4.4),

SHMM cluster analysis (Crossa and Cornelius 1997) and SREG model with GGE biplot were

used to group environments into sub-groups with negligible COI. The dendrogram generated

using cluster analysis of the G×E data, following SHHM model, is presented in Figure 4.1. In the

110

first fusion level, R11N was identified as the N-environment with the highest frequency of COI

with the other environments. In the second fusion level, the remaining 7 environments were

grouped into two subgroups; one included all of the SNF-dependent environments (R11Rh, B13Rh,

B12Rh and E12Rh) and one of the fertilizer-dependent environments (B13N). The other two

fertilizer-dependent environments, B12N and E12N, formed another group in the dendrogram. As

expected, the frequency of COI, estimated following the Azzalini-Cox’ test (Azzalini and Cox,

1984), decreased by moving down the branches of the dendrogram (Figure 4.1).

In SREG analysis, both PC1 and PC2 were significant (P < 0.0001), accounting for 40 and

20 percent of the genotype and genotype by N within environment variation, respectively (Figure

4.2). The environment marker positioned farthest from the origin of the biplot, indicating greater

within-environment variation and hence greater discrimination ability for selecting genotypes for

their yielding ability, was R11N followed by R11Rh, B13Rh and B13N were. R11N with the largest

projection from the biplot origin was the highest yielding environment and even though it had

the greatest variation, it had strong COI with other environments and therefore was not

representative of overall genotypic performance across the test environments. The environments

with their markers closest to the biplot origin (E12Rh, B12Rh, E12N, and B12N) all received low

precipitation over the growing cycle and most likely were affected by some levels of drought

stress. Three groups of N environments can be identified in the bi-plot: a single environment

group formed by R11N; a second group, including B12N and E12N, clustered together, both with

high N and low precipitation; and a third group, including R11Rh, B12Rh and E12Rh, and B13Rh

(all low N environments) and B13N, which formed another cluster. The environments within each

of the two clusters are closely associated, in terms of genotypic rank. However, the two clusters

in the opposing directions of the bi-plot form two COI pattern groups with significant genotypic

111

rank change. Of the two clusters identified in the biplot, one included all the SNF-dependent

environments, and one of the fertilizer-dependent environments, B13N, and the other included

two fertilizer dependent environments, E12N and B12N (Figure 4.2). In accordance with the

SHMM cluster analysis, R11N with its vector positioned at close to a 90ᴼ angle with other

environments was identified as an N environment with its genotypic ranking unrelated with the

others’.

Yan et al. (2000) suggested that if the correlation between PC1 and genotypic main effects is

close to unity, then the GGE biplot can be used to identify which-won-where patterns. The

coefficient of correlation between PC1 and the genotypic main effects was 0.64 (P < 0.0001).

Due to relatively weak association between PC1 and the genotypic main effects the biplot was

not used to study wining genotypes within sub-groups of environments. However, it is clear from

the biplot that selections in one environment or groups within one cluster will not necessarily be

the best genotypes in the other cluster of environments, which may justify application of a

stability analysis for genotypic selection.

4.4.3 Stability analysis

The scatter plot of the regression coefficient λi against genotypic yield was partitioned into

different sectors by drawing horizontal and vertical lines representing population mean and the

95% confidence limits for λi and genotypic yield (Fig. 4.3). The average seed yield of the RILs

and parental lines across all environments ranged from 1.45 to 2.23 t ha-1

.The high-SNF parental

line, Mist, and 52% of the RILs yielded higher than the population mean. Six percent of the

RILs, 7 genotypes, fell in the middle right sector of the scatter plot with higher than average

112

yield and within one confidence limit of the average value of the stability parameter. These RILs

were identified as high yielding genotypes with average stability. These genotypes are those that

are well adapted to a range of environmental variation, regardless of source of N. Among these

lines, one RIL, designated AG 09-126-137 had the smallest deviation (σ2

fi = 0.02) from the

regression coefficient and can therefore be considered the most stable RIL. The top right sector

of the scatter plot included 6% of the RILs and the high SNF parental genotype, Mist. These

RILs yielded more than the population average with higher than average value of λi, and may be

grouped as genotypes adapted to favourable environments. Four percent of the RILs, in the

bottom right sector of the scatter plot with higher than average yield and with lower λi can be

identified as lines adapted to low yielding conditions. Among the seven genotypes, categorized

as high yielding genotypes with average stability, 4 genotypes including AG 09-126-137 were

also selected in the SREG biplot as the best performing lines with high genotypic main effect and

close to zero PC2 value.

4.5 DISCUSSION

The RIL population studied here, which was derived from a cross between high- and low-

SNF genotypes, showed considerable variation in seed yield across all SNF- and N fertilizer-

dependent environments. Across all environments, there was no indication of an advantage of N

fertilizer application over SNF in beans. However, the main effect of N management seemed to

be mostly dependent on other environmental variables, perhaps specifically the availability of

soil moisture. While under optimum rainfall SNF-dependent environments, in general, yielded

higher (Belwood 2013) or not differently (Rockwood 2011) than the fertilizer-dependent

environments, in low rainfall testing sites, the SNF-dependent treatments yielded lower than the

113

N fertilizer-dependent treatments. This is in agreement with the previous reports (e.g., Coleto,

2014; Devi et al., 2013; Arrese-Igor et al., 2011; Purcell et al., 2004) of inhibition of SNF by

moisture stress. It is known that insufficient soil moisture limits the productivity of the crop

(Abebe et al., 1998) through an adverse impact on plant source strength by reducing canopy

absorption of photosynthetically-active radiation (Earl and Davis, 2003) Carbon limitation

induced by water shortage reduces SNF (Gil-Quintana et al., 2013), which subsequently restricts

the availability of nitrogen compounds essential for seed production (e.g., Arrese-Igor et al.,

2011) and reduces the yield potential of the crop.

Significant genotype by N management within environment interaction effects, associated

with significant genotypic rank change, was evidenced by differential genotypic responses to N

management treatments across test environments. While some of the observed G×E can perhaps

be described by different responses of the RILs to N management, the data suggested that other

environmental variables, possibly the shortage of precipitation during the growing season in

some environments, may have been important contributors to overall G×E. Our attempt to

identify sub-sets of N environments with reduced COI, using SHMM and SREG model analyses,

resulted in identification of two reduced-COI subgroups of N environments, in which all SNF-

dependent environments grouped together. Even though genotypic variation was greater in the

optimum moisture than the dry N environments, the genotypic rank change within the sub-

groups was minimal. This suggested that even though drought was likely the major cause of

reduced yield due to its negative effect on SNF, the relative ranking of genotypes was not

drastically changed among SNF-dependent environments.

114

In terms of genotypic rank change, most of the SNF-dependent environments were in strong

COI with fertilizer-dependent environments. This may suggest that selecting genotypes that

perform well regardless of N management may not be an easy task. We believe that this is

mainly due to the environment-dependent nature of SNF, which is significantly influenced by

many external factors such as soil moisture (Gil-Quintana et al., 2013), soil pH (Bordeleau and

Prévost, 1994), phosphorous availability (Pereira and Bliss, 1987), and soil temperature (Piha

and Munns, 1987; Bordeleau and Prévost, 1994). Compared with SNF, the effect of N fertilizer

seems to be less environment-dependent, which might explain why growers, at least in the case

of dry beans, prefer N fertilizer application over SNF.

This lack of consistency of genotypic responses to SNF justified the application of stability

analysis to identify genotypes which maintain their performance regardless of N management

strategy. In stability analysis, 6% of the population yielded more than the population average

with their stability parameter close to the population average. Four genotypes out of these 8

entries were identified as winning genotypes across all environmental conditions in both stability

analysis and SREG model, suggesting that it is feasible to select genotypes that perform well

under both SNF-dependent and fertilizer-dependent management, which is consistent with a

previous report by Fageria et al. (2014).

In the SREG model 12, 6, 6 and 15 genotypes performed around the corresponding average

in Rockwood 2011, Elora 2012, Belwood 2012 and Belwood 2013, respectively, (data not

shown), while 13% of genotypes performed around average across all environments. Under

limited rainfall, in Elora and Belwood 2012, the genotype ranking was different from other sites

with optimum moisture conditions. Again, this supports the environmental dependency of SNF

115

and emphasizes water scarcity as a likely cause of lack of consistent genotypic responses to N

management strategies.

116

Table 4.1 Soil properties of trial environments presented for samples taken from 0-15 and 15-45 cm depth.

Soil Property

Rockwood Belwood Elora

Belwood

2011

2012

2012

2013

Soil depth (cm)

0-15 15-45

0-15 15-45

0-15 15-45

0-15 15-45

Organic matter (%) 3 1.5

4.9 3

4.4 2.3

3.5 3

P (bicarb, ppm) 7 19

5 4

82 23

11 4

P (Bray-P1, ppm) 16 25

6 5

219 39

14 7

K (ppm) 28 20

47 37

111 60

14 7

Mg (ppm) 190 145

315 265

365 335

175 170

Ca (ppm) 1330 970

2080 2040

2740 3520

1680 1590

Na (ppm) 8 3

2 1

13 14

6 8

Al (ppm) 850 1117

808 900

675 587

777 787

Nitrate (ppm) 4 3

3 2

5 4

7 9

pH 7.1 6.9

7.5 7.7

7.6 7.9

7.3 7.3

CEC† (meq/100g) 9.2 7.3

13.2 12.5

17.1 20.6

10.1 10.1

K/Mg 0.05 0.04 0.05 0.05 0.1 0.05 0.13 0.13 †CEC, Cation Exchange Capacity

117

Table 4.2 Maximum, minimum, and mean temperature, and total monthly precipitation data at trial environments during 2011 to 2013 (available at: weather.gc.ca/canada_e.html).

Rockwood (2011)

May June July August September Growing season

Max Temp† °C 17.6 22.4 28.0 25.2 20.5

Min Temp‡ °C 8.8 12.5 16.5 14.6 11.3

Mean Temp °C 13.2 17.4 22.2 19.9 15.9

Precipitation mm 120 112 38 125 79 473

Belwood (2012)

Max Temp °C 21.7 24.2 28.7 24.8 19.7

Min Temp °C 8.3 12.6 15.5 13.1 8.4

Mean Temp °C 15.0 18.4 22.1 19.0 14.1


Elora (2012)

Max Temp °C 21.8 24.4 28.7 25.3 20.2

Min Temp °C 7.5 12.2 14.0 11.8 7.3

Mean Temp °C 14.7 18.3 21.3 18.6 13.8


Belwood (2013)

Max Temp °C 20.0 22.0 25.1 24.1 20.3

Min Temp °C 7.1 12.5 15.3 12.5 8.8

Mean Temp °C 13.6 17.3 20.2 18.3 14.6

Precipitation mm 106 137 158 53 105 559 † Mean daily maximum temperature for the month ‡ Mean daily minimum temperature for the month

118

SNF N SNF N SNF N SNF N

Mist 1.8 2.1 1.8 1.8 1.6 2.3 2.1 2.1Sanilac 1.4 1.1 1.2 1.4 0.7 1.3 1.3 1.8RILs Max 3.1 4.1 2.4 3.3 2.2 3.1 3.1 4.1 Min 0.8 0.7 0.6 0.6 0.4 0.4 0.9 0.8 Mean 2.3 2.3 1.4 1.6 1.5 1.6 2.2 2.1

CI95% 2.2-2.3 2.2-2.4 1.3-1.2 1.5-1.6 1.0-1.1 1.6-1.7 2.2-2.2 2.0-2.1

Mist 49 42 43 35 44 35 52 45Sanilac 30 31 27 25 28 30 32 35RILs Max 65 64 50 41 49 45 66 49 Min 40 40 36 35 34 32 38 38 Mean 49 43 43 46 47 38 44 45

CI95% 48-47 41-44 41-45 44-48 45-49 36-40 41-46 43-47

Mist 113 114 112 111 113 114 113 114Sanilac 114 114 109 111 112 114 114 113RILs Max 131 135 120 119 126 120 142 142 Min 110 101 107 110 107 106 110 110 Mean 115 114 113.3 114.1 114 113 120 118

CI95% 114-115 113-115 113-114 113-114 113-115 112-114 119-121 117-119

Mist 40 43 40 39 41 41 41 37Sanilac 41 41 38 36 39 36 39 36RILs Max 48 54 46 53 48 55 46 54 Min 34 22 30 28 33 32 30 38 Mean 41 41.7 40 42 40 46 39 47

CI95% 40-41 41-42 41-42 41-42 41-42 45-47 38-39 43-48

SPAD(2nd fully expanded leaf) (mg Chlorophyll m-2)

Maturity (days)

Seed yield (t ha-1

)

Flowering (days)

Table 4.3 Maximum, minimum and mean and 95% confidence interval (P =0.05) of

Least-square means of grain yield, days to flowering, days to maturity and

chlorophyll concentration (SPAD) of the 2nd fully expanded leaf at 50% flowering

stage of 140 recombinant inbred lines and 2 parental lines , Sanilac and Mist in

2011, 2012, and 2013 in SNF-dependent (SNF) and N fertilizer-dependent (N)

management.

Rockwood-2011 Belwood-2012 Elora-2012 Belwood-2013

119

Fixed factor F-test P-value F-test P-value F-test P-value F-test P-value

G† 1.68 <.0001 3.94 <.0001 1.14 ns 2.13 <.0001

Random factors

S2‡‡

(Se§§)P-value S2

(Se)P-value S2

(Se)P-value S2

(Se)P-value

E‡ 0.20

(0.188)ns¶¶ 95.4

(194.49)ns

10.3

(11.39)ns

4.3

(7.64)ns

N(E)§ 0.05

(0.035)ns

16.7

(21.86)ns

1.8

(1.64)ns

1.9

(2.10)ns

Blk (E × N )¶ 0.002

(0.0017)ns 0.00 ns 0.0 ns

0.23

(0.27)ns

Iblk(Blk × E×N)# 4×10-3

(22×10-4)0.04

6.5

(4.11)<.0001

0.5

(0.57)ns

1.2

(0.286)<.0001

G × N (E)†† 0.07

(0.008)<.0001

12.8

(8.96)<.0001

14.1

(1.99)<.0001 0.0 ns

Residual0.04

(0.006)<.0001

102.8

(6.20)<.0001

35.9

(1.76)<.0001

7.2

(0.34)<.0001

¶¶ns, not significant

§§Standard error

‡‡ Variance component estimate

††Genotype by nitrogen within environment effect

#Incomplete block within block by environment by nitrogen effect

¶Bock within environment by nitrogen effect

§Nitrogen(0 anf 100 kh N ha-1) with in environments effect

Seed yield (t ha-1) Flowering (days) Maturity (days) SPAD(2nd fully expanded leaf)

Table 4.4 F -test of fixed effect of entry and variance component estimates (S2) and their

standard error (Se) of random effects in the mixed-model analysis of mapping population, their

parents and checks (R99 and OAC Rico) tested in multiple location-years (environment) in

Ontario, Canada during 2011-2013.

‡ Environment (location-yer) effect

† Genotype effect

SPAD(2nd fully expanded leaf)Seed yield (t ha-1) Days to flowering Maturity (Day)

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Figure 4.1 Diagram of dendrogram which is generated from SHMM analysis for grouping eight environments, under two different

nitrogen (N) managements including symbiotic N2 fixation(SNF)-dependent and N fertilizer-dependent conditions (N), and cross over

interaction percentage (COI%) for each subgroup. Numbers on dendogram branches are the COI% in each branch based on Azzalini-

Cox’ test (Azzalini and Cox, 1984).

123

CHAPTER 5

Response to Selection for Improved Nitrogen Fixation in Common Bean3

3 A version of this chapter has been submitted for publication as Farid, M., H. J. Earl, and A.

Navabi. 2015. Response to selection for improved Nitrogen fixation in common bean

(Phaseolus vulgaris L.). Crop Sci. Submitted.

124

5.1 ABSTRACT

Breeding for high symbiotic nitrogen (N) fixation (SNF) in common bean (Phaseolus

vulgaris L.) is expected to contribute to reduced application of chemical fertilizers in cropping

systems involving common bean. The magnitude of variation and the genetic and phenotypic

correlation among SNF ability, seed yield and related traits were studied in a population of 140

F4-derived F5 recombinant inbred lines (RIL), developed from a cross between high- and low-

SNF bean genotypes ‘Mist’ and ‘Sanilac’, respectively. The experiment was conducted in a total

of 5 location-years in Ontario, Canada, from 2011 to 2013, which based on the total precipitation

during the growing season were grouped into stress- and non-stress test sites. Moreover, in each

test site, two nitrogen supply management strategies, SNF-dependent and N fertilizer-dependent,

were simulated separately in the field by inoculating the seed with a commercial Rhizobium

leguminosarum bv. phaseoli and by application of N fertilizers at 100 kg ha-1

, respectively. The

genetic variation was significant for SNF potential, measured as percentage of nitrogen derived

from atmosphere (%Ndfa), and related traits, while genotypes responded differently to

environments and the N management strategies. The heritability of the traits ranged from 14% to

71% and 4% to 25% in non-stress and in stress environments, respectively. There was no

significant correlation between %Ndfa and seed yield, indicating that selection for high SNF

genotypes does not necessarily lead to greater seed yield and that selection for both traits should

be performed simultaneously. A selection index was computed to select for lines with higher

yield in SNF-dependent management than N-fertilizer-dependent. Selection using the index

identified twenty percent of the RILs with better performance in SNF-dependent environments.

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5.2 INTRODUCTION

Common bean (Phaseolus vulgaris L.), with a total production of 23 million tonnes of dry

seeds and 1.7 million tonnes of green beans in 2010 (Global Biodiversity Information Facilities,

2015) grown on more than 30 million ha of land, worldwide, is an important food legume. Dry

beans are rich sources of protein for over 500 million people, mainly in Latin America and

Africa (Fageria et al., 2011), and also provide low-fat carbohydrates (Díaz-Batalla et al., 2006),

fiber (Díaz-Batalla et al., 2006), folate (Hefni et al., 2010) and minerals such as calcium, zinc,

copper, potassium, iron and phosphorous (Ribeiro et al., 2012), all over the world.

Despite dry bean’s importance, it is usually planted in the marginal lands with its average

yield, on a global scale, estimated to be less than 20% of its potential yield (Fageria et al., 2011).

Availability of nitrogen is a major environmental and management factor, limiting bean

production. Nonetheless, dry beans are generally known as being relatively weak N2 fixers

compared to other legumes (Martínez-Roméro, 2003). Therefore, inorganic nitrogen fertilizers

(N) are often applied by farmers to maximize yield. Generally, plants can uptake only about 30

to 40% of the nitrogen applied to the soil in the form of N fertilizers (Raun and Johnson, 1999).

The increased application of the chemical fertilizers in agricultural lands, over the last 6 decades

(Sutton et al., 2013) on one hand, and the fate of the unused inorganic N in the soil, on the other

hand, are causing environmental concerns (Liang et al. 2013).

Previous studies (Graham, 1981, Miranda et al. 1991; Bliss et al., 1993; Farid and Navabi

2015) have provided evidence for the existence of variation in the symbiotic N2 fixation (SNF)

ability of bean genotypes. Identification of high yielding bean genotypes with superior SNF

126

ability across different environments can potentially contribute to reduced application of N

fertilizer in agricultural lands, which is both economically and environmentally beneficial.

An understanding of the association between SNF and agronomic traits could be very

helpful in exploring surrogate measure(s) for indirect selection of high-yielding and high-SNF

varieties in common bean breeding programs. In previous studies, negative association between

nitrogen fixation and δ13

C, an indicator of water use efficiency in plants, has been reported

(Kumaarasinghe et al., 1992; Knight et al., 1993; Farid et al., 2015). Rennie and Kemp (1983)

also showed that indeterminate and climbing beans were stronger N2 fixers than the bush type

beans, noting that these beans are usually late maturing, and with a longer vegetative growth

period and later flowering time, have a longer N fixation time.

On the other hand, dry bean breeders have over the years selected genotypes of beans that

are resistant against bacterial diseases. Among the bacterial diseases of common bean, common

bacterial blight (CBB), caused by Xanthomonas axonopodis pv. phaseoli (Smith), has been a

major disease of common bean, in North America and elsewhere, for which bean breeders have

introgressed resistance through inter-specific crosses to close relatives e.g., P. acutifolius

(Thomas and Waines, 1984; Scott and Michaels, 1992). While there are at least two reports of

genetic association between CBB resistance and nodulation traits (Nodari et al., 1993; Souza et

al., 2000), it is still not well understood whether or not selection for resistance against the

bacterial pathogen causing CBB, over the years, has reduced the ability of the bean plant to

establish symbiotic relationships with beneficial bacteria.

127

An understanding of the existing genetic diversity for yield, SNF and their related traits as

well as the magnitude of genetic correlation among these plant characteristics, which may

influence the efficiency of selection in plant breeding, is important for an efficient selection

strategy to improve SNF ability of dry beans while maintaining its yielding ability. The

objectives of the present study were, therefore, to (i) investigate the genetic variability,

heritability and the correlation among seed yield and SNF-related traits at the phenotypic and

genetic levels in a bi-parental population derived from crosses of high- and low-SNF genotypes,

(ii) estimate the realized and expected response to direct and indirect selection for seed yield,

SNF and related traits, and (iii) select genotypes with higher yielding ability in SNF-dependent

conditions compared with inorganic N-dependent production environments.

5.3 MATERIALS AND METHODS

5.3.1 Plant Materials

A population of 140 F4-derived F5 recombinant inbred lines (RILs), developed from a

cross between two navy bean genotypes, a high SNF white bean genotype, ‘Mist’, (Farid et al.,

2015) and a low SNF genotype, ‘Sanilac’, (Bliss, 1993), using a single seed descent approach,

was used for this study. The parental lines, a non-nodulating mutant, R99 (Park and Buttery,

1997), and it’s wild type, OAC Rico (Beversdorf, 1984) were included as checks to make a total

of 144 entries.

128

5.3.2 Field trials

Field trials were conducted in a range of different environments, chosen based on their low

N availability, following soil analysis of samples taken prior to planting, in addition to not being

seeded to dry beans for at least 10 years prior to the experiment. Field site-years included 5

locations over 3 years i.e., one location in 2011, two locations in 2012 and two locations in

2013. These included a farmer’s field near Rockwood (43o39’56”N, -80

o9’54”W, elevation 353

m elev.) in 2011, a farmer’s field near Belwood (43o40’16”N, -80

o11’34”W, elevation 430 m

elev.) and a field site at the University of Guelph Elora Research Station near Elora

(43°38'27.8"N 80°24'20.4"W, elevation 379 m elev.) in both 2012 and 2013, all in Ontario,

Canada in the Grey Brown luvisols soil zone. Data collected in Elora 2013 was not included in

the analysis due to severe flooding. Trial sites were sprayed, pre-planting, with S-metolachlor

(Syngenta Crop Protection Canada, IncInc., Guelph, Ontario), Imazethapyr Pursuit (BASF

Canada, Mississauga, Ontario ) and TrifluralinTreflan (Dow AgroSciences Canada Inc., Calgary,

Alberta) at the rate of 2.3 L ha-1

, 200 mL ha-1

and 1.5 L ha-1

, respectively. The experimental

design in each environment was a two-replication 12 ×12 unbalanced square lattice design

including 144 entries. At each site, two sets of the experiment were planted, side by side,

simulating two nitrogen management practices i.e., SNF-dependent and N fertilizer-dependent

management. Plots were seeded using a plot-planter (Fabro Enterprise Ltd., Swift Current,

Saskatchewan, Canada). Each experimental unit consisted of a 4-row, 2.7 m long plot with 36-

cm row-spacing, planted the second week of June of each year. Both sets of the trial at every

location, were fertilized with 200 kg ha-1

of 0-20-20 of N-P-K fertilizers to supply plants with 40

kg ha-1

of P2O5 (17.6 kg P ha-1

) and 40 kg ha-1

K2O (33.2 kg K ha-1

), pre-planting. The SNF-

dependent set was inoculated, immediately prior to planting, with a peat based culture of

129

rhizobia, at the rate of 2.5 g of Rhizobium leguminosarum bv. phaseoli in a commercial product

(Becker Underwood, Saskatoon, Saskatchewan, Canada) per kg of seed to give approximately

5×105 bacteria/seed. The N fertilizer-dependent set received 294 lbs ha

-1 of 34-0-0 of N-P-K

fertilizer, in the form of ammonium nitrate, to supply 100 lbs N ha-1

at planting. The

combination of locations, years and N management provided a total of 8 environments, of which

four were SNF-dependent [Rockwood -2011-0 (R11Rh), Belwood-2012-0 (B12Rh), Elora-2012-0

(E12Rh), and Belwood-2013-0 (B13Rh)], and four were N-dependent environments [Rockwood -

2011-100 (R11100), Belwood-2012-100 (B12100), Elora-2012-100 (E12100), and Belwood-2013-

100 (B13100)].

5.3.3 Data Collection

Leaf chlorophyll concentration was measured using a SPAD 502 Plus Chlorophyll Meter

(Spectrum Technologies, Inc., Aurora, USA) at the 50% flower stage on the second top-most

fully expanded leaflets, SPAD, (four observations for each of the three leaflets on three plants in

each experimental unit). Days to flowering and physiological maturity, 95% of pods turned

yellow, were recorded as the number of days from planting to the day when 50% of plants in the

plot reached flowering, or until pods turned yellow, respectively. At harvest maturity (18 -20 %

seed moisture content), all plants in each plot were harvested using a Wintersteiger plot combine

(Wintersteiger AG, Ried im Innkreis, Upper Austria, Austria) with a Classic Seed-Gauge

weighing system by Harvest-Master (Juniper Systems Inc., Utah, USA) and plot seed weight and

moisture content were recorded. Two 200-g seed samples were collected, and dried in a forced

air oven at 60 oC for about 48 h. Seed protein content was then measured on one of the samples

using a Near Infrared (NIR) Portable Seed Analyzer (model Zx50, Zeltex, Hagerstown, MD,

130

USA) on a moisture-free basis. The second seed sample was ground finely and passed through a

100 mesh (0.149 mm) sieve. Five to 6 mg of ground seed sample was weighed into a tin capsule

(8×5 mm; Isomass Scientific Inc., Calgary, AB, Canada). The capsules were then closed,

compressed and placed in 96-well micro plates. Samples were shipped to the Agriculture and

Agri-Food Canada-Lethbridge Research Centre (Lethbridge, Alberta, Canada) to determine 15

N

abundance (δ15

N) and carbon isotope discrimination (δ13

C) using gas chromatography-mass

spectrometry (GC-MS), following methods explained by Shearer and Kohl (1993) for δ15

N

(15

N/14

N) using the light glutamic acid, NIST-8573, (NIST, National Institute of Standards and

Technology, Gaithersburg, MD, USA) into the same capsules.

Percent nitrogen derived from the atmosphere (%Ndfa) was calculated for each

experimental unit following the equation given by Shearer and Kohl (1986) as:



where δ15

Nref. plant is δ15

N for reference plant, the non-nodulating mutant R99 (Park and

Buttery, 1997), δ15

Nfixing plant is the δ15

N of the dry bean genotype tested and the correction factor,

B is by definition the δ 15

N value of the legume obtaining its entire N from N2 fixation in an N-

free medium. The B-value was obtained, as proposed by Peoples et al. (2009), by taking the

average of δ15

N measurements of a total of 20 randomly selected bean genotypes grown in an N

free medium in a greenhouse at the University of Guelph, Guelph, Ontario, Canada.

Carbon isotope discrimination δ C (‰), was calculated for each experimental unit using

the same samples taken for δ15

N as:

131

𝛿C(‰) =(𝛿𝑎 − 𝛿𝑝)

(1 +𝛿𝑝

1000)

where 𝛿𝑎 is the isotopic ratio of 13

C to 12

C (-8‰) in the atmospheric air and 𝛿𝑝 is isotopic

ratio of 13

C to 12

C in the seed sample.

A sample of the RIL population was planted separately in an artificially-inoculated

common bacterial blight nursery at the AAFC-Greenhouse and Processing Crops Research

Centre in Harrow, Ontario, Canada over 3 years from 2011 to 2013. Each year, two replications

of the population were planted in the nursery. Each experimental unit consisted of a hill plot

planted on rows at every 50 cm with 7 to 10 seeds each with 50-cm row-spacing. The nursery

was artificially inoculated at the fourth trifoliate leaf stage with spore suspensions of three

isolates of X. axonopodis pv. phaseoli (18, 118 and 98) by spraying the leaves with a suspension

of 107 CFU mL-1

inoculum. The CBB severity, defined as the average percentage of necrotic

leaf area, was rated 14 and 21 days after inoculation (as described by: Mutlu et al., 2005). The

disease severity was recorded based on the infected leaf area, across all leaves on the plant, on a

0 to 5 visual scale, where 0 = no symptoms, 1= < 5%, 2 = 5-10%, 3 =10-25%, 4 = 25-50%, and 5

= 50-100% (Yu et al. 2000).


Statistical analyses were carried out using SAS 9.3 (SAS Institute Inc., Cary, NC, USA).

Data were subjected to a multi-environment analysis of variance in mixed model using the

PROC MIXED procedure in SAS (version 9.3, SAS Institute, Cary, NC, USA, 2007). The effect

132

of genotypes, G, was considered as a fixed effect and the effects of environment, E, nitrogen

within environment, N(E), block within environment by nitrogen interaction, Blk(E×N),

incomplete block within block by environment by nitrogen interaction, Iblk(Blk×E×N), and

genotype by nitrogen within environment interaction, G×N(E), were considered random. For

traits that were recorded only in one of the two N-management environments in each year

(%Ndfa and δ13

C), and CBB, which was recorded only in the CBB nursery, the mixed model

analysis was performed with G as the fixed effect and E, block within environment, Blk(E),

incomplete block within block by environment interaction, Iblk(Blk×E), and genotype by

environment interaction, G × E, as random effects. The COVTEST statement was used to

examine the significance of the variance of the random effects in the model. The significance of

the fixed effects was examined using an F-test of the type III sums of squares. Least squared

means (lsmeans) were computed for fixed effects using the LSMEANS statement in SAS. The

standard error of LSmean values were estimated and pair-wise tests of significant differences

were performed using the PDIFF statement.

Components of phenotypic variance were estimated using SAS macros developed by

Holland (2003), using the REML algorithm in the PROC VARCOMP procedure, in which all the

source of variation were considered random effects. The broad-sense heritability (ℎ2) was

estimated as

ℎ2 =σ𝑔

2

σ𝑝2

× 100

133

The expected genetic gain (Allard 1960; 𝐺𝐴𝑒) following 10 % selection intensity was

calculated as:

𝐺𝐴𝑒 = 𝑘 . σ𝑝 . ℎ2

where 𝑘 is constant (1.755 for 10% selection intensity), σ𝑝 is phenotypic standard

deviation. The realized (observed) genetic gain following the same 10 % selection intensity was

estimated as

𝐺𝐴𝑜 = �̅� 𝜇

where �̅� and 𝜇 are the average value of the selected subset and the unselected population,

respectively. 𝐺𝐴𝑒 and 𝐺𝐴𝑜 were also estimated as percentages of the genotypes mean for each

trait.

For each genotype, the best linear unbiased predictor (BLUP) value was estimated, in order

to estimate an un-shrunken genotypic value, using the PROC MIXED procedure by including

SOLUTIONR in the RANDOM statement in the random model. Coefficients of phenotypic

correlation were then estimated using the BLUP values in the PROC CORR procedure.

Coefficients of genetic correlation (rg) were estimated as:

𝑟𝑔 =𝐶𝑜𝑣𝑔𝑖𝑗

√(𝜎𝑔𝑖2 × 𝜎𝑔𝑗

2)

134

using SAS macros introduced by Holland (2006), where 𝐶𝑜𝑣𝑔𝑖𝑗 is the estimate of the

covariance of the ith

and jth

traits and 𝜎𝑔𝑖2 and 𝜎𝑔𝑗

2 are the estimates of variances of the ith

and jth

traits, respectively. A symbiotic nitrogen fixation relative efficiency index SNFI was also

computed for each genotype as

𝑆𝑁𝐹𝐼𝑖 =𝐺𝑌𝑆𝑁𝐹𝑖 − 𝐺𝑌𝑁𝐴𝑖

𝐺𝑌𝑁𝐴𝑖× 100

where 𝐺𝑌𝑆𝑁𝐹𝑖 and 𝐺𝑌𝑁𝐴𝑖 are yield LSMEANS of the i

th genotype in the SNF-dependent

management, and in the N fertilizer-dependent management, respectively.

The PROC CORR procedure of SAS was used to compute Pearson’s and Spearman’s

correlation coefficients between pairs of traits, and principle component analysis with the

Genotype × Trait (GT) biplot analysis (Yan and Rajcan, 2002) was implemented to examine the

multi-variable associations among traits and with symbiotic nitrogen fixation efficiency index

(SNFI) using the genotypic lsmeans values for, seed yield (SY), days to maturity (DM) , nitrogen

derived from atmosphere (%Ndfa), second fully expanded leaf chlorophyll concentration

(SPAD), response to CBB, and days to flowering (DF). The model for the principle component

analysis, using the standardized values of the selected traits including SNFI was:

𝛼𝑖𝑗 − 𝛽𝑗

𝜎𝑗= ∑ 𝛾𝑛𝛿𝑖𝑛

∗ 𝜌𝑗𝑛∗

2

𝑛=1

+ 휀𝑖𝑗 = ∑ 𝛿𝑖𝑛∗

2

𝑛=1

𝜌𝑗𝑛∗ + 휀𝑖𝑗

where 𝛼𝑖𝑗 is the value of the ith genotype for the jth trait, 𝛽𝑗 is the mean value of all

genotypes for the jth

trait, 𝜎𝑗 is the standard deviation of the jth

trait, 𝛾𝑛 is the singular value for

135

principle component n (PCn), 𝛿𝑖𝑛 is the PCn score for the ith

genotype, 𝜌𝑗𝑛 is the PCn score for

the jth

trait, and 휀𝑖𝑗 is the residual associated with genotype i in trait j. Genotype × Trait biplots

were constructed by plotting PC1 scores against PC2 scores for each genotype and each trait.

5.4 RESULTS

There were significant differences among the RILs and between parental lines (P <0.0001)

for all traits, except for number of days to maturity, across all environments. Even though the

effect of environment was not significant for all traits, G×N(E) and G×E effects in N-fertilizer-

dependent and SNF-dependent environments were significant for all the traits except SPAD. It

indicated that for the traits seed yield, days to flowering, days to maturity, seed protein content,

%Ndfa, CBB infection score, and δ13C genotypes responded differentially to the two N

management strategies (Table 5.1 and Table 5.2). For the variance component analysis and

heritability estimates, based on the amount of rainfall, the environments were categorized into

two groups. The first group was the optimum moisture group, which received more than the 30-

year precipitation average (418 mm), including R11Rh, B13Rh, R11100, and B13100. The second

group was limited moisture (dry) environments of Elora 2012 (E12Rh and E12100) and Belwood

2012 (B12Rh and B12100) with lower than the 30-year precipitation average

In general, traits had higher genetic variances in optimum environments than in the dry

environments. This indicated lower contribution of genetic variance to total phenotypic variation

under moisture stress conditions. The estimates of heritability were low to moderate, ranging

from 0.03 to 33, for traits across all environments. Estimates of heritability were higher in the

optimum environments (ranging from 0.32 to 71) than that of stress (dry) environments (ranging

136

from 0.04 to 13). In the optimum environments, days to flowering, seed yield and %Ndfa had

moderate heritability (ℎ2 = 71%, ℎ2 = 40%, and ℎ2 = 48%, respectively) while days to maturity

had relatively lower heritability estimates (ℎ2 = 14%). In the dry environments, estimates of

heritability were generally lower than in the optimum environments ranging from 4% to 25%

(Table 5.3).

The estimates of the expected genetic gain (𝐺𝐴𝑒) were less than the observed genetic gain

(𝐺𝐴𝑜) at 10% selection intensity for all traits across all environments, regardless of moisture

stress conditions (Table 5.3). Moreover, both 𝐺𝐴𝑒 and 𝐺𝐴𝑜 , for all the traits, were generally

higher in optimum environments than in dry environments. It indicated that selection will be

more effective under optimum environments.

The estimates of SNFI in the population ranged from -40% to 45% (Figure 5.1) with an

average of -0.04%. Using the 95% confidence limits of the mean SNFI values, the RILs were

grouped into 3 categories. The group with SNFI estimates within one confidence limit of the

mean included 14% of the RILs with statistically similar yield in SNF-dependent and N-

fertilizer-dependent environments. The group with SNFI estimates higher than one confidence

limit of the mean included the high SNF parent, Mist, and 27% of the RILs with better yielding

response in SNF-dependent environments. Finally, the group with SNFI estimates lower than one

confidence limit of the mean included the low SNF parent, Sanilac, and 59% of the RILs with

lower yielding ability in SNF-dependent environments than in N fertilizer-dependent

environments.

137

Around 30% of the estimated phenotypic (rp) and 36% of the estimated genetic (rg)

correlations were significant, P < 0.05, (Table 5.4). %Ndfa was significantly and positively

correlated with the number of days to flowering (rp = 0.30 and rg = 0.34, respectively). Seed

yield had a significant and negative genetic correlation with seed protein percentage (rg = -0.51).

Among all traits, SPAD2nd

leaf, taken at 50% flowering, was genetically associated with both seed

yield and %Ndfa (rg = 0.10, P = 0.05; rg = 0.13, P = 0.05). Additionally, there were significant

correlations between δ13

C and seed protein percentage (rp = -0.25, P < 0.01; rg = -0.37, P < 0.05),

and between SPAD2nd

leaf and days to flowering (rp = 0.17, P < 0.05; rg = 0.56, P < 0.05). The

first two PCs of the GT biplot explained a total of 81 % (74 % PC1 and 7% PC2) of the total

variation (Figure 5.2). The cosine of the angle between the vectors of two traits (or genotypes)

approximates the correlation coefficient between the two. The distance of the markers of traits or

genotypes from the origin of the bilpot represents the amount of variation for that trait or

genotype. SNFI among all traits showed the highest diversity. The GT biplot indicated close

association between measures of SNFI and days to maturity, DM, (r = 0.30, P < 0.001) and

clustered with %Ndfa, SPAD, and days to flowering (Figure 5.2).

5.5 DISCUSSION

The objectives of this study were to examine the genetic variation, heritability and the

genetic and phenotypic correlations among seed yield and %Ndfa and related traits in a bi-

parental population of dry bean, in order to investigate the response to direct and indirect

selection for high yield, high SNF potential, and related traits. The plant material was derived

from a cross between previously-identified high- and low-SNF genotypes of dry beans, which

138

together with multi-environment evaluations under SNF-dependent and N fertilizer-dependent

environments provided a range of genetic and environmental variation for the study.

The population was found to have high variation for %Ndfa and related traits in individual

test environments and across environments. Availability of genetic variation along with moderate

to high heritability for the majority of %Ndfa-related traits indicated the feasibility of breeding

for improved SNF potential in common bean. Heritability of a trait is known to predict the

probability of improvement of a phenotypic trait through selection (Robinson et al., 1949), while

the favourable selection environment has often been reported to be one that maximizes the

genetic variation of trait(s) (Ceccarelli et al., 1998). Under optimum moisture environments,

higher genotypic variance and ℎ2 values were observed for all traits than in the dry

environments. This was in agreement with previous reports of lower heritability of the traits

under stress conditions, which is believed to be the reason for higher efficiency of selection

under non stress environments (Eid, 2009; Bänziger et al., 1997; Ceccarelli, 1996; Whitehead

and Allen, 1990; Johnson and Geadelmann, 1989). Therefore, breeders have often argued that

genetic gain from selection for adaptation to both stress and optimum environments will be

higher if performed in favorable or near optimum conditions (Cited by: Ceccarelli et al., 1998).

However, Jinks and Connolly (1973) believed that selection for desirable traits should be

conducted based on average performance in two or more contrasting environments. There are

also other reports indicating higher heritability of the quantitative traits under stress

environments (Bolaños and Edmeades, 1993). Ceccarelli (1987) reported that selection for grain

yield was more efficient under stress conditions than selection under favourable conditions,

when dry areas were the target environment. Van Ginkel (1994), however, argued that lower

heritability of some traits under stress conditions is not a true estimation and it is just because of

139

significant G×E. In our study G×E and G×N(E) were significant for all the traits, with the

exception of SPAD, and ℎ2 was always smaller in the stress environments for all the traits.

Estimates of coefficients of genetic correlation among traits were generally greater than the

corresponding phenotypic correlation coefficients. From these, about 32% were significantly

different from zero. The positive genetic correlation between %Ndfa and days to flowering is of

importance from a physiological point of view. Luthra et al. (1983) reported that any reduction in

the availability of photo-assimilates for nodules may result in reduced nitrogen fixation.

Therefore, higher %Ndfa values could possibly be achieved by longer vegetative stage of growth

because of later flowering time. This is in agreement with findings in SNF studies in pigeon-pea

by Kumar Rao and Drat (1987). Moreover, %Ndfa was found to be positively correlated with

SPAD readings at the flowering stage, which is again in agreement with previous reports in

alfalfa lupin and soybean (Antipchuk et al., 1990), and in peanut (Dinh et al., 2013). The SPAD

was significantly correlated with seed yield, which conforms to previous findings in dry bean

(Güler and Özçelik 2007).

Korkovelos and Goulas (2011) demonstrated the heritable nature of SPAD in maize, which

was confirmed in our study by relatively high broad-sense heritability estimates for this trait.

Therefore, SPAD could be recommended as a possible surrogate measure for indirect selection

for genetic improvement of bean varieties with high SNF and yielding ability. Results are in

agreement with Ramaeker et al. (2013) who believed that SPAD is a desirable phenotyping tool

in breeding for SNF ability improvement, though they indicated that its efficacy for use as an

indirect trait for selecting for SNF ability would only be effective if plants are assessed in a low-

N environments.

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Negative genetic correlation was observed between seed yield and protein, which was

consistent with previous findings in soybean (Eskandari et al., 2013) and maize (Mural et al.

2012), but did not conform to the report by Kibite and Evans (1984), who indicated that inverse

relationships between seed yield and seed protein content do not have a genetic basis and are

solely phenotypic.

Hafeez et al. (1998) and Appiah et al. (2015) reported a positive correlation between SNF

and seed yield in chickpeas and cowpea, respectively. Our study, however, does not point to any

significant correlation between yield and %Ndfa. Considering that Hafeez et al. (1998) and

Appiah et al. (2015) measured SNF as the amount of N2 fixed per unit area (Nfix) which is

mathematically a function of seed yield (Peoples et al., 2002), it can be expected that the two

characteristics would be highly correlated. In our study, on the other hand, SNF was measured as

the percentage of N derived from atmosphere (%Ndfa) in the seed, which is independent of seed

yield. Van Kessel and Hartley (2000) suggested that N2 fixation and seed yield are significantly

correlated in low-stress, high-yielding, environments. If N2 fixation supplies plant N demand

over the seed filling period, then seed yield will increase. When overall N supply through N2

fixation cannot meet plant requirements during seed filling, N will be remobilized from leaves to

the grain. Remobilization of N from the leaves will limit the photosynthetic capacity of the

canopy and in turn restrict the seed yield. Therefore, fluctuation of the ability of N2 fixation to

supply N needs of plants could explain lack of significant correlation between seed yield and

%Ndfa in our experiment.

Fageria et al. (2014), in a greenhouse study, identified bean genotypes with higher yielding

ability in a SNF-dependent culture system, compared with N-fertilized conditions. In this study,

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we simulated SNF-dependent and N fertilizer-dependent management systems, in the field, by

either applying rhizobia or fertilizer at selected field sites with low soil nitrogen availability.

Using the overall yield data of genotypes in SNF-dependent and N fertilizer-dependent

environments, we computed a selection index (SNFI) which was used to identify lines with high

yield under SNF-dependent environments. Results not only identified relatively high performing

RILs in SNF-dependent compared to N-fertilizer-dependent conditions, but also confirmed the

relatively stable nature of SNFI, which can be used as a selection index in improving SNF

potential in dry bean maintaining the yielding ability of genotype. Even, more than 12% of the

RILs had high SNFI estimates and consistently performed better in SNF-dependent

environments.

The significant association between SNFI and days to maturity indicated that selection for

improved yield under SNF-dependent environments may result in later maturity. In the GT biplot

the vector of SNFI was in the same direction, and with close association, with the vectors of days

to maturity, %Ndfa, days to flowering, and SPAD, indicating that corelated response to selection

can be expected with selection for any of these traits. Luthra et al. (1983) believed that any

decline in availability of photo-assimilate to nodules could weaken nitrogen fixation. It is also

known that plant source size affects the SNF ability of the plant (Bethlenfalvay et al,. 1978).

Therefore, longer vegetative growth phase, because of later flowering, empowers the ability of

plant to carry out N2 fixation. If greater source size is accompanied by a larger sink size, longer

seed filling period due to later maturity is expected to result in higher SNF potential and higher

leaf N status and subsequently greater yielding ability.

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In spite of the association of SNFI with maturity, %Ndfa, SPAD, and days to flowering,

and seed yield with SPAD, lack of association between %Ndfa and seed yield may suggest that

selection for high SNF genotypes does not necessarily lead to greater seed yield and that

selection for both traits should be performed simultaneously. Such multiple trait selection can be

done through application of selection indices such as the one used in this study, SNFI, which

imposes selection for high yield and independence from N fertilizers at the same time.

Despite the reports claiming association between resistance to the bacterial pathogen

causing CBB and nodulation traits (Souza et al., 2000; Nodari et al., 1993) our study does not

indicate any association between %Ndfa and CBB resistance, either at the phenotypic or genetic

level. The resistant parent of the population, Mist, is a derivative of inter-specific crosses to P.

acutifolius and P. coccineus, which were performed to introgress CBB resistance into common

bean. Results suggest that despite long-term breeding efforts to improve CBB resistance in

common bean, selection has not had any negative impact on the rate of rhizobial infection and

therefore SNF potential of common bean.

143

Fixed factor† F-test P-value F-test P-value F-test P-value F-test P-value F-test P-value

Genotype (G) 1.68 <.0001 3.94 <.0001 1.14 ns 2.13 <.0001 1.33 0.016

Random factors‡ S2 Se S2 Se S2 Se S2 Se S2 Se

E 0.20ns 0.188 59.41

ns 154.49 10.35ns 11.389 4.31

ns 7.64 0.01ns 0.008

N(E) 0.05ns 0.035 11.73ns 17.859 1.76ns 1.643 1.90ns 2.098 0.01ns 0.006

Blk(E × N ) 0.00ns 0.002 0.00ns 0.000 0.00ns 0.000 0.23ns 0.273 0.06ns 0.054

Iblk(Blk × E×N) 0.00* 0.002 4.46*** 4.111 0.45ns 0.570 1.20*** 0.286 0.00ns 0.000

G × N(E) 0.07*** 0.007 10.82*** 7.559 14.10*** 1.994 0.00* 0.000 0.10*** 0.02

Residual 0.14*** 0.006 92.62*** 4.232 35.91*** 1.764 7.20*** 0.337 0.21*** 0.014

† Genotype main effect ‡ E, N, Blk, iblk are environment, Nitrogen, block, and incomplete block, respectively.ns

Not significant; *, **, and *** are significant at 0.05, 0.01, and 0.001, respectively.

Table 5.1 F -test of the fixed effect of genotype and variance component estimates (S 2 ) and their standard error (Se ) of random effects in the mixed-

model analysis of 140 recombinant inbred lines of a bi-parental population and their parental lines, Mist and Sanilac, and checks tested in multiple location-

years under N-dependent and SNF-dependent environments in Ontario, Canada during 2011-2013.

Seed protein

(%)

Seed yield

(kg ha-1)

Flowering

(days)

Maturity

(days)SPAD

144

Fixed factor† F-test P-value F-test P-value P-value

G 1.3 0.03 1.56 0.001 2.43 <0.0001

Random factors‡ S

2 Se S2 Se S

2 Se

E 20.43ns 18.778 95.41ns 194.490 10.35ns 11.389

Blk (E ) 0.20ns 0.170 0.00ns 0.000 0.00ns 0

Iblk(Blk × E) 0.37ns 0.219 6.48*** 4.111 0.45ns 0.57

G × E 7.47*** 0.716 12.82*** 8.959 14.10*** 1.994

Residual 13.63*** 0.603 102.82*** 6.201 35.91*** 1.764

CBB¶ δ13C# (‰)Ndfa § (%)

Table 5.2 F -test of fixed effect of entry and variance component estimates (S 2 ) and their standard

error (Se ) of random effects in the mixed-model analysis of mapping population and their parents

tested in multiple location-years (environment) at 0-level of N in Ontario, Canada during 2011-2013.

† Genotype main effect

‡ E, N, Blk, iblk are environment, Nitrogen, block, and incomplete block, respectively.

§ Nitrogen derived from atmosphere ¶ Common bacterial blight severity# Carbon discriminationns Not significant; *, **, and *** are significant at 0.05, 0.01, and 0.001, respectively.

145

Trait Min Max Mean σ2p σ2g h 2% (Se) GAo GAe

Overall 1.52 2.30 1.88 0.18 0.01 3 (1.9) 0.32 0.02

Opt. environments 1.52 2.95 2.26 0.15 0.06 40 (4.7) 0.51 0.27

Dry environments 1.02 1.98 1.49 0.20 0.01 4 (2.0) 0.32 0.03

Overall-N-0 1.18 2.17 1.72 0.12 0.02 14 (4.0) 0.33 0.09



Overall-N-100 0.76 2.71 1.89 0.21 0.01 7 (4.0) 0.49 0.06



Overal 0.11 0.40 0.28 0.006 0.0005 10 (3.4) 0.21 0.01



Overall 32 72 48 182.61 60.08 33 (4.7) 15.00 7.83

Opt. environments 37 72 48 59.65 42.49 71 (2.5) 11.00 9.62

Dry environments 33 67 45 0.004 0.0005 12 (8.4) 13.00 0.01

Overal 110 122 116 23.49 0.31 1 (1.9) 3.83 0.09



Overall 14 27 21 10.00 1.14 11 (4.4) 3.09 0.61



Overall 25.84 27.01 26.42 0.18 0.03 15 (4.4) 0.44 0.11


Dry environments 25.15 26.85 26.01 0.23 0.03 13(6.6) 0.59 0.11

Overall 35.89 43.46 39.67 1.32 0.36 27 (4.4) 2.74 0.55



CBB§ 2.00 4.11 2.91 0.48 0.05 11 (5.1) 0.69 0.13

† Nitrogen derived from atmosphere

‡ Carbon discrimination

§ Common bacterial blight severity

Table 5.3 Minimumn (Min) , maximum (Max), mean, phenotypic variance (σ2p), genetic variance (σ

2g),

heritability (h 2 ) and its standard error (Se), observed genetic advance (GAo) and expected genetic advance

(GAe) following 10% selection in a population of 140 recombinant inbred lines of a cross between Mist

and Sanilac navy beans for different traits.

Seed yield

(kg ha-1)

SPAD (2nd top leaf)

Ndfa †

(%)

Flowering

(days)

Seed protein

(%)

Maturity

(days)

δ 13C‡

(‰)

146

0

Maturity

(days)Ndfa

(%)†

SPAD 2nd

top leaf

δ 13 C

(‰)

Protein

(%)CBB ††

Flowering

(days)

r p -0.10 0.09 0.04 0.13 0.14 0.19** -0.24

r g -0.03 0.14 0.10* 0.23 0.51* 0.19 0.44

r p 0.19* -0.01 0.04 0.12 -0.09 0.35*

r g 0.27 0.23* 0.03 0.14* 0.40 _

r p 0.11 0.009 0.01 -0.05 0.30*

r g 0.13* 0.10 0.21 0.26 0.34*

r p 0.06 0.14 -0.10 0.17*

r g0.31* 0.07 0.03 0.56*

r p 0.25** 0.11 0.16*

r g 0.37* 0.30 0.18

r p 0.05 0.07

r g 0.07 0.08

r p 0.18*

r g 0.30

† Correlations involving Ndfa , N2 fixationand δC were estimated using data from only the SNF-dependent

environments data.

†† Corrlations involving CBB was consisted of the data from 3 years of data taken in CBB nursery at Harrow,

Ontario and Processing Crops Research Centre, Agri-Food Canada Research Centre, Harrow, Ontario, Canada.

* and ** are significant at 0.05 and 0.01, respectively. Significant r P tested by the Student’s t test. Significance of

r g was tested by estimating 95% confidence limits of the rg estimates, rg intervals that did not include zero were

considered significantly different from zero.

δ 13C (‰)

CBB

Table 5.4 Phenotypic (r p ) and genetic (r g ) correlations among grain yield and other traits estimated in a

population of 140 recombinant inbred lines ofa a cross between Mist and Sanilac navy beans tested in multiple

environments in Ontario, Canada during 2011-2013.

Protein (%)

Seed yield

(kg ha-1)

Maturity

(days)

Ndfa (%)

SPAD 2nd

top leaf

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Figure 5.1 SNF relative efficiency index % (SNFREI) of 140 mapping population and their parents tested across multiple location-years at two N-levels of 0 ,with inoculation, and 100 kg N ha-1, without inoculation, in Ontario, Canada during 2011-2013. Group i,SNFRI = 0, same yielding response of genotype(s) to N application and inoculation; group ii, SNFREI > 0 , bettr yielding response of genotype(s) to inoculation than that of N application; group iii, SNFREI < 0 , better yielding respose of genotype(s) to N application than that of inoculation.

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Figure 5.2 Genotype by Trait (GT) biplot based on common bacterial blight (CBB), seed yield, symbiotic nitrogen fixaion

effeciency index (SNFI ), nitrogen derived from atmosphere (Ndfa ), second fullly expanded lef chlorophyl concentration

(SPAD), and days to flowering data on a population of 140 recombinant inbred lines derived from a cross between Mist

and Sanilac navy beans, their parental lines, and checks tested in eight environments under two nitrogen systems, N-

dependent and SNF-dependent conditions, in Ontario, Canada.

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CHAPTER 6

Quantitative Trait Loci for Symbiotic Nitrogen Fixation and Related Traits in Common Bean

(Phaseolus vulgaris L.)

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6.1 ABSTRACT

A study was conducted to better understand the inheritance of symbiotic nitrogen fixation

(SNF) and related traits in a bi-parental F4:5 recombinant inbred line (RIL) population (n = 140) of a

cross between low- and high-SNF Middle American common bean (Phaseolus vulgaris L.)

genotypes ’Sanilac’ and ’Mist’. The RILs were phenotyped for the percent nitrogen derived from

the atmospheric air (%Ndfa) and SNF-related traits in multi-environment field trials and for

nodulation potential in growth room assays. The population was also genotyped with genome-wide

single nucleotide polymorphic (SNP) markers. Composite interval mapping detected a total of 42

QTL significantly associated with %Ndfa and related traits. A QTL on Pv08 was repeatedly

detected in different tests, accounting for 19% of the phenotypic variation in %Ndfa. Moreover,

smaller effect QTL were detected for %Ndfa on PV01 and Pv07 accounting for up to 15% of the

variation in optimum and low rain fall environments. The Pv07 QTL for %Ndfa was in genetic

linkage with QTL for carbon isotope discrimination (δ13

C), an indicator of water use efficiency

(WUE), suggesting either a pleiotropic effect or linkage of genetic factors influencing both traits.

Three candidate genes associated with QTL for %Ndfa were identified in the genomic region of the

%Ndfa QTL on Pv08. These candidate genes encode SNARE-associated Golgi, 14-3-3, alcohol

dehydrogenase related protein family, auxin responsive protein, and Leucine-Rich Repeat

Receptor-Like Protein Kinase.

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6.2 INTRODUCTION

The biological process of converting atmospheric nitrogen (N2) into ammonia through

symbiotic relationship between legumes and nitrogen fixing bacteria is referred to as symbiotic

nitrogen fixation (SNF). This beneficial role of legumes in rotation or in companion cropping with

other species has been known since the ancient times (Burris, 1974), even though the biology of

SNF was not well understood until the 1960s when the first successful experiments with cell-free

extracts of nitrogen-fixing bacteria were reported by Carnahan et al. (2005). Cropping systems

involving N2-fixing legumes offer opportunities to reduce inorganic nitrogen (N) fertilizer

application and improve the overall nitrogen use efficiency of the cropping systems.

Common bean (Phaseolus vulgaris L.), with a global production of 2.28×106 tons of dry

beans and 21.36×106 tons of green beans on 29.05×10

6 ha and 1.54×10

6 ha of land, respectively, in

2013, (FAOSTAT; 2015) is the third worth widely grown grain legume after soybean and peanuts.

Common bean supplies 20% of the protein consumed by people around the world, and is also a rich

source of low-fat carbohydrates, fiber (Díaz-Batalla et al., 2006), folate (Hefni et al., 2010) and

minerals such as calcium, zinc, copper, potassium, iron and phosphorous (Ribeiro et al., 2012).

Nevertheless, among members of the legume family, the common bean is known to have

relatively lower SNF ability (Martínez and Roméro, 2003; Isoi and Yoshida, 1991), often receiving

inorganic nitrogen fertilizer to supply its N demands. Reports of genetic diversity among dry bean

genotypes for SNF potential (Farid and Navabi 2015; Bliss et al., 1993; Kumarasinghe et al. 1992;

Miranda et al. 1991; Graham, 1981), however, support the idea that the SNF potential of common

bean can be improved through breeding. Such improvements, if achieved, are expected to

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contribute to development of more sustainable and environment-friendly cropping systems,

especially in low-input cropping systems.

A number of studies have attempted to identify quantitative trait loci (QTL) in legumes,

mostly with a central focus on SNF-related traits, particularly nodule wt., number and size. Hwang

et al. (2014) reported eight QTL in a soybean (Glycine max (L.) Merr.) recombinant inbred line

(RIL) population associated with nodule number and seven QTL for nodule size. Santos et al.

(2013), also working with soybean, detected three QTL for nodule number and one QTL for the

ratio of nodule dry weight to nodule number. Nicolas et al. (2006) reported two QTL, explaining up

to 15 % of the phenotypic variation for nodule number and dry wt. in a soybean RIL population.

Bourion et al. (2010) reported nine QTL for nodule number and four QTL for nodule dry weight in

a pea (Pisum sativum L.) RIL population.

In common bean, Nodari et al. (1993) detected four QTL for nodule number on Pv01, Pv03

and Pv07 in a RIL population derived from an inter-gene pool cross between a low nodulating

Middle American small seeded bean (BAT93) and a high nodulating large seeded Andean bean

(Jalo EEP558). The study also reported that one of the Pv07 QTL was significantly associated with

resistance to common bacterial blight (CBB; caused by Xanthomonas campestris bv. phaseoli).

Later on, Tasi et al. (1998) screened the same RIL population in two different N levels and

confirmed the pervious findings by Nodari et al. (1993) under low N availability conditions. They

also reported another QTL for nodule number on Pv04 under high levels of N. Souza et al. (2000)

identified 15 markers associated with nodule number on seven linkage groups in the same BAT93 x

Jalo EEP558 population under low N.

Souza et al. (2000) identified four QTL associated with both nodule number and CBB

resistance and concluded that the QTL for nodule number and CBB resistance in common bean

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may have the same genetic origin. They indicated that the stability of these QTL was a function of

the adaptability of genotypes to diverse soil fertility. Ramaeker et al. (2013) studied nodulation

traits of nodule number (NN) and nodule dry weight (NDW) in a RIL population generated from a

cross between a Middle American genotype and an Andean dry bean genotype, G2333 × G19839,

respectively. They reported two QTL for NDW on chromosomes 3 and 4 and two QTL associated

with NN located on chromosomes 3 and 5 from their studies in a greenhouse. In the most recent

Kamfwa et al. (2015) studied nodulation traits of NDW and NN on a diverse panel of Andean dry

bean genotypes. They reported two QTL; both were located on chromosome 9, with up to 12 %

explanation of the variability for NN in one year of a two years field trial.

Even though there have been several QTL mapping studies on nodulation traits in legume

crops, only a limited number of studies report QTL for SNF traits. Tanya et al. (2005) reported two

QTL associated with plant acetylene reduction activity (ARA), as a measure of SNF potential in

soybean. A QTL for ARA was also reported by Tominaga et al. (2012) on chromosome 4 of the

model legume, Lotus japonicas, with pleiotropic effect on SNF and nodule weight. Even though

application of ARA as a measure of SNF potential of legumes is known as a simple, quick and

inexpensive method (Hardey et al., 1973), it has been criticized for its accuracy in field studies

(Boddey, 1987). These criticisms arose from difficulties with root sampling, frequent calibration

requirements (Unkovich et al., 2008), and high sensitivity of ARA to environmental conditions

especially soil moisture (Huang et al., 1975).

A more efficient method of SNF measurement in the field is the natural 15

N abundance

(Shearer and Kohl, 1986). Ramaekers et al. (2013) studied the QTL associated with SNF, estimated

using the natural 15

N abundance method, and related traits in a RIL population derived from a cross

between a high SNF Middle American climbing bean genotype (Graham and Halliday 1976;

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Graham and Rosas 1977; Kumarasinghe et al. 1992) and a low SNF bush type Andean genotype.

The study identified two QTL accounting for up to 21% of phenotypic variation in nitrogen (N %)

in whole plants, shoots and roots, in the greenhouse. One of the two QTL overlapped with a QTL

identified for total N content in a one location-year field trial in a different population. In another

greenhouse experiment, they detected two QTL on Pv01 and Pv04 accounting for up to 18% of

variation in total plant N fixed. Most recently, Kamfwa et al. (2015) detected significant QTL on

chromosomes 3 and 9 for nitrogen derived from atmosphere (%Ndfa) in the shoot at flowering, and

for %Ndfa in seed and chromosome 7 for %Ndfa in the shoot at flowering, in an Andean diversity

panel, in one field environment (location- year) out of two field environments.

The majority of genetic studies of either SNF or its related traits in dry bean have been

conducted using the progeny of inter-gene pool bi-parental crosses between Middle American and

Andean genotypes. This has mainly been done to take advantage of a greater genetic diversity

between the two genepools. However, phenotypic abnormalities and reduced productivity of

progenies are the main concerns with inter-genepool crosses (Johnson and Gepts, 1999), which may

have affected the ultimate results of these studies. Moreover, not only for common bean, but also

for all other legumes most genetic studies of SNF have been conducted in controlled conditions,

with only a few studies considering SNF measurement in the field in a limited number of

environments (Ramaekers et al. 2013; Kamfwa et al., 2015).

Unlike previous studies, we examined a RIL population derived from an intra-genepool cross

between two Middle American bean genotypes, which were previously identified as high- and low-

SNF genotypes, tested in multi-environment field and growth room trials. The objectives of this

study were to investigate the QTL for SNF and related traits and to identify candidate genes in the

identified QTL regions.

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6.3 MATERIALS AND METHODS


A population of 140 F4:5 RILs of a cross between two Middle American navy bean varieties

‘Mist' and ‘Sanilac’, the two parental lines, the non-nodulating mutant, R99 (Park and Buttery,

1997) and its wild type ‘OAC Rico’ (a total of 144 entries) were included in this study. The

parental lines of the RIL population were chosen based on their significant difference in SNF

ability detected in previous studies. Mist is an indeterminate upright navy bean, which was found to

be a high SNF genotype in our previous work (Farid and Navabi 2015), while Sanilac (Anderson et

al., 1960) is a determinant bush type bean, reported to be a low SNF genotype (Bliss, 1993; Farid

and Navabi 2015).

6.3.2 Growth-room assay

Four seeds of each genotype were planted in 1-L plastic pots with drainage holes, filled with

an N-free medium, MVP Turface (Profile Products, LLC 750 Lake Cook Rd, Suite 440 Buffalo

Grove, IL 60089); with a pH of 6.7 in a growth-room at the University of Guelph. Seeds were

inoculated, immediately prior to planting, with a peat based culture of rhizobia, at the rate of 2.5 g

of Rhizobium leguminosarum bv. phaseoli in a commercial product (Becker Underwood,

Saskatoon, Saskatchewan, Canada) per kg of seed to give approximately 5×105 bacteria/seed.

Growth-room day/night temperature was 25/18°C. Day length was set to 16 h using overhead light

with the flux of photosynthetically active radiation (PAR) of approximately 250 μmol m-2

s-1

.

Approximately two weeks after planting, seedlings were thinned to one per pot. The experimental

design was a randomized complete block design (RCBD) with six replicates planted sequentially

with a 5 to 10-d interval between replicates from January to December 2013, in order to spread the

156

workload. Plants were kept well-watered at all times by using daily subsurface drip irrigation

through two vinyl tubes for each pot, equipped with one 12.5-cm slotted drip spike. A nitrogen free

fertilizer containing H3PO4 (18.7 g L-1

), KHCO3 (37.5 g L-1

), MgSO4 7H2 (3 g L-1

), 3 g L-1

chelated

micronutrient mixed (Plant Products Co. Ltd., Brampton, Ontario, Canada) was added to irrigation

water, in 200-ml of a 1% solution, once a week after the first trifoliate was fully-expanded.

Twelve SPAD readings were recorded from the 3rd

trifoliate of each plant, with four readings

on each leaflet, using a SPAD 502 meter (Spectrum Technologie, Inc., Aurora, USA). The

experiment was terminated 42 days after planting at the onset of flowering. Plants were harvested

and roots were thoroughly washed. Next, nodule numbers were counted and nodules were dried in

a forced air oven at 80oC for about 48 h and 100-nodule dry weight and nodule unit weight as the

ratio of nodule weight to nodule number were determined.

6.3.3 Field assay

Field trials were conducted in a total of four environments during 2011 to 2013 in field sites

chosen based on their low N availability, following soil analysis performed prior to planting in

addition to having had no dry bean or soybean history for at least 10 years prior to the experiment.

Test environments were a farmer’s fields near Rockwood (43o39’56”N, -80

o9’54”W, elevation 353

m) in 2011, a farmer’s field near Belwood (43o40’16”N, -80

o11’34”W, elevation 430 m) and the

University of Guelph Elora Research Station (43°38'27.8"N 80°24'20.4"W, 379 m) in 2012, and a

farmer’s field near Belwood (43o40’16”N, -80

o11’34”W, elevation 430 m) in 2013, all in the Grey-

brown luvisols soil zone, in Ontario, Canada. Based on the climatic data obtained from the weather

stations closest to the field sites, Rockwood 2011 and Belwood 2013, with total precipitation of

473 mm and 579 mm, receptively, of rain during the growing season, had higher than the 30 year

average and were considered optimum soil moisture environments, while both locations in 2012


157

had less than the 30-year average (Belwood with 349 mm and Elora with 292 mm precipitation

during the growing season) and were considered dry environments.

All field sites were sprayed, pre-planting, with S-metolachlor (Syngenta Crop Protection

Canada, Inc., Guelph, Ontario), Imazethapyr Pursuit (BASF Canada, Mississauga, Ontario ) and

Trifluralin (Dow AgroSciences Canada Inc., Calgary, Alberta) at the rate of 2.3 L ha-1

, 200 mL

ha-1

and 1.5 L ha-1

, respectively. The experimental design in each site was a 12 ×12 partially

balanced lattice with two replications. The field was fertilized with 200 kg ha-1

of N free 0-20-20 of

N-P-K fertilizer to supply plants with 40 kg ha-1

of P2O5 and K2O at the time of planting. One

hundred and thirty five seeds of each genotype were inoculated, immediately prior to planting, with

a peat based culture of rhizobia, at the rate of 2.5 g of Rhizobium leguminosarum bv. phaseoli

commercial product (Becker Underwood, Saskatoon, Saskatchewan, Canada) per kg of seed to give

approximately 5×105 bacteria/seed. Inoculated seeds were planted using a plot-planter (Fabro

Enterprise limited, Swift Current, Saskatchewan, Canada) in 4-row, 2.7 m long plots with 36 cm

row-spacing in the second week of June of each year.

As a measure of leaf chlorophyll concentration, leaf greenness was recorded using a SPAD

502 Chlorophyll Meter at 50% flowering from the second top-most fully-expanded leaf between

9:00 am and 5:00 pm. Four observations were recorded for each leaflet on three plants in each

experimental unit. Days to flowering and physiological maturity were recorded as the number of

days from planting to 50% flowering and the day when 50% of pods turned yellow, respectively.

At harvest maturity (18 -20 % seed moisture content), all plants in each plot were harvested using a

Wintersteiger plot combine (Wintersteiger AG, Upper Austria, Austria) with a Classic Seed-Gauge

weighing system by Harvest-Master (Juniper Systems Inc., Utah, USA) and plot seed weight and

moisture content were recorded. Two sub-samples (200 g each) of seeds harvested from each plot

158

were dried in a forced air oven at 60oC for about 48 h and seed protein percentage were measured

on one of the samples using a Near Infrared (NIR) Portable Seed Analyzer model Zx50 (Zeltex,

130 Western Maryland Parkway, Hagerstown, MD, USA) on a moisture-free basis. A seed sub-

sample of 94 randomly selected RIL, the two parental lines, and the non-nodulating mutant, R99

were ground finely and passed through a 100 mesh sieve. Afterwards, 5 to 6 mg of ground seed

sample was weighed into a tin capsule (8×5 mm; Isomass Scientific Inc. 140, 5700 - 1st Street

S.W., Calgary, AB. Canada) and closed, compressed and placed in 96-well micro plates. Samples

were shipped to the Agriculture and Agri-Food Canada, Lethbridge Research Centre (Lethbridge,

Alberta, Canada) to determine 15

N abundance (δ15

N) and carbon isotope discrimination value

(δ13

C) using gas chromatography-mass spectrometry (GC-MS) following the methods explained by

Shearer and Kohl (1993) using the light glutamic acid, NIST-8573, (NIST, National Institute of

Standards and Technology, Bureau Drive, Stop 1070, Gaithersburg, MD, USA) in the same kind

and size of capsules.

Percent nitrogen derived from the atmosphere (%Ndfa) was calculated for each experimental

unit following the equation given by Shearer and Kohl (1986) as:



where δ15

Nref. plant is δ15

N for reference plant, the non-nodulating mutant R99 (Park and Buttery,

1997), δ15

Nfixing plant is the δ15

N of the dry bean genotype and B is the δ 15

N value of the legume

grown obtaining its entire N from N2 fixation in an N-free medium. The B-value was obtained, as

proposed by Peoples et al. (2009), by taking the average of δ15

N measurements of a total of 20

randomly selected bean genotypes grown in an N free medium in a greenhouse at the University of

Guelph, Guelph, Ontario, Canada.

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The carbon isotope discrimination δ13

C was estimated as

𝛿13𝐶(‰) =(𝛿𝑎 − 𝛿𝑝)

(1 +𝛿𝑝

1000)

where 𝛿𝑎 is the atmospheric air isotopic ratio of 13

C to 12

C (-8 ‰) and 𝛿𝑝 is isotopic ratio of 13

C to

12C in the seed sample.

The RIL population and its parental lines were also planted in a CBB nursery at the

Greenhouse and Processing Crops Research Centre, Agriculture and Agri-Food Canada Research

Centre, Harrow, Ontario, Canada over 3 years from 2011 to 2013. Each experimental unit consisted

of a hill plot planted on rows every 50 cm with 7 to 10 seeds each. The population was inoculated

at the fourth trifoliate leaf stage with spore suspensions of three isolates of X. axonopodis pv.

phaseoli (18, 118 and 98) by spraying the leaves with a suspension of 107 CFU mL-1

inoculum. The

severity of CBB was visually estimated as the percentage of necrotic leaf area in the plot, rated 14

and 21 days after inoculation (Multu et al., 2005). The disease severity was rated based on leaf area

infection with a 0 to 5 scale where 0 = no symptoms, 1= < 5%, 2 = 5-10%, 3 =10-25%, 4 = 25-

50%, and 5 = 50-100% (Yu et al. 2000).


Normal distribution of residuals was tested using the PROC UNIVARIATE procedure of

SAS 9.3 (SAS Institute Inc., Cary, NC, USA). A multi-environment analysis of variance was

conducted for each trait in a mixed model analysis using the PROC MIXED procedure of SAS

(version 9.3 SAS Institute, Cary, NC, USA, 2007). The effect of genotypes, G, was considered

fixed and the effects of environment, E, block within environment, Blk(E), incomplete block

within block by environment, Iblk(Blk×E), and genotype by environment interaction, G×(E), were

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considered random. The COVTEST statement was used to estimate the standard error (Se) for the

variance of the random effects and to examine their significance using a standardized normal Z

distribution. The fixed effect test of significance was done using the F-test of type III sums of

squares. Least squared means (LSmeans) were computed for the fixed effects using the LSMEANS

statement in SAS. The standard errors of LSmean values were estimated and pair-wise test of

significant differences were performed using the PDIFF option.

6.3.5 Genotyping

The population was grown in a growth room at the University of Guelph. At the second

trifoliate, the first few leaves of 94 RILs, which were randomly chosen for SNF assays, along with

the parents were collected for DNA isolation. Collected samples were transferred to liquid nitrogen

and stored at -80 °C in a freezer. About 0.5g of each sample was ground in liquid nitrogen.

Genomic DNA was extracted using the method explained by Yu et al. (1999). Genomic DNA of

each RIL was used for genotyping with a total of 6166 SNP markers. The Pv768 common bean

Illumina GoldenGate array (Illumina Inc., San Diego, CA) developed at the University of

Saskatchewan, Canada (Kirstin Bett, unpublished) and the Pv5398 common bean Illumina

GoldenGate array (Illumina Inc., San Diego, CA; Schmutz, et al. 2014) were used for genotyping.

Genotyping with Pv768 was performed at the University of Saskatchewan, while genotyping with

5398SNP markers was performed at the McGill University Genome Québec Innovation Center.

Genotyping was carried out according to the standard assay protocol

(http://www.illumina.com/technology/goldengate_ genotyping_ assay.ilmn). Products generated by

this assay were read with an Illumina HiScan (Illumina Inc., San Diego, CA) and the resulting data

were clustered for allele calling using GenomeStudio software version 2010.3 (Illumina Inc., San

Diego, CA). Allele calls were visually inspected for errors in automatic allele calling and corrected

http://www.illumina.com/technology/goldengate_%20genotyping_%20assay.ilmn

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where deemed necessary. Any calls that were not clearly one allele or the other were reported as

missing data to avoid errors.

6.3.6 Linkage mapping and QTL analysis

The software JoinMap 4.0 (Van Ooijen, 2006) was used to construct a linkage map of the

polymorphic SNP markers. The segregation rate of markers was first tested in Joinmap 4.0 and

distorted markers (chi-square threshold of 0.01) were omitted from further analysis. The LOD

(logarithm of odds) score of 5.0 was set as the minimum LOD score, Kosambi as the mapping

function, and regression mapping as the mapping algorithm to construct linkage groups. After

grouping, MapChart (Voorrips, 2002) was used to make linkage group figures. The genetic linkage

maps were aligned with the physical map of the SNPs based on their positions as identified in a

BLAST search in the Phaseolus Bean Genome sequence (Goodstein et al., 2011) in MapChart 2.3

using the chart option of Homol-1 as described by Voorrips (2002). QTL designations were

assigned following Pedrosa-Harand et al.(2008).To examine the associations between phenotypic

values and genotypes, composite interval mapping (CIM; Zeng, 1993; 1994) was performed in

WinQTL Cartographer Version 2.5 (Wang et al., 2011). For CIM, permutation tests (1000

permutations, α = 0.05) were used to determine the threshold values of the likelihood ratio (LR)

values, for each trait (Churchill and Doerge, 1994; Doerge and Churchill, 1996). Then LR values

were converted to LOD scores as explained by Lander and Botstein (1989). The CIM analysis was

performed using the standard model (Model 6) with a walk speed of 1 cM and forward-backward

stepwise regression to set the number of marker cofactors. The cofactors within 10 cM on either

side of the QTL were excluded from the model. All linkage groups and QTL were visualized using

MapChart 2.2 (Voorrips, 2002).

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6.3.7 Candidate Gene Identification

The P. vulgaris reference genome sequence version 1.0, developed by Schmutz et al. (2014),

available at: http://www.phytozome.net [accessed 20 April 2015], was used to search for candidate

genes within the immediate genomic region (±20 kb) in the vicinity of the marker at the peak LOD

score value of the most repeatable QTL, within and across all field environments. The Jbrowse in

Phytozome v10.1 was conducted to browse the genome to search for the potential candidate genes

for %Ndfa.

6.4 RESULTS

6.4.1 Phenotypic data analysis

Significant genotypic variation (Table 6.1 and 6.2), highly significant genotype ×

environment interactions (Table 6.1), continuous frequency distributions, and transgressive

segregation were observed for %Ndfa and the other traits (Figure 6.1). The two parental lines were

significantly different for all traits. Sanilac had significantly higher nodule number in the growth

room assays, CBB severity, and carbon isotope discrimination (δ13

C), and significantly lower

%Ndfa, 100-nodule weight, SPAD reading and seed yield, as well as earlier flowering date than

Mist across all environments and in every single location-year.

6.4.2 Linkage map

From the total of 6166 SNP markers, 1203 SNPs were polymorphic between the two parental

lines. From these, 39 remained unlinked. The linkage map consisted of 11 linkage groups. These

linkage groups comprised 198 and 966 polymorphic SNP markers from Pv768 and Pv5398 panels,

respectively. The total map distance was 3200 cM with an average interval of 3.6 cM between

http://www.phytozome.net/

163

adjacent markers and a per-chromosome map distance ranging from 19 cM (Pv10) to 498 cM

(Pv02). Linkage and physical maps were reasonably aligned for most chromosomes except Pv02

and Pv07, in which a 20-cM and a 90-cM region, respectively, were found to be reversed.

6.4.3 QTL mapping

A total of 42 significant QTL was identified throughout the 3-year field and growth room

assays (Table 6.3). These QTL were distributed on 10 out of 11 linkage groups with the presence of

clusters of QTLs in different chromosomal regions (Figure 6.2).

6.4.3.1 Chlorophyll concentration (SPAD)

Four QTL were detected for SPAD on Pv02, Pv03, Pv06 and Pv08, designated SPAD2,

SPAD3, SPAD6, and SPAD8 with significant negative additive effects -1.7, -1.3, -2.1, and -2.0,

respectively. These QTL explained 13%, 11%, 13% and 19% of the variation, respectively, in

SPAD readings in the growth room. Although none of these QTL were associated with SPAD in

the field assays, two QTL on Pv07 and Pv11, SPAD7 and SPAD11, respectively, were detected

(LOD>3.0) for SPAD across field environments accounting for 14% and 13% of the variation. The

QTL associated with SPAD in the field had positive additive effects of 0.31 and 0.61 while QTL

for SPAD in the growth room had negative additive effects.

6.4.3.2 Nodule traits

Four QTL were detected (LOD > 3) for number of nodules in the growth room. Two of

these QTL, NN9.1 and NN9.2 were on Pv09, in repulsion phase, 84.9 cM apart. The markers

closest to the peak LOD value of the two QTL were “sc00016ln1258381_645222_T_G_

26331833” and “sc00030ln901868_598673_A_C _40799116”, with additive effects 33 and -31on

164

number of nodules, respectively, accounting for 11% and 9% of the phenotypic variance. The other

two QTL, NN1 and NN4, were on linkage groups Pv01 and Pv04 accounting for 11% and 15% of

the variation, respectively, and with additive effects 37 and -46 on nodule numbers.

Nodule weight and number in the field assays were only measured in Rockwood 2011. No

significant QTL were detected for nodule weight in the field. However, a QTL on Pv08, NN8, was

significant for nodule number, with additive effect of 14 nodules and accounting for 15% of the

variation.

6.4.3.3 Flowering date

Three QTL were detected on Pv01, Pv08 and Pv11 with significant effect on flowering date

including DTF1, DTF8 and DTF11, respectively. From these, the QTL on Pv08, DTF8, reduced the

flowering time by 4 days (LOD=3.2), accounting for 12 % of the variation. The other two QTL on

Pv01 (LOD=4.0) and Pv11 (LOD=5.0) delayed flowering date by 4 and 5 days, respectively.

6.4.3.4 Maturity

One QTL, MT9, was found significantly associated with number of days to physiological

maturity across all environments. This QTL on Pv09 (LOD = 3.2) accounted for 13% of the

phenotypic variation with an additive effect of 1.3 days. The marker sc00105ln590179_432554_

A_G_94086672 was closest to the peak LOD score for this QTL.

6.4.3.5 Common bacterial blight

Three QTL of CBB3, CBB5, and CBB8 on Pv03, Pv05 and Pv08, respectively, were

significantly associated with common bacterial blight resistance, accounting for 13%, 13% and

17% of variation, respectively. From these, CBB3 on Pv03 had a positive additive effect of 0.3 and

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the other two QTL CBB5 and CBB8, located on Pv05 and Pv08, respectively, had negative additive

effect values of 0.2 and 0.13, respectively.

6.4.3.6 Carbon isotope discrimination

For Carbon isotope discrimination (δ13

C), as an indicator of water use efficiency (Farquhar et

al., 1989), two QTL were detected on Pv01, ∆1, and Pv07, ∆7, each accounting for 13% and 11% of

the phenotypic variation. The ∆1 had a positive additive effect of 0.11 % and the ∆7 had a negative

additive effect of -0.08 % on δ13

C.

6.4.3.7 Seed protein content

Seven QTL were detected on 4 different chromosomes for seed protein content including

PRO4, PRO6, PRO7.1, PRO7.2, PRO7.3, PRO7.4, and PRO9. Of these, four were on Pv07

spanning a 29 cM region, one of which, PRO7.1, with LOD=8.0 being a major QTL accounting for

23% of the phenotypic variation with an additive effect of 0.49 %. Among the other Pv07 QTL two,

PRO7.2 and PRO7.4, had negative additive effects accounting for 11% and 15% of the variation,

respectively, and PRO7.3 had an additive effect of 0.51%, accounting for 22% of variation. Other

QTL were detected on Pv04, PRO4, Pv06, PRO6, and Pv09, PRO9, accounting for 17%, 15% and

10% of the variation. They had significant additive effects of 0.56%, 0.58% and -0.46%,

respectively.

6.4.3.8 Seed yield

Six QTL were detected significantly associated with seed yield (LOD > 3.0) on 6

chromosomes, including Pv02, Pv03, Pv04, Pv06, Pv07, and Pv11. From these, 3 QTL SY2 on

Pv02, SY4 on Pv04, and SY6 on Pv06, were significantly affecting seed yield in low rain fall

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conditions of Belwood and Elora 2012. SY2 on Pv02 accounted for 14% of variation, and could be

considered as a major QTL for seed yield in dry conditions. The other QTL SY2.2 on Pv02, SY4 on

Pv04, SY6 on Pv06, accounted for 11%, 14%, and 11%, respectively, of seed yield variations in dry

environments. All of these QTL except SY6 had negative additive effects ranging from -80 to -160

kg ha-1

. For seed yield under optimum soil moisture conditions in Rockwood 2011 and Belwood

2013, only one QTL, SY3, which accounted for 11% of the phenotypic variance, was identified on

Pv03 with a small but significant positive additive effect of 110 kg ha-1

. In addition, two genomic

regions SY7 and SY11 on Pv07 and Pv11, respectively, were significant, accounting for 9% and

21% of the variation in seed yield across all environments. These QTL had negative additive effects

of 60 and 130 kg ha-1

.

6.4.3.9 Percent Nitrogen derived from atmosphere

To avoid from anymathematicalassociation between SNF ability and seed yield percent

nitrogen derived from atmosphere (%Ndfa) was considerd as an indicator for SNF ability in this

study. Overall 8 QTL were significantly associated with %Ndfa at individual location-years and

(or) across all environmental conditions. For %Ndfa in optimum rainfall environments (Rackwood

2011, and Belwood 2013) two QTL, Ndfa 1 and Ndfa8.4, were detected on Pv01 and Pv08

accounting for 15% and14% of the variation with negative additive effect -2.83% and -2.94%,

respectively. The other QTL Ndfa 8.1and Ndfa8.3, associated with the marker “sc00089ln640327_

323100_T_C_84188591”, were detected in Belwood 2013 and Rockwood 2011 accounting for 12%

and 19% of the variation with negative additive effects -3.32%, -3.26% and -1.64, respectively.

Another QTL, Ndfa7, was significantly associated with %Ndfa in Rockwood 2011 on Pv07, which

accounted for 15% of total phenotypic variation and with significant positive additive effect of

2.50%.

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In low rainfall environments, only in Elora 2012, one QTL was found significantly

associated with %Ndfa. Ndfa5, on Pv05 explained 17% of the variation in %Ndfa in Elora 2012

with a positive additive effect of 3.00 %. Grouping the environments into low and optimum rainfall

conditions revealed three other genomic regions including Ndfa7, Ndfa 1 and Ndfa 8.4 associated

with %Ndfa. Of these, the first one, Ndfa7, on Pv07 was associated with %Ndfa under dry

conditions accounting for 14 % of the variance and the other two QTL Ndfa1 on Pv01 and Ndfa8.4

onPv08 were associated with %Ndfa in optimum rainfall environments, accounting for 15 and 14 %

of total variance, respectively. Two QTL on linkage group Pv01 and Pv08 (LOD=3.0; LOD=4.0,

respectively) were detected for %Ndfa across optimum rainfall environments, both with negative

additive effects 2.94% and 2.82%, respectively. They accounted for 15% and 14% of phenotypic

variation, respectively. The only QTL on linkage group Pv07, Ndfa7, detected for %Ndfa in dry

condition (LOD=4.0) had a positive additive effect 2.31% accounted for 14% of the phenotypic

variance.

6.4.4 Repeatable and overlapping %Ndfa QTL

For %Ndfa across all environmental conditions just one genomic region in the vicinity of the

marker “sc00089ln640327_323100_T_C_84188591” on Pv08 was detected as carrying a QTL,

Ndfa8.1, which accounted to 17% of the variation in %Ndfa and with negative additive effect of

1.64%. This genomic region was detected as carrying QTL for %Ndfa in both optimum

environments, Rockwood 2011 and %Ndfa in Belwood 2013.

Three genomic regions on Pv01, Pv07 and Pv08 were repeatedly detected as carrying QTL

for %Ndfa across the environments, in optimum and dry conditions, and in single location years.

The %Ndfa QTL Ndfa7 on Pv07, associated with %Ndfa in dry environments, was in close genetic

linkage with the QTL detected for δ13

C, ∆7, (cM). These QTL had opposing additive effects on

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%Ndfa and δ13

C, which is expected from the negative association between %Ndfa and δ13

C.

Moreover, the Pv01 genomic region associated with %Ndfa in optimum conditions, Ndfa1 was in

close genetic linkage (23 cM) with the QTL detected for flowering time, DTF1, with -3% additive

effect on %Ndfa in optimum environments and 4 days additive effect on days to flowering. A QTL

for %Ndfa in dry environments, Ndfa7, was in genetic linkage (2cM) with the QTL for δ13

C, ∆7,

which also had a positive additive effect (0.11%) on δ13

C. Similarly, the Pv07 genomic region,

which carried a QTL for %Ndfa in dry conditions and δ13

C, was found significantly associated with

protein content. The additive effects of this region for %Ndfa under dry conditions, δ13

C, and

protein content were 2.31%, -0.08‰ and 0.51%, respectively.

In addition, a genomic region on Pv02 was also significantly associated with both %Ndfa in

Belwood 2012 and seed yield in dry environments with opposing additive effects 4% for %Ndfa

and -70 kg ha-1

for seed yield in the dry environments. A QTL, NN8 on Pv08, accounting for 13%

of the variation for number of nodules in the field (Rockwood 2011) was associated with the

marker “PvSNP23p310253” at the peak LOD score, with a negative additive effect of 14 nodules. It

showed a close genetic linkage (9 cM) with a QTL detected for %Ndfa in optimum conditions.

6.4.5 %Ndfa Candidate genes search

Search in the genome sequence of the genomic regions with repeatedly detected significant

QTL for % Ndfa on Pv01, Pv07, and Pv08 in the reference Phaseolus Genome sequence (available

at http://www.phytozome.org; accessed 11 May 2015) in +20 kb region stretching each QTL

resulted in detection of 10 candidate genes with potential role in SNF in accordance with the

ontology of the genes (Table 6.4).

169

The genomic region identified for “%Ndfa across all environments” was linked to three

genes including Phvul.008G004000, Phvul.008G004300, and Phvul.008G004400.

Phvul.008G004000 encodes an “SNARE associated Golgi protein” (N-ethylmaleimide-sensitive

factor attachment protein receptor). Moreover, this gene was also associated with %Ndfa QTL in

both optimum rainfall location-years of Rockwood 2011 and Belwood 2013.

Phvul.008G004400 was another gene associated with the genomic region on Pv08. This gene

encodes alcohol dehydrogenase related proteins. Phvul.008G018100 gene was linked to the marker

associated with the significant genomic region on Pv08 associated with %Ndfa across all optimum

environments. This gene encodes “Chitinase Class I” proteins.

The other potential candidate gene in this genomic region was Phvul.008G017800, which

encodes an “aldo/keto reductase family”. Two other prospective candidate genes were located on

both sides of the genomic region associated with %Ndfa across optimum rainfall environments on

linkage group Pv01. One of these genes, Phvul.001G034300, encodes an abscisic acid receptor,

polyketide cyclase PYR/PYL family. The other gene, Phvul.001G033500, encodes a

hemoglobinase family protein member with a cysteine-type endopeptidase activity.

The gene Phvul.007G04350 was identified in the genomic region on Pv07 with the QTL

associated with %Ndfa in dry conditions. This gene encodes a receptor-like kinase gene from the

Leucine-Rich Repeat family, LRR-RLKs.

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6.5 Discussion

6.5.1 Phenotypic data analysis

Significant variation, continuous frequency distributions, and transgressive segregation were

observed for %Ndfa and related traits. Unlike previous QTL mapping studies in common bean

(Nodari et al., 1993; Tasi et al., 1998; Souza et al., 2000; Ramaeker et al., 2013), in which QTL

were reported in inter-genepool populations, the population studied here was derived from crosses

of two Middle American small-seeded white beans, which is expected to result in the identified

QTL being potentially more applicable in plant breeding programs.

Presence of significant G×E for %Ndfa and related traits in this study conforms to the

previous reports of environmental dependency of SNF (Bliss, 1993; Peoples et al. 1995; Ramaeker

et al., 2013; Farid and Navabi, 2015). The environmental dependency was also apparent for most

SNF-related traits. This dependency could be due to variation in genotypic response to

environmental variables known to influence SNF in legumes i.e., soil moisture (Devi et al., 2013),

soil phosphorous (Namayanja et al., 2014; Divitoa and Sadras, 2014), potassium and sulfur (Divitoa

and Sadras, 2014), and soil salinity (Faghire et al., 2011) of soil. Based on the significant G×E,

which appeared to be associated with significant genotypic rank change (Farid et al, 2015) we

chose to conduct QTL analysis for %Ndfa and related traits using not only the overall multi-

environment data, but also on the single environment (location-year) of data.

6.5.2 QTL study

To prevent an overestimation of QTL effects, likely caused by Bayesian distribution due to

relatively small population size (Xu, 2003), a relatively dense genetic map was constructed using

two available common been SNP panels, Pv768 and Pv5398. Moreover, LOD scores for identifying

171

the significant QTL associated with the traits and optimizing the genotyping information a

composite interval mapping (CIM) analysis with 1000 permutations was carried out to determine

threshold LOD score for declaring significant QTL. Accordingly, a total of 45 QTL were detected

for various traits, distributed across 10 out of 11 linkage groups with the proportion of the variance

accounted for by the QTL ranging from 9 to 23%.

Although significant G×E interaction was observed for different traits over environmental

conditions, a number of consistent and repeatable QTL were detected for some traits. Two different

QTL were detected for %Ndfa in dry and optimum environments on Pv07 and Pv08, at 3.0 and 1.5

Mbp, accounting for 14 and 17% of the phenotypic variance of the traits, respectively. The genomic

region associated with %Ndfa across all environments on Pv08, Ndfa8.1, at 0.5 Mbp was also

detected as being significantly associated with %Ndfa in optimum soil moisture environments

(Rockwood 2011, and Belwood 2013). This genomic region on Pv08 appears to be a major QTL for

%Ndfa across all environmental conditions.

The association between SNF potential and water use efficiency has been previously reported

(Kumaarasinghe et al., 1992; Knight et al., 1993; Farid and Navabi, 2015). In our study, two QTL

were identified for %Ndfa in optimum and dry conditions on Pv01 and Pv07 at 3.0 and 3.4 Mbp,

respectively. Both of these QTL were in close proximity (47 cM on Pv01 and 2 cM on Pv07) with

the QTL associated with δ13

C at 42.5 Mbp and at 2.6 Mbp on the physical map. While the QTL on

Pv07, Ndfa7.1 at 3.4 Mbp, had a positive additive effect on %Ndfa, the other QTL , at 2.6 Mbp, was

associated with δ13

C in the dry environments with a negative effect. This genetic linkage of the

QTL for %Ndfa and δ13

C is in agreement with the previous reports of negative correlation between

nitrogen fixation and δ13

C, a measure of water use efficiency, which provides a genetic explanation

for positive association between SNF ability and WUE (Kumaarasinghe et al., 1992; Knight et al.,

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1993; Farid and Navabi 2015). The other QTL for %Ndfa in optimum rainfall environments,

Ndfa1at 3 Mbp, was in genetic linkage with the δ13

C, at 42.5 Mbp, with opposing additive effects,

which agrees with previous reports of negative correlation between these two traits in drought stress

environments (Kumaarasinghe et al., 1992; Farid and Navabi, 2015).

The Pv01 QTL for %Ndfa at 3.0 Mbp was also in close proximity (23 cM) with a QTL for

days to flowering, DTF1 at 1.1 Mbp. This is in agreement with previous reports that genotypes with

longer vegetative growth phase, and climbing genotypes compared with bush types, often have

higher SNF ability (Graham and Halliday 1976; Graham and Rosas 1977; Kumarasinghe et al.

1992; Ramaeker et al. 2013). Results of this study indicate that any breeding efforts to improve

SNF (% Ndfa in our study) should consider selecting for high WUE level and an optimum

vegetative growth length that can photosynthetically support an optimum nodulation of the bean

genotypes.

Higher N status of the plant throughout the growing season, especially flowering stage,

results in higher seed protein content (McMullan et al., 1988; Zhao et al., 2005). As a result, a high

SNF bean genotype is expected to have higher N status throughout the growing season, which

should result in higher seed protein content. The genomic region on Pv07, which carries a major

QTL for seed protein content, PRO7.4 at 2.5 Mbp, accounting for up to 23% of the variation, was

consistent with the report by Casañas et al. (2013), who reported a significant QTL associated with

seed protein content on Pv07 in common bean. This QTL, PRO7.4 at 2.5 Mbp, in close proximity

with the QTL for %Ndfa in dry conditions, Ndfa7.1 at 4.0 Mbp, (10 cM) and δ13

C, at 2.0 Mbp (12

cM) in our study.

Previous studies of SNF in legumes have used greenness and chlorophyll content in the shoot

tissues as an indirect indicator of SNF potential, when legume species are grown under N deficient

173

conditions (Tint et al., 2011). In our growth room assays, QTL for SPAD were detected on Pv02,

Pv03, Pv06 and Pv08 at physical positions 2.8, 2.3, 24.5 and 57.5Mbp while one QTL, SPAD7, at

the physical position 4.8 Mbp was detected in association with SPAD in the field. Of these, the

QTL for SPAD on chromosomes 6 and 8 were also reported by Ramaeker (2013) in the same

physical regions and in close proximity with the genomic regions with significant QTL in the field

and growth room assays, respectively. The repeatability of SPAD QTL between our growth room

and field assays and with the study by Ramaeker et al. (2013) seem to be low and with opposing

additive effect between field and growth room assays. These unexpected results may be due to

significantly different light intensity and quality between the field and the growth room assays and

how they might influence gene expression.

Yield QTL were identified on Pv02, Pv03, Pv04, Pv06, Pv07, and Pv11 which accounted for

11%, 11%, 14%, 15%, 9%, and 21% of pheonotypic variations. SY2 (0.90 Mbp), SY3 (38.3 Mbp),

SY4 (44.5 Mbp), SY6 (21.5 Mbp), SY7 (42.5 Mbp), and SY11 (2.1 Mbp) were detected in different

environments or across environments. The more frequent QTL seed yield reported in common bean

were detected on chromosome 3 by Blair et al. (2006), Wright and Kelly (2011) Blair et al. (2012)

Checa and Blair (2012) Mukeshimana et al. (2014). While Mukeshimana et al. (2014) reported just

one QTL for seed yield on chromosome 9, three QTL on chromosomes 2, 4 and 4 and two QTL on

chromosomes 7 and 11 were detected in stress conditions and across all environments.

Nodule weight and number have been recommended as indirect selection criteria for SNF

potential in legumes (Wadisirisuk and Weaver, 1985). In our study 4 QTL were detected for

number of nodules in growth-room assays, one on each of Pv01 and Pv04, and two on Pv09, in

repulsion linkage within an 85 cM interval, at 4.6 Mbp, 4.0 Mbp, 2.7 Mbp, 4.6 Mbp, and 25.4 Mbp

on physical map. A total of 15 % of the variation was explained by the major QTL. Existence of a

174

QTL on Pv04, NN4 at 4.0 Mbp, for nodule number in controlled conditions was previously reported

in a diefferent RIL population, derived from an inter-genepool cross in two different greenhouse

studies (Tsai et al. 1998 and Noradi et al. 1993). For nodule number in the field, which was assayed

only in one location-year (Rockwood 2011), a QTL was identified on Pv08, 2.7Mbp, explaining

13% of total variance which was found at 9 cM and 41 cM distance from a QTL for %Ndfa across

all environments, Ndfa8.1 at physical position of 0.5 Mbp, and in optimum rainfall conditions.

Ramaeker et al. (2013) detected two different QTL for nodule number on Pv03 and Pv05 in the

field condition. Kamfwa et al. (2015) in a genome-wide association study of %Ndfa in the Andean

diversity panel detected such an association between QTL for nodule number and %Ndfa on a

different chromosome, Pv09. Lack of repeatability of detected QTL between controlled and field

environments suggests that selection for nodulation traits should be conducted in the field, but this

is a labor-intensive task, which is often not affordable in most breeding programs. Moreover, our

study does not provide any evidence for association between QTL for nodule number and SNF

potential, indicating that direct SNF assays such as 15

N natural abundance (Shearer and Kohl, 1986)

may be more suitable assays in breeding for high SNF potential in common beans than the other

SNF estimation methods.

In our study we also attempted to examine whether or not breeding for resistance to bacterial

diseases (CBB in this case), which has involved introgression from related species through inter-

specific crosses (Scott and Michaels, 1992), may have negatively influenced the symbiotic

relationship with beneficial bacteria. Previous report by Noradi et al. (1993) indicated the presence

of a common QTL for nodule number and CBB on Pv07. In our study, the high SNF parent of the

RIL population, Mist, is a derivative of crosses to CBB resistant varieties and lines, which had been

derived from inter-specific crosses to P. acutifolius. We did not identify any common genomic

175

region controlling both traits. However, the major CBB QTL for CBB resistance at the end of Pv08,

at physical location of 59.4 Mbp. At the population level, however, genetic association between

CBB and %Ndfa was found in our previous genetic correlation reports using the same population’s

data (Farid et al. submitted). This result indicated that long term breeding of common beans for

resistance to CBB, at least for the strains screened in our study, did not adversely influence their

SNF ability.

6.5.3 Potential candidate genes for %Ndfa

Ten genes were identified in the vicinity of the significant %Ndfa QTL as candidate genes for

SNF. The gene Phvul.008G004000 which encodes an SNARE-associated Golgi protein (N-

ethylmaleimide-sensitive factor attachment protein receptor is one of the candidate genes. SNARE

associated Golgi protein was previously reported in Medicago truncatula (Catalano et al., 2007;

Wang et al., 2010), common bean (Blanco et al., 2009), and Lotus japonicus (Hakoyama et al.,

2012), with a role in plant-Rhizobium interactions. This protein through the Golgi apparatus can

target numerous membrane proteins in the symbiosome membrane (Catalano et al., 2004). Several

SNARE proteins have been recognized with potential roles in resistance against pathogenic bacteria

(Kalde et al. 2007, respectively) and fungi (Feechan et al., 2013), but we did not detect any close

genetic linkage between CBB and %Ndfa QTL.

The other candidate gene is Phvul.008G004300, which encodes 14-3-3 protein family

member. This protein family is known to have critical roles in the nodulation process especially in

early developmental stages (Radwan et al., 2012; Gil-Quintana et al., 2013). Phvul.008G004400

was the other gene associated with %Ndfa, which encodes for alcohol dehydrogenase- related

proteins. Genes encoding this kind of protein have been reported as nodule-induced metabolic

factors in Lotus japonicus (Colebatch et al., 2004). Phvul.008G001500 and Phvul.008G001400

176

might be considered as two other candidate genes associated with %Ndfa. These genes encode for

an “auxin responsive protein” and Leucine-Rich Repeat Receptor-Like Protein Kinase, LRR-RLK

(Sánchez-Lopez et al., 2011), respectively. Ramaeker et al. (2013) also reported an auxin

responsive protein as a candidate gene for QTL associated with yield and N accumulation ability.

The LRR-RLK’s role in nodule development was reported by Sánchez-Lópes et al. (2011) in

common bean. Therefore, Phvul.008G001400 could be considered as another candidate gene

conditioning %Ndfa. In the study by Kamwfa et al. (2015), LRR-RLK was reported as another

candidate gene controlling the SNF ability in the Andean diversity panel, considering its role in

nodule development in common bean(Sánchez-Lópes et al., 2011).

The gene Phvul.008G018100 was also in the vicinity of the genomic region on Pv08 which

was significantly associated with %Ndfa across all optimum environments. Phvul.008G018100

encodes for “Chitinase Class I” protein. This group of proteins is known for their important role in

plant-rhizobia symbiotic interactions. They are known to enable the host plant to hydrolyze the

lipo-chitooligosaccharide (nod factor) produced by nitrogen-fixing bacteria (Perret et al. 2000;

Cullimore et al. 2001; Kasprzewska 2003; Colebatch et al., 2004) and to start the infection process.

The other potential candidate gene in this genomic region was Phvul.008G017800, associated

with the %Ndfa QTL in optimum rainfall conditions. This gene encodes an aldo/keto reductase

protein family. This group of proteins has a function in the nodule-inducing process in legumes

(Leborgne-Castle and Bouhidel, 2014).

Two other candidate genes were located either side of a significant SNP marker on Pv01,

associated with %Ndfa across optimum rainfall environments. One of these genes,

Phvul.001G034300 encodes an abscisic acid receptor, of the polyketide cyclase PYR/PYL family

with a known function through declining effect of abscisic acid on leghaemoglobin and

177

consequently may influence SNF (González et al., 2001). In accordance with the genetic linkage of

QTL for %Ndfa in optimum rainfall conditions and δ13

C in this genomic region, and the well-

known role of abscisic acid in plant water stress, Phvul.001G034300 could be suggested as a

candidate gene with pleotropic effect on %Ndfa and δ13

C. The other gene, Phvul.001G033500,

encodes a hemoglobinase family protein member with a cysteine-type endopeptidase activity.

These proteins have been reported as nodule development inhibitors in legumes (Vorster et al.,

2013). Assuming a similar role for this protein in common bean, it could be consider another

possible candidate gene controlling %Ndfa in non-stress conditions.

The gene Phvul.007G04350 linked to the QTL associated with %Ndfa in dry conditions, which

encodes a LRR-RLK. The same candidate gene for this QTL previously reported by Kamwfa et al.

(2015) at the same physical position on common bean genome in an Andean diversity panel.

The gene Phvul.007G04350 is also associated with a QTL associated with δ13

C, in accordance

with genetic linkage between %Ndfa in dry conditions and δ13

C on Pv07. This may suggest the

importance of quicker nodulation events in dry regions. Therefore, it suggests that the genetic

control of timing of nodulation in the field should perhaps be considered as a trait for future genetic

studies of SNF. Moreover, future functional genomic studies are needed to confirm the roles of the

candidate genes detected in our study either on %Ndfa or WUE.

178

Fixed factor† F-test P-value F-test P-value F-test P-value F-test P-value F-test P-value F-test P-value F-test P-value F-test P-value F-test P-value

G 1.3 0.03 1.56 0.001 2.43 <0.0001 1.69 <0.0001 5.45 <0.0001 1.26 0.05 1.96 <0.0001 1.16 0.04 1.4 0.01

Random factors‡S2 Se S2 Se S2 Se S2 Se S2 Se S2 Se S2 Se S2 Se S2 Se

E 20.43ns 18.778 95.41ns 194.49 0.08ns 0.09 0.18ns 0.159 11.8ns 10.108 15.98ns

16.2281.35ns

1.2960.00*

0.002

Blk (E) 0.20ns 0.17 0.00ns 0.000 0.10ns 0.015 0.02ns 0.02 57.09ns 88.632 0.00ns 0 0.19ns 0.235 0.00ns 0.000 0.00ns 0

Iblk(Blk × E) 0.37ns 0.219 6.48*** 4.111 0.00ns 0.000 0.0002* 0.00013 57.42** 23.113 0.00ns 0.004 0.01ns 0.004 1.06* 0.515 21.50ns 36.249

G×E 7.47*** 0.716 12.82*** 8.959 0.11*** 0.03 0.02*** 0.005 12.82*** 8.959 2.16* 1.285 3.54*** 0.422 2.52** 1.025

Residual 13.63*** 0.603 102.82*** 6.201 0.41*** 0.028 0.21*** 0.007 112.91*** 14.516 46.03*** 1.855 4.06*** 0.213 5.64*** 0.71 1133.20*** 99.333

Maturity

(days)Ndfa §

(%)CBB¶ δ 13C#

(‰)

Table 6.1 F -test of fixed effect of entry and variance component estimates (S 2 ) and their standard error (Se ) of random effects in the mixed-model analysis of 140 recombinant inbred lines of a cross

between the high SNF bean, Mist, and the low SNF bean, Sanilac, tested in multiple locations field assays in Ontario, Canada, 2011-2013.

_

Seed protein

(%)

Nodule numbers

(Rockwood 2011)SPAD

_

¶ Common bacterial blight severity# Carbon discriminationns Not significant; *, **, and *** are significant at 0.05, 0.01, and 0.001, respectively

Seed yield

(kg ha-1)

Flowering

(days)

† Genotype main effect ‡ E, N, Blk, iblk are environment, nitrogen, block, and incomplete block, § Nitrogen derived from atmosphere

179

Fixed factor† F-test P-value F-test P-value F-test P-value

G 1.63 <0.0001 1.25 0.04 1.09 0.05

Random factors‡ S2 Se S2 Se S2 Se

Blk 11.95* 34.88 715.82ns 534.598 10.09ns 17.476

Residual 49.11*** 78.5 1349.35*** 1030.509 104.80*** 119

ns Not significant; *, **, and *** are significant at 0.05, 0.01, and 0.001, respectively

Table 6.2 F -test of fixed effect of entry and variance component estimates (S 2 ) and their standard error (Se ) of random

effects in the mixed-model analysis of 140 recombinant inbred lines of a cross between the high SNF bean, Mist, and the

low SNF bean, Sanilac, tested in a growth room assay with 6 replications at the University of Guelph in Ontario, Canada,

2013.

‡ Blk, iblk are environment, nitrogen, block, and incomplete block, respectively.

† Genotype main effect

SPAD Nodule numbers100 Nodule weight

(g)

180

LODR2

(%)

TR2

(%)

Additive

effect

growth room 2013 SPAD2 Pv 02 125.8 2.8 3.6 13 34 -1.7674



Field-across environments SPAD7 Pv 07 347.9 8.4 3.0 14 44 0.3132


Field-across environments SPAD11 Pv11 118.3 10.0 4.0 13 36 0.6251

Field-across environments CBB3 Pv 03 279.8 40.8 4.6 13 51 0.3158

Field-across environments CBB5 Pv 05 236.4 40.3 3.0 13 19 -0.1505

Field-across environments CBB8 Pv 08 438.1 59.4 4.3 13 47 -0.1598

Field-across environments DTF1 Pv 01 4.2 1.1 3.0 11 21 -4.4001

Field-across environments DFT8 Pv 08 362.3 56.8 3.2 12 31 -3.6051

Field-across environments DTF11 Pv 11 367.8 1.1 4.6 13 51 4.9150

Field-across environments MD3 Pv 03 73.9 1.9 3.5 12 36 -0.9483

Field-across environments MD9 Pv 09 152.1 16.5 3.2 13 22 1.2645

Field- optimum Ndfa1 Pv 01 148.9 3.0 3.0 15 17 -2.9412

Rockwood 2011 Ndfa7.2 Pv 07 195.9 10.7 4.0 15 32 2.5015

Elora 2012 Ndfa5 Pv 05 152.3 37.6 3.3 17 22 3.0000

Field-dry Ndfa7.1 Pv 07 19.2 3.4 4.0 14 27 2.3062

Field-across environments Ndfa8.1 Pv 08 3.3 0.5 4.0 17 24 -1.6367

Belwood 2013 Ndfa8.2 Pv 08 3.3 0.5 4.0 19 34 -3.2642

Rockwood 2011 Ndfa8.3 Pv 08 3.6 0.5 3.0 12 25 -3.3210

Field- optimum Ndfa8.4 Pv 08 35.7 1.5 4.0 14 30 -2.8291

Chr, Chromosome

3rd leaf SPAD

Common Bacterial Blight

Floweing (days)

Maturity (days)

Table 6.3 Quantitative trait loci (QTL) analysis for RIL population drived from Sanilac × Mist cross in the

fied trial at 4 location-years (4 location over 3 years, 2011-2013) and 2 nitrogen fertilizer levels ( 0; 100

kg N ha-1

), and in the growth room (April-Nov., 2013).

ChrField/Growthroom assay Year QTLPosition

(cM)

Position

(Mbp)

Identified QTL (P < 0.01)

N derived from atmosphere (%)

181

LODR2

(%)

TR2

(%)

Additive

effect

growth room 2013 NN1 Pv 01 38.6 46.0 3.0 11 34 36.7610

growth room 2013 NN4 Pv 04 0.0 0.4 3.3 15 30 -45.7396

Rockwood 2011 NN8 Pv 08 44.4 2.7 3.0 13 14 -13.5552

growth room 2013 NN9.1 Pv 09 103.8 7.6 3.3 11 41 33.2815

growth room 2013 NN9.2 Pv 09 188.7 25.4 3.2 9 54 -31.4789

Field-across environments ∆1 Pv 01 196.1 42.5 3.0 13 16 0.1146

Field-across environments ∆7 Pv 07 20.7 2.6 3.3 11 41 -0.0835

Field-dry SY2 Pv 02 74.2 0.9 3.6 11 42 -0.1590

Field- optimum SY3 Pv 03 161.1 38.3 3.3 11 35 0.1092

Field-dry SY4 Pv 04 348.8 44.5 3.0 14 15 -0.0834

Field-dry SY6 Pv 06 92.0 21.5 3.6 15 21 0.1518

Field-across environments SY7 Pv 07 49.5 42.5 3.0 9 43 -0.0607

Field-across environments SY11 Pv 11 37.4 2.1 6.4 21 44 -0.1344

Field-across environments PRO4 Pv 04 34.6 1.3 3.9 17 28 0.5579

Field-across environments PRO6 Pv 06 57.7 13.1 3.3 15 16 -0.5162

Field-across environments PRO7.1 Pv 07 17.0 3.2 3.3 11 42 -0.3942

Field-across environments PRO7.2 Pv 07 162.4 48.8 4.6 15 44 -0.5579

Field-across environments PRO7.3 Pv 07 42.5 44.3 5.6 22 46 0.5087

Field-across environments PRO7.4 Pv 07 8.9 2.5 8.0 23 58 0.4868

Field-across environments PRO9 Pv 09 158.4 17.2 3.3 10 45 0.3183

Chr, Chromosome

Seed yield (t ha-1)

Seed protein (%)

QTL ChrPosition

(cM)

Position

(Mbp)

Identified QTL (P < 0.01)

Nodule Number

Carbon isotope discrimination(%)

Table 6.3 Continued…

Field/Growth room assay Year

182

QTL Chr Mbp Effecive in Locus name Gene

Phvul.008G004000 SNARE associated Golgi protein (Leborgnne and Bouhidel, 2014)

Phvul.008G004300 14-3-3 PROTEIN (Radwan et al. 2012 - in Soybean SNF)

Phvul.008G001400 Leucine-Rich Repeat Receptor-Like Protein Kinase (Sanchez-Lopez et al., 2011)

Phvul.008G004400 Alcohol dehydrogenase related protein (Colebatch et al., 2004)

Across optimum rainfall environments Phvul.008G018100 Chitinase class I (Perret et al., 2000; Colebatch et al., 2004)

Rockwood-2011 & Belwood-2013 Phvul.008G017800 Aldo/keto reductase family (Colebatch et al., 2004)

Phvul.001G034300 Abscisic acid receptorPolyketide cyclase PYR/PYL family / dehydrase

and lipid transport (González et al., 2001)

Phvul.001G033500Hemoglobinase family member/ Peptidase C13 family with a

cysteine-type endopeptidase activity (Vorster et al., 2013)

Ndfa7 Pv 07 32.3-35.9 Across low rainfall environments Phvul.007G043500 Leucine-Rich Repeat Receptor-Like Protein Kinase (Sanchez-Lopez et al., 2011)

Pv 08Ndfa8.1

Table 6.4 Candidate genes detected for Ndfa in a 40 bp in the genomic region of significant and repeatable Ndfa QTL .

Chr, Chromosome

Across optimum rainfall environments 30.2-34.5Pv01Ndfa1

15.2-15.6Pv 08Ndfa8.4

Position

Across all environments0.38-0.46

183

Figure 6.1 Maximum, minimum, mean (vertival solid line) and 95% confidence interval (dashed lines) of least square means for nodule number and 100 nodule weight (g), SPAD reading on the 3rd fully expanded leaf in growth room assay, seed yield (t ha-1), nitrogen derived from atmosphere (%) in optimum soil moisture (Ndfa-opimum), dry (Ndfa-dry) and across all environmrnts (Ndfa), nodule number in field (Rockwood 2011), common bacterial blight severity (0-5 scale), carbopn isotope discrimination (%), seed protein content (%), days to maturity and days to flowering in field assays, for 140 recombinant inbred lines of a cross between the high SNF bean, Mist, and the low SNF bean, Sanilac, tested in multiple locations in Ontario, Canada, 2011-2013.

184

Figure 6.2 Genetic linkage map of bean chromosomes in the Sanilac x Mist common bean RIL population, indicating the location of the quantitative trait loci (QTL). QTL and corresponding marker(s) are shown using different colours. Pv10 is not presented because no QTL was detected on it.

185

Figure 6.2 continued...

186

Figure 6.2 continued...

187

CHAPTER 7

General Conclusions and Future Directions

188

7.1 General Conclusions

Common bean (Phaseolus vulgaris L.) is generally known as a week N2-fixing legume

(Martínez-Roméro, 2003). The complex inheritance of symbiotic nitrogen fixation (SNF),

coupled with its environmental-dependency and high genotype by environment variation makes

it difficult to achieve significant progress in breeding for high SNF in common bean. Therefore,

the research projects reported in this thesis were conducted to investigate the variation in SNF

potential, the environmental and genotype by environmental effects on SNF and response to

selection for SNF and to better understand the genetic basis of SNF in common bean.

In the first step, I attempted to explore the genetic variation for SNF and related traits. A

small group of 12 dry bean genotypes, but diverse in terms of physiological and morphological

characteristics, selected from the two common bean gene pools, including 3 Andean and 9

Middle American beans, were examined for SNF and related traits in the field and in controlled

environments. Trials included inoculation treatments, which were performed immediately prior

to planting, with a peat-based culture of rhizobia, at the rate of 2.5 g of Rhizobium

leguminosarum bv. phaseoli commercial product (Becker Underwood, Saskatoon, Saskatchewan,

Canada) per kg of seed to give approximately 5×105 bacteria/seed. A non-nodulating mutant,

R99, was used in the SNF trials as the reference plant to estimate SNF potential, measured as

nitrogen derived from atmosphere (%Ndfa), through the natural 15

N abundance method (Shearer

and Kohl, 1986) in the field. Significant inter- and intra-gene pool discrepancies were detected

for %Ndfa and related traits in the field assays. Moreover, environmental and genotype by

environmental effects significantly influenced %Ndfa and its related traits in the field. The

Andean bean genotypes showed greater nodulation ability compared to the Middle American

genotypes in the greenhouse, while the Middle American genotypes were generally superior in

189

terms of nitrogen fixation under optimum soil moisture conditions in the field in 2011. Nitrogen

fixation was generally associated with seed yield and carbon isotope discrimination (δ13

C), an

indicator of water use efficiency (WUE), in the field. Noticeably, two Middle American

genotypes, Mist and Sanilac, had contrasting extreme high and low values for proportion of

nitrogen derived from atmosphere (%Ndfa) and other SNF-related traits. These two Middle

American common bean genotypes were the parents of an F4-derived recombinant inbred lines

(RIL) population, developed using a single seed decent approach.

The second study was conducted to investigate if bean genotypes had stable yielding

ability across N fertilizer-dependent and SNF-dependent production practices using the F4:5

population of 140 RILs of the cross between Mist and Sanilac. The experiment in each location

was conducted under two different N management strategies, SNF- and N fertilizer-dependent,

across multiple environments and the study examined each genotype for stability of their

performance. The study highlighted the high level of environment dependency of SNF as

genotypes responded differentially to SNF-dependent and N fertilizer-dependent environments,

even though N management did not significantly affect the overall yield. It was revealed that

environmental variables, likely drought stress during the growing season in some environments,

affected the response of genotypes to N-management strategies. High environmental dependency

was observed for SNF in this study, which indicated that selecting genotypes that perform well

regardless of N management, while possible, may not be an easy task. However, among the RILs

with higher than average seed yield, the stability analysis identified 6% as generally-adapted to

all environments, regardless of N management.

Success of breeding for high SNF in common bean could therefore be explained as a

function of variation and heritability of SNF and related traits and genetic and phenotypic

190

correlation among traits that are being selected for. Therefore, the magnitude of these parameters

for SNF and related traits were studied in the third study on the same RIL population over multi-

environment field trials. The environments (location-years), based on the total precipitation

during the growing season, were grouped into stress- and non-stress test sites. The genetic

variation was significant for %Ndfa and related traits, while genotypes responded differently to

environments and N management strategies. There was no significant genetic correlation

between %Ndfa and seed yield, indicating that selection for high SNF genotypes does not

necessarily lead to greater seed yield and that selection for both traits should be performed

simultaneously. A selection index, identified as SNF relative efficiency index, was then

computed to identify lines with the same or higher yielding ability in SNF-dependent

management than in N-fertilizer-dependent conditions. Selection using the index identified

twenty percent of the RILs with better performance in SNF-dependent environments. This index

was significantly associated with days to maturity, indicating that selection for improved yield

under SNF-dependent environments may result in later maturity or vice-versa.

Moreover, upper leaf chlorophyll concentration, SPAD, was significantly associated with

both %Ndfa and yielding ability in this study, which indicated that greenness of dry bean plants

on the top of canopy, when grown under low N conditions, might be a good criterion to assess

the SNF and predict yielding ability of genotypes under SNF-dependent environments, and for

selection for high-SNF genotypes. The heritability of the traits ranged from 14% to 71% and 4%

to 25% in non-stress and in stress environments, respectively. Almost all the traits, including

%Ndfa and seed yield, had higher heritability under non-stress conditions. It can be concluded

that any selection attempts to improve SNF and yielding ability should be performed under

optimal soil water conditions.

191

There has been previous research (Nodari et al., 1993; Tasi et al., 1998; Souza et al., 2000;

Ramaeker et al., 2013) studying the genetic basis of SNF and/or nodulation traits in biparenal

RIL population derived from crosses between genotypes from the two Pheseolus gene pools. In

this thesis I report the results of a quantitative trait loci (QTL) analysis to better understand the

inheritance of SNF and related traits. However, my study differs from previous research in that I

studied a bi-parental F4:5 RIL (n = 94) population developed from a cross between two bean

genotypes from the same gene pool i.e., the Middle American genotypes ’Sanilac’ and ’Mist’.

Significant variation and transgressive segregation was observed for SNF and related traits in

field and growth room assays. Composite interval mapping, using genome-wide SNP markers,

detected a total of 43 QTL significantly associated with %Ndfa and related traits. A QTL on

Pv08 was consistently detected in different tests, accounting for 19% of the phenotypic variation

in %Ndfa. Moreover, smaller effect QTL were detected for %Ndfa on chromosomes PV01 and

Pv07 accounting for up to 15% of the variation in optimum and low rain fall environments. The

QTL for %Ndfa across dry environments on Pv07 was in genetic linkage with a QTL for carbon

discrimination (δ13

C), an indicator of WUE, suggesting either a pleiotropic effect or linkage of

genetic factors influencing both traits. This showed the importance of considering WUE and

early nodulation ability of common bean genotypes in the field for future breeding efforts for

efficient SNF in common bean. Although genetic linkages were detected between QTL for

%Ndfa in dry conditions and δ13

C (2 cM) and seed protein content (10 cM) on chromosome

Pv07, no genetic association was detected between %Ndfa and common bacterial blight (CBB)

infection severity.

To identify the potential candidate genes for %Ndfa, within the immediate genomic region

(±20 kb) in the vicinity of the marker at the peak LOD score value of the most repeatable QTL

192

was examined, within and across all field environments. The P. vulgaris reference genome

sequence version 1.0, developed by Schmutz et al., (2014), available at:

http://www.phytozome.net [accessed 20 April 2015] was browsed using Jbrowse in Phytozome

v10.1.

Three candidate genes associated with QTL for %Ndfa were identified in the genomic

region of the %Ndfa QTL on Pv08. These candidate genes encode SNARE associated Golgi,14-

3-3, and Alcohol dehydrogenase related protein family members which have role(s) in SNF

(Hakoyama et al., 2012; Gil-Quintana et al., 2013; Colebatch et al., 2004, respectively). Five

more potential candidate genes were detected for %Ndfa under different environmental

conditions as follows: one gene associated with a genomic region on Pv08 for %Ndfa across all

optimum environments coding for “Chitinase Class I” proteins. Two genes associated with the

genomic region associated with %Ndfa across optimum rainfall environments on linkage group

Pv01. One of these genes encodes an abscisic acid receptor, polyketide cyclase PYR/PYL

family. The other gene encodes a hemoglobinase family protein member with a cysteine-type

endopeptidase activity. One potential candidate gene was detected in probable association with

genomic region on Pv07 for %Ndfa in dry conditions. This gene encodes a receptor like kinase

gene from the Leucine-Rich Repeat family, LRR-RLKs. All of these genes were previously

reported with a role in SNF.

7.2 Future Directions

Six percent of the RIL population studied in my thesis were identified as generally

adapted to a wide range of environmental conditions, regardless of N management. Examination

of their performance in future yield trials under SNF- and N fertilizer-dependent multi-

http://www.phytozome.net/

193

environmental studies is suggested. Moreover, these genotypes could be used as suitable

germplasm for crossing in breeding programs to develop high-yielding bean genotypes for low

‘N’ input management systems, with potentially higher SNF.

Any future selection effort for SNF and yielding ability improvement in dry bean is

suggested to concurrently be performed in non-stress conditions when considering WUE and

early nodulation ability of bean genotypes. Significant differences were observed for SNF

between the two Middle American genotypes among selected bean genotypes in this study

Consequently, a genome-wide association analysis of SNF in a diverse panel of Middle

American bean genotypes to better understand the genetic architecture of SNF is suggested.

A number of candidate genes were identified in the genomic region of the significant

SNF-related QTL. Future functional genomic studies are needed to confirm the role of the

candidate genes detected in our study for %Ndfa, WUE, or both.

194

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Genotype δ15

NMist -2.126

Sanilac -1.844

OAC 09-3 -2.203

OAC Thunder -1.688Zorro -2.655

SXB 415 -2.642AC Compass -2.313

OAC Rico -2.177Chinook 2000 -1.755Red Rider -1.854

Majesty -1.886RIL 1 -1.984RIL 2 -1.744RIL 3 -2.642RIL 4 -1.788RIL 5 -1.666RIL 6 -1.848RIL 7 -1.668RIL 8 -1.621RIL 9 -1.655B value -1.988

Appendix I. Genotypes that were grown in N-free

media in growth room to estimate the B-values in 15N natural abundance method.

RIL, Recombinant inbred line of a cross between

low SNF genotype, Sanilac, and high SNF

genotype, Mist.

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