Agricultural Performance in Semi-Arid Tropics of India

Ind. Jn. of Agri. Econ.Vol. 56, No. 1, Jan.-March 2001

Agricultural Performance in Semi-Arid Tropics of India

P.K. Josh, B.S. Chandel, S.M. Virmani and J.C. Katyal*

INTRODUCTION

The Indian Semi-Arid Tropics (SAT) is the most prominent agro-ecological zonein the country's agricultural economy. It accounts for more than one-third (116.4million hectares) of the total geographical area, and contributes almost half offoodgrains, and two-thirds of the oilseed production in the country (Katyal et al.,1997). More than 70 per cent of the workforce in this region virtually depends onagriculture. A variety of crops are cultivated in the SAT region because of a largespatial variability in the agro-climatology, like rainfall, soils, length of growingseason, water resources, etc., and it is the same reason to experience a wide range ofproduction cOnstraints also. To name a few include inappropriate drainage(responsible for soil salinity and waterlogging), prolonged dry spell (causing droughtsituation), high rainwater run-off (resulting in severe soil erosion), among others.

Recognising the importance of SAT in the national economy and inherentproduction constraints, key questions about its agriculture and sustainability includesthe following: What has been the historical performance of agriculture in SAT?Which factors historically promoted and retarded the performance of agriculture inSAT? And, what lessons can be drawn, and what support can be initiated for a highgrowth and sustainable agriculture in the SAT region. The present study attempts torespond to these key questions for crafting appropriate development strategies andpolicy options.

II

THE APPROACH

The study is confined to the erstwhile 136 districts (now 156 reorganised), whichhave the characteristics of SAT environment. These districts are located in 11 statesincluding Delhi and the Union Territory of Diu. District-level data on key variables

* Principal Scientist, National Centre for Agricultural Economics and Policy Research, New Delhi-110 012;Senior Scientist, National Academy of Agricultural Research Management, Hyderabad-500 030; Principal Scientist,International Crops Research Institute for the Semi-Arid Tropics, Patancheru-502 324 (Andhra Pradesh) and Director,National Academy of Agricultural Research Management, Hyderabad-500 030 (Andhra Pradesh), respectively.

The authors are grateful to T.G.Kelley and P. Parthasarthy for their help in providing the district-level databaseand M. Asokan for data analysis.

72 INDIAN JOURNAL OF AGRICULTURAL ECONOMICS

were collated from various published and unpublished sources, including the District-

Level Data Set maintained at the International Crops Research Institute for the Semi

Arid Tropics (ICRISAT) from 1960 to 1990.

The SAT region is adaptable to cultivate a variety of crops due to wide agro-

climate diversity. These range from water thirsty crops like rice and sugarcane to low

water requirement crops like millets, oilseeds, pulses, etc. To better identify

constraints and propose appropriate solutions, prominent cropping systems were

delineated using the cluster analysis. The purpose of cluster analysis is to group

observations of a data set into clusters such that the observations within a cluster are

similar, that is, they resemble each other more than they resemble those in different

clusters (Nerlove et. al., 1996). The share of each crop in the gross cropped area

during the triennium average ending 1990-91 was used as a key criterion to delineate

the clusters of districts having similarity in crops and cropping systems.

Agricultural performance in the SAT regions is probed using five important

indicators, which included (a) agricultural income and efficiency, (b) yield of major

crops, (c) risk in crop production, (d) crop intensification, and (e) crop diversifica-

tion. These indicators were scrutinised for three-year averages ending 1970-71; 1980-

81; and 1990-91. In the subsequent discussion these are referred as 1970, 1980 and

1990. The crop diversity was measured using Simpson index which is equal to

{1- Eaj2 (Eaj)2 where ai is area under j-th crop and Ea.; is total cropped area.

Annual compound growth rates of all major crops in each cropping system were

also computed to examine the rate of change in the level of yields. Growth rates were

computed for two time periods: (a) 1970-71 to 1979-80; and (b) 1980-81 to 1990-91.

The former period is characterised as the green revolution period. During this period,

Indian agriculture witnessed a qualitative and quantitative change in foodgrain

production. The second period is reckoned as the post-green revolution period, when

the new technology was largely stabilised, and the pace of its adoption slowed down.

To determine the factors which promoted or retarded the agricultural

performance, three key areas were investigated. These included new technology,

infrastructure and socio-economic factors. A number of relevant components were

identified under each key factor, which possibly explain the sources of agricultural

performance differences in SAT. The components of new technology - irrigation,

area under high-yielding varieties (HYVs) and fertiliser use, were combined to

calculate Technology Diffusion Index (TINDX) by assigning equal weight to each

component. Thus TINDX = E wi {TECHVTECHin where \NT; is weight of i-th

technology and TECHii is value of i-th technology for j-th cropping system. The

Normalised Technology Diffusion Index denoted as ADJ TINDX was calculated by

dividing the values of TINDX for different cropping systems by its maximum value.

AGRICULTURAL PERFORMANCE IN SEMI-ARID TROPICS OF INDIA 73

III

RESULTS AND DISCUSSION

(A) Cropping Systems in SAT

A cropping system is usually referred to as a combination of crops within a givenyear (Willey et al., 1989). To identify the important cropping systems in the SATregion, cluster analysis was done. The analysis yielded 14 major cropping systems inthe SAT environment. A list of districts under each cropping system is given in theAppendix. The two cropping systems, namely, sugarcane-wheat, and sugarcane-rice,are ignored because of two obvious reasons: (a) less number of districts in eachsystem, and (b) highly intensive and commercial systems. The salient features of theremaining 12 cropping systems took up for further analysis are presented in Table 1.

TABLE 1. PREDOMINANT CROPPING SYSTEMS IN SAT AND THEIR IMPORTANT CHARACTERISTICS

Gross cropped area

Soil type000 ha Per cent Annual rainfall (mm)(1) (2) (3) (4) (5)

Rice 11,079.4 12.60 1,170 Red sandy and loamy soilRice-wheat 11,829.9 13.46 781 Alluvial soil, red loamy

Ragi 3,059.6 3.48 954 Red sandy

Groundnut 8,412.6 9.57 726 Medium black soil, redsandy soil

Pearlmillet-wheat 4,533.6 5.16 . 737 Alluvial soil, deep blacksoil

Pearlmillet-wheat-mustard 5,887.6 6.70 588 Grey brown soil, alluvialsoil

Wheat-chickpea 4,028.4 4.58 838 Alluvial soil, red and blackmixed soil

Maize-wheat 4,802.4 5.46 820 Mixed red and blackshallow soils, alluvial soil

Soyabean 2,584.8 2.94 912 Deep black soil

Cotton-sorghum 11,923.9 13.56 1,021 Black soil

Sorghum 7,563.8 8.60 758 Medium black soil

Pearlmillet-sorghum 9,314.2 10.59 929 Medium black soil

•It may be noted that rice-wheat, and rice based cropping systems are located in

relatively better endowed and favourable regions, and are considered to be the mostcommercial systems. On the other hand, cropping systems, like sorghum, pearlmillet-sorghum, and cotton-sorghum, are confined to marginal and fragile environments,which are vulnerable to degradation of soil and water resources. The plight of thesesystems is that they are characterised as rainfed, subsistence, poverty ridden, andprone to degradation. According to Walker and Ryan (1990), more than 250 millionpeople live and depend heavily on these types of rainfed systems for theirsubsistence.


(B) Agricultural Performance in SAT

This section discusses the results on selected indicators to investigate thedifferences in agricultural performance across the identified cropping systems in theSAT region.

(i) Agricultural income and efficiency

The most important indicator, which elucidates agricultural performance, is theagricultural income. In the present analysis, it was defined as the value of agriculturalproduction of 46 food and non-food crops in each cropping system. This informationwas drawn from the District Profile 1995 (CMIE, 1995), which computed the valueof agricultural output in terms of unit area and per capita for majority of the districtsin India. In the present analysis, district-level values of agricultural output werepooled and analysed according to the cropping systems in the SAT region. Theaverage values of agricultural output in terms of unit area and per capita for allcropping systems are given in Table 2. As expected, the value of agricultural outputwas the highest in rice based, and rice-wheat based cropping systems and hence,sometimes termed as high-income cropping systems. These systems covered a littleover 30 per cent of the districts in SAT, and contributed about 50 per cent offoodgrain production. On the other extreme, pearlmillet-sorghum, cotton-sorghum,and sorghum based systems ironically yielded the lowest value of agricultural outputand referred to as low-income cropping systems. Considering the efficiency

of rice-wheat system as 100 per cent, the agricultural efficiency index was

TABLE 2. VALUE OF AGRICULTURAL OUTPUT AND EFFICIENCY INDICESIN DIFFERENT CROPPING SYSTEMS

Cropping system

(1)

Values of agricultural output Agriculturalefficiency index

(4)Rs./ha(2)

Rs./capita

(3)

Rice 5,500 847 102

Rice-wheat 5,408 1,486 100

Ragi 4,694 1,032 87

Groundnut 4,303 971 80

Pearlmillet-wheat 3,733 826 69

Pearlmillet-wheat-mustard 3,244 966 60

Wheat-chickpea 2,821 875 52

Maize-wheat 2,707 677 50

Soyabean 2,611 977 48

Cotton-sorghum 2,176 727 40

Sorghum 2,160 878 40

Pearlmillet-sorghum 1,990 730 37


computed to compare the status of other cropping systems, by dMding the value of

agricultural output per hectare of the respective cropping system with the value of

output per hectare of this system. It was observed that sorghum, cotton-sorghum, and

pearlmillet-sorghum systems lagged far behind (60 per cent or more) rice, and rice-

wheat based cropping systems.Disaggregating the value of output within each cropping system provided more

clear information on variations in agricultural performance (Table 3). It is interesting

to note that the percentage of districts which attained agricultural income more than

Rs. 6,000 per ha in high-income cropping systems (rice and rice-wheat) was more

and ranged from 30 to 47 per cent. A chance that district(s) having these cropping

systems and average income below Rs. 3,000 per ha was one-sixth only. On the other

extreme, a majority of the districts (more than 80 per cent in the same system) having

TABLE 3. FREQUENCY DISTRIBUTION OF DISTRICTS ACCORDING TO THEVALUE OF AGRICULTURAL OUTPUT AMONG CROPPING SYSTEMS

(per cent)

Value of agricultural output levels (Rs.tha)

Cropping system <3,000 3,000 — 6,000 > 6,000

(1) (2) (3) (4)

Rice 16 37 47

Rice-wheat 70 30

Ragi 14 57 29

Groundnut 42 33 25

Pearlmillet—wheat 14 86 .

Pearlmillet-wheat-mustard 56 44

Wheat-chickpea 78 22

Maize-wheat 73 18

Soyabean 80 20

Cotton-sorghum 87 13

Sorghum 89 11

Pearlmillet-sorghum 87 13

Overall 47 36 17

sorghum, pearlmillet-sorghum, cotton-sorghum, wheat-chickpea, and soybean basedcropping systems achieved income less than Rs. 3,000 per ha. None of the districts inthese systems could attain an income level of Rs. 6,000 per ha. Few districts achievedincome more than Rs. 6,000 per ha in ragi, groundnut, and maize-wheat basedcropping systems. These results implicitly suggest that more number of poor farmers,with a large number under abject poverty, may be depending for their subsistence inthese cropping systems.. Earlier, Ahluwalia (1978) established the relationshipbetween agricultural performance and poverty, and revealed a negative relationship

between these two. It may be mentioned that high-income cropping systems represent

most favourable and well-endowed agricultural region in SAT. On the other extreme,pearlmillet-sorghum, cotton-sorghum, and sorghum based systems describe the poor


endowed and most harsh environment in SAT. The soils in these systems aremarginal and prone to degradation. Low income from agriculture in_this environmentdoes not allow poor farmers to invest on agricultural activities (Joshi et a/.,1996 a, b).

(ii) Yields of major crops

Agricultural income is largely determined by the yields of major crops in theregion. We examine here the differences in yields of prominent crops in differentcropping systems .(Table 4). It may be noted that the yields of rice and wheat wereinvariably higher. Obviously, it was due to the introduction and widespread adoptionof improved biological and mechanical technologies in rice and wheat. Theperformance of millet and sorghum in their respective cropping systems was not verysatisfactory. The yield levels of sorghum in the sorghum based cropping system wereat the poorest state, 505 kg per ha in 1970 and 650 kg per ha in 1990. Althoughsorghum yields were modest in millet-sorghum based system, the performance ofmillet was quite miserable, 291 kg per ha in 1970 and 453 kg per ha in 1990. Butmillet yields were about one tonne per ha in millet-wheat or millet-wheat-mustardbased cropping systems. There are two obvious reasons for better performance ofmillets in these cropping systems. Firstly, these systems are restricted to relativelyless favourable pockets in the Indo-Gangetic plains, but are characterised far betterthan the millet-sorghum based cropping systems. Secondly, these regions witnessedthe spillover effects of improved crop and resource management technologies of riceand wheat which benefited millet farmers in those regions. The performance of maizeyield in all the cropping systems, and of ragi in a majority of the cropping systemswas far superior than those of millets and sorghum. Chickpea yields were modest(750 kg per ha in 1990) in wheat-chickpea based system, but more than 900 kg per hain maize-wheat, millet-wheat-mustard, and rice-wheat based cropping systems.Regarding three most popular oilseeds crops, the yield of groundnut, mustard andsoybean was around one tonne per ha in their respective cropping systems. Therewas a phenomenal increase in the yield of mustard from a low level of 561 kg per ha

in 1970 to 1,075 kg per ha in 1990. Such an impressive increase in the yield ofmustard may be due to improved seed distribution of mustard under the OilseedTechnology Mission of the Government of India initiated in 1987. Cotton yield (lintproduction) ranged between 200 and 300 kg per ha in a majority of the districts.However, it was observed to be less than 200 kg per ha in the cotton-sorghum based

cropping system.The above results clearly indicate that the harsh and difficult environment in SAT

was lagging far behind the well endowed regions like rice and rice-wheat based

cropping systems in terms of crop yields. The productivity of inferior crops like

sorghum and pearlmillet in the worst kind of environment was too low and deserve

serious attention. There is a need to substantially increase the Research and


Development (R & D) efforts to break the existing yield frontiers of inferior crops in

the most fragile areas of SAT.

TABLE 4. YIELD LEVELS OF MAJOR CROPS IN DIFFERENT CROPPING SYSTEMS(kg./ha)

Cropping system

(1)

Major crop

(2)

Triennium ending

1970

(3)

1980(4)

1990

(5)

Rice Rice 1.,461 1,986 2,722

Rice-wheat Rice 1,046 1,668 2,295

Wheat 1,638 2,001 2,792

Ragi Ragi 693 1,221 1,058

Groundnut Groundnut 810 924 995

Pearlmillet—wheat Pearlmillet. 795 703 1,077

Wheat 1,472 1,701. 2,302

Pearlmillet-wheat-mustard Pearlmillet 597 645 1,077

Wheat 1,426 1,822 2,778

Mustard 561 571 1,075

Wheat-chickpea Wheat 885 1,006 1,704

Chickpea 573 $70 750

Maize-wheat Maize 935 874 1,477

Wheat 1,235 1,577 2,204

Soyabean Soyabean 1,008

Cotton-sorghum Cotton* 92 101 124

Sorghum kharif 586 928 1,154

Sorghum rabi 513 677 614

Sorghum Sorghum kharif 505 729 650

Sorghum rabi 519 618 • 530

Pearlmillet-sorghum Pearlmillet 291 315 453

Sorghum kharif 651 1,175 1,250

Sorghum rabi 365 477 597

* Cotton lint production in bales.

(iii) Growth in yields

Compound growth rates and coefficients of variation in yields of important crops

in each cropping system for two time periods were computed and presented in Table

5. It is observed that the yields of some inferior crops showed decelerating trends

during the 1981-90 period. More specifically, yield growth rates of sorghum in

sorghum and cotton-sorghum based cropping systems, chickpea in wheat-chickpea

based cropping system, and ragi in ragi based cropping system were negative during

the 1981-90 period. Growth rates of all these crops, except chickpea, were positive


. and quite impressive during the 1970-80 period. Growth rates of cotton in cotton-sorghum based system and soybean yields showed rising trends. These crops mayeither replace sorghum to some extent or encourage the traditional cropping systemof cotton-sorghum intercropping. In rice and rice-wheat based cropping systems,yield growth rates of major crops showed positive trend during the 1981-90 periodand values observed to be quite high.

TABLE 5. COMPOUND GROWTH RATES AND COEFFICIENTS OF VARIATION IN YIELD OFMAJOR CROPS IN DIFFERENT CROPPING SYSTEMS, 1970-80 AND 1981-90

(per cent)

Cropping system Major cropCompound growth rates Coefficients of variation

1970-80 1981-90 1970-80 1981-90(1) (2) (3) (4) (5) (6)

Rice Rice 1.96 3.08 17.7 18.5Rice-wheat Rice 5.90 2.55 25.2 15.6

Wheat 2.03 2.89 15.2 10.8Ragi Ragi 4.61 -2.21 29.3 20.3Groundnut Groundnut 1.12 0.95 28.6 37.5Pearlmillet-wheat Pearlmillet 0.16 3.67 30.8 27.6

Wheat 1.99 2.64 16.4 12.8Pearlmillet-wheat-mustard Pearlmillet -0.70 3.28 39.6 35.0

Wheat 2.53 2.78 17.4 16.5Mustard 0.81 1.55 37.1 20.4

Wheat-chickpea Wheat 0.07 2.97 19.2 16.7Chickpea -1.41 -0.89 21.9 15.3

Maize-wheat Maize -3.01 2.38 41.0 41.7Wheat 1.71 3.95 17.0 20.8

Soyabean Soyabean 5.48 32.5Cotton-sorghum Cotton 1.77 2.47 29.1 35.7

Sorghum kharif 9.10 2.16 36.7 33.0Sorghum rabi 6.91 - 1.02 33.3 25.7

Sorghum Sorghum kharif 5.13 -0.12 42.2 38.4Sorghum rabi 2.89 -0.83 32.1 23.9

Pearlmillet-sorghum Pearlmillet 4.14 4.14 28.7 33.7Sorghum kharif 9.71 2.54 45.6 24.4Sorghum rabi 9.22 4.27 38.6 27.5

The above analysis revealed: (a) chickpea, ragi and sorghum were either pushedto more marginal lands or neglected due to expansion of a few relatively higherprofitable crops, which resulted in their poor performance, (b) land degradation mightbe posing a constraint in enhancing the crop yields as these regions are prone tosevere land degradation, and (c) spillover effects of improved technology with respectto rice and wheat in rice and wheat related cropping systems started benefiting othercrops and SAT area.

(iv) Risk and uncertainty in SAT

It is evident that agriculture is too risky and uncertain in SAT. , It is morepronounced in the drier regions of SAT than in the irrigated ones. Coefficients of


variation in yield of prominent crops in all the cropping systems were computed for

two time periods: (a) 1970-80 and (b) 1981-90 (Table 5). It was observed that

chickpea, rice, and wheat were more stable than other crops in a majority of the

cropping systems. On the other hand, groundnut, pearlmillet, and kharif sorghum

showed higher instability in yield which revealed more risk to these crops.

Temporal comparison of coefficients of variation in yields of major crops showed

that except groundnut in groundnut based, and cotton in cotton-sorghum based

cropping systems, these declined or remained almost stable. These trends indicate

introduction and adoption of improved and stable genotypes of all crops, except

groundnut and cotton. These trends are welcome, but further decline in risk and

uncertainty needs greater attention in the drier areas of SAT.

The analysis revealed that the sorghum and pearlmillet related cropping systems

were low yielding and more risky. On the other hand, wheat and rice were low risky

and high yielding crops in their respective systems. Risk in agriculture influences

investment decisions and adoption of improved technologies (Binswanger, 1979).

Technology and policy interventions are, therefore, urgently required to minimise

risk and uncertainty, and enhance the productivity of inferior crops in marginal and

harsh environments.

(v) Crop intensification

Cropping intensity was considered as a measure of agricultural performance to

represent utilisation of land resource. It was measured as the ratio of gross cropped

area and the net cropping area. Crops like cotton, pigeonpea and sugarcane cover the

land more than a season, therefore, their duration was considered while computing

the cropping intensity.It was noted that 18 per cent of the districts in SAT were having cropping

intensity of more than 160 per cent in 1990. There were only 5 per cent of the

districts in 1970 which were showing cropping intensity of more than 160 per cent.

On the other extreme, there were about 37 per cent districts in 1970, which were

having cropping intensity of less than 120 per cent in 1970. Their number declined to

20 per cent in 1990, implying that 17 per cent of the districts moved to higher crop

intensification.Spatial and temporal differences in cropping intensity with respect to different

cropping systems were examined and the results are presented in Table 6. As

expected, the crop intensification was the highest in rice-wheat based cropping

system (169 per cent), followed by cotton-sorghum based cropping system (156 per

cent), and pearlmillet-wheat based system (150 per cent). All these systems, except

cotton-sorghum based, prevail in favourable and better endowed regions. On the

other hand, the lowest cropping intensity was noted in ragi based cropping system

(115 per cent), wheat-chickpea based cropping system (116 per cent) and pearlmillet-

sorghum and groundnut based cropping systems (125 per cent).


Crop intensification has increased in all the cropping systems between 1970 and1999. The rapid increase was observed in rice-wheat based cropping system (25 percent), followed by soybean based cropping system (14 per cent), and pearlmillet-wheat based' cropping system (11 per cent). The lowest increase was observed in ragibased system (3 per cent), wheat-chickpea based system (4 per cent), and groundnutbased system (5 per cent).

TABLE 6. AVERAGE CROPPING INTENSITY IN DISTRICTS OF DIFFERENT CROPPING SYSTEMS(per cent)

Cropping system

(1)

Triennium ending

1970(2)

1980

(3)

1990(4)

Rice 125 129 135Rice-wheat 144 155 169Ragi 112 115 115Groundnut 120 124 125Pearlmillet-wheat 137 145 150Pearlmillet-wheat-mustard 126 135 137Wheat-chickpea 112 115 116Maize-wheat 133 142 144Soyabean 124 127 138Cotton-sorghum 144 146 156Sorghum 118 124 128Pearlmillet-sorghum 114 118 125

The analysis clearly showed that better endowed regions utilised lands moreintensively than the marginal environments, and took a lead in agriculturaldevelopment and prosperity.

(vi) Crop diversification

Crop diversification is an important strategy to manage rainfed agriculture inenhancing agricultural production and improving sustainability of natural resources.It is defined as the increased variety of agricultural crops produced, and can beachieved by (a) planting new crops in newly opened lands beyond the lands used fortraditional crops, and (b) adding new crops to traditional crops in the same landthrough more intensive crop rotation or intercropping (Hayami and Otsuka, 1994).

In this study, crop diversification was measured using the Simpson Index ofdiversity for triennium averages ending 1970, 1980 and 1990, and the results aregiven in Table 7. An overview of the table exhibited that agriculture in SAT is highlydiversified. However, at an aggregate level, the trends indicated that agriculture inSAT was slowly moving towards less diversification, or specialisation to someextent. There were about 54 per cent of the districts which were highly diversified(diversity index greater than 0.80) in 1970. Only 45 per cent of the districts remained

in this category in 1990, implying that about 9 per cent of the districts moved towards

lesser diversification.


TABLE 7. DIVERSITY INDICES IN DIFFERENT CROPPING SYSTEMS

Cropping system

(1) •.-,

Triennium ending

1970(2)

1980

(3)1990(4)

Rice 0.6896 0.6691 0.6950Rice-wheat 0.8012 0.7533 0.7044Ragi 0.7297 0.7434 0.7529Groundnut 0.7690 0.7506 0.7519Pearlmillet-wheat 0.8084 0.7921 0.7816Pearl millet-wheat-mustard 0.7929 0.7906 0.7675Wheat-chickpea 0.7821 0.7991 0.8079Maize-wheat 0.8168 0.8084 0.8140Soyabean 0.8178 0.8149 0.8044Cotton-sorghum 0.7413 0.7749 0.8081Sorghum 0.8439 0.8484 0.8605Pearlmillet-sorghum 0.7671 0.7872 0.7955

Comparing the diversification between different cropping systems, it wasobserved that rice based cropping system has the lowest diversification index,followed by rice-wheat based cropping system. The diversity index was the highest insorghum based system. The diversity index substantially declined in rice-wheat basedcropping system, from 0.80 in 1970 to 0.70 in 1990. This clearly indicated that manydistricts in rice-wheat based cropping system were moving towards specialisation.Groundnut, millet-wheat, and soybean based cropping systems also exhibiteddeclining trends in crop diversification.

The main purpose of crop diversification is to minimise risk due to crop failureand price fluctuations. A relationship was established between diversity index andyield risk of major crops in different cropping system. A positive relationship wasobserved between diversification and risk in crop production in dryland areas, whichrevealed more crop diversification in higher risk environments. To cite an example,sorghum based system was a low yielding-high risk environment, where farmersdiversified crops to the maximum extent. On the other hand, rice and rice-wheatbased systems were high yielding and low-risk environments, where cropdiversification was the lowest. These systems are gradually moving towardsspecialisation.

The analysis suggested that while developing improved technologies for marginaland vulnerable environments, technological options should have components of cropdiversification complemented by higher yields.

(C) Factors lifluencing Agricultural Peiformance

Three types of factors, namely, improved technology, infrastructural developmentand socio-economic aspects, were hypothesised to influence agricultural


performance. This section intends to elucidate the role of these factors in influencingagricultural performance.

(i) Improved technologies

The combination of nutrient inputs, improved varieties and an assured soilmoisture regime through irrigation have been the basis of green revolution that hasbeen so marked by improved grain production in India (Burford et al., 1989). Thesethree critical components of improved technology, namely, irrigation, fertiliser andimproved varieties, were analysed to examine their role in creating differences inagricultural performance in the SAT region. Table 8 gives the status of all thesetechnology components in different cropping systems. It was observed that theirrigated area was the highest in rice-wheat based cropping system, followed bypearlmillet-wheat, and rice based cropping systems in 1990. Irrigation developmentthrough surface and groundwater received highest priority in rice-wheat based systemduring 1970-90, followed by millet-wheat based system. The rest of the croppingsystems are yet to achieve the irrigation level of rice-wheat based system whichexisted two decades ago. These cropping systems are lagging far behind rice-wheatand rice based cropping systems in irrigation development, which is the major sourceof shaping agricultural performance.

TABLE 8. DIFFUSION STATUS OF IMPROVED TECHNOLOGIES INDIFFERENT CROPPING SYSTEMS, 1970 AND 1990

Cropping system

(1)

Gross irrigated area(per cent)

Area under HYVs(per cent)

Fertiliser use(kg/ha)

1970(2)

1990

(3)

1970(4)

1990

(5)

1970(6)

1990

(7)

Rice 44.26 52.52 17.40 80.01 30.74 142.45

Rice-wheat 50.30 76.30 16.87 68.76 21.52 120.53

Ragi 22.34 22.72 16.50 75.17 18.74 96.17

Groundnut 26.09 29.93 18.31 73.53 23.12 85.21

Pearlmillet-wheat 36.52 59.15 13.03 63.02 13.81 80.48

Pearlmillet-wheat-mustard 25.57 45.40 8.94 61.31 5.10 45.18

Wheat-chickpea 19.05 29.65 3.42 51.26 3.69 32.27

Maize-wheat 25.90 35.80 6.17 38.34 9.05 49.14

Soyabean 4.44 19.83 3.26 76.16 3.13 52.18

Cotton-sorghum 6.02 13.90 13.19 70.18 7.14 51.85

Sorghum 11.46 19.22 5.43 41.49 4.12 35.86

Pearlmillet-sorghum 9.94 19.78 6.60 47.30 7.15 50.46

Note: 1970 and 1990 refer to triennium averages ending 1970-71 and 1990-91 respectively.

In irrigated areas, the adoption of improved varieties followed irrigation

development during the mid-sixties and the early seventies. Examples are adoption of

improved HYVs of rice and wheat. These varieties were responding to water. But

with the investment in R & D on rainfed environments, several improved varieties of


many dryland crops were developed and released. These were widely adopted in

dryland areas (Table 8). In most marginal environment, like sorghum and pearlmillet-

sorghum based cropping systems, the adoption of HYVs of cereals was more than 40

per cent, which was less than 10 per cent two decades ago. In addition to pearlmillet,

sorghum and maize, many drought escaping and disease resistant varieties of

pigeonpea and chickpea were widely adopted by the farmers in the dryland areas

(Bantilan and Joshi, 1996; Joshi et al., 1996 a, b). Although the adoption of improved

varieties was not reflected in the yield levels of pearlmillet, sorghum, pigeonpea and

chickpea, these were expected to prevent decline in yields due to land degradation in

marginal environments.The fertiliser consumption was also found to be the highest in rice-wheat based

cropping system (Table 8). Fertiliser consumption in sorghum based system wasslightly above 30 kg per ha in 1990, which was approximately at the same level ofrice based system in 1970, a lag of about two decades. However, fertiliserconsumption in dryland areas has shown an increasing trend. A positive relationshipwas observed between adoption of HYVs and fertiliser consumption. Obviously, theadoption of fertiliser responsive improved HYVs was responsible for increase infertiliser consumption in dryland areas. Four apparent reasons for low fertiliserconsumption in marginal and dryland environments are: (a) lag in fertiliser researchin the dryland agriculture because of the earlier views that low and variable rainfallwas a major constraint (Burford et al., 1989), (b) relatively low response of fertiliserin sorghum and pearlmillet based cropping systems, as compared to rice and rice-wheat based cropping systems, (c) uncertainty of the responses of crops to fertiliserapplication in dryland areas (Jha and Sarin, 1984), and (d) higher area allocation tolegumes which improves soil fertility but is not accounted for in fertiliserconsumption statistics.

A Technology Diffusion Index was developed by assigning equal weight to eachcomponent. It was found that at an aggregate level, the adoption of improvedtechnology was the highest in rice-wheat, and rice based cropping systems in 1990(Table 9). Assuming that technology adoption in rice-wheat based cropping systemwas 100 per cent in 1990, it was only about 38 per cent in sorghum based system, 44per cent in pearlmillet-sorghum based system, 46 per cent in wheat-chickpea basedsystem, and 48 per cent in maize-wheat based system. There is a positive correlationbetween technology diffusion and performance of agriculture in terms of agriculturalincome, efficiency, crop yields, and stability. To improve the performance ofagriculture in lagging regions, there is a need to design the technology options, whichsuit the needs of poor farmers in SAT.


TABLE 9. TECHNOLOGY DIFFUSION INDICES IN DIFFERENT CROPPING SYSTEMS, 1970 AND 1990

Cropping system

(1)

Triennium ending

1970 1990

TINDX

(2)

ADJ _TINDX

(3)

TINDX

(4)

ADJ TINDX

(5)

Rice 0.943 1.00 0.894 0.990Rice-wheat 0.875 0.928 0.903 1.000

Ragi 0.650 0.689 0.634 0.702

Groundnut 0.755 0.800 0.634 0.702

Pearlmillet-wheat 0.630 0.668 0.710 0.786

Pearlmillet-wheat-mustard 0.389 0.412 0.560 0.620

Wheat-chickpea 0.230 0.244 0.418 0.463

Maize-wheat 0.383 0.407 0.431 0.478

Soyabean 0.122 0.130 0.523 0.580

Cotton-sorghum 0.355 0.377 0.471 0.522

Sorghum 0.220 0.233 0.340 0.376

Pearlmillet-sorghum 0.263 0.279 0.400 0.443

Note: TINDX refers to technology diffusion index, and ADJ TINDX refers to normalised technology diffusion

index.

(ii) Infrastructure

Bank credit, rural roads and markets were identified as the most importantindicators of infrastructural development, which possibly influence agriculturalperformance (Table 10). Among these three indicators, bank credit and agriculturalperformance demonstrated expected relationship. It indicated that better endowedregions and better performing cropping systems borrowed more money from thebanks for agricultural activities. On the other hand, poor endowed regions borrowedtoo little amount in relation to better endowed regions. One possible reason may bethat the returns to investment in agriculture are less than the bank rate, of interest inthe marginal and poor endowed regions.

With respect- to roads, no consistent relationship with agricultural performancewas observed. It should not be inferred that roads do not contribute in acceleratingagricultural performance. There were several schemes and projects of the government

for developing good road network. The schemes were for the development of the

entire economy of the region. Therefore, the effect of roads may be captured if time-

series data are analysed.

(iii) Socio-econonzic factors

As expected, the composition of socio-economic indicators varies substantially

across cropping systems. Table 10 presents the important socio-economic variables in


different cropping systems. It may be noted that illiteracy and dependency onagriculture were consistently at higher levels in the marginal environment. Literacyplays a vital role in adopting new technologies. Higher illiteracy poses a constraint intechnology transfer, influences credit delivery systems, and market intelligence.Similarly, higher dependency in agriculture results in declining labour productivity.An appropriate literacy programme, linked with technology transfer, should beaggressively launched in marginal environments. There is also a need to initiateactivities to transfer workforce from agriculture to other sectors. It will help ingenerating additional resources for investment in agriculture.

TABLE 10. SELECTED INFRASTRUCTURE AND SOCIO-ECONOMIC INDICATORSIN DIFFERENT CROPPING SYSTEMS

Infrastructureindicators

Socio-economic indicators

Bank Road Rural Female Workforce Average PopulationCropping system credit' length literacyb literacyb in agri- size of densityb

(Rs./ha) (km./100 (per cent) (per cent) culture and holding' (No./sq.km.)sq./km.) allied • (ha)

activity'(per cent)

(1) (2) (3) (4) (5) (6) (7) (8)Rice 2,379 44.44 46.60 33.71 69.02 1.55 277Rice-wheat 1,306 28.96 43.28 28.66 68.02 1.11 553Ragi 2,055 76.02 47.12 41.24 76.45 1.61 249Groundnut 1,639 25.82 49.28 41.92 63.99 3.33 182Pearlmillet-wheat 1,286 40.39 45.61 32.25 69.04 1.60 455Pearlmillet-wheat-

mustard967 44.84 41.96 32.61 65.87 2.06 413

Wheat-chickpea 589 20.26 37.71 26.49 73.06 2.38 231Maize-wheat 715 22.02 31.84 27.64 78.69 1.96 392Soyabean 702 49.72 35.23 30.41 68.67 4.51 236Cotton-sorghum 683 41.32 49.16 42.28 71.52 3.23 221Sorghum 671 47.88 34.76 26.78 76.74 3.25 . 193Pearlmillet-sorghum 821 52.19 54.67 37.35 70.07 3.31 227

Source: CMIE (1995).

Notes: a. Per hectare bank credit to agriculture has been worked out on the basis of gross cropped area of 1985-86.b. Calculation based on 1991 Census.c. Data pertain to 1985-86 Agricultural Census.

It is interesting to note that the size of holding is higher in the marginalenvironments as compared to better endowed regions, like rice and rice-wheat basedcropping systems. Due to poor yields and unfavourable environment in the fragilelands, the investment capacity of the farmers living in subsistence level is perhapsconstrained. Eventually, despite the larger holding size, agriculture did not perfornithe way it performed in the favourable regions.


IV

CONCLUSIONS AND POLICY ISSUE

All indicators like agricultural income, yield levels, their stability, cropping

intensity and crop diversity, selected to investigate agricultural performance revealed

large variations in them within the SAT region. The performance of superior crops,

like rice and wheat, was remarkable, while that of inferior crops, like pearlmillet and

sorghum, was quite dismal. Pearlmillet-sorghum, cotton-sorghum, and sorghum

based cropping systems proved to be the systems of low-income and higher risk in

comparison to rice, and rice-wheat based cropping systems. The crops like chickpea,

ragi and sorghum were either pushed to more marginal lands or neglected due to

expansion of a few relatively higher profitable crops which resulted in their poor

performance and land degradation might be posing a constraint in enhancing the crop

yields. The variation in agricultural performance was largely due to differences in

agricultural potential, which was. influenced by the uneven endowments of natural

resources, besides lack of diffusion of improved technology, inadequacy of bank

credit and other infrastructures, and low status of socio-economic factors especially

literacy.Drier areas need special attention because agriculture in these regions lagged far

behind the highly and partially irrigated areas. Special focus to accelerate agricultural

performance is required in pearlmillet-sorghum, sorghum and cotton-sorghum based

cropping systems to reduce disparity within the SAT region. It can be met through

increased R&D efforts to develop suitable technologies for harsh and risky

environments. It is obvious that returns to research investment in harsh and low

potential areas will always be less than well endowed regions. This should not be the

sole criterion for research allocation in such environments. The research methods of

the past need to be complemented by additional ones that have more effective

interface with the farmer communities in such environment. A two-way research

continuum, land-to-lab-to-land rather than lab-to-land alone is important. An

innovative action and participatory research approaches to identify problems, set

priorities, refinement of indigenous technical knowledge (ITK) and development and

evaluation of technologies will make an impact on the agricultural performance and

socio-economic status. Otherwise, a large population with pervasive poverty and

workforce depends on agriculture in these regions and any negligence will lead to

further imbalance and impoverishment in the SAT region.

Received March 2000. Revision accepted March 2001.


APPENDIX

LIST OF STATES AND DISTRICTS UNDER DIFFERENT CROPPING SYSTEMS

Cropping system States/districts(1) (2)

Rice Andhra Pradesh: East Godavari, Guntur, Karimnagar, Khammam, Krishna,Medak, Nalgonda, Nellore, Nizamabad, Warangal, West Godavari.Karnataka: Shimoga.Tamil Nadu: Chengalpet, Kanya Kumari, Madurai, Ramanthapuram, Thanja-vur, Tiruchirapalli, Tirunelveli.

Rice-wheat Haryana: Jind, Karnal.Punjab: Amritsar, Ferozepur, Kapurthala, Ludhiana, Patiala, Sangrur.Uttar Pradesh: Pratapgarh, Raibareilly, Shahjahanpur, Unnao, Varanasi,Allahabad, Etawah, Fatehpur, Hardoi, Jaunpur, Kanpur, Lucknow.

Ragi Karnataka: Bangalore, Chikmagalur, Hassan, Kolar, Mandya, Mysore,Tumkur.

Groundnut Andhra Pradesh Chittoor, Cuddapah, Kurnool.Gujarat: Amrelli, Bhavnagar, Junagarh, Rajkot.Karnataka: Chitradurga.Tamil Nadu: Coimbatore, North Arcot, Salem, South Arcot.

Pearlmillet-wheat Gujarat: Kaira.Haryana: Gurgaon, Rohtak.Maharashtra: Nasik.Uttar Pradesh: Aligarh, Badaun, Etah.

Pearlmillet-wheat-mustard Gujarat: Mehsana.Haryana: Mahendragarh.Madhya Pradesh: Morena.Rajasthan: Alwar, Bharatpur, Jaipur, Sawai Madhavpur.Uttar Pradesh: Agra, Mathura.

Wheat-chickpea Madhya Pradesh: Bhind, Dataia, Gwalior, Shivpuri.Rajasthan: Kota.Uttar Pradesh: Banda, Hamirpur, Jalaun, Jhansi.

Maize-wheat Gujarat: Panchmahals, Sabarkantha.Madhya Pradesh: Jhabua.Rajasthan: Banswara, Bhilwara, Bundi, Chittorgarh, Dungarpur, Udaipur.Uttar Pradesh: Bulandshar, Farrukabad.

Soyabean Madhya Pradesh: Dewas, Dhar, Indore, Shahjahanpur, Ujjain.Cotton-sorghum Andhra Pradesh: Adilabad.

Gujarat: Ahmedabad, Baroda, Bharuch, Surendranagar.Karnataka: Dharwad.Madhya Pradesh: East Nimar, West Nimar.Maharashtra: Jalgoan, Parbhani, Akola, Amravati, Buldhana, Nanded,Yavatmal.

Sorghum Andhra Pradesh: Mahbubnagar.Karnataka: Belgaum, Bidar, Gulbarga, Raichur.Maharashtra: Osmanabad.Rajasthan: Ajmer, Jhalawar, Tonk.

Pearlmillet-sorghum Karnataka: Bijapur.Maharashtra: Ahmednagar, Aurangabad, Beed, Pune, Sangli, Satara, Solapur.

Sugarcane-wheat Uttar Pradesh: Meerut, Moradabad, Muzaffarnagar.

Sugarcane-rice Gujarat: Surat.Maharashtra: Kolhapur.


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