1

Farm-Level Performance of Genetically-Modified Cotton: A Frontier

Analysis of Cotton Production in Maharashtra

Kambhampati, U.1, Morse, S.

2, Bennett, R.

3 and Ismaal, Y.

3

1 Corresponding author. Department of Economics, The University of Reading,

Whiteknights, PO Box 218, Reading RG6 6AA.

2 Department of Geography, School of Human and Environmental Sciences, The

University of Reading, PO Box 227, Reading RG6 6AB Email: s.morse@reading.ac.uk

Tel +44 (0) 118 3788736 Fax +44 (0) 118 9755865

3 Department of Agricultural and Food Economics, The University of Reading, PO Box

237, Reading RG6 6AR, UK Tel: +44 (0) 118 3786478 Fax: +44 (0) 118975 6467

Abstract

In this paper, we analyse the yield increases resulting from the cultivation of Bt cotton in

Maharashtra, India. The study relies on commercial farm, rather than trial, data and is

therefore the first of its kind based as it is on real farm and market conditions. Findings

show that since its commercial release in 2002 Bt cotton has had a significant positive

impact on yields and on the economic performance of cotton growers in Maharashtra.

This difference remains even after controlling for different soil and insecticide inputs into

the production of Bt cotton. There is also significant spatial and temporal variation in this

‘benefit’, and much can depend upon where production is taking place and the season.

Keywords: Bt cotton, India, bollworm

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Introduction

The commercial cultivation of genetically modified (GM) insect resistant cotton is

increasing in pace across the globe. Bt cotton utilizes a gene from the bacterium Bacillus

thuringiensis (Bt) that codes for proteins (endotoxins) toxic to Lepidoptera (e.g. the bollworm complex)

and some Coleoptera. There are a number of forms of the Bt endotoxin, the most common of which is

based upon the protein Cry1Ac (Gonzalez-Cabrera et al., 2003). The variation in form of the endotoxin

does allow breeders to ‘stack’ the genes in the plant thereby widening the resistance and making it more

durable (Tabashnik et al., 2000). This is important as while commercial cultivation increases it is reasoned

that the chances of a breakdown in the resistance also becomes more likely. However, in practice the

sustainability of Bt cotton is more than just a matter of the durability of the resistance but is also deeply

entwined with the economics of production. Given that Bt cotton requires less expensive insecticide for the

control of bollworms its seems reasonable to conclude that resource-poor farmers in the developing world

will be eager to adopt. Allied with this, of course, is the requirement for less labour to apply the insecticide

and potential benefits in terms of health given that the insecticides are poisonous, albeit to varying degrees.

Evidence to date suggests that this is the (see James 2002), and the countries where research has

taken place now includes South Africa (Bennett et al. 2003; Ismael et al. 2002),

Argentina (Qaim and De Janvry 2002), Mexico (Traxler et al. 2001), Indonesia (Manwan

and Subagyo 2001), China (Pray et al. 2002) and India (Naik 2001 and Qaim and

Zilberman 2003). All of these studies have shown that growing Bt cotton can reduce costs

and hence improve the gross margin of cotton production. Some of these studies, such as

Ismael at al. (2002) and Bennett et al. (2003) for South Africa, have been based on

farmers’ practice while others (Naik, 2001; Qaim and Zilberman, 2003) are based on trial

data. Either approach has both advantages and disadvantages, but using trial data has

been opne to the criticism that yields will be positively biased by the artificial conditions

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that exist in such trails. Cultivation under ‘normal’ conditions, it is argued, is likely to

result in lower yields. This has resulted in the FAO’s call for more ‘market based studies’

that will accurately reflect the agronomic and economic environments faced by growers

of Bt cotton (FAO, 2004). While this is of course true on a global scale it can be argued

that it is of particular import in those countries which are major cotton producers such as

China and India.

Commercial planting of Bt cotton in India was first allowed in 2002, albeit with much

controversy. Some 38,000 hectares were planted with Bt cotton in 2002, and 12,000 of

these were in the state of Maharashtra, In that same year some 17,658 farmers grew Bt

cotton in the major cotton districts of Maharashtra With India accounting for 16% of

global cotton production, it is an important source of supply in world markets. In

addition, with 25% of the world’s total cotton area, this production is an important source

of livelihoods within the country. Any technology that helps to increase yields is

therefore likely to be extremely significant for the economy. This paper seeks to address

the gap noted by FAO 92004) by using commercial farm data to explore the benefits, if

any, which accrue to farmers adopting Bt cotton compared to those who do not. Given the

importance of the cotton industry in India and the current global debate on the use of GM

technology in developing countries, obtaining timely results regarding the performance of

Bt cotton are extremely important.

The analysis concentrates on addressing whether Indian farmers have experienced yield

and economic gains from growing Bt cotton hybrids released by a company affiliated to

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Monsanto (Mahyco-Monsanto) compared to a complex of non-Bt hybrids and cultivars.

Therefore, since the beginning of commercial cultivation of Bt cotton in India in 2002,

there have been three full seasons of Bt cotton cultivation (2002, 2003 and 2004).

However, only data for the 2002 and 2003 seasons are presented in this paper as data for

2004 were not available in a form for analysis at the time of writing

There are two species of cotton grown in Maharashtra (Gossypium hirsutum and G.

arboretum), but most of the cotton grown (73% of cotton area) is an intra-hirsutum

hybrid, with the remainder being covered with improved (non-hybrid) G. hirsutum and G.

arboreum cultivars. There are three Mahyco-Monsanto Bt cotton hybrids grown in the

sub-regions, MECH-162 Bt, MECH-184 Bt and MECH-12 Bt. Popular non-Bt varieties

are Bunny, Tulsi, NHH-44 and JK-666.

Methodology

The data for the 2002 and 2003 cotton seasons are based on a questionnaire survey

carried out by agricultural extension workers of the Maharashtra Hybrid Seeds Company

(Mahyco). Both the survey and the data were independently monitored by four teams

from the Indian Genetic Engineering Approval Committee (GEAC) as well as scientists

from the Central Institute for Cotton Research (CICR). The data were then submitted to

GEAC for evaluating the performance of the first GM cotton in India. Maharashtra was

selected on the basis that it is the biggest cotton growing state in India. Some 2,709

farmers (15.43%) from 1,275 villages in 16 (out of 31 in Maharashtra) districts were

randomly selected and interviewed in three cotton-growing sub-regions of the state

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(Khandesh, Marathwada, Vidarbha). The data for the 2003 cotton season covered a larger

area in four cotton growing states (Maharashtra, Gujarat, Madhya Pradesh and

Karnataka). However, the information gathered was more limited than that of the 2002

survey. .

This paper employs a sub-set of the survey data by focussing on farmers from

Maharashtra State. In most cases, farmers grew both Bt and conventional cotton varieties

in different plots on the same farm, and this provides some ‘control’ for a number of

producer-related factors that might influence performance of the technology (such as

entrepreneurial ability, age, experience and expertise in growing the crop, access to other

inputs such as credit and irrigation). The data provide comparison across some 7,751

plots in 2002 and 1,580 plots in 2003.

Analysis of the data took place using the ‘Production Function Frontier’ approach. This

was pioneered by Farrell (1957) and seeks to define the maximum possible output that a unit

can produce, given input bundles ‘x’. This is the efficiency or ‘best-practice’ frontier. While

average production functions attribute differences between firms to random factors,

stochastic frontier functions isolate differences in inefficiency and random differences

amongst producers by dividing the error term into a deterministic component and a random

one. In this methodology, realised output is seen as bounded from above by the stochastic

frontier (Schmidt and Sickles, 1984) and technical inefficiency is seen as the amount by

which a firm’s actual output falls short of the efficiency frontier.

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The stochastic frontier production function was first proposed by Aigner et al. (1977) and

Meeusen and van den Broeck (1977). This was specified for cross-sectional data and

consists of a production function of the usual type:

Qit = (Xit) eit

(1)

where Qit is the value of output, Xit is the vector of inputs, is the vector of parameters

estimated and is the random disturbance term composed of two parts.

it = vit - uit (2)

where vit is a normally distributed error term that represents statistical noise, while uit is a

truncated (non-negative) error term that represents technical inefficiency.

To estimate a frontier production function for the data, we began by trying a number of

specifications – linear, quadratic, log-linear and log-log. The log-log specification was

found to provide the best fit, hence the dependent variable in our analysis is the log of

yield per acre.. A complete model was estimated for the 2002 data for which a wide range

of variables was available. A more parsimonious model was estimated for data pooled

over two years (2002 and 2003) because the 2003 data collection effort was more limited

and did not include variables such as soil quality and irrigation. The pooling of the data

for 2002-3 in the second estimation will help us to consider whether the results for 2002

reflect a ‘first year of production’ impact and/or whether there is change over time.

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Table 1 provides summary statistics of the variables used in the analysis, as well as the

results of tests for statistical differences in these variables for the Bt and non-Bt varieties.

The specifications of the frontier production function models are shown in Table 2. All

inputs are included in the model – land, pesticide sprays, soil type and irrigation (the

latter two only for 2002) – for which there are data. However, it should be noted that in

neither year were data collected for farm labour or for the amount of fertilizer applied to

the plots, and the results therefore carry the implicit assumption that labour inputs are the

same on all plots. This is a very simplistic assumption as labour requirements for

bollworm insecticide sprays are likely to be reduced for Bt plots while labour

requirements for harvesting are likely to be increased. Therefore, omitting these variables

could cause a bias in the estimates, which is not always easy to determine. In spite of this,

it was felt that the estimates provide useful information about the economic prospects of

the Bt cotton variety in India.

In our model the four determinants of yield we have used are:

(a) Soil quality. The soil quality variable within the data is a discrete variable representing

light, medium and dark brown soil. We expect yields to be highest on dark soils and we

expect this to be true for both the Bt and non-Bt varieties. We therefore included soil

quality in our model as two separate binary variables (DARKSOIL and MEDSOIL),

leaving out LIGHTSOIL to avoid the dummy variable trap. Table 1 indicates that there

was no statistical difference in soil types in the two growing seasons.

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(b) Irrigation. We included the number of irrigations (LNIRRI) and hypothesised that the

plots that are irrigated are likely to produce higher yields. Summary statistics (Table 1)

indicate that there are about 3.2 irrigations on average per plot but that this is not

significantly different for Bt and non-Bt varieties.

(c) Insecticides. We included two variables for insecticide sprays – sucking pest sprays

(SPSPRAY; for jassids, aphids etc.) and bollworm pest sprays (BWSPRAY; for

bollworms). In general we assume that the greater the amount of insecticide applied then

the smaller is the loss from insects and presumably the higher the yield. Since the Bt

varieties are expected to provide some protection against bollworm attack (at least in the

early stages of crop development) it is expected that BWSPRAY input on Bt plots should

be lower. This assumption is based on farmers being aware of the nature of the Bt

technology. It is also logical to assume that bollworm insecticide when used on Bt plots

will have a smaller impact on productivity than when used on non-Bt plots. Note that

some positive effect of BWSPRAY on yield would be expected even on Bt plots as the

plant resistance only operates against younger instars of the bollworm larvae. Older

instars are less affected by the Bt endotoxin but can, of course, continue to cause damage

and are killed by insecticide1. Table 1 indicates that while the number of SPSPRAYS is

very similar for the Bt and non-Bt varieties, the number of BW sprays is significantly less

on Bt plots (1.44 as compared to 3.84 on non-Bt plots).

1 There is the possibility that response to irrigation and spraying may be quadratic in nature (i.e.

diminishing returns) but when tested this form of response was not significant. This seems to imply that

farmers are aware of the costs of over-irrigation and over-spraying.

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(d) Plant resistance. This is the use of the Bt hybrid, and is, of course, assumed to be

related to the use of insecticide. Variation in yield for Bt cotton is captured by the

DUMBT term. If the Bt variety is more productive than the non-Bt variety, then we

would expect the coefficient of this variable to be positive, and previous studies based on

trial data in India and elsewhere certainly lead us to expect this. Preliminary summary

statistics (Table 1) confirm that the yield for Bt varieties is significantly higher than for

non-Bt varieties. Interaction terms between DUMBT and each of the inputs such as

insecticide have been included in order to capture the possibility that the impact of each

input on yield may vary between the Bt and non-Bt varieties.

Land (LANDHOLD) is not included as an input into the production function because all

factors are included on a ‘per acre’ basis. Instead, land is included as a variable that will

determine the efficiency with which the inputs are utilised. Thus, we hypothesise that

small farms would be more intensively cultivated and hence have higher yields, while

larger farms may benefit from economies of scale in cultivation and therefore have better

gross margins even if yields are lower.

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Results: Frontier Production Function Estimates

As indicated above, the frontier production function estimates indicate the ‘best practice’

in an industry. In this context, the estimates have to be interpreted slightly differently

from average production function estimates. We will begin by considering the results for

the 2002 season. Table 2 indicates that the Bt technology has a particularly large positive

effect on yield/acre (DUMBT), even after allowing for the influence of other inputs such

as number of insecticide sprays, soil type and irrigation. Yield increases for both the Bt

and non-Bt varieties as the number of irrigations (LNIRRI) increases. However, there is

an additional positive and highly significant impact of irrigation on the Bt varieties

(DUMIRRI) indicating that one unit of irrigation will have a larger impact on Bt output

than on non-Bt output. This suggests that the yield response of Bt varieties to irrigation is

significantly higher than that of non-Bt varieties. However, there may be a seasonal effect

here and the response may have more to do with the variety carrying the Bt gene rather

than the Bt characteristic itself.

Our results also indicate that, as expected, yields are highest on the heavier, darker soils

(DARKSOIL) than on medium or light soils. However, we find that soil type does not

have a significant additional impact on the productivity of the Bt varieties (DUMMSOIL

and DUMDSOIL are both insignificant).

Table 2 confirms that the use of sucking pest sprays increases the yield of cotton both for

the Bt and the non-Bt varieties (LNSSP and DUMLNSP respectively). It is possible that

the Bt technology increases the productivity of these sprays because of the higher

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potential crop yields to be protected. The impact of bollworm spray, on the other hand, is

more varied. It significantly increases the yield of the non-Bt type (LNBWSP) but the

negative DUMLNBW coefficient shows that the yield response of the Bt variety to

bollworm sprays is lower. This is not surprising given that the Bt technology already

protects the crop from bollworm.

Finally, we included land as a variable that reflects economies of scale. Our results

indicate that in general, size of plot is not significant in influencing productivity for the

farms in our sample. This is true for both non-Bt (LANDHOLD) and Bt varieties

(DUMLANDH) varieties, though the latter is marginally significant at 80% level. Thus,

our results indicate that Bt varieties may do marginally better on smaller plots than non-

Bt varieties but that in general, area does not matter either in conferring economies of

scale or in increasing yields through increasing the intensity of cultivation.

Finally, Table 2 also confirms that yields vary spatially. Region 1 (Vidarbha) had a

significantly lower yield in 2002 than Regions 2 or 3 (Marathwada and Khandesh

respectively). Region 2, on the other hand, is not significantly different in terms of yields

than the other regions. Our results also indicate that the highest yields for the Bt variety

are in Khandesh, followed by Vidarbha and Marathwada. A more detailed statistical

analysis of the regions indicates that there are a number of reasons for this pattern

including intensity of input use. We will discuss these in the next section.

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Pooled Estimates for 2002-3.

The above analysis considers data only for the first year of production of Bt cotton

(2002). Therefore it is unable to consider whether there is a time-varying pattern of yields

in Maharashtra. More specifically, it is possible that the first year of production was

particularly good or bad (in terms of weather, challenge by pests etc.). The first year of

production is also a learning period for farmers and therefore production may be higher in

the second year. To take these factors into account, we re-estimated the model for data

pooled over both 2002 and 2003. Since there was no information available on soil type or

number of irrigations for 2003, we estimated a more parsimonious model for the pooled

data. This could, of course, give rise to omitted variable bias. We tested the possible

direction of such a bias by estimating the model for 2002 after excluding these variables

and find that it does not change the direction of any of the coefficients.

Results for the pooled data (also in Table 2) are similar to those for the 2002 cross-

section. The variable DUMBT again shows the positive impact of the Bt variety on yields

compared to conventional varieties, this time over both seasons. The magnitude of this

variable is rather large (0.498) and the coefficient is also highly significant. Sucking pest

sprays (LNSSP) and bollworm sprays (LNBWSP) both have a positive effect on yields.

However, bollworm pest sprays have a larger impact on non-Bt yields compared to Bt

yields (DUMLNBW). This is not surprising, as discussed earlier. Sucking pest sprays, on

the other hand, have a smaller impact on Bt yields compared to non-Bt (DUMLNBW).

One possible explanation is that insecticides designed to control sucking pests may

incidentally provide some control of bollworm pests in the earlier stages of crop growth.

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Once again, Regions 1 and 2 do not perform as well as Region 3 (Khandesh), as shown

by the negative signs on these coefficients, with Region 1 (Vidarbha) being the least

productive of the three regions. Interestingly, the coefficients for DUMREG1 and

DUMREG2 also show that the Bt cotton varieties have significantly higher yields in

Region 3 than in Regions 1 and 2.

Turning now to the time-pattern of yields over the two years, we find that the coefficient

of YEARDUM is negative and significant indicating that cotton yield was generally

lower in 2003 than in 2002. Our results however indicate that the yield of the Bt varieties

is significantly higher in 2003 than in 2002 (DUMYEAR). Thus, yields on the Bt

varieties do seem to have increased with experience. YEARLNSP shows that sucking

pest sprays had a greater impact on yields in the first season and YEARLNBW shows

that bollworm sprays had a relatively greater impact in the second season and since the Bt

varieties are generally better protected from these pests, it is perhaps not surprising that

non-Bt yields were lower in the second season than the first.

Further Analysis of Regional Differences

Insert Tables 3a, 3b and 3c here.

Our results indicated that Khandesh had the highest yields for both Bt and non-Bt

varieties amongst the three regions that we sampled. What might explain this pattern?

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Descriptive statistics of yields and input use in each of the three regions indicate that

Khandesh falls between Vidarbha and Marathwada in terms of the average size of

landholdings and cotton area planted per farm and in terms of seed cost per acre. These

factors do not readily explain the high yields in Khandesh. Farmers in this region,

however, seem to be more intensive in their use of certain inputs – sucking pest and

bollworm sprays and irrigation – than farmers in either of the other two regions. Table 3c

also confirms that the proportion of plots with dark soil is highest in Khandesh (19 %) as

opposed to 5.4% in Marathwada and 7% in Vidarbha2.

This pattern is repeated in the intensity of input use in Bt cotton production in the three

regions. Table 3b indicates that Khandesh has more irrigation and more sucking pest

sprays on Bt plots than the other two regions. However, unlike for non-Bt cotton, farmers

in Khandesh also seem to be better informed about the Bt variety and use less bollworm

sprays on their Bt plots than other farmers.

The analysis presented above here using commercial planting data found lower (but still

substantial) average yield increases of 45% and 63% for Bt plots across seasons

compared to non-Bt than found from trial results. In assessing the benefits of the GM

2 Generally, Khandesh is more productive using irrigation resources and high inputs.

Farmers in Marathwada generally use less inputs and are more dependent on rain for

cotton cultivation. Vidharbha is largely rain fed but comes under assured rainfall areas,

farmers in this part generally uses very low inputs and their spending on pesticides are the

lowest in country and so is yield (Rana, 2003 personal communication).

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technology it is important to recognise that there are likely to be a number of factors that

could be contributing to the increased performance of Bt cotton. The first and most

obvious is the Bt gene technology itself. The second is the cotton variety used as the base

for the Bt variety and the performance of this variety under local conditions. For

example, it could be that the Bt cotton varieties use better yielding hybrids than some of

the conventional cotton grown. Thus there may be both a Bt technology effect on

performance (i.e. yield) and a hybrid effect. Thirdly, it may be that more efficient farmers

take up the new technology. These farmers may already be achieving higher yields than

non-adopters before the technology. Data on farmer characteristics would be required to

analyse these factors in more detail. In addition, adopters may have planted the Bt seed

on their better land and given it more attention than conventional (given the relatively

high cost of the Bt seed). Each of these factors may therefore be contributing to the

higher yields that are measured for the Bt varieties. It has not been possible to separate

out each of these possible effects from the data available and there is therefore a need for

further data collection and research to try to separately identify the technology effect

from other possible effects.

Conclusion

This study is the first of its kind in India based as it is on ‘real’ farms and markets rather

than the more artificial conditions that exist with trials. Findings show that since its

commercial release in 2002 Bt cotton has had a significant positive impact on yields and

on the economic performance of cotton growers in Maharashtra. However, it is important

to note that there is spatial and temporal variation in this ‘benefit’, and much can depend

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upon where production is taking place and the season. Further data are required in

particularly on labour costs, farmer characteristics and cotton varieties to identify the

origin of the increased yield. However, if the apparent advantages of GM cotton to

farmers in India can be sustained then there could be a significant positive impact on

farmers’ livelihoods and on agricultural gross domestic product for India.

References

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USA, Development Prospects Group, The World Bank, 2004.

Bennett, R., T.J. Buthelezi, Y. Ismael and S. Morse. “Bt Cotton, Pesticides Labour and

Health: A Case Study of Smallholder Farmers in the Makhathini Flats, Republic

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Agriculture 2003-2004. Agricultural Biotechnology. Meeting the Needs of the

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Gonzalez-Cabrera, J., Escriche, B., Tabashnik, B. E. and Ferre, J. 2003. Binding of

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James, C. “Global Review of Commercialised Transgenic Crops Featuring Bt

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Cotton.” ISAA Brief No. 26. Ithaca, USA, International Service for the

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Qaim, M. and D. Zilberman. “Yield Effects of Genetically Modified Crops in Developing

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Tabashnik, B. E., Patin, A. L., Dennehy, T. J., Liu, Y. B., Carriere, Y., Sims, M. A. and Antilla, L. (2000).

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2001.

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Table 1. Summary Statistics for Inputs and Outputs

Season 2002 Season 2003

Variables non-Bt Bt Sig. non-Bt Bt Sig.

Cotton area (acres) 2.4 (1.6) 1.56 (1.7) *** 2.76 (3.2) 2.37 (3.4) ***

Soil type (3 categories) 2.22 (0.6) 2.23 (0.6) ns - -

No. irrigations 3.29 (1.7) 3.23 (1.6) ns - -

No. sucking pest sprays 2.25 (0.8) 2.24 (1.1) *** 2.2 (0.9) 2.37 (1.1) **

No. bollworm pest sprays 3.84 (2.6) 1.44 (1.7) *** 3.11 (1.1) 0.71 (0.8) ***

Total costs (seed plus insecticide)

(Rupees/acre) 2,048 (840.8) 2,349 (638.2) *** 2,160 (823.3) 2,206 (431.7) ***

Cotton yield (quintals/acre) 6.09 (9.0) 8.83 (12.8) *** 5.59 (2.5) 9.1 (3.6) ***

Price of cotton (Rupees/quintal) 2,037 (224.6) 1,953 (415.1) *** 2,499 (90.9) 2,504 (90.5) ns

Revenue from cotton yield

(Rupees/acre) 12,577 (20,195) 18049 (26,945) *** 14,001 (6,361) 22,807 (9,216) ***

ns = not significant at 5%

** P < 0.01

*** P < 0.001

Results are means and standard deviations in parentheses. Raw data failed Anderson-Darling test for normality, even with

transformation, therefore data have been compared with the Kruskal-Wallis non-parametric test.

Table 2. Model results for season 2002 and for combined seasons 2002/2003.

2002 2002 and 2003

Coefficient t-ratio Coefficient t-ratio

Intercept 1.875 61.46 *** 1.961 82.340***

Years (2002=0; 2003=1) YEAR -0.120 -3.201***

Logarithm no. irrigations LNIRRI 0.107 8.944***

Logarithm no. sucking pest sprays LNSSP 0.059 3.360** 0.116 7.186***

Logarithm no. bollworm sprays LNBWSP 0.046 3.037** 0.051 3.396***

Medium Soil (=1) MEDSOIL 0.008 0.423

Dark Soil (=1) DARKSOIL 0.120 6.071***

Size of Landholdings (acres) LANDHOLD -0.001 -1.454

Vidarbha (1; others=0) REGION1 -0.191 -10.43*** -0.195 -12.332***

Marathwada (1; others=0) REGION2 -0.021 -1.137 -0.028 -1.751

Bt varieties (0= non-Bt; 1=Bt) DUMBT 0.490 11.828*** 0.498 16.950***

DUMLNSP 0.094 3.651*** 0.083 3.731***

DUMLNBW -0.150 -7.490*** -0.146 -7.277***

DUMMSOIL -0.031 -1.216

DUMDSOIL -0.018 -0.620

DUMIRRI 0.064 3.259***

DUMLANDH -0.001 -1.796

DUMREG1 -0.193 -6.380*** -0.164 -6.566***

DUMREG2 -0.226 -7.349*** -0.167 -6.543***

Interaction terms (with YEAR) YEARBT 0.175 5.122***

YEARLNSP -0.083 -3.289***

YEARLNBW 0.106 3.582***

Mu -113.215 -0.098 -127.154 -0.111

Lambda 20.159 0.198 21.001 0.223

Sigma 5.901 0.198 6.482 0.224

Log likelihood -3670.07 -5257

ns = not significant at 5%, * P < 0.05, ** P < 0.01, *** P < 0.001

The specification of the 2002 model is:

LNYIELD = f(LNLAND, LNIRRI, LNSOIL, LNSSP, LNBWSP, DUMBT, REGION1, REGION2,

DUMBT*all previous variables)

The specification of the pooled 2002/2003 data model is:

LNYIELD = f(YEAR, LNSSP, LNBWSP, DUMBT, REGION1, REGION2, DUMBT*all previous

variables, YEARLNSP, YEARLNBW )

where:

YIELD = yield (quintals per acre)

LAND = number of acres held by each farmer

IRRI = number of irrigations (where rainfed cotton is held as 0)

SOIL = soil type (coded 1,2,3 where 1= light soil, 2=medium brown soil and 3=dark brown soil).

SSP = number of sucking pest sprays

BWSP = number of bollworm sprays

REGION1 = If region is Vidarbha, then 1; else = 0

REGION2 = If region is Marathwada, then 1; else = 0

DUMBT = dummy term denoting Bt varieties (=1, if Bt; else 0).

Table 3a: Regional Differences in Input Use (All Varieties) in 2002

Khandesh Marathwada Vidarbha

Mean Std.Dev. Number Mean Std.Dev. Number Mean Std.Dev. Number

YIELDVAL 18240.60 8186.78 1879 16277.70 40733.80 2541 12680.50 26041.40 3019

IRRIGAT 3.27 1.29 1942 2.09 1.64 2575 3.02 1.62 2756

SPSPRAY 2.43 0.68 1974 2.27 11.51 2717 2.11 0.76 3029

BWSPRAY 3.57 26.61 1871 2.04 1.43 2665 3.39 29.28 2968

LANDHOLD 14.81 14.39 2009 12.96 15.52 2721 18.75 14.91 3063

COTLAND 8.07 8.66 2009 4.25 5.37 2721 9.52 7.91 3063

SEEDCOST 879.90 552.94 1927 856.51 587.03 2718 882.74 553.65 3042

Table 3b: Regional Differences in Input Use (Bt Varieties) in 2002

Khandesh Marathwada Vidarbha

Mean Std.Dev. Number Mean Std.Dev. Number Mean Std.Dev. Number

YIELDVAL 25217.50 8399.94 684 17531.10 5896.98 1024 16227.10 42597.90 1095

IRRIGAT 3.32 1.33 700 2.13 1.65 1096 3.06 1.59 997

SPSPRAY 2.41 0.68 711 2.34 17.67 1150 2.04 0.77 1092

BWSPRAY 1.24 16.19 610 1.08 0.98 1119 1.95 23.88 1042

LANDHOLD 14.42 13.79 722 12.43 15.27 1153 18.84 14.98 1106

COTLAND 7.85 8.37 722 4.08 5.29 1153 9.42 8.01 1106

SEEDCOST 1589.65 112.43 718 1419.57 500.56 1150 1598.26 106.21 1104

1

Table 3c: Regional Differences in Soil Quality (2002)

All Cotton Bt Cotton

Soil Type

Khandesh Marathwada Vidarbha Khandesh Marathwada Vidarbha

Number

of plots

% Number of

plots

% Number

of plots

% Number

of plots

% Number of

plots

% Number

of plots

%

DARKSOIL

373

19.02

123

5.42

204

7.09

125

17.66

68

6.47

59

6.48

MEDSOIL

753

38.40

1180

52.01

2190

76.12

277

39.12

784

74.60

482

52.91

LITESOIL 835 42.58 966 42.57 483 16.79 306 43.22 199 18.93 370 40.61

Total 1961.00 2269.00 2877.00 708.00 1051.00 911.00