Agricultural Resource Management Survey (ARMS) data for 2003 were used to estimate two binary logit models for two definitions of genetically modified (GM) cottonseed adoption. Results indicate conservation tillage did not positively affect adoption of GM cotton with either of these definitions, while adoption of GM cotton in the previous year did. Refuge cotton also did not affect these adoption decisions for the study year.Agricultural Resource Management Survey (ARMS), binary logit model, conservation tillage, cotton, genetically modified seed, herbicide-resistant cotton, jackknife procedure, refuge cotton, stacked-gene cotton, technology adoption, Crop Production/Industries,

Banerjee, Swagata (Ban)

Martin, Steven W.

English

Research Papers in Economics

  1
A Binary Logit Analysis of Factors Impacting Adoption of 
Genetically Modified Cotton 
 
Swagata “Ban” Banerjee 
g_horse2000@yahoo.com, banerjee@drec.msstate.edu  
(Corresponding Author) 
and 
Steven W. Martin  
smartin@ext.msstate.edu
                     
Selected Poster presented at the 
Southern Agricultural Economics Association Annual Meetings 
Dallas, Texas, February 2-5, 2008 
          
  
 
Abstract 
Agricultural Resource Management Survey (ARMS) data for 2003 were used to estimate two binary logit 
models for two definitions of genetically modified (GM) cottonseed adoption. Results indicate conservation 
tillage did not positively affect adoption of GM cotton with either of these definitions, while adoption of 
GM cotton in the previous year did. Refuge cotton also did not affect these adoption decisions for the study 
year.  
 
 
Key Words: Agricultural Resource Management Survey (ARMS), binary logit model, conservation tillage, cotton, 
genetically modified seed, herbicide-resistant cotton, jackknife procedure, refuge cotton, stacked-gene cotton, 
technology adoption.  
 
 
 
 
 
 
 
 
 
 
Copyright © 2008 by Swagata “Ban” Banerjee and Steven W. Martin 
 
All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by 
any means, provided that this copyright notice appears on all such copies. 
 
 
Swagata Banerjee is post-doctoral associate and Steven Martin is associate professor and extension 
economist, both at the Delta Research and Extension Center of Mississippi State University.    2
Introduction 
The U.S. Environmental Protection Agency has mandated an Insect Resistance Management (IRM) 
program that attempts to preserve the benefits and insect protection of Bacillus thuringiensis (Bt) cotton. 
According to that mandate, growers planting Bt cotton are required to follow the IRM practices designed to 
keep some lepidopteran populations from being exposed to the Bt protein. Thus, a refuge of non-Bt cotton 
must be planted. Other than Bt (insect-resistant) cotton, genetically modified (GM) types of cotton include 
herbicide-resistant (HR) cotton and stacked-gene (SG) cotton, the latter combining the traits of insect 
resistance with herbicide resistance. 
  
Conservation tillage in general and no-till practices in particular have increased over the past few years 
(Figures 1 and 2). Conservation tillage (either no-till or reduced tillage practice) is reported to reduce soil 
erosion by preventing reduction in crop residue on the soil surface (Harper). Yet, despite the apparent 
advantages of conservation tillage in reducing soil erosion, soil degradation, runoff, and in improving soil 
quality (Edwards; Sandretto), some farmers adopt no-till or minimum-till while others do not.  
With the use of HR or SG seed technology, it indirectly reduces the use of residual herbicides and increases 
profit potential of the farmer (Marra, Pardey, and Alston). The use of conservation-tillage (CT) practices 
may be even more important in cotton production than in other row crop production because of the minimal 
amount of residue left on the soil surface. Crop residues after planting averages 3% for cotton compared 
with 29% for corn (U.S. Department of Agriculture). 
  
Past studies have attempted to show the relationship between adoption of GM (specifically HR) seed and 
adoption of conservation tillage, and determine the causal effects between them. For example, Fernandez-
Cornejo and McBride’s cross-sectional study for 1997 using USDA’s ARMS data investigated a potential 
simultaneous relationship between HR soybean seed and CT practices using two simultaneously estimated 
binomial probit models, and compared those results with two single-equation probit models. Their study 
suggested that accounting for simultaneity was important for the no-till decision but not for the seed-use 
decision. In a more recent study with time-series data for 1992-2004, Roberts et al. used Bayesian theorem 
and a two-equation simultaneous logit analysis to find that the introduction of the HR cottonseed in 
Tennessee increased the probability that farmers would adopt conservation tillage and farmers who had 
previously adopted CT practices were more likely to adopt HR cottonseed. They concluded that the 
simultaneous adoption of conservation tillage and HR cottonseed reduced soil erosion and residual 
herbicide use, and increased profit. However, with ARMS data for 2003, Banerjee et al. found no evidence 
of a simultaneous relationship between these two adoption decisions. 
  
The overall objective of this study was to identify factors that led to the adoption of GM cotton in the 
United States. Specifically, the study sought to identify farm and farmer characteristics driving the 
production of GM cotton in the United States in general. In particular, the impact (or otherwise) of 
conservation tillage (i.e., no-till and reduced till: ridge-till, strip-till, and mulch-till) and conventional cotton 
used as refuge on the adoption of GM cottonseed across the United States and within selected regions was 
studied.  
 
 
Data and Methods 
This study used two definitions of genetic modification of cottonseed adoption: Bacillus thuringiensis (Bt) 
cotton or HR cotton (denoted as BH); and Bt, HR, or SG cotton (denoted as BHS). The survey respondents were classified as adopters of a particular type of GM cotton according to these definitions. The “yes” 
responses received for these definitions as percentages of the sample used in the regressions were 65% and 
86%, respectively. Two single-equation binary logit models (Maddala) were estimated using the same set 
of independent variables, except for the relevant adoption variable of the previous year that uses the 
definition specific to the relevant adopter classification. 
  
Administered by USDA-ERS, data from the 2003 ARMS, which is a random sample of cotton farms across 
the United States, were used to estimate the two binary logit models with two definitions of genetic 
modification. The dependent variable for each equation indicated the adoption of GM cotton (with the 
relevant GM definition) vis-à-vis its non-adoption (i.e., adoption of conventional cotton). The explanatory 
variables for each equation included the following:  
1)  demographic information of the survey respondents/farmers: age, if college graduate, gross annual 
farm income, length of tenure in cotton farming; 
2)  farm characteristics: farm size, farm labor expense, refuge size in field surveyed, percentage of farm 
acres in cotton, if they grew genetically engineered cotton (using the relevant definition) in the 
previous year (2002), if any part of their surveyed field was declared highly erodible land (HEL) by 
the Natural Resources Conservation Service (NRCS), if they no-tilled in 2002, cotton acres 
harvested, percentage of cotton acres harvested using one or more CT equipment; and  
3)  region-specific dummy variables: one for each cotton-producing region, the 17 cotton-producing 
states being divided into five regions. 
  
The logit equation is written as (Greene):  
(1)                                                    
x
x
e
e
Y
'
'
1
) 1 Pr(
β
β
+
= = ,  
with the cumulative distribution function given by:  
(2)                                                       
x e
x F
' 1
1
) ' (
β β
+
= , 
where β' represents the vector of parameters associated with the factors x.                                                                       
  Assuming the probability that farmer n will choose to produce cotton using a particular technology 
(BH or BHS seed) is equal to the proportion of cotton farmers using that technology, the individual 
empirical models to be estimated may be specified as:       
(3)  ,
02
17
14
13 12 11 10 9 8
7 6 5 4 3 2 1 0
T j
j
i
iREGION GFI TEN EDU AGE PNT YIELD
REFUGE HEL CTP CTA LABEXP BH CONSTILL BH
ε β β β β β β β
β β β β β β β β
+ + + + + + + +
+ + + + + + + =
∑
=
 
 
(4)  ,
02
17
14
13 12 11 10 9 8
7 6 5 4 3 2 1 0
G j
j
i
iREGION GFI TEN EDU AGE PNT YIELD
REFUGE HEL CTP CTA LABEXP BHS CONSTILL BHS
ε γ γ γ γ γ γ γ
γ γ γ γ γ γ γ γ
+ + + + + + + + +
+ + + + + + + =
∑
=
 
where  
CONSTILL is a dummy variable indicating whether or not CT practices were adopted;  
BH02 is a dummy variable indicating if Bt or HR cotton was grown in the prior year, 2002;  
BHS02 is a dummy variable indicating if Bt, HR, or SG cotton was grown in the prior year, 2002;  
LABEXP is the total labor expense on the farm ($100,000);  
CTA denotes the total harvested cotton acres (dryland and irrigated) on the farm (100 acres);  
CTP is the percentage of cotton acres on the farm;  
  3HEL is a dummy variable indicating if NRCS classified any part of the field surveyed as HEL;  
the REFUGE dummy indicates if refuge cotton was used in the survey year;  
YIELD is lint yield in pounds per acre harvested or expected in 2003;  
PNT is a dummy variable indicating if no-till was used in the surveyed field in the year before the survey, 
2002;  
AGE is the age of the principal farm operator;  
EDU is a dummy variable indicating the surveyed farm operator’s education level (whether or not college 
graduate);  
TEN is the length of tenure (in years) of the operator household for the surveyed field;  
GFI is the gross farm income in 2003 ($100,000);  
REGION is a set of four dummies:  
REGION1 including Alabama, Florida, and Georgia;  
REGION2 including North Carolina, South Carolina, and Virginia;  
REGION4 including Texas, Oklahoma, and Kansas; and  
REGION5 including Arizona, California, and New Mexico; with  
REGION3 (Arkansas, Louisiana, Mississippi, Missouri, and Tennessee) excluded as the reference region to 
compare other regions with;  
T ε and  G ε  are random error terms;  
β’s and γ’s are parameters to be estimated; and   
the subscript n for the n
th farmer is suppressed for clarity. 
 
 
Results  
Results (as given by the maximum likelihood estimates from equations 3 and 4) indicate conservation 
tillage (CONSTILL) did not affect adoption of GM cotton with either definition for the study year (2003) at 
the 5% level, though it did seem to cause a reduction in the likelihood of BH adoption (equation 3) at the 
10% level (Table 1). The latter counter-intuitive result brings to light one of the issues regarding the use of 
cross-sectional data. Instead of establishing causality, effects, determinants, or impacts, our regression 
results may be read as implying correlations, relationships, and associations. The “correlations” presented, 
however, assume ceteris paribus conditions. The weak negative correlation becomes weaker and no more 
significant even at the 10% level when stacked-gene cotton adopters are included in the mix, as apparent 
from the BHS adoption model (equation 4) estimation. It is plausible that farmers who had not adopted CT 
practices before BH seed was introduced might have first adopted BH seed and then decided that they 
could improve efficiency by adopting CT practices sometime during or before 2003. A similar study in a 
later year might make this variable positive and significant.   
   
Whether or not GM cotton was used in the prior year (2002) positively affected the adoption of GM cotton 
in the study year using both definitions at the 1% level. Percentage of cotton acres on the farm (CTP) also 
positively affected both adoption decisions at the 1% level. The variable for Region 1 was significant at the 
5% level and negative in the BH equation, with a marginal effect of -0.173, indicating that, estimated in 
isolation, this region (southeastern states of AL, FL, and GA) was 17.3% less likely to adopt BH relative to 
cotton farmers in Region 3 (AR, LA, MS, MO, TN). 
 
Previous experience of no-till (PNT) in the BH equation and education level (EDU) in the BHS equation 
were significant only at the 10% level, with EDU having an unexpected negative sign.  
  
  4  5
Labor expense, cotton acres on farm, HEL, refuge size, yield, tenure, gross farm income, and age were not 
significant factors in determining adoption of GM technologies in cotton production for the study year.  
 
 
Conclusions 
Recent studies involving adoption of conservation tillage and HR cotton have attempted to determine if the 
adoption of conservation tillage impacts the adoption of GM cottonseed and/or vice versa. If CT adoption 
impacts GM cottonseed adoption, then conservation tillage can indirectly affect residual herbicide use and 
farm profits. The current study using cross-sectional ARMS data does not provide strong evidence of such 
a correlation with either of the two definitions of genetic modification used. Refuge cotton also did not 
affect GM cottonseed adoption using any definition in this study. However, the limitations of a cross-
sectional study using complex data such as from the ARMS are apparent.   
 
 
References 
Banerjee, S.B., S.W. Martin, R.K. Roberts, J.A. Larson, R. Hogan, J.L. Johnson, K.W. Paxton, and J. 
Reeves. “Adoption of Conservation-Tillage Practices in Cotton Production.” Selected Paper 
presented at the Southern Agricultural Economics Association annual meeting, Mobile, Alabama, 
February 4-7, 2007. 
Edwards, W.M. “Effects of tillage and residue management on water for crops.” Crop Residue 
  Management to Reduce Erosion and Improve Soil Quality: Appalachia and Northeast Region. R.L. 
  Blevins and W.C. Moldenhauer, eds., U.S. Department of Agriculture – Agricultural Research 
  Service, Conservation Research Report No.41, 1995. 
Fernandez-Cornejo, J. and W.D. McBride. “Adoption of Bioengineered Crops.” USDA –  
  Economic Research Service, Agricultural Economic Report No. 810, 2002.   
Greene, W.H. Econometric Analysis. 3
rd ed. New York, NY: Macmillan Publishing Company, 1993. 
Harper, J.K. “Economics of Conservation Tillage.” Pennsylvania State University College of  
  Agricultural Sciences Cooperative Extension, 1996. Internet site: 
  http://pubs.cas.psu.edu/FreePubs/pdfs/uc130.pdf (Accessed January 22, 2008). 
Maddala, G.S. Limited-Dependent and Qualitative Variables in Econometrics. Cambridge, UK:  
  Cambridge University Press, 1983.  
Marra, M.C., P.G. Pardey, and J.M. Alston. “The Payoffs to Transgenic Field Crops: An  
  Assessment of the Evidence.” AgBioForum 5(2003):43-50. Internet site: 
  http://www.agbioforum.org/v5n2/v5n2a02-marra.htm (Accessed January 22, 2008). 
Roberts, R.K., B.C. English, Q. Gao, and J.A. Larson. “Simultaneous Adoption of Herbicide-Resistant and 
  Conservation-Tillage Cotton Technologies.” Journal of Agricultural and Applied  Economics 
  38,3(December 2006):629-43.  
Sandretto, C. “Crop Residue Management.” Agricultural Resources and Environmental Indicators, 
  Agricultural Handbook 712, pp. 155-74. U.S. Department of Agriculture – Economic Research 
  Service, 1997.  
U.S. Department of Agriculture – ERS. “Agricultural Resources and Environmental Indicators, 1996-97.” 
Agricultural Handbook 712. Economic Research Service, Washington, DC, 1997.   6
Table 1. Parameter Estimates and Marginal Effects from the Single-Equation Logit Models for 
Bt/Herbicide-Resistant (BH) and Bt/Herbicide-Resistant/Stacked-Gene (BHS) Cottonseed 
BH Cottonseed Equation  
 
BHS Cottonseed Equation 
Explanatory 
Variable
a
Parameter 
Estimate 
Marginal 
Effect
b
  Explanatory 
Variable
a
Parameter 
Estimate 
Marginal 
Effect
b
           
Constant** 
 
 
-3.311 
(1.648)
c 
 
  Constant** 
 
 
-4.717 
(1.898)
c 
 
 
CONSTILL*  -1.030 
(0.550) 
 
-0.116 
(0.062)
d
  CONSTILL  -0.639 
(0.952) 
<-0.001 
(<0.001)
d
BH02*** 
 
 
4.615 
(0.520) 
 
0.519 
(0.058)
 
 
  BHS02*** 
 
 
7.906 
(1.516) 
0.002 
(<0.001)
 
 
LABEXP 
 
 
-0.022 
(0.147) 
-0.003 
(0.017) 
 
  LABEXP 
 
 
-0.718 
(0.706) 
<-0.001 
(<0.001) 
CTA 
 
 
-0.068 
(0.052) 
-0.008 
(0.006) 
 
  CTA 
 
 
-0.054 
(0.081) 
<-0.001 
(<0.001) 
CTP*** 
 
 
2.486 
(0.744) 
0.280 
(0.084) 
 
  CTP*** 
 
 
4.285 
(1.090) 
0.001 
(<0.001) 
 
HEL 
 
 
-0.453 
(0.812) 
-0.051 
(0.091) 
 
  HEL 
 
 
-0.467 
(1.490) 
<-0.001 
(<0.001) 
REFUGE  -0.050 
(0.042) 
 
-0.006 
(0.005) 
  REFUGE  0.618 
(4.365) 
<0.001 
(0.001) 
YIELD  <-0.001 
(<0.001) 
 
<-0.001 
(<0.001) 
 
  YIELD  <-0.001 
(0.002) 
<-0.001 
(<0.001) 
PNT* 
 
 
1.124 
(0.631) 
0.126 
(0.071) 
 
  PNT 
 
 
0.975 
(1.300) 
<0.001 
(0.001) 
AGE 
 
 
0.021 
(0.014) 
0.002 
(0.002) 
 
  AGE 
 
 
0.040      
(0.042) 
<0.001 
(0.001) 
EDU 
 
 
-0.264 
(0.473) 
0.030 
(0.053) 
 
  EDU* 
 
 
-1.340      
(0.800) 
<-0.001 
(<0.001) 
TEN 
 
-0.019 
(0.016) 
-0.002 
(0.002) 
  TEN 
 
-0.042      
(0.049) 
<-0.001 
(<0.001)   7
     
GFI 
 
 
0.017 
(0.059) 
 
0.002 
(0.007) 
 
  GFI 
 
 
0.068    
(0.134) 
<0.001 
(0.001) 
 
REGION1
e** 
 
 
-1.539 
(0.778) 
-0.173 
(0.087) 
 
  REGION1
e
 
 
-2.115      
(1.721) 
<-0.001 
(0.001) 
REGION2 
 
 
-1.139 
(0.696) 
-0.128 
(0.078) 
 
  REGION2 
 
 
3.089       
(14.838) 
<0.001 
(0.004) 
 
REGION4 
 
 
-0.309 
(0.771) 
-0.035 
(0.087) 
 
  REGION4 
 
 
-1.407      
(1.322) 
<-0.001 
(0.001) 
REGION5 
 
 
0.188 
(0.665) 
0.021 
(0.075) 
 
  REGION5 
 
 
-1.445      
(1.529) 
<-0.001 
(0.001) 
           
Log Likelihood = -5,607.622    Log Likelihood = -10,566.661 
McFadden R
2 = 0.4527     McFadden R
2 = 0.7835 
Adjusted McFadden R
2 = 0.4513    Adjusted McFadden R
2 = 0.7823 
Prediction Success:   
   Concordant   85.3% 
   Discordant    14.5% 
   Tied                0.2% 
  Prediction Success:   
   Concordant   96.3% 
   Discordant      2.1% 
   Tied                1.5% 
Number of Observations = 537 in sample 
≈ 9,128 farms in expanded full sample  
 
  Number of Observations = 898 in sample 
≈ 12,126 farms in expanded full sample  
Number of BH Adopters = 349 in sample 
≈ 5,342 farms in expanded full sample  
 
  Number of BHS Seed Adopters = 769 in 
sample ≈ 9,163 farms in expanded full 
sample  
Note: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.  
a Explanatory variables are defined in Table 1. 
b A marginal effect indicates the change in predicted probability of adopting the relevant technology for a unit change in an 
explanatory variable. Marginal effects of continuous variables were calculated at the means of the data. For dummy variables, a 
value of 0 was used if the mean was less than 0.5 and a value of 1 if the mean was greater than or equal to 0.5.  
c Numbers in parentheses below parameter estimates are respective asymptotic delete-a-group jackknife standard errors of those 
estimates. Parameter estimates were obtained using the NASS “full-sample weight” variable.     
d Numbers in parentheses below marginal effects are respective asymptotic delete-a-group jackknife standard errors of those 
effects. Marginal effects were obtained using the NASS “full-sample weight” variable.    
 
e Regional dummy variables compare adoption relative to cotton farmers in Region 3 (AR, LA, MS, MO, TN). 99.3
10 3 .8
109 .2 109.0
103.1
112 .6
95
100
105
110
115
M
i
l
l
i
o
n
s
 
o
f
 
a
c
r
e
s
1994 1996 1998 2000 2002 2004
 
Source: Conservation Technology Information Center. 
Figure 1. Conservation-tillage adoption in the United States, 1994-2004
  838.9
42.9
47.8
52.2
55.3
62.4
35
40
45
50
55
60
65
M
i
l
l
i
o
n
s
 
o
f
 
a
c
r
e
s
1994 1996 1998 2000 2002 2004
 
Source: Conservation Technology Information Center. 
Figure 2. No-till adoption in the United States, 1994-2004 
 
  9

Adoption of Conservation-Tillage Practices

Crop Residue Management.” Agricultural Resources and Environmental Indicators,

Department of Agriculture – ERS. “Agricultural Resources and Environmental Indicators,

Econometric Analysis. 3 rd ed.

Economics of Conservation Tillage.” Pennsylvania State University College of Agricultural Sciences Cooperative Extension,

Effects of tillage and residue management on water for crops.” Crop Residue Management to Reduce Erosion and Improve Soil Quality: Appalachia and Northeast Region.

Limited-Dependent and Qualitative Variables in Econometrics.

The Payoffs to Transgenic Field Crops: An Assessment of the Evidence.” AgBioForum 5(2003):43-50. Internet site:

A Binary Logit Analysis of Factors Impacting Adoption of Genetically Modified Cotton

A Binary Logit Analysis of Factors Impacting Adoption of Genetically Modified Cotton

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