adverse health experiences, risk perception and pesticide...
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ADVERSE HEALTH EXPERIENCES, RISK PERCEPTION AND PESTICIDE USE BEHAVIOR
BY
MUHAMMAD KHAN
A Thesis Submitted in Partial Fulfillment of the Requirements for the
Degree of DOCTOR OF PHILOSOPHY IN ECONOMICS
2012
FUUAST School of Economics Sciences Federal Urdu University of Arts, Science & Technology (FUUAST)
Islamabad.
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TO
MY PARENTS
BROTHERS AND
SISTERS
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ACKNOWLEDGEMENTS
Several individuals deserve acknowledgement for their contributions in one way or another during this study. My sincere and deepest appreciation goes to my supervisor Professor Dr. Rehana Siddiqui for her close supervision and professional advice throughout the course of research work. I know I was the source of much headache for her but no option. I especially thank her for taking time to read my work in her extremely busy schedule. I am also grateful for all that I have learned from her.
Second among this list is Prof. Dr. Nawab Haider Naqvi, Distinguish Professor of Economics and Director General of this University. Without his vision, support and passion to develop research in this university, we could not have come so far. He is the only source of hope in our most difficult times in this university. In a very personal way providing us with support and solution, he has managed to keep track of our studies. His invaluable services for the department, particularly for students are very much appreciated.
My special thanks are given to Dr. Abdul Salam and Dr. Muhammad Iqbal for their review and valuable comments on earlier drafts of the thesis. My appreciation goes to my teachers Dr. Adiqa Kiani, Dr Aitzaz Ahmed, Dr.Abdul quyyam, Dr. Imtiaz Ahmed, Dr. Waseem Shahid Malik, Saeed ahmed sheikh and Dr Seeme Malik.
I am indebted to my friend Iftikhar ul Husnain for his contribution which extends to several fronts. My thanks also go to all my classmates, particularly Zafar ul Husnain, Naeem Akram, Ihtasham ul Haq Padda and Saima Akhter Qureshi for their co-operation during the course of my study. I would like to acknowledge the support provided by Dilshad Ahmad, Kashif Bhatti and field enumerators during the fieldwork. My brother Kashif Mehmood assisted me in entering part of the data into computer who also deserves special commendation.
To get this goal, many people have made sacrifices for me, but my family members have borne a great deal. I want to thank most sincerely to my father. Absolutely words can’t express my gratitude to him. Without h is keenness and encouragement I might have not done this work. Thanks for insisting and pushing me forward. I know he will be the happiest person once this thesis is over. I am very grateful to my mother who raised me and instilled values in me. I count myself blessed to have mother like her. Thanks for countless prayers.
My heart-felt thanks go to my sisters Saima and Shumaila who made my life easier. I acknowledge the concerns and encouragement of my brothers also. I extend my deepest sense of gratitude to my grandmother and my aunts for their prayers, best wishes and moral support.
Finally, I thankfully acknowledge that this work would not have been possible without the financial support of the Higher Education Commission (HEC) Pakistan. Muhammad Khan
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Contents
ACKNOWLEDGEMENTS III
ABSTRACT XII
CHAPTER 1: INTRODUCTION 1
1.1 BACKGROUND AND MOTIVATION 1
1.2 STATEMENT OF THE STUDY PROBLEM 4
1.3 OBJECTIVE OF THE STUDY 7
1.4 CONTRIBUTION AND SIGNIFICANCE OF THE RESEARCH 7
1.5 SCOPE AND ORGANIZATION OF THE STUDY 10
CHAPTER 2: REVIEW OF LITERATURE 12
2.1 PESTICIDE USE AND HEALTH IMPACTS 12
2.2 PESTICIDE USE AND THE ENVIRONMENT 18
2.3 ECONOMICS OF PESTICIDE USE 20
2.3.1 PESTICIDE USE AND HEALTH COST 20 2.3.2 PESTICIDE USE AND NATURAL BIOLOGICAL RESOURCE DEGRADATION 21 2.3.2.1 BIODIVERSITY (RENEWABLE BIOLOGICAL CAPITAL RESOURCES) 21 2.3.2.2 PEST SUSCEPTIBILITY (NON-RENEWABLE BIOLOGICAL CAPITAL RESOURCES) 22
2.4 PSYCHOLOGY AND ECONOMICS 23
2.4.1 THE LINK BETWEEN PSYCHOLOGY AND ECONOMICS 23 2.4.2 USE OF PSYCHOLOGY IN ECONOMICS 25 2.4.2.1 THEORY OF REASONED ACTION(TRA) AND THEORY OF PLANNED BEHAVIOR (TPB) 26 2.4.2.2SOCIAL-COGNITIVE THEORY 27 2.4.2.3 THE COMMON SENSE MODEL 28 2.4.2.4 HEALTH BELIEF MODEL 29
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2.5 ECONOMIC COST OF PESTICIDE USE 34
2.6 THE CONTINGENT VALUATION METHOD 36
2.6.1 ECONOMIC EVALUATION OF HEALTH COST USING WTP 37
2.7 PESTICIDE USE BEHAVIOR 40
2.8 INTEGRATED PEST MANAGEMENT 50
2.9 SUMMARY 53
CHAPTER 3: CROP SECTOR IN PAKISTAN: MAJOR CROPS AND PESTICIDE USE 55
3.1 SIGNIFICANCE OF AGRICULTURE SECTOR IN THE ECONOMY 55
3.2 SELECTED MAJOR CROPS AND CHARACTERISTICS OF AGRICULTURAL PRODUCTION 56
3.2.1 COTTON 57 3.2.2 RICE 58 3.2.3 SUGARCANE 59 3.2.4 WHEAT 59
3.3 PESTICIDE USE AND PRODUCTION OF MAJOR CROPS 60
3.3.1 THE PATH DEPENDENCE (PESTICIDE TREADMILL) 62
3.4 MANAGEMENT OF PESTICIDE USE AND INTEGRATED PEST MANAGEMENT 64
3.4.1 IPM STATUS IN PAKISTAN 64
3.5 AGRICULTURAL EXTENSION 65
3.6 SUMMARY 68
CHAPTER 4: STUDY AREA, SURVEY DESIGN AND DATA COLLECTION 70
4.1 SELECTION OF STUDY AREA 70
4.2 DEVELOPMENT OF SURVEY QUESTIONNAIRE 72
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4.3 DATA METHODOLOGY 73
4.3.1 FIELD SURVEY 75 4.3.2 SAMPLE SIZE 76
4.4 VALIDITY AND RELIABILITY ANALYSIS 76
4.4.1 RELIABILITY ANALYSIS 78 4.4.2 VALIDITY TESTS OF CVM 80
4.5 SUMMARY 82
CHAPTER 5: SURVEY RESULTS 84
5.1 BACKGROUND INFORMATION 84
5.1.1 AGE AND EDUCATION OF THE FARMERS 84 5.1.2 HOUSEHOLD CHARACTERISTICS 85 5.1.3 LAND OWNERSHIP AND FARM CHARACTERISTICS 85
5.2 PESTICIDE SAFETY KNOWLEDGE, INFORMATION SOURCE AND AVERTING BEHAVIOR 86
5.2.1 SOURCES OF INFORMATION ABOUT SAFETY PRACTICES 86 5.2.2 PESTICIDE SAFETY KNOWLEDGE AND AVERTING PRACTICES 87 5.2.3 RISK PERCEPTION 89 5.2.4 PESTICIDE PRACTICES AND USE OF PROTECTIVE MEASURES 91
5.3 CROP PROTECTION METHODS AND PESTICIDE APPLICATION 93
5.3.1 CROP PROTECTION METHODS IN STUDY AREA 93 5.3.2 PESTICIDE SPRAY FREQUENCY 94 5.3.3 USE OF PESTICIDE BY TOXICITY CLASSIFICATION 95
5.4 HEALTH AND ENVIRONMENTAL IMPACTS OF PESTICIDE USE 97
5.4.1 HEALTH EFFECTS OF PESTICIDE USE 97 5.4.2 IMPACT OF PESTICIDE USE ON THE ENVIRONMENT 100
5.5 WILLINGNESS TO PAY FOR SAFER PESTICIDE 100
5.6 SUMMARY 103
CHAPTER 6: THE CONCEPTUAL FRAMEWORK 106
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6.1 HEALTH BELIEF MODEL AND PESTICIDE USE BEHAVIOR 106
6.2 HEALTH EXPERIENCE, RISK PERCEPTION AND SAFETY BEHAVIOR (MODEL 1) 108
6.2.1 EMPIRICAL MODEL 110
6.3 HEALTH EXPERIENCE, FARMERS’ ATTITUDES AND ENVIRONMENTALLY SOUND BEHAVIOR OF PESTICIDE USE (MODEL 2) 114
6.3.1 EMPIRICAL FRAMEWORK 116
6.4 FARMER’S WILLINGNESS TO PAY FOR INTEGRATED PEST MANAGEMENT (MODEL 3) 118
6.4.1 EMPIRICAL MODEL 120
6.5 SUMMARY 121
CHAPTER 7: ANALYSIS OF PESTICIDE USE BEHAVIOR 123
7.1 HEALTH EXPERIENCE AND FARMERS’ ATTITUDES 123
7.1.1 ORDERED PROBIT RESULTS FOR RISK PERCEPTION OF PESTICIDE USE 125
7.2 HEALTH EXPERIENCE, RISK PERCEPTION AND SAFETY BEHAVIOR 130
7.3 HEALTH EXPERIENCE, RISK PERCEPTION AND ENVIRONMENTALLY SOUND BEHAVIOR OF PESTICIDE USE 134
7.4 FARMER’S WILLINGNESS TO PAY FOR INTEGRATED PEST MANAGEMENT 138
7.4.1 RESULTS OF ORDERED PROBIT MODEL 139
7.5 SUMMARY 143
CHAPTER 8: CONCLUSION AND POLICY IMPLICATIONS 145
8.1 CONCLUSION 145
8.2 POLICY IMPLICATIONS 150
8.3 FUTURE RESEARCH PRIORITIES 154
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REFERENCES 156
APPENDIXES 169
APPENDIX 1: FIGURES 169
APPENDIX II: TABLES 173
APPENDIX III: PESTICIDE LEGISLATION IN PAKISTAN 185
APPENDIX IV: DISTRICTS PROFILES 189
APPENDIX V: DESCRIPTION OF VARIABLES IN EMPIRICAL MODELS 193
APPENDIX VI : SURVEY QUESTIONNAIRE 198
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List of tables
Table 3.1. Production of Selected Major Crops in Pakistan (000 tonnes) _____________________________ 56 Table 3.2. Yield of major crops in Pakistan _______________________________________________________ 61 Table 4. I. Province wise share of cotton production _______________________________________________ 70 Table 4.2.Distribution of sample population by district ____________________________________________ 76 Table 4.3. Reliability analysis Item-Total Statistics ________________________________________________ 79 Table 4.4.Validity test in the implementation of the CV ____________________________________________ 81 Table 5.1. Distribution of education attainment by age groups _____________________________________ 84 Table 5.2. Distribution of farm size by household size _____________________________________________ 85 Table 5.3.Distribution of farm size and farm ownership (%) ________________________________________ 86 Table 5.4. Farmer’s behavior about safety instruction _____________________________________________ 88 Table 5.5. Per acre use of pesticide (kg) with different level of Risk perception ________________________ 90 Table 5.6. Main reasons, for not taking protective measures (%)____________________________________ 93 Table 5.7.Total amount of pesticide applied by WHO classification__________________________________ 96 In table 5.7, the sum of the total amounts of active ingredient used under the WHO classification system is provided. Most of the pesticides (54.7%) in use are moderately hazardous (category II) . Moreover, cotton accounts more than 70% of total pesticide use in this study area (see table 5.8). _____________________ 96 Table 5.8. Use of pesticide on selected crops by WHO classification (%) ______________________________ 97 Table 5.9. Distribution of Willingness to pay responses (%)________________________________________ 101 Table 5.10. Distribution of Mean WTP by district ________________________________________________ 101 Table 5.11. Distribution of willingness to pay by farm size (%) _____________________________________ 102 Table 5.12. Distribution of WTP by risk perception (%) ____________________________________________ 102 Table 5.13. Distribution of WTP by income ______________________________________________________ 103 Table 7.1.Pearson correlation coefficients (District Lodhran) ______________________________________ 123 Table 7.2.Pearson correlation coefficients (District Vehari) _______________________________________ 124 Table 7.3. Ordered probit results for risk perception ______________________________________________ 126 Table 7.4. Predicted probabilities and marginal effects for risk perception categories ________________ 127 Table 7.5. Results of ordered probit regression for protective behavior _____________________________ 131 Table7.6. Predicted probabilities and marginal effects from the estimat ed model____________________ 132 Table7.7. Maximum likelihood estimates of Probit for the use of alternative pest management practices____________________________________________________________________________________________ 134 Table 7.8.Predicted probabilities and marginal effects from the estimated probit model ______________ 135 Table 7.9. Estimated coefficients of Ordered Probit Model for positive WTP _________________________ 139 Table 7.10. Predicted probabilities and marginal effects from the estimated model __________________ 141
List of figures
Figure 2.1. Health Belief Model _________________________________________________________________ 32 Figure 3.1. Pesticide consumption in Pakistan (mt) ________________________________________________ 60 Figure 4.1. Map of Punjab Province _____________________________________________________________ 71 Figure 5.1. Farmer’s sources of information (%) __________________________________________________ 87 Figure 5.2. Farmers perception of pesticide risk (%) _______________________________________________ 90 Figure 5.3. Use of protective equipments during spray (%) _________________________________________ 92 Figure 5.4. Mean pesticide application on different crops __________________________________________ 95 Figure 5.5. Distribution of farmers’ attitudes towards the affect of pesticide on their health ___________ 98 Figure 5.6. Distribution of health effects experienced by farmers (%) ________________________________ 99 Figure 6.1. Relationship between health experiences, risk perception and pesticide use behavior ______ 107
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List of appendix Figures
Figure 1A.Farm ownership status .............................................................................................................................169 Figure 2A. Pesticide spray frequency by district .....................................................................................................169 Figure 3A. Use of protective measures by district .................................................................................................170 Figure 4A.Farmer’s perception of pesticide risk by district (%) ............................................................................170 Figure 5A. Farmers’ attitudes towards health effects of pesticide use in Vehari .............................................171 Figure 6A. Farmers’ attitudes towards health effects of pesticide use in Lodhran ..........................................171 Figure 7A. Distribution of mean pesticide application on vegetables ...............................................................172
List of appendix Tables
Table 1A. Distribution of farm size by district ____________________________________________________ 173 Table 2A. Distribution of farm size by farm ownership in Lodhran __________________________________ 173 Table 3A. Distribution of farm size by farm ownership in Vehari ___________________________________ 173 Table 4A. Distribution of farmer’s age _________________________________________________________ 174 Table 5A.Distribution of education attainment by age in Vehari ___________________________________ 174 Table 6A.Distribution of education attainment by age in Lodhran __________________________________ 174 Table 7A.WHO Hazard Classification of pesticides _______________________________________________ 175 Table 8A. Pesticide use by WHO hazard classification by district ___________________________________ 175 Table 9A.Crop wise pesticide use by WHO hazard classification in Vehari (%) ________________________ 175 Table 10A. Crop wise pesticide use by WHO hazard classification in Lodhran (%) ____________________ 176 Table 11A.WHO Category wise pesticide use on cotton by farm size (%) ____________________________ 176 Table 12A. WHO Category wise pesticide use on wheat by farm size (%) ____________________________ 176 Table 13A.WHO Category wise Pesticide use on vegetables by farm size (%) ________________________ 177 Table 14A. WHO Category wise pesticide use on other crops by farm size (%) _______________________ 177 Table 15A.Amount of pesticide used (Kg/per acre) by farm size ____________________________________ 177 Table 16A. Distribution of income by age group _________________________________________________ 178 Table 17A.Main source of information for farmers in study area __________________________________ 178 Table 18A. Use of IPM by method ______________________________________________________________ 179 Table 19A.Percentage of farmers who follow instructions on pesticide labels by level of education ____ 179 Table 20A.Descriptive statistics of important variables (district Lodhran) ___________________________ 180 Table 21A.Descriptive statistics of important variables (district Vehari) _____________________________ 180 Table 22A: Na me of the districts and share of total area under cotton in Punjab province ____________ 181 Table 23A. List of sample villages used for survey ________________________________________________ 182 Table 24A. Area, production and per hectare yield of major cotton producing countries (2005 -2006)___ 183 Table 25A.Area, production and per hectare yield of major rice producing countries (2005 -2006)______ 183 Table 26A. Area, production and per hectare yield of major sugarcane producing countries (2005 -2006)184 Table 27A.Area, production and per hectare yield of major wheat producing countries (2005-2006) ___ 184
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List of abbreviation
APO Agricultural Pesticide Ordinance
BSCV Burewala Strain of Cotton Virus
CCRI Central Cotton Research Institute (Multan)
CSM Common Sense Model
CV Contingent Valuation
CVM Contingent Valuation Method
EU European Union
FAO Food and Agriculture Organization
FFS Farmers Field School
GDP Gross Domestic Product
GR Green Revolution
HBM Health Belief Model
IPM Integrated Pest Management
LCV Leaf Curl Virus
LD Lethal Dose 50%
LFS Labour Force Survey
NARC National Agriculture Research Center
NFDC National Fertilizer Development Center
NOAA National Oceanic and Atmospheric Administration
NPS Non Point Source
PARC Pakistan Agriculture Research Center
SCT Social Cognitive Theory
SLT Social Learning Theory
TOF Training of Facilitators
TPB Theory of Planned Behavior
TRA Theory of Reasoned Action
UN United Nations
WHO World Health Organization
WTP Willingness to Pay
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Abstract
For Pakistan’s economy, agriculture is the most important sector. It contributes about 22
percent of the gross domestic product (GDP) and employs 45 percent of the national
employed labour force. It supports directly or indirectly about 65 percent of the
population living in rural areas for their sustenance. It also contributes about 65 percent
to total export earnings derived from raw and processed agricultural commodities.
It is evident that pesticides are used for the benefits. However, use of pesticide leads to
negative externalities for the farmers and the society. Negative externalities may include
such as effects on human health, loss of bio-diversity, degradation of natural ecosystems
and irreversible changes in the environment. Various kinds of pesticides have been used
on a large scale in Pakistan since the early 1950s to protect crops from damages inflicted
by insects and diseases. After liberalization of pesticides in 1980, pesticide use increased
dramatically in Pakistan reaching 117513 metric tonnes in 2005 which was only 12530
metric tonnes in 1985. The massive increase in pesticide consumption is not translated
into productivity improvements rather accompanied by a huge cost in terms of human
health and degradation of the environment.
It is well established that the use of pesticides on the farm is largely governed by
voluntary behavior. Therefore, it is important to understand what drives farmer’s
behavior of pesticide use. Such information is critical to identify the prospects and
constraints to the adoption of alternative crop protection policy. According to
microeconomic consumer theory, individuals make choices following their preferences.
However, economic theory does not focus to the processes of individual’s reasoning
behind choices. Cognitive models in Public Health and Social Psychology argue that
persons who have had adverse health experiences are likely to undertake greater
preventive behavior. This study combines an approach from social psychology with micro
economic consumer theory to understand individual’s reasoning behind their decisions.
Further, it also examines the health implications of pesticide use as caused by behavior of
the farmers which help to inform policy makers about productivity reducing effects of
pesticide use.
A survey of 318 farmers in Vehari and Lodhran districts of Southern Punjab was drawn.
Results indicate that farmers are frequently exposed to pesticides. Over 90 percent
farmers reported at least one health problem in district Lodhran, where as in district
Vehari, almost 80 percent farmers reported the same. However, they appeared to give low
priority to health considerations and grossly under-estimating pesticide’s health risk
where almost all the farmers did not visit hospital or doctor for proper medication. This
misperception is largely translated into practical behavior where farmers were found
heavily skewed towards pesticide use for pest management and the use of protective
measures to avoid direct exposure of pesticides is not sufficient. Low level of education
combined with cultural/local beliefs regarding health effects of pesticide use is the main
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reason of this comportment. Moreover, about 80% pesticides used in the study area are
highly or moderately hazardous. In terms of crops, cotton alone received over 70% of total
quantity. Similar pattern appeared in terms of toxicity, where cotton consumed over 88%
of highly hazardous and moderately hazardous pesticides. Farmers were found to be
overusing pesticides. They were also found applying pesticides very frequently. During
survey 73 percent of them reported that they applied pesticide more than 10 times on
cotton in a season. The spray frequency is as high as 16 on cotton crop in one season.
There is a dearth of formal training and information on proper use and safe handling of
pesticides. Most of the farmers did not know about IPM, hardly few of them using it
which helps them reduce dependence on pesticides.
The analysis supports the hypothesis that farmers who have had negative health
experiences related to pesticide use are more likely to have heightened risk perceptions
than farmers who have not had such problems. Education and training are also important
determinant of risk perception. Association also existed between the experience of health
problems and the use of protective measures. The results, however, do not support the
hypothesis that the farmers who have had negative health effects from pesticide use are
more likely to adopt alternative pest management practices. This however does not mean
that farmers who have had such experiences do not care about the effects of pesticide use.
The lack of information or access to alternative pest management practices is the likely
reason. The Contingent Valuation (CV) analysis shows that farmers are willing to pay
premium for safe alternatives of pesticides which support our argument.
Finally, research findings have some important implications, for example, the empirical
relation that appears to exist between training of safe handling and alternative pest
management would suggest that trained farmers significantly and effectively substitute
IPM for pesticide use. Hence, to improve awareness, necessary for better choices of
pesticide use, specific and relevant information regarding the health effects and
environmental risks of using pesticide should be provided to farmers through training
programs. For this, government should restructure current pro-pesticide extension
system and design effective outreach programs, such as farmer field schools which deal
specifically with health risk of pesticide use, averting behavior and better management of
pests. One such program (e.g. National IPM program) is already in place but with
limited coverage which needs to be strengthened and broadened through increased efforts
by government and NGOs to educate farmers which may help reduce dependency on
pesticide while at the same time maintaining or improving production. Further, policy
interventions should also include the restructuring of incentives and punishment to
reduce availability of highly toxic insecticides.
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Chapter 1
Introduction
1.1 Background and motivation
The synthetic pesticides are an integral part of present day farming. Indeed, they
have significant contribution in the improvement of crop yield by killing pest which may
otherwise inflict huge damage to crops and in some cases destroy whole crops. It is said
that without pesticide use1, the level of yields and safety of today's crops could not be
possible (Rola and Pingali, 1993). However, on the other hand, this role of pesticide is
accompanied by disutility in the form of health impairment and environmental damage.
The increasing use of pesticide is held responsible for millions of poisoning in the
world. World Health Organization‘s estimates show that pesticide use causes 30, 00,000
cases of poisoning and 20,000 deaths annually across the globe. The majority of these
cases are reported from developing countries (WHO, 1990). Studies have also
documented the health effects of pesticide use e.g. cancer, kidney, lung, liver damage,
neurological and developmental disorder in children that may be the direct result of
either acute or chronic effect of pesticide exposure (Pimentel et al, 1996). In addition,
renal toxicity, reproductive problems and dermatomes have been found associated with
chemical pesticides. It has also been identified that pesticides are associated with
1Any substance (usually chemical) or mixture of substances intended to destroy, control or prevent any
pest (including vectors of̀ human and animal diseases, unwanted species of animals and plants) causing
harm or interfering with the production, processing, transport or storage of food and agricultural
commodities (Retrieved, 12-08-2010, http://en.wikipedia.org/wiki/Pesticide).
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infertility in agriculture workers who had been exposed to the pesticides (Potashnik,
1987).
Pesticide use in Pakistan grew at the rate of 11.6 percent on average over the last
twenty years or so, reaching 117513 metric tonnes in 2005 which was only 12530 metric
tonnes in 1985. This massive increase in pesticide use has caused a huge cost in terms of
human health and environment. Studies have noted that indiscriminate pesticide use in
agriculture in Pakistan has led to development of pest resistance against the pesticide
which ultimately resulted in many fold increase in their population (Azeem et al, 2002;
Iqbal et al, 1997; Hasnain, 1999). Due to extensive use of pesticide, the flora and fauna
have been destroyed causing imbalance in agro-ecosystem and biodiversity (Iqbal et al,
1997). Studies have also noted that in cotton growing areas of the country, the
population of natural enemy pests has declined substantially (Hasnain, 1999; Iqbal et al,
1997; Rehman, 1994; Nasira, 1996).
Further, Azeem et al (2002) estimated health and environmental cost of
pesticide use in nine districts of cotton belt in Punjab province. The result shows that
cost of pesticide use is worth 11941 million Pak-rupees per year. While estimating
health and environmental cost they reported that about 1.08 million persons were
subjected to pesticide associated sickness, among those 24000 persons were hospitalized
because of serious illness and about 271 fatalities were happened in these districts. A
study in Multan division reported that 22 out of 25 blood samples of farmers were found
contaminated with pesticide residues (Hassan, 1994). Similarly, another study reported
the result of blood samples obtained from female cotton pickers in cotton growing areas
of Punjab which shows that nearly 74% female cotton pickers had blood (AChE)
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inhibition between 12.5 to 40 percent, while 25 percent of them were in dangerous
condition where blood AChE inhibition was between 50-87.5 percent (Jabbar et al,
1992).
The above evidences indicate that current crop protection system is a serious
threat to agriculture sustainability in cotton growing areas of Pakistan. Farmers are using
pesticide in rising quantities due to rising pest resistance and the indiscriminate use of
pesticide has led to huge health and environmental cost to rural communities. Therefore,
there is an urgent need to address pesticide issues, so that rural communities can be
secured from pesticide associated health and environmental damage.
The literature shows that indiscriminate use of pesticide and associated negative
externalities in terms of health and environment can be avoided by providing proper
information, raising awareness and changing farmer‘s attitude and behavior regarding
pesticide use (Ibitayo, 2006; Dasgupta et al, 2005a; Forget, 1991). Since, farmers are
directly concerned in their role as principal polluters and victims of the pollution, the
first step in developing crop protection policy is to investigate farmer‘s attitudes and
behaviors regarding pesticide use (Koh & Jeyaratnam;1996, Dasgupta, 2005a; 2005b;
Ajayi, 2000). The information regarding farmer‘s attitudes and behaviors is critical to
identify the constraints as well as prospects to widespread adoption of environmentally
sound crop protection policy (Ajayi, 2000).
A strand of literature in different geographical settings is of the view that health
and environmental problems of pesticide appear to be due to lack of education,
knowledge and information in developing countries (see e.g. Forget, 1991; Koh and
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Jeyaratnam, 1996; Ibitayo, 2006; Jors, 2006). However, latest studies have shown that
despite high levels of knowledge regarding health impact of pesticide use, farmers use
toxic pesticides frequently and taking little/no personal safety measures (see e.g. Kishi,
2002; Clarke, 1997; Meulenbeit, 1997; Garcia, 2002; McCauley, 2004; Yassin, 2002;
Sivayoganathan, 1995; Blair, 1997). Similarly, Damalas noted that although farmer‘s
knowledge about pesticide associated health hazards was high, the avertive measures
taken were not satisfactory and very high risk practices were common (Damalas, 2006).
All these studies however, do not provide satisfactory answer to the questions that why
some farmers, despite high level of knowledge of health risks, do not respond to health
promotion and what factors influence how those exposed to pesticide risk transform that
risk into self protective behavior? Through series of observations, this study attempts to
close this research gap by providing answers to the above mentioned questions in order
to better inform pesticide policy in the country.
1.2 Statement of the study problem
The microeconomic models of consumer behavior are based on observed
choices. The economic researchers generally use these models to study consumer
preferences and behavior. According to classical microeconomic consumer theory,
individuals make choices following their preferences. However, classical
microeconomic models of consumer behavior are poor in explaining and predicting
consumer behavior and do not focus to the processes of individual‘s reasoning behind
choices. One major problem with these models is that they ignore many of noneconomic
factors that play important role in guiding individual behavior (Huang, 1993). Basically
health related risk is influenced by a large number of factors, e.g. the cognitive and
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social factors exert strong influence over how and when individual engage in self
protective behavior (Leventhal, 1983). Theories of cognitive psychology show that at
personal level risk understandings is developed through cognitive analytic system and
intuitive experiential system. Experiential or observed information is more meaningful
to change behavior than abstract information and experiences drive most of the risk
perceptions and outcomes (Severtson, 2006; Leventhal, 1983). Therefore, one of the
factors in pesticide use behavior is, whether farmers have experienced any personal
health effect from pesticide use (Lichtenberg et al, 1999). As health psychology
literature says that most of the information and knowledge in our lives come from actual
personally relevant experiences or observations rather than from intellectual exercises.
Williamson (2003) in the context of farmer‘s field schools says that it has been found
that adults learn best from experience; firsthand knowledge is superior to information
received from others. Further, the literature in health psychology recommends the
application of behavioral theory in the present context e.g. to explain the relationship
between health experience and pesticide use behavior (Severtson, 2006). This study
therefore combines an approach from social psychology with new classical theory to
illustrate individual reasoning behind their decisions of pesticide use (Pouta, 2003). The
health belief model from social psychology provides a conceptual framework for this
study. This framework increases our understanding that how farmer‘s preferences are
formed viz-e-viz pesticide related health issues (Pouta, 2003).
Secondly, with the growing concerns of negative health effects of pesticide use
particularly due to misuse or overuse of pesticides, little or no use of protective clothing
during mixing or spraying pesticides, the use of extremely or highly hazardous
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pesticides and non-adherence to safe application techniques, it is important to discuss
health implications of pesticide use in monetary terms (Ajayi, 2000). The estimation of
health cost of pesticide use will guide policy makers to take under consideration the
negative health effects from pesticide use which ultimately reduce crop productivity. In
future, these effects are expected to be higher since most of the labour force in
agriculture in Pakistan is young and if the current use of pesticide continues with limited
adherence to pesticide safety measures, a large share of rural population will be at risk of
not only acute but also chronic adverse health effects. Thus, monetary estimates of
human health cost of pesticide use will help policy makers: first, for effective allocation
of resources to necessary health and safety programmes that can safeguard agriculture
workers and rural communities and second, for formulation of new rules and regulations
to promote safe use of pesticides in the country (Atreya, 2005). However, economic
valuation of health cost of pesticide use is constrained by the measurement challenges
because of different value components (market and non-market component2) of human
health. As for as the literature on economic valuation of health cost of pesticide use is
concern, it has focused on market components of pesticide use, e.g. it has used indirect
approaches like costs of illness. However, it is well understood that this is not true
economic measurement of health cost and a more comprehensive measurement of health
valuation has to include non-market component as well. The Contingent Valuation
(CV)3 method is designed to serve this purpose. The Contingent Valuation method
includes both market as well as non-market component of human health cost. Therefore,
this study uses Contingent Valuation approach to measure health cost of pesticide use.
2 market components, e.g cost of illness and non-market component, e.g cost of pain and discomfort
3For accurate measurement of the health costs of pesticide use, non -market value should also be
considered. For this purpose, the contingent valuation (CV) approach has been proposed.
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The information from farmers‘ point of view can provide important contribution to the
design of policies and regulations that aims to reduce negative effects of pesticides.
1.3 Objective of the study
The present study attempts to apply the HBM to explore farmer‘s behavior of
pesticide use and to analyze the implications for safe alternative pest management
techniques. Other objective of the study is to estimate the value of the improved health
from reduced pesticide pollution.
The specific objectives of the study are:
a) To identify health problems caused by pesticide use in farmers.
b) To evaluate the relationship between health effects, risk perceptions and safety
behavior.
c) To examine the level of awareness and affect of household characteristics in
pesticide use decision.
d) To understand determinants of environmentally sound pest management
adoption by farmers.
e) To determine farmer‘s willingness to pay for the improved health or to estimate pesticide associated health cost to farmers.
f) To identify factors that affect farmer‘s decision to pay for safe alternative pest management techniques.
1.4 Contribution and Significance of the research
The contribution of the study is vast and it serves many purposes. It is likely to
fill the gap in literature as a detailed study exploring farmer‘s attitude and behavior and
underlying factors determining their decision making regarding pesticide use in Pakistan
is non-existent. The study through series of observations highlights preventive behavior
8
at personal and environmental levels. The study is unique in the sense that it creatively
used Health Belief Model from health psychology and combines it with new classical
micro economic theory to demonstrate farmers reasoning behind their decisions of
pesticide use. Although Health Belief Model is potentially relevant to farmer‘s behavior
of pesticide use but this model has not been applied in any empirical economic research
to understand farmer‘s pesticide use behavior in any developing country. The use of
health psychology concepts leads better interpretation of the phenomena and helps
policy makers to reach better solutions of agriculture pollution in the country.
Moreover, there are small numbers of studies in the literature of social
psychology in developed countries context and these studies targeted to farmer‘s
perceived health threat as health belief model postulates (Napier & Brown, 1993; Tucker
& Napier, 1998). While this study used a more direct measure of health risk than
farmer‘s perception of health risk. It used actual health effects of pesticide use
experienced by farmers rather than perceived threats of pesticide use. In this way an
important problem in the research of health belief model framework is sought out that
emerges because of the use of different questions to determine the same risk perception
across studies. Consequently, it not only made comparison of results across studies
possible but also ease the hectic job of researchers to design appropriate scales or tests
for the components of HBM. Thus this work not only broadens pesticide use behavior
literature but also expands the health-belief model approach.
The economic burden of the substantial health effects that farmers bear due to
pesticide pollution must be accounted for in economic policy and planning. Such
valuation will be useful information to policy makers to allocate resources for necessary
9
health and safety programmes that can safeguard rural communities. Since valuation of
health effects includes both market and non-market components, it is difficult to
integrate health cost of both components because market does not exist for non-market
goods. Therefore, the valuation of pesticide use related health cost in most of the studies
has focused market components of health cost and ignored non-market component.
However, a comprehensive measurement should include non-market component also.
To overcome this issue, this study used contingent valuation approach. The CVM
includes both market and non-market components, it provides better estimates of health
cost which are of more interest to the policymaker. Further, willingness to pay approach
is based on the individual preferences which give more suitable basis for making
decisions about changes in welfare. Although in environmental and resource economics,
the use of this technique is common but very scarce in pesticide use context. Therefore,
this study also broadens the extent of willingness to pay approach by using it in pesticide
use context, particularly in Pakistan. Further, it adopts the information-behavior
framework from social psychology to study farmer‘s WTP for environmentally sound
pest management. This analysis provides better estimates of health costs which may help
policy makers to understand true value of health benefits of integrated pest management.
From overall policy perspective, the study offers an opportunity to understand
the behavior and attitude of farmers regarding pesticide use, safety measures and
underlying factors such that policymakers can easily identify what shapes farmer‘s
choices of pesticide use and to focus capacity building efforts in the area. Keeping under
consideration the IPM activities of National IPM Programme of Pakistan in the study
area, the study likely to identify the underlying problems in the course of adopting IPM
10
and resultantly helps to facilitate the design of future activities of the National IPM
Programme.
From a researcher‘s perspective, it provides opportunities for insights into the
determinants that motivate voluntary self protection in rural communities in general, and
that shed light on the match between risk-reducing policy interventions and the people
they are planned to benefit.
In addition, the study promises an opportunity to health professionals (policy
makers & researchers) to have insight into the health psychology of rural communities.
The results of the study can also be implemented to other environmental and hea lth
problems in rural areas of Southern Punjab.
1.5 Scope and organization of the study
Chapter two consolidates broad range of previous literature related to
economics of pesticide use, pesticide use behavior, health and environmental cost of
pesticide use and farmer‘s knowledge, attitude and perception regarding health risk of
pesticide. Chapter three presents an overview of the agriculture sector and its
importance in the economy. It discusses the characteristics of major crops e.g.
production of major crops and the role of pesticide consumption in productivity. It also
highlights health and environmental cost of pesticide use and d iscusses integrated pest
management status in Pakistan. In chapter four, information about study area and
research is presented. The sampling technique and selection of sample size are discussed
in detail.
11
In chapter five, survey based information on farmers with reference to land
characteristics, knowledge, attitude, health effects, pesticide practices and willingness to
pay for IPM are presented. Chapter six of this study discusses the conceptual
framework referencing health belief model and its relevance to explain farmer‘s decision
making behavior of pesticide use. The empirical models based on the study framework
are also presented. Chapter seven presents the analysis of pesticide use behavior.
Chapter starts by discussing simple methods like descriptive and summary statistics to
analyze data informally. Then devotes much space for analysis and discussion on
farmer‘s attitudes and behavior regarding pesticide use. It also contains analysis of
willingness to pay for integrated pest management. Chapter eight concludes this study,
presents policy implications and identifies priority areas of future research.
12
Chapter 2
Review of Literature
2.1 Pesticide use and health impacts
The desire for economic benefits (profits) is, most probably a strong motivation
for the use of agricultural pesticide (Ibitayo, 2006). However, the extensive use of
synthetic pesticide results in several health threats. In addition, pesticide residues in air,
water and foods have serious health implications for general pub lic. According to WHO
(1990) pesticides have been found in the air even after the use of long time ago, leading
to affect humans, wildlife and biodiversity. Further, they bioaccumulate and travel
globally.
Farmers are believed as the most vulnerable group of people to pesticide
exposure all over the world, because they are directly involved in mixing and spraying
dangerous liquids. Further, the less protected and unsafe use of pesticide increases the
chances for exposure substantially. Farm workers who apply pesticide and people who
live close to pesticide treated farms such as farm houses and people living nearby are
showing the highest level of pesticide exposure (Mcduffie, 1994). In addition to farmers
and their families who receive direct exposure of pesticide, a large proportion of
population may well be at risk of developing chronic health effects due to toxic pesticide
residues in the air, drinking water and food items (al-Saleh, 1994). The accurate
epidemiological data is largely missing which make proper assessment of pesticide
associated health effects very difficult. According to the National Research Council
(1984), data necessary for detailed health exposure assessment existed for only 10% of
13
pesticide. Litchfield (2005) provided a detailed discussion on pesticide associated acute
poisonings in agricultural workers of developing countries. He acknowledged that
pesticides related acute poisonings cases in developing countries are seriously under
reported. Farmers usually do not go to hospitals or health centers for proper treatment.
Most of the health effects are treated as minor effects of pesticides. Therefore, a small
fraction of the several million cases of pesticide associated health effects is usually
registered worldwide. The Bell (2006) reported similar results in USA. He presented a
case-control analysis of factors linked with reporting a high pesticide exposure event
(HPEE) by pesticide applicators and spouses. Study suggests that pesticide poisoning
surveillance data may seriously underreport the frequency of pesticide related poisoning
events.
Number of studies has documented the potential health effects such as cancer 4,
reproductive health problems, liver damage, kidney, lung and neurological problems
and developmental disorder in children that may be the direct result of either acute or
chronic5 effect of pesticide exposure (Pimentel et al, 1996). Further, reproductive
problems6 like increased risks of preterm birth have also been related to chemical
pesticide. Pregnancy loss and infertility is expected be high with some types of pesticide
exposures (Garcia, 1999; Sanborn, 2004). A research of National Cancer Institute
reported that pregnant women residing within nine miles of agriculture farms treated
4Pesticide associated cancers include: skin cancer, lung cancer, brain cancer, rectal cancer, ovarian
cancer, breast cancer, bladder cancer, liver cancer, stomach cancer, kidney cancer, multiple myeloma,
prostate cancer, pancreatic cancer, leukemia,, testicular cancer, soft-tissue sarcomas, and non-Hodgkin’s
lymphoma’ ( People & the Planet, 2007; Thomas, 1989).
5 Chronic health problems may include birth defects, neurological disorders, cancers, infertility and other
reproductive disorders (WHO, 1990). 6 In addition, studies have found that pesticide exposures to mothers, fathers, or both leads to increased
risks of preterm birth and fetal growth retardation.
14
with synthetic pesticide may have increased risk of losing an unborn baby to birth
defects. They are also identified to be associated with infertility in agricultural farm
workers who had been exposed to the pesticide (Potashnik, 1987; Schafer, 1968).
Because of direct exposure of pesticide or pesticide residues in the environment, sterility
is found in humans, generally in males. A study found that the young males in the lower
Columbia River and males in Florida's Lake Apopka have lesser reproductive organs
than the males in regions of their respective habitats that are not contaminated with
pesticides (Colborn et al, 1996).
It has also been found that agricultural workers in developing countries are at the
top of pesticide poisonings risk resultant from the unsafe practices. Research highlights
that although develop countries use more than 2/3 of total pesticide produced in the
world but the number of fatalities occur in these countries are less than half of all
pesticide- induced deaths (Pimentel et al, 1996). But the scenario is very different in
developing countries, where the share of pesticide poisonings and resultant deaths are
very high. World Health Organization‘s estimates show that pesticide use causes 30,
00,000 cases of poisoning and 20,000 deaths annually7 across the globe. The majority of
these cases are reported from developing countries (WHO, 1990). The latest studies
report pesticide associated fatalities as high as fifteen times higher8 than WHO
estimates.
7 The United Nations (UN) has estimated that about 2 million poisonings and 10,000 deaths occur each
year from pesticide and most of these occurring in developing countries (Quijano R, 1993).
In the United States, an estimated 67,000 pesticide associated poisonings and twenty-seven accidental
fatalities are reported every year (Pimentel et al, 1996). 8 Latest studies showed that the actual deaths may be around 300 000 every year (Eddleston, 2000; Rao,
2005).
15
Research has concluded that most of the pesticide related health and
environmental problems are occurring due to lack of knowledge and awareness,
misperception of hazard, insecure attitudes and unsafe practices (Dasgupta, 2005a).
Incorrect believes about pesticide hazard; scarce occupational safety standards,
protective and caring facilities; unsatisfactory enforcement; poor labeling of pesticide;
low level of education or illiteracy; and inadequate knowledge of pesticide hazards
(Pimentel et al, 1996). Evidence in different geographical settings suggests that farmers
use more toxic pesticides because they kill insects quickly (Dasgupta, 2005a).
According to WHO (1990) the pesticides, banned in developed countries, are still
extensively produced in developed nations for export to developing countries. The
farmers of less developed countries are using these pesticides on large scale,
deteriorating the already serious health and environmental problems in these countries
where many products of the WHO category I and II are still used at large scale. The
studies also revealed that farmers in developing countries are not expert in dealing with
pesticide. The expertise at their disposal for pesticide handling is often unsuitable:
sprayers are usually defective, defensive equipments9 are either lacking or unsuitable to
use, and first-aid provisions are largely missing. The studies found that lack of
information, knowledge and awareness are the chief contributing factors of pesticide
intoxication and dangerous work practices in developing countries (Forget, 1991). The
lack of information, knowledge and awareness in turn leads to misperception about
pesticide and pesticide poisoning and ultimately unsafe behavior that largely reduces the
9 Despite the much potential for pesticide exposure, workers who apply pesticide in the field often do not
use proper safety equipment, even when safety equipments are available. Further, many applicators do
not receive training of safe pesticide handling (Natural Resources Defense Council, 1998; Buckley, 2004).
16
ability of farmers and pesticide applicators to protect themselves aga inst pesticide
hazards (Ibitayo, 2006).
In Brazil, Recena (2006) found that pesticide associated poisoning rate was very
high among male farmer between the age periods of 15 to 49 years and insecticides were
main cause factor. Similarly Wim Hoek (2005) studied acute effects of pesticide
exposure in Sri Lanka. He compared different socio-demographic characteristics such as
age and education and adverse life events in cases and control group. He found that most
of the cases (84%) were because of deliberate self-poisoning. He reported that they had
lower educational level and were more probably unemployed. Study found that
individual‘s past experience of pesticide poisoning, mental disorder and heavy
dependence on alcohol were the main risk factors. Similar to intentional poisoning in Sri
Lanka, Srinivas Rao (2005) investigated the pesticide poisoning in Warangal district in
Andhra Pradesh, Southern India. The result shows that overall case fatality ratio was
more in India than found in Sri Lanka. Men and women ratio was (57%, 43%)
respectively with all pesticide types. A study carried out by Nhachi, Loewenson (1993)
in Zimbabwe‘s commercial farming sector shows that about 50% of workers on the
farms were exposed to organophosphates during spraying which is categorized as
extremely hazardous and is banned in developed countries. Adding literature on
pesticide related illness; Calvert (2008) studied pesticide-related acute occupational
poisonings among youths in United States and found that insecticides were involved for
nearly all of these illnesses (68%). However, he reported that the majority of poisonings
were of minor severity (79%). Similarly, Meulenbeit (1997) described a prospective
study which aims to determine the extent and severity of acute pesticide poisoning and
17
identifying working conditions that lead to these poisonings in Netherland. He found a
direct relation between exposure to pesticide and acute health problems in 37 out of 54
pesticide poisoning events. He found that in 67% of the cases, pesticide exposures took
place during mixing and other preparatory activities; repair of application equipment
(14%) and during re-entry (14%). In most accidents (74%) technical defects were
identified as major risk factors for exposure. Interestingly most of the workers had good
knowledge regarding pesticide poisonings and were aware of the risk of using pesticide,
but they were still careless in taking adequate protective measures, especially during
preparatory and reparation activities. Continuing with the literature on pesticide related
injuries Garry (2002) identified pesticide poisoning‘s related birth defects. Data of the
536 pesticide applicators with children was collected. It was found that children of
pesticide applicators had confirmed three times higher birth defects than the national
average. Some other side effects are also identified in health literature.
Adding up literature on pesticide health effects Ngatia (1980) noted statistically
significant reduction of plasma cholinesterase enzyme in employees who handled a
variety of chemical pesticide. Crossley (1999) in a case study of a farmer/commercial
applicator identified the relationship between occupational pesticide exposure and
neuropsychological brain functions. This case study suggests that specified brain
functions may be depressed during occupational exposures to pesticide and high levels
of work-related stress and fatigue. Dasgupta (2005b) assessed the main factors of
pesticide associated poisoning in Vietnam. Data from 482 farmers participating in both
survey and clinical tests were collected and analysed. Reported results of blood
cholinesterase tests suggest that the incidence of poisoning from exposure to
18
organophosphates and carbonates pesticide is quite high in Vietnam. However, the
farmers who usually use protective measures showed lower incidence of pesticide
poisoning. Maramba (1988) reported similar results. The comparison of medical tests of
farmers who usually take protective clothing while handling pesticides and farmers who
undertake pesticide operations without safety measures shows that hemoglobin levels
are significantly higher for farmers who usually take protective clothing while handling
pesticides than those who miss safety measures.
2.2 Pesticide use and the environment
There are also growing concerns about the consequences of pesticide use on the
environment. In addition to negative human health implications, the pesticides are also
responsible for the damage of environment. The pesticide use has caused domestic
animal poisonings, the death of useful predators and parasites, residues in air, fishery
and aquatic bodies‘ losses, the damage of flora and fauna, unintentional crop exposures,
death of birds and honeybees and undesirable residue in food items have all credited to
pesticides (Pimentel et al, 1992). It has been recognized that the chemical pesticide
residues are the key contributor to the destruction threats fac ing many endangered
species. Smith reported that U.S Government listed 663 threatened and endangered
species in 1995, of which 165 were linked to herbicides and other pesticides (Smith). In
addition, populations of honeybees10 which are necessary for pollinating many crops
have shrunk sharply and pesticide use is the primary suspect behind this aberrations.
10
The research shows that number of honeybee colonies in U.S. farmlands dropped from 4.4 million in
1985 to < 1.9 million in 1997 due to direct and indirect effects of pesticides (Horrigan, 2002 ). Exposures
to pesticides weaken honeybees’ immune system which makes them more vulnerable to natural enemies.
Pesticides also disrupt their reproduction system and development (Horrigan, 2002).
19
Pollution sources are usually classified as Point11 and Non-point.12 The NPS has
contributed to create deed zones in rivers and oceans, endangering the world‘s most
valuable stores of freshwater13 and survival of aquatic life (Woodwell et al, 2001). Fiore
et al (1986) in a study of women who had constantly taken groundwater for drinking
purpose and digested low level pollution reported confirmation of considerably reduced
immune response. In Japan, Sudo (2002) studied pesticide associated water pollution in
Lake Biwa. He measured the amount pesticides in the water at entering level and when it
crossing the boundary of Lake. The result indicated that although amount of pesticides
generally decreased in the water but does not totally eliminated which calls for serious
attention by the authorities. Similarly, Ntow (2005) in his paper highlighted the study on
Volta Lake in Ghana. The study tested pesticide residues in surface water and sediments
and found that pesticide residues are present in the water without any significant
contamination. Result tends to be different by region. Novak (1998) examined pesticide
concentration in the shallow groundwater of an eastern coastal plain watershed in U.S.
The study found that the nearly all (91%) of the wells had no detections for 11
compounds commonly used in the watershed.
11
Pollution originating from a single source, such as a discharge pipe from a factory or sewage plant, is
known as Point Source Pollution. 12
Pollution which does not originate from a single source, or point, is known as Non-point Source
Pollution (NPS). NPS pollution arises from many everyday activities that take place in residential,
commercia, and rural areas. Non Point Source (NPS) pollution is caused by rainfall or snowmelt moving
over and through the ground. As the runoff moves, it picks up and carries away pollutants into lakes,
rivers, wetlands, coastal waters, and even underground sources of drinking water. 13
The U.S. Geological Survey Pesticide National Synthesis Project tests surface water, ground water, and
sediments for 76 pesticide and seven pesticide breakdown products all over the country. The survey
reported that 90% of streams and 50% of wells had positive tests for at least one pesticide.
20
2.3 Economics of pesticide use
2.3.1 Pesticide use and health cost
Pesticide is the most familiar way to control pests. It helps farmers to kill pests
that would otherwise reduce the yield obtained from fields. This role of pesticide, on the
other hand is accompanied by disutility in the form of health impairment. A farmer,
who wants to maximize utility, faces two opposite forces, a positive income effect (in
terms of increased production) which requires higher use of pesticide and a negative
health effect which requires the use of less pesticide. In the beginning, the use of
pesticide may improve welfare of farm household through better crop productivity and
more profits. The farmers may continue using more chemical inputs to enhance farm
production up to certain maximum level but since pesticide is by nature a poison, the
further increase in pesticide use leads to serious health effects to farmer. The negative
health effects of pesticide use have serious implication14 on farm production. It is due to
the reason that labour is the central input in crop production and in less developed
countries farms and farm workers are highly interdependent (Ajayi, 2000; Archibald,
1988). In addition to short term health effects, there is now growing evidence of chronic
effects of pesticide use which indeed impose potential negative effects on farm
production in future. Given that agriculture labor is the central input in crop production
particularly in less developed countries, the use of pesticide therefore lowers potential
output not only in short run but also in the long run (Campbell, 1976).
14
The negative implication may manifest in a lower level of farm production (e.g. through a reduction in
the number of farm labour that are available to work at farm). It may also lead to decrease farm income
for the agricultural household (e.g. through a reduction in the farm output). Another negative effect is that
it may lead to a reduction in the amount of leisure time available for the household (through a reduction
in the leisure time available for sick worker or more stress of work for the healthy members o f farm
household who have to work more and harder to fill in for sick members).
21
2.3.2 Pesticide use and natural biological resource degradation
In addition to direct cost of pesticide use e.g. monetary cost of controlling pest
and taking protective measures, pesticide use also accompanies two types of indirect
cost also. The first is the health cost which is discussed above and the second is the
natural resource cost. Natural resource cost refers to the depletion of the natural
biological resources which maintain a natural regulatory mechanism in the ecosystem.
The biological resources exist in two major forms — renewable and non-renewable
resources (Ajayi, 2000). In the following sections, information regarding both types of
resources is discussed.
2.3.2.1 Biodiversity (renewable biological capital resources)
―Nature is comprised of biological diversity. Soil is one of the most diverse
habitats on earth. It contains one of the most diverse assemblages of living organisms –
bacteria, protozoa, fungi and invertebrate animals‖15 which serve to maintain
productivity of agro ecosystems by keeping the population of pests and predators in a
reasonable balance (Anne-Marie Izac, et al.), and hence provides invaluable services to
keep pests in check in agro ecosystems (Ajayi, 2000). When agricultural inputs like
pesticide and fertilizer are used, they disturb the balance and as a result soil biodiversity
declines. The disturbance in the balance and resultant reductions in biological diversity
seriously damage natural function which ultimately leads to reduce the ability of agro-
ecosystems to resist any turmoil and/or unexpected strain e.g pest infestation (Waibel,
1996).
15
Giller, et al, (1997)
22
2.3.2.2 Pest susceptibility (non-renewable biological capital resources)
In Botany, ―susceptibility is the extent to which a plant, vegetation complex or
ecological community would suffer from a pathogen if exposed.‖16 The natural
susceptibility of pests acts as natural predator and hence, provides invaluable service for
easy control of pests. ―Increasing the use of pesticide leads to a cumulative buildup of
adaptation processes within an ecosystem, and pests increasingly adapt to the chemicals
and become more resistant to them. The increase in pest resistance gradually degrades
the biological capital of pest susceptibility. Pest susceptibility is a fixed quantity in an
ecosystem and it can be exhausted‖ (Ajayi, 2000). When non-renewable biological
capital is depleted due to continued pesticide usage, pest develops resistance and even
greater amounts of pesticides are needed to obtain the same level of crop production
(Ajayi, 2000). As a result, pest resistance increases the cost of pesticide use.
The above discussion indicates that pesticide use decision is a tradeoff between
high yield in current time period and potential production loss through negative health
effects and biodiversity loss in the future time period. Economic theory suggests that
decision-making on pesticide use depends on the net effects of these two opposing
attributes of pesticide. If the pesticide use decisions include only direct costs of
pesticide use and ignore future costs (e.g. pest resistance, biodiversity loss and chronic
health costs), the pesticide use decision may be sub optimal because excluding negative
externalities of production can overstate productivity gains from pesticide use as some
cost of production is not counted. ―Rola and Pingali (1993) demonstrate that explicit
16
Giller, et al, (1997).
23
accounting for (human) health costs substantially raises the cost of using pesticide‖
(Ajayi, 2000). ―It follows therefore that accounting for the costs of health hazard, pest
resistance and the destruction of the natural control potential of an ecosystem changes
the relative economic advantage of self-regulating measures‖17 of pest management such
as Integrated pest management (IPM) versus external inputs like pesticide (Waibel,
1996). It must be bear in mind that unlike other input costs, farmers generally unable to
identify and manage pesticide related health impairment, biodiversity loss and pesticide
resistance costs. As a result, health and environmental costs cannot be easily accounted
by individual farmers and ultimately leads to sub-optimal pesticide use decisions.
2.4 Psychology and Economics
2.4.1 The Link between Psychology and Economics
There is an intrinsic relationship between psychology and economics, since
much of the economics has focused on the interaction that takes place in the market.
Indeed the market is often a defining characteristic of economics. At the same time
economists have extended their domain to include among others, family relationship,
leader follower relationship or even criminality. Clearly economic behavior resides in
social environment which makes a connection between social psychology and
economics both appropriate and practical (Kirkpatrick, 2007).
When tracing history of economics, during classical period, one comes to know
that economics and psychology had close link. The best example is ―The Theory of
17
Ajayi (2000)
24
Moral Sentiment‖ written by Adam Smith, a text clearly describing psychological
underpinnings of individual behavior (Kirkpatrick, 2007). During the neo-classical
period, psychological principles had been used in the analysis by many important figures
such as Edgeworth, Pareto, Irving Fisher and Keynes. At the end of 1940s economists
started developing more formal and practical models. They shifted their focus from
economic decision that assumed perfect information and maximizing behavior to
decision making which may not be rational necessarily. Herbert Simon‘s theory of
―Bounded Rationality‖ clearly states that due to mental constraints and moral
consideration not all alternatives are examined (Luce, 2000). Such decisions and
behaviors clearly run against rational consideration.
During 1960s researcher started using cognitive psychology in economic
decision making. Brain was the main focus of this research that act as an information
processing unit. Over time number of psychological effects has been translated into
behavioral economics concerning human judgment and decision-making (Luce, 2000).
'Prospect theory: An Analysis of Decision Under Risk' written by Kahneman18 and
Tversky in 1979, 'Theory of Crime' written by Becker in 1967 used cognitive
psychological techniques to explain individual behavior that diverge from neo-classical
theory in many economic decision making (Kahneman, 2003). Another seminal work
that explained psychological concepts into economic theory is from Herbert Simon
through theory of Bounded Rationality in which he explained that occasionally people
18
Daniel Kahneman was awarded with the Nobel prize in 2002 "for having integrated insights from
psychological research into economic science, especially concerning human judg ment and decision-
making under uncertainty.
25
are satisfied with irrational behavior instead of maximization principle as economic
theory postulates (Hogarth, 1987).
In short, psychology has a strong impact on economics in many ways.
Psychological concepts helped to reach a better understanding of economic behavior. It
has made experimental research a widely used research method and extended the view
of human nature by showing pro-social aspects in people‘s preferences (Vigna, 2007). In
this way psychology adds meat to the bones of economics.
2.4.2 Use of Psychology in Economics
Psychological factors are very important for many economic decisions. For
example, the classical microeconomic models based on consumer‘s observed choices
have generally been employed by the researchers to study individual preferences. These
models are based on the assumption of perfect market and hence assume specifically
that individuals have perfect knowledge about the market and they make rational
decisions based on utility maximization. However, one obvious shortcoming of classical
models of consumer behavior is that they fail to explain reasons behind consumer
behavior and also do not consider sociological and psychological factors that guide
consumer behavior. In this regard, social psychology is more successful in interpreting
these phenomena than is a plain economic model.
Health communication research has recommended the application of cognitive
psychological or behavioral models to understand relationship between information and
individual responses (Severtson, 2006). Several psychological models are potentially
relevant to farmer‘s pesticide use behaviors and they can contribute in the formulation of
26
policy interventions necessary to promote safety behaviors and observance to pesticide
associated health impairments (Munro, 2007). The availability of these theories, at the
same time, making it difficult for a researcher to select the most relevant one for the
current research. Therefore, these models need to be thoroughly studied to assess their
appropriateness to the present research. The next section provides a short description of
health psychology theories, their strengths and weaknesses, specifically within the realm
of understanding farmer‘s behavior of pesticide use.
2.4.2.1 Theory of Reasoned Action(TRA) and Theory of Planned
Behavior (TPB)
Originally, the Theory of Reasoned Action was developed in the context of
social psychological studies of behavior and attitudes. Later it has been widely used in
applied research in fields like family planning behavior, nuclear risk, health behavior,
voting behavior and of consumer behavior (Ajzen, 1985). In 1985, Theory of Planned
Behavior (TPB) expanded the theory of reasoned action by including an additional
element of behavior, the so called perceived behavioral control. This element has been
added in the theory to consider certain situations and environment where individual‘s
behavior is largely determined by the factors beyond his/her control. The originators of
the theory argue that individual will only perform those behaviors where he is
confidence that he has a control over it (Marcoux and Shope, 1997).
Individual intention is the main determinant of the behavior in both ‗Theory of Planned
Behavior‘ and Theory of Reasoned Action. The TRA and TPB both explain that the
individual intention is the best way to understand behavior and therefore, one should
measure behavioral intention in order to understand behavior (Marcoux and Shope,
27
1997). The behavioral intention however, depends on attitude of the individual and
subjective norm. The attitude is an individual‘s positive or negative evaluation of the
behavior while subjective norm is the social pressure on an individual to perform certain
behavior (Armitage, 1999). According to these models, if attitude and the subjective
norm are both favorable, there is more chances that individual perform certain behavior
(Armitage, 1999; Munro et al, 2007; Bandura, 2004).
Further, it is argued that certain other variables that affect attitudes or subjective
norms can also influence the individual behaviors. However, research has shown limited
support for this theory (Munro et al, 2007). Sutton (1997) has suggested that additional
explanatory variables (such as social and economic variables) should be incorporated to
improve both of the theories. He also stressed for more conceptualization (Munro et al,
2007).
2.4.2.2Social-Cognitive Theory
This theory explains that human behavior is a dynamic and ongoing process. The
personal, environmental and human factors influence each other in this process to
regulate human motivation and action (Bandura, 1997; Redding, 2000). According to
Social-cognitive theory (SCT), three main determinants determine the probability that an
individual will change health behavior: I) Self-efficacy; II) Goals; III) Outcome
expectancies. It describes that if individual is sure about personal self-efficacy, he can
change behavior even if he faces constraints to act and if an individual is not sure about
personal self-efficacy, he will not be ready or convince to act. This theory also suggests
that health behavior may also be influenced by the goals and expected outcomes (Munro
28
et al, 2007). In sum, this theory proposes that behavior will be performed for a certain
action if an individual is sure and confident to execute the behavior.
The weaknesses of this theory are that non-voluntary factors can also affect individual
behavior which this theory largely ignored. Further, this theory is also limited in scope
and do not explain external influences on behavior. Another limitation is that it lacks an
individualized approach.
2.4.2.3 The Common Sense Model
Another model of social psychology which provides theoretical support for the
study of health and protective behavior is Common Sense Model. The Common Sense
Model (CSM) assumes that individuals make mental image of their sickness based on
two type of information accessible to them. First; concrete or factual information and
second; abstract or nonfigurative information. This information helps shaping strategy to
cope with an illness (Leventhal et al, 1983). There are three main sources of
information which direct an illness representation. First; the general information already
learned (memory). Second; information from individual‘s social environment like
parents, family members, friends or other people or information from any authoritative
persons like doctors. Finally, through personal experiences with the health effects.
Individual‘s personal characteristics e.g. education, age, access to media and cultural
background are also important factors (Severtson, 2006). The main sources of concrete
information are personal experiences. The influence of concrete information in shaping
representations and behavior is much more than abstract information. The Common
Sense Model has following dimensions: Identity, Cause, Consequences, Timeline and
Control.
29
1 Identity represents how people recognize and label a threat.
2 The cause represents those factors that are believed to be responsible for causing
the illness.
3 The consequences dimension refers to beliefs regarding the impact of the illness
on overall quality of life.
4 Timeline represents an individual‘s perception about the course of disease.
5 The cure/control refers to individual‘s confidence regarding effectiveness of
coping behaviors (e.g. taking protective measures help to avoid direct exposure
of pesticide use).
The major flaw of CSM model is that it totally focuses on individual
characteristics and ignores socio-economic environment in development of
representation (Munro et al, 2007).
2.4.2.4 Health Belief Model
This model is developed in 1952 by Godfrey Hochbaum,19 when he started
research to identify the factors that lead individuals to decide to have their examination
for prior detection of TB (Hochbaum, 1956). Since then, this model has been widely
used as a research tool in an array of health and environmental settings (Lichtenberg et
al, 1999). Over time the domain of this model has been extended to explain general as
well as specific health motivation for health behavior (Green, 2010; Strecher, 1997).
19
The health belief model is developed by researchers at the United States Public Health Service in the
1950s and Godfrey Hochbaum in itiated the first research on the HBM in 1952.
30
This theory postulates that a person who had experienced health effect is more likely to
take safe behavior, if he/she; 1). Believes that the illness can be avoided; 2). Believes
that by adopting suggested safety measures, illness can be avoided and; 3). Sure that
he/she can effectively take suggested safety measures.
Basically ―Health Belief Model‖ encourages a person to adopt positive health
actions using the desire and will to avoid illness as the key inspiration. For example, in
the current settings, pesticide exposure has negative health effect and the desire to avoid
direct exposure from pesticide can be used to motivate farmers into practicing protective
and safe use of pesticide. Broadly ―Health Belief Model‖ is based on six key concepts.
1) Perceived threat: It is further classified into two parts; perceived susceptibility
and perceived severity.
Perceived susceptibility: One's own belief of the chances of receiving a health
condition that may seriously affect one's health.
Perceived severity: One's personal belief of severity of health condition, for
example, pain and discomfort, reduced productivity and less time available for
work, extra economic burden, problems with day to day jobs and difficulties with
family relationships.
2) Perceived benefits: The believed effectiveness of strategy proposed to decrease
the risk of sickness.
31
3) Perceived barriers: The possible negative consequences that may result from
adopting certain health actions, such as physical and psychological stress and
economic loss.20
4) Cues to action: The variables that force or stimulate an individual to take
necessary steps to avoid illness or health threat. These stimuli may be internal or
external.
5) Modifying variables: Demographic, social, psychological and economic factors
that influence an individual's perceptions and thus indirectly affect behavior.
6) Likelihood of action: These include all those factors that indicate the probability
of taking suggested health action to prevent disease (Green, 2010). These factors
jointly affect an individual to undertake the recommended preventive health
action.
In short, the Health Belief Model postulates that individuals‘ behavior change is
a function of individuals‘ mental appraisal of the barriers and benefits of taking certain
action (Munro et al, 2007). If perceived health effects are serious and net benefits 21 of
taking action are positive, there is a more probability that individual will take action.
20
Due to these barriers, action may not take place, even though an individual may believe that the
benefits to taking action are effective. This may be due to barriers. Barriers relate to the characteristics of
a treatment or preventive measure may be inconvenient, expensive, unpleasant , painful or upsetting. These
characteristics may lead a person away from taking the desired action. 21
According to this model, the perceived seriousness of, and susceptibility to a disease influence
individual's perceived threat of disease. Similarly, perceived benefits and perceived barriers influence
perceptions of the effectiveness of health behavior. In turn, demographic and socio -psychological
variables influence both perceived susceptibility and perceived seriousness, and the perceived benefits
and perceived barriers to action. It is concluded that High-perceived threat, low barriers and high
perceived benefits to action increase the likelihood of engaging in the recommended behavior (Becker et
32
Figure 2.1. Health Belief Model
Individual perception Modifying Factors Likelihood of Action
Source: Strecher and Rosenstock (1997).
This study selected the health belief model to gain better understanding of
relationships between health experience, risk perception and pesticide use behavior. The
health belief model has been chosen for the present study because of several reasons; (1)
the health belief model considers individual as active info rmation processor and
independent decision maker. Since pesticide use is largely governed by voluntary
behavior, hence health belief model best suits in present circumstances; (2) another
advantage of HBM is its simplicity that makes it attractive to understand health
al, 1979).
Demographic & socioeconomic
Variables:
(Age, Sex, Personality, Knowledge
about the disease, socioeconomic
variable, etc)
Perceived Benefits
of preventive action
minus Perceived
barriers to
preventive action
Perceived threat of Disease
Perceived
susceptibility to
disease
Perceived
seriousness
(severity) of disease
Likelihood of taking
recommended
Preventive health
action Cues to action
Mass media Campaigns
Advice from other
Illness of family member or friend
Newspaper or Magazine article
33
behavior. Health Belief Model does not follow strict guidelines22 like other models of
health psychology to predict health behavior. Instead it describes the framework in
which each individual variable contributes in the prediction of health behavior (Nejad et
al, 2005). Although this lake of proper guidelines is considered a shortcoming of this
model and often a reason of heavy criticism, but at the same time, the flexibility of the
construct makes this model very attractive23 among researchers and it is the most
frequently used model in health psychology; (3) though, HBM is a health-specific
model, it allows socio-economic variables to be included in the model which affect
health motivation. Because of the features, discussed above, the HBM has received
much wider support from practitioners, academia and researchers (Munro et al, 2007).
There are few studies in pesticide use behavior literature that sought help from
social psychology to explain behavior. A seminal work in this regard is done by
Lichtenberg and Zimmerman (1999). Referencing social psychology they examined
specific hypothesis that ―whether or not adverse health experiences play a part in
shaping attitudes.‖24 The research has shown a strong support for health belief model.
The result indicated that there is significant relation between health effects that farmers
have experienced from the use of chemical pesticides and their risk perception toward
the seriousness of health effects. Study also found a strong relation between health
experiences from pesticides and the use of environmentally sound pest management
practices. Similarly Napier and Brown (1993) highlighted very well-built results for
22
The model comprises a series of broadly defined constructs that might explain the variance in health
behavior but there are no clear operational guidelines regarding relationships between them 23
Most health belief model based research to date has incorporated only selected component of HBM
(Munro et al, 2007). 24
Lichtenberg and Zimmerman (1999).
34
farmers who use pesticides and fertilizer in Ohio State USA and related their health risk
perception with environmental attitudes. The authors found that ―respondents who
supposed their families to be threatened by fertilizers and pesticide in groundwater likely
to perceive groundwater contamination to be chief environmental problem and were
more willing to compel land operators to alter production practices to keep groundwater
resources safe.‖25 Tucker and Napier (1998) in a study found that perceived negative
health effects from pesticide associated contaminated groundwater was the strongest
predictor of farmer‘s attitude.
2.5 Economic cost of pesticide use
Keeping in view, the chronic and acute poisonings, and other environmental
problems, economic cost of the application of pesticide seems to be very high. David
Pimentel (2005) estimated the economic cost of pesticide associated health and
environmental damage in United States. He estimated that the cost was as high as $10
billion. The distribution of major losses due to pesticide use was as follows; the cost to
public health was $1.1 billion, the development of pest resistance against pesticide was
$1.5 billion, the losses to crops and vegetation was $1.4 billion, the birds, honeybees and
animal losses due to pesticide use was $2.2 billion and groundwater contamination was
$2.0 billion per year. Following Pimentel, Azeem et al. (2002) measured health and
environmental cost of chemical pesticide use in Pakistan. The estimated cost of pesticide
use is 11941 million rupees per year. Most of the cost is caused through production
losses, amounted 5667 million due to resistance development in the pests. The damage
to animal amounted 1304.5, while health cost of pesticide use is estimated as more than
25
Napier and Brown (1993)
35
1032 million, including treatment cost, workday loss, and fatalities. Pesticide residue in
food chain are estimated more than109 million. The economic evaluation of pesticide
use shows that negative externalities of pesticide are very high in Pakistan which needs
urgent attention of policy makers.
A notable work in the context of South Asia is done by Wilson (2000) which
provides detail on respective issues. He justified that regardless of producing record
yields, the current agricultural practices in South Asia are unsustainable. Farmers are
totally dependent on heavy doses of chemical inputs, like fertilizer and pesticide. Due to
this dependence a high cost has arisen in terms of human health and natural
environment. Human health cost includes treatment/medical cost and time costs (work
days lost of ill workers and care giver‘s time lost). Human health prob lems also reduce
efficiency/ ability to work on lands which has it cost. He further described that overall
productivity of farm is affected because of indiscriminate use of pesticide and fertilizers.
The larger quantity of chemical pesticide depletes natural capital by destroying natural
predators of pests, disrupting ecological balance and reduces soil productiveness. As a
result of declining land productivity due to rise of pests and other diseases, larger
quantities of chemical fertilizer and pesticide have to be used in the production process,
which increase the costs of input use. Another type of cost of chemicals is agricultural
toxic run off which pollute water and affects other production processes, such as
production of fisheries which provides farmers an additional source of income.
Moreover, the safety measures taken to keep away from exposure to pesticide, though
insufficient, also incur cost.
36
2.6 The Contingent Valuation Method
Valuation is difficult for the outcomes such as reducing the risk o f human illness
because they are nonmarket goods (the goods that are not sold and purchased in any
market). Due to absence of market, Special techniques are required to study consumer
choices and preferences for environmental goods. One such technique is Contingent
Valuation Method (CVM). In this method individuals are directly questioned about their
willingness-to-pay for a given good or service. This is a survey based technique where
―respondents are offered a hypothetical market and they are asked to express their WTP
for existing or potential environmental goods or services not reflected in any real
market‖. ―The monetary values obtained in this way are thought to be contingent upon
the nature of the constructed market, and the commodity described in the survey
scenario‖ [Garming et al (2006)]. The respondent‘s answers help researcher to drive
demand curve for an environmental good and service directly in the absence of market
data (Hanemann; 1994).
Although there has been long-standing awareness in Contingent Valuation
technique in environmental and resource economics, the approach got momentum in
recent years when researchers in environmental and resource economics have made
increasing use of this approach to estimate the value of many type of environmental
goods and services (Carson, 2000a). The Contingent Valuation technique is of great use
because of its flexibility to measure value. It allows the estimation of an array of non-
market goods.26 Although Contingent Valuation (CV) is the most commonly used non-
market valuation method, the debate over the reliability of CV however, continues to
26
This is the only technique to measure passive use value
37
exist. However, researchers and experts in CV method have suggested that many of the
so-called problems with this technique can be resolved by expert plane and proper
implementation of the survey (Carson 2000a).27 In addition, CVM is direct
(hypothetical) measure and focuses on ex-ante behavior before some changes take place
whereas the indirect methods (e.g. travel cost and hedonic pricing) concern with ex-post
behaviors. Thus, from policy perspective the estimates of changes in welfare are
theoretically better approached using CV method than using indirect methods (Doherty,
1993).
2.6.1 Economic evaluation of health cost using WTP
As noted above, like many other environmental goods, economic evaluation of
health cost of pesticide use is embarrassed by the practical obstacles because of different
value components of human health; market component such as the cost of illness,
productivity loss, work days loss (are those on which a person is unable to engage in
ordinary gainful employment)28 and non market component like cost of discomfort.
Since it is difficult to integrate market and non-market elements of health cost of
pesticide use in a health cost model, the most of the researchers measuring health cost of
pesticide use have focused on the market components of health cost.29 Different
researchers used different approaches, for example, Ajayi (2000) and Huang et al.
27
For detail see: Portney R.P (1994); “The contingent valuation debate”: why should economists care.
Journal of economic perspectives-volume 8, number 4-fall 1994-pages 3-17
Carson & Flores (2000a); Contingent valuation: controversies and evidence. EScholarship Repository,
University of California. See at http://repositories.cdlib.org/ucsdecon/96-36R
Hanemann W.M (1994); valuing the environment through contingent valuation. Journal of economic
perspectives- volume 8, number 4-fall 1994-pages 19-43. 28
U.S department of health, education and welfare
29 Market components include, estimating the costs of illness, work days loss and productivity loss.
38
(2000) measured health cost by calculating treatment cost of pesticide associated health
effects and the amount of work days lost because of illness which obviously a
conservative measure of health cost. Others like Rola et al. (1993) included negative
effects on farm production (due to illness of family labour) and estimated cost of chronic
illnesses (Garming et al, 2006). Since economic perspective on health focuses on effects
that people are aware of and want to avoid, that is, the health problems that would
decrease their utility. Therefore, most of clinical research that focuses on health effects
is questionable significance to individuals, and is difficult to relate to individual‘s
perception and behavior [Freeman, 2003, valuing longevity and health (p. 317)].
Keeping in mind that individual‘s preferences give better/suitable basis for
making decisions about changes in their welfare, reduction in health effects should be
measured according to individual‘s preferences or willingness to pay. Hence, the
Contingent Valuation30 is proposed to accomplish this task. Contingent Valuation
measure health cost which is based on individuals‘ preferences. Many economists such
as Carson (2000b) called CV a useful tool and valid measure for benefit-cost analysis.
Through benefit-cost analysis, welfare economics, attempts to explain possible change
in utility due to minor change in economic variable. Typically, changes in utility are
demonstrated in monetary terms that are either to be taken from or given to individuals
to keep their overall utility constant. Theoretically, the same law is applied to non-
market goods and services, ―that is, the maximum amount an individual would pay to
avoid losing, or gaining, access to the good‖ (Lipton, 1995).
30 CV is better measure of health cost of pesticide use since; it also includes non-market value.
39
In empirical literature few Contingent Valuation studies can be found on health
effects of pesticide use. Wilson (1999) measured willingness to pay of small scale
farmers to avoid pesticide associated illness in Sri Lanka. The research indicated that
major determinant of farmer‘s willingness to pay for avoidance of pesticide exposure are
their income and size of the household. However, surprisingly, it was found that
education of the farmer and their age has insignificant relation with their willingness to
pay bid. The results further indicated that the farmers who experienced negative health
effects resulting from pesticide use are more likely to pay more to avoid pesticide
exposure. Similarly, the time spent by a farmer in undertaking pesticide mixing and
spraying operations is also important variable to determine WTP bids. The paper shows
that health effects from exposure to pesticides, income of the household and size of the
household are important determinant in the adoption of safe and environmentally
sustainable agricultural practices.
Similarly, Garming and Waible (2006) presented results of Contingent Valuation
study in Nicaragua. The author attempts to assess the cost of health effects of pesticide
use among vegetable farmers. The study indicated that on average farmers are willing to
pay 28% more to avoid pesticide associated health risks. Willingness to pay largely
depends on farmers‘ pesticide associated health risks and income level which supports
Wilson (1999) results. In another study, Cuyno (1999) estimated farmers‘ willingness
to pay in Philippines for the reduction of negative health effects resulted from pesticide
use. The investigation shows that Philippines farmers were willing to pay 22% of
pesticide costs for better health. In Canada, Cranfield and Magnusson (2003) conducted
a survey to measure consumers‘ willingness to pay for pesticide residue free foods.
40
Questionnaire was divided into two parts. The questions regarding pesticide related
health concerns and their relation with low chemical input foods and in the second part
questions related to sustainability of agriculture. The respondents were asked to express
their willingness to pay in real monetary amounts rather than in percentage which helped
respondents to avoid mental calculation. The order probit estimates show that more than
65% respondents were willing to pay up to ten percent premium for low chemical input
foods relative to conventional food while five percent respondents were willing to pay
up to ten percent premium for low chemical input foods relative to conventional foods.
The age of the respondents, income and being female are significant determinants of
willingness to pay. Further, respondent‘s concerns regarding health and environment are
important determinants of consumer choices for low external input foods. Differently
Huang (1993) used information processing theory from social psychology and analyzed
the relationship between consumer risk perceptions and their attitudes with their
willingness-to-pay for pesticide residue-free produce. Research shows that personal
experience with pesticide appears to influence consumer perceptions and attitudes
toward pesticide residues on fresh produce which in turn influence their willingness-to-
pay for residue-free produce. The demographic variables, like married female with
children and those who are employed have strong concern about pesticide residues on
fresh foods and more likely willingness-to-pay for fresh foods.
2.7 Pesticide use behavior
The principal work-related health and environmental problems to pesticide occur
in the mixing and using of dangerous synthetic materials. Farmers must knowledgeable
and skilled about pesticide handling because this action is an integral part of their
41
agricultural operation and has significant economic consequences (Blair, 1997). The
research has shown that to control pests in developing countries, farmers use pesticide
extensively. A study in Kenya reported more than 97 percent farmers using pesticides
during the whole or most part of cropping season (Ibitayo, 2006). Considerable research
has been dedicated to evaluating pesticide use practices and behavior in selected
circumstances. Akhtar (1985) noted misuse of pesticide in Sind, Pakistan. He noted that
the selection of timing of spray is very important. The effect of sprays on cotton yield
was maximum when spraying during pest attack .The farmers who spray before and
after pest attack achieve even smaller yield than average. The study indicates that
efficient use of pesticide can reduce input, health and environmental cost. Similarly in a
study Azeem et al (2002) pointed out the over use of pesticide on cotton in Punjab. He
noted that least efficient farmers31 attained the same yield by spending 70% more than
the best of their counterparts. They also stated that most of the cotton picker women
were not aware of using caution during picking. The overuse of pesticide has also been
reported in other countries of South Asia. Dasgupta, et al (2005a) reported that more
than 47 percent of farmers were overusing pesticides in Bangladesh. In the same study,
they also reported that more than 87% respondents explicitly admitting to taking little or
no safety measures while using pesticides. A well build study, examining farmer‘s
knowledge and awareness that leads to serious behavior of pesticide use by Kishi (2002)
identified that knowledge of the farmers regarding the health hazards of pesticide use is
not enough to change their behaviors. The study found that principal concern of farmers
is to avoid economic losses, not their health. Therefore, he emphasized that IPM field-
31
Due to lack of information and poor decision, in selection and timing of spray.
42
school guidance can provide farmers a workable alternate to eliminate unnecessary
pesticide use. On the same lines in Egypt, Ibitayo (2006) investigated the farmer‘s
behavior of pesticide use. The result indicated that almost all the farmers do not wear
protective measures, when mixing or applying pesticide. Thirty three percent of them
reported that they never wear long pants when applying pesticide and their knowledge
was so narrow that about 62% were not sure that whether pesticides leave residues on
plants or not. Likewise, 62.2% were not sure that pesticide may pollute groundwater.
Another study supporting the same conclusion is conducted by Clarke (1997) in Ghana.
It describes knowledge, attitudes and practices of pesticide applicators regarding safe
handling of pesticide. The result identified that the respondents‘ use of safety measures
vary from poor to moderate; 27% answered that they never put on any protective
clothing and other measures when mixing or applying pesticides. Surprisingly farmers
had substantial information and awareness about the usefulness of these safety measures.
The study also identified that very risky practices were common in Ghana; re-entry
period was very short and pesticides were used frequently on regular basis. Farmer‘s
homes were being used as stores for pesticide. Similarly, Kimani and Mwanthi (1995)
noted unsafe behavior of pesticide use in Kenya. They found that most of the farmers
were found using old and leaky household equipment for measuring, mixing and
supplying pesticides. The reason of this unhealthy practice was the unaffordability of
adequate equipment. Similarly Salameh and Baldi (2004) undertake a survey to study
knowledge and practices about pesticide safety among agricultural workers in Lebanon.
They reported that more than half of the respondents replied very poor safety items
while mixing, loading or spraying pesticide.
43
A study by Yassin (2002) in Gaza Strip also strengthening and supporting above
cited literature on pesticide use behavior. The author observed that only 20% of the
farmers in study area said that they take safety measures to protect them from direct
exposure of pesticides. The study found very risky practices in the area, despite high
levels of knowledge by farmers on health impact of pesticide (97.9%). The prevalence
of negative health symptoms was related to use of highly toxic pesticides. The most
common symptom was burning sensation. The highest percentage of toxicity symptoms
was found among the farm workers who re-entered into the fields within one hour of
pesticide application. Evidence continues supporting unsafe behavior and poor
knowledge regarding safety. In United States, Blair (1997) found that many farmers
were able to provide information on amount of pesticide purchased, application rate
employed, and acres treated. A larger proportion, however, provided ―don‘t know‖
responses to the questions about amount of pesticide purchased (27%) and use of active
ingredient on crops (20%). In another study, McCauley (2004) prepared a 20-items true
- false safety knowledge test with the aim to test pesticide knowledge among farm
workers in Oregon, United States. Two types of respondents were selected for the study,
the adult farmers and adolescent migrant farm workers. Overall, 414 farm workers were
interviewed. The mean test score was 78.4%. Both adult farmers as well as adolescent
migrant farm workers faced difficulty with the questions related to health effects of
pesticide use. In Sri Lanka, Sivayoganathan (1995) described similar results where
farmers were found having good knowledge about the benefits of wearing protective and
safety measures when mixing or applying pesticides. But surprisingly there was no
noteworthy effect of knowledge on safety behavior. The most important reason for this
44
behavior was uneasiness. This study also examined association between uses of
protective measures and reporting of health symptoms. The result shows that the farmers
who used protective measures while spraying reported significant few health symptoms
compared to the farmers who do not use protective measures.
In Malaysia, Nordi (2002) reported that use of protective measures significantly
reduced negative health symptoms. The farmers who used good condition sprayers, who
are non smoker and who change clothes after spraying reported significantly less health
symptoms. Chitra (2006) in India found that majority of farmers (75 percent) either use
moderately hazardous or highly hazardous pesticides. The study also indicated that 88
percent farmers used no protection while handling pesticides and more than half mixed
different brands of pesticides. Pesticide retailers were the major source of information
about pesticide for 56% of farmers. The farmers reported excessive sweating (36.5%),
dry/sore throat (25.5%), eyes irritation (35.7%) and excessive salivation (14.1%). Result
suggests that excessive sweating, eye and throat problems were significantly related to
pesticide exposure. Similarly in a more comprehensive study Kishi (1995) in Indonesia
assessed the correlation between direct exposure to pesticides and signs and symptoms
of toxicity. Results showed that heavy doses of pesticides considerably affect farmer‘s
health. The negative health problems were found considerably high during spraying
seasons than during non-spaying seasons. The spray frequency per week, the use of
highly and extremely hazardous pesticides and the contact of skin with pesticide liquid
were positively related with negative health problems. The study advised that farmers
should be motivated to reduce the frequency of spray through widespread training in
integrated pest management.
45
Dasgupta (2005a) found in a study that only 4% of Bangladeshi farmers got IPM
training. This lack of IPM training and knowledge regarding safe handling of pesticides
lead to misperception among farmers regarding pesticide hazards. Study found that more
than 34% of farmers in Bangladesh under classify pesticide hazard. In a study Recena
(2006) reported farmer‘s knowledge regarding pesticide safety and pesticide use
practices in Brazil. About all, respondent reported that they generally use pesticides.
More than half of them reported pesticide toxicity symptoms. The study found that
adoption of safety measures has strong negative correlation with health symptoms.
Study reported that knowledge of the farmers regarding pesticide health effects was
fairly high e.g. more than 90% answered that pesticides are dangerous to human health,
but less than 20% used protective clothes during pesticide application. Likewise,
Damalas (2006) studied knowledge of the farmers regarding safety issues of pesticide
use and actual practices among tobacco farmers in Greece. He reached to the conclusion
that although farmers' knowledge of pesticide health and environmental hazards was
very high, but the use of protective measures was poor. He proposed that increased
stress on the proper use of protective measures is essential for changing farmer‘s
behavior. Jors (2006) studied the extent and causes for occupational pesticide
intoxication in Bolivia. The study recognized that most toxic pesticides are being used
by the farmers who have had almost no information; how to use pesticide and how to
protect themselves against the poisoning of intoxication. Symptoms of intoxications
were commonly associated with spraying operations. The occurrence of symptoms was
influenced by the hygienic and personal protective measures taken during pesticide
spraying operations. Delgado (2004) reported health consequences of pesticide exposure
46
in Brazil. Agricultural workers were interviewed by using questionnaire containing
information on the use of pesticide, health status, use of protective measures, pesticide
exposure related symptoms, disposal of agrochemical containers, and technical
assistance. As a rule, pesticide are handled carelessly and almost all 92 percent workers
involved in the mixing, loading, and spraying of insecticides and fungicides used, no
protective clothing or equipment what so ever. Some 62 percent of them reported at least
one illness related with mixing or spraying pesticide. The most often reported symptoms
were headache, dizziness, nausea, vomiting, skin irritation, and blurred vision. A large
number 21% of affected workers required medical care.
Kunstadter (2001) studied the pesticide use in Chiang Mai, Thailand. The author
found that most of workers know health hazards of pesticide use, but they usually fail to
take protective measures necessary to avert pesticide‘s direct exposure. Medical
screening results showed that about 20-69% of 582 farmers had unsafe levels of
cholinesterase inhibition, which indicates that they had exposure to extremely hazardous
pesticides e.g. organophosphate and carbamate. In addition study also found that those
individuals who do not actually apply pesticide showed as high exposure as among those
who applied pesticides. This suggests that exposure by routes is also an important source
of exposure in addition to direct contact. Similarly, in Ghana Ntow (2006) studied the
farmers' risk perceptions of pesticide use in a vegetable production area. Result shows
that farmers are involved in a variety of inappropriate practices of pesticide use which
caused high level of poisoning symptoms to farmers. The farmers who do not take
protective measures reported significantly high rate of poisonings that those farmers
who normally use protective clothing. The age of the farmers has significant negative
47
relation with poisoning symptoms and farmers with less than 45 years of age were
identified as the most vulnerable to poisoning symptoms.
Garcia (2002) in his study identified different socio-demographic characteristics of
farmers that determine pesticide exposure in agricultural workers in Valencia, Spain.
Most of the respondent farmers got primary education or less. Farmer sprays pesticide
throughout the year. Due to lack of education and awareness farmers were found using
protective measures inappropriately. Further, more than sixty-five percent workers used
no safety measures. Although, over 90% farmers reported good knowledge of pesticide
associated health risks but this knowledge does not translated into actual field practices.
The study found that age of the farmers, overall income or education exert no significant
effect on the use of personal protection. Schenker (2002) investigated the relationship
between occupational exposure and use of protective measures among California State
farmers. The analysis of data shows that more than 93% of respondent farmers reported
using personal safety measures. Further, pesticide associated health risk perception was
associated with safety behavior.
Aragon et al, (2001) adding literature on pesticide use behavior highlighted some
other important reasons for unsafe pesticide use practices in Nicaragua. The study
identified the factors like poverty and insufficiency or unavailability of protective
devices as the main responsible factor for dangerous work practices. Cultural factors
were also been identified as affecting the farmers' behavior in such a way that leads to
dangerous practices. The result suggests that safety education and training programs on
occupational health should be designed keeping in view the socio-economic and cultural
factors. Similarly while describing threats to agriculture sustainability in South Asia
48
Wilson and Tisdell (2001) explained that the market systems support the use of pesticide
in agriculture. Farmers usually continue to use pesticide in rising quantities even though
the high external costs. The use of pesticide may be beneficial in short run. However, in
long run, the use of pesticides cause not only health effects but also undermines
environment through several negative externalities. Actually the damage to agricultural
land, human and environmental hazard from pesticide use occurs after some period of
time. Hence, in initial phases costs of pesticide use may not be very severe but in the
long run because of several reasons, pesticide use become part and parcel of crop
production and farmers become totally dependent on ‗unsustainable‘ agricultural
systems.
In a more comprehensive study Huang (2003) analysed the productivity effects
of pesticide use on rice production in China. The study indicated that use of pesticides is
beneficial if productivity effects are considered. The result indicated that due to pest
attacks the yield loss could have reached as high as 40% if no pesticide is used.
However, on the other hand, the study showed that health effects of pesticide use are
very high. The health examination shows that both acute and chronic health effects are
closely linked with pesticide exposure. Although the health cost valuation of this study
was limited because it included only treatment cost of few visible acute effects but still it
was more than 15% of total pesticide cost. The study indicates that if the cost of chronic
health effects is also be included, total health cost may exceed the total private cost of
pesticide use. While highlighting the reasons of pesticide use, this study identified that
farmer‘s perception of yield loss due to pest attacks is the main reason. Among others
quality of pesticides and agricultural extension services are major determinant of
49
pesticide use. Therefore, the better solution to avoid negative externalities of pesticide
use is to improve safety practices, correct use of pesticides and ground conditions or
environment so that farmers could avoid external cost of pesticide use. Similarly Dung
(2003) analyzed the impact of pesticide use on farm productivity and health in Vietnam.
He found that farmers have little knowledge regarding pests and pesticides, they spray
very frequently and resultantly health hazard and environmental degradation is severe.
The study indicated that many formulations that are banned in the country are being
used heavily. Farmers were also not taking safety measures during pesticide application.
As a result, a higher frequency of pesticide associated health symptoms was noted. In a
more comprehensive study, Rola and Pingali (1993) reported the investigation of the
impact of pesticide use on crop production, farmer‘s income and long-term health. The
authors provided a framework for evaluating pest management‘s techniques. They
openly added pesticide associated health effects into the production analysis. When the
associated health costs of pesticide use are counted as a production cost, pesticide use
cuts rice productivity instead of improving it. The authors reported a strong support for
sustainable investment in research that can help reduce pesticide use by farmers.
Differently in Tanzania, Ngowi (2001) investigated the knowledge, attitudes and
practices of agricultural extension workers with respect to health effects of pesticide use.
Results showed that the most of the extensionists knew that pesticide could enter the
human body but only a quarter perceived pesticides as an important problem in the
community they served. The majority showed knowledge and awareness of potential
health hazards of the different pesticides used in their service areas, but they did not
identify what pesticides were responsible for poisoning.
50
2.8 Integrated pest management
Integrated Pest Management (IPM) is a common-sense method which is based on
cultural practices that farmers have used for hundreds of years. The examples are crop
rotation, sowing and harvest time alteration, pest traps, removing crop residues, using
varieties resistant to pests and using botanical pesticide (e.g neem). In any given location
Integrated Pest Management (IPM) methods are specific to production characteristics.
As a result, a general ideology usually applied and no specific standards are set (NARC,
2008). The FAO defines IPM as a pest management system that utilizes all suitable
techniques in the context of specific environment. It combines cultural control32 and less
toxic pesticides to check pest populations to economically manageable levels.
Integrated pest management techniques are considered not only substitute to pesticide
but also safer and environmentally sound. But somehow perceived less productive as
compared to pesticide. Dasgupta et al (2004) compared the profitability of IPM and
conventional farming in Bangladesh. The result shows that there is no significant
difference in the productivity of IPM and the productivity of conventional farming.
Since it is established that IPM decreases pesticide related health and environmental
damage, it comes out to be more profitable than conventional pesticide use. Similarly,
Azeem, at al (2004a), provided evidence from Pakistan that Farmers Field School based
training significantly enhanced farmers' skills for better management practices. Field
observations show that FFS farmer‘s capacities to control pest problem have improved a
lot relative to the non-FFS farmers. The analysis also indicates that farmers' dependence
on pesticides is reduced significantly through training on cultural and biological
32
e.g crop rotation, hand picking of pests/weeds, use of pheromones to trap pests
51
methods. Azeem, et al (2004b) also evaluated impacts of IPM on biodiversity and bio-
safety in Khairpur district of Sindh province. Once again, result shows that total doses of
pesticides were largely reduced (up to 43%) on FFS farms relative to non-FFS farms. It
has also been noted that farmers reduced pesticide use up to 54% which led to decrease
in pesticide associated health effects and resultantly improved labour productivity. The
FFS graduate farmers have also shown resilience under panicking pest flare up
situations.
Continuing with pesticide impact studies in Pakistan, Echols, et al, (2004)
compared the impact of FFS training on number of pesticide applications on cotton crop
between FFS trained farmers and non-trained control village farmers. Results showed
that after attending Farmers Field Schools farmers reduced pesticide spray from 13.1 to
only 6.8 sprays on cotton in a season. However, over the same period, control village
farmers reduced only 0.5 sprays. The analysis further indicated that effect of farmer field
schools was also observed on neighboring cotton farmers. In another impact study,
Azeem, et al (2004c) investigated organizational gains of the Farmers Field Sc hool
(FFS) in Khairpur district of Sindh. Although the gains were estimated just after one
year of the participation, the results show that FFS farmers joined in greater numbers
(100%) through imparting crop management and group functioning skills. It has also
been noted that FFS graduate farmers‘ social recognition was even higher than partial
graduate farmers. Similarly, Azeem, et al (2004d) estimated the impact of Farmer Field
Schools (FFS) on human capital e.g. knowledge up-gradation, decision-making skills
enhancement and experimentation among participating communities in Sindh. A
significant change in farmers‘ knowledge on pests, water requirements indicators, and
52
IPM as an environment friendly approach has been observed. Farmers significantly
improved their knowledge regarding recognition of beneficial and harmful pests and
their actions and countering interactions with each other. Results further indicated that
FFS-farmers age and education level were strongly associated with the beneficial and
harmful pest recognition, decision-making capacities and outcomes of these decisions in
terms of gross margins. It is concluded that FFS training module implemented in
Pakistan helped farmers in learning skills with greater rigor and clarity.
Azeem, et al (2004e) also analyzed the impacts of FFS- based IPM on rural poverty in
Sindh province of Pakistan. Khairpur district was selected for impact analysis. Results
show that FFS training significant increase production resultantly farmer‘s income
which helped reduces poverty profile from 71 % to 55%. The neighboring communities
from FFS villages marginally benefited through following the cotton management
practices of FFS farmers. Major factors contributing were better yields (around 30%),
low pesticide cost (55%) and relative savings on fertilizer costs. The poverty reduction
attribute of FFS approach suggests its wider application for the actualization of poverty
reduction dreams of the rural poor. Hruska (2002) also supported integrated pest
management (IMP) training. He investigated the impact of training of Nicaraguan
resource-poor maize farmers in the use of (IPM). Three groups of farmers were
examined for two years: the highly trained farmers, the trained farmers and a group of
"control" farmers. After two years, the trained farmers found using fewer pesticides and
hence they have experienced less health effects than the farmers who did not receive
IPM training.
53
2.9 Summary
Farmers use pesticide to maximize agricultural output on limited acres of land.
However, the extensive use of these pesticides results in considerable health and
environmental damage. According to WHO (1990) pesticide use causes 3.5 to 5 million
acute poisonings a year. Further, studies have also documented long term potential
health effects of pesticide use like cancer, reproductive health problems, kidney, lung,
and liver damage, neurological and developmental disorder in children that may be the
direct result of either acute or chronic effect of pesticide exposure.
From an environmental perspective, pesticide use has caused domestic animal
poisonings, the death of useful predators and parasites, residues in air, fishery and
aquatic bodies‘ losses, the damage of flora and fauna, unintentional crop exposures,
death of birds and honeybees and undesirable residue in food items have all credited to
pesticides. Pesticide use has polluted ground and surface water, as the pesticide runoff
moves, it carries away pollutants into lakes, rivers, wetlands, coastal waters, and even
underground sources of drinking water. Pesticide use has also contributed to create deed
zones in rivers and oceans, endangering the world‘s most valuable stores of freshwater
and survival of aquatic life. It has been recognized that the chemical pesticide residues
are the key contributor to the destruction threats facing many endangered species.
From economic perspective, pesticide use decision is influenced by two opposite
forces, a positive income effect which requires higher use of pesticide and a negative
health and environmental effect which requires less use pesticide. The decision-making
on pesticide use depends on the net effects of these two opposing attributes of pesticide.
If a farm worker is aware of the negative health effects of pesticide use, he/she would
54
choose to use more protective clothing or look for alternative technologies of pest
management. This role of information is investigated through out of this study
referencing ―Health Belief Model‖ a health behavior theory of social psychology.
The economic valuation of health impacts of pesticide use is also of interest,
since it has important policy implication. However, one critical problem is that market
usually non-existent for many of environmental goods and services. To overcome these
limitations, economists have developed Contingent Valuation Method (CVM). This is a
survey based technique in which respondents are asked how much they are willing to
pay for potential environmental change not reflected in any real market.
Considerable research has been dedicated to evaluating pesticide exposures and
pesticide use behavior in selected circumstances which concluded that most of the
pesticide related health and environmental problems are occurring due to lack of
knowledge and awareness, insecure attitudes, misperception of hazard and unsafe
practices. Research has shown that farmers has developed incorrect believes about
pesticide hazard which led to insecure attitude and unsafe practices. It is therefore
recommended that farmer‘s attitude and behavior should be changed in order to save
them from pesticide hazards. One such technology is Integrated Pest Management,
successfully used all over the world to reduce pesticide use. Many studies around the
globe have emphasized that IPM field-school guidance can provide farmers a workable
alternate to eliminate unnecessary pesticide use.
55
Chapter 3
Crop Sector in Pakistan: Major Crops and Pesticide Use
3.1 Significance of agriculture sector in the economy
The agricultural sector plays a pivotal role in economic development of the
country. It provides food to the population and contributes substantial share of foreign
exchange for the country. In spite of structural shift towards industry, agriculture is still
the largest sector in the economy and contributes 21.8 percent of the GDP. Its
contribution in total employment is also significant. It provides employment to over 44
percent of total employed labour force (Pakistan Economic Survey, 2008-09). It is also a
main source of income for the rural population. This sector also provides input to
number of industries like textile, sugar and food industries. Thus, through forward and
backward linkages, this sector also contributes to employment generation in industrial
and service sectors of the economy. Hence, in one way or another, it is the most
important sector in Pakistan economy. Although Pakistan has witnessed an increased
contribution in total exports from non-agricultural sector, agriculture continues to
dominate in foreign trade33and its contribution constitutes more than 60 percent of the
total exports (Chang et al, 2007).
33
Through exports of raw products such as rice and cotton and semi-processed and processed products
such as cotton yarn, cloth, carpets and leather production.
56
3.2 Selected major crops and characteristics of agricultural production
To a great extent, agriculture in Pakistan is supported by major crops.34 In 2008-
2009, the major crops contributed 33.4 percent to agricultural value added as opposed to
a 12 percent contribution from minor crops (Pakistan Economic Survey, 2008-09). It is
thus evident that socio-economic development of the country is critically dependent
upon agriculture. Therefore, the use of agricultural resources of the country needs to be
strengthened on a sustainable basis. The introduction of Green Revolution technologies
in Pakistan helped the country to get significant increase in the production of the major
crops. For example, wheat production increased from 3.3 million tonnes in 1950-51 to
22.42 million tonnes in 2008-2009. During the same period, rice production rose from
0.86 million tonnes to 6.95 million tonnes. The production of cotton reached 2.01
million tonnes (11.81 million bales) during 2008-2009 while reaching maximum at
14.26 million bales in 2004-2005.Sugarcane production reached 63.9 million tonnes
during 2007-2008.
Table 3.1. Production of Selected Major Crops in Pakistan (000 tonnes)
Years Wheat Rice Cotton (000 bales) Sugarcane
1985-86 13923.0 2918.9 7154.5 27856.3
1986-87 12015.9 3486.3 7759.7 29925.8
1987-88 12675.1 3240.9 8632.9 33028.8
1988-89 14419.2 3200.2 8385.1 36975.7
1989-90 14315.5 3220.1 8559.8 35493.6
1990-91 14565.0 3260.8 9627.7 35988.7
1991-92 15684.2 3243.1 12822.2 38864.9
1992-93 16156.5 3116.1 9053.8 38058.9
1993-94 15213.0 3994.7 8041.1 44427.0
1994-95 17002.4 3446.5 8697.1 47168.4
1995-96 16907.4 3966.5 10594.9 45229.7
34
Major crops include; Wheat, Cotton, Sugarcane, Rice, Maize, Gram, Tabacco, Bajra, Jowar, Barley,
Rapeseed, and Mustard (Pakistan Economic Survey).
57
1996-97 16650.5 4304.8 9374.2 41998.4
1997-98 18694.0 4333.0 9183.8 53104.2
1998-99 17857.6 4673.8 8790.2 55191.1
1999-00 21078.6 5155.6 11240.0 46332.6
2000-01 19023.7 4802.6 10731.9 43606.3
2001-02 18226.5 3882.0 10612.6 48041.6
2002-03 19183.3 4478.5 10210.6 52055.8
2003-04 19499.8 4847.6 10047.7 53419.0
2004-05 21612.3 5024.8 14265.2 47244.1
2005-06 21276.8 5547.2 13018.9 44665.5
2006-07 23294.7 5438.4 12856.2 54741.6
2007-08 20929.0 5563.0 11655.0 63920.0
2008-09 24033.0 6952.0 11819.0 50045.0 Source: Agricultural Statistics of Pakistan (2008).
3.2.1 Cotton
Cotton is the most important cash crop and economic sector35 of Pakistan. It
contributes 7.3 percent of agriculture value addition and about 1.6 percent to GDP of the
country (Pakistan Economic Survey, 2008-2009). Pakistan stands as fifth largest
producer, third largest exporter of raw cotton, fourth largest consumer, and the largest
exporter of cotton yarn in the world (Chang et al, 2007). Over the years, Pakistan
witnessed fluctuating trends in cotton production. During 1980s a rapid growth in the
yield has been witnessed ranges from 364 kilograms per hectare in 1982-83 to 769
kilograms in 1991-92. During 1990s the cotton crop experienced a huge crisis. This
crisis reached at peak in 1991-92 caused by the Leaf Curl Virus (LCV). This crisis led to
significant decrease in yields of cotton which dropped from 769 kg per hectare to
between 500 and 600 kg per hectare. In 2000s, a new strain of virus known as Burewala
Strain of Cotton Virus started damaging the cotton crop in district Vehari during 2001.
35
Cotton and textile products dominate exports, accounting for over 55 percent of the export earnings.
Cotton production supports Pakistan’s largest industrial sector, the textile industry, comprising more than
400 textile mills, 700 knitwear units, 4,000 garment units, 650 dyeing and finishing units, nearly 1,000
ginneries and 300 oil expellers (Pakistan annual cotton report, 2009).35
It is by any measure Pakistan’s
most important economic sector.
58
After emergence of this new strain of virus, the yield loss again started increasing
(Irshad, 1999; Pakistan Economic Survey, 2008-2009).
When compares with other cotton producing countries, the average yield is
comparatively much low in Pakistan. Pakistan‘s average yield in 2005-2006 is 714
which not only lower than industrially developed nations like U.S.A but also lower than
closest neighbors like India and Uzbekistan. According to government documents,36 the
main factors for low productivity of cotton include: Leaf Curl Virus incidence and other
pests attack, limited or inappropriate use of modern technology and water shortages at
critical stages. There are also some other social and economic problems that impede
productivity includes: lack of awareness and illiteracy, high cost of inputs, lack of
extension services and insecurity in the market.
3.2.2 Rice
Rice is very important food and cash crop in Pakistan. It is grown on more
than10 percent of the total cropped area of the country. The production of rice is about
6.95 million tonnes. In terms of export earnings, the export of rice accounted for 11.4
percent of the foreign exchange in 2008-2009 (Pakistan Economic Survey, 2008-2009).
Despite the fact that rice production and yield is increasing, the rice productivity is low
compared to major rice growing countries of the world. Factors responsible are: shortage
of irrigation water, weak extension services and limited or inefficient use of modern
technology.
36
Agricultural Perspective and Policy (2004), Ministry of Food & Agriculture, Government of Pakistan.
59
3.2.3 Sugarcane
Sugarcane is also an important cash crop of the country. The sugarcane crop
occupies about 5 percent of the total cropped area in the country. Its share in overall
GDP is 0.7 percent. The total production during the year 2008-2009 has reached at 50.0
mt (Pakistan Economic Survey, 2008-09). Sind province accounts for 25 to 30 percent
of sugarcane land while 60 to 65 percent of the area under sugarcane production is
provided by Punjab. The per acre productivity of sugarcane is higher in Sind province
compared to Punjab province. Pakistan's sugarcane yield averages about 48 tonnes per
hectare, well below the world average of above 60 tonnes, and below neighboring
India's yield of 61 tonnes.
3.2.4 Wheat
Wheat is the largest food crop in Pakistan. It contributes 2.8 percent to GDP. Its
total cultivated area during the year 2008-09 is reported 9062 thousand hectors. The
production estimates of wheat are 23.4 million tonnes during the same year (Pakistan
Economic Survey, 2008-09). The wheat production increased from 3.4 million tonnes in
1948 to 23.4 million tonnes in 2008-09, a significant increase in terms of production.
Pakistan also witnessed productivity improvements over the years. ‗During 1970s wheat
production increased by 1.17 percent, in 1980s it grew at 1.57 percent, in 1990s at 1.83
percent, during 2000s by 2.49 percent and 2.8 percent in 2007-2008 (PARC, 2008).
Although, there is an upward trend in the wheat production in the country yet yield per
hectare in Pakistan is far less than the other countries. The main reasons for low
productivity includes: limited use of high yielding variety seed, improper fertilizer use,
60
inefficient control on weed infestation, irrigation water shortage, soil degradation and
weak extension system (PARC, 2008).
3.3 Pesticide use and production of major crops
The agricultural crops are subject to pests attacks. Particularly, the cotton is the
most vulnerable to pest attacks. The use of pesticides as crop protection technology
begun in 1952 in Pakistan and the Government provided full support for the use of
pesticide to save crops from pests and diseases (Rasheed, 2007). Pesticide consumption
has increased tremendously over the last two decade, reaching 117513 metric tonnes in
2005-06 which was only 12530 metric tonnes in 1985.
Figure 3.1. Pesticide consumption in Pakistan (mt)
Pesticide consuption in Pakistan
0
20000
40000
60000
80000
100000
120000
140000
19851987
19891991
19931995
19971999
20012003
2005
Years
pes
tici
de
Qu
anti
ty
PESTICIDE
CONSUMPTION
Source: Agricultural Statistics of Pakistan, various years.
In terms of crops, pesticides are intensively used on cotton in Pakistan which
accounts for about 80 percent of the total consumption of active ingredient of pesticide
(NFDC, 2002). Most of the pesticides used are insecticides. The colossal increase in
pesticide use from 1980 when the pesticide trade was liberalized and transferred to the
private sector raised serious concern about sustainability of pesticide use. The field
61
evidences (Poswal et al, 1998; Iqbal et al, 1997; Hasnain, 1999; Azeem et al 2002)
indicate that farmers have moved to high levels of dependence on the use of pesticide.
This reliance on pesticide has led to increased future costs of pest‘s control since
indiscriminate use of pesticides leads to disturb the agro-ecological balance between
pests and predators.
Table 3.2. Yield of major crops in Pakistan
Years Wheat
(Kg/ hectare)
Rice
(Kg/ hectare)
Cotton
(Kg/ hectare)
Sugarcane
(tonnes/ hectare)
Pesticide
use (Mt)
1985-86 1881 1567 515 35.7 12530
1986-87 1559 1688 527 39.6 14499
1987-88 1734 1651 572 39.2 14848
1988-89 1865 1567 544 42.2 13072
1989-90 1825 1528 560 41.5 14607
1990-91 1841 1546 615 40.7 14743
1991-92 1991 1826 769 43.4 20213
1992-93 1947 1622 543 43.0 23439
1993-94 1894 1626 488 46.1 20279
1994-95 2081 1622 558 46.7 24864
1995-96 2018 1835 601 47.0 43375
1996-97 2053 1912 506 43.5 43219
1997-98 2238 1870 528 50.3 38004
1998-99 2170 1928 512 47.8 41576
1999-00 2491 2050 641 45.9 45680
2000-01 2325 2021 624 45.4 61299
2001-02 2262 1836 579 48.1 47592
2002-03 2388 2013 622 47.3 69897
2003-04 2373 1970 572 49.7 78133
2004-05 2586 1994 760 48.9 112928
2005-06 2519 2116 714 49.2 117513
2006-07 2716 2107 711 53.2 12530 Source: Agricultural Statistics of Pakistan, various years, Ministry of Food & Agricultur, Islamabad,
Pakistan.
The evidences from cotton growing areas have revealed that dependency on
pesticide use has already led to create resistance among pests, further reinforcing
farmer‘s reliance on chemical pesticide. For example Poswal et al. (1998) and Husnain
62
(1999) have reported that the rapid increase in pesticide consumption has destroyed the
delicate balance between pests and predators in cotton growing areas of Pakistan without
contributing any productivity improvements. The best examples are the experiences with
the major outbreaks of the Cotton Leaf Curl Virus (CLCV) in early 1990s, Burewala
Strain of Cotton Virus and Mealy Bug in the beginning of 2000s which have done
colossal damage to cotton crop. The pesticide dependence due to buildup of pest
resistance problem is explained by the path dependence or pesticide treadmill.
3.3.1 The path dependence (pesticide treadmill)
The main idea of path dependence is that the historical antecedents with a
production system increasingly influence the contemporary performance of that system
(Ajayi, 2000). In the beginning, policy makers have many options to choose production
systems which may provide increasing returns. However, when these production
technologies compete for potential adopters in the market, insignificant random events
or external interventions take place in such a way that in the development process of a
technology competition tilts in favor of one technology (Ajayi, 2000; Arthur, 1989). In
the absence of information regarding negative effects of the technology, policy makers
believe that they choose best technology available. Hence, adopted technology
increasingly floods into the production system and the positive feedbacks initially
emerged, led the technology enjoy the advantages of economy of scale. On the other
hand because of unfavorable conditions (artificial interventions that are biased against
them), the potential competitor technologies become nearly out of competition. In this
way, a premature and inefficient industry turns into standardization.
63
The same argument follows in case of pesticide technology in Pakistan. In the
beginning, the use of pesticide increased agricultural productivity through controlling
pest attacks. With this initial success at home and the similar success stories in other
countries, the government launched intervention programs to promote the use of
chemical pesticides at large scale. These programs such as subsidies on pesticide and
provision of easy availability of these chemicals shifted the competition in favor of
pesticide based pest control technology compared to other alternative pest control
technologies. The pesticide subsidies and pro-pesticide extension encourage farmers to
use more chemical pesticide and not to use other pest control methods. In this way,
almost all agricultural support measures (directly or indirectly) reinforce the dependence
on chemical-based control. On the contrary, alternative pest management methods are
driven out from the market and pest control technology in Pakistan became almost
synonymous with the use of pesticides.
In spite of some success in protecting crops, pesticide is nonetheless
―accompanied by negative externalities such as pest resistance, degradation of biological
capital and human health. Current information indicates that the rate at which insects are
developing resistance to chemical insecticides is increasing.‖37 Azeem et al (2002) stated
that the condition of the environment and agricultural sustainability in cotton growing
areas of Punjab are going steeply downhill. Despite tremendous increase in pesticide
use, cotton crops cannot be properly protected from pest‘s damage.
Although pesticide use has negative externalities and it has exhibited inefficient
production, but still the use of chemical pesticides is the only crop protection technology
37
Ajayi (2000)
64
and there is no shift away from it. The farmers have only single solution to pest
resistance or ineffectiveness of the available pesticides and this is either to use more
pesticides or change with more toxic one. In this way chemical pesticide use based
control of pests set off a vicious circle which made pesticide use self-reinforcing in the
country.
3.4 Management of pesticide use and integrated pest management
During early 70s through Agricultural Pesticide Ordinance (APO, 1971), the
Government of Pakistan tried to regulate production and consumption of pesticides.38
The legislation regarding specifications of pesticide exists in the Agricultural Pesticide
Rules 1973. Regulations have also been developed for safe use of pesticides (Rasheed,
2007). Recognizing and realizing health and environmental hazards attached to pesticide
use, reliance on Integrated Pest Management (IPM) has been stressed in the National
Agricultural Policies. Further, provincial agriculture departments execute pest
warning/scouting on regular basis to check excessive use of pesticide. In addition,
through print and electronic media farmers are advised to apply pesticide only when the
pest population crosses the economic threshold level (Rasheed, 2007). By these
measures government encourages judicious use of pesticide in the country.
3.4.1 IPM status in Pakistan
The disappointment with the extension methodology of Training and Visit (T &
V) system has been expressed openly at global level during 1990s which led the
movement for reforms in present extension system all over the world. Today, Farmers
38
See appendix III
65
Field Schools (FFS) approach has been accepted and adopted by almost every country as
a better extension methodology due to its participatory features.
Now this approach is also developing in Pakistan. The National lPM Programme
of Pakistan39 is established in December 2000(NARC, 2008). This programme40 helps
sharpen awareness of the value of biodiversity and ecosystem services and facilitate
farmers to maintain and preserve biodiversity, soil as well as of wild species.41 The
economic soundness of IPM has already been identified and established by the research
studies in the cotton zone of the Punjab during 1995-96 that pesticide use can be reduced
by at least 50% without compromising on yields (NARC, 2008). Although, IPM has
been institutionalized through National IPM Programme and accepted as a strategy for
sustainable agriculture development in the country, yet concerted efforts are still
required by the government to educate the farmers on a large scale.
3.5 Agricultural extension
Agricultural extension is part and parcel of modern day agriculture. All over the
world, it has been playing a significant role in improving crop productivity by offering
technical advice regarding input use e.g. pest management, water management and soil
conservation. Currently, extension systems are extending their domain by including
marketing linkages and participatory extension approaches. ―Thus extension systems are
becoming complex networks of various stakeholders like researchers, NGOs, extension
39
For more detail See: www.Nat-IPM.gov.pk.
40 The key to this IPM strategy lies in the conservation of natural enemies to reduce or replace reliance on
chemical pesticide (www.Nat-IPM.gov.pk)
41In IPM programme, chemical pesticides are generally considered a last resort; they are used only when
there is a genuine need requiring emergency response. However, when choosing pesticide, the least
hazardous pesticide is usually chosen.
66
workers, farmers and departments of agriculture‖ [Ministry of Food & Agriculture,
(2004), Agricultural Perspective and Policy].
In Pakistan, the federal government is responsible for policy planning, resource
mobilization and inter-provincial coordination for agriculture and education sectors
while the provincial governments are responsible for implementation of all programs.
The overview of the extension system in Pakistan reveals that extension activities have
been managed since 1947, using various extension models. During 70s government
launched a program to supply agricultural inputs to farming community at their
doorsteps. The main purpose of the program was to enhance crop yields. Under this
program farmers were encouraged to use more inputs like improved seed, fertilizers and
pesticide which were supplied at subsidized rates. Extension staff used all the efforts to
convince and encourage the farming communities to use improved inputs for higher
agricultural productivity. This program was failed to accomplish desired goals. Two
main reasons were identified as the main problems in this program. One; the program
was very costly, since government was paying huge subsidy for inputs. Second; the
extension staff was giving no time for extension activities and they became just
merchants of fertilizers, seeds and pesticide. This program was ended in 1978 and a new
model called ―Training and Visit System (T&V)‖ was introduced. Marketing of
agricultural inputs was handed over to private sector and subsidies were withdrawn
(Khooharo, 2008). Under T&V extension system, the extension staff was assigned
assignments like: 1) conducting the demonstration plots to show latest technology; 2)
imparting training programs for Field Assistants about the technology. This system also
could not continue primarily because of lack of sufficient funds which limited the
67
mobility of extension staff. The role of government sector extension to provide
information about safe use of pesticide is also limited owing to the problems of poorly
motivated staff, little knowledge and skills of field extension workers, inadequate
operational funds and less number of extension staff. Due to above mentioned problems
in public sector extension farmers do not get proper information regarding selection and
safe handling of pesticide. The private sector extension usually targets large and
progressive farmers. Therefore, average farmer has no option except to be totally
dependent on pesticide dealers or sales agents who usually are not trained and their
prime motive is profit maximization. Consequently, colossal losses are taking place due
to over/misuse of pesticide.
As a whole, agriculture sector in Pakistan faces problems of weak planning, limited
funds, lack of trained staff, limited training opportunities, inadequate linkage with
research and education and little use of modern technology (Agricultural Perspective
and Policy, 2004). In addition, there are some specific problems related to agricultural
extension. For example, in present agricultural extension system, there is a very weak
mechanism for micro planning at village level. To meet the challenges of monitoring,
coordination and communication, technical and manpower capacity of extension system
needs to be strengthened. Well designed extension education programs may be made
mandatory for registration of companies. Alternate methods of pest control may be
encouraged in the process, e.g. farmer field schools on IPM may be fully supported so as
to optimize health and environmental risks and also curtail pesticide import bill. In
addition Agricultural Perspective and Policy (2004) has highlighted number of strategic
68
options to improve the effectiveness of extension system in Pakistan which are given
below:
―Establishment of a national- level body to develop and implement national
extension policy.‖
―Enhance and improve the mandate of extension by covering topics such as
marketing, input synchronization and the environment in addition to the transfer
of agricultural technology.‖42 Also increase extension staff per district.
Provide information technology tools to facilitate extension in the fields.
Revise and improve the extension education curriculum.
Broaden the technical mandate of extension and adopt participatory extension
services.
3.6 Summary
In Pakistan, agriculture sector has significant contribution in the economy.
Indeed, the production of major crops has increased over the years but most of the
increase in production is largely attributed to greater area planted rather than by
increased productivity per hectare. The data shows that despite rapid increase in
pesticide use in Pakistan, the productivity effects of pesticide use are insignificant.
Despite many important advances in legislation, government interventions to regulate
the usage of pesticide have largely failed. Given the Pakistan‘s agriculture settings and
cash crops security situation, it can be expected that current crop protection practices
will likely continue to be the main system in the country. There will be a growing use of
42
Agricultural Perspective and Policy (2004)
69
agricultural pesticide because farmers recognize pesticide as important input for
agricultural production. ―The trust on pesticide for plant protection is expected to lead to
more dependence on and to rising use of pesticide due to rapid development of
resistance among pests‖ (Huang et al, 2003). This not only a serious health threat to
farmers but also putting consumer‘s health at stack. A fundamental shift is required from
pesticide based pest management to more sustainable environmental friendly methods.
A variety of successful alternative methods are available that have the potential to
reduce pesticide use. These methods need to be standardized. IPM impact studies
suggest that collective action by the government and other stakeholders is vital for
environmentally safe pest management in the country. Agricultural research and
extension programs need to be scaled up to minimize pesticide use and to promote
environmentally friendly pest control.
70
Chapter 4
Study Area, Survey Design and Data Collection
4.1 Selection of study area
Because of differences in the use of pesticide on different crops, data from
Pakistan agriculture statistics43 were collected to find the composition of pesticides used
in different crops and geographical areas. Cotton has been identified as the major crop,
which accounts for more than 70% of total pesticides used in Pakistan (Rasheed, 2007).
Whereas more than 80% of cotton is produced in Punjab province and being the center
of cotton crop, cotton zone of the Punjab has been recognized as the most intensive with
respect to pesticides use.
Table 4. I. Province wise share of cotton production
Province Area
(000, hector)
Production
(Millions bales) % Of production
Punjab 2.460 10.50 80.77
Sindh 0.570 2.40 18.46
KPK 0.002 0.01 0.076
Baluchistan 0.040 0.09 0.69
Total 3.072 13.00 100.0 Source: Agriculture statistics (2008)
Two districts Vehari & Lodhran (highlighted in the map of Punjab below) in
cotton growing areas of Punjab province were selected for the study which are
historically famous for cotton production and have a long history of pesticides use
(approximate 50 years). Both districts represent more than 17% area under cotton
43
Agriculture Census 2000, procedure & data tables Punjab, Government of Pakistan, Statistics Division
Agricultural Census Organization Lahore.
71
cultivation in Punjab44. Agriculture is a backbone of the economy of both the districts.
Vehari is ranked third by area after Rahim Yar Khan and Bahawalpur in Punjab. It is
also one of the most intensive pesticide use area. Similarly Lodhran is famous for higher
per acre cotton production in Punjab and one of the most intensive area with respect to
pesticide use.45
Figure 4.1. Map of Punjab Province
In addition, the selection of these districts is also based on the understanding that
a reasonable data of farmers currently using IPM could be available and that the farmers
of these districts are very much aware of IPM since the government has undertaken the
44
See List of main cotton producing districts in appendix table 22A.
45 See: www. pakisan.com
72
activities of Farmers Field School (FFS) and Training of Facilitators (TOF) under the
umbrella of National Integrated Pest Management (IPM) programme 46 in these districts.
4.2 Development of survey questionnaire
The development of survey questionnaire can be described by following phases.
4.2.1 Preliminary phase
The preliminary phase is the period of construction and design of the survey
questionnaire for this study. The construction of questionnaire is based on the
questionnaires used in the similar World Bank studies in Bangladesh and Vietnam. The
World Bank team designed and supervised the surveys in both countries. The surve ys
were carried out in the winter of 2003 in Vietnam and in the summer of 2003 in
Bangladesh47. From the World Bank surveys, a modified survey was constructed for the
present study which by design, focused on major pesticide intensive crop e.g. cotton.
The questionnaire was also guided by the Contingent Valuation (CV) guidelines and
data requirements for the pesticide use behavior and willingness to pay (WTP) analysis.
4.2.2 Pre-testing
After the initial draft of the questionnaire is designed, an investigation visit was
carried out for general familiarization with the research area and testing of the contents
of the questionnaire. The familiarization and testing process was assisted by the use of
some informant interviews to obtain information about the actual set-up on the ground in
the study area. Overall 21 interviews with initially designed questionnaire were
46
Source: www.Nat-IPM.gov.pk
47 These questionnaires were used to collect information on pesticide use and practices, applicator
precautions and averting behavior, knowledge, risk perceptions and health effects.
73
completed.
4.2.3 Finalization of questionnaire
Using the background knowledge and information from the reconnaissance visit,
the questionnaire was modified and improved accordingly. Final version of the
questionnaire was used to collect requisite information on pesticide use. In detail,
questionnaire included farmers‘ characteristics, background information, the health
effects of pesticide use, other alternatives to pesticides such as integrated pest
management techniques, income, size of the farm and attitudes regarding the use of
pesticide and protective equipment or clothes during preparation and application of
pesticide, training, pesticide poisoning experience in the past and sources of info rmation.
The survey questionnaire was divided into seven sections;48 section one includes area
and property information; second contains personal information and household
characteristics of farmers; the third deals with farmer‘s pesticide use behavior; next
contains questions about health of the farmer; part five contains information regarding
protective measures taken and sixth includes environmental problems observed by the
farmers in their areas. Last includes willingness to pay for safe alternative to pesticide.
4.3 Data methodology
The study used a combination of purposive and probabilistic sampling.
Purposive sampling method was used primarily because the lists49 of farmers using
pesticide in cotton growing areas could not be found, actually were not available.
48
See survey questionnaire in appendix VI 49
The lack of adequate lists may automatically rule out systematic sampling, stratified sampling, or
another sampling design.
74
To study a small subset of a larger population, the cluster sampling was used to collect
data economically. Clusters50 were selected for their proximity to tehsil or district
headquarters and availability of resident‘s information which provides basis to make
lists of farmers using pesticide in the selected areas. Ideally a cluster should be as
heterogeneous as the population itself. Therefore a problem may arise with cluster
sampling if the characteristics and attitudes of the elements within clusters are too
similar. This problem to an extent may be mitigated by constructing clusters that are
composed of diverse elements and by selecting a large number of sampled clusters
(Cooper et al, 2000). Hence as a sampling strategy, after the selection of study districts,
all three tehsils were chosen for survey as the representative area. At least three villages
(clusters), from every tehsil were selected purposively in each district to get the
pesticide-related information from a sample of pesticide applicators and farmers.
Unfortunately, in each selected village, any type of list of the farmers using pesticide
could not be obtained from the agriculture officer/extension, simply because it was not
available. In each village, we got help from well informed men to make farmer‘s list
who knew almost all farmers, who use pesticide in their respective villages. Overall, 915
farmers from both the districts, 412 from district Vehari and 503 from district Lodhran
were enlisted. The names of farmers were sorted by alphabetical order and assigned a
number. A random sample of 400 farmers was drawn without replacement using
www.random.org/nform.html. Finally, 318 interviews were successfully completed in
both districts. The overall response rate (i.e. successful interviews completed) was 80%,
including 85% response rate for Lodhran district and 75% response rate for Vehari
50
Cluster sampling is classified as a probability sampling technique either because of the random
selection of the clusters or because of the random selection of elements within each cluster (Cooper,
2000).
75
district. The overall refusal rate (i.e. farmers were contacted and refused participation)
was 0%. The remaining 20% of names drawn were either not available at the time of
interview or the information collected were not completed.
4.3.1 Field survey
The survey adopted face to face interview method for filling in the questionnaire.
We carried out pre-testing for the survey in winter-spring51 2007-2008 and then a team
of five members carried out the survey of farmers in summer 2008. Four data collectors
with the background of conducting surveys were hired locally with the help of employee
of Central Cotton Research Institute (CCRI) Multan. One of the data collectors was
MBA, two others were higher secondary certificate holder and one of them was
graduate. Their knowledge of the local environment and their access and contact 52 with
farmers added much to the quality of the data collected and they also helped to save time
and cost. To minimize possible biases, the survey was carried out with an agreement that
the identity of the respondents will not be revealed at any stage. The supervisor was also
responsible to check the filled in questionnaires for completeness on daily basis and
monitor enumerators53 as they return with completed interview to ensure quality of data
collected.
51
There are two main growing seasons Winter-Spring and Summer-Autumn in Pakistan. Although the
survey was carried-out in a single season but farmers were asked thorough questions about pesticide and
pesticide use that covered the whole year period. Thus the survey enclosed information for both growing
seasons in the area. 52
Enumerators/village guides were responsible for contacting and arranging interviews with their
assigned interviewees and accommodating respondents by meeting them in a convenient location for the
interview. 53
Monitoring involves on the spot monitoring, questions regarding respondent’s impressions while giving
answers to different questions. The meeting place for interview and time consumed ect.
76
4.3.2 Sample size
The random sampling technique was used to select sample farmers which ensure
that every household must has an equal probability of being included in the sample
irrespectively of their farm size in the study area. The sample of 318 farmers is
considered reasonably appropriate to provide reliable estimates of farmer‘s behavior of
pesticide use. Out of 318 sampled farmers, a sample of 149 farmers is taken from district
Vehari and 169 farmers from district Lodhran. The entire sample of farmers is drawn
from 30 Villages/Mouzas out of which 11 from district Vehari and 19 from district
Lodhran54 (the selected villages in Lodhran were relatively smaller). After the data were
collected, the supervisor did the job of data entry and cleaning. Later author rechecked
data entry, cleaned and assigned codes. See table 4.2 for detail distribution of sample
population.
Table 4.2.Distribution of sample population by district
District Tehsil Sample size Total size
Vehari
Mailsi 55
149 Borewala 52
Vehari 42
Lodhran
Lodhran 54
169 Dunya Pur 66
Kahrore Pacca 49
4.4 Validity and Reliability analysis
In any research, Researchers/analysts must show that instruments being used are
reliable, since without reliability research results will no more be replicable, which is
fundamental to the scientific research. Since present study is a survey based research and
54
See appendix table 23A for detail of villages
77
reliability issues of the survey results may arise. At the same time, a part of this research
is Contingent Valuation (CV) which comes under severe criticism for using stated
preferences in consumer choice problem instead of actual behavior and controversy
continues to exist between researchers regarding validity and reliability of CVM.
Therefore, this issue is discussed in the present context.
―Validity refers to the correspondence between what one wished to measure and
what was actually measured.‖55 The best way to measure validity of any research is to
compare with some criterion known to be correct. There has not any such criterion so far
been developed to which such comparison be made. Furthermore, ―no such criterion
exists to which any other consumer surplus estimate can be compared, irrespective of the
econometric technique used or whether the good is private or public‖ (Gunatilake,
2003). What researchers can do in such cases and how validity of a research can be
determined? The economists have developed some approaches which researcher may
adopt to determine validity of his research. The two commonly used approaches are;
construct validity and convergent validity. ―Construct validity refers to how well the
measurement is predicted by factors that one would expect to be predictive a priori.
Convergent validity can be taken only when measurements of the phenomena of interest
are available using two different techniques.‖56 In terms of reliability of a survey
research, two approaches are commonly used and famous among researchers. ―One is
the chronological/temporal stability of the estimate if two different samples of the
sample population are interviewed with the same survey instrument at two different
55
Carson, et al, (2000a)
56 Garming et al, (2006).
78
points in time. The other is the classic test-retest reliability where an original sample of
respondents is later re- interviewed using the same survey tool.‖57
4.4.1 Reliability analysis
Reliability is ―the correlation of an item, instrument or scale with a hypothetical
one which truly measures what it is supposed to‖ (Garson, 1999). Reliability can be
measured by many ways, one way of measurement is, if two persons who are same in
terms of scale being measured, should produce same result. ―In statistical terms each
item in the scale should produce results consistent with overall questionnaire.‖58
Reliability analysis allows researchers to understand about individual item and its
relationship with overall construct (Field, 2005).
The most common measure of scale reliability is Cronbach‘s Alpha denoted by
( ) which measures internal consistency of items in a given scale. The value of Alpha
varies from 0 to 1, if alpha equals zero, no item measures true score. Alpha equals 1.0
when all items measure only the true score and there is no error component in the scale.
= Number of items in scale
= Variance of item
= Variance of total score
From which it can be seen that alpha measures true variance over total variance.
Cut-off criteria: A moderate cut-off of .60 is common in social sciences and
57
Carson et al, (2000a)
58 Garson (1999)
79
exploratory research. However, some researchers are of the view that the value of alpha
should not be less than .70 in order to retain an item in a scale and some others even
emphasis on a cut-off of .80 (Garson, 1999). The Statistics in table 4.3 shows a
reasonably good reliability. The value of Cronbach's Alpha is as high as 0.7059, a
generally accepted value. Hence questionnaire appeared to have good internal
consistency.
Table 4.3. Reliability analysis Item-Total Statistics
Variables
Scale Mean
if Item
Deleted
Scale Variance if
Item Deleted
Corrected
Item-Total
Correlation
Cronbach's
Alpha if Item
Deleted
Risk
perception 16.2987 20.633 .409 .666
Age 16.1541 21.563 .328 .683
Farm size 15.7673 18.116 .612 .616
Income 15.5912 17.977 .710 .596
WTP 16.8019 19.692 .560 .635
Health effect 18.2547 25.610 .090 .708
IPM 18.9403 25.034 .284 .697
Training 18.9340 25.153 .242 .699
Education 15.8365 18.888 .274 .730
Intraclass Correlation Coefficient
Intraclass
Correlation
95% Confidence
Interval F Test with True Value 0
Lower
Bound
Upper
Bound Value df1 df2 Sig
Single
Measures .205 .169 .246 3.322 317.0 2536 .000
Average
Measures .70 .647 .746 3.322 317.0 2536 .000
59
Kline (1999, cited in Field 2005) said that although the .80 value of Cronbach's Alpha is generally
considered good scale, however when studying psychological and behavio ral construct, the values below
even .70 can realistically be expected because of the diversity of construct being measured.
80
4.4.2 Validity tests of CVM Two widely used validity assessments are; content and theoretical validity.
Content validity deals with survey design. It measures whether the good defined in the
survey is correct and the subject good can measure the correc t value. It further considers
whether respondents are provided sufficient information about the good? Is the payment
vehicle for the good and the scenarios presented are acceptable and plausible? Research
has shown that careful survey design and pre-testing are the good tools to enhance
content validity (Garming et al, 2006). The theoretical validity refers to the idea that the
preferences for environmental goods follow the same rules as the preferences for
conventional goods, that is, the value of environmental good should vary with the
quantity of the good and Willingness to Pay (WTP) should be sensitive to income of the
respondents and their attitudes towards the good. ―Attitudes towards the good, e.g.
concerns about pesticide poisoning and experience of illness, as well as budget
constraints and risk measures like intensity of pesticide use are expected to have an
impact on farmers‘ valuation of pesticide related health‖ (Garming et al, 2006).
In addition, study followed NOAA guidelines for good practices in CVM
obtained by Portney (1994). Table 4.4 gives a snapshot of the validity criteria used in the
implementation of field survey. The approach used to describe health for the willingness
to pay scenario was taken from ―Garming (2006)‖. ―Health was represented as an
attribute of a pesticide, which was offered in a hypothetical purchase situation‖
(Garming, 2006).
81
Table 4.4.Validity test in the implementation of the CV
Validity Implementation in survey Method of assessment
test?
Content validity
Response rates Analysis of comments of respondents with zero WTP.
Definition of the good Pesticide without health risks
Payment vehicle Pesticide price Familiarity Farmers’ heavily dependent
pesticide
Acceptance of the Questionnaire
Modifications after pre-tests
Construct validity Theoretical validity Household characteristics
Pesticide related health experiences Perception/attitudes
Ordered probit model on
WTP
Source: Adopted with changes from Garming et al (2006)
The pesticides are very popular among farmers in the study area and since they
believe that pesticide use is the only crop protection technology that provides effective
control over pests, most recently used or heavily dependent pesticide was used to
increase farmers‘ familiarity with the environmental good, ―the IPM‖ in present
scenario. The other possible scenarios could also be used as different studies included
e.g. ―the willingness to invest in IPM.‖ However, pretest of the questionnaire and
informant interviews of farmers showed that IPM activities are very much limited in the
area and most of the farmers are not familiar with IPM. Therefore, the use of IPM as
product ―might not reflect true reference and would have reduced the plausibility of this
scenario for the farmers. Thus the most practical description remains chemical pesticide
which farmers are very familiar with, rendering the ‗low toxicity pesticide option‘ as the
most feasible option for the CV survey.‖60 Following standard practice in CVM
60
Garming et al, (2006)
82
analyses, the farmers were asked,61 suppose you were able to have access to a pesticide
that was just as effective as the one(s) you are using now, but it did not have any short-
or long- term health effects. Thinking about the health effects you have experienced with
your current use of pesticide, how much would you be willing to pay for the use of the
safer pesticide? ―The price premium, he would be willing to pay for a pesticide with the
same characteristics except the health risks of the product, was then established as the
WTP for the health attribute. Willingness to Pay was calculated as the product of
purchased amount of the pesticide and the price premium.‖62
4.5 Summary
This chapter discusses research methodology used for this study. To select study
area, data from the Pakistan agriculture statistics (Agriculture Census 2000) were
collected to find the composition of pesticide use in different crops and geographical
areas. Cotton has been identified as the major crop, which consumes much of pesticide
used in Pakistan. Whereas more than 80% of cotton is produced in Punjab province and
being the center of cotton crop the cotton zone of the Punjab has been recognized as the
most intensive with respect to pesticide use. Overall two districts (Lodhran & Vehari) of
the cotton belt in Punjab province were selected for the study.
61 As a standard practice after being informed of the CVM scenario , farmers were asked that suppose you
were able to have access to a pesticide that was just as effective as the one(s) you are using now, but it did
not have any short- or long- term health effects. Thinking about the health effects you now experienced
with your current use of pesticide, how much would you be willing to pay for the use of the safer
pesticide? Please also understand that to pay for this alternative; you would have less money for other
items. This amount classified into categories, 1= Not willing to pay, 2= willing to pay from 1 percent up
to 5 percent premium, 3= willing to pay up to 6 percent to 10 percent premium, 4= willing to pay up to 11
percent to 20 percent premium, 5= willing to pay over and above 20 percent premium. 62
Garming et al, (2006)
83
A well- designed, comprehensive and pre-tested questionnaire was used to
collect data from both the districts in 2008 and face to face interviews were conducted.
The questionnaire was based on similar World Bank studies in Bangladesh and Vietnam.
The multistage cluster sampling was used to collect data economically. Hence as a
sampling strategy, after the selection of study districts, all three tehsils of each district
were chosen for survey as the representative area. At least three villages from every
tehsil were selected in each district to get the pesticide use related information from a
sample of pesticide applicators and farmers. In each village, well informed men were
hired to make farmer‘s list in their respective villages. Overall 915 farmers from both
the districts, 412 from district Vehari and 503 from district Lodhran were enlisted. A
random sample of 400 farmers was drawn without replacement using
www.random.org/nform.html and overall 318 interviews were completed successfully.
Out of 318 sampled farmers, a sample of 149 farmers is taken from district Vehari and
169 farmers from district Lodhran.
The reliability and validity issue of survey research have also been discussed in
detail in the present context. The statistics of Reliability Analysis shows a reasonably
good reliability value (.70) of the survey results. Hence questionnaire appeared to have
good internal consistency. Following NOAA guidelines for good practices in CVM, the
study also undertaken content and theoretical validity tests which ultimately improved
the analysis.
84
Chapter 5
Survey Results
5.1 Background Information
5.1.1 Age and education of the farmers
All the surveyed farmers were male; this is because usually the spraying
operations are done by male in Pakistan. Age ranges from 18 to 66 years, with an
average age of 33.3 years approximately. Most of the farmers 113 were in age groups
21-30 (35.5%) and 101 were in age group of 31-40 (31.8%). The table 5.1 displays the
education attainment of different age groups.
Table 5.1. Distribution of education attainment by age groups
Age categories
Education attainment
Illiterate Up to Primary
Middle Matric Higher secondary and above
Total
Up to 20 5 (27.8%) 6 (33.3%) 2 (11.1%) 3 (16.7%) 2 (11.1%) 100.0
21-30 32 (28.3%) 25 (22.1%) 14 (24.8%) 29 (13.3%) 13 (11.5%) 100.0
31-40 27 (26.7%) 33 (32.7%) 15 (24.8%) 17 (6.9%) 9 (8.9%) 100.0
41-50 10 (20.0%) 13 (26.0%) 3 (36.0%) 19 (8.0%) 5 (10.0%) 100.0
51-60 9 (25.7%) 11 (31.4%) 5 (14.3%) 8 (22.9%) 2 (5.8%) 100.0
61+ 1 (100.0 %) 0.0 0.0 0.0 0.0 100.0
Over 73% of respondents had received education of different levels. About 6 percent
of them also obtained graduation degree, whereas 26.5% of respondents had never in the
school and could not read or write. In terms of higher education categories (matric and
above) the farmers up to age 40 years are better educated than their older counterparts,
this is probably due to changing attitude towards schooling and more opportunities
available than the past. However, overall distribution is more or less same for all age
categories.
85
5.1.2 Household characteristics
The average number of members per household 63 is 6.52. The average household size
differs in districts, (6 in Lodhran and 6.8 in Vehari). The cross-tabulation of household
and farm size is noteworthy.
Table 5.2. Distribution of farm size by household size
Farm size
(Acre)
Household
(Number)
Associated
members
(Number)
Average
household size
(Number)
Percentage of
sample
population
Up to 2.50 36 184 5.1 11.3
2.60-5.0 63 334 5.3 14.15
5.01-10.0 79 450 5.7 25.15
10.01-25.0 120 816 6.8 36.16
25.01-50.0 10 76 7.6 8.80
50.01-100 6 41 6.9 3.14
100+ 4 33 8.3 1.25
Total 318 1934 6.52 100
The table shows that with the increase of farm size, the household size is also increasing.
This positive relationship is little unexpected, as one may expect small farmers usually
having large families relative to large landholders. However, result is quit analogous to
the Microeconomic Household Theory of fertility, since farm size is synonymous to
wealth and main source of income generation for farmers. Further, to draw some
conclusion results also need to be cross-checked with other variables like income and
education levels of the household.
5.1.3 Land ownership and farm characteristics
The land ownership data indicate that the majority of farmers 75.5 percent owned
land.64 More than 10 percent have rented from land owning families and 6 percent of the
63
A household is defined to comprise all usual residents, where they live together and eating from the
same kitchen, share common facilities and mutual reciprocal responsibility.
86
respondents are sharecropper. About 8 percent of them have mixed arrangements. Most
of the fields cultivated in the area were inherited from parents.
Table 5.3.Distribution of farm size and farm ownership (%)
Farm size
(Acres)
Farm ownership
Total Own the
farm
Rental
arrangement Sharecropper
Up to 2.50 72.2 13.9 13.9 100.0
2.51-5.0 73.0 20.6 6.3 100.0
5.01-10.0 81.0 11.4 7.6 100.0
10.01-25.0 92.5 5.0 2.5 100.0
25.01-50.0 100.0 0.0 0.0 100.0
50.01-100 100.0 0.0 0.0 100.0
100+ 100.0 0.0 0 100.0
A large number of the farmers surveyed 99 (31%) hold either 5 or less than 5
acre of land. In terms of large land holding, only few of them had 50 acres or over, and
most of them in district Lodhran, while a large percentage of respondent farmers (more
than half) can be said small farmers in terms of land holding. The respondents average
land area was 13.5 acres in district Vehari, and 14.5 acres in Lodhran district. (District-
wise distribution is presented in appendix II).
5.2 Pesticide Safety Knowledge, Information Source and Averting
Behavior
5.2.1 Sources of information about safety practices
Sources of communication to farmers about pesticide issues are important since
it influences the knowledge of the farmers which help them to improve their agricultural
practices, health and environment. The sources of information in the study area which
influences farmers in their application of pesticide are very limited and skewed. About
64 See Figure.1A: Farm ownership status of the sampled farmers in Appendix 1.
87
40% farmers said that they obtained information from Pesticide Sellers or Pesticide
Company in the market and 22% from neighbors, fellow farmers, relatives and others.
Only 7% received any information from agriculture extension. About 31% said that they
never received any information regarding safety issues of pesticide use. The majority of
farmers (41%) obtain information from two or more sources. The result shows that most
of the farmers never received any expert technical advice regarding pesticide use.
Figure 5.1. Farmer‘s sources of information (%)
5.2.2 Pesticide safety knowledge and averting practices
The pesticide labels contain very useful information. Therefore, it is very important
source of information for users. It helps farmer to understand what toxic chemical this
bottle contains, for what purpose it may be used, what safety measures are needed while
using this substance and how adequately it should be prepared for application (Meisner,
2005). Although 73 percent farmers were literate and 91% farmers received information
from different sources, hardly 23% farmers followed the instructions on the labels.
Similarly 48 percent farmers wear gloves or plastic bag (an alternate to gloves) on hands
while mixing pesticide. However all the farmers were reported using sticks to mix
88
pesticides. Similarly, all the farmers interviewed said that they do not spray pesticide
against wind.
Although few farmers (4%) said that they eat or drink when spraying, the
numbers are high (11%) for the farmers who usually smoke while spraying. Similarly,
14% of the farmers placed their mouth on the sprayer‘s nozzle while cleaning it. Most of
the farmers (77) replied that they do not wash pesticide bottles and sprayers in the canal.
However, only 3 % farmers displayed any signboard or an empty pesticide bottle on
their field after applying the pesticide.
The storage of pesticide bottles is a safety concern. The data show that 81%
farmers usually place pesticide bottles in their houses that are out of children‘s reach.
Similarly, the empty pesticide bottles also raise health threats, therefore their disposal is
a matter of concern. The result shows that almost all the farmers keep empty pesticide
bottles either in the living house or field. Few of them threw them outside the house on
garbage dump. The farmers who kept empty pesticide bottles, usually sell to bottle
collector, few (1.2%) farmers said that they used these bottles for domestic purposes.
Table 5.4. Farmer‘s behavior about safety instruction
Safety Instructions Percentage of farmers
who received Information
about Instructions
Percentage of
farmers who follow
these Instructions
Read the labels and follow the Instructions.
91 23
Do not mix pesticides with bare
Hands.
84 48
Mix pesticides with a stick. 84 100
Do not spray pesticide against the
wind.
100 100
Do not place your mouth on nozzle of the sprayer and blow on
it.
78 86
89
Do not eat or drink while spraying pesticide.
96 96
Do not smoke while spraying
pesticide.
96 89
Do not wash pesticide bottle and sprayer in the canal.
65 77
Display a signboard after pesticide application
12 3
Do not keep other things in the pesticide bottle or package.
95 1.2
Tear up the pesticide package into pieces and bury them under the
ground.
79 0
Do not keep pesticide where you keep other things.
96 89
Keep pesticide under lock so that
they are out of the reach of children.
96 81
5.2.3 Risk perception
Perception of a pesticide‘ risk influences the dose decision by farmers (Dasgupta,
2005a). It is important to know that whether farmers perceive pesticide a risk to their
health (Meisner, 2005). Identification of their perception is very important in the design
of any safety program. According to the study results, the majority (88%) of farmers
believed that they are at risk while using pesticide. Farmers were also asked to rank the
risk. Five categories were presented and scaled as shown in the figure 5.2. More than
half 52 % reported some small risk, 23% a medium amount of risk, 10% believed that
the risk is large and significant, 3% said that the risk is very toxic, however 12%
believed that there is no risk at all.
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Figure 5.2. Farmers perception of pesticide risk (%)
It is important to note that risk perception has important bearing on dose decision. The
table 5.5 shows that the farmers who believe that pesticide use has no effect on health
use significantly more amount of pesticide in a season than the farmers with higher level
of risk perceptions.
Table 5.5. Per acre use of pesticide (kg) with different level of Risk perception
Risk perception Both districts Vehari Lodhran
No risk at all 13.5 13.0 13.9
some small risks 11.1 11.4 10.9
A medium amount of risk 11.9 12.1 11.7
A large/ significant amount
of risk 12.6 12.3 13.0
very toxic risks 12.5 12.1 13.0
In comparison by district, the perception of pesticide risk in district Lodhran is
not very heightened despite the fact that most of the farmers (90 percent) experienced
health effects during the course of mixing and applying pesticide. The possible reason of
low perception is that most of the farmers might not believe that health effects are
caused by pesticide use. From the survey responses we came to know that many of the
farmers believed that most of the diseases are common in rural communities which can
be experienced by any man any time (see figure 4A and table 19A). This attitude is not
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restricted to farmers in the Lodhran. Kishi et al (1995) reported that farmers in Indonesia
were found accepting many of health problems as part of their work. Ajayi (2000) also
reported analogous evidence. He described that over time farmers usually do not care
about the health problems associated with pesticide spraying. With the passage of time,
pesticide associated health problems are considered as routine matter (Ajayi, 2000).
In district Vehari, on average farmers experienced less number of health effects
than the farmers in district Lodhran, however, they tended to perceive pesticide risk
much higher than the farmers in district Lodhran. This could partly be explained by
higher level of education. Further the severity of the poisonings may also be a possible
reason.
5.2.4 Pesticide practices and use of protective measures
When farmers undertake spraying operations, they are naturally face direct toxic
exposure. This toxic exposure cause number of negative health effects.65 However, the
health effects of pesticide use can be avoided by taking safety measures (Dasgupta,
2005a). In our survey only 8% farmers reported receiving basic training on the safe
handling of pesticides, while 89% said that neither had they any access to nor did they
know who provides this training. However, 3% have access but they are not interested.
The 44 percent farmer said that during mixing pesticides, dangerous liquid touch their
hands, 3 percent reported the same incident with their feet. Another fact describing
unsafe practices is the re-entry time in the field after application, 74 percent farmers re-
65
Depending on the pesticide’s toxicity and the dose absorbed by the body, pesticide exposure can
produce intoxication symptoms within few minutes or hours, in case acute toxicity is high. The general
acute effects identified by different studies are headache, flu, skin rashes, blurred vision, eye irritation and
other digestive problems. In addition, prolonged exposure to pesticides can lead to many chronic health
problems like cardiopulmonary problems, adverse dermal effects, cancer and neurological and
hematological symptoms (Dasgupta, 2005).
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enter within 24 hours after pesticide application, out of which 5 percent re-enter right
after application/within few hours. It is a common practice in the area to visit the
sprayed field after 24 hours to see the effects of pesticide. The re-entry time is very short
which may lead to serious health problem.
Most of the respondents said that they partially cover their body with protective
clothing. The use of masks and glasses were almost nonexistent, but farmers usually use
cloth to cover their faces instead of mask which could be said a substitute of mask in
present circumstances. Also the use of gloves and boots were limited, only 13% and 4%
of them used gloves and boots respectively. Figure 5.3 shows the types of protective
clothing66 respondent farmers usually used while spraying.
Figure 5.3. Use of protective equipments during spray (%)
The main reasons for not using protective clothing was; already high cost of inputs, non-
availability of these materials, uncomfortable67 to wear due to hot weather and lack of
66
It is difficult to determine the effectiveness of these equipment as protection because these clothes are
usually made of cotton or cotton materials and may absorb mild pesticide mixtures during spraying, thus
bringing the toxic chemicals even closer to the applicator’s body. 67
Hot weather has been identified as the main factor for lack of use of protective clothing by pesticide
applicators.
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information. Due to limited income, many farmers do not even able to purchase these
materials and hence compromise their health. Due to lack of awareness regarding health
hazards of pesticide use 46% sample farmers normally do not take bath with soap after
pesticide application; they also do not consider it necessary to take bath after spray.
However, majority of the farmers (88) % said that they change their spraying clothes
shortly after spraying operation, only few of them do not change their spraying clothes
normally.
Table 5.6. Main reasons, for not taking protective measures (%)
Main reasons
Items
Due to high
cost
Uncomfortable to
wear Not Available No Need
Boots 2 94 1 3
Hat/Head cover 2 88 5 5
Gloves 13 33 51 3
Masks 13 57 27 3
Glasses 11 39 49 1
5.3 Crop protection methods and Pesticide application
5.3.1 Crop protection methods in study area
Principally, all the farmers were found dependent upon chemical pesticides.
Pesticide is regarded as very important for successful production. Farmers openly said
that they cannot grow crops without pesticide. Although many of them believed that
spraying pesticide is dangerous but they said that they have ―no other option‖ at all. The
powerful distribution systems of pesticide manufacturers and pro-pesticide extension
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have led to erosion of cultural methods in the area. Only few farmers (9%)68 use IPM
methods as complementary to pesticide which help them reduce number of pesticide
applications on cotton crop. Farmers were also asked about the reasons for not using
these practices. The main reason for not using IPM was the uncertainty about
effectiveness of IPM practices. Most of the farmers fully believe that they cannot secure
their crop without using pesticide. Some alternative pest control practices that farmers in
the study area apply include; crop rotation, pest scouting, traps, enemy pests and the
‗burn‘ system. However, most of the farmers do not know about IPM.
5.3.2 Pesticide spray frequency
The survey found that farmers often apply pesticide very frequently. It was quite
common for farmers (73 percent) to use pesticide more than 10 times on cotton in a
season. The spray frequency is as high as 16 on cotton crop in one season. Almost all the
farmers found mixing several different brands together and the common reason of this
practice was better control over different type of insects at a time.
Mean application for different crops is shown in figure 5.4. On average, the
farmers in district Vehari (11.8 spray) were found spraying frequently than the farmers
in district Lodhran (11.3spray) in a season on cotton crop. However, comparison of both
districts shows that spray frequencies on wheat and vegetables 69 are almost same in both
districts. The result suggests that pesticides are getting less and less effective against
pests in the region as described by Azeem et al, 2002; Iqbal, et al 1997; Husnain, 1999;
and Poswal et al, 1998. Therefore, results are the indication of development of pest
68
See appendix table 18A.
69 See appendix Figure.2A
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resistance to pesticide over time. Many other studies have also noted this comportment
in the region (Dasgupta, 2005a; Chitra, 2006). The occurrence of pest resistance in the
cotton producing zones of Punjab tends to increase the cost of producing cotton and it
also leads to increased health and environmental problems in the region.
Figure 5.4. Mean pesticide application on different crops
5.3.3 Use of pesticide by toxicity classification
Pesticide use can be measured by many ways. The well established measurement
indicators include; by number of pesticide applications on a crop in a season, by
absolute quantity of pesticides used, and a ―measure of the relative risk or toxicity‖ of
the pesticide. The first two types of measurements e.g. absolute quantity of pesticides
used and number of pesticide applications are easy to handle and interpret, ―however,
factoring in the relative risk of each pesticide requires the adoption of a methodology
that can rank one pesticide as more toxic than another‖ (Meisner, 2005). The
methodology adopted for this study is described below.
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By simply summing all pesticides measured as kg of active ingredient used in
crop protection. ―To gauge the relative toxicity of each active ingredient, a measure
called the LD50 (or lethal dose 50%) is used.70 LD50 is a statistical estimate of the
number of milligrams (mg) of toxicant per kilogram (kg) of bodyweight required to kill
50% of a large population of test animals.71
Pesticides with a lower LD50 value are
more toxic‖ (Meisner, 2005). To better understand the extent of risk exposure, the study
used widely-known categorical method developed by the World Health Organization72
(WHO) which is also based on the LD50 measure.73
Pesticides are divided into 4 major
hazard groups: Category Ia & Ib (extremely hazardous& highly hazardous), Category II
(moderately hazardous), Category III (slightly hazardous), and Category U (unlikely to
present acute hazard if used safely).
Table 5.7.Total amount of pesticide applied by WHO classification
Category Total (kg A.I.) Percent
Extremely hazardous (Ia) 0.0 0.0
Highly hazardous (Ib) 1137.8 23.3
Moderately hazardous (II) 2666.0 54.7
Slightly hazardous (III) 878.5 18.0
Unlikely (U) 193.1 4.0
Total 4875.4 100
In table 5.7, the sum of the total amounts of active ingredient used under the WHO
classification system is provided. Most of the pesticides (54.7%) in use are moderately
70
The WHO recommended classification of pesticide by hazard and guidelines to classification 2004. 71
It is based on experiments with animals 72
See table 7.A in appendix for WHO hazard classification methodology. 73
The WHO toxicity rating is based on the lowest published oral LD50, typically tested on rats. While
WHO ratings generally reflect acute toxicity, they also take into account other toxic effects such as
reproductive and developmental toxicity (WHO, 2002; Meisner, 2005).
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hazardous (category II). Moreover, cotton accounts more than 70% of total pesticide use
in the study area (see table 5.8).
Table 5.8. Use of pesticide on selected crops by WHO classification (%)
Category I Category II Category III Category U
Cotton 68.7 87.2 33.2 61.8
Vegetables 21.8 9.0 18.6 28.0
Wheat 0.5 1.6 45.9 0.0
Others 8.9 2.3 2.3 10.3
Total 100 100 100 100
Note that extremely hazardous (category Ia) is non-existent but highly hazardous
(category Ib) is still being used by a large fraction of farmers. The more concerning is
the use of this class of pesticide on vegetables in large quantity, seriously threatening
consumer health and calls for immediate attention of policy makers and regulators.
5.4 Health and environmental impacts of pesticide use
5.4.1 Health effects of pesticide use
Farmers were questioned74 do they believe that pesticide has short term or long
term negative health effects? More than 84 percent farmers interviewed believed that
pesticide could have some affect on their health. The distribution of perceived health
impacts is displayed in Figure 5.5 which shows how farmers rated the effect of pesticide
74 Are farmer’s self-reported health effects a credible measure? The detailed and comprehensive
information for farmers is non-existent and actually beyond the scope of present study. However, as
Dasgupta (2005a) explained that the studies using medical tests of farmers conducted on rice and
vegetable farmers in Philippines, Indonesia and Vietnam revealed that 58% - 99% of the farmers exposed
to pesticide had at least one health effect (Kishi et al., 1995; Antle and Pingali, 1994; Rola and Pingali,
1993). These evidence suggest that the degree of upward bias may not be large (Dasgupta et al ., 2005a).
98
on their health. 48% and 22% said the effects were little and some small, compared to
8% and 1% believing it large and very large (respectively). 16% of farmers answered ―I
do not know‖; however 5% said that pesticide had no influence on their health. Most of
the farmers who do not perceive pesticide as a health hazard, either never had suffered
from poisoning or they do not know that the illness was because of pesticide.
Figure 5.5. Distribution of farmers‘ attitudes towards the affect of pesticide on their health
Farmers were also asked whether they have experienced any health problem while
dealing with pesticides. Almost 82 percent of farmers said they have experienced health
effects during pesticide application. The most common signs75 and symptom76
experienced were eye (irritation: 33%), neurological (headaches: 26%, dizziness: 13%),
gastrointestinal (vomiting: 9%), respiratory (shortness of breath: 10%), dermal (skin
irritation: 33%) and (fever: 2%).
As many as 34% farmers have experienced multiple health effects. The
maximum numbers of symptom reported were 6. To see whether farmers also believe
75
*Sign: something you can observe or see that requires an examination
76 *Symptom: something a person feels but you cannot see.
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that these symptoms and signs were due to pesticide operations, 63% farmers believed
that the above mentioned signs and symptoms were related to pesticide use. More than
44 % of them strongly believed that signs and symptoms they experienced were due to
pesticide operations.
Figure 5.6. Distribution of health effects experienced by farmers (%)
When suffering from illness, most of the farmers cure themselves by using
home-made remedies such as drinking lemon juice, saltish water in case of vomiting and
massage to the body with bitter oil (tara mera ) in case of skin irritation. While many
farmers believe that these symptoms are routine matter or common and they are not
worry about them. Only few of them visited doctor because the illness was serious.
These results tend to analogous to other studies. Kishi et al (1995) reported that only
24% of all the pesticide applicators who reported symptoms took medication and less
than 1% of farmers who experienced health effects visited hospital for proper
examination. Similarly, Ajayi (2000) noted that more than 80% pesticide applicators do
not think that during pesticide application they encounter any extraordinary health
problem that is beyond the normal level. Only in 2% cases, the person with pesticide
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associated symptom visited health care centers and hospitals to seek formal medical
assistance.
5.4.2 Impact of pesticide use on the environment
In addition to health effects, it is also important to highlight the consequences of
pesticide use on the environment. It was reported that surface water is polluted in study
area. More than fifty percent farmers in the survey reported that canal water may be
polluted from pesticide residues because of frequent pesticide use. They said that
pesticide, although unintentionally went into canal through washing pesticide bottles and
sprayers. They further reported that few years back in spraying season they used to see
dead fishes and frogs in canals and ponds and dead birds in fields but now they are non-
existent in the area. Pesticides are reported as the main contributing factor behind this
destruction. While describing air pollution, many of the farmers (37%) reported smell of
chemicals in the air. Some of them also said that some times in spraying season they
cannot take breath easily when they are in the fields.
5.5 Willingness to Pay for safer pesticide
This study also aims to know how much value, farmers place for their health.
Information was also collected on farmer‘s attitudes towards better pest management
techniques such as IPM which is supposed to be environmental friendly. Farmers were
asked how much they are willing to pay for IPM, keeping in view the environmental and
health effects of current practices of pesticide use. The summary of the farmer‘s
responses for both the districts is presented in table 5.9.
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Respondents were given two choices to indicate his WTP. They can either express
their WTP in actual monetary amounts (in Rs) which later converted into percentages or
in percentage directly which ultimately reduce farmer‘s need to think about prices of
different pesticides and make mental calculations. The result shows that out of 318
respondents 73 (22.9 percent) farmers are not willing to pay any premium.
Table 5.9. Distribution of Willingness to pay responses (%)
Willingness to pay category Both Districts Vehari Lodhran
Not willing to pay 22.9 17.4 27.8
Willing to pay = (1-5) % premium 21.6 26.2 17.8
Willing to pay = (6-10) % premium 39.8 36.9 42.6
Willing to pay = (11-15) % premium 1.9 2.0 1.8
Willing to pay = (16-20) %premium 13.2 17.4 9.5
Willing to pay = above 20 % premium 0.3 0.0 0.6
Total 100 100 100
The zero responses are significantly higher in district Lodhran which was
expected, since farmers in district Lodhran are relatively less educated as well as having
less mean income and risk perception. This is evident from the table 5.10. The results,
therefore, appear logical and consistent with the literature.
Table 5.10. Distribution of Mean WTP by district
Mean WTP (%) Mean WTP amount in (Rs)
WTP in Lodhran 7.5 542
WTP in Vehari 8.8 628
Total sample WTP 8.1 582
Overall, the mean willingness to pay appears to be very low, as compared to
other studies such as Garming et al. (2006) found that farmers in Nicaragua willing to
pay 28% more, the total cost of pesticide. Similarly, Cuyno (1999) found that
Philippines farmers were willing to pay 22% more of the total pesticide costs. This is
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however not surprising, if considering data we come to know that most of the farmers
are poor.
In this study, size of the farm is used as a proxy to wealth. It is expected that, the
farmers having more land are more willing to pay for alternative safe methods of pest
management than those with less acres of land. The table 5.11 shows that it seems true;
the farmers who hold more land, are more willing to pay for safe alternatives than those
who hold less land.
Table 5.11. Distribution of willingness to pay by farm size (%)
Farm size WTP
Total No WTP Up -5 % 6 -10% 11-20% 20% +
Up to2.50 0.9 3.8 6.3 1.6 0.3 12.9
2.6-5.0 2.8 4.7 6.9 2.8 0.9 18.2
5.0-10.0 1.9 6.9 11.9 3.1 0.9 24.8
10.1-25.0 2.5 7.2 18.9 5.7 3.5 37.7
25.1-50.0 0.0 0.3 2.5 0.0 0.3 3.1
50.1-100 0.0 0.0 1.6 0.3 0.0 1.9
100+ 0.0 0.3 0.9 0.0 0.0 1.3
Total 8.2 23.3 49.1 13.5 6.0 100.0
The respondents‘ beliefs concerning pesticide associated health risks are also
important determinant of WTP in the present context. Results showed that the farmers
who believe that pesticide use has medium amount of risk and significant amount of risk
to their health are more willing to pay than the farmers who believe that pesticide use
has no risk to their health or it has some small risk.
Table 5.12. Distribution of WTP by risk perception (%)
Risk perception WTP
Total No WTP Up -5 % 6 -10% 11-20% 20% +
No risk at all 21.2 51.6 16.4 10.8 0.0 100.0
some small risks 8.4 28.6 46.8 11.0 5.2 100.0
A medium amount of risk 5.4 21.6 51.4 16.2 5.4 100.0
A significant amount of risk 6.7 11.1 53.3 15.6 13.3 100.0
very toxic risks 0.0 0.0 60.0 40.0 0.0 100.0
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The willingness to pay for safe alternatives or IPM increases with income which
is evident from the table 5.13. The table shows that the farmers in lower categories of
income are less likely to fall in higher categories of WTP compared to the farmers in
higher income categories. The results are consistent with expectations and economic
theory.
Table 5.13. Distribution of WTP by income
Income WTP
Total No WTP Up -5 % 6 -10% 11-20% 20% +
Up to 5000 30.4 42.3 15.8 11.5 0.0 26
5000-1000 9.0 27.8 49.3 11.1 2.8 144
1000-15000 4.1 21.9 53.4 13.7 6.8 73
15000-20000 10.8 10.8 37.8 24.3 16.2 37
20000-25000 4.3 8.7 52.2 17.4 17.4 23
25000 + 6.6 6.7 80.0 6.7 0.0 15
5.6 Summary
In this chapter, survey based information on farmers with reference to farmer‘s
characteristics, their knowledge, attitude, health effects and pesticide practices are
presented. The age distribution of sample farmers varies from 18 to 66 years, with an
average age of 33.3 years approximately. Most of the farmers (67.3%) were in age
groups 21-30 and 31-40 years. The information on farmer‘s characteristics shows that
about one-third of the respondents cannot read or write. Over 73% of respondents
received education of different levels. The majority of farmers 75.5 percent owned land.
More than 10 percent have rental arrangements and 6 percent were sharecropper.
Majority of the farmers surveyed were small scale farmers (about 31% hold either 5 or
less than 5 acre of land). The average household size in district Lodhran is 6 and in
district Vehari is 6.8.
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The survey results show that all the farmers were dependent upon synthetic
pesticide and they regard pesticide an integral part of agriculture. Most of them do not
know about IPM. Farmers have been found frequently exposed to pesticide. The
prevalence of this exposure is more in district Lodhran, where over 90 percent farmers
reported at least one health problem. This ratio is less in district Vehari, where almost 80
percent farmers appeared to report these problems. The risk perception would be
expected to be heightened in district Lodhran than in district Vehari. The results
however, do not support this logic. The farmers in district Lodhran more likely to give
low priority to health considerations and grossly under-estimating pesticide risk. Low
level of education combined with cultural/local beliefs regarding health conditions is the
possible reason of this misperception.
Farmers appeared to give low priority to health considerations and grossly under-
estimating pesticide risk. They tended to consider those health effects as common
problems. This misperception is largely translated in practical behavior where almost all
the farmers did not visit hospital or doctor for medication. The misperception of farmers
on the potential hazards of pesticide to health also appeared to have much impact on
field practices of pesticide use, where more than 80% pesticide used were fall in WHO
categories of highly and moderately hazardous. In terms of crops, cotton alone received
over 70% of total quantity. Similar pattern were appeared in terms of toxicity, where
cotton consumed over 88% of highly and moderately hazardous pesticides. The use of
highly hazardous pesticides in huge quantity on vegetables raises serious health concern
and calls for immediate attention of policy makers. Further, it was quite common for
105
farmers (73 percent) to use pesticide more than 10 times on cotton in a season. The
spray frequency is as high as 16 on cotton crop in one season.
Most of the farmers partially covered their body with protective clothing while
mixing or spraying pesticide despite knowing possible health hazard. The formal
training and information on safe use of pesticide is almost non-existent. Farmers are
totally dependent on pesticide dealers and salesmen. Agricultural extension services are
extremely limited in the area. The chapter also discusses farmer‘s willingness to pay for
practices like IPM which are supposed to be environmentally friendly. The results show
that farmers are willing to pay at least 8.1% more money for safe alternative crop
protection approaches, if it guarantees no damage to their agriculture economy. Support
for safe alternative was higher among those farmers who had higher income, higher risk
perception, higher education and who lived in district Vehari.
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Chapter 6
The Conceptual Framework
6.1 Health Belief Model and Pesticide Use Behavior
This study seeks theoretical support from Health Belief Model to understand
farmer‘s safe behavior of pesticide use. Within the framework of Health Belief Model,
the motivation of a person to adopt a positive health behavior can be classified into
following categories:
i) Individual perceptions: These are the factors that deal with the importance of
health to the individual. These factors affect the perception of disease or illness and
they are perceived susceptibility and perceived severity of the disease.
ii) Modifying behaviors: These factors include individual characteristics and
demographic variables.
iii) Likelihood of action: These include all those factors that indicate the
probability of taking suggested health action to prevent disease (Green, 2010). These
factors jointly affect an individual to undertake the recommended preventive health
action.
A major problem that emerged out of the HBM framework is that it largely lacks
accepted scale. Therefore, different researchers adopted scales on their own and used
different questions in the questionnaire to illustrate the same risk perception.
Consequently, it made very difficult to compare different studies and to identify most
appropriate scales of the HBM (Green, 2010).The present study has adopted more direct
approach to apply HBM in the context of farmer‘s health behavior which avoids many
107
of such problems. Instead of using a respondent‘s perceived susceptibility, following
Lichtenberg and Zimmerman (1999) this study uses actual negative health experience
associated with pesticide use which is a more direct measure of health risk relative to
perceived health risk. The susceptibility component of health belief model ―is the one
most closely analogous to the health experiences that farmers have reported in
connection with pesticide‖ (Lichtenberg et al, 1999). The actual experience of health
problem heightens individual‘s perception regarding health threats. The individual‘s
heightened perception regarding health threats may or may not motivate farmers to
change their behavior with respect to pesticide use and safety (Lichtenberg et al, 1999).
The conceptual framework used in the present study is depicted in figure 6.1.
Figure 6.1. Relationship between health experiences, risk perception and pesticide use behavior
Modifying Factors
Demographic & socioeconomic
Variables: (Age, Education,
Knowledge about the disease,
Economic and Psychological
variables, etc). Mass media
campaigns, Advice from others,
Environmental damage (e.g. death of
fish, frog, and birds)
Pesticide label, Printed material
Perceived Benefits of
preventive action
minus Perceived
barriers to
preventive action
Heightened risk perception
Actual health
experiences
Likelihood of taking
Preventive health
action/alternative
pest management
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Based on conceptual understanding, three independent models of information
behavior linkages have been constructed in this chapter. The first model discusses
pesticide use behavior at personal level. This model links farmer‘s adverse health
experiences and risk perceptions to the safe (protective) behavior of pesticide use. In the
second model, pesticide use behavior is discussed at environmental level to explore
broader utility of health belief model. This model examines the adoption of
environmentally sound practices by farmers. It specifically relates health experience and
risk perception to environmentally sound behavior of pesticide use. In the third model,
Contingent Valuation Method is combined with health belief model to understand and
interpret the value of environmentally sound practices. These models have been
discussed in detail in the following sections.
6.2 Health experience, risk perception and safety behavior (Model 1)
Pesticides are by nature a poison. Therefore, unsafe use of pesticides likely to
impair health of the farmers. Being the most important agricultural input and because of
close interdependence between farm and farm worker, the negative health status of the
farmer can affect farm production77 significantly and overall welfare of farm household.
One of the efforts by the farmers to protect themselves from direct exposure of pesticide
use, improve pesticide practices and reduce potential health effects of chemical use is to
undertake protective measures. However, the decision to use protective measures
depends on cost (barriers; e.g. monetary cost of protective measures and cost of
77
The production may decrease, through a reduction in the number of farm labour that are available to
work at farm, through a reduction in the farm output and through a reduction in the leisure time available
for sick worker or more stress of work for the healthy members of farm household who have to work more
and harder to fill in for sick members.
109
discomfort)78 which requires less use of safety measures and benefits (improved health)
which requires higher use of protective measures. A farmer will use protective measures
only if he believes that he will be better off by doing so. Analogously a farmer will use
protective measures only if he believes that positive health benefits of using protective
measures (perceived benefits) are greater than the cost (perceived cost) of using
protective measures.
At the same time, the decision to use protective measures is determined by the
level of awareness and quality of information. If information gap exists, health costs of
pesticide use are most likely not to be included in decision-making which may result in
sub-optimal choices. If a farm worker is aware of the health consequences of pesticide
use on overall household welfare, he/she would choose to use more protective clothing
or look for alternative technologies (e.g. IPM). Thus, the accuracy of information and
knowledge79 of farmers regarding pesticide safety are key issues in pesticide safety
decision making behavior.
In studying how farmers react to information about pesticide associated health
effects, this section builds an empirical model that links farmer‘s adverse health
problems and risk perceptions to safety behavior of pesticide use. The model assumes
that as farmer gets information that certain negative health effect is caused by pesticide,
he formulates perception80 about the substance and his cognitive process converts
perception into mind-set, and he/ she eventually makes a choice (Huang, 1993). Theory
78
The use of protective measures in hot and humid climate makes farmers upset
79 The knowledge about health effects, the risk perception and the importance that farmers attach to health
issues are important determinants of safety behavior. 80
Perceptions are an individual’s subjective mental construct which is vibrant and active in nature, that
is, over time, it can change considerably (Huang, 1993).
110
also reveals that there are certain other variables that influence perceptions and hence
behavior e.g. knowledge, information, personal characteristics and cultural environment.
6.2.1 Empirical model
On the basis of conceptual framework, an empirical model is designed that
analyze the relationships between health experience, risk perception and farmer‘s
decision-making behavior. The framework of empirical model as follows:
RP = g (HE, Z) + ξ1 (1).
SB = h (RP, Z) +ξ2 (2).
Where RP represents farmer‘s perception of pesticide associated health risk, HE
represent health effects experienced by farmers while using pesticide, Z represent other
variables included in the equation to measure farmers r isk perception, SB defines the
safety or protective behavior and ξis represent random errors.
The disturbances are such that
Cov (ξ1, ξ2) =0
―That is, the same period disturbances in different equations are uncorrelated
(technically, this is the assumption of zero contemporaneous correlation)‖ Gujarati,
2004).
Now considering first equation, on the right hand side it contains only
explanatory variables and ―by assumption they are uncorrelated with the disturbance
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term ξ1, the equation satisfies the critical assumption, the uncorrelatedness between the
explanatory variables and the stochastic disturbance terms‖ (Gujarati, 2004).
The same argument carries when we consider the second equation because RP is
uncorrelated with ξ2. Actually we do not have simultaneous equation problem in this
situation. It is clear from the structure of the system that there is no interdependence
among the endogenous variables. Thus RP affects SB but SB does not affect RP. RP
equation exhibits unilateral causal dependence, hence one can proceed with the
estimation of single equation separately (Gujarati, 2004).
Returning back to study framework, it is assumed that risk perception of
pesticide can be determined by variety of factors like health effects experienced by
farmers, the level of education and pesticide handling training (Dasgupta, 2005). Hence
equation (1) assumes that farmers formulate their perception from available information
and demographic characteristics. Thus, it is rewritten as:
RISK PERCEPTION = f (HEALTH EXPERIENCES, AGE, EDUCATION, TRAINING, INCOME, DISTRICT DUMMY) + ξ1
The above equation shows that risk perception of a farmer is determined by
health experiences, age, education, income and training. The district dummy is also
included in the equation to see possible differences in farmer‘s perception with re spect
to region (districts). The risk perception (RP) variable is constructed from the survey
data, where respondents were given five options of likert scale statements from ‗No risk
at all to fatal risk‘ and were asked to rank their concerns. The answers were given codes
112
accordingly and were included in the equation as risk perception data. Risk perception is
expected to be positively related with health experiences, education, training and age.81
The equation (2) is rewritten as:
SAFETY BEHAVIOR = f (HEALTH EXPERIENCES, RISK PERCEPTION, AGE,
EDUCATION, TRAINING, INCOME, DISTRICT DUMMY) + ξ2
The safe behavior is specified as the function of health experiences, risk
perception, age, education, income and training.
The dependent variables in both equations ―risk perception‖ and ―safe behavior‖
are multiple response variables that they demonstrate an intrinsic order. Therefore, one
must look for ―ordered qualitative response models‖ to analyze above equations. Two
broad choices, the logistic or standard normal density functions, are readily available. If
is the logistic density, the resulting probability model is the ordered logit; if is
the standard normal density, the resulting probability model82 is the ordered probit. To
model health experience and farmer‘s attitudes, an ordered probit model is preferred
over order logit. An ordered probit was preferred over others because it allows
researcher to calculate predicted probabilities and marginal effects83 directly (Cranfield,
2003). Marginal effects explain the change in predicted probability due to a change in
explanatory variable. The framework used in the study is as follows:
81
Assuming, more age people generally more conscious and caring about health compared to young
people. 82
Both of these densities are symmetric, bell-shaped curves, although the logistic distribution has heavier
tails than the standard normal. Since the distributions are similar, the results derived using two models
are quite similar. 83
Parameter estimates can also be used to calculate the marginal effects of explanatory variables on the
predicted probabilities
113
Ỳ= x'ß + e e│x ~ Normal (0, 1)
Where
Ỳ = the latent (or unobserved) variable,
X = vector of explanatory variables,
ß = vector of parameters
e = independently and identically distributed error term with mean zero and variance
one (Wooldridge: P (505)).
Let < < - - - - < j be unknown cut points and define:
y=0 if Ỳ≤ 1
y=1 if 1< Ỳ≤ 2
y=j if Ỳ> j
The cut points are very useful in ordered probit model84 because they allow us to
compute predicted probability and marginal effects (Hoffmann, 2004).
84 Given the standard normal assumption for e, it is straightforward to derive the conditional distribution
of y given x; we simply compute each response probability:
P (y=0│x) = P (Ỳ≤ a1│x) = P (xß + e ≤ a1│x) = xß)
P (y=1│x) P ( 1 < Ỳ ≤ 2│x) = X ß 1 X ß)
.
.
114
6.3 Health experience, farmers’ attitudes and environmentally sound
behavior of pesticide use (Model 2)
Pesticide use behavior discussed in the previous section explained that the
information regarding health threats from pesticide use help farmers to promote
solutions at personal levels. This section discusses pesticide use decision at
environmental level to explore broader utility of Health Belief Model in understanding
the role of concrete information (health experiences), in pesticide use behavior.
Therefore, health behavior theory is combined with consumer choice problem.
One basic premise in new classic welfare (utility) economics is that individuals
are best judges of their welfare and that inferences can be drawn about welfare (utility)
for each individual by observing the individual‘s choice of bundles of goods and
services (Gunatilake, 2003). Suppose a consumer (farmer) who consumes a product
(pesticide) approaches the same product (pesticide) but in a more health and
environmentally sound form. A farmer who moves from consuming a usual pesticide to
the one which is assumed potentially safe to the health and environment, presumably he
believes that either the choice of the safe pesticide increases his utility or it keeps same
at the original level. If utility of farmer does not increase, then he is not rationally be
willing to change chemical pesticide with safe alternative, as an increase in efforts
results in a lower level of utility compared to the original level. If utility of farmer
increases by using integrated pest management, then he may be more likely willing to
adopt IPM, provided that the present choice does not lower utility beyond the base leve l.
P (y= j-1│x) P ( j-1 < Ỳ ≤ j│x) = j X ß j-1 X ß)
P (y= j│x) P (Ỳ > j│x) = 1- j X ß)
115
Specifically, a farmer‘s preference for safe alternative entirely depends on change in
utility:
Safe alternative f U
Where U is the change in utility and f 0……….. ………............... (1)
The above equation shows that an individual farmer‘s choice for safe alternative
depends on the change in utility in terms of improved health due to consumption of IPM.
―Since the choice of one product over another is a discrete one, it is convenient to cast
choice in a random utility85 setting. In this setting, an individual‘s utility function, and
hence utility arising from the choice of alternative, is composed of a deterministic
component and a random component. The deterministic component reflects observable
alternative specific factors (i.e., attributes) that influence the level of utility realized by
choosing the ith product. The random component represents unobservable factors, such
as unobservable variations in preferences, random individual behavior and measurement
error.‖86 Alternative i is chosen if and only if the utility arising from its choice exceeds
the utility arising from the currently consumer product. 87
85
Random utility theory is characteristically identified with preferences that are associated with the
design of discrete choice experiments. In the random utility model, the utility function is expressed as Ui
X ß , where Ui is the utility arising from the choice of the ith alternative, X i ß is the deterministic
component of the utility function, Xi is a vector of observable, alternative specific factors that influence
utility, ß is a parameter vector and is the random component. 86
Cranfield et al, (2003)
87 Alternative i is chosen if and only if Ui Uj for all j i (or that U Ui Uj ). Willingness to
adopt IPM can be re-written, without loss of generality, as IPM X ß , where X Xi Xj and
i j . Put another way, the ith
alternative is chosen if and only if the change in utility (arising from a switch in products consumed) is positive.
116
6.3.1 Empirical framework
Based on the conceptual framework, an empirical model is designed to analyze
the relationship that links health experience and risk perception in a farmer‘s decision-
making process for alternative pest management. The framework is spec ified as follows:
RP = g (HE, Z) + ξ1 (1).
ESB = h (RP, Z) +ξ2 (2).
Where RP represents farmer‘s perception of pesticide‘s health risk, HE represent
health experiences a farmer faced while using pesticide, Z represent all other variables
included in the equation to measure farmers risk perception, ESB defines the
environmentally sound behavior of pesticide use and ξis represent random errors.
The disturbances are such that Cov (ξ1, ξ2) =0
The Equation (2) is restated as:
ENVIRONMENTALLY SOUND BEHAVIOR OF PESTICIDE USE = f (RISK PERCEPTION, HEALTH EXPERIENCES, AGE, FARM SIZE, INCOME,
EDUCATION, TRAINING, DISTRICT DUMMY) + ξ2
Thus in the equation, farmers‘ behavior of pesticide use is specified as a function
of risk perception, health experiences, age, education, income, farm size and training.
The region dummy is also included in the equation to see possible differences in
farmer‘s decision with respect to location. To test the stated hypotheses, the
environmentally sound behavior (ESB) variable is constructed based on data collected
117
from the survey, where respondents were asked that thinking about adverse health
effects of pesticide use, whether they adopted any alternative pest management
technique such as integrated pest management which is supposed to be environmentally
sound. A positive answer is taken as environmentally sound. Environmentally sound
behavior is expected to be positively related with health experiences, education, training,
income and age.
The dependent variable takes the form of binary response variable, hence binary
response (probit or logit) models are available. The probit model will be used here. The
latent variable yi as follows:
Where is independent of , is a K _ 1 vector of parameters, and e│x ~ Normal (0, 1).
Instead of observing , we observe only a binary variable indicating the sign of :
We can easily obtain the distribution of yi given xi:
118
where P88 (y │x means the probability that an event occurs given the values of x
and ei is the standard normal variable and denotes the standard normal cumulative
distribution function (cdf) which can be written explicitly in the present context as:
( ) =
=
6.4 Farmer’s Willingness to Pay for Integrated Pest Management
(model 3)
Market usually non-existent for many of environmental goods and services.
Nonetheless, individuals are benefited from the use of these goods and services and
losses of such environmental goods and services lead to reduce utility of these
individuals. To overcome these limitations, economists developed some modern
approaches for assessing changes in value for these goods in the absence of markets.
One such technique is Contingent Valuation (CV). In this method individuals are
directly questioned about their willingness-to-pay for a given good or service. This is a
survey based technique where ―respondents are offered a hypothetical market and they
are asked to express their WTP for existing or potential environmental goods or services
not reflected in any real market‖. ―The monetary values obtained in this way are thought
to be contingent upon the nature of the constructed market, and the commodity
described in the survey scenario‖ [Garming et al (2006); Khan, (2010)]. The answers
88 Since P represents probability that an event will occur, it is measured by the area of standard normal
curve from to .
119
provided us a direct way to obtain demand curve89 for an environmental good or service
(Hanemann, 1994). Since economic perspective on health focuses on effects that people
are aware of and want to avoid, that is, health effects that would decrease their utility
(Khan, 2010). Keeping in mind that individual‘s preferences give better/suitable basis
for making decisions about changes in welfare, health cost of pesticide use should be
measured according to individual‘s preferences or willingness to pay. Hence, Contingent
Valuation Method (CV) is used to serve this purpose.
Microeconomic theory provides necessary elements to model the decision
process of an individual‘s choice of non-market good. In Contingent Valuation Method,
the Hicksian concept of compensated demand functions is generally used to study the
change in the amount of a non-market good like health with respect to a constant utility
for the individual consumer. Suppose the initial utility of farm household (U1) is given
as the sum of health (H1) and all other goods, expressed as income (Y1). Now suppose
the health supply is improved from H1 to H2, holding income constant at initial utility,
the farmer moves to a higher indifference curve and hence utility level (U2). The value
of the change in health supply is measured as that amount of income that the farmer is
willing to pay (WTP) in order to be indifferent about the change in health i.e. to remain
on his initial utility level. Conceptually, the economic valuation can be represented by
indirect utility framework as given below:
U1 = Y1 + H1 = Y1 – C (WTP) + H2
89
That could not otherwise be seen from the market data.
120
Where, U1 = the initial utility, Y1 = the current income, H1 = the base level health, H2 =
the improved health, and WTP = the amount of income a farmer is ready to pay in order
to gain improved health status while maintaining a constant level of utility. The research
has shown that willingness to pay is influenced by number of factors that may include
characteristics of the farmers and quality and attributes of the environmental product.
6.4.1 Empirical Model
The willingness-to-pay variable is in the form of multiple response variable and
since it also has intrinsic order. Therefore, an ordered qualitative response model is
preferred over multinomial model for empirical analysis. The WTP model is expressed
as:
WTP* X ß
Where WTP*= the unobserved dependent variable measuring willingness-to-pay,
X= vector of independent variables,
ß= is a vector of parameters
an independently and identically distributed error term with mean zero and variance
one. ―If a farmer‘s WTP* falls within a certain range, their WTP is assigned a numerical
value that reflects the category in which their unobserved willingness-to-pay lies‖
(Cranfield et al, 2003).
The probability of WTP for the J finite categories is expressed as:
Pr (WTP= j-1) j X ß j-1 X ß) j J
121
Where is a Cumulative Density Function (CDF), which measures the
probability of WTP. Two broad choices, the logistic or standard normal density
functions, are readily available. If is the logistic density, the resulting probability
model is the ordered logit; if is the standard normal density, the resulting probability
model is the ordered probit. To model willingness to pay for environmentally sound pest
management, an ordered probit model is preferred over ordered logit. An ordered probit
was preferred over others because it allows researcher to calculate predicted
probabilities90 and marginal effects91 directly (Cranfield, 2003). Marginal effects explain
the change in predicted probability due to a change in explanatory variable. The
explanatory variables included in the model are household characteristics, pesticide
associated health effects, attitude or risk perception, experience with pesticide poisoning
and income.
6.5 Summary
This chapter highlights the role of information in pesticide use behavior. The
discussion on conceptual framework used to model pesticide use behavior highlights the
role and utility of Health Belief Model (HBM) to understand farmer‘s decision making
behavior. The Health Belief Model postulates that the person who experience negative
health problem is more willing to adopt preventive behavior to avoid that problem. It has
90
Predicted probabilities indicate the chance of the average farmer being willing -to-pay a premium
falling within each of the categorical premium levels. These provide valuable insight into farmer
preferences as they can be used to gauge the level of farmer WTP for safe pesticide products.
91 Parameter estimates can also be used to calculate the marginal effects of explanatory variables on the
predicted probabilities
122
been assumed that when a farmer encounters a health problem associated with pesticide,
his risk perception heightens regarding health consequences of pesticide use, resultantly
he/she choose to use more safety measures or look for safe alternative technologies of
pest management. Based on the conceptual framework, three separate models of
information behavior linkages have been designed.
The first model examines how farmers react to information about health effects
of pesticide use at personal level. This model assumes that negative health experience
induces farmers to undertake greater preventive measures. Therefore, an empirical
model is used to estimate the link between farmer‘s adverse health experiences and risk
perceptions to the safety behavior. In the second model, pesticide use behavior is
addressed at environmental level. Using the understanding of Health Belief Model, an
empirical model is developed that examines the adoption of environmentally sound
practices by farmers. It relates negative health experience and risk perception to
environmentally sound behavior of pesticide use. This model is more appropriate for
policy purposes.
In the third model, a framework for farmer‘s WTP for environmentally sound
pest management practices has been constructed. For this purpose, Contingent Valuation
Method is combined with health belief model to estimate the value of environmentally
sound pest management practices. Contingent Valuation Method is very useful in the
measurement of value of environmental goods and services, because it also includes
non-market value component.
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Chapter 7
Analysis of Pesticide Use Behavior
7.1 Health experience and farmers’ attitudes
In this section, one of the hypotheses that links pesticide associated health
experiences with farmer‘s attitudes is analyzed. Specifically, it is assumed that those
farmers who have experienced any negative health effect while pesticide application are
expected to have heightened concerns about health risk associated with pesticide use92
(Lichtenberg et al, 1999). To test this hypothesis risk perception is regressed with
independent variables like health experience, age, education, training, income, farm size
and geographical area. To have an initial understanding of the degree of relationships
between main variables used in the models, a simple matrix of correlations for both the
districts is presented in table 7.1 and 7.2.
Table 7.1.Pearson correlation coefficients (District Lodhran)
Lodhran
Variable
Name Age Income Education Perception
Health
Effects Training
Age
Income .564**
Education .391** .519**
Perception .500** .581** .514**
Health
effects .117 .175** .141 .558**
Training .564** .609** .431** .457** .133 *-Significance at 0.01 **-Significance at 0.05
92
This hypothesis is important because attitudes often drive environmentally protective behavior
124
Table 7.2.Pearson correlation coefficients (District Vehari)
Vehari
Variable Name Age Income Education Perception
Health
effects
Training
Age
Income .412**
Education -.139 .376**
Perception .064 .478** .637**
Health effects -.116 .004 .132 .370**
Training .096 .194* .130 .138 .003 *-Significance at 0.01 **-Significance at 0.05
The evidence indicates that many characteristics in both districts are not same.
Contrary to district Vehari, the age in district Lodhran is positively and significantly
correlated with all the variables though not significant with health effects. The positive
association between age and education in district Lodhran needs explanation. Before we
explain this result, it is useful to combine income of the individuals as well, since there
is also positive association between age and income in the district. The result underlines
the general phenomenon of rural communities in the country and cotton belt of Punjab in
particular, the so called widespread poverty. Although the general awareness regarding
education is high and educational facilities are improved than past, but for most of the
farming communities‘ poverty is still a main barrier to get their children in school.
Hence they join farming as early as youth and they are usually the ones who perform
agricultural activities, such as pesticide use. However, in the case of high income
(large/medium land holding) farmers, the children usually attend school and elders
perform these duties. This seems to be true when we see significant correlation between
income and education in the same district (see table 7.1). The result, though not
significant is quite opposite in Vehari district. Further, the positive correlation between
125
income and health effects is also worth noticing in district Lodhran. It can be interpreted
that low income farmers usually less likely to report health effects than high income
farmers. The data reveals that 37 percent farmers who experienced health effects during
or after pesticide use do not believe that the problems may necessarily be related to
pesticide use. They believe that these symptoms are common. Most of these are small
scale uneducated farmers. The correlation between health effects and training is positive
but insignificant in district Vehari as well as district Lodhran. Similarly, the correlation
between health effects and education is insignificant in both district Vehari and Lodhran.
The positive associations between education and perception and education and income
are very much expected.
7.1.1 Ordered probit results for risk perception of pesticide use
The estimated coefficients of ordered probit are presented in table 7.3. The
estimated model has a Pseudo R2 about 0.1302. The null hypothesis which explicitly
assumes that all the coefficients in the model are jointly equal to zero is rejected at 1%
level. The results of the ordered probit model are in line with most of the
assumptions/expectation. As expected, the results indicate that the farmers who reported,
they have experienced an adverse health effect while using pesticides have positive
association with risk perception. The result also shows that this association is very
strong and significant (see table 7.3). The risk perception also increases significantly
with more grades of education. This is explained by the fact that education increases
general awareness level of farmers regarding negative effects of pesticide on health and
environment. Almost all the results are analogous to prior expectations except farm size
and age. Both farm size and age of the farmers are negative to risk perception. The most
126
likely interpretation for farm size is that the large land holders are likely to use more
protective measures, observe less health effects and perceive less health risk of pesticide
use. Another interpretation for the result is that they may not be applying pesticide
regularly and usually get this job done by hired labour. The interpretation for age
variable is that over time farmers are used to of the health problems/illness that are
associated with pesticide spraying. They take them a routine matter and usually ignore
them. The analogous reasoning is explained by Ajayi, (2000).This relationship is
however, not significant. The district dummy shows that the perception is comparatively
less likely heightened in Lodhran than Vehari which is expected since education levels
of farmers are higher in Vehari than the education levels of farmers in Lodhran.
Table 7.3. Ordered probit results for risk perception
Variable Risk perception
Education .0669755*** (4.74)
Age -.0029556 (-0.46)
Income .0403085** (4.30)
Health effects .8227102*** (5.17)
Farm size -.0042312** (-2.03)
District Dummy(Vehari) .4222231*** (3.42)
IPM Training .8616012*** (5.59) Log likelihood = -426.70839, Pseudo R2 = 0.0895, LR chi2 (12) = 83.88***
93
Note: Z-scores in parenthesis .
In short, the variables like health impairment, farmer‘s education, as well as
training are statistically important variable to explain the variation in farmer‘s
perception of pesticide risk.
Since the maximum likelihood estimates (estimated coefficients) are not
marginal effects in non- linear models. Therefore in addition to coefficient estimates,
93
* - significant at the 10% level. ** - significant at the 5% level. *** - significant at the 1% level.
127
marginal effects are also presented. Table 7.4 presents predicted probabilities and
marginal effects for the five risk perception categories. These predicted probabilities do
not show any compact picture, when taken alone. However, they are very informative
when we see marginal change in an exogenous variable on these predicted probabilities.
Table 7.4. Predicted probabilities and marginal effects for risk perception categories
Perception=1 Perception=2 Perception=3 Perception=4 Perception=5
Predicted probabilities
.09560712 .33493779 .297088 .24219804 .03016905
Marginal effects
Age .0004939 .0006487 -.0001768 -.000767 -.0001988
Farm size .0007185 .0009439 -.0002572 -.0011159 -.0002892 Health effects
-.1909826 -.1495263 .1103873 .2025511 .0275705
Education -.0118074 -.0389282 .0105851 .0449501 .0081762
Income -.0069491 -.0426779 .0116047 .0492798 .0089637 District dummy -.0490742 -.1065122 .0289621 .1229888 .022371
The table 7.4 has two panels, the upper panel shows predicted probabilities and the
lower indicates the marginal effect for all explanatory variables. Since model includes
both continuous and binary variables, the interpretation of continuous and binary
variables is not same. In the case of continuous variables we usually interpret results
following our OLS regression understanding, such as o ther thing being constant, a unit
change in exogenous94 factor results a change in predicted probability equal to the size
of marginal effect. However the interpretation is different for binary variable95 that is the
94
Marginal effects for continuous variables case are calculated as:
Where is the normal probability distribution function. 95 In case of binary variables, the marginal effects are discretely approximated using the difference in
predicted probabilities when the discrete variable is set equal to one and zero:
128
marginal effect represents change in predicted probability provided that respondent falls
into that category. Note also that the marginal effects across all risk perception
categories (which are five here) must sum to zero for a particular explanatory variable
by definition96 (Cranfield et al, 2003).
Starting with the age variable of the farmers, they are likely to perceive no risk or
low risk, and less likely to perceive high risk with the increase of age. This is against our
expectations since we are taking age as the proxy of farming/pesticide use experience.
There are two possible explanations for the result. First; with the passage of time,
farmers are used to of these problems and they do not take these effects very serious. It
seems true, because many farmers believe that these health effects are routine matter and
they are ready to accept a certain level of health effects. This comportment may well
explain the lack of pesticide related health awareness. Second; these farmers may using
more protective measures and resultantly have not experienced/witnessed any health or
environmental problem.
The same is the case of farm size. The variable has positive marginal effects for
the first category as well as for the second category of risk perception (e.g., no risk at all
and low risk) but negative marginal effects for other three categories. An interpretation
for this result is that the land holding represents farme r‘s wealth. The more is the land,
the more is the farmer‘s ability to purchase protective measures. The farmers who are
using more protective measures are less likely to be effected from pesticide, hence less
(Cranfield, 2003).
96 Since the predicted probabilities for all the categories of risk perception must sum to one, the change in
probabilities for these categories must sum to zero.
129
likely to perceive risk perception. Another interpretation for the result is that they may
not be applying pesticide regularly and usually get this job done by hired labour,
resultantly; their perception of pesticide risk is low. The health effect variable has
negative marginal effects for the first category as well as for the second category of risk
perception (i.e., no risk at all and low risk) but positive marginal effects for other three
categories and this influence is very large for third (medium risk) and fourth (high risk)
categories. These results are very much expected since it is hypothesized that farmers
who experienced negative health impairment are more likely to perceive higher pesticide
risk. In short, the result indicates that health experiences strongly influence farmer‘s
attitudes. This result is very much similar to number of previous studies 97 and
underlying theory. Similarly, the marginal effects of training on no risk at all, low
risk/some small risk and medium amount of risk categories are negative, however the
marginal effects of training on high risk category and extremely high risk category is
positive and very strong. The finding is paralle l to that found by Lichtenberg and
Zimmerman (1999).
The marginal effect of education is negative for the first two categories of risk
perception, however it is positive for the higher classes. This suggests that farmers with
more education are less likely to perceive no risk of pesticide use on health and/or low
level of health risk and more likely to perceive high risk, medium amount of risk and
extremely high risk. Holding all other things equal, there is a higher probability of being
in lower perception categories when farmer‘s education is low compared to when
farmer‘s education is higher. Differently, more educated farmers are more likely to
97
(Lichtenberg et al, 1999;Dasgupta etal,2005a; Huang,1993)
130
perceive high risk perception relative to less educated ones. The income variable follows
similar pattern, though not significant. The marginal effect for the first and second
category of perception is negative but positive for higher classes of perception. One
possible interpretation for this result is that the high income farmers usually have more
access to information and more access to education, compared to low income/small land
holders since agriculture extension services are heavily skewed towards progressive
farmers. This is also evident from the farmer‘s responses in both the districts.
Farmers in district Vehari relative to the farmers of d istrict Lodhran are more
likely perceive greater risk to health from the use of pesticide. The marginal effects are
also stronger for Vehari than for Lodhran. Such differences are not surprising given the
diversity of the population between districts and relatively higher level of education
among sampled farmers in Vehari.
7.2 Health experience, risk perception and safety behavior
The seriousness with which farmers view health problems associated with
pesticide has been analyzed from two different angles (from personal safety perspective
and from environmental perspective). The behavioral factor studied in this section was
the extent, to which farmers used safety measures to avoid pesticide exposure when they
believe that they have experienced negative health effects from pesticides.
Result shows that farmers who experienced health symptoms during mixing or
spraying pesticide are more likely to adopt protective measures, ceteris paribus. The
findings support the hypothesis that there is a strong linkage between adverse health
effects and protective behavior. Seriousness of health risk is important factor in shaping
131
individual‘s behavior. In line with previous literature and theoretical background
individual‘s risk perception appeared as an important factor to convince farmers to take
more protection. Thus, this evidence suggests that pesticide associated negative health
problems act as a signal, changing farmer‘s future behavior toward pesticide safety. The
result is consistent with theory and priori expectations.
It is widely accepted that education enhances awareness regarding health which
can be seen from the table 7.5. The more educated farmers reported taking more
protective clothing than farmers with less education. The result implies that education
exerts a significant effect on the decision to adopt protective measures. The farmers who
got training of safe pesticide handling reported significantly higher concern about
protection. This could be interpreted as indicating that the more learned farmers in terms
of safety are more likely to select higher level of protection than non-trained farmers.
Similar findings were noted by Lichtenberg and Zimmerman (1999). District controls
reveal that protection level to avoid direct exposure from pesticide is not significantly
different in both districts.
Table 7.5. Results of ordered probit regression for protective behavior
Variable Dependent variable:
Protective behavior
Health Effect .6161179 (0.001)
Risk perception .1402597 (0.037)
Age .1077838 (0.103)
Education .1421597 (0.000)
Training .8896106 (0.000)
Income -.1684239 (0.047)
District Dummy -.028836 (0.823)
Farm size .0579337 (0.464) The values in parenthesis are P values. Log likelihood = -373.34005, Pseudo R2 = 0.0920,
LR chi2 (12) = 75.62 (0.000)
132
The negative relationship between income and protective measures is the
harshest which can‘t be explained properly. Results do not provide any evidence of
statistical association between the age of farmers and level of protection. Again same
arguments can be presented that with farming experience farmers are ready to accept
certain level of health effects and they consider them a routine matter. Therefore their
risk perception is low and they are less likely to take protective measures. Similarly no
significant relation was found between farm size and level of protection in the study
area. Overall, results indicate that farmers who have had health experiences do care
about the effects of pesticide application and do engage in safety practices. Table 7.6
shows the predicted probabilities and marginal effects for different categories of
protective measures evaluated at the means of the sample data.
Table7.6. Predicted probabilities and marginal effects from the estimated model
Prot = 2 Prot = 3 Prot = 4 Prot = 5 Prot = 6 Prot =7
Predicted probabilities .01565593 .15487806 .58602928 .20037795 .03541904 .00763975
Marginal effects
Age -.0042346 -.0230953 -.0064368 .0239071 .0075909 .0022687
Farm size -.0022761 -.0124137 -.0034598 .01285 .0040801 .0012194 Health effects
-.0376577 -.1454786 .0181434 1236833 .0326121 .0086976
Training -.0194261 -.1412899 -.1611067 .1836292 .0950573 .0431361 Education -.0055852 -.0304612 -.0084897 .0315319 .0100119 .0029923 Income .0066171 .0360889 .0100581 -.0373574 -.0118616 -.0035451 Perception -.0055106 -.030054 -.0083762 .0311104 .009878 .0029523 District dummy
.0011329 .0061788 .0017221 -.006396 -.0020308 -.000607
The health effect has negative marginal effects for first two categories but
positive marginal effects for higher categories of protection level. This is expected since
133
it is hypothesized that farmers who experienced negative health impairment are more
likely to use protection to avoid pesticide exposure.
The age of the farmers appear to affect averting behavior positively. With the
increase of age, farmers are more likely to be caring and using protective measures. The
same applies on farm size. The variable has positive relation with averting behavior. The
marginal effects for the higher categories of protection are positive. Thus more the size
of farm, the more is the farmer‘s ability to purchase protective measures and the more
likely he uses protective measures. The income variable however does not follow same
pattern which is surprising.
The marginal effects of training, education and risk perception have strong
influence on farmer‘s averting behavior. There is a higher probability of being in lower
categories of protection when farmer‘s education, training and risk perception are low
compared to when same are higher. Alternatively, more educated and trained farmers
are more likely to wear protective measures relative to less trained and less educated
ones. Although farmers in district Vehari relative to farmers of district Lodhran are
more educated but they are less likely to use protection. The result may indicate that
negative health effects have stronger impact on averting attitude of farmers than their
education.98 The result follows ‗health belief model‘ which states that actual experience
of adverse health effects (concrete information) have stronger impact on individual‘s
coping behavior than general pool of knowledge (abstract information/education).
98
More than 90% farmers in district Lodhran experienced health effects compared to about 80% farmers
in district Vehari.
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7.3 Health experience, risk perception and environmentally sound
behavior of pesticide use
This section examines to what extent farmers engage in pest management
practices that are considered environmentally sound in relation to health effects. A
binary variable is constructed by asking farmers that whether or not they use an
alternative to pesticide or engage in any activity which tends to reduce reliance on
pesticide. A probit model was used to examine the relationship between health
experience and alternative pest management practices. The probability of alternative pest
management practices used was also assumed to be a function of farm size, farmer‘s
characteristics and farmer‘s attitudes toward pesticide-related health experiences. The
incorporation of the additional variables controls for factors that may be associated with
health experience as well as decisions about using alternative pest management practices
and thus allows isolation of the effects of health experience. The probit results for the
use of alternative pesticide use as the function of health effects and other variables are
presented in the tables 7.7 and 7.8.
Table7.7. Maximum likelihood estimates of Probit for the use of alternative pest
management practices
Independent variable Dependent variable = IPM
Estimated coefficients Z-scores (P)
Perception .4762618 2.97 (0.003)
Health effects -.1566526 -0.41 (0.687)
Training 2.012281 6.87 (0.000)
Farm size -.006086 -0.85 ( 0.396)
Income .0074283 0.32 ( 0.749)
Age .0071272 0.50 (0.616)
Education .0809879 2.43 (0.015)
District dummy -1.155681 -3.21 (0.001)
Constant -2.400256 -3.53 (0.000) -Values in parenthesis are P values
135
The probit results did not support the hypothesis that farmers who have had an
adverse health experience related to pesticide use are more likely to adopt sound
behavior of pest management than farmers who have not had such experie nces. Neither
farm size nor income of the farmer had any effect on alternative pesticide use. Once
again, age is not significant to alternative pesticide use. Like previous result, training in
safe-handling of pesticide had positive effect on alternative pesticide use and this effect
is very strong also. Once again, education significantly affects alternative pest
management practices. Among districts, alternative pest management practices are more
likely prevalent in district Lodhran.
Predicted probabilities and marginal effects from the estimated probit model are
presented in table 7.8. Result shows that the farmers with heightened risk perception are
more likely to adopt alternative pest management practices than farmers with less
heightened perception. Similarly, controlling for other variables, the probability of
alternative pesticide use among more educated farmers is higher than less educated
farmers.
Table 7.8.Predicted probabilities and marginal effects from the estimated probit model
Variables
Dependent variable = IPM
Predicted probability=.0311968
Marginal effects Z-scores (P)
Perception .0306913 2.77 (0.006)
Health effects -.0130484 -0.44 (0.657)
Training .4323269 6.87 (0.000)
Farm size -.0026829 -0.22 ( 0.826)
Income .006383 0.47 ( 0.640)
Age -.0017495 -0.17 (0.866)
Education .0137491 2.41 (0.016)
District dummy -.0750914 -3.21 (0.001)
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The data did not appear to confirm that farmers who experienced health
problems while using pesticide are more likely to adopt alternative pest management
than farmers who have not had such experiences. Multiple reasons as reported by
farmers may explain this comportment.
The most important reason of not using alternative pest management techniques
is that farmers in study area either have no information about the availability of
alternative techniques to pesticide or have no access to these alternatives. So they
are forced to use pesticide despite their reservations.
Generally, farmers are over cautious about economic losses. Since pesticides are
easily available even at door-steps, they tend to use pesticide frequently to avoid
crop damage. They do not want to use any alternative pest management
technique that is not well tested or that is not believed as effective as chemical
pesticide. Further IMP is not practiced on a large scale; therefore most of the
farmers are unaware of its utility.
Practically, most of the farmers are uneducated coupled with non-existent
agriculture extension services let pesticide companies succeed to convince
farmers through powerful advertising that without the use of pesticide, high
yields may not be possible; hence pesticides are considered an integral part of
present day agriculture in the study area. Furthermore, these
companies/pesticide dealers also succeeded to speeding up the use of chemicals
in agriculture by providing different services and offering lucrative incentives
involving distribution of pesticides, sprayers and fertilizers on advance or in
many cases free distribution of these items, and lotteries/prizes which ultimately
137
lead to encourage the use of pesticide over other natural alternatives available to
farmers.
Agriculture extension is pro-pesticide in the study area. Further, it is also not
oriented to the shift of information related to the dangers inherent in the use of
pesticide. Due to cultural believes regarding health effects or farmers inability to
distinguish health effects related to pesticide use, it is likely that health effects
arising from pesticide use are grossly-underestimated. Farmers take many of
health effects a routine matter and are not very serious to take steps to avoid
these problems.
Controlling for other variables, the probability of alternative pesticide use among
farmers who received training of alternative pesticide use/safe handling is significantly
higher than the farmers who did not receive such training. Hence training appears to
discourage pesticide use in the study area. However, evidence indicates that there is lack
of formal training on safe handling and IPM use. Only 10% of the farmers reported
receiving formal training on safe handling and better management of pesticide. The
result is very much similar to that found by Dasgupta (2005a) in Bangladesh where
farmers reported similar trend. Therefore, speeding up the formal training in IPM may
be a workable solution to reduce health and environmental damages. However, strong
institutional support is required to extend the scale of IPM training.
Coming to insignificant relationship between age and alternative pesticide use.
The age of the farmers appeared in the negligence of pesticide related health
impairments. As reported by Ajayi (2000) that with the increase of age (experience of
pesticide spraying), farmers are likely to think less of the health problems that are
138
associated with pesticide use. They are ready to accept a certain level of pesticide
associated illness that in turn reflects their hesitation to adopt alternative pesticide use.
Above explanation seems applicable in case of present study, since age appears negative
but non-significant to risk perception.
Finally, the insignificant results of farm size and income indicate that in addition
to farmer‘s health characteristics, wealth characteristics are also less likely to motivate
farmers to adopt more sustainable practices. The analysis underscores the fact that
human capital characteristics (e.g. education, training and awareness) of farmers appear
to influence their decision for more sustainable practices than land characteristics (e.g.
land size).
7.4 Farmer’s Willingness to Pay for Integrated Pest Management
Since alternative pest management trainings and farmer‘s field schools are
largely not available or not accessible to farmers in the study area, it is important to
understand farmer‘s demand for these practices. This part of analysis presents estimation
of farmer‘s willingness to pay for alternative pest management practices. The price
premium for alternative pest management practices by farmers may indicate that
extension programs like IPM that effectively reduce the use of pesticide, are of great
value which in turn can provide motivation to policy makers to continue implementation
of IPM.
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7.4.1 Results of ordered probit model
The estimated coefficients of the ordered probit model and the corresponding p-
values are shown in Table 7.9. Out of nine explanatory variables, five are significant and
have expected signs. Importantly, these variables are theoretically-motivated variables.
The Pseudo R2 is 0.5167 and the overall null hypothesis is rejected99 at the 1% level.
Table 7.9. Estimated coefficients of Ordered Probit Model for positive WTP
Variables Estimated coefficients100
Education .2190725*** (4.64)
Perception 1.293249*** (11.46)
Training -.4451418 (-1.43)
IPM -.023301 (-0.08)
Farm size .1811018* (1.86)
Age .0718007 (0.363)
Health effects .6933518*** (3.36)
Income .7846149*** (7.13)
District dummy(Vehari) -.027809 (-0.18) Log likelihood = -206.46517, Pseudo R2 = 0.5167, LR chi2 (12) = 441.41***
Regarding personal/household characteristics, it comes as no surprise that
income variable approximated by the sum of all the household expenditures, either in
cash or in goods is positively related to WTP. Thus, purchasing power of the farmers is
highly significant determinant of WTP. Whereas low income farmers cannot decide
freely on environmental friendly pest management for higher prices. Similarly, the
coefficient for education is consistently highly significant to a positive WTP. Continuing
with personal/household characteristics, Contrary to Garming et al, (2006) adoption of
integrated pest management practices may not always be positively associated with
99
The null hypothesis that the estimated coefficients are jointly equal to zero is rejected at the one percent
level.
Note: Z-scores are given in parenthesis. * - significant at the 10% level. ** - significant at the 5% level. *** - significant at the 1% level.
140
WTP. This is supported by the fact that a consumer will be least interested to pay for the
good which he/she already have; that is, they already practicing IPM successfully. The
training variable follows the same argument, the farmers who already got training of
safe handling of pesticide, probably are least interested to pay more for safer pesticide.
Again, the age variable has no impact on WTP, the more the age of the farmers, the less
probability he pays for IPM. This variable has consistent result throughout the analysis
and this result has already been interpreted well above.
Of the health and exposure-related variable, the health experience was positively
and significantly associated to a positive WTP. Similarly perception of risk is
significantly related to positive willingness to pay. Moreover results indicate that the
association between the farmers‘ risk perception and WTP is very strong. Thus risk
perception is the most important determinant for positive WTP. The size of the farm is
significant to the positive WTP in present analysis which was very much expected since
it is an indicator of wealth. With respect to regions, WTP is not significantly different in
both the districts.
The predicted probabilities for the five willingness to pay categories are reported
in table 7.10. The reported probabilities indicate the likelihood that farmers are willing-
to-pay some premium for safe pesticide which possibly improve their health. The table
has two panels, the upper panel reports predicted probabilities and the lower indicates
the marginal effect for all explanatory variables. Model includes both continuous and
binary variables.
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Table 7.10. Predicted probabilities and marginal effects from the estimated model
WTP(=0) WTP (1-5
%)
WTP (6-
10%)
WTP (11-
20%)
WTP (20 %
& above)
Predicted probabilities
.03946155 .38032781 .57760992 .00260072 7.644e-10
Marginal effects
Age -.0061195 -.0219439 .0274859 .0005775 3.40e-10
Perception -.1102229 -.3952452 .4950665 .0104016 6.13e-09
Health effects -.0869063 -.1842272 .2676826 .0034509 1.66e-09
IPM .0020161 .0071064 -.0089395 -.000183 -1.05e-10
Training .0503218 .1256599 -.1735827 -.002399 -1.05e-09
Farm size -.0154352 -.0553487 .0693273 .0014566 8.58e-10
Education -.0186714 -.0669534 .0838628 .001762 1.04e-09
Income -.0668723 -.2397955 .3003572 .0063106 3.72e-09
District dummy
.0023701 .008499 -.0106455 -.0002237 -1.32e-10
Starting from top of the table, age of the farmers, nevertheless not significant
more likely to pay premium for safer pesticide since we also assume age as the proxy of
farming/pesticide use experience, suggests that farmers who have been using pesticide
since long are more likely to perceive higher risk and therefore willing to pay premium
for safer pesticide. This can also be explained in terms of income of the farmers. Old
farmers are more likely having higher income and more empowered. The ―risk
perception‖ variable is negative for the first and second categories of willingness-to-pay,
but for the other three willingness-to-pay categories, it has positive marginal effects.
Moreover, the marginal effect tends to be very strong for the category ―medium amount
of risk‖. Thus the farmers who perceive pesticide a health risk are more likely to be
willing to pay premium relative to those who do not perceive pesticide a health hazard.
The pesticide related health effect variable has negative marginal effects for first
two categories of WTP but positive marginal effects for other three categories of WTP.
These results are analogous to priory expectation. Logically negative health experiences
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from the pesticide are more likely to influence farmer‘s attitudes to pay higher premium
for safe pesticide. The marginal effect of education is negative for the first two
categories of WTP but it is positive for the higher categories of WTP. This suggests that
holding other things same, there is more chance for a farmer to be in lower WTP
categories when his education is low compared to when farmer‘s education is higher.
Alternatively, more educated farmers are more likely to pay more for safe pesticide use
relative to less educated farmers.
The marginal effects of training and IPM variables for the first two categories are
positive, such that the farmers who got training of safe handling of pesticide use and
farmers who currently practicing IPM are more likely to pay either no more money or up
to five percent more and very less likely willing to pay higher premium for safe use of
pesticide. The income variable shows opposite pattern. The marginal effect for the first
and second categories of WTP is negative however these effects are positive for other
three categories. This is because higher income farmers can afford premium. The farm
size variable follows same reasoning. This variable is an indicator of individual‘s wealth
which ultimately expands farmer‘s budget constraints. Thus more the size of farm, the
more likely farmer willing to pay premium for safe use of pesticide. The result is
parallel to priory expectation and consistent with the theory.
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7.5 Summary
This chapter presents the econometric analysis of pesticide use behavior. In this
chapter, three different hypotheses are tested. The results supported the hypothesis that
farmers who have had negative health experience related to pesticide use are more likely
to have heightened perception than the farmers who have not such experience.
The results also support the hypothesis that there is a strong linkage between adverse
health effects and protective behavior. Seriousness of health risk is important factor in
shaping individual‘s behavior. As expected individual‘s risk perception appeared as an
important factor to convince farmer to take more protection. However, results tend to be
different and do not support the hypothesis that farmers who have had negative health
effects from pesticide use are more likely to adopt environmentally better management
practices. This does not indicate that attitudes of farmers on pesticide related health
effects are nominal (since their knowledge is not replicated in the farming practices).
Actually, the farmers in study area either haven‘t information regarding alternative pest
management techniques or haven‘t access to these alternatives. So they are forced to
adopt pesticide despite their reservations.
This chapter also highlights the results of contingent valuation method to
measure health cost of pesticide use from farmer‘s point of view. Analysis shows that
farmers are ready to pay at least 8.1% more of their total current pesticide cost for
avoiding pesticide related health risks. All the relevant indicators of WTP such as risk
perception, previous experience of pesticide related poisoning, education and income are
significant predictors for the positive WTP which are important for the ―Theoretical
144
validity‖ of the study. Compared to the other studies in literature (Garming et al, 2006,
Cuyno, 1999) mean willingness to pay is relatively small. This is not surprising, since
most of the farmers are poor (small-scale farmers), and uneducated, hence cannot afford
premium. From the results it is evident that health effects provide motivation for farmers
to pay more for practices like IPM that reduce dependence on pesticide use which in
turn is a strong motivation for policy makers to expand the scope of this technique
through more research on IPM and its implementation on grass root level.
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Chapter 8
Conclusion and policy implications
8.1 Conclusion
Rising pest problems as well as easy availability of pesticides to farmers that
results from liberalization of pesticide market led to increased use of pesticides in crop
protection practices. Consequently, consumption of pesticides in Pakistan has reached
up to 117513 metric tonnes in 2005 which was only 665 metric tonnes in 1980. The
indiscriminate use of pesticide leads to both direct and indirect costs in terms of health
and environment. Indirect costs include pest resistance, degradation of biological capital,
loss of bio-diversity, negative effects on human health and irreversible changes in the
natural ecosystems which ultimately effect sustainability of agriculture.
The evidences from cotton growing areas have indicated that health impairments
of farm workers and environmental damage are mounting because of growing
dependence on pesticide use. The experience with the major outbreaks in 1992-93
onwards has shown that overuse of pesticide has led to destruction of natural enemy
populations in cotton growing areas of the country. As a result of destruction of natural
enemy populations, insects have developed resistance to chemical pesticide which led to
tremendous increase in pesticide use without any improvement in effectiveness of crop
protection particularly cotton in Pakistan.
From the discussion it is obvious that rising use of pesticides is not an optimal
option to protect crops from pest damage. However, given the Pakistan‘s agriculture
146
settings and cash crops security situation, it is expected that current crop protection
practices will likely continue to be the main agriculture system101 in the country because
farmers believe that pesticide use is the sole crop protection technique. The trust on
pesticide as the only crop protection technique leads to further dependence on pesticides.
It is therefore obvious that the environmentally sound pest management system should
be developed on urgent basis which ensures high yield crops while maintaining good
health of farmers and agriculture sustainability in Pakistan. However, before
undertaking such developments, it is important to understand farmer‘s behavior of
pesticide use. The information regarding farmer‘s behavior is critical to identify the
prospects and constraints to the adoption of environmentally sound crop protection
policy. What becomes striking about the pesticide use in Pakistan is that, despite the
recognition of the severity of problem, no systematic study exists in Pakistan that
discusses farmer‘s behavior of pesticide use.
The present study analyzed the existing crop management system or actual
practices in the field and behaviors with the view to identifying the prospects and the
constraints in the adoption of more sustainable practices. The study used Health Belief
Model from health psychology and combined it with new classical Microeconomic
theory to demonstrate farmers reasoning behind their decisions of pesticide use. The use
of psychological model improves understanding of farmer‘s pesticide uses behavior and
contributes to the design of more effective policy interventions to promote safe pest
management. The study through series of observations highlights preventive behavior at
101
Non-existent or lack of information about alternative pest management practices and pro -pesticide
extension services provided by pesticide companies led to convince farmers that pesticide use is the only
pest management technique.
147
personal and environmental levels. The study reveals that pest management is pro-
pesticide in Pakistan. Government policies (pro-pesticide extension system, soft rules for
import of pesticide and other support measures)102 either directly or indirectly encourage
farmers to use pesticide to achieve higher crop yields. Over the years pesticide
encouragement policies have led to eliminate alternative cultural and traditional
practices among farmers in cotton growing areas. Farmers in the study area are not well
conversant to integrated pest management and they have no choice except to use
pesticide, even their health concern. Study results underscore the significance of
providing information relevant to pesticide safety and health issues.
The result shows that about one-third of the respondents is illiterate and cannot
read or write. It has been noted that farmer‘s low level of education is the main reason of
incorrect beliefs regarding pesticide toxicity which acts as a constraint in the adoption of
alternative practices. Farmers are frequently exposed to pesticide. The prevalence of this
exposure is more in district Lodhran, where over 90 percent farmers reported at least one
health problem than in district Vehari, where almost 80 percent farmers appeared to
report these problems. They also appeared to give low priority to health considerations
and grossly under-estimating pesticide risk. They tended to consider those health effects
as common problems. This misperception is largely translated in practical behavior
where almost all the sample farmers did not visit hospital or doctor for proper
medication. Low level of education combined with cultural/local beliefs regarding
health conditions is the main reason of this misperception.
102
Agricultural Pesticide Ordinance 1971 and Agriculture Pesticide Rules 1973 were amended in favor of
importers in the Form 16 and 17 in 1992 and 1997, respectively. According to Form 16 and 17, the
pesticide registered in other countries can be imported without going through pesticide trials at two
research stations to test its efficacy against the target pests for two seasons (anonymous).
148
The misperception of farmers on the potential hazards of pesticide to health appeared
to have much impact on field practices of pesticide use, where farmers are found heavily
skewed towards pesticides and taking few safety measures. More than 80% pesticide
used is highly or moderately hazardous. In terms of crop, cotton alone received over
85% of total quantity. Other crops include vegetables 9% and wheat 5%. Similar pattern
appeared in terms of toxicity, where cotton consumed over 90% of highly hazardous
pesticide and about 89% moderately hazardous pesticide. Farmers also found applying
pesticides very frequently. It is quite common for them (73 percent) to use pesticide
more than 10 times on cotton in a season. The spray frequency is as high as 16 on cotton
crop in one season.
Although farmer‘s knowledge of pesticide and safety practices is reasonably good
but practically non-existent. Most of the farmers partially covered their body with
protective clothing while mixing or spraying pesticide. The formal IPM training and
information are largely non-existent in the study area. Most of the farmers did not know
about IPM, hardly few of them are using these alternatives as supplementary to reduce
their dependence on pesticide. They are totally dependent on pesticide dealers and
pesticide salesmen for information. Agricultural extension services are very limited in
the area. However, the encouraging is that most of the farmers are ready to pay a
premium for safe alternative crop protection approaches.
The analysis supported the hypothesis that farmers who have had negative health
experience related to pesticide use are more likely to have heightened risk perception
than farmers who have not experienced such health problems. Education and training are
also important determinants of risk perception. The findings also support the hypothesis
149
that there is a strong linkage between adverse health effects and protective behavior.
Seriousness of health risk is important fac tor in shaping individual‘s behavior. In line
with previous literature and theoretical background individual‘s risk perception appeared
as an important factor to convince farmers to take more protection. Again education and
training appeared as an important determinant of protective behavior. The results
however, tend to be different and do not support the hypothesis that the farmers who
have had negative health effects from pesticide use are more likely to adopt IPM. The
lack of information or access to these methods is likely a contributing factor which did
not allow many farmers to have proper awareness about alterna tive pest management
practices. The non-existent alternative methods of pest control as well lack of
information regarding these methods and pro-pesticide extension made farmers biased in
favor of pesticide use. As a result, alternative methods are locked out and crop
protection technology became almost synonymous with pesticide use. Therefore,
farmers consider pesticide as the only crop protection method in this part of Pakistan.
The positive and significant effect of risk perception on adoption of alternative pest
management practices and willingness to pay a premium for IPM support this argument.
Hence, improving farmers‘ awareness and access to other methods will be necessary for
their adoption of alternative crop protection practices.
Finally, the study concludes that cultural believes (ignorance) regarding pesticide
related health effects, lack of information regarding and/or non-existent alternative pest
management and fear of economic losses remain the main barriers in adoption of more
sustainable pest management practices. In addition, direct or indirect loans and
incentives offered by pesticide companies combined with powerful advertisement
150
network perpetuating the vicious circle of pesticide use and serving as the chief barriers
to switching to alternative pest management strategies. Therefore, farmers must be
informed about negative externalities of pesticide use and they should be trained enough
to use pesticide correctly and safely and avoid its misuse and overuse, so that farmers
could internalize the negative health and environmental externalities of pesticide use and
find better pest management solution.103 There is also an urgent need to convince
farmers that pesticide use is not the only way of controlling pests. Hence, improvement
in farmer‘s knowledge and awareness regarding pesticide safety issues is critical. The
availability of alternative pest management techniques is also an issue which should be
resolved. Although some farmers are fully convinced to adopt Integrated Pest
Management and many others are willing to pay a higher price to adopt Integrated Pest
Management, but such techniques are largely absent in study area. The study stresses
that increasing use of farm pesticide cannot be effectively checked if there is no practical
alternative pest management technology available.
8.2 Policy implications
The results of the study bear some implications for policy formulation. It must be
noted that negative health and environmental externalities caused by indiscriminate use
of pesticide are severe. These externalities are affecting a large share of the farming
community in study area and there exist solutions that can contribute significantly in the
103
In seeking for a better solution to pest management problems and negative externalities of pesticide
use, the priority issues are not just how to set up regulations and policies that would ban pesticide use in
crop production, but how to use pesticide correctly and safely.
151
improvement the health of farmers and the environment. Based on the research results,
these areas should receive priority attention.
The government must understand that the use of pesticides in Pakistan
particularly in cotton growing areas of the country is promoted by official
intervention. Through series of interventions, the government encouraged the
farmers to adopt pesticides as crop protection technique and over time, due to
this policy pesticides became part and parcel of crop protection which ultimately
led to the massive use of highly toxic pesticides by farmers. Further, due to
multiple factors, a common farmer does not take protective measures necessary
for the safety. Resultantly, the increasing use of pesticide is held responsible for
thousands of health poisoning and environmental damage every year. Therefore,
it is the government who must take responsibility to control the massive use of
pesticides. The present situation can be changed only when government shows
even stronger commitment to reduce pesticide use and as a result, the health and
environmental hazards than to the initial campaigns to encourage farmers to use
pesticides in the first place. Realizing the situation, over time government made
laws to control toxic substances and to ensure their safe use but enforcement of
these laws remains insufficient. An important policy measure is that government
may take appropriate steps to control the use of highly hazardous pesticides.
Policy interventions may include the restructuring of incentives/punishment104 to
reduce availability of highly toxic insecticides.
104
Subsidy for less toxic pesticides and tax for highly toxic pesticides
152
The government should make serious efforts to reverse the present situation. The
promotion of environmentally safe use of pesticides requires substantial
allocation of funds for research and training in integrated pest management. The
countries that have succeeded to get rid of pesticide trap, spent generous amount
of economic resources on research and training. The best example is the
Indonesia that invested about 1 million U.S dollars a year in integrated pest
management research and training in 1980s and by 1990s Indonesia was able to
raise crop yield by 12% with remarkably low pesticide use (Pimentel, 1997;
Wilson, 2001).
One of the corner stone of this research is that the public resources diverted to
provide information regarding health and safety related issues can be effective
even when public investment for more comprehensive and detailed intervention,
such as provision of alternative pest management or enforcement of pesticide
related laws are lacking. The government should strengthen information and
services105 provided by the agriculture extension for plant protection. The
government may also engage other stakeholders106 in this process.
The study recognizes that education is a powerful tool for improving farmer‘s
awareness regarding pesticide related health and environmental problem that
farmers need to address according to their specific exposure circumstances. The
finding that education is positively and significantly related to farmers‘ risk
105
There is a need to overhaul current extension services by improving their knowledge on the changing
trends of pest populations. 106
The interventions can take many forms, including media events, NGOs and community programs
undertaken to promote awareness and understanding of the risk issues. Intervention should also include
social institutions (e.g., community leaders) that can help making farmers become aware of the risk and
subsequently leads to some sort of change in knowledge, attitudes and behaviors.
153
perception and behavior toward adopting protective measures and alternative
pest management offers important policy implication. It implies that innovative
and practical educational programs on health and safety would address incorrect
beliefs, misperception and misinformation and to facilitate farmer‘s
understanding of pesticide borne health risks. The continuous stress on the basic
safety measures would be an immediate solution to dangerous spray practices
and wrong habits which put farmer‘s health at jeopardy. The study results
however, stress that these educational programs need to target small land holders
and less educated farmers that appear to have less knowledge regarding health
and safety issues. But it must be noted that education alone is not enough to
address this issue. To improve the degree of success, training of safe and better
pest management practices is also necessary.
The results which indicate that heightened risk perception and IPM training are
the main determinants of safe behavior of pesticide use offer opportunities to
integrate IPM technology into current crop protection methods. The feasibility of
the IPM technology has been highlighted by many studies (e.g Azeem et al, 2002
& 2004) which were conducted in the same cotton area of Punjab. In addition,
the common belief among farmers in the area that most of the pesticides have
lost effectiveness against pests and they are not as dangerous to many pests as
before makes this claim stronger that the farming community in study area will
warmly welcome IPM methods of crop protection. Further, the argument is also
supported by the analysis which shows that farmers are willing to pay a premium
for IPM.
154
There is a need to develop and provide protective equipments feasible to the hot
and humid climate of Pakistan. These should also be affordable and accessible
for the average farmer.
The basic health-care facilities are generally lacking in rural areas which needs to
be strengthened because they provide first aid facilities107 to the farmers. The
local dispensaries and health care practitioners may better serve this purpose.
Therefore, they should be trained and supplied proper anti dotes. The emergency
poison centers should also be established in the area.
There is a need to inform farmers regarding the banned pesticide, their health and
environmental impacts. It would be much better if they are provided the list of
these pesticides.
8.3 Future Research Priorities
The present research offers some future research possibilities which are given below.
The future research may explore better information tools and more suitable
education programs for farmers regarding health and safety issues. The research
needs to determine what appropriate information tools and practical education
programs should be offered to farmers, in what way they would be most effective
and useful, and how they should be conveyed.
―Economic concerns of the farmers override their health concern‖ is well
distinguished by this research and therefore, immediate economic benefits like
crop yield likely to serve as a driving force in the acceptance of integrated pest
management. It must be noted that farmers take pesticide as a ‗reference point‘
107
The research shows that primary health care approach is most suitable for such situations.
155
against which they would evaluate integrated pest management and they will
adopt alternative pest management techniques only when they believe that
returns from doing so are positive. Improving awareness of the farmers regarding
integrated pest management methods alone may not guarantee that they will
substitute integrated pest management with pesticide use. The results of other
studies (e.g Ajayi, 2000) also indicated that farmer‘s decision regarding adoption
of integrated pest management would be purely based on economic returns of
these methods in comparison to pesticide use. Future studies should focus on
IPM‘s impact on productivity and profitability. Productivity estimates are
important to convince farmers to shift production practices at la rge scale. This
information also helps policy makers to understand whether or not, direct future
resources towards IPM program.
There is a need for further exploration of farmer‘s behavior on the line of current
research approach. It may be done by increasing the coverage in terms of area
and crops. This type of research greatly benefit policy makers to understand
whether the problems are similar in other geographical areas as identified by this
research which ultimately increase pressure on policy makers to change
agricultural production to more sustainable path.
156
References
Agriculture Census Organization. 2000, ―Agriculture census: Procedure and Data Tables Punjab‖, Statistics Division, Lahore, Pakistan.
Ajayi, O. O. 2000, ―Pesticide Use Practices, Productivity and Farmers‘ Health: The Case
of Cotton-Rice Systems in Côte d‘Ivoire, West Africa‖, (Pesticide Policy Project Publication Series Special Issue No. 3, November 2000).
Ajzen, I.1985, From intentions to actions: A theory of planned behavior, Heidelberg: Springer. (pp. 11-39).
Akhatar, S. 1985, ―Effect of the timing and number of sprays on cotton yields in Sind: An explanatory analysis‖, The Pakistan development review, Vol. XXIV, summer 1985.
Al-Saleh, I. A. 1994, ―Pesticide: A Review Article‖, Journal of Environmental
Pathology, Toxicology and Oncology, Vol. 13, No. 3, Pg. 151-56. Anne-Marie Izac et al. ―Agricultural Intensification, Soil Biodiversity and Ecosystem
Function‖ Antle, J. M., Pingali, P. L. 1994, ―Pesticide, Productivity and Farmer Health: A
Philippine Case Study‖, American Journal of Agricultural Economics 76: 418-430. Aragon, A., Aragon, C., Thörn, A. 2001, ―Pests, peasants, and pesticide on the Northern
Nicaraguan Pacific Plain‖, International journal of occupational and environmental health, ISSN 1077-3525.
Armitage, C. J., Conner, M. 1999, ―The theory of planned behaviour : Assessment of predictive validity and `perceived control‖, British Journal of Social Psychology: 38, 35-
54.
Atreya, K. 2005, ―Pesticide use knowledge and practices: A gender differences in Nepal‖, Alternative Development and Research Center (ADRC), Kathmandu, Nepal.
Archibald, S.O. 1988, ―Incorporating Externalities into Agricultural Productivity Analysis‖ In: Capalbo S.M. and J.M. Antle (eds) Agricultural Productivity:
Measurement and Explanation. Washington D.C.: Resources for the Future. Arthur, W.B. 1989, ―Competing Technologies, Increasing Returns and Lock-in by
Historical Events‖, The Economic Journal 99: 116-131.
Azeem, M., Iqbal, M., Ahmad, I., Soomro, M. H. 2002, ―Economic evaluation of pesticide use externalities in the cotton zones of Punjab, Pakistan‖, The Pakistan development review 41:4 part 2 pp.683-698.
157
Azeem, M., Ahmad, I., Echols, W. G. 2004a, ―Impact of an FFS-based IPM Approach on Farmer Capacity, Production Practices and Income: Evidence from Pakistan‖,
Pakistan Agricultural Research Council, National IPM Programme, NARC, Islamabad.
Azeem, K. M., Soomro, M. H., Ahmad, I. 2004b, ―Impact of IPM based plant protection practices on biodiversity and bio-safety: Evidence from Pakistan‖, GCP/RAS/164/EC: Impact Study of IPM in Pakistan. Pakistan Agricultural Research Council, National IPM
Programme, NARC, Islamabad.
Azeem, M., Soomro, M. H., Ahmad, I. 2004c,‖ Impacts of FFS Group Activities on the Organizational Capacities of Farmers: Evidence from Pakistan‖, GCP/RAS/164/EC: Impact Study of IPM in Pakistan. Pakistan Agricultural Research Council, National IPM
Programme, NARC, Islamabad.
Azeem, M., Soomro, M. H., Ahmad, I. 2004d, “Impacts of FFS Approach on Skills Enhancement and Decision Making Capacity of Farmers: Evidence from Pakistan‖, GCP/RAS/164/EC: Impact Study of IPM in Pakistan. Pakistan Agricultural Research
Council, National IPM Programme, NARC, Islamabad.
Azeem, M., Soomro, M. H., Ahmad, I. 2004e, ―Impact of FFS based IPM knowledge and practices on rural poverty reduction: Evidence from Pakistan”, GCP/RAS/164/EC: Impact Study of IPM in Pakistan. Pakistan Agricultural Research Council, National IPM
Programme, NARC, Islamabad.
Bandura, A.1997, Self-efficacy: The exercise of control, New York: W. H. Freeman and Company; 1997.
Bandura, A.2004, ―Health promotion by social cognitive means‖, Health Education Behavior. 31:143–164.
Becker, M.H., Maiman, L.A., Kirscht, J.P., Haefner, D.P., Drachman, R.H., Taylor, D.W., 1979 ―Patient perceptions and compliance: Recent studies of the Health Belief
Model‖, Baltimore: Johns Hopkins University Press; pp. 78–109.
Bell, Sandler, Alavanja. 2006, ―High Pesticide Exposure Events among Farmers and Spouses Enrolled in the Agricultural Health Study‖, Journal of Agricultural Safety and Health Vol. 12 (2).
Blair, A.1997, ―Comparison of Two Techniques to Obtain Information on Pesticide Use
from Iowa Farmers by Interview‖, Journal of Agricultural Safety and Health, 1997. Buckley, N.A., Karalliedde, L., Dawson, A., Senanayake, N., Eddleston, M. 2004,
―Where is the evidence for the management of pesticide poisoning – is clinical toxicology fiddling while the developing world burns‖ Journal of Toxicology, Clinical
Toxicology 42, 1–4.
158
Calvert. 2008, ―Acute pesticide poisoning among agricultural workers in the United States, 1998-2005‖, American Journal of industrial Medicine, 2008.
Campbell, H.F. 1976, ―Estimating the Marginal Productivity of Agricultural Pesticide:
The Case of Tree-Fruit Farms in the Okanagan Valley‖, Canadian Journal of Agriculture Economics 24(2): 23-30.
Carson, R. T., Flores, N. A. 2000a, ―Contingent Valuation: Controversies and evidence‖, Department of Economics, UCSD.
Carson, R. T. 2000b, ―Contingent Valuation: A User‘s Guide‖, Environmental Science Technology, 34 (8), 1413 -1418.
Chang, M., Khan, S. 2007, Sustainable Cotton Production in Pakistan, WWF – Lahore,
Pakistan. Chitra, G.A., Muraleedharan, V.R., Swaminathan, T., Veeraraghavan, D. 2006, ―Use of
pesticide and its impact on health of farmers in South India‖, Journal of occupational and environmental health.
Clarke, E. E., Levy, A. S., Calvert, I. A. 1997, ―The problems associated with pesticide use by irrigation workers in Ghana‖, Occupational Medicine, Vol. 47, Issue 5, Pg. 301-
308
Colborn, T., Myers, J.P., Dumanoski, D. 1996, ―Our stolen future: how we are threatening our fertility, intelligence and survival: a scientific detective story‖, New York: Dutton.
Cooper, D. R., Emory, C.W. 2000, Business Research Method, 5th edition 2000.
Cranfield, J. A. L., Magnusson, E. 2003, ―Canadian Consumer‘s Willingness-to-pay for Pesticide free food Products‖, An Ordered Probit Analysis. International Food and
Agribusiness Management Review Volume 6, Number 4.
Crossley. M. 1999, ―Neuropsychological Functioning and Health-related Symptoms in a Commercial Pesticide Applicator during High- and Low- level Exposure Seasons‖, Journal of Agricultural Safety and Health.
Cuyno, C.M. 1999, ―An Economic Evaluation of the Health and Environmental Benefits of the IPM Program (IPM CRSP) in the Philippines‖, Agricultural and Applied Ecoomics. Blackburg, Virginia Polytechnic Institute and State University: 133.
Damalas, C.A., Georgiou, E.B., Theodorou, M.G. 2006, ―Pesticide use and safety
practices among Greek tobacco farmers: A survey‖, International journal of Environmental Health, Oct; 16(5):339-48
159
Dasgupta, S., Meisner, C., Wheeler, D. 2004, ―Is Environmentally-Friendly Agriculture
Less Profitable for Farmers? Evidence on Integrated Pest Management in Bangladesh‖, World Bank Policy Research Working Paper 3417.
Dasgupta, S., Meisner, C. 2005a, ―Health Effects and Pesticide Perception as Determinants of Pesticide Use: Evidence from Bangladesh‖, World Bank Policy
Research Working Paper 3776, November 2005
Dasgupta, S., Meisner, C., Wheeler, D., Thi, Lam N., Xuyen, K. 2005b, ―Pesticide Poisoning of Farm Workers: Implications of Blood Test Results from Vietnam‖, World Bank Policy Research Working Paper 3624.
Delgado, I. F., Paumgartten, F.J. 2004, ―Pesticide use and poisoning among farmers
from the county of Paty do Alferes, Rio de Janeiro, Brazil‖, Cad Saude Publica. Available: <www. ncbi. nlm.nih.gov/pubmed>
Doherty, R. 1993, ―The Contingent Valuation Method‖, Department of Economics University of West England, CSERGE Working Paper PA 93-01
Dung, N.H., Dung, Tran Thi T. 2003, ―Impact of Agro-Chemical Use on Productivity and Health in Vietnam‖, Research Reports, http://203.116.43.77/publications/research1
/ACF122.html
Eddleston. 2000, ―Patterns and problems of deliberate self poisoning in the developing world‖, Quarterly Journal of Medicine 93, 715–731.
Echols, W. G., Soomro, M. H. 2004, ―Impact of the FAO-EU IPM Programme for Cotton in Asia on the Environment‖, Pakistan Agricultural Research Council National
IPM Programme, NARC, Islamabad. FBS. 2008, ―Pakistan Labour Force Survey 2007-2008‖, Government of Pakistan,
Statistics Division, Federal Bureau of Statistics, 2008.
Field, A.P. 2005, Discovering statistics using SPSS, chapter 15. London: Sage. Finance Division. 2009, ―Economic Survey 2008-2009‖, Government of Pakistan,
Economic Adviser‘s Wing, Islamabad, Pakistan.
Fiore, M.C., H.A. Anderson, R. Hong, R. Golubjatnikov, J.E. Seiser, D. Nordstrom, L. Hanrahan, Belluck, D. 1986, ―Chronic exposure to aldicarb-contaminated groundwater and human immune function‖, Environmental Resources. 41: 633-645.
Forget, G. 1991, ―Pesticide and the third world‖, Journal of Toxicology and
Environmental Health, Vol.32, Pg. 11-13
160
Freeman, M. A. 2003, ―The Measurement of Environmental and Resource Values—Theory and Methods‖, 2nd Edition, Washington: Resources for Future.
Garming, H. Waibel, H. 2006, ―Willingness to pay to avoid health risks from pesticide, a
case study from Nicaragua‖, Working Paper 2006 No. 4. Development and Agricultural Economics, Faculty of Economics and Management, University of Hannover, Germany.
Garson, G.D. 1999, ―Reliability Analysis‖ < http://www2.chass.ncsu.edu/garson/pa765/reliab.htm# intraclass>
Garry V.F. 2002, ―Birth defects, season of conception, and sex of children born to pesticide applicators living in the Red River Valley of Minnesota, USA‖, Environmental
Health Perspectives, Vo.110, No. 3, Pg. 441-449.
Garcia, A.M., Fletcher, T., Benavides, F.G., Orts, E. 1999, ―Parental agricultural work and selected congenital malformations‖, American Journal o f Epidemiology 149: 64–74.
Garcia, A.M., Ramirez, A., Lacasaña, M. 2002, ―Pesticide application practices in agricultural workers‖, Available: <www.ncbi.nlm.nih.gov/pubmed>
Giller, K.E., Beare, M.H., Lavelle, P., Izac, A.-M.N., Swift, M.J.,1997 ―Agricultural intensification, soil biodiversity and agroecosystem Function‖, Applied Soil Ecology
volume 6: pp 3-16
Global Agricultural Information Network. 2009, ―Pakistan annual cotton report‖, Office of global analysis: USDA foreign agricultural services, Islamabad Pakistan.
Green, L. W. 2010, ―Health Belief Model‖, Available: <Encyclopedia of Public Health>: retrieved on 16-06-2010.
Gujarati, N. D. 2004, Basic Econometrics, 4th Edition: McGraw-Hill.
Gunatilake, H. 2003, Environmental valuation: theory and applications, Chapter 6, Contingent Valuation.
Hanemann, W.M. 1994, ―Valuing the Environment Through Contingent Valuation‖, Journal of Economic perspective, No. 4, 19-43.
Hassan, I.J. 1994, ―The cure that kills‖, Herald (a monthly English magazine: September
issue), Karachi. Hasnian, T. 1999, ―Pesticide Use and Its Impact on Crop Ecologies: Issues and
Options‖, Sustainable Development Policy Institute (SDPI), Islamabad. Working Paper Series.
161
Hochbaum, G. 1956, ―Why People Seek Diagnostic X-Rays‖ Public health Reports 71:32780.
Hogarth, R. M., Reder, M. W. 1987, ―Rational Choice: The contrast between economics
and psychology‖, Chicago: University of Chicago Press. Horrigan, L., Lawrence, R. S., Walker, p. 2002, ―How Sustainable Agriculture Can
Address the Environmental and Human Health Harms of Industrial Agriculture ‖, Environmental Health Perspectives.
Hoek, W. V. D., Konradsen, F. 2005, ―Risk factors for acute pesticide poisoning in Sri Lanka‖, Tropical Medicine and International Health, volume 6.
Hoffmann, J.P. 2004, ―Generalized Linear Models: An applied approach‖ Pearson
Education, Inc. Huang, C. L. 1993, ―Simultaneous-Equation Model for Estimating Consumer Risk
Perceptions, Attitudes and Willingness-to-Pay for Residue-Free Produce‖, Journal of Consumer Affairs, Vol. 27.
Huang, J., Qiao, F., Zhang, L., Rozelle, S. 2003, ―Farm Pesticide, Rice Production, and Human Health‖, <http://203.116.43.77/publications/research1 /ACF268.html>
Huang, J., Qiao, F., Zhang, L., Rozelle, S. 2000, ―Farm Pesticide, Rice Production, and
Human Health‖, CCAP's Project Report 11. Center for Chinese Agricultural Policy (CCAP), Chinese Academy of Agricultural Sciences.
Hruska, A., Corriols, M. 2002, ―The Impact of Training in Integrated Pest Management among Nicaraguan Maize Farmers: Increased Net Returns and Reduced Health Risk‖,
International Journal of Occupational Environmental health 2002; 8: 191–200 Ibitayo, O. 2006, ―Egyptian Farmers‘ Attitudes and Behaviors Regarding Agricultural
Pesticide: Implications for Pesticide Risk Communication‖, Risk Analysis, Vol. 26, No. 4, 2006.
Iqbal, Z., Zia, K. & Ahmad, A. 1997, ―Pesticide Abuse in Pakistan and Associated Human Health and Environmental Risks‖, Pakistan Journal of Agriculture science, Vol.
34 (1-4).
Irshad, M. 1999, ―Implications and Management of Insecticide Resistance in Agricultural Pests‖, Pakistan Journal of Biological Sciences 2:4, 1650-1654.
Jabbar, A., Mallick, S. 1992, ―Pesticide and environmental situation in Pakistan‖, Sustainable Development Policy Institute (SDPI), Islamabad. Working Paper Series.
162
Jeyaratnam, J. 1990, ―Acute pesticide poisoning: a major global health problem‖, World Health Stat Q 1990; 43: 139-44.
Jors, E. 2006, ―Occupational pesticide intoxication among farmers in Bolivia‖, A cross
sectional study. Environmental health. Kahneman, D. 2003, Well-being, the foundations of hedonic psychology, Russell Sage
Foundation.
Khan, M. 2010,‖ Economic Evaluation of Health Cost of Pesticide Use: Willingness to Pay Method‖, Conference proceedings, 25th Annual General Meeting & Conference (AGM). Pakistan Society of Development Economists (PSDE), March 16-18, 2010,
Islamabad.
Khooharo, A.A. 2008, ―A study of public and private sector pesticide extension and marketing services for cotton crop‖, PhD thesis, department of agricultural education, extension & short courses, faculty of agricultural social sciences, Sindh agriculture
university, Tando Jam, Pakistan.
Kimani, V. N., Mwanthi, M. A. 1995, ―Agrochemicals exposure and health implications in Githunri Location, Kenya‖, East African Medical Journal, 72(8), 531–535.
Kirkpatrick, C. D., Dahlquist, J. R. 2007. Technical Analysis, The Complete Resource for Market Technicians. pp. 49.
Kishi, M. Hirschhorn, N. Qjajadisastra, M. Satterlee, L. N. Strowman, S. Dilts, R. 1995, ―Relationship of Pesticide Spraying to Signs and Symptoms in Indonesian
Farmers‖, Scandinavian Journal of Work & Environmental Health 21: 124-133.
Kishi, M. 2002, ―Farmers' perceptions of pesticide, and resultant health problems from exposures‖, International journal of occupational and environmental health.
Koh, D., Jeyaratnam, J. 1996, ―Pesticide hazards in developing countries‖, Science of the Total Environment 188(Suppl. 1), S78–S85.
Kunstadter. 2001, ―Pesticide exposures among Hmong farmers in Thailand‖, International Journal of occupational and environmental health 7(4), 313-325.
Leventhal, H., Safer, M., Panagis, F. D. 1983, ―The impact of communications on the
self-regulation of health beliefs, decisions, and behavior‖, Health Education Quarterly, 10(1), 3–29. Lichtenberg, E., Zimmerman, R. 1999, ―Adverse Health Experiences, Environmental
Attitudes, and Pesticide Usage Behavior of Farm Operators‖, Risk Analysis, Vol. 19,
No. 2
163
Lipton D.D.1995, ―Economic Valuation of Natural resources‖, A Handbook for Coastal
Resource Policymakers. National Oceanic and Atmospheric Administration (NOAA),
USA.
Litchfield, M. 2005, ―Estimates of acute pesticide poisoning in agricultural workers in
less developed countries‖, Toxicology Review.
Luce, R. D. 2000, ―Utility of Gains and Losses: Measurement-theoretical and
Experimental Approaches‖, New Jersey: Lawrence Erlbaum Publishers.
Maramba, N. C. 1988, ―Assessment of Adequacy of Protection of Pesticide Handlers
II‖, A Final Project Report, Guagua, Pampanga, Philippines.
Marcoux, B. C., Shope, J. T. 1997, ―Application of the Theory of Planned Behavior to
adolescent use and misuse of alcohol‖, health education research, Theory & Practice,
Vol.12 no.3 1997 Pages 323-331
McCauley L. A., Shapiro S. E., Scherer J. A., Lasarev M. R. 2004, ―Assessing Pesticide
Safety Knowledge Among Hispanic Migrant Farm workers in Oregon‖, Journal of
Agricultural Safety and Health Vol. 10(3): 177-186.
McDuffie, H.H. 1994, ―Women at work: agriculture and pesticide‖, Journal of
Occupational Medicine 36: 1240-1246.
Meisner, C., DECRG. 2005, ―Poverty-Environment Nexus Report‖, Pesticide Use in the Mekong Delta, Vietnam.
Meulenbeit, J., Varies, I. D. 1997, ―Acute Work Related Poisoning by Pesticide in the Netherlands‖, A One Year Fellow up Study.‖ Przegl Lek, 54(10): 665-670.
Ministry of Food & Agriculture, 2008, Agricultural Statistics of Pakistan, Government of Pakistan, Islamabad.
Ministry of Food & Agriculture, 2004, Agricultural Perspective and Policy, Government
of Pakistan, Islamabad. Munro, S., Lewin, S. Swart, T., Volmink, J. 2007, ―A review of health behaviour
theories: how useful are these for developing interventions to promote long-term medication adherence for TB and HIV/AIDS?‖ BMC Public Health. 2007; 7: 104.
Biomed Central Ltd. Napier, T. L., Brown, D. E. 1993, ―Factors affecting attitudes toward groundwater
pollution among Ohio farmers‖, Journal Soil Water Conservation 48,432-439 (1993).
National Research Council, 1984, Toxicity Testing, Strategies to Determine Needs and Priorities. Washington DC: National Academy Press, 1984.
164
Natural Resources Defense Council, 1998, Trouble on the Farm: Growing Up with
Pesticide in Agricultural Communities. Available: http://www.nrdc.org/health/kids/farm/ farminx.asp.
NARC. 2008, ―National IPM programme‖ Pesticide Policy Analysis Project. Available : < www.Nat-IPM.gov.pk>
Nasira, N. 1996, Invisible Farmer, Khoj Lahore.
Nejad, L. M., Weirthem, E. H., Greenwood, K. m. 2005, ―Comparison of the Health
Belief Model and the Theory of Planned Behavior in the prediction of dieting and fasting behavior.‖ E- Journal of social psychology: Social section. 1 (1): 63-74.
NFDC 2002, ―Pesticide use survey report 2002‖, National Fertilizer Development Center, Government of Pakistan. Islamabad, Pakistan.
Ngatia, J., Mgeni, A. Y. 1980, ―The effects of continuous exposure of organophosphorus and carbamate insecticides on cholinesterase (CHE) levels in
Humans‖, Field worker exposure during pesticide application, Pg. 98-102, Eds. W.F. Tordoir, EAH van Heemstra, Amsterdam: Elsevier.
Ngowi, A.V., Maeda, D.N., Partanen, T.J. 2001, ‗Assessment of the ability of health care providers to treat and prevent adverse health effects of pesticide in agricultural
areas of Tanzania‖, International Journal of Occupational Medicine Environmental Health. 2001;14(4):349-56.
Nhachi, F. Lowenson, P. 1993, ―Toxicology of pesticide and the occupational hazards of pesticide use and handling‖, UZP, Harare, Zimbabwe.
Nordi. 2002, ―Effects of safety behaviors with pesticide use on occurrence of acute
symptoms in male and female tobacco-growing Malaysian farmers‖, Department of public health and occupational medicine, University of Tokyo, Japan.
Novak, j. M. 1998, ―pesticide and metabolites in the shallow groundwater of an eastern coastal plain watershed‘, American Society of Agricultural Engineers.
Ntow, W.J., Gijzen, H.J., Drechsel, P. 2006, ‗Farmer perceptions and pesticide use practices in vegetable production in Ghana‖, Pest Manage. Sciences., 62 (4), 356-365.
Ntow, W.J., 2005, ―Pesticide residues in Volta Lake Ghana‖, Lakes and Reservoirs:
Reservoirs Management., 10, 243-248. Pakistan Agriculture Research Council, 2008, National Coordinated Wheat Programme,
Available:< [email protected];[email protected]>
People & the Planet, 2007. Health and pollution fact file,
165
Available:< www.peopleandplanet.net/section.php?section=12&topic>
Pimental, D., Acquay, H., Biltonen, M. 1992, ―Environmental and Economic Costs of Pesticide Use‖, Bioscience 42, 750-60.
Pimentel, D., Greiner, A. 1997, ―Environmental and socioeconomic costs of pesticide use‖, In Pimentel, D. (Ed.), Techniques for Reducing Pesticide Use: Economic and
Environmental Benefits. John Wiley and Sons, Chichester, pp. 51–78.
Pimentel, D. 2005, ―Environmental and Economic Costs of the Application of Pesticide Primarily in the United States‖, Environment, Development and Sustainability (2005) 7: 229–252.
Pimentel, D. 1996, ―Public health risks associated with pesticide and natural toxins in
foods‖, University of Minnesota, 1996. Pimentel, D., Greiner, A. 1996, ―Environmental and socio-economic costs of pesticide
use‖, In D. Pimentel, ed. Techniques for Reducing Pesticide: Environmental and Economic Benefits. Chichester, England: John Wiley & Sons. In press.
Portney, P. R. 1994, ―Contingent Valuation Debate: Why economis t should care‖, Journal of economic perspective-volume 8, number 4, 3-17.
Poswal, M. A., Williamson, S. 1998, ―Stepping off the Cotton Pesticide Treadmill‖,
Preliminary Findings from a Farmers' Participatory Cotton IPM Training Project in Pakistan. CABI Bioscience Centre, Rawalpindi.
Potashnik, G., Yanai, I. 1987, ―Dibromochloropropane (DBCP)‖, An 8-year reevaluation of testicular function and reproductive performance. Fertility and Sterility
47: 317-323. Pouta, E. 2003, ―Attitude-Behavior Framework in Contingent Valuation of Forest
Conservation‖, Academic Dissertation, Faculty of Agriculture and Forestry of the University of Helsinki.
RANDOM. ORG. 2008, ―Random Sampling‖ Retrieved from
<www.random.org/nform.html.>
Rao, S., Venkateswarlu V., Surender T., Eddleston M., Buckley N. A. 2005, ―Pesticide poisoning in south India: opportunities for prevention and improved medical management‖, Tropical Medicine and International Health.
Rasheed, B.M. 2007, ―Country report on international code of conduct on the
distribution and use of pesticide‖, Department of plant protection, Ministary of Food, Agriculture & Livestock, Government of Pakistan 2007.
166
Ray, M. L. 1974, ―Consumer Initial Processing: Definitions, Issues and Applications‖, in Buyer/Consumer Information Processing, Chapel Hill: The University of North
Carolina Press: 145-156.
Recena, M.C.P., Caldas, E.D., Pires, D.X., Pontes, E.R.J.C. 2006, ―Pesticide exposure in Culturama, Brazil—knowledge, attitudes, and practices‖, Environmental Research 102 (2), 230–236.
Redding, C.A., Rossi, J.S., Rossi, S.R., Velicer, W.F., Prochaska, J.O. 2000, ―Health
behaviour models‖, International Electronic Journal of Health Education. 3:180–193. http://www.oiiq.org/SanteCoeur/docs/redding_iejhe_vol3_nospecial_2000.pdf
Rehman, K., Roofi, A.R. 1994, ―In Search of Harmony‖, (Ed. Nasira Habib), Lahore.
Rola, A. C., Pingali, P. L. 1993, ―Pesticide, Rice Productivity, and Farmer‘s Health: an economic assessment‖, Washington DC: International Rice Research Institute, World Resources Institute.
Salameh, P. R., Baldi, I., Brochard, P., Abi Saleh, B. 2004, ―Pesticide in Lebanon: A
knowledge, attitude and practice survey‖, Environmental Research, 94(1), 1–6. Sanborn, M., Cole, D., Kerr, K., Vakil, C., Sanin, L.H., Bassil, K. 2004, ―The Ontario
College of Family Physicians. Pesticide Literature Review‖, Available:<http://www.ocfp.on.ca/local/files/Communications/Current%20Issues/
Pesticide/Final%20 Paper%2023APR2004.> Schafer, M. L. 1968, ―Pesticide in Blood‖, Cincinnati, Ohio, USA, in Residue Reviews,
Vol. 24, 1968, ed F.A. Gunther.
Schenker, M., Orenstein, M., Samuels, S. 2002, ―Use of protective equipment among California farmers‖, American Journal of Industrial Medicine, 2002 Nov; 42(5):455-64.
Severtson, D. J., Baumann, L., Brown, R. L. 2006, ―Applying a Health Behavior Theory to Explore the Influence of Information and Experience on Arsenic Risk
Representations, Policy Beliefs, and Protective Behavior‖, Risk Analysis, Vol. 26, No. 2, 2006.
Sivayoganathan, C., Gnanachandrnn, C., Lewis, J. 1995, ―Protective measure use and symptoms among agropesticide applicators in Sri Lanka‖, Social Science and Medicine,
Vol. 40, Pg. 431–6. Smith, M., Lewandroski, J. K., Noel, D. U., ―Agricultural residue as a source of risk‖,
Review of agricultural economics- volume 22, number 2-pages 313-325.
167
Strecher, V. J., Rosenstock, I. M. 1997, ―The Health Belief Model‖, In Health Behavior and Health Education: Theory, Research, and Practice, eds. K. Glanz, F. M. Lewis, and
B. K. Rimer. San Francisco: Jossey-Bass.
Sudo, M., Kunimatso, T., Okubo, T. 2002, ―Concentration and loading of pesticide residues in Lake Biwa‖, Water Resources. 36, 315-129.
Sutton, S.1997, ―Transtheoretical model of behaviour change‖, Cambridge: Cambridge University Press; 1997. pp. 180–182.
Thomas, P.T., House, R.V. 1989, ―Pesticide- induced modulation of immune system‖, Pages 94-106 in N. N. Ragsdale and R. E. Menzer, eds. Carcinogenicity and Pesticide:
Principles, Issues, and Relationships. Washington, DC.
Tucker, M., Napier, T. L. 1998, ―Perceptions of risk associated with use of farm chemicals: Implications for conservation initiatives‖, Environmental Management 22,575-587 (1998).
Vigna, S.D. 2007, ―Psychology and Economics: Evidence from the field‖, National
Bureau of Economic Research, Working Paper 13420 http://www. nber.org/papers/w13420
Waibel, H. 1996, ―The Economics of Crop Health Management‖, Giessener Beitraege zur Entwicklungs forschung Bd. 23: 31-44.
Wikipedia 2010, ―Definition of pesticide‖ Retrieved, 12-08-2010,
http://en.wikipedia.org/wiki/Pesticide).
Wikipedia 2009, ―Vehari district profile‖ Retrieved, 12-02-2009,
http://en.wikipedia.org/wiki/Vehari
Wikipedia 2009, ―Lodhran district profile‖ Retrieved, 12-02-2009,
http://en.wikipedia.org/wiki/Lodhran Williamson, S. 2003, ―Economic costs of pesticide reliance‖, PAN UK, IPM features, pesticide news, 61.
Wilson, C. 2000, ―Environmental and human costs of commercial agricultural
production in south Asia‖, International Journal of Social Economics 27, 816–846. Wilson, C. 1999, ―Pesticide avoidance: Results from a Sri Lankan Study with Health
Policy Implications‖, in Economics of pesticide, sustainable food production, and organic food market, edited by D. C. Hall and L. Joe Moffot, Elsevier Publicat ion.
Wilson, C., Tisdell, C., 2001, ―why farmers continue to use pesticide despite environmental, health and sustainability costs‖, Ecological Economics. 39, 449–462.
168
Woodwell, G. M., Wurster, C. F., Isaacson, P. A. 2001, ―DDT residues in an East Coast estuary: a case of biological concentration of a persistent insecticide‖
Wooldridge, J.M. ―Econometric Analysis of Cross Section and Panel Data‖, available:
<http://mitpress.mit.edu/Wooldridge-EconAnalysis.> World health organization (WHO). 2006, ―Recommended classification of pesticide by
hazard, and guidelines to classification 2004‖, Geneva.
World Health Organization (WHO). 2003, ―Adherence to long-term therapies: Evidence for action‖, Geneva. World health organization (WHO). 2002, ―Recommended classification of pesticide by
hazard, and guidelines to classification 2000-2002‖, Geneva. Available at: http://www.who.int/ipcs/publications/pesticides_hazard/en/
World health organization (WHO). 1992, ―International Programme on Chemical Safety‖, The WHO recommended classification of pesticide by hazard and guidelines to
classification 1992-1993. Geneva.
World Health Organization (WHO). 1990, ―Public Health Impact of Pesticide Used in Agriculture‖, Geneva.
Yassin, M. M., Abu Mourad, T. A., Safi, J. M. 2002, ―Knowledge, attitude, practice and toxicity symptoms associated with pesticide use among farm workers in the Gaza Strip ‖,
Occupational and Environmental Medicine; 59:387-393
169
Appendixes
Appendix 1: Figures
Figure 1A.Farm ownership status
Figure 2A. Pesticide spray frequency by district
170
Figure 3A. Use of protective measures by district
Figure 4A.Farmer‘s perception of pesticide risk by district (%)
171
Figure 5A. Farmers‘ attitudes towards health effects of pesticide use in Vehari
Figure 6A. Farmers‘ attitudes towards health effects of pesticide use in Lodhran
172
Figure 7A. Distribution of mean pesticide application on vegetables
173
Appendix II: Tables
Table 1A. Distribution of farm size by district
Vehari Lodhran
No of farmers Farm size Average land
holding
No of
farmers Farm size
Average
land holding
18 Up to2.50 1.8 18 Up to2.50 1.7
25 2.6-5.0 3.36 20 2.6-5.0 3.25
36 5.0-10.0 6.7 44 5.0-10.0 6.85
62 10.1-25.0 14.3 53 10.1-25.0 14.45
6 25.1-50.0 25.9 22 25.1-50.0 27.4
0 50.1-100 0 7 50.1-100 59.3
2 100+ 375 5 100+ 130
Total 149 169
Table 2A. Distribution of farm size by farm ownership in Lodhran
Farm size
Farm ownership
Total On the Farm
Rental
Arrangement Sharecropper
Up to2.50 72.7 13.6 13.6 100.0
2.6-5.0 79.2 20.8 0.0 100.0
5.0-10.0 88.6 4.5 6.8 100.0
10.1-25.0 95.2 0.0 4.8 100.0
25.1-50.0 100.0 0.0 0.0 100.0
50.1-100 100.0 0.0 0.0 100.0
100+ 100.0 0.0 0 100.0
Table 3A. Distribution of farm size by farm ownership in Vehari
Farm size
Farm ownership
Total Owner the farm
Rental
arrangement Sharecropper
Up to2.50 63.2 21.1 15.8 100.0
2.6-5.0 73.5 17.6 8.8 100.0
5.0-10.0 71.4 20.0 8.6 100.0
10.1-25.0 89.7 10.3 0.0 100.0
25.1-50.0 100.0 0.0 0.0 100.0
50.1-100 100.0 0.0 0.0 100.0
100+ 100.0 0.0 0 100.0
174
Table 4A. Distribution of farmer‘s age
Age % No.
11-20 04 13 21-30 35 111
31-40 31 96 41-50 21 67
51-60 09 29 61-70 32 01
Total 100 318
Table 5A.Distribution of education attainment by age in Vehari
Education attainment
Age
categories Illiterate
Up to
Primary Middle Metric
Higher
secondary
Graduation
and above Total
≤ 20 0 12.5 50.0 25.0 12.5 0.0 100.0
21-30 12.2449 22.4 30.6 22.4 4.1 8.2 100.0
31-40 36.53846 21.2 21.2 17.3 0.0 3.8 100.0
41-50 38.70968 12.9 29.0 9.7 6.5 3.2 100.0
51-60 44.44444 22.2 33.3 0.0 0.0 0.0 100.0
61+ 0 12.5 50.0 25.0 12.5 0.0 100.0
Table 6A.Distribution of education attainment by age in Lodhran
Education attainment
Age
categories Illiterate
Up to
Primary Middle Metric
Higher
secondary
Graduation
and above Total
≤ 20 0.0 60.0 40.0 0.0 0.0 0.0 100.0
21-30 14.5 37.1 25.8 9.7 3.2 9.7 100.0
31-40 36.4 27.3 22.7 2.3 4.5 6.8 100.0
41-50 36.1 38.9 5.6 16.7 2.8 0.0 100.0
51-60 23.8 28.6 14.3 9.5 14.3 9.5 100.0
61+ 0.0 0.0 0.0 100.0 0.0 0.0 100.0
175
Table 7A.WHO Hazard Classification of pesticides
Pesticide Class
LD50 for the rat (mg/kg body weight)
Oral
Solids Liquids
Ia (extremely hazardous) 5 or less 20 or less
Ib (highly hazardous) 5-50 20-200
II (moderately hazardous) 50-500 200-2000
III (slightly hazardous) 500-2000 2000-3000
IV (unlikely if used safely) Over 2000 Over 3000
WHO recommended classification of pesticide by hazard and guidelines to classification 2004.
Source: Murphy .H (2002) & WHO (2006)
Table 8A. Pesticide use by WHO hazard classification by district
Category
Lodhran Vehari
Amount(kg
A.I) %
Amount(kg
A.I) %
Extremely hazardous (Ia)
0.0 0.0 0.0 0.0
Highly hazardous (Ib) 615.5 24.1 522.2502 22.5
Moderately hazardous
(II) 1394.3 54.5 1271.682 54.9
Slightly hazardous (III) 455.9 17.8 422.5585 18.2
Unlikely (U) 92.7 3.6 100.412 4.3
Total 2558.5 100.0 2316.9027 100
Table 9A.Crop wise pesticide use by WHO hazard classification in Vehari (%)
Crops Highly
hazardous
Moderately
hazardous
Slightly
hazardous Unlikely (U)
Cotton 23.1 69.0 6.0 1.9
Vegetables 29.0 37.4 17.6 16.0
Wheat 2.7 20.2 77 0.1
Others 57.0 28 5.9 9.1
176
Table 10A. Crop wise pesticide use by WHO hazard classification in
Lodhran (%)
Crops Highly
hazardous
Moderately
hazardous
Slightly
hazardous Unlikely (U)
Cotton 22 64.9 10.1 5
Vegetables 40.5 32.2 19.6 7.7
Wheat 2.1 6.7 90.3 0.9
Others 47.8 35.5 5.6 10.3
Table 11A.WHO Category wise pesticide use on cotton by farm size (%)
Farm size Extremely
hazardous
Moderately
Hazardous
Slightly
Hazardous Unlikely
0.1- 2.5 28 53 19 0
2.6- 5.0 32 56 10 2
5.1- 10 22 63 13 2
10.1-25 18 66 11 5
25.1-50 33 59 8 0
50.1-100 22 71 7 0
100+ 25 69 6 0
Table 12A. WHO Category wise pesticide use on wheat by farm size (%)
Farm size Extremely
hazardous
Moderately
Hazardous
Slightly
hazardous Unlikely
0.1- 2.5 29 40 31 0
2.6- 5.0 22 37 41 0
5.1- 10 19 43 38 0
10.1-25 19 33 35 13
25.1-50 41 26 33 0
50.1-100 19 42 24 15
100+ 0 33 43 24
177
Table 13A.WHO Category wise Pesticide use on vegetables by farm size (%)
Farm size Extremely
hazardous
Moderately
hazardous
Slightly
hazardous Unlikely
0.1- 2.5 50 50 0 0
2.6- 5.0 12 38 28 22
5.1- 10 21 23 28 28
10.1-25 12 53 32 3
25.1-50 24 46 21 9
50.1-100 10 40 48 2
100+ 11 57 32 0
Table 14A. WHO Category wise pesticide use on other crops by farm size (%)
Farm size Extremely
hazardous
Moderately
hazardous
Slightly
hazardous Unlikely
0.1- 2.5 0 10 90 0
2.6- 5.0 0 20 5 75
5.1- 10 30 15 25 30
10.1-25 61 25 11 3
25.1-50 0 76 24 0
50.1-100 0 100 0 0
100+ 0 0 0 0
Table 15A.Amount of pesticide used (Kg/per acre) by farm size
Farm size Cotton Wheat Vegetables Others
0.1- 2.5 11.76 1.5 6.9 2.7
2.6- 5.0 9.5 1.45 7.3 0.8
5.1- 10 10.5 1.2 8.1 3.1
10.1-25 9.75 1.8 8.4 3.6
25.1-50 10 1.65 6.5 2.6
50.1-100 11.5 1.3 7.1 0.6
100+ 12 1.6 6.3 0
178
Table 16A. Distribution of income by age group
Income
Age (years) Rs= up to 10000
Rs=10001-20000
Rs> 20000 Total group
≤ 20 2.8 1.3 0.0 4.1
21-30 18.2 12.6 4.4 35.2 31-40 18.9 7.2 4.1 30.2
41-50 10.1 8.8 2.2 21.1
51-60 3.5 4.4 1.3 9.1 61+ 0.0 0.3 0.0 0.3
Total 53.5 34.6 11.9 100.0
Table 17A.Main source of information for farmers in study area Read the labels on the bottle/package and follow the Instructions (if you cannot read, please get help from
others who can read).
Agric ministry
(11% )
Sales person/companies
(37% )
Others
(41% )
Never heard
(9% )
Do not mix pesticide with bare Hands. While mixing, wear hand gloves and glasses/eye shield.
7% 50% 23% 16%
Mix them with a stick.
7% 50% 23% 16%
While cleaning the sprayer’s nozzle do not place your mouth on it or blow on it.
6% 36% 28% 22%
Before s praying pesticide take all protective measures such as wearing hand gloves, head cover; face shield,
full sleeve shirt/kurta, full length trousers /shalwar, and shoes.
7% 56% 16% 17%
Do not spray pesticide against the wind. Determine wind direction first and then s pray.
7% 57% 36% 0%
Do not eat or drink while s praying pesticide.
7% 57% 29% 4%
Do not smoke while spraying pesticide. The reaction may be toxic or even fatal.
7% 57% 29% 4%
Do not wash pesticide bottle or pesticide sprayer in the Pond/canal.
7% 17% 30% 35%
Wash and clean the sprayer and your clothes at a far distance from the pond.
7% 11% 25% 46%
After applying the pesticide on your field, dis play a signboard or an empty pesticide bottle, so that
everybody sees and understands that you s prayed pesticide on that field.
3% 1% 4% 88%
Do not keep other things in the pesticide bottle or package.
179
7% 57% 29% 5%
Tear up the pesticide package into pieces and bury them under the ground.
5% 3% 11% 79%
Keep pesticide under lock so that they are out of the reach of children.
7% 56% 28% 4%
Do not keep pesticide where you keep other things.
7% 56% 28% 4%
In the event of an accident, provide first aid to the patient, Following the instructions on the label of that
particular pesticide bottle. Take the patient and the pesticide Bottle to the doctor as soon as possible.
7% 3% 10% 77%
Keep the children and domestic cattle and poultry birds out of the immediate area.
9% 33% 40% 18%
Note: The four columns under every informat ion row represent different sources of informat ion in
percentages. The first column indicate % informat ion received from agricu lture extension, second
showing % information received from sales person/pesticide company, third column represents %
informat ion received from other sources(i.e. NGOs, relatives, fellow farmers, neighbors, public media and
self), last column shows % of farmers who did not received any information regarding above stated
informat ion.
Table 18A. Use of IPM by method
IPM Methods No. of farmers
Decreased dosage 12
Change with less toxic pesticide 17
Decreased no. of applications 30
Manual clearing 30
Enemy plants 9
Crop rotation 21
Variation in sowing and harvesting time 2
Biological methods 30
Other measures of IPM. 29
Table 19A.Percentage of farmers who follow instructions on pesticide labels
by level of education
Education level Both districts Vehari Lodhran
Illiterate 5.0 5.0 5.0
Primary 16.0 18.0 14.0
Middle 10.0 11.0 8.0
Matric 14.0 19.0 9.0
Higher secondary 51.0 48.0 53.0
Graduate 60.0 65.0 54.0
180
Table 20A.Descriptive statistics of important variables (district Lodhran)
Variables Minimum Maximum Mean Std. Deviation
Perception 1 5 2.6 1.1
Health effect 0 1 0.9 0.3
Training 0 1 0.1 0.4
Age 15 66 32.3 9.8
Income 6 70 16.2 9.4
Education 0 16 5.6 4.3
IPM 0 1 0.1 0.4
Farm size 1 175 14.5 25.2
Table 21A.Descriptive statistics of important variables (district Vehari)
Variable Minimum Maximum Mean Std. Deviation
Education 0.0 16.0 5.9 4.3
Age 18.0 60.0 34.2 10.4
Income 5.0 60.0 18.0 9.2
Perception 1 5 2.9 1.1
Health effect 0 1 0.8 0.4
Training 0 1 0.1 0.3
Farm size 1.0 110 13.5 16.4
IPM 0.0 1.0 0.1 0.3
181
Table 22A: Name of the districts and share of total area under cotton in Punjab province
District’s Name Area under
cotton crop
(In acres)
% Of Total Area
under cotton in Punjab province108
Number of
associated H.Hs* with
farming in each district
Rahim Yar Khan 798518 13.3 191024
BahawalPur 623173 10.3 149636
Bahawalnagar 473048 7.0 139640
Vehari 561312 10.0 119752
Pakpatan 49895 1.0 79305
Toba Tek Sing 111670 2.0 84011
Sahiwal 214171 3.5 99780
Khaniwal 498457 8.0 119363
Lodhran 444177 7.5 77667
Multan 418568 7.0 103515
Muzaffargarh 632236 11.0 205964
Total 2319279 82 1369657
Source: Agriculture census 2000, procedure & data tables Punjab, Government of Pakistan statistics
division Agricultural census organization Lahore. * H.Hs stands for households
108
% o f total area under cotton in Punjab is calculated by the formula= area under cotton crop in each
District/ Total area under cotton crop in Punjab province
182
Table 23A. List of sample villages used for survey
S.No. Name of village
No. of
respondent
interviewed
Tehsil District
1 HARI CHAND 16 MAILSI WAHARI
2 ALAM PURA 13 MAILSI WAHARI
3 MAZA GHAUSE 16 MAILSI WAHARI
4 QAZI QALDA 10 MAILSI WAHARI
5 CHACK-204/E-B 20 BOREWALA WAHARI
6 CHAK 255-EB 23 BOREWALA WAHARI
7 CHAK 203-EB 9 BOREWALA WAHARI
8 CHACK 547 EB 17 WAHARI WAHARI
9 MOZA GHAFFOR
WAH 9
WAHARI WAHARI
10 CHACK 248/E-B 7 WAHARI WAHARI
11 CHACK 249/E-B 9 WAHARI WAHARI
12 MASTA CHOWKI 5 PAKA KRORE LODHARAN
13 CHANAN WALA 7 PAKA KRORE LODHARAN
14 CAIAN WALA 18 PAKA KRORE LODHARAN
15 CHORIAN WALA 8 PAKA KRORE LODHARAN
16 DABE WALA 4 PAKA KRORE LODHARAN
17 BAGHAR WALA 7 PAKA KRORE LODHARAN
18 MERAN PUR 8 DUNIA PUR LODHARAN
19 CHACK 360/W-B 9 DUNIA PUR LODHARAN
20 CHAK No 366/W-B 5 DUNIA PUR LODHARAN
21 KHOOH BUKSH WALID MOZA BASTI BOHARH
5 DUNIA PUR
LODHARAN
22 WAHID BUKSH
KOTLY BAJWAH 7
DUNIA PUR
LODHARAN
23 CHAK 361/W-b 13 DUNIA PUR LODHARAN
24 CHAK No 372/W-B 7 DUNIA PUR LODHARAN
25 CHAK No 365/W-B 12 DUNIA PUR LODHARAN
26 KALO WALA 10 LODHARAN LODHARAN
27 MOZA KOT HAGI 11 LODHARAN LODHARAN
28 MOZA WAHI
SALLAMAT RAY 9
LODHARAN LODHARAN
29 PIPLI WALA 16 LODHARAN LODHARAN
30 MOZA SALSADAR 8 LODHARAN LODHARAN
183
Table 24A. Area, production and per hectare yield of major cotton producing countries (2005-2006)
Countries Area (000 hectares) Prod. (000 tonnes) Yield (kgs/ hectare)
China 5060 17100 3379
U.S.A 5586 12876 2305
India 8826 7500 850
Pakistan 3193 7279 714
Brazil 1254 3727 2972
Uzbekistan 1390 3770 2712
Turkey 600 2290 3817
Turkmenistan 600 1000 1667
Australia 335 1397 4170
Greece 365 1232 3375
Syria 218 1024 4697
Egypt 315 820 2603 Source: Agricultural statistics of Pakistan, Ministry of Food & Agriculture (2008), Government of
Pakistan, Islamabad.
Table 25A.Area, production and per hectare yield of major rice producing countries (2005-2006)
Countries Area (000 hectares) Prod. (000 tonnes) Yield (kgs/ hectare)
China 29030 181900 6266
India 43400 130513 3007
Indonesia 11801 53985 4575
Bangladesh 11100 41104 3703
Viet Nam 7339 36341 4952
Thailand 10200 27000 2647
Myanmar 6270 24500 3907
Philippines 4000 14615 3654
Brazil 3936 13141 3339
Japan 1706 11342 6648
U.S.A 1361 10126 7440
Pakistan 2520 7538 2991
Republic of Korea 980 6435 6566
Egypt 650 6200 9538 Source: Agricultural statistics of Pakistan, Ministry of Food & Agriculture (2008), Government of
Pakistan, Islamabad
184
Table 26A. Area, production and per hectare yield of major sugarcane producing countries (2005-2006)
Countries Area (000 hectares) Prod. (000 tonnes) Yield (kgs/ hectare)
Egypt 135 232320 121000
Brazil 5767 420121 72849
China 1326 87600 66063
Pakistan 966 47244 48907
Mexico 645 45195 70070
Colombia 432 39849 92243
Australia 441 37485 85000
Philippines 380 31000 81579
U.S.A 374 24751 66179
Indonesia 360 29300 81389
Argentina 305 19300 63279
South Africa 312 21725 69631
Guatemala 190 18500 97368
India 3750 15000 61952 Source: Agricultural statistics of Pakistan, Ministry of Food & Agriculture (2008), Government of
Pakistan, Islamabad
Table 27A.Area, production and per hectare yield of major wheat producing
countries (2005-2006)
Countries Area (000 hectares) Prod. (000 tonnes) Yield (kgs/ hectare)
China 22950 97000 4227
India 26500 72000 2717
U.S.A 20283 57280 2824
Russia 23045 47608 2066
France 5281 36878 6983
Germany 3174 23693 7465
Pakistan 8358 21612 2586 Source: Agricultural statistics of Pakistan, Ministry of Food & Agriculture (2008), Government of
Pakistan, Islamabad
185
Appendix III: Pesticide Legislation in Pakistan
Agricultural Pesticide Ordinance 1971
Agricultural pesticide Ordinance (APO) 1971 was issued on 25th January, 1971.
The main objective of APO was to regulate and monitor import, manufacture, sale/
distribution and use of pesticide in the country. Over time, the ordinance was amended
by issuing Acts in different years up to 2005 in order to make this ordinance compatible
with the modern day requirements.
The ordinance provided provision for the constitution of the Agricultural
Pesticide Technical Advisory Committee (APTAC) to direct the Government on
technical matters arising out of administration of this ordinance and to execute any other
role assigned to it by or under this ordinance. The APTAC is authorized to appoint sub-
committee consisting of specialists/experts for the consideration of particular matters as
it may consider necessary.
The APO also provided a provision for establishment of pesticide laboratory at
Federal or Provincial level to carry out the functions e.g. analysis of pesticide to ensure
their originality and specification. Government experts are provided authority for
checking the pesticide samples in the laboratory. The provision of Inspectors is also
given under this ordinance. Any Inspector is authorized within the specified local limits
for which he is appointed, to enter upon any premises where pesticide are stored, no
matter whether these pesticide are in containers or in bulk and take samples from for
examination. The APO also announces penalties for the offences and other
misappropriations.
186
The Agricultural Pesticide Rules, 1973
The Agricultural Pesticide Rules (APR), 1973 provides powers to the
Government to make rules in consultation with Agricultural Pesticide Technical
Advisory Committee (APTAC) for carrying out the provisions of this ordinance.
Pesticide Ordinance 2005
Under Pesticide Ordinance 2005, import, export, manufacture, formulation, sale,
distribution and use of pesticide are as follow:
1. Registration of Pesticide: No person shall import, manufacture,
formulate, repackage, holds in stock for sale or advertise any pesticide which has
not been registered in the prescribed manner. Any person intending to import,
manufacture, formulate, repackage, hold in stock for sale or advertise any
pesticide, may apply to the department for registration of the pesticide under
identified trade mark. It must also satisfy the department that the pesticide is
effective for the purpose for which it is claimed to be effective and that the
pesticide is not generally detrimental/ injurious to environment, human or animal
health when applied according to directions. Further, the pesticide must not
belong to formulations banned in Pakistan.
2. Cancellation of registration: If at any time after the registration of a
pesticide, the Federal Government is of the opinion that the registered pesticide
is leading to violation of the provision of this Act, the Director General may,
after giving an opportunity of being heard, cancel the registration with intimation
to the Federal Government.
187
3. Export: A pesticide registered in Pakistan, can be exported subject to
intimation to the department in the prescribed form and in conformity with any
other law and any relevant international convention or protocol for the time
being in force.
4. Renewal of registration of a pesticide: If a person who holds a previous
registration certificate desires that the registration of a pesticide be renewed, the
Federal Government may under this Act renew the registration for a further
period of three years, provided that no change has taken place in the ingredients
of that pesticide.
5. Testing at port of entry and exit: Every consignment of any pesticide
imported into or exported from Pakistan shall be invariably tested by the
Government Analyst and if found to be adulterated or sub-standard, incorrectly
or misleadingly tagged, the Federal Government may disallow the import or
export of such pesticide and may also cancel the registration of such pesticide.
6. Labeling: No person shall import, sell or advertise unless package
containing the pesticide is marked in printed characters in such manner as may
be prescribed. Certification of distributor or dealer who fails to maintain
prescribed requirements shall be cancelled.
7. Regulation of use: No person shall use any pesticide in violation of the
rules made under this Act.
188
8. Regulation of manufacture, formulation and repackaging: No
person shall engage in the manufacture, formulation, repackaging of a pesticide
including that of intermediates, without obtaining prior certification from the
department.
9. Renewal of certification of manufacturing, formulation or repacking
plant: The Federal Government may, upon application of the expiry of the
certification of a plant, renew the certification for a further period of five years.
189
Appendix IV: Districts profiles
District Vehari
Geography: Vehari109 is a district in the Punjab province of Pakistan. It is known as
city of cotton, is located at 30°1'60N 72°20'60E at an altitude of 135m (446ft). The total
area of the district is 4,364 square kilometers. It is about 93 kilometers in length and
approximately 47 kilometers in breadth. It borders with Bahawalnagar and Bahawalpur
on the southern side, with Pakpatan on the eastern, with Khanewal and Lodhran on
western and with Sahiwal and Khanewal on northern side. It lies about 100 kilometers
from the regional metropolis of Multan and about 25 kilometers north of the river
Satluj110. The district of Vehari is administratively subdivided into three tehsils, Mailsi,
Burewala and Vehari.
Weather: Like other districts of Southern Punjab, the summer in Vehari is very hot.
The summer season starts from April and continues until October. May, June, and July
are the hottest months in the district. The mean maximum and minimum temperatures
for these months are about 47 and 28 degrees Celsius. During summer dry, hot and dusty
winds are common in the district. The winter season lasts from November to March.
December, January and February are the coldest months. The mean maximum and
minimum temperatures for this period are about 22 and 4 °C. Fog is very common
during winter. In most parts of the district rain falls during the monsoon season from
109
The name Vehari means low lying settlement by a flood water channel. The district lies along the right
bank of the river Sutlej which forms its southern boundary. Information regarding district Vehari obtained
from Wikipedia. For further information and detail
See: http://en.wikipedia.org/wiki/Vehari
110
See: http://en.wikipedia.org/wiki/Vehari
190
July to September. During winter season there is very little rain.
Agriculture Economy: The district consists of plain area with fertile land. As a
part of Indus plain it has the best cultivated land which is suitable for cotton, wheat and
other agricultural crops. According to Agriculture Census (2000) the area under cotton
crop in district Vehari is 561312 acres which represents 10 percent of total area under
cotton crop in Punjab. The associated households with agriculture in the district are
119752. Its land is irrigated with the fertile water of Chenab and Ravi rivers. Vehari
district has a big canal system with two canals namely Pakpatan and Mailsi canal. The
total number of canals including their minors in the district is 19 with a total length of
about 1,380. The agricultural products of the district include; mangoes in the summer
and guava and other citrus fruits in the winter. Vehari is considered the capital of cotton
production in this part of Pakistan, with dozens of cotton processing factories and
cottonneseed oil manufacturing plants. In addition, sugarcane farming and processing is
also common.
Population and Culture: According to Population Census (1998) the total
population of the district is 2090416. The main languages are Saraiki, Punjabi, Pashto,
Hindko and Urdu. The native population of the study area are the Arain (the desandants
of Umayyad Arabs from Areeha, who were known as their Arabic name Areehai which
change to Arain) is the most prominent tribe/cast of the district. Other tribes/casts
include Khichhi, Jahiyas, Daultana and Khakwani Pathans those appeared on the Vehari
scene towards the end of the 19th century. The Khakwani came here as huge landowners
and are still probably the largest single family that owns the highest acres of lands. Joint
191
family system is common and all the members of the household111 usually live in the
same house. In cases where they do not, the mutual economic and interdependent
relationship remains the principal cohesive factor among them.
District Lodhran
Geography: Lodhran is a district in the Punjab province of Pakistan.112 It is located at
29°31'60N 71°37'60E and lies on the northern side of River Sutluj. It is bounded to the
north by the districts of Multan, Vehari and Khanewal, to the south by Bahawalpure, to
the east by Vehari and Bahawalpur while district Multan lies on the western side.
Lodhran is spread over an area of 1,790 square kilometers and is subdivided into 3
tehsils Dunya Pur, kahror Pacca and Lodhran. The main towns of the district are:
Qutabpur, Gogran, Dhanot, Danwran, Rajapur, Dakhano Gharo, Choki Masti Khan,
Borhanpur, Amirpur Sadat, Fatehpur, Makhdoom Ali and Jalla Arain.
Agriculture: The main crops of the district are cotton and wheat. Some others include;
rice, sunflower and sugarcane. The main fruit that are cultivated include; citrus, mango
and guava, while the main vegetables are onion and cauliflower. According to
Agriculture Census (2000) the area under cotton crop in district Lodhran is 444177 acres
which represents 7.4 percent of total area under cotton crop in Punjab. The associated
households with agriculture for their livelihood in the district are 77667.
Climate: The climate of the district is hot and dry in summer and cold in winter. The
maximum and minimum temperature ranges between 42C0 and 28C0 in summer.
111
Households consist of individuals who share mutual reciprocal responsibility, i.e. people who are
obliged to look after each other (to feed, house and clothe) and who in return owe the responsibility to
render services, particularly for farm activities (Ajayi, 2000). 112
Information regarding district Lodhran obtained from Wikipedia for further information and detail see http://en.wikipedia.org/wiki/Lodhran
192
During winter, the temperature fluctuates between 21C0 and 5C0. The entire district is
smooth plain. The average rainfall in the district is 71 millimeters.
Population and Culture: According to Population Census (1998) the total
population of the district is 1171800 (Density 422/ km²). The main languages are Saraiki
and Urdu. The native populations of the district are the Rajput, Kanjo, Dogar, Baloch
and Arain. Joint family system is common and usually all the members of the family live
in the same house. In few cases where they do not, the mutual economic and
interdependent relationship remains the principal cohesive factor among them.
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Appendix V: Description of variables in empirical models
The number and type of variables to be included in a pesticide use model vary
depends on the objectives and hypotheses being tested, and the limitations imposed by
the data availability (Ajayi, 2000).
Table. Description of variables included in the empirical models
Variables Description
Environmentally sound
behavior (IPM)
Dichotomous variable represents whether or not farmer
use any IPM. 1 = Yes, 0 = No
Farm size Acres of land cultivated
Risk perception Farmer‘s perceived risk associated with pesticide use.
5=extremely high , 1= No risk at all
Education Number of years of formal schooling, categorized as 1= illiterate, 7= graduates and above
Training Dummy variable represents whether farmer got
training of pesticide use or not. 1 = Yes, 0 = No
Age Age of pesticide applicator in years
Income Farmer‘s monthly income in rupees
Geographical area District dummy, 0=Lodhran, 1=Vehari
Health effects Whether farmer experienced health problem or not? 1 = Yes, 0 = No
Willingness to pay Farmer‘s Willingness to pay to avoid health risk, 1= Not willing to pay, 5= willing to pay over and above 20 percent premium.
Risk perception: This variable measures whether or not farmers perceive pesticide a
potential danger to their health, particularly when mixing and applying pesticide. It is
very important in the course of behavior change since it motivates individuals to adopt
measures to protect themselves from negative environmental conditions. Risk perception
is specified as no risk at all=1 to very high risk=5. In defining the variable, the study
follows a similar method used by Lichtenberg and Zimmerman (1999). For empirical
model, risk perception is specified as dependent variable. The health experience, age,
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education, training, income and geographical area are specified as independent variables.
The model thus controls for farm operator and farm characteristics that may influence
health experience in order to isolate the effects of health experience on attitudes.
Health effects: As farmers mix and spray pesticide, they are naturally exposed to the
toxicity of the chemicals. Exposure to pesticide can lead to number of health effects,
depending on the pesticide‘s toxicity and the dose absorbed by the body (Dasgupta,
2005a). Health effects variable is very important in the course of behavior change. It
heightens risk perception which ultimately motivates individuals to take protective
measures to minimize health risk. Health effect is specified as whether or not farmers
experienced negative health effects during or short after mixing or spraying operations.
The health effects of pesticide exposure are manifested as specific symptoms or a
combination of multiple symptoms. Building on WHO information as well as earlier
studies, 10 types of symptoms were first identified. The question was also left open to
include others if reported (any). The study focuses on acute health effects, as a detailed
medical examination of sample farmers was beyond the scope of this study. Study relied
on self-reported health effects, where farmers were questioned if they experienced any
health impairment after mixing and spraying pesticide. Following Dasgupta (2005a), the
health effects variable is defined as whether a farmer experienced at least one symptom
(=1) or not (=0). Given the results of previous studies and theoretical background health
effects is expected to have a positive relationship with risk perception, protective
behavior and alternative pest management practices.
IPM: The IPM variable is very important in the present context since this study makes
an explicit link between illness experiences and coping strategies. It measures whether
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or not farmers adopt alternative pest management technique such as integrated pest
management which is supposed to environmentally sound. It is worth knowing that IPM
focuses on the adoption of various pest management practices regarded as
environmentally sound/beneficial and either substituting for or supplementing pesticide
use while not necessarily eliminating pesticide use.
Education: Education is expected to have positive impact on coping behavior. The
more educated people are expected to rank higher risk perception and subsequently
adopting IPM practices owing to better awareness. For the purpose of analysis, the
respondents were grouped into seven groups based on the education level— from 1=
illiterate, 2= 1 year of schooling up to 4 years, 3=from 5 years up to the 7 year s, 4= 8
years up to 9 years of schooling, 5= 10 years up to 11 years, 6=12 years up to 13 years
and 7= 14 years and above.
Income: Income is the total monetary equivalence of all expenditures made by the
household in the farm of cash plus total value of household grown agriculture products
kept for household‘s consumption during a month. The household grown products also
includes livestock‘s produced dairy products. Household were also asked about
variations in income during different seasons. 113 The income is defined in rupees and is
expected to impact risk perception, protective behavior and IPM positively. It is based
on the reasoning that high income individuals are more likely better aware and better
informed and can afford protective measures.
113
Based on the understanding that livestock generates products like milk, eggs and the like items are not
always same throughout the year. Similar reasoning holds for agricultural products like fruits and
vegetables.
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Age: This variable represents farmers‘ age and is used as a proxy for farmer‘s
experience and management capacity of pesticide operations. Compared to youth, adult
are also assumed to be more caring. Given that farming is the major vocation in the
study area and most of the individuals are introduced to farming as early as their youth,
it is assumed that their age will better reflect pesticide hazard (Ajayi, 2000). As prior
expectation age is positively related to risk perception, protective behavior and IPM.
Training: Training is also a variable of interest. An individual usually undertake
training with the ultimate goal to avoid pesticide exposure. A trained farmer being better
informed is expected to perceive more risk and engage in better management practices.
The training variable is defined as whether a farmer got training of safe handling of
pesticide (=1) or not (=0)?
Farm size: Farm size is also included in the model to see the possible differences in
attitudes regarding pesticide use among small and large land holders. Based on the prior
evidences (pesticide use survey, 2002; Jeyaratnam, 1990; Forget, 1991) which states that
agriculture extension services often limited to big landholders, farm size is assumed to
be positive to risk perception and alternative pest management practices. Additionally,
farm size is taken as the proxy of duration of pesticide exposure, since larger the farm
size, higher the likelihood that farmers spend additional hours in spraying/farming
activities. Therefore, carrying higher probability of being exposed to pesticide.
Willingness to pay: Farmers were also asked about their willingness to pay for safe
alternatives like IPM. The amount was classified into categories, 1= Not willing to pay,
2= willing to pay from 1 percent up to 5 percent premium, 3= willing to pay up to 6
percent to 10 percent premium, 4= willing to pay up to 11 percent to 20 percent
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premium, 5= willing to pay over and above 20 percent premium. From policy
perspective this variable is very important. If farmers have positive willingness to pay
for avoiding pesticide related health risks. It provides strong motivation for policy
makers to continue research on IPM and its implementation which is very limited in the
area.
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Appendix VI : Survey Questionnaire
My name is_________ and I am from federal Urdu university of
Arts, Science & technology Islamabad. The purpose of this questionnaire is to investigate the use of pesticide by farmers, and the health &Environmental effects of pesticide use. It is for research purposes only. Please answer the questions to be best of your knowledge. Answers will be kept completely confidential and will only be presented in a summary format.
Do you agree to participate in this survey? 1. Yes 2. No
If yes, continue the survey Time started: ________________
Name ________________
Village ________________
Tehsil ________________
District ________________
Address __________________________________________
__________________________________________
PESTICIDE USE IN PAKISTAN
Survey Questionnaire
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Part 1: Area and Property Information
A.1 How would you define the farm ownership?
1. Own the farm 2. Rental arrangement
3. Sharecropper 4. Others ________
A.2 what is the approximate farm size?
1. less than 1 acre 6. 10 to less than 25 acres
2. 1 to less than 2.5 acre 7. 25 to less than 50 acres
3. 2.5 to less than 5 acres 8. 50 to 100 acres
4. 5 to less than 7.5 acres 9. More than 100 acres
5. 7.5 to less than 10 acres
Part 2: Personal General Information
B.1 Gender of the respondent
1. Male 2. Female B.2 Age of the respondent Years __________
B.3 How many people, including yourself, lives in your immediate household? (A household is defined to comprise all usual residents, where they sleep and share
common facilities) # of persons__________). B.4 what is the total monthly (cash) expenditure of the household? ______________ B.4.a What is the approximate value of all household grown products used only for
household consumption (use additional sheet if required)?
Product name Quantity (kg)
Price Product name
Quantity (kg)
Price
B.6 what is the highest level of education you have completed (in case not decision maker, please, also complete second row)?
Household‘s Education
Illiterate Under-
primary
Primary Middle Secondary Higher
Secondary
Graduation
& above
1.
Respondent
2. Head or decision
maker
Note; 1. Illiterate (can‘t read or write); 2. Under- primary (1-4 years of schooling; 3. Primary (5 years of
schooling); 4. Middle (6-8 years of schooling); 5. Secondary (9-10 years of schooling); 6. Higher
Secondary (12 years of schooling); 7. Graduation and above (14 years and above schooling)
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Part 3: Pesticide Application
C.1 How long have you been applying pesticide? _____ Months ____ years. C.2 Do you mix different brands of pesticide before application?
1. Yes 2. No (if no, please go to C.2.b) C.2.a If yes, please specify the brand and mixture you use for each crop (use additional
sheet it required).
Crop name
No. of application/
Spray
Brand Name
Amount You mix
Prescribed Quantity
Price
1
2
3
4
5
6
7
8
9
10
11
12
13
14
C.2.a.1 What is the main reason why you mix the pesticide this way?
Please specify)__________________________
C.2.b If you use single brand, please specify the brand and quantity for each crop.
Crop name
No. of application/
Spray
Brand Name
Amount You mix
Prescribed Quantity
Price
1 2
3 4
5 6
7 8
9
10
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C.3. Has the use of pesticides increased over the years? 1. Yes 2. No
C.3.a if yes, please give reason?
1. everybody else increased
2. Pesticide supplier said so 3. Pesticides are not effective
4. to make sure that it worked 5. I do not know
6. Other_________________ (please specify)
C.4 on a scale of 1-5, how much risk do you think you are exposed to while using
pesticide on this farm?
No risk at all
some small risks
A medium amount of risk
A large and significant amount of risk
Very toxic risks
C.5 Do you also use alternative pest management methods to control pest?
1. Yes 2. No (if no, please go to D.7.b)
C.5.a If yes, which method you use to reduce dependence on pesticide
Methods Please explain
Decreased dosage
Decreased no. of applications Change with less toxic pesticide Manual clearing Light traps Crop Rotation Variation in sowing and harvesting time
Enemy plants Biological methods Other measures of IPM.
e.g_______________
C.5.b If no, why you did not adopt any measure. Please specify ________________
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Part 4 Health
The next section is related to health. Please recall the best you can about any problems that you may have experienced.
D.1 Have you ever had any of the following symptoms after applying pesticide
during the last year?
1. Eye irritation 6. Fever
2. Headache 7. Convulsion
3. Dizziness 8. Shortness of breath
4. Vomiting 9. Skin irritation
5. Diarrhea 10. Other (specify) ______
D.2 How sure or confident you are that the symptoms you experienced were caused
by exposure to pesticide?
1. Not sure 2. Little
3. Rather 4. Very
5. Extremely 6. I don‘t know
D.3 Did you visit doctor?
1. Yes 2. No D.3.a. If yes what did he diagnose (code of disease*114)?
1. ________ 6. ________ 2. ________ 7. ________ 3. ________ 8. ________ 4. ________ 9. ________ 5. ________ 10. ________
D.3.a.1 How much did it cost you (Doctor‘s fee+ medicine cost + transportation cost)?
________________ D.3.b If no, why, please explain __________________
D.4 How long did that (those) symptoms last? (In hours/days).
1. ________ 6. ________ 2. ________ 7. ________ 3. ________ 8. ________
114 * 1. Eye irritation; 2. Headache; 3. Dizziness; 4. Vomiting; 5. Diarrhea; 6. Fever; 7.
Convelsion; 8. Shortness of breath; 9. skin irritation; 10. Others
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4. ________ 9. ________ 5. ________ 10. ________
D.5 How many days you spent in bed because of illness? _________
D.6 Do you think that pesticide use and/or exposure, overall, has any negative short-
term and long-term impacts on health?
1 2 3 4 5 6
No effect
Little effect
Some effects Large effects
Fatal effects I don't know
Part 5: Protection and safety
E.1 Have you ever-received basic training on safe handling and applying pesticide?
1. Yes 2. No
E.1.a if yes from where you got this training?________________________ E.1.b If no basic training, do you have access to someone who provides such training?
1. Yes 2. No E.1.b.1 If YES, who? ____________________________
E.2 when purchasing pesticide, are you usually supplied with information on the
pesticide, such as pamphlets or instructions, describing safety issues.
1. Yes 2. No
E.2.a If YES, do you read and follow the instructions in the pamphlets?
1. Yes 2. No
E.3 what do you typically wear while applying pesticide?
Protective
measures
Use Reason if protective measures not used
Costly Not available Unnecessary uneasy Others
Boot 1. yes
2. no
Hat 1. yes
2. no
Shirt/qamis 1. yes
2. no
Gloves 1. yes
2. no
Eye glasses 1. yes
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/goggles 2. no
Shalwar/lungi 1. yes
2. no
Mask 1. yes
2. no
others 1. yes
2. no
E.4 Do you take a bath right after spraying?
1. Yes
2. No (If no, why ____________________________) E.5 Do you change clothes right after spraying?
1. Yes
2. No (If no, why ___________________________)
E.6 How long is it after application before you re-enter the field? __ Hours __Days E.7 when you mix/use pesticide, does the liquid come into contact with any part of
your body?
1. Yes 2. No
E.7.a If YES, which part?
1. Hands 2. Feet
3. other part (Please specify) ____________
E.8 Please indicate the main source of the following instructions that you may have
received, and also tell, do you follow these instructions.
Instructions Information Source
Do you follow
If no, please explain why?
Read the labels on the
bottle/package and follow the Instructions.
1
2
3
4
5
YES
NO
Do not mix pesticides with bare Hands.
1
2
3
4
5
YES
NO
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Mix pesticides with a stick. While mixing, wear hand gloves and eye shield.
1
2
3
4
5
YES
NO
While cleaning the sprayer‘s nozzle do not place your mouth on it or
blow on it.
1
2
3
4
5
YES
NO
Before spraying pesticide take all
protective measures such as wearing hand gloves, head cover;
face shield, full sleeve shirt/kurta, full length trousers/shalwar, and shoes.
1
2
3
4
5
YES
NO
Do not spray pesticide against the wind. Determine wind direction
first and then spray.
1
2
3
4
5
YES
NO
Do not eat or drink while spraying
pesticide. 1
2
3
4
5
YES
NO
Do not smoke while spraying pesticide. The reaction may be toxic or even fatal.
1
2
3
4
5
YES
NO
Do not wash pesticide bottle or pesticide sprayer in the pond or
canal.
1
2
3
4
5
YES
NO
Pesticide from the bottle or Sprayer
will contaminate the water of the 1
2
YES
206
pond or canal and will be deadly for the fish, cattle, birds and people.
3
4
5
NO
Wash and clean the sprayer and your clothes at a far distance from
the pond.
1
2
3
4
5
YES
NO
After applying the pesticide on your
field, display a signboard or an empty pesticide bottle, so that everybody sees and understands
that you sprayed pesticide on that field.
1
2
3
4
5
YES
NO
Do not keep other things in the pesticide bottle or package.
1
2
3
4
5
YES
NO
Tear up the pesticide package into pieces and bury them under the ground.
1
2
3
4
5
YES
NO
Keep pesticide under lock so that
they are out of the reach of children. 1
2
3
4
5
YES
NO
Do not keep pesticide where you keep other things.
1
2
3
4
5
YES
NO
In the event of an accident, provide
first aid to the patient, following the instructions on the label of that particular pesticide bottle. Take the
patient and the pesticide
1
2
3
4
YES
NO
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Bottle to the doctor as soon as possible.
5
Keep the children and domestic
cattle and poultry birds out of the immediate area.
1
2
3
4
5
YES
NO
Agri. Ministry Official=1, Pesticide Suppliers=2, NGOs =3, others =4, Never heard this
before=5
Part 6: Environment F.1 Have you ever heard or witnessed any of the pesticide-related accidents below in your
local area?
1 Water contamination, please describe _______________________
2 Air contamination, please describe _________________________
3 Death of fish, frogs, birds, bees, please describe ___________________
Part 7: Willingness to pay Now we are going to ask you a question about alternative pest management. Suppose that you were able to have access to a pesticide that was just as effective as the one(s) you are using now, but it d id not have any short-term or long-term
negative health effects. Thinking about the health effects you have experienced or observed with your current use of pesticides, how much would you be willing
to pay for the use of safer pesticide? (Note also that it will reduce your income for other purposes) __________________%
THANK YOU FOR CO-OPERATION TIME FINISHED_________________