24ps0521#pesticide externalities in agriculture#_kavitha,d.c._ (1)

8/13/2019 24ps0521#Pesticide Externalities in Agriculture#_Kavitha,D.C._ (1)

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Pesticide externalities in agriculture: Economic and environmentalassessment of IPM technology in cotton cultivation in India

Kavitha, D.C., S. Suryaprakash and G.N.Nagaraja

Abstract

Cotton continues to remain the back bone of Indian rural economy. However, it isa crop of high inputs with high risk. It suffers considerable damage from insect pests at

all stages of crop growth. Thus, the crop accounts for over 50 per cent of total pesticide

use in Indian agriculture. Sole reliance and continued use on pesticides has resulted in

development of resistance in existing pests, resurgence, death of beneficial insects,residues of toxic pesticides in food and water, disturbance in natural agro-ecosystem and

problems related to environmental pollution and health hazards. In this regard, Integrated

Pest Management (IPM) has emerged as the answer to sustain crop yield, greatly reducethe harm of pesticides to bio-diversity and the environment. IPM is a pest management

strategy that employs an array of complimentary methods in pest management. It is an

ecological approach that can significantly reduce the use of pesticides. IPM is the costeffective, economically viable, technically feasible, environmentally compatible,

ecologically sustainable and socially acceptable technology to limit the harmful effects of

pesticides.

This study is an attempt to assess the economics of IPM technology and to

identify and to estimate the externalities of IPM technology vis-- vis non-IPM

technology in cotton production in the cotton belt of Karnataka state, India. The data for

the study was drawn from 140 cotton farms 80 IPM farms and 60 non-IPM (control)farms. The IPM farms realized nearly 47 per cent higher gross returns from cotton

production over the control farms, with hardly 4 per cent increase in cost of production.The functional analysis showed that the mean technical efficiency on IPM farms was

94.55 per cent as against 73.26 per cent on non-IPM farms. Environment Impact

Quotient (EIQ) was used as a measure of the negative externality associated withpesticide use in cotton. It reduces the environmental impact information to a single value,

thus facilitates in judging the external effects of various alternate technologies in pest

management. The EIQ under recommended IPM practice was 6.971and in farmers IPM

practice it was 9.526 while that on non-IPM farms the EIQ was 28.66, clearly indicatingthe huge reduction in the pesticide externality due IPM technology. Thus, adoption of

IPM technology in cotton cultivation not only increases farmers income but also reducesthe environmental damages of pesticides.

Key words: Integrated Pest Management, Economics, Externalities of IPM, Technicalefficiency, Environment Impact Quotient


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1. Introduction

In the recent past, the changes in the cropping systems and adoption of

intensive cultivation in India have not only increased the productivity, but has also

brought in several problems of pests and diseases, besides environmental hazards due to

indiscriminate use of pesticides. Cotton is the most important traditionally cultivated

commercial crop in India. In fact, India accounts for the largest area under cotton in the

world (9 million ha.). Cotton production has increased by over 820 per cent from the 2.79

million bales produced at the time of independence in 1947 (www.ikisan.com). The

characteristic of prolonged reproductive phase of cotton plant with squares, flowers and

bolls at various phases of development offers continuous food supply and shelter for the

uninterrupted breeding and dispersal of pest in this crop. Sucking pests (jassids, aphids,

thrips, whitefly) and boll worms (spotted boll worm, American boll worm, tobacco cut

worm and pink boll worm) cause significant loss in cotton yield. The yield loss ranges

from 20 to 50 per cent in addition to deterioration in cotton quality. This has resulted in

indiscriminate use of pesticides throughout the crop growth period, initially to control

sucking pests and to control pests attacking the bolls in later stages.

The present crisis in cotton revolves around the main issues such as rising cost of

production, indiscriminate use of pesticides without adequate pest suppression, inability

to enhance production during the bountiful monsoon due to ineffective water

management, deterioration in genetic purity, inadequate price support and paucity of

infrastructure to ensure value addition at farmers level (Mayee et al, 2002)

In order to overcome the adverse effects of over reliance on pesticides, the current

thrust is on promoting pest management practices which are ecologically sound,

economically viable and socially acceptable such as Integrated Pest Management (IPM).

IPM involves judicious combination of feasible pest management components like

mechanical, cultural, biological and chemical to keep the pest population below the

economic threshold level. Of the total pesticides consumed, cotton accounts for the

maximum consumption of 54 per cent.


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Integrated Pest Management (IPM) is a pest control strategy that uses an array of

complementary methods: natural predators and parasites, pest-resistant varieties, cultural

practices, biological controls, various physical techniques and pesticides as a last resort.

It is an ecological approach that can significantly reduce or eliminate the use of

pesticides. The main focus of IPM is usually insect pests, but IPM encompasses diseases,

weeds and any other naturally occurring biological crop threats. IPM, as applied in

agriculture, is the use of the most cost effective, economically viable, technically feasible,

environmentally compatible, ecologically sustainable and socially acceptable

combination of physical, cultural, mechanical, chemical and biological methods to limit

the harmful effects of crop pests.

In this back ground, this paper makes an attempt to assess the economics and

efficiency of IPM technology in cotton production, to study the influence of IPM in

cotton production and to estimate the externalities of IPM technology vis--vis

conventional technology.

2. Data and Methodology

Cotton is cultivated across the country both under rainfed and irrigated conditions. For

the purpose of the study, Karnataka state in southern India was chosen. The district

accounting for the largest area under cotton, cultivated mainly in rainfed lands, viz.,

Dharwad district was identified for the study. The other reason for the choice of the district

is that IPM technology is fairly well popularized here. A sample of 80 farmers was

randomly chosen from the list of IPM practicing farmers. In addition, 60 farmers not

practicing IPM were randomly chosen as control for effective comparison. Cost-return

analysis, production function analysis, logit analysis and environment impact quotient field

use rating were employed in data analysis to satisfy the study objective.

2.1 Factors influencing cotton production : Cobb-Douglas production function

The Cobb-Douglas type production function was employed since the regression

coefficients directly represent the elasticities.


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The specific Cobb-Douglas type production function used was:

UXXXXXXaXYbbbbbbb 76543217654321 . (1)

Where,Y = Gross income (Rs. /acre)a = Intercept, a scale parameter

X1 = Seeds (Rs. /acre)X2 = Organic manure (Rs. /acre)X3 = Human labour (Rs. /acre)

X4 = Bullock / Machine labour (Rs. /acre)

X5 = Chemical fertilizers (Rs. /acre)

X6 = Plant protection chemicals (Rs. /acre)X7 = IPM components (Rs. /acre)

U = Error term

bi = Output elasticities of respective inputs, the summation of which givesthe returns to scale.

The equation upon logarithmic transformation takes the linear form.The parameters

were estimated using the Ordinary Least square (OLS) method.

2.2 Efficiency in cotton production : Frontier production function analysis

The economic efficiency of a production function is basically a function of

technical efficiency and allocative efficiency. Technical efficiency is a physical measure of

efficiency and is measured as the ratio of the output of an individual farm to that of the

potentially achievable output. The frontier production function was derived from the Cobb-Douglas type production function fitted to the gross income from cotton cultivation (Farrel,

1957 and Timmer, 1971). The technical efficiency was worked out using potential output

that can be realized from a set of inputs. The potential output is given by

meYY * .. (2)

Where,

Y*= Potential gross income that could be derived from cotton cultivation

Y = Estimated gross income from cotton cultivationem= Highest positive error term

The potential gross return was estimated by adding the highest positive error term to

the intercept of the production function. The potential gross income from cotton cultivation

for each farmer was worked out and the technical efficiency of each farm was assessed as

follows:


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Technical efficiency of gth

farm =g

g

Y

Y*

(3)

Where,

Yg is actual gross income from cotton cultivation on gth

farmYg

* is the potential gross income attainable from cotton cultivation on gth farm.

The allocative efficiency is an economic measure as against technical efficiency,

which is a physical measure. A production activity is allocatively efficient when the value

of the marginal product (VMP) of a factor is equal to its marginal factor cost (MFC).

The output of Cobb-Douglas type production function fitted for both IPM and non-

IPM farms was used to compute the allocative efficiencies. The first differential itself was

the VMP of the factor as the dependent variable was the gross income from cotton

cultivation. Since all the independent variables in regression are the cost of inputs, the MFC

of all factors was unity. Thus the allocative efficiency measure of all factors are given by

the equation

VMP XiAllocative efficiency = ----------- .. (4)

MFCXi

Where,

X

YVMP

VMP = Value marginal product

= input co-efficient

Y= Geometric mean of gross income

X = Geometric mean of input

The value marginal product of the inputs were worked out by multiplying the respective

input co-efficient with the geometric mean level of output and divided by the geometric mean

level of respective input. Allocative efficiency equal to unity represents efficient allocation

while less than or more than unity represents over or under use of the factor concerned,

respectively.


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Adoption of IPM technology: Logit analysis

The data was subjected to logit analysis to work out the probabilities of adoption of IPM

technology in cotton cultivation. The adoption of IPM technology may be influenced by

several social and economic factors. Therefore, to understand the degree and direction of

influence of each factor in the adoption of the technology, the logistic regression model

was used. Although ordinary least square (OLS) estimates can be computed for binary type

dependent variable, the error terms are likely to be heteroscedastic leading to inefficient

parameter estimates. Thus classical hypothesis tests, such as t-ratios, are inappropriate

(Pindyck and Rubinfield, 1981). An alternative proposal is to use Linear Probability Model.

However, if a linear probability model is used, the predicted values may fall outside the 0-1

intervals, thereby violating the basic concepts of probabilities. The use of logit model,

which gives maximum likelihood estimators, can overcome most of the problems

associated with the linear probability models and provide parameter estimators, which are

asymptotically consistent and efficient so that the analogue of the regression t-test can be

applied.

The probability of willingness to practice IPM by the farmers depends on a number of

factors. If the farmer practices or willing to practice IPM in cotton (it includes both IPM

farmers and willing non-IPM farmers), a value of one is assigned for the dependent

variable. If a farmer is not willing to practice IPM, a zero value was assigned. The logit

analysis pertains to practice of IPM in cotton crop in the study area.

The functional form of the logit model was specified as

iiii

i

i uXbaZP

PLog

1. (5)

Where a= intercept

Pi= The probability that a farmer will adopt IPM technology

bi= Logit coefficientXi= Independent variables

ui= The error term

i

i

P

P

1= The odds ratio in favour of adoption of IPM.


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The independent variables considered were:

X1=Age (in years)X2=Education (in years of schooling)

X3=Agriculture income ( in Rs./farm)

X4=Non-agricultural income ( in Rs./farm)

X5=Land holding (in acres)X6= Gross returns from cotton (in Rs./acre)

2.4 Influence of IPM practice on cotton cultivation : Regression analysis

Regression analysis is a useful tool in analyzing the factor productivity in any

production activity including farming. A linear regression model was employed to

estimate the influence of IPM practice on gross returns in cotton cultivation.

The empirical model was:

Y= a + b1X1 + b2X2 + b3X3 + b4X4 + b5X5 + b6X6 + b7X7 + b8D1 .. (6)

Where,

Y = Gross returns (Rs. /acre)

a = Intercept, a scale parameter

X1 = Seeds (Rs. /acre)

X2 = Organic manure (Rs. /acre)X3 = Human labour (Rs. /acre)

X4 = Bullock / Machine labour (Rs. /acre)

X5 = Chemical fertilizers (Rs. /acre)

X6 = Plant protection chemicals (Rs. /acre)

X7 = IPM inputs - others (Rs. /acre)

D1 = Intercept dummy (1 for IPM and 0 for non-IPM)

bi = Regression co-efficients of the respective independent variables.

In the analysis, the intercept dummy variable was introduced for the pooled data

(of IPM and non-IPM farmers) in order to test the hypothesis that IPM shifts the

intercept. The use of intercept dummy indicates the two forms of production function

(one for IPM with the coefficient of intercept dummy variable and the other for non-IPM

without the co-efficient of intercept dummy variable).


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2.5 Relative externalities associated with IPM practices :EIQ

According to Baumol and Oates (1992), externality is present whenever some

individuals utility or production relationship including real (i.e., non-monetary) variables

whose values are chosen by others, without particular attention to the effect in thevictims welfare. For this effect on the victim/beneficiary, the victim does not receive any

compensation or does not pay any fee. According to Mishan (1971), externality is an

unintended or incidental byproduct of some legitimate activity, which arises where ever

the value of production function or consumption of a firm depends upon the activity of

others.

Environmental impact quotient (EIQ)

A model was developed by Kovach et al (1995), which is called the

environmental impact quotient (EIQ) of pesticides. This model reduces the environmental

impact information to a single value. To accomplish this, an equation was developed

based on the three principal components of agricultural production systems: a farm

worker component, a consumer component, and an ecological component. Each

component in the equation was given equal weight in the final analysis, but within each

component, individual factors were weighted differently. The coefficients used in the

equation to give additional weight to individual factors were also based on a one to five

scale. Factors carrying the most weight were multiplied by five, medium-impact factors

were multiplied by three, and those factors considered to have the least impact were

multiplied by one. A consistent rule throughout the model was that the impact potential

of a specific pesticide on an individual environmental factor is equal to the toxicity of the

chemical times the potential for exposure. Stated simply, environmental impact is equal

to toxicity times exposure. The details of the methodology developed by Kovach et al

(1995) are presented here under.

The EIQ Equation

The formula for determining the EIQ value of individual pesticides is listed below

and it is the average of the farm worker, consumer, and ecological components.

EIQ={C[(DT*5)+(DT*P)]+[(C*((S+P)/2)*SY)+(L)]+[(F*R)+(D*((S+P)/2)*3)+(Z*P*

3)+(B*P*5)]}/3 (7)


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DT = dermal toxicity, C = chronic toxicity, SY = systemicity, F = fish toxicity, L =

leaching potential, R = surface loss potential, D = bird toxicity, S = soil half-life, Z = bee

toxicity, B = beneficial arthropod toxicity, P = plant surface half-life.

Farm worker risk was defined as the sum of applicator exposure (DT* 5) plus

picker exposure (DT*P) times the long-term health effect or chronic toxicity (C). Chronic

toxicity of a specific pesticide was calculated as the average of the ratings from various

long-term laboratory tests. These tests were designed to determine potential reproductive

effects, teratogenic effects, mutagenic effects, and oncogenic effects. Within the farm

worker component, applicator exposure was determined by multiplying the dermal

toxicity (DT) rating to small laboratory mammals (rabbits or rats) times a coefficient of

five to account for the increased risk associated with handling concentrated pesticides.Picker exposure was equal to dermal toxicity (DT) times the rating for plant surface

residue half-life potential (the time required for one-half of the chemical to break down).

This residue factor takes into account the weathering of pesticides that occurs in

agricultural systems and the days to harvest restrictions that may be placed on certain

pesticides.

The consumer component was the sum of consumer exposure potential

(C*((S+P)/2)*SY) plus the potential groundwater effects (L). Groundwater effects were

placed in the consumer component because they are more of a human health issue than a

wildlife issue. The ecological component of the model was composed of aquatic and

terrestrial effects and was the sum of the effects of the chemicals on fish (F*R), birds

(D*((S+P)/2)*3), bees (Z*P*3), and beneficial arthropods (B*P*5). The environmental

impact of pesticides on aquatic systems was determined by multiplying the chemical

toxicity to fish rating times the surface runoff potential of the specific pesticide (the

runoff potential takes into account the half-life of the chemical in surface water).

The impact of pesticides on terrestrial systems was determined by summing the

toxicities of the chemicals to birds, bees, and beneficial arthropods. Because terrestrial

organisms are more likely to occur in commercial agricultural settings than fish, more

weight was given to the pesticidal effects on these terrestrial organisms.


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After the data on individual factors were collected, pesticides were grouped by

classes (fungicides, insecticides/miticides, and herbicides), and calculations were

conducted for each pesticide. Where toxicological data were missing, the average for

each environmental factor within a class was determined, and this average value was

substituted for the missing values. Thus, missing data did not affect the relative ranking

of a pesticide within a class.

EIQ Field Use Rating

Once an EIQ value has been established for the active ingredient of each

pesticide, field use calculations can begin. To accurately compare pesticides and pest

management strategies, the dose, the formulation or percent active ingredient of the

product, and the frequency of application of each pesticide need to be determined. To

account for different formulations of the same active ingredient and different use

patterns, a simple equation called the EIQ Field Use Rating was developed. This rating is

calculated by multiplying the EIQ value for the specific chemical by the percent active

ingredient in the formulation by the rate per acre used

EIQ Field Use Rating = EIQ * % active ingredient * Rate .. (8)

With this method, comparisons of environmental impact between pesticides and different

pest management programs can be made.

3. Results and discussion

3.1 Relative economics of IPM

The profitability of a production activity is determined by the cost involved in the

application of new technology, crop productivity and output price. There was no

difference in output price received by the IPM and non-IPM farmers. Thus, given the

output price, productivity and cost of technology are the main determinants of

profitability.

The cost of variable inputs on IPM farms was of the order of Rs. 5908 per acre

which is marginally higher compared to non-IPM farms (Rs. 5696/acre). The cost


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incurred on human labour accounted for the highest share (29.05 % and 25.71 %,

respectively) in both the cases (Table 1). The expenditure on plant protection chemicals

in non-IPM farms (Rs. 966) was more than double the expenditure incurred on IPM

farms (Rs. 466). This is due to the fact that non-IPM farmers relied solely on chemical

pesticides for pest control. The cost incurred on chemical fertilizers was also high in case

of non-IPM farms (Rs. 1246), while in case on IPM farms emphasis was on using organic

manure. The cost incurred on IPM components (excluding chemical pesticides),

including the expenditure on pheromone traps, trap crop, HaNPV, trichocards, Neem

seed kernel extract and summer ploughing, together (Rs. 354) constituted 5.99 per cent of

the total variable cost (Table 2).

The gross returns realized in case of IPM and non-IPM farms were Rs. 10,642 and

Rs. 7254, in that order. Higher returns on IPM farms than non-IPM farms is due to higher

quantity of output (5.7 quintals against 4.2 quintals per acre) realised. The return per

rupee of cost is higher at 1.63 on IPM farms and 1.18 on non-IPM farms. It is the

increase in yield rather than cost change that has benefited the IPM farmers.

3.2 Resource use efficiency in cotton cultivation

The real impact of IPM can be understood only if they are standardized to

comparable levels of scale and input use. The adjusted coefficient of multiple

determination was 0.64 and 0.71 for IPM and non-IPM farms respectively, indicating that

64 and 71 percent of variability in gross returns were explained by the variables

considered in the model (Table 3).

The regression coefficients of organic manure (0.19), human labour (0.47),

fertilizer (0.18) and IPM components (0.115) on IPM farms were significant suggesting

that increase in the use of these factors over and above the present level leads to a

significant increase in the gross returns. The co-efficient pertaining to plant protection

chemicals (-0.02) was negative and significant on non-IPM farms. This clearly brings out

the fact that there is excessive use of pesticides in cotton cultivation and that practice of

IPM technology results in lower cost on this count and increases the net returns.


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3.3 Technical efficiency in cotton cultivation

The frontier output level was computed and the technical efficiency of each

individual farm was worked out (Table 4). The mean technical efficiency of IPM farmers

was 94.55 per cent, while that on non-IPM farms was a low of 73.26 per cent. Among thedifferent categories of efficiency level, major proportion of the IPM farmers (81.25 %)

were in high efficiency group (more than 90 % technical efficiency) compared to non-IPM

farmers (56.71 %). Another interesting observation is that none of the IPM farms were in

low efficiency (below 80 %) group, while a sizeable non-IPM farms (23.36 %) fall in this

group. This clearly shows that efforts in promotion of IPM along with better production

practices have been fairly successful.

3.4 Allocative efficiency in cotton cultivation

The allocative efficiency of the inputs of organic manure and machine/bullock

labour in case of both IPM farms (0.45 and 0.13) and non-IPM farms (0.34 and 0.10) were

less than unity but greater than zero indicating that they have been over used but are still in

the rational region of the production (Table 5). However the allocative measure of plant

protection chemicals of both group of farms (-1.83 and -0.15) were negative indicating that

their use was in the irrational region (III region) of the production function. This brings out

the fact that more efforts are required to make the non-IPM farmers realize the futility of

unscientific and unwarranted use of chemical pesticides in cotton production. The very

high allocative efficiency of IPM components (3.46 for IPM farms and 10.88 for non-IPM

farms) signals that there is a vast scope for increasing its usage and achieve higher net

returns from cotton.

3.5 Influence of IPM technology on cotton production

The per acre gross returns from cotton were regressed on the contributing factors

like seeds, organic manure, human labour, bullock / machine labour, chemical fertilizers,

plant protection chemicals and IPM inputs to analyze the relationship between the gross

returns and input cost (Table 6). An intercept dummy variable for differentiating between

the IPM and non-IPM farms was introduced. This intercept dummy variable introduced


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to capture the overall difference in the input use and gross returns between the farmers

practicing IPM and the non-IPM was 1297.40 and was statistically significant. This

indicates that these two types of farmers significantly differed with regard to the factors

not considered in the production function for IPM and non-IPM farms. The use of IPM

components contributed significantly to the gross returns (2.03) thus, use of IPM

components other than plant protection chemicals will positively and significantly

influence the gross returns in cotton cultivation. Human labour (3.63) and fertilizers

(1.02) contributed positively and significantly to the gross returns. The contribution of

plant protection chemicals to the gross returns is negative (-1.68) and significant.

3.6 Factors influencing adoption of IPM technology

The probability of adopting IPM technology in cotton cultivation was worked out

considering the independent variables like age, education, agricultural income, non-

agricultural income, land holding and returns from cotton (Table 7). The results indicate

that returns from cotton (1.059), age (0.958) and Land holding (0.555) are the major

components influencing adoption of IPM technology. The coefficient pertaining to the

age and land holding were negative. The exponential coefficient for age and land holding

is less than one because cotton production using IPM technology is labour intensive

hence adoption over a larger area is difficult. Again the farmers become risk averters as

the age increases and do not venture into any less-known and perceived-risky practice.

Using the results obtained from logistic regression the odds ratio was worked

out. The odds ratio was 3.91, which implies that for every one farmer not willing to adopt

IPM, there are chances of nearly four farmers willing to adopt IPM technology.


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3.6 Relative externalities of IPM and non-IPM production packages

Several pesticide combinations are used to control various pests which attack

cotton at various stages of its growth. To identify the pesticide combination which is least

toxic and to compare the EIQ field use rating of recommended and farmers practice, EIQ

field use rating was estimated for farmers practice under both IPM and non-IPM farms

and compared with the EIQ of recommended IPM and conventional pest management

practices. The pesticides used to control the insect pests are imidacloprid, quinolphos,

endosulphan, rogar, nuvacron and cyphermethrin which have EIQ values of 37.2, 23.2,

40.5 41.2 and 54.4, respectively. The EIQ uses information on active ingredient,

quantity used per acre and number of applications to calculate EIQ field use rating.

The magnitude of environmental impact quotient under recommended IPM

practices in cotton production was the least at 6.971 (Table 8) and in farmers IPM

practice it was higher at 9.527 (Table 9). The EIQ in recommended conventional pest

protection practices was still higher at 19.520 (Table 10). But the highest EIQ was 28.659

for farmers with conventional practices on non-IPM farms (Table 11). It is worth noting

here that lower the value of EIQ, lower is the externality (damage) caused. Two facts

emerge from the results. There is still scope for reducing the magnitude of external

damage caused by IPM technology (from 9.527 to 6.971). The non-IPM farmers can

reduce the magnitude of externality from a very high of 28.659 to 19.520 by merely

following the recommended conventional pest control practices and can further reduce

the magnitude by adoption of IPM technology. This makes out the case for wider

adoption of IPM technology in cotton, since it not only benefits the farmers, but also

protects the environment.

Out of the pesticides used by respondents rogar had highest EIQ value followed

by cyphermethrin, nuvacron and endosulphan. Quinolphos was found to be relatively

least toxic.


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3.7 Farmer s opinion of impact of I PM

The opinion about the impact of IPM was collected from the IPM farmers (Table

12). Eighty seven per cent of farmers felt that there is significant reduction in the number

of pesticide sprays and 84 per cent of the farmers witnessed labour saving for spraying

activity through adoption of IPM technology. IPM resulted in higher employment

generation (44 %) and the health hazards due to pesticide use were also minimized (23

%). There was no much change in the output quality and the higher income was mainly

due to higher output.

4. Conclusions

The concept of IPM has been in existence since long in the form of cultural,

physical and natural interventions. It is only in the recent years that the concept has been

revitalized in response to increasing technological failure of chemical pest control

technology and its negative externalities to environment and human health. IPM appears to

be an effective alternative to chemical pest control.

Technical efficiency improvements are apparent in IPM farms with a larger number of

farms achieving an efficiency rating of above 90 per cent. This higher technical efficiency

may be attributed to higher net returns realized on IPM farms. The allocative efficiency of

IPM components is very high indicating that there is a great scope for increasing its use in

cotton cultivation. There is a need to convince the farmers of the benefits of IPM

technology in terms of economic returns and productivity as these are the major driving

forces in changing the farmers perception towards new technology. Adoption of IPM

technology in cotton cultivation not only increases farmers income but also reduce the

environmental damages of pesticides. This aspect of the IPM technology needs emphasis in

the promotion of IPM in agriculture in general and in cotton cultivation in particular. The

number and quality of trainings on IPM technology need to be increased for effective

dissemination of information. Voluntary agencies and private institutions may be

associated with IPM technology dissemination, to supplement the efforts of government

machinery.


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References

BAUMOL, W.J. AND OATES, W.E., 1992,The theory of environmental policy.

Prentice-Hall Inc. New Jersey.

BIRTHAL S. PRATAP, SHARMA, O.P. AND KUMAR SANT, 2000, Economics ofIntegrated Pest Management: Evidences and Issues. Indian Journal of AgriculturalEconomics, 55(4): 644-659.

FARELL, M. J., 1957, Measurement of Productive Efficiency,Journal of Royal Statistical

Society, 120: 253-290.

KISHORE, N. M., 1994, The effects of pesticide externalities on cotton cultivation in

Andhra Pradesh.Indian Journal of Agricultural Economics, 49 (3):544-48.

KOVACH, J., PETZOLDT, C., DEGNI, J. AND TETTE, J, 1995, A Method to Measure

the Environmental Impact of Pesticides (IPM Program), Cornell University,

New York State Agricultural Experiment Station Geneva, New York.

MAYEE C.D., RAJENDRAN, T.P. AND VENUGOPALAN M.V., 2002, Surviving

under pressurized trade. Survey of Indian agriculture. The Hindu. June 18

2002:9

MISHAN, E.J., 1971, The post-war literature on externalities: An interpretative essay,

Journal of Economic Literature, 9: 2.

PINDYCK, R.S. AND RABINFIELD, 1981, Econometric Models and Economic

Forecasts. Second Edition, McGraw-Hill, London: 310.

RAJARAM, V., RAMAMURTHY, R. AND KRISHNADOSS, D., 2000, Integrated Pest

Management in Cotton.Insect Environment, 6(1): 47-48


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Table 1: Cost and returns in cotton cultivation

(Rs. /acre)

Particulars IPM farms Non-IPM farms

Seed 577 (9.77) 625 (10.97)

Organic manure 452 (7.65) 211 (3.70)

Human labour 1716 (29.05) 1465 (25.71)

Bullock/ tractor charges 827 (14.00) 747 (13.11)

Chemical fertilizers 1079 (18.26) 1246 (21.87)

Plant Protection Chemicals 466 (7.89) 966 (16.95)

IPM inputs 354 (5.99) 14 (0.25)

Interest on working capital @ 8 %per annum 437 (7.40) 422 (7.40)

Sub total (A) 5908 (100.00) 5696 (100.00)

Fixed cost

Depreciation on farm machinery and equipments 175 179

Land revenue 21 22

Interest on fixed assets@ 11 % per annum 166 171

Sub total (B) 362 372

Marketing cost (C) 232 183

Cost of cultivation (D=A+B+C) 6502 6251

Gross Returns (Rs) 10642 7254

Net Returns (Rs) 4140 1003

Returns per rupee of cost 1.63 1.18

Note: Figures in the parentheses indicate percentages to total variable cost


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Table 2: IPM components used in cotton cultivation

(Rs/acre)

Table 3: Relative resource use efficiency in cotton cultivation - Results of Cobb-Douglas production function

Variables Regression co-efficients (elasticities)

IPM farms Non-IPM farms

Gross returns (Rs.) 10,642 7,254

Intercept 0.83** 1.18**

Seeds (Rs.) 0.15 0.28*

Organic manure (Rs.) 0.19* 0.01

Human labour (Rs.) 0.47** 0.57*

Machine/bullock labour (Rs.) 0.01 0.01

Fertilizer (Rs.) 0.18* -0.03

PPC (Rs.) -0.08 -0.02*

IPM components (Rs.) 0.115* 0.001

Adjusted R2 0.64 0.77

Note: ** and * indicate significance at 5 and 10 per cent levels, respectively

Sl. No. IPM component Units cost

1 NSKE 1.062 Kg 82.50

2 HaNPV 500 LE 10.00

3 Trichocards 5Nos. 26.25

4 Trap crop seeds 0.5 kg 38.56

5 Pheromone traps 2 Nos. 160.00

6 Summer ploughing 0.0625 tractor hours 37.50

Total 354.81


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Table 4: Distribution of cotton farmers according to technical efficiency levels

(per cent)

Sl. No. Particulars IPM Farms Non-IPM Farms

1High efficiency group

(91% and above)81.25 56.71

2Medium efficiency group(80-90%)

18.75 19.93

3Low efficiency group

(Below 80%) - 23.36

4 Average efficiency (%) 94.55 73.26

Table 5: Allocative efficiency in cotton production

Sl.

No.Input

IPM Farms Non-IPM Farms

MVP MFC MVP/MFC MVP MFC MVP/MFC

1 Seeds (Rs) 2.77 1 2.77 3.25 1 3.25

2 Organic manure (Rs) 0.45 1 0.45 0.34 1 0.34

3 Human labour (Rs) 2.92 1 2.92 2.82 1 2.82

4Machine/bullocklabour (Rs)

0.13 1 0.13 0.10 1 0.10

5 Fertilizers (Rs) 1.78 1 1.78 -0.17 1 -0.17

6Plant protection

Chemicals (Rs)-1.83 1 -1.83 -0.15 1 -0.15

7IPM components(Rs)

3.46 1 3.46 10.88 1 10.88


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Table 6: Influence of IPM technology on cotton cultivation

Sl. No. Variable Co-efficients

1 Intercept 2272.26**

2 Seeds (Rs) -0.16

3 organic manure (Rs.) 0.49

4 Human labour (Rs.) 3.63**

5 Machine/bullock labour (Rs.) -0.04

6 Fertilizers (Rs.) 1.02*

7 PPC (Rs.) -1.68**

8 IPM components (Rs.) 2.03**

9 Intercept dummy 1297.40**

Adjusted R 0.68


Table 7: Factors influencing adoption of IPM technologyLogit analysis


Sl. No. Independent variable Co-efficient Exp. Of

1 Age -0.043* 0.958

2 Education 0.249 1.283

3 Agriculture income 0.045 1.046

4 Non agril. Income 0.014 1.014

5 Land holding -0.589** 0.555

6 Returns from cotton 0.057** 1.059

7 Constant 2.697 14.842

Odds ratio 3.91


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Table 8: Environmental impact quotient of recommended IPM practices in

cotton cultivation

Pesticide EIQ

Active

ingredient

Quantity

(litres/acre)

Number of

applications

EIQ field

rating

quinolphos 23.2 0.25 1.00 1 5.800

acetamiprid 20.8 0.20 0.02 1 0.083

cypermethrin 54.4 0.10 0.20 1 1.088

Total environmental impact quotient 6.971

Table 9: Environmental impact quotient of farmers IPM practices in cotton

cultivation

Pesticide EIQ

Active

ingredient

Quantity

(litres/acre)

Number of

applications

EIQ field

rating

Imidacloprid 37.2 0.15 0.01 1.00 0.056

Quinolphos 23.2 0.25 0.28 0.35 0.558

Endosulphan 40.5 0.35 0.33 0.34 1.584

Rogar 74.0 0.40 0.36 0.69 7.245Nuvacron 41.2 0.40 0.01 0.85 0.084



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Table 10: Environmental impact quotient of recommended conventional pest

protection practice in cotton cultivation

Pesticide EIQ

Active

ingredient

Quantity

(litres/acre)

Number of

applications

EIQ field

rating

Imidachloprid 37.2 0.70 0.01 1 0.260

dimethoate 41.2 0.30 0.48 1 5.933

dicofol 29.9 0.18 0.60 1 3.319

endosulphan 40.5 0.35 0.66 1 9.356

cypermethrin 54.4 0.10 0.12 1 0.653


Table 11: Environmental impact quotient of farmers plant protection

practice on non-IPM cotton farms

Pesticide EIQ

Active

ingredient

Quantity

(litres/acre)

Number of

applications

EIQ field

rating

Quinolphos 23.2 0.25 0.41 0.55 1.317

Endosulphon 40.5 0.35 1.12 1.12 17.670

Rogar 74.0 0.40 0.37 0.73 7.930

Cyphermethrin 54.4 0.25 0.10 0.15 0.204

Nuvacron 41.2 0.40 0.22 0.43 1.538


Table 12: Farmers opinion of impact of IPM technology

Indicators

Nature of impact

(positive/negative) percentage

1 Economic impactpositive 84a. In terms of labour saving on spraying

b. Output Quality (premium price) positive 15

2 Environmental impact in terms of no. ofsprays positive 87

3 Health hazard positive 23

4 Social-economic impact in terms of

employment generation positive 44

24ps0521#pesticide externalities in agriculture#_kavitha,d.c._ (1)

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