Environmental efficiency analysis of Basmati rice production in Punjab, Pakistan: implications for sustainable agricultural development.
Abedullah ; Kouser, Shahzad ; Mushtaq, Khalid 等
The intensive use of chemicals worked as a catalyst to shift the
production frontier but the most critical factor of maintaining a clean
environment was totally ignored. The present study attempts to estimate
the environmental efficiency of rice production by employing the
translog stochastic production frontier approach. The data are collected
from five major Basmati rice growing districts (Gujranwala, Sheikupura,
Sialkot, Hafizabad, and Jhang) of Punjab in 2006. Chemical weedicides
and nitrogen are treated as environmentally detrimental inputs. The mean
technical efficiency index is sufficiently high (89 percent) but the
environmental efficiency index of chemical weedicides alone is 14
percent while the joint environmental efficiency index of chemical
weedicides and nitrogen is 24 percent implying that joint environmental
efficiency is higher than chemical wcedicide alone. It indicates that
substantial reduction (86 percent) in chemical weedicide use is possible
with higher level of productivity. Moreover, it is likely to contribute
a considerable decrease in environmental pollution which is expected to
enhance the performance of agriculture labour. The reduction in chemical
weedicides will save Rs 297 per acre and Rs 1307.3 million over all from
the rice crop in Punjab, improving the profitability of rice growing
farmers by the same proportion. Empirical analysis indicates that
reduction in environmental pollution together with higher level of
profitability in rice production is achievable.
JEL classification: N5, O 13
Keywords: Rice Production, Environmental Efficiency, Weedicide,
Fertiliser (NPK), Stochastic Translog Frontier
1. INTRODUCTION
Rice is one of the most important food crops that augment and earn
foreign exchange for the national economy.. It contributes more than two
million tonnes to our food requirements and is a major source of
employment and income generation in the rice growing areas of the farm
land. Rice is the third largest crop in terms of area sown, after wheat
and cotton. It was cultivated on over 2.9 million hectares in 2008.
Accounting for 5.9 percent of the total value added in agriculture and
about 1.3 percent to GDP [Pakistan (2009a)] its importance in the
national economy is obvious. Pakistan has two major rice producing
provinces, Punjab and Sindh. Both provinces account for more than 88
percent of total rice production. Punjab, due to its agro-climatic and
soil conditions has assumed the position of a major centre of Basmati
rice production, accounting for nearly all the Basmati rice the country
produces.
It is well documented that the use of fertiliser and pesticides
(insecticides, weedicides and herbicides) in agriculture has increased
manifolds since the introduction of the so-called green revolution. The
intensive use of inputs has worked as a catalyst to shift the production
frontier of almost all grain crops to feed the growing population but
the most critical factor of maintaining a clean environment has been
totally ignored. Pesticides play an important role in raising
agricultural yields in developing countries. They offer the most
attractive low cost method of increasing output per hectare of land and
give the farmer a high economic return for his labour and investment.
The use of pesticides has considerably increased in developing countries
however its advantages seem to have not been fully exploited [Nguyen, et
al. (2003)]. It is observed that the quantity of agrochemicals used in
the agricultural system of Pakistan has increased more than four times
just in seventeen years i.e., from 1990 to 2007. The total quantity of
agrochemicals consumed increased from 20213 tonnes in 1990 to 94265
tonnes in 2007 and in value terms, the consumption increased from 5536
million Rupees to 10534 million Rupees for the same period [Pakistan
(2009b)]. The negative impact of these agrochemicals on human
productivity, environment and ground water quality has been neglected in
the past, posing a grave threat to the sustainability of agriculture
production system.
The increasing awareness about the role clean environment plays in
human productivity has intensified the demand to eliminate or minimise
the negative externalities of different production systems. Like any
other production system, agriculture also generates positive and
negative externalities. The challenge for scientists is to minimise or
eliminate the negative externalities to sustain the clean environment
for future generations while increasing the productivity level through
modern technologies or reducing environmental pollution by sustaining
productivity levels with the given set of technologies. Fertiliser,
pesticides, weedicides and herbicides are the major inputs that cause
environmental and ground water pollution in agriculture sector. These
inputs could be re-allocated in a way that environmental pollution was
significantly reduced by keeping output levels within a given framework
of production technologies and available resources.
A significant body of literature exists dealing with the technical
and allocative efficiency in different crops and in different regions
[Good, et al. (1993); Ahmed and Bravo-Ureta (1996); Wilson, et al.
(1998); Wadud (1999); Wang and Schmidt (2002); Larson and Plessman
(2002); Villano (2005); Abedullah, et al. (2007)] but little work has
been done to estimate the environmental efficiency of agro-chemicals
(weedicide, pesticide, herbicide and fertiliser) in agricultural
production system [Reinhard, et al. (1999); Zhang and Di-Xue (2005) and
Wu (2007)] which is expected to play an important role in the reduction
of environmental pollution. According to our knowledge there is no study
in respect of Pakistan that deals with environmental efficiency. The
present study hopefully would fill this gap. The objective of the
present study is to estimate the environmental efficiency of chemical
weedicides and fertiliser in rice production by employing a stochastic
production frontier approach.
The scheme of the paper is as follows. The next section presents
the conceptual framework and delineates the empirical model with
variable specification to explain the estimation procedure of technical
and environmental efficiency. This section also explains the selection
of sample and the data collection procedure. Empirical results are
presented and implications are derived in the subsequent section.
Section 4 discusses the limitation of data. The summary and conclusion
is presented in the last section.
2. METHODOLOGY AND DATA COLLECTION PROCEDURE
The methodology is defined in two steps: conceptual framework and
empirical model. The conceptual framework discusses general procedure
adopted to estimate the technical and environmental efficiency while the
empirical model explains the details of production function
specification and mathematical manipulation employed to estimate
environmental efficiency. The last part of this section explains the
data collection procedure used for empirical analysis.
2.1. Conceptual Framework
There are two main approaches (with a number of sub-options under
each) to measure technical efficiency (TE). These include, stochastic
frontier (parametric approach) and data envelop analysis (DEA), also
named as non-parametric approach. These two methods have a range of
strengths and weaknesses which may influence the choice of methods, in
particular with regard to application and constraints. The advantages
and disadvantages of each approach have been discussed by Coelli (1996),
Coelli and Perelman (1999). The present study is employing a stochastic
frontier production approach introduced by Aigner, et al. (1977); and
Meeusen and van den Broeck (1977), later on followed by a number of
studies. Following their specification, the stochastic production
frontier can be written as,
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1)
where, [y.sub.i] is output for the i-th farm, [x.sub.i] is a vector
of k inputs, [beta] is a vector of k unknown parameters,
[[epsilon].sub.i] is an error term. The stochastic frontier is also
called "composed error" model, because it postulates that the
error term [[epsilon].sub.i] is decomposed into two components: a
stochastic random error component and a technical inefficiency component
as follow,
[[epsilon].sub.i] : [v.sub.i-[u.sub.i] ... ... ... ... ... ... ...
(2)
where, [v.sub.i] is a symmetrical two sided normally distributed
random error that captures the stochastic effects beyond the
farmer's control (e.g., adverse weather, natural disasters and what
the farmer might call 'his luck')., measurement errors, and
other statistical noise. It is assumed to be independently and
identically distributed N(0, [[sigma].sup.2.sub.v]). Thus, [v.sub.i
]allows the frontier
to vary across farms, or over time for the same farm, and therefore
the frontier is stochastic. The term [u.sub.i] is one sided ([u.sub.i]
[greater than or equal to] 0) efficiency component that captures the
technical efficiency of the i-th farmer. The variance parameters of the
model are parameterised as:
[[sigma].sup.2.sub.s] = [[sigma].sup.2.sub.v] +
[[sigma].sup.2.sub.u]; [gamma] = [[sigma].sup.2.sub.u]
/[[sigma].sup.2.sub.s] and 0 [less than or equal to] [gamma] [less than
or equal to] 1 ... ... ... ... ... (3)
The parameter [gamma] must lie between 0 and 1. The maximum
likelihood estimation of Equation (1) provides consistent estimators for
[beta], [gamma], and [[sigma],sup.2.sub.s] parameters. Hence, Equation
(1) and (2) provide estimates for [v.sub.i] and [u.sub.i] after
replacing [[epsilon].sub.i], [[sigma].sup.2.sub.s] and [gamma] by their
estimates. Multiplying by [e.sup.-vi] both sides of Equation (1) and
replacing [beta]'s with maximum likelihood estimates, yields
stochastic production frontier as:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (4)
where, [y.sup.*.sub.i] is the observed output of the i-th farm
adjusted for the statistical random noise captured by [v.sub.i]
[Bravo-Ureta and Rieger (1991)]. All other variables are as explained
earlier and [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] is the
vector of parameters estimated by the maximum likelihood estimation
technique. The technical efficiency (TE) relative to the stochastic
production frontier is captured by the one-sided error components
[u.sub.i] [greater than or equal to] 0, i.e.
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (5)
The technical efficiency index in Equation (5) can be defined as
the ratio of the observed to maximum feasible output which is estimated
by employing the traditional stochastic production frontier approach
while according to Reinhard, et al. (2000, 2002) the environmental
efficiency index can be defined as the ratio of minimum feasible to the
observed use of an environmentally detrimental input, given technology
and the observed levels of output and conventional inputs.
Pittman (1983) was the first to consider environmental effects as
undesirable outputs while estimating the Tornqvist index of productivity
change. However, undesirable outputs cannot be priced in the markets
because markets do not exist for such products; hence the modeling of
undesirable products is feasible only if the undesirable outputs can be
valued by their shadow prices. The author used econometric techniques to
estimate shadow prices of demand for biochemical oxygen generated in the
process of converting wood pulp to paper for thirty Michigan and
Wisconsin mills, but it is observed that shadow prices are constant
across all the observation. Following Pittman (1983), Fare, et al.
(1989) and Fare, et al. (1993) also modeled environmental effects as
undesirable outputs. All these studies include environmental effects in
the output vector, and then to obtain inclusive measures of technical
efficiency, and occasionally, productivity change, incorporate the
generation of one or more environmental effects as by-products of
production process [Reinhard, et al. (1999)]. However, Pittman (1981) is
the first who modeled pollution as an input in the production function
and later his approach is refined and modified by Haynes, et al. (1993),
Haynes, et al. (1994), Hetemaki (1996), Boggs (1997) and Reinhard, et
al. (1999). These seminal works have considered environmental effects as
a conventional input rather than as an undesirable output which
distinguished their study from the earlier literature. Recently this
approach has been adopted by Reinhard, et al. (2002), Zhang and Xue
(2005) and Wu (2007). Following the later group of studies we also
incorporated environmental effects (weedicide and fertiliser) as a
conventional input in the production process. Different studies have
used different variables as environmental determinant according to their
objectives and availability of data. We consider weedicides and
fertiliser as environmentally detrimental in rice production however
since pesticides are being used only by a small number of farmers (less
than 15 percent) and on an average its impact on the production process
is not expected to be significant. Following Reinhard, et al. (1999) we
estimated technical and environmental efficiency separately.
The mathematical representation of environmental efficiency can be
written as:
EE = min {[PHI}: F(X, [PHI]Z) [greater than or equal to] Y } [less
than or equal to] 1 ... ... ... ... (6)
where, F(X, [PHI]Z) is the new production frontier and (X, Z)
[epsilon] [R.sub.+] (a set of positive real numbers) while X and Z are,
respectively a vector of conventional and environmentally detrimental
input and Y [epsilon] [R.sub.+] is yield estimated by employing maximum
likelihood estimation technique as defined earlier in Equation 1. To
obtain the environmental efficiency index, a new frontier production
function as defined in Equation 6 could be developed by replacing the
observed environmentally detrimental input vector Z with [PHI]Z and
setting [u.sub.i] = 0, representing a function at full technical
efficiency. The environmental efficiency is explained by employing the
definition of Reinhard, et al. (2000); Reinhard et al. (2002) as EE =
[PHI]Z/Z and then by taking natural logarithm on both sides of the
equation, it can be written with more detail as below: (1)
Ln EE = Ln {PHI]Z-LnZ = Ln([PHI]Z/Z) = Ln[PHI] ... ... ... ... (7)
Where, "LnEE" is the logarithm of environmental
efficiency and it is equal to the logarithm of new frontier function
with [u.sub.i] =0 minus the original frontier function when [u.sub.i]
[not equal to] 0.
2.2. Empirical Model
There is only one output in our case and therefore, as discussed by
Wu (2007) we estimate a stochastic production frontier rather than a
stochastic distance function to relate the environmental performance of
individual farms to the best of environment-friendly farming. To
minimise the misspecification of model we have used a stochastic
translog production frontier and under the assumption of one
environmentally detrimental variable X7 (which is represented by Z due
to environmentally detrimental variable), the translog production
frontier is defined as below:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] ... (8)
Where Ln represents the natural logarithm, Y is the yield in maunds
per acre, X~ is tractor hours used for land preparation, [X.sub.2] is
amount of seed in kg, [X.sub.3] is the number of irrigations, [X.sub.4]
is the amount of labour in hours per acre, [X.sub.5] is per acre active
nutrient of Phosphorus and Potash (PK) in kg, [X.sub.6] is per acre
active nutrients of nitrogen (N) in kg, and Z is the cost of chemical
weedicide in Rupees per acre and it is also considered as the
environmentally detrimental variable. The Equation (8) can be estimated
by employing Frontier Version 4.1 developed by Coelli (1994). The new
stochastic frontier function as discussed above in empirical framework
can be obtained by replacing Z with [PHI]Z in Equation (8) in such a way
that technical inefficiency of each farmer approaches to zero (i.e.,
[u.sub.i] =0) that exists in the original frontier function (Equation
8). It should be noted that [PHI] is environmental efficiency index.
Hence, the new translog function can be written as,
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] ... ... (9)
By subtracting Equation (8) from Equation (9) and with little
mathematical manipulation the result can be written as:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] ... ... ...
(10)
By employing the result of Equation (7) in Equation (10) it can be
modified as follow:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] ... (11)
Now Equation (11) can be solved for LnEE by using the quadratic
equation formula as below: (2)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] ... ... ... ...
(12)
The environmental efficiency "EE" from Equation (12) can
be estimated just by taking the exponent of this equation i.e.
EE=exp(LnEE)={PHI]=([PHI]Z/Z) ... ... ... ... ... (13)
It should be noted that * is the environmental efficiency index as
discussed earlier. In case of two environmentally detrimental variables
(active nutrients of nitrogen and cost of chemical weedicide) the
description for "LnEE" as described in Equation (12) is
changed as follow:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] ... ... ... ...
... (14)
In case of translog production function the elasticities are not
the coefficient of production function as in case of Cobb-Douglas.
However, the elasticity of output with respect to different inputs in
case of translog production function can be estimated by taking
derivative of Equation (8) with respect to logarithm of any specific
input as shown below:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
It should be noted that Xv has been represented by Z in Equation 8
and the above equation can be written in more general form as follow:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] ... ... ... ...
(15)
where, "i" stands for the number of explanatory
variables. The cross elasticity of substitution for input factor
"j" and "k" can be written by following the formula
developed by Ferguson (1969) as follow:
[H.sub.jk]=[[[beta].sub.jk] / ([S.sub.j]+[S.sub.k])] + 1 ... ...
... ... ... ... (16)
A positive elasticity of substitution implies that two input
factors "j" and "k" are complementary while a
negative elasticity of substitution indicates a competitive relationship
between two inputs.
2.3. Data Collection Procedure
Analysis is carried out by using primary data on input-output
quantities and prices from 500 farm households' belongings to five
major basmati rice growing districts in terms of
production--"Gujranwala, Sheikupura, Sialkot, Hafizabad, and
Jhang" of Punjab Province [Pakistan (2005)]. From each of these
districts 100 farmers are selected by choosing 25 from each tehsil. Four
teshils from each district (because most of the districts in our sample
have four or less than four tehsils) and 2 villages from each teshil are
randomly selected. From the first village in each teshil 12 farmers and
from the second village 13 farmers are randomly selected, in order to
make 25 from each teshil. The number of villages in each tehsil
increased accordingly where districts have less than four tehsils in
order to maintain the sample of 100 farmers from each district. A well
structured and field pre-tested comprehensive interviewing schedule is
used for the collection of detailed information on various aspects of
rice farmers in 2006. The mean value of inputs and output are reported
in Table 1. Only fifteen percent farmers in our sample are using
pesticides and that is why it is not reported in the table and neither
it is considered as an environmentally detrimental variable.
3. RESULTS AND DISCUSSIONS
The results of Maximum Likelihood Estimates (MLE) for translog
production function are reported in Table 2 which can be used to test
the null hypothesis that no technical inefficiency exists in rice
production. It should be noted that the values of log-likelihood
function for the stochastic frontier model and the OLS fit are
calculated to be 237.40 and 229.22, respectively and reported in Table
2. This implies that the generalised likelihood-ratio statistic for
testing the absence of technical inefficiency effect from the frontier
is calculated to be LR =-2*(229.22-237.40) = 16.36 which is estimated by
the Frontier 4.1 and reported as the "LR" test of the one
sided error. The value of likelihood-ratio "16.36" exceeds the
critical value of "10.371" obtained from Table 1 of Kodde and
Palm (1986) for the degree of freedom equal to 5 at five percent level
of significance. It should be noted that degree of freedom is equal to
the number of restriction in null hypothesis. The log likelihood ratio
test indicates that technical inefficiency exists in the data set and
therefore, null hypothesis of no technical inefficiency in rice
production is rejected.
The parameters of translog stochastic frontier production are
reported in Table 2. These results of production function are employed
to estimate the elasticities of output with respect to different inputs
as explained in Equation 14 and summary statistic of these output
elasticities are reported in Table 3. The output elasticities of tractor
hours (used in land preparation) and irrigation are negative, while that
of seed, labour, PK (active nutrients of phosphorus and potash), N
(active nutrients of nitrogen) and cost of weedicide are positive. The
elasticity of tractor hour is negative but it is not clear why it is so.
The coefficient of tractor hour is 0.09 with negative sign and it
implies that by increasing one percent of tractor hours, the yield
declines by 9 percent. In order to explain its negative sign, more
specific soil related information is required which is missing in our
data set. The elasticity of seed is positive in rice production. Rice is
a water intensive crop and it requires high quantities of water compared
to other crops. Such a large quantity of water is not available from
irrigated sources and therefore, farmers depend more on ground water in
rice production areas. The quality of ground water is poor in the rice
zone areas and the negative elasticity of number of irrigations is due
to poor ground water quality. But if we had information on the
distribution of number of irrigations from canal water and ground water,
it would have made our statement more reliable. However, the negative
elasticity coefficient for irrigation reflects wasteful irrigation
practices and expenditures as well as posing environmental problems. It
also emphasises the need for farmers' education in crop irrigation,
need for testing the quality of tubewell water and its suitability for
irrigation. The use of unfit tubewell water may be posing an
environmental problem as well. The elasticity of labour and active
nutrients of PK and active nutrients of N are positive which are 28, 9
and 3 percent respectively and these results are according to prior
expectations. It implies that if labour, active nutrients of PK, and
active nutrients of N are increased by 100 percent then output will
increase by 28, 9 and 3 percent, respectively, implying that the
contribution of labour is higher than the joint contribution of
fertiliser PK and N nutrients. Rice is a labour intensive crop and that
is why elasticity of labour is highest and positive followed by active
nutrients of nitrogen. The elasticity of weedicide is also positive
implying that if the cost of weedicide increases by 100 percent then it
contributes to increase in yield by 7 percent.
The cross elasticities of substitution are estimated by employing
Equation 15 and results are reported in Table 4. The negative value of
cross elasticities of substitution indicates a competitive relationship
while the positive value reflects the complementary relationship between
the two inputs. It is observed that tractor hours and seed, tractor
hours and labour, seed and labour, seed and active nutrient of PK,
number of irrigations and active nutrients of N, and active nutrients of
phosphorus and potash "PK" and active nutrients of nitrogen
"N" all have competitive relationship, while all others have
complementary relationship. Competitive relationship between two inputs
indicates that decline in one input can be compensated with the other,
implying that inputs are substitutable in the production process.
Complementary relationship implies that output can be raised by
increasing both the inputs simultaneously.
The technical efficiency of rice production in Pakistani Punjab is
estimated by employing Equation 8 and results are summarised in Table 5.
The results indicate that technical efficiency of rice production is
reasonably high ranging from 0.59 to 0.97 with an average value of 0.89.
This implies that rice production could be increased up to 11 percent
from the given set of resources, just by using the available resources
more efficiently. It is observed that 62 percent farmers are technically
more than 90 percent efficient and only 12 percent farmers are
technically less than 80 percent efficient, implying that distribution
of farmers is skewed towards high technical efficiency, and that is why
average technical efficiency is reasonably high.
As discussed earlier we have assumed the cost of chemical weedicide
and active nutrients of nitrogen (N) as environmentally detrimental
variables. The environmental efficiency of chemical weedicide is
estimated by employing Equation 12 and 13 and results are reported in
Table 6. The mean environmental efficiency of chemical weedicide in our
sample group is only 0.14, ranging from 0.00 to 0.73, implying that
environmental efficiency is considerably less than technical efficiency.
Our finding reveals that the average level of rice output can be
sustained or even increased by reducing 86 percent of chemical weedicide
use. Such substantial reductions in chemical weedicide use will not only
increase profitability of rice production by decreasing cost of Rs 296.7
per acre but it is also expected to significantly contribute in the
improvement of environmental quality. (3) The significant reduction in
environmental pollution is expected to increase the productivity of
other resources such as land and labour. Rice was grown on 4.4 million
acres of land in Punjab in 2006 [Pakistan (2006)]. Hence, Rs 1307.3
million can be saved each year from the reduction in use of chemical
weedicide in Punjab with higher level of output. From the frequency
distribution of environmental efficiency, it is observed that 93 percent
farmers have less than 50 percent environmental efficiency and remaining
7 percent farmers fall in the range of 50 to 80 percent category of
environmental efficiency. There is no farmer in our sample who has more
than 80 percent environmental efficiency of chemical weedicide use. The
distribution of joint environmental efficiency of chemical weedicide and
active nutrients of nitrogen "N" is depicted in Table 7. It is
observed that average joint environmental efficiency is almost double
(0.24) the average environmental efficiency of weedicide alone (0.14).
The higher environmental efficiency score of two detrimental variables
might be due to more efficient and judicial use of nitrogen in rice
production. The higher environmental efficiency of nitrogen use leads to
improvement in the joint effect of two detrimental variables but still
substantial scope exists to improve environmental efficiency that can be
explored. It appears there is a lot of wasteful expenditure in the use
of these chemicals which needs to be economised. It is obvious that the
use of fertilisers has assumed great importance in farm production and
perhaps is the principal component of the out of pocket expenditures in
the production of rice. Our results revealed that a large amount of
nitrogen could also be saved with improvement in environmental
conditions and higher level of output.
4. LIMITATION OF DATA
It should be noted that primarily this data was collected for
another study and at the time of data collection the focus was not on
environmental efficiency. This would mean that important information
that a study on environmental efficiency would require was not obtained.
Especially, in order to justify the negative sign of the elasticity of
irrigation we should have had more detailed information on sources of
irrigation which is missing in our case. Similarly, we do not have
detailed information on soil characteristics of the farms which is again
required to justify the negative sign of the elasticity of tractor hours
used for land preparation. Hence, future researchers should be mindful
of these weaknesses while organising their study.
5. SUMMARY AND CONCLUSION
The present empirical study is based on a sample data of 500 rice
farmers collected from five major rice growing districts in Punjab.
First of all, we tested the presence of technical inefficiency in our
data set and we rejected the null hypothesis of no technical
inefficiency in our sample data. The output elasticity of tractor hours
and irrigation is negative, while the output elasticity of seed, labour
and active nutrients of PK and active nutrients of N, and weedicide cost
is found to be positive. The cross elasticities of substitution for
different inputs are also estimated in order to observe the nature of
relationship between different inputs in the production process. On an
average technical efficiency is found to be 89 percent in our sample
farmers.
Environmental efficiency is estimated by assuming a single
(chemical weedicide) and two environmentally detrimental variables
(chemical weedicide and active nutrients of nitrogen) in major rice
production districts of Punjab. The environmental efficiency of chemical
weedicide is found to be 14 percent only. It suggests that a substantial
improvement in resource allocation can be made by reducing 86 percent of
chemical weedicide in rice production with higher level of output. It
could help to improve the profitability of Rs 296.8 per acre in rice
production that totals to an expected saving of Rs 1307.3 from the
reduction in the use of chemical weedicides. Moreover, it is likely to
alleviate the problem of environmental pollution by sustaining the
productivity of the agriculture system. Moreover, it is expected to
increase the productivity of agricultural labour. The joint
environmental efficiency of two detrimental variables (chemical
weedicide and active nutrient of nitrogen) is 24 percent which is almost
71 percent higher than the single detrimental variable (chemical
weedicide). This might be due to the reason that though fertiliser is
being used more efficiently in rice production but still substantial
scope exists that can be explored. Nitrogen which is a major source of
cash input can be substantially saved without affecting the level of
output, and with higher level of environmental quality.
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(1) According to Reinhard, et al. (2002) and Reinhard, et al.
(2000) the environmental efficiency is the ratio of minimum feasibility
to an observed input which is environmentally detrimental.
(2) In the quadratic formula there are both positive and negative
(+) outside the under- root term but we took only positive because
[u.sub.i] = 0 only if we will consider the positive sign outside the
under-root term.
(3) Rs 60 = $1.
Abedullah <
[email protected]> is Assistant Professor,
Department of Environmental and Resource Economics, University of
Agriculture, Faisalabad. Shahzad Kousar <
[email protected]>
is Lecturer, Department of Environmental and Resource Economics,
University of Agriculture, Faisalabad. Khalid Mushtaq
<
[email protected]> is Assistant Professor, Department of
Agricultural Economics, University of Agriculture, Faisalabad.
Table 1
Summary Statistics of the Sample
Variables Mean Median Maximum Minimum
Yield (Mounds/Acre) 35.0 35 55.0 18.0
Tractor (Hours) 3.8 3.5 12.3 0.5
Seed (Kg) 5.0 4.0 6.0 2.5
No. of Irrigations 8.0 10.0 16.0 5.0
Labour (Hours) 180.0 175.0 220.0 142.0
Nutrients of PK (Kg) 22.5 23.0 57.5 0.0
Nutrients of N (Kg) 34.5 32.0 70.5 0.0
Weedicide Cost (Rs) 345.1 275.0 400.0 40.0
Variables Std. Dev
Yield (Mounds/Acre) 5.7
Tractor (Hours) 1.7
Seed (Kg) 0.8
No. of Irrigations 3.2
Labour (Hours) 36.3
Nutrients of PK (Kg) 9.4
Nutrients of N (Kg) 9.8
Weedicide Cost (Rs) 33.7
Table 2
Coefficients of Translog Production Function with Maximum
Likelihood Estimation (MLE) Technique
Parameters Coefficients t-ratio Parameters
[B.sub.0] -0.63 -0.39 [B.sub.17]
[B.sub.1] -0.35 -1.24 [B.sub.23]
[B.sub.2] -1.49 -1.40 [B.sub.24]
[B.sub.3] 0.85 1.64 [B.sub.25]
[B.sub.4] 1.03 3.18 [B.sub.26]
[B.sub.5] 0.18 1.08 [B.sub.27]
[B.sub.6] 0.09 0.65 [B.sub.34]
[B.sub.7] 0.32 1.14 [B.sub.35]
[B.sub.11] -0.12 -2.42 [B.sub.36]
[B.sub.22] 0.37 0.53 [B.sub.37]
[B.sub.33] -0.42 -1.69 [B.sub.45]
[B.sub.44] -0.10 -0.76 [B.sub.46]
[B.sub.55] 0.02 2.29 [B.sub.47]
[B.sub.66] 0.00 -0.34 [B.sub.56]
[B.sub.77] -0.01 -0.42 [B.sub.57]
[B.sub.12] -0.03 -0.42 [B.sub.67]
[B.sub.13] 0.08 1.05 sigma-squared
[B.sub.14] 0.02 0.37 gamma
[B.sub.15] -0.01 -0.63 Log Likelihood
[B.sub.16] 0.01 0.48
Parameters Coefficients t-ratio
[B.sub.0] 0.01 0.39
[B.sub.1] 0.41 2.43
[B.sub.2] -0.05 -0.37
[B.sub.3] -0.01 -0.43
[B.sub.4] -0.06 -0.90
[B.sub.5] 0.04 0.43
[B.sub.6] 0.04 0.29
[B.sub.7] 0.00 -0.06
[B.sub.11] -0.03 -0.55
[B.sub.22] -0.08 -0.80
[B.sub.33] -0.02 -0.75
[B.sub.44] -0.05 -1.04
[B.sub.55] -0.04 -0.54
[B.sub.66] 0.00 0.82
[B.sub.77] -0.01 -0.97
[B.sub.12] 0.05 1.16
[B.sub.13] 0.07 1.72
[B.sub.14] 0.81 7.41
[B.sub.15] 237.4
[B.sub.16]
Table 3
Output Elasticity of Translog Fuitction
Variables Mean Median Maximum
Tractor (Hours)=[X.sub.1] -0.09 -0.10 0.13
Seed (Kg)=[X.sub.2] 0.07 0.06 0.72
No. of lrrigations=[X.sub.3] -0.11 -0.12 0.57
Labor (Hours)=[X.sub.4] 0.28 0.26 0.83
Nutrients of PK (Kg)=[X.sub.5] 0.09 0.10 0.17
Nutrients of N (Kg)=[X.sub.6] 0.03 0.04 0.09
Weedicide Cost=[X.sub.7] 0.07 0.07 0.23
Variables Minimum Std. Dev
Tractor (Hours)=[X.sub.1] -0.25 0.06
Seed (Kg)=[X.sub.2] -0.37 0.13
No. of lrrigations=[X.sub.3] -0.44 0.13
Labor (Hours)=[X.sub.4] 0.12 0.08
Nutrients of PK (Kg)=[X.sub.5] -0.08 0.04
Nutrients of N (Kg)=[X.sub.6] -0.30 0.03
Weedicide Cost=[X.sub.7] -0.39 0.07
Table 4
Cross Elasticities of Substitution
Mean Median Maximum Minimum
[X.sub.12] -0.09 -0.10 0.13 -0.25
[X.sub.13] 0.07 0.06 0.72 -0.37
[X.sub.14] -0.11 -0.12 0.57 -0.44
[X.sub.15] 0.28 0.26 0.83 0.12
[X.sub.16] 0.09 0.10 0.17 -0.08
[X.sub.17] 0.03 0.04 0.09 -0.30
[X.sub.23] 0.07 0.07 0.23 -0.39
[X.sub.24] -2.70 3.29 1845.27 -2702.15
[X.sub.25] -10.53 7.47 855.04 -7736.51
[X.sub.26] 3.98 0.14 1152.59 -21.05
[X.sub.27] 4.79 1.53 2168.90 -237.88
[X.sub.34] 52.13 -2.70 25342.45 -731.92
[X.sub.35] 6.69 -1.05 1622.54 -118.89
[X.sub.36] -6.34 -19.34 60618.75 -24943.67
[X.sub.37] 1.52 -0.15 822.60 -152.75
[X.sub.45] 1.12 0.59 240.20 -54.80
[X.sub.46] 5.26 -12.80 9777.10 -1452.65
[X.sub.47] 2.00 5.13 1277.58 -2738.88
[X.sub.56] -0.63 0.10 107.96 -149.27
[X.sub.57] 1.11 1.07 11.87 -8.40
[X.sub.67] 12.40 5.93 1206.11 -656.76
Std. Dev.
[X.sub.12] 0.06
[X.sub.13] 0.13
[X.sub.14] 0.13
[X.sub.15] 0.08
[X.sub.16] 0.04
[X.sub.17] 0.03
[X.sub.23] 0.07
[X.sub.24] 185.83
[X.sub.25] 364.13
[X.sub.26] 56.08
[X.sub.27] 97.89
[X.sub.34] 1137.34
[X.sub.35] 99.61
[X.sub.36] 3403.14
[X.sub.37] 47.67
[X.sub.45] 14.70
[X.sub.46] 509.44
[X.sub.47] 151.12
[X.sub.56] 15.12
[X.sub.57] 1.28
[X.sub.67] 112.03
Table 5
Technical Efficiency Estimates
Cumulative Cumulative
Value Count Percent Count Percent
[0.6, 0.69] 6 1.2 6 1.2
[0.7, 0.79] 56 11.2 62 12.4
[0.8, 0.89] 126 25.2 188 37.6
[0.9, 1] 312 62.4 500 100
Total 500 100.0 500 100.0
Table 6
Environmental Efficiency Estimates for Weedicide Only
Cumulative Cumulative
Value Count Percent Count Percent
[0.0, 0.09] 266 53.2 266 53.2
[0.1, 0.19] 103 20.6 369 73.8
[0.2, 0.29] 56 11.2 425 85
[0.3, 0.39] 24 4.8 449 89.8
[0.4, 0.49] 15 3 464 92.8
[0.5, 0.591 24 4.8 488 97.6
[0.6, 0.69] 9 1.8 497 99.4
[0.7, 0.79] 3 0.6 500 100
Total 500 100.00 500 100.00
Table 7
Environmental Efficiency Estimates for Weedicide and Fertiliser
Cumulative Cumulative
Value Count Percent Count Percent
[0.0-0.09] 37 7.4 37 7.4
[0.1-0.19] 105 21 142 28.4
[0.2-0.29] 230 46 372 74.4
[0.3-0.391 106 21.2 478 95.6
[0.4, 0.49] 20 4 498 99.6
[0.5, 0.59] 2 0.4 500 100
Total 500 100.00 500 100.00
