measuring efficiency of cotton cultivation in pakistan: a restricted production frontier study

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Research ArticleReceived: 19 January 2014 Revised: 4 March 2014 Accepted article published: 13 March 2014 Published online in Wiley Online Library: 30 April 2014

(wileyonlinelibrary.com) DOI 10.1002/jsfa.6652

Measuring efficiency of cotton cultivation inPakistan: a restricted production frontier studyMuhammad Arif Wattoa,c* and Amin Mugeraa,b

Abstract

BACKGROUND: Massive groundwater pumping for irrigation has started lowering water tables rapidly in different regions ofPakistan. Declining water tables have thus prompted research efforts to improve agricultural productivity and efficiency to makeefficient use of scarce water resources. This study employs a restricted stochastic production frontier to estimate the level of, andfactors affecting, technical efficiency of groundwater-irrigated cotton farms in the Punjab province of Pakistan.

RESULTS: The mean technical efficiency estimates indicate substantial technical inefficiencies among cotton growers. Onaverage, tube-well owners and water buyers can potentially increase cotton production by 19% and 28%, respectively, withoutincreasing the existing input level. The most influential factors affecting technical efficiency positively are the use of improvedquality seed, consultation with extension field staff and farmers’ perceptions concerning the availability of groundwaterresources for irrigation in the future.

CONCLUSIONS: This study proposes that adopting improved seed for new cotton varieties and providing better extensionservices regarding cotton production technology would help to achieve higher efficiency in cotton farming. Within the context offalling water tables, educating farmers about the actual crop water requirements and guiding them about groundwater resourceavailability may also help to achieve higher efficiencies.© 2014 Society of Chemical Industry

Keywords: technical efficiency of cotton; stochastic frontier analysis; restricted and unrestricted translog production function

INTRODUCTIONAlthough the agrarian economy of Pakistan is mainly dominatedby wheat, cotton, rice and sugarcane crops, cotton productionremains the most important agricultural commodity. Owing toits strategic importance in the international export market, cot-ton production has always been under the agricultural policylimelight in Pakistan. Cotton production accounts for 6.9% of thevalue added in agriculture and contributes 1.4% to the coun-try’s gross domestic production (GDP). With 9.80% share in theglobal cotton production, Pakistan remained the fourth largestcotton producer during the year 2011–12. Over the same period,Pakistan’s yarn and apparel exports contributed to 26% and14%, respectively, in the global cotton export market. At thenational level, cotton exports account for 46% of the country’sentire exports and the industry employs 35% of the total indus-trial labour force.1,2 By any measure, cotton production and pro-cessing are the most important economic sectors of Pakistan’seconomy. Therefore, national economic growth is greatly influ-enced by the volume and value of cotton production and itsby-products.

As a result of policy support for the cotton industry, the areaunder cultivation and production has increased by 33% and163%, respectively, since 1980, while domestic consumption hasincreased by almost 400% over the same period.2,3 Although in thepast agricultural policies have focused on increasing cotton pro-ductivity, despite devoting much attention, yields in cotton pro-duction on per hectare basis have remained low. Under the currentgovernment policy, the Pakistan Central Cotton Committee aims

to increase cotton production by 40–60% as a national strategyto achieve a target of 19.1 million bales by 2015. The major com-ponents of this strategy include: (i) increasing the area under cot-ton cultivation and per hectare yields; (ii) encouraging adoptionof genetically modified cotton varieties; (iii) improving produc-tion technology; (iv) subsidizing fertilizers; and (v) managing inte-grated pest management.4 However, despite widespread policyefforts and other encouraging incentives, the expected outcomesmay not be realized. Ongoing water stress may undermine thepotential of this policy, which unfortunately has not been takeninto consideration.

Cotton production is associated with excessive water applica-tions. However, irrigation water is becoming an increasingly scarceresource for agricultural production in Pakistan. Evidence suggeststhat the Indus River Basin of Pakistan is one of the most depletedriver basins in the world. Surface water resources are not only defi-cient but are also highly variable over time and space. Due to

∗ Correspondence to: Muhammad Arif Watto, School of Agricultural andResource Economics, The University of Western Australia M089, 35 Stirling High-way, Crawley WA 6009 Australia. E-mail: [email protected]

a School of Agricultural and Resource Economics, The University of WesternAustralia M089, 35 Stirling Highway, Crawley WA 6009 Australia

b The UWA Institute of Agriculture, The University of Western Australia M089, 35Stirling Highway, Crawley WA 6009 Australia

c The University of Agriculture, Faisalabad, Pakistan

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Measuring efficiency of cotton cultivation in Pakistan www.soci.org

spatiotemporal variations in surface runoff, farmers have startedaugmenting their irrigation supplies with groundwater abstrac-tions. Over the last half-century, the rapid increase in groundwa-ter use has evolved as a ‘silent revolution’ carried out by millionsof farmers. Since 1960, groundwater share to the total water sup-ply has increased by more than 50%.5,6 Although groundwaterresources have played a key role in agricultural production, over-drafting of groundwater resources is now at a critical juncture.6 – 9

Massive groundwater abstractions of 60 km3 y−1 have exceededthe recharge rate of 55 km3 y−1, resulting in substantial depletionof groundwater aquifers.10 The rapid depletion of groundwaterresources is making relative accessibility of groundwater resourceseconomically unviable, which would have serious repercussionson the sustainability of irrigated agriculture in Pakistan.6 – 8,11 – 13

However, despite declining water tables, water productivity in cot-ton cultivation (0.22 kg m−3) is low when compared to the othermajor cotton-producing countries.14

Given the water shortage backdrop, the widespread stagna-tion in cotton yields has prompted research efforts to improveefficiency and productivity of cotton farming and to makeefficient use of dwindling water resources. The objective ofthis paper is to estimate farm-level technical efficiency (TE) ofgroundwater-irrigated cotton farms in the Punjab province of Pak-istan. The contribution of this study resides in its methodologicaland empirical application as well. Methodologically, this paper isamong the few studies which have used the three-stage restrictedproduction frontier to estimate TE. Empirically, to the best of ourknowledge, this is the only study using a unique dataset of 172groundwater-irrigated cotton farms from the Punjab province ofPakistan to estimate the level and factors affecting their TE.

The rest of the paper is organized as follows. The next sectiondescribes the stochastic production frontier to estimate TE. Thethird section describes the data and principal features of the studyareas. The results are presented in the fourth section. The finalsection draws conclusions and provides some policy implications.

METHDOLOGICAL FRAMEWORKEstimation of TELet the production technology be described by the stochasticproduction function proposed by Aigner et al.15 and Meeusen andvan der Broeck16 as follows:

yi= f

(x

i; 𝛽

)exp

(𝜀

i≡ v

i− u

)(1)

where yi

denotes the amount of crop output for farm i (i = 1, … ,N); x

irepresents the vector of conventional inputs; 𝛽 is a vector of

parameters to be estimated; 𝜀i is a composed error term consistingof v

i, a symmetric and normally distributed error term that is

independently and identically distributed as N(

0, 𝜎2v

), intended

to capture the exogenous random forces which are beyond thecontrol of the farmers, and u

i≥ 0 is a non-negative random error

term independently and identically distributed as N+(

0, 𝜎2u

), that

captures the shortfall of output from the production frontier.The stochastic version of the output-oriented TE of a specific

farm is expressed as

TEi= y

i∕[

f(

xi; 𝛽

)exp

(v

i

)](2)

Applying this to the translog specification in Eqn (1), TE can bemeasured as

TEi= exp

(−u

i

)(3)

Since ui ≥ 0 and 0≤ exp(−ui)≤ 1 , technical inefficiency has tobe separated from statistical noise in the composed error term.Battese and Coelli17 have proposed the TE estimator as

TEi= E

[exp

(−u

i

) | (𝜖i

)](4)

Empirical modelLet the unknown production frontier (1) be specified by thefollowing translog specification:

ln yi= 𝛽

0+

j∑j=1

𝛽jln x

ji+ 1

2

(j∑

j=1

j∑k=1

𝛽jk

ln xji

ln xki

)+ v

i− u

i(5)

Where yi denotes the level of production; xi represents the vec-tor of conventional inputs as described in Eqn (1); 𝛽 is a tech-nology parameter to be estimated; vi is a random error term,independently and identically distributed as N

(0, 𝜎2

v

)and ui is a

non-negative random error term independently and identicallydistributed as N

(0, 𝜎2

u

). To separate the stochastic and inefficiency

effects in the model, we need to impose a distributional assump-tion. In this study, inefficiency is modelled explicitly as a functionof known characteristics and exogenous effects, such that

ui= 𝛿0 +

j∑j=1

𝛿jzij+ 𝜆 i (6)

where zi is a vector of variables which explain efficiency differ-entials among farmers, 𝛿 is the associated inefficiency parametercoefficient, and 𝜆 is a random variable defined by the truncationof the normal distribution with mean zero and variance 𝜎2 wherethe point of truncation is −z

i𝛿 such that 𝜆 i ≥ zi𝛿.18

The translog production function is the best investigated func-tional form and is widely used in efficiency estimation models.However, there are several concerns about the flexibility andtheoretical consistency when estimating a translog productionfunction.19 Since micro-economic theory requires that a produc-tion function should be monotonically increasing in all inputs20

and quasi-concave,21 it is necessary to test the estimated pro-duction frontier for theoretical consistency and, if necessary,to impose them. The monotonicity restrictions require holding𝜕y

i∕𝜕x

i≥ 0 ∀i, x, for all observations.22,23 However, imposing

global convexity restrictions greatly restricts the flexibility of thefunctional form,19,21 and hence should be applied to ensure localquasi-concavity. Henningsen and Henning20 argue that whenestimating a production function under the assumption of outputmaximization, it is not necessarily needed to be quasi-concave.Monotonicity can be imposed by using Bayesian techniques24,25

or a non-parametric approach26 or a three-step procedure.20

In this study, we follow Henningsen and Henning’s20 three-stepprocedure to adjust the model. First, we estimate the translog fron-tier and extract the unrestricted parameters and their covariancematrix. Second, we estimate restricted parameters through a min-imum distance approach as follows:

(7)

subject to𝜕f

(x, 𝛽0

)𝜕x

≥ 0 ∀i, x

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www.soci.org MA Watto, A Mugera

Equation (7) is solved using quadratic programming to get therevised set of coefficients 𝛽0 that confirm whether the monotonic-ity assumption holds. These restricted parameters 𝛽0 are asymp-totically equivalent to the restricted parameters of one-stage max-imum likelihood estimation.27 Finally, the stochastic frontier model(adjusted-restricted) is re-estimated as

ln yi= 𝛼

0+ 𝛼

1ln y − v

i+ 𝜖

i(8)

where yi= f

(x, 𝛽0

). That is, the only input is the estimated frontier

output based on the restricted parameters. The parameters 𝛼0 and𝛼1 represent final adjustments to the parameter estimates. Theadvantage of the three-step approach is that the parameter valuesestimated in the first stage provide appropriate starting valueswhere the variance–covariance matrix limits the degree to whichthese parameters are altered when imposing monotonicity in thenon-parametric component.28

DESCRIPTION OF THE DATAStudy districtsThe study is conducted in the Jhang and Lodhran districts of thePunjab province of Pakistan. In the study districts, cotton farmingheavily relies on groundwater for irrigation purposes. However,farmers in the study area solely depend on groundwater forirrigation in the Jhang district, and partly on canal water in theLodhran district. In the Lodhran district, canals supply water onlyduring the Kharif ∗ season. The canal water† contribution duringthe Kharif season of 2010–11 was observed to range between20% and 44% of the total irrigation requirements. Therefore, themajority of the irrigation water comes from groundwater which ispumped mainly through electricity-operated tube-wells.

Tube-well installation costs are very high due to deep watertables and are further projected to increase due to rapid fallingof water tables. The cost of bowering a tube-well at a depth of24 m is seven times that of a tube-well at a depth of 6 m.29 Forthis study, the variation in the bore depth was observed to rangebetween 60 and 99 m in Lodhran district and between 33 and57 m in the Jhang district during this field survey in 2010–11.We find that owing to low water tables and the high installationcost, tube-well populations are relatively less dense in northernparts of the Jhang and southern part of the Lodhran district. As aresult, farmers generally engage in informal groundwater trading.Such informal groundwater transactions have increased access toirrigation water for tenants and smallholder farmers who do notown tube-wells. However, water buyers sometimes have equityconcerns under such informal market settings.30 Despite the factthat the cost of buying water is three to four times more thanthat of pumping groundwater, water buyers face delays in gettingwater for irrigation.30 – 32

∗There are two cropping seasons in Pakistan: Kharif and Rabi. Kharif startsfrom June and July and goes to October and November, while the Rabiseason starts from September and October and continues to April and May.However, cropping time varies geographically across the country. Cotton isa Kharif crop.†In Punjab canal water is distributed equitably proportional to the farm sizeas a fixed weekly rotation which is allocated through a calibrated orificefrom the watercourse. The Water Management Department has computeda common conversion factor of 102.98 to convert the discharge water intocubic meters.

Data collection and variable definitionA multi-stage sampling technique was used in data collection.In the first stage, one tehsil‡ was selected purposively from theLodhran and the Jhang districts. In the next stage, 10 villageswere selected at random from each purposively selected tehsil.Finally, from each village ten groundwater users (five tube-wellowners and five water buyers) were selected randomly to obtainthe differential impact of tube-well ownership and to reveal thedifference of amount of water applied and production gains fortube-well owners and water buyers. Data were collected froma total sample of 200 farming households. However, only 92tube-well owners and 80 water buyers cultivated cotton during thecropping season of 2010–11.

The data were collected using an interview schedule. We col-lected information on various inputs and output quantities. Theinputs are measured as (i) seed and fertilizer in kilograms per acre,(ii) total labour, consisting of hired (casual and permanent) andfamily labour in hours per acre, (iii) farm operations as number ofapplications per acre and (v) groundwater use in cubic metres peracre. Cotton yield (output) is also measured in kilograms per acre.

Various studies have used different approaches to computethe volume of irrigation water. However, they do not give actualestimates of water used. For example, Gedara et al.28 measured thequantity of water used in rice production in Sri Lanka, which wasrelated to the proportion of total land owned by the farmer andtotal quantity of water released, assuming that this was distributedevenly across the irrigated area. Sharma et al.33 measured waterby the number of times water was released to the farm from themain water source in the Tarai of Nepal. In contrast to the surfacewater volumes, groundwater use estimates are more realistic andreliable. In this study, we collected information about the numberof irrigations for a particular crop, duration of water applicationper irrigation event, borehole depth, diameter of suction pipe andpower of the engine used to pump water. Using this information inan approximate estimation model, as used by Eyhorn et al.34 andSrivastavaa et al.,35 we measured groundwater extraction in litresusing the following formula and then converted into cubic metres:

Q = t × 129574.1 × BHP[d +

(255.5998 × BHP2) ∕d2 × D4)

] (9)

where Q represents the volume of water in litres, t is the totalirrigation time, d is the depth of bore, D is the diameter of thesuction pipe, and BHP is the power of the engine.

The descriptive statistics of the variables used in the SFA modelare presented in Table 1. The table compares selected variables forboth the tube-well owners and water buyers used in the analysis. Itis evident from the descriptive statistics that on average there is noconsiderable variation in the use of farm inputs including per acreseed rate, labour use and fertilizer application. Similarly, outputproduced by the tube-well owners and water buyers do not varyconsiderably. In contrast, there is some variation in the numberof farm operations and irrigation water applied by the tube-wellowners and water buyers. On average, tube-well owners used 7%more groundwater than water buyers. The average cotton yield is836 kg acre−1, with a maximum of 1400 kg acre−1 for tube-wellowners and 821 kg acre−1, with a maximum of 1200 kg acre−1, forwater buyers.

‡Tehsil is an administrative unit. A district usually comprise of five to sixtehsils (sub-districts).

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Table 1. Summary statistics of the variables used in the empirical model

Tube-well owners Water buyers

Variable Mean SD Mean SD

Economic data

Farm production (kg) 8473 6199 4598 3811

Seed quantity (kg) 88.75 65.94 48.56 42.27

Labour (h) 3396.75 2522.90 1814.45 1549.29

Fertilizer (kg) 2300.88 1866.25 1231.02 1226.89

Machinery cost (Rs) 39027.80 25896.19 22303.19 18785.18

Irrigation water (m3) 24074.09 17842.71 12143.78 10084.01

Farm characteristics

Farmers age (years) 45.435 8.961 42.363 8.138

Farmers education (years of schooling) 5.674 4.401 3.750 3.733

Proportion of farmers with dummy variables

0 1 0 1

Land tenureship (0 = tenants, 1 = owners) 18.48 81.52 18.75 81.25

Off-farm income (0 = no, 1 = yes) 82.61 17.39 88.75 11.25

Seed (0 = not improved, 1 = improved) 73.91 26.09 73.75 26.25

Extension services (0 = no, 1 = yes) 66.30 33.70 73.75 26.25

Water shortage perceptions

Salinity perception (0 = no, 1 = yes) 73.91 26.09 80 20

Is water table declining? (0 = no, 1 = yes) 25.00 75.00 76.25 23.75

Effect on cropping patterns (0 = no, 1 = yes) 44.57 55.43 46.25 53.75

The average age of farmers is 45 years for tube-well owners and42 for water buyers, ranging from 27 to 60 years. The statisticson education reflect a lack of education, with 27% of tube-wellowners and 43% water buyers having had no formal education.Amongst tube-well owners and water buyers the vast majority ofthe surveyed farms cultivate their own land. We see that 17% oftube-well owners and 11% of water buyers have one or anothersort of off-farm business activities. Almost the same proportion oftube-well owners and water buyers among the surveyed cottonfarms used improved quality seed. Access to extension advice andto other information sources, e.g. radio, television and newspapers,indicates that tube-well owners seek relatively more extensionadvice compared to water buyers. There is a very little differencein perception of tube-well owners and water buyers concerningsalinity level and its impact on future cropping patterns. However,more tube-well owners perceive that the water table is fallingrapidly compared to water buyers.

ESTIMATION RESULTS AND DISCUSSIONThe parameter estimates of the stochastic frontier model are pre-sented in Table 2 and the estimates of the inefficiency model arepresented in Table 3. The estimated parameters of the unrestrictedand restricted models show clear differences; however, these dif-ferences, with one exception, are smaller than standard errors oftwo, as can be seen from the ‘difference/standard error’ column.

The initial maximum likelihood estimates of the production fron-tier indicate that none of the variables fully satisfy monotonic-ity conditions for all observations (Table 4). Irrigation water sat-isfies monotonicity conditions for only 78% of the total obser-vations. Similarly, quasi-concavity is satisfied for only 29% of thetotal observations in the initial model. Monotonicity conditionsare fully satisfied for all observations and all variables in theadjusted model. Likewise, quasi-concavity is also improved in the

final adjusted model, where 95% of the observations satisfied theconditions.

We can interpret this scenario as, for the remaining 5% of obser-vations that are not quasi-concave, the individual inefficiencyscore may be either over- or underestimated.19 Since the standardmicro-economic theory requires satisfying quasi-concavity underthe profit-maximizing assumption, Henningsen and Henning(2009)20 argue that TE concept assumes that producers tend tomaximize their output given their input quantities rather thanto maximize profit. Thus, in contrast to monotonicity conditions,satisfying the quasi-concavity assumption is not necessarilyimportant. We see that the intercept term in the final step is notsignificantly different from zero, while the scaling coefficient is notsignificantly different from 1. From these results we can infer thatthe three-step procedure has not introduced substantial bias intothe model.28

The partial production elasticities with respect to all inputs arereported in Table 5. It is evident from the results that productionis inelastic with respect to each of the inputs included in themodel. The elasticities in the sample mean are almost identicallyranked under both estimations. Seed variable exhibits the largestpartial production elasticity, while labour displays the lowest.The elasticities relating to seed and labour were slightly lowerin the unrestricted estimate compared to the restricted estimate.Irrigation water, with an elasticity of 0.079, is ranked fourth outof the five variables included in the model. Similar results werereported by Karagiannis et al.36 in their study for out-of-seasonGreek vegetable farms. Regardless of the impact of measurementunits, cotton production is highly responsive to the type andquality of seed (0.41), while it is least responsive to labour (0.039)and irrigation water (0.079), respectively. The returns to scale,derived from the sum of input elasticities, is estimated to be1.174, suggesting that cotton farms on average are operatingunder increasing return to scales. The cross-product of the input


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Table 2. Restricted and unrestricted model parameter estimate

MLE estimates Minimum distance estimates Final SFA estimates

Parameter Estimate SE Coefficient Difference Diff/SE Estimate SE

Constant −13.523* 7.351 −13.236 0.287 0.039 −13.534Ln seed (kg) 1.108 1.946 0.962 −0.146 −0.075 0.944Ln labour (h) −1.990* 1.145 0.059 2.049 1.790 0.023Ln fertilizer (kg) 0.480 1.615 0.524 0.044 0.027 0.497Ln machinery (No. of farm operation) 1.936 2.186 1.344 −0.592 −0.271 1.334Ln water (m3) 1.071 1.023 0.345 −0.727 −0.711 0.315Ln seed × seed −0.549 0.427 −0.538 0.012 0.027 −0.585Ln seed × labour −0.545** 0.195 −0.128 0.417 2.138 −0.167Ln seed × fertilizer 0.423 0.286 0.342 −0.081 −0.283 0.312Ln seed × machinery 0.004 0.295 0.014 0.010 0.035 −0.022Ln seed × water 0.348 0.219 0.120 −0.228 −1.041 0.086Ln labour × labour −0.218 0.220 −0.004 0.215 0.975 −0.040Ln labour × fertilizer 0.370** 0.172 0.094 −0.276 −1.607 0.059Ln labour × machinery 0.561** 0.272 0.030 −0.530 −1.948 −0.006Ln labour × water −0.195 0.168 −0.016 0.179 1.063 −0.053Ln fertilizer × fertilizer −0.604** 0.257 −0.444 0.160 0.623 −0.490Ln fertilizer × machinery 0.011 0.265 0.037 0.027 0.101 0.001Ln fertilizer × machinery −0.102 0.188 −0.020 0.082 0.436 −0.057Ln machinery × machinery −0.332 0.361 −0.110 0.222 0.614 −0.149Ln machinery × water −0.194 0.194 −0.050 0.144 0.742 −0.088Ln water × water 0.088 0.252 −0.030 −0.118 −0.470 −0.068Model variance 𝜎2 = 𝜎2

u + 𝜎2v 0.055*** (0.011) 0.058*** (0.012)

Variance ratio 𝛾 = 𝜎2u∕

(𝜎2

u + 𝜎2v

)0.833*** (0.107) 0.827*** (0.100)

Intercept −0.036 (0.541)IcFitted 1.000*** (0.234)

Table 3. Inefficiency model estimates

Initial estimates (MLE) Final estimates (adjusted model)

ParameterCoefficient

estimate SECoefficient

estimate SE

𝛿AGE 0.007*** (0.002) 0.007*** (0.002)𝛿EDC −0.003 (0.007) −0.001 (0.007)𝛿OFIN −0.131 (0.102) −0.168 (0.112)𝛿LTS 0.173** (0.088) 0.149* (0.099)𝛿SDQ −0.221** (0.088) −0.176** (0.088)𝛿TWO −0.027 (0.064) −0.053 (0.065)𝛿EXT −0.282*** (0.097) −0.294*** (0.102)𝛿WTD 0.001 (0.065) 0.003 (0.065)𝛿SPER −0.048 (0.074) −0.030 (0.073)𝛿GWSH −0.254*** (0.092) −0.262*** (0.100)

Asterisks indicate significance at *10%, **5% and ***1%.

elasticities are relatively small, suggesting that there is limitedopportunity for input substitution.

Table 6 presents the TE estimates derived from both the unre-stricted and restricted models. The average TE score for tube-wellowners is estimated at 81%, while for water buyers the averageTE score is 71% under both the unrestricted and restricted mod-els. However, the TE estimates from the unrestricted and restrictedmodel are highly correlated with a 0.99 correlation coefficient, indi-cating the robustness of results under both estimations. The bestfit line as shown in Fig. 1 between the TE estimates of both modelsalso indicates that both TE estimates are highly correlated. We usedthe root mean square error (RMSE) and mean absolute error (MAE)

Table 4. Proportion of farms satisfying the monotonicity andquasi-concavity conditions

Variable Maximum likelihood model Final adjusted model

MonotonicitySeed 93.1% 100%Labour 67.7% 100%Fertilizer 94.2% 100%Farm machinery 97.7% 100%Irrigation water 78% 100%Quasi-concavity 28.9% 95.4%

Table 5. Partial production elasticities for the sample mean from theunrestricted and restricted models

Variables Maximum likelihood model Final adjusted model

Seed 0.409 0.455Labour 0.039 0.079Fertilizer 0.288 0.273Farm machinery 0.359 0.323Irrigation water 0.079 0.079

to check the difference between the predicted and original out-come of both models. The RMSE estimates (0.37 and 0.369) andMAE estimates (0.296 and 0.295) of the unrestricted and restrictedmodel suggest than imposing restrictions does not affect the pre-dictive power of the models. Since TE is the final outcome in theestimation procedure, comparing both TE estimates indicates that


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Table 6. Frequency distribution of technical efficiency for tube-wellowners and water buyers from the unrestricted and restricted models

Tube-well owners Water Buyers

EfficiencyRange

TE(unrestricted)

TE(restricted)

TE(unrestricted)

TE(restricted)

<30 0 0 0 030-40 0 0 0 040-50 1 2 7 750-60 7 5 10 960-70 12 12 19 1970-80 16 15 14 2080-90 28 31 19 1490-100 28 27 12 12Mean 0.810 0.810 0.729 0.725Std. Dev. 0.133 0.131 0.146 0.146Minimum 0.405 0.412 0.405 0.413Maximum 0.966 0.967 0.962 0.959

0.4

0.5

0.6

0.7

0.8

0.9

1

0.4 0.5 0.6 0.7 0.8 0.9 1

TE

est

imat

es f

rom

the

rest

rict

ed m

odel

TE estimates from the unrestricted model

Figure 1. Technical efficiency estimates of the restricted and unrestrictedmodels.

equality of means test (t-test) for both TE estimates cannot berejected at 1%.

The estimated results suggest that taking measures can poten-tially increase cotton production given the existing resourceendowments. Based on our dataset, we have computed thatcotton growers on average produce 0.67 kg cotton using 1 m3

groundwater. Although these estimates are higher than the pre-vious estimate of 0.22 kg m−3,14 there is considerable scope forimproving water productivity and efficiency. Increased cotton pro-duction may necessitate improved efficiency given the shortageof irrigation water. We find that water buyers remain technicallymore inefficient than tube-well owners. Meinzen-Dick37 foundthat tube-well owners were better off in terms of farm productiv-ity compared to water buyers, presumably as a result of greatercontrol over groundwater access and supplies. Nevertheless, waterbuyers are risk averse to uncertain and delayed irrigation supplies.As groundwater trading is informal, it is highly influenced by thesocial ties between tube-well owners and water buyers. Hencethe absence of a formal contract sometimes leads to inequities inwater allocation and distribution among the buyers.30,38 Moreover,due to ongoing energy crises water buyers face more uncertaintiesand delays in obtaining water for irrigation and it is highly likelythat delayed water application may decrease the marginal prod-uct of other inputs such as fertilizer, labour and chemical inputs.

Consequently, water buyers remain technically more inefficientthan tube-well owners.

As far as estimates of the inefficiency§ model (Table 3) areconcerned, the estimated coefficients and standard errors of theunrestricted and restricted models differ slightly in some cases.However, the difference is not statistically significant. We see thatfarmers’ education, off-farm business activities and tube-wellownership do not significantly affect TE. As expected, old farmersand tenants have slightly lower TE levels than their younger coun-terparts. We find that use of improved seed varieties and extensionservices play a significant role in improving TE. Results indicatethat farmers who perceive that over-extraction of groundwaterresources may cause its quality and availability to deteriorate aregenerally more efficient than those farmers who think otherwise.

Most of the estimated coefficients in the inefficiency modelconfirm to a priori expectations about their impact on efficiencylevels. Our estimates indicate that farmers’ age significantlyimpacts the level of TE. Other studies suggest that old farmers aremore sceptical of adopting new farming techniques and technolo-gies and hence lag in agricultural production, for example.39,40

The coefficient of land tenure status indicates that non-ownersare more technically efficient than land owners. These resultscontradict the common intuition that, ceteris paribus, land ownersusually invest more in new production technologies and, con-sequently, increase their expected returns.40 – 42 However, somestudies have also reported a negative impact of land ownershipon farm efficiency.43 Nonetheless, our results support the notionthat farmers who rent land will also devote extra effort in man-agement oversight to generate returns above what they pay forrent; hence they are more efficient. As expected, education andextension services have a positive impact (education is positivebut non-significant, while extension is positive and significant)on TE, supporting the premise that increases in human capitalenable farmers to improve resource utilization and thus achievehigher efficiencies. In the literature, we find mixed results for theefficiency and education relationship; e.g. Karagiannis et al.36 andSolís et al.44 found the impact of education significant, while Haji45

and Speelman et al.40 found education impact non-significant.These mixed results indicate that using general years of schoolingcould not be a substitute for specialized education; e.g. agricul-tural education has different requirements compared to the socialsciences. The impact of extension services on TE is consistentwith the commonly established assumption that farmers whotend to seek more extension advice and get involved in trainingprogrammes are technically more efficient than those who haveless or no contact with extension staff.41,46

The results for seed quality show a statistically significant posi-tive association between seed quality and TE. We find that off-farmincome is positively associated with TE, suggesting that with alter-native income resources farmers may have a better edge to pur-chase and use an optimal input mix, which in turn results in betterefficiency gains.36 The impact of tube-well ownership on TE impliesthat tube-well owners have better assurance and control over irri-gation in terms of spatiotemporal crop requirements and hencetheir expected returns are higher than the water buyers. Amongstthe explanatory variables representing farmers’ perceptions aboutgroundwater resource, perception about salinity and the potentialimpact on future cropping pattern are positively associated with

§The estimated positive coefficients in the inefficiency effects model indi-cate that variables have a negative effect on technical efficiency.


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TE, while perception about decline in water tables is negativelyassociated with TE.

CONCLUSIONThe main objective of this study was to estimate the level of,and factors affecting, TE among groundwater-fed cotton farmsin Pakistan. The results obtained from a cross-sectional data of172 cotton growers, including 92 tube-well owners and 80 waterbuyers, indicate considerable technical inefficiencies.

We find that, on average, tube-well owners are more techni-cally efficient than water buyers. Our results indicate that tube-wellowners and water buyers can potentially increase cotton produc-tion by 19% and 28%, respectively, without increasing the exist-ing input level. The estimated results on production elasticitiesindicate that cotton production is highly responsive to the typeand quality of seed, and is least responsive to labour and irriga-tion water, implying potential reductions in labour and irrigationwater use. While one of the key underlying research objectivesis to suggest measures to improve TE in cotton production, thisstudy suggests that adopting improved seed for new cotton vari-eties and providing better extension services for cotton produc-tion technology would help to achieve higher efficiency. In partic-ular, within the context of falling water tables, educating farmersabout the actual crop water requirements and guiding them aboutthe groundwater resource availability may also help in achievinghigher efficiencies. As water buyers are generally down the watersupply chain and are likely to face more water uncertainties, pre-sumably they remain more inefficient than the tube-well owners.We suggest that policy intervention in groundwater markets func-tioning to improve water allocation could improve equity of accessand presumably TE amongst water buyers.

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measuring efficiency of cotton cultivation in pakistan: a restricted production frontier study

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