TECHNICAL EFFICIENCY OF SUGARCANE PRODUCTION IN DISTRICT DERA ISMAIL KHAN

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1 Sarhad J. Agric. Vol. 29, No.4, 2013 TECHNICAL EFFICIENCY OF SUGARCANE PRODUCTION IN DISTRICT DERA ISMAIL KHAN *GHAFFAR ALI 1, SYED MEHTAB ALI SHAH 1, DAWOOD JAN 1, ABBASULLAH JAN 1, MOHAMMAD FAYAZ 1, IRFAN ULLAH 1 and MUHAMMAD ZAFARULLAH KHAN 2 1. Department of Agricultural & Applied Economics, The University of Agriculture, Peshawar- Pakistan 2. Department of Extension Education & Communication, The University of Agriculture, Peshawar- Pakistan *Corresponding author: ghaffarali@aup.edu.pk ABSTRACT This study aimed at investigating the technical efficiency of sugarcane production in district Dera Ismail Khan (D. I. Khan) using stochastic production function. The study used primary data collected from 100 respondents of sugarcane growers during harvesting year The tractor hours, seed rate, labor days, irrigation numbers, chemical fertilizer, farm yard manure (FYM) and herbicide were the variables for finding technical efficiency while age, experience and education of sugarcane growers were taken as the technical inefficiency factors. The results revealed that the elasticities of technical efficiency for tractor hours, seed rate, labor days, irrigation numbers, chemical fertilizer, FYM and herbicides were found 0.185, 0.102, 0.145, 0.093, 0.084, and 0.05 respectively. All the variables of technical efficiency showed positive and significant effect on the production of sugarcane with the exception of seed in district D.I.Khan. The mean technical efficiency index was 0.77 while the minimum and maximum efficiency values were 0.57 and 0.91 respectively. The mean value suggesting that the farmer s output can be improved 23% through improved resource allocation. The factors affecting technical inefficiency showed negative relationship with inefficiency. The results further indicated that age, experience and education having positive relationship with production and play a vital role in the production of sugarcane. The study recommends that proper policies should be formulated to educate farmers through extension services that will reduce inefficiency in their operations, eventually it will lead them to get maximum returns from their scarce resources. Keywords: Technical efficiency, stochastic production function, production, elasticity, sugarcane. Citation: Ali, G., S. M. A. Shah., D. Jan., A. Jan., M. Fayaz., I.Ullah and M.Z. Khan Technical efficiency of sugarcane production in district Dera Ismail Khan. Sarhad J. Agric. 29(4): INTRODUCTION Agriculture is the divine gift to meet the basic needs of human on the earth. In Indo-Pak subcontinent agriculture has been considered as the most important and rewarding economic activity since ancient times. Agriculture is the major sector and a dominant driving force for the growth and development and the basic source of livelihood for the population of Pakistan. This sector contributes almost 21 percent to the GDP and employs about 45 percent of the total work force. It has a vital role in ensuring food security, economic growth and reducing poverty. The quality of life of the people of Pakistan can be improved by making agriculture an efficient, productive and profitable sector. (GoP, 2012). Pakistan occupies a significant position in cane producing countries and ranks fifth position in cane acreage and almost 15 th position in sugar production in the world (PSMA, 2009). Sugarcane is an important source of income and employment for the agricultural community and is the second major cash crop of Pakistan. Its share in value added in agriculture and GDP is 3.7 and 0.8 percent, respectively (GoP, 2012). Production of sugarcane crop is complex process and depends on use and combination of different inputs such as labor, land, capital, management practices and other various factors. The variations in use and combination of various factors of production affect the crop yield. These combinations are considered as technology. Farmers experience difference in crop yield that is the result of using varying level and combination of inputs. Furthermore there is a broad gap in the yields of farmer s field and experimental stations showing the suboptimal use of inputs. Technical efficiency studies the conversion of various physical inputs such as labor inputs, land inputs and other semi finished goods and raw materials into outputs. This study is an attempt to estimate the extent of technical efficiency of sugarcane production, to identify a factor that influences such efficiency of sugarcane production and to explore the potential for improving production efficiency of farmers.

2 Ghaffar ali, et al. Technical efficiency of sugarcane production in district Dera Ismail Khan 586 MATERIALS AND METHODS This study was conducted in district D.I. Khan of Khyber Pakhtunkhwa where sugarcane is amongst the major cash crops of the area. Three villages were selected from district D.I. Khan that includes Shorkot, Gara Hayat and Haji Mora. The majority of the farmers of these villages are sugarcane growers. To obtain an appropriate sample size about 9.43% of the total sample size from the villages proportional allocation sampling technique was used (Cochran, 1977). For obtaining the required sample several authors and res earchers have used proportional allocation technique which is defined as: Where: 1 = Number of Sampled Farmers in each Village = Total Number of farmers in = Total Sample Size = Number of villages in the study area th village = Total Number of farmers in the Research Area Selected number of the sample farmers from each village of the study is given as follow: Table 1. Village Wise Distribution of Sample Farmers in the Study Area District Villages No. of Farmers Sample Size Shorkot D.I Khan Gara Hayat Haji Mora Total Source: Local Revenue Department district D.I. Khan. Analytical Framework The Stochastic Frontier Production Function is used for the estimation of technical efficiency of sugarcane. The Stochastic frontier production model also called composed error model was developed by Meeusen and Julien van Den Broeck (1977) and Aigner et al. (1977). The work of Meeusen and Julien van Den broeck (1977) and Aigner et al. (1977) was based upon the measurement of technical efficiency by Farrell (1957). Assuming an appropriate production function the stochastic production frontier is given as follows. Where: = The output of sugarcane obtained in [Kg/Acre] by farmer i. = The inputs per unit for farmer i. = Represents the parameters to be estimated = The composed error term for i th farmer. The error term composed of two components which is essential idea behind the stochastic frontier model...4

3 Sarhad J. Agric. Vol. 29, No.4, Where is the symmetric ) and covers all the stochastic effects which are outside the farmer s control such as breakdown, weather and other natural disasters. The term shows technical inefficiency in the sense that it estimates the short fall of output from its maximum possible output value given the stochastic frontier. The technical efficiency is measured by the ratio rather than by the ratio used by deterministic models. This simply differentiates the technical inefficiency of the factors of interruption that are outside of the farmer s control. It also captures the measurement errors and observation of the dependent variable under the farmer s control inefficiency of the farmers (Aigner, et al. 1977). Estimation of technical inefficiency of sugarcane growers and captures the technical It is assumed for the estimation of technical inefficiency that the symmetric error is distributed as N (0, ) and a non-negative error is distributed as N (0, ), is half normal. Technical inefficiency is expressed as follows:.5.6 Where: 7 = vector of socio economic characteristics of farmer i, = Parameters to be estimated = Age of farmers (years) = Education level = farming experience (years) = Composed Random error term. The maximum likelihood (ML) estimates of the various parameters of the frontier model are estimated such that the variance parameters are expressed in terms of the parameterization of where the -parameter have a value between zero and one. Gamma indicates that the systematic influence of the unexplained variables by the production functions is the dominant sources of random error. Gamma shows the amount in variation in output resulting from the technical inefficiencies of the farmers (Taru et al. 2011). RESULTS AND DISCUSSION The results of the analysis carried out on the basis of outlined objectives are as under: Socioeconomic Characteristics of the farmers This section highlights the prominent socioeconomic aspects such as age, education and farming experience of the sample farmers. Age Group of the Farmers Age is the main factor and plays a vital role in the rejection or selection of new practices and modern technology. Person s age is accepted to have great contribution towards personal learning, personality development, cognition, attitude and such properties adapted to a person s skills and experience over his life time and help out in correct judgment. It was observed that majority (52%) of the respondents belongs to the age group of years who were the middle aged persons of the society and showed more interest in sugarcane cultivation. (Table 2).

4 Ghaffar ali, et al. Technical efficiency of sugarcane production in district Dera Ismail Khan 588 Table 2. Age group of sample farmers Age group (years) Number %age Above Total Education Level Education performs a vital role in the use of modern technology as educated people are more efficient as compare to the uneducated that are more orthodox. Most of the farmers 36% had primary education, 24% had middle education, 13% were matriculate and above and 27% were illiterate. (Table: 3). Table 3. Educational Level of Selected Respondents Educational Level Number %age Illiterate Primary Middle Matric & above Total Farming Experience Farming experience is another major factor which determines the use of scarce resources. It is evident from the data in Table 4 that 38 percent sugarcane growers involved in sugarcane farming from last years, followed by 32 percent who have more than 30 years farming experience. This indicated that most of the sugarcane growers had sufficient experience of sugarcane cultivation. (Table 4). Table 4. Farming Experience of sugarcane Growers Experience (years) Number %age Above Total Summary Statistics of Socioeconomic Factors The Table 5 describes the summary statistics of socioeconomic factors of sugarcane farmers including age, education and experience. The mean value of farmer s age was found 52 years with the range of 36 to 62 with the standard deviation of 7.3. Similarly the education mean was found 4.5 and ranges from 0 to 10 years with standard deviation of 3.9. The experience ranges from 8 to 40 with average value of 20 and standard deviation of 8.8. Table 5. Summary Statistics of Socio-Economic Factors Factor Minimum Maximum S.D Mean Age Education Experience Productivity of farm inputs The analysis showed (Table 6 ) that there is a positive response of output to the increase in inputs. The values of Tractor hours, seed, labor, irrigation number, chemical fertilizer, FYM and pesticides/herbicides are 0.185, 0.102, 0.145, 0.093, 0.084, and respectively. This means that 1 percent increase in each of the input keeping rest of the inputs constant will increase the output by 0.185, 0.102, 0.145, 0.093, 0.084, and percent respectively.

5 Sarhad J. Agric. Vol. 29, No.4, Technical inefficiency Technical inefficiency is usually expected to be affected by the social, economic, and demographic factors of the farmers. The estimated technical inefficiency model is specified to encompass the most important variables contributing to the technical efficiency of the farmers. The estimated value for is statistically significantly different from zero, which means that sugarcane farmers in D.I Khan are not using their inputs efficiently to get maximum level of output. Age is the most inefficiency factor affecting the output. The more the age of the farmer is the more he will have knowledge about his production process. The estimated negative value (-0.145) of age of the farmer confirms the fact that the efficient allocation of resources to get the maximum level of output is directly related to the age of the farmers. The estimated co-efficient for education is (-1.53). The negative sign of the education shows that, education has direct impact on enhancing the efficiency of the farmers in resource allocation. The more the educated farmers the higher yield of the sugarcane is expected accordingly. The parameter of the experience is found negative (-0.563) as well, showing that inefficiency is reduced with the gain in experience. Table 6. The maximum likelihood estimates for parameters of the stochastic production function for the D.I Khan Sugarcane farmers. Variable Coefficient t-ratios Constant Tractor Hrs Seed Labor Irrigation No Chemical Fertilizer FYM Pesticides/Herbicides Inefficiency Model Constant Age Experience Education Variance Parameters Sigma Square Sigma u Gamma Technical Efficiency (TE) Percentage Minimum % Maximum % Mean % Source: survey data. Variance Estimates The estimated value for γ is found to be which means that 40.3% variance is because of inefficiency in the operation of the farmers. Technical Efficiency The mean value of technical efficiency was observed to be 77% which means that 23% improvement in output is possible using the given resources. CONCLUSIONS AND RECOMMENDATIONS The study was carried out in the three villages of district Dera Ismail Khan of Khyber Pakhtunkhwa namely Shorkot, Gara Hayat and Haji Mora. It was aimed to calculate the technical efficiency of sugarcane production and to evaluate the factors affecting the efficiency of the farmers in D.I. Khan. The sample of 100 respondents was selected through proportional allocation technique. The parameters were estimated using the stochastic frontier production

6 Ghaffar ali, et al. Technical efficiency of sugarcane production in district Dera Ismail Khan 590 function, through maximum likelihood estimation. The results of the summary statistics of socioeconomic characteristics showed that higher percentage of sugarcane growers was in the age group of and most of the farmers are qualified up to primary level that is 36 percent. The descriptive statistics of the experience level of the farmers in the study area indicated that highest number of sugarcane growers fall in the experience range of years. The estimated production elasticities were 0.185, 0.102, 0.145, 0.093, 0.085, 0.073, and for tractors hours, seeds, labors days, irrigation, chemical fertilizer, FYM and herbicides respectively. These elasticities means that 1% increase in each of the inputs keeping the rest constant will bring 0.185, 0.102, 0.145, 0.093, 0.085, 0.073, and percent increase in the production of sugarcane. The analysis of stochastic frontier production function yielded variance parameters used to calculate, which was found to be On the basis of it can be concluded that 40.3% variations in the output are due to Inefficiency. The Inefficiency was found to be the function of age, experience and education with coefficients of , and The inefficiency model estimates revealed that all three variables have a negative relationship with the inefficiency, which means aged, experienced and educated farmers are supposed to be more efficient in their operations and can get maximum output using the same level of inputs. The average value of technical efficiency was calculated 77%, which shows that there is possibility of increasing production up to 23% by using the same level of inputs. Proper policies are needed to educate farmers through Agricultural extension services to reduce inefficiency in their operations and to get maximum returns from their scarce resources. All the inputs have positive relationship with output, so government and other input supplying agencies need to ensure timely availability of production inputs; like seeds, fertilizers and pesticide/herbicides to sugarcane producers. Government may also like to extend provision of timely and adequate credit facilities to the farmers on affordable terms to enhance their efficiency in production. REFERENCES Aigner, D. J. C., A. K. Lovell and P. Schmidt Formulation and estimation of stochastic frontier production function model. J. Econ. 6: Cochran, W. G Sampling Techniques, 3 rd edition. Johan Wiley and Sons, New York. pp Farrell, M. J The measurement of productive efficiency. J. Royal Stat. Soc. 120(3): Government of Pakistan Economic Survey of Pakistan Finance Div. Ministry of Finance. Meeusen, W. and Julien van Den Breock Efficiency estimation from Cobb-Douglus Production Function with composed error. International Economic Review. 18(2): PSMA Annual report (2009), Pakistan Sugar Mills Association, Islamabad. Taru, B, Lawal, and I. Tizhe Technical efficiency of sole cowpea production in adamawa state, Nigeria: A Cobb-Douglas Stochastic Frontier Function. Journal of Economics and International Finance. 3(8):