Document Type : Research Article-en
Authors
1 M.Sc: graduate student,, Urmia university
2 PhD Graduate, Agricultural Economics, University of Tabriz, Iran
Abstract
Given today’s existing limitations, providing a healthy, adequate and high-quality food for the fast-growing population of the world is a great challenge. The limitations exist in all fields, including resources and factors affecting production in the agricultural sector. The only solution to guarantee food security is the use of available sources effectively to deliver more and higher-quality products i.e., improving efficiency. The assessment of the efficiency of agricultural productions is also an important issue in the process of development in countries. On the other hand, agriculture is a risky activity and is affected by various factors such as climatic conditions, pests and diseases, fluctuations in inputs and outputs prices, financial risk, human risk, and input risk in production. Among these, the risk of inputs in production is important because of creating variation in production and yield of the output. Therefore, Agricultural activities are risky in comparison with other production activities, and the risk is also often accompanied with inefficiency. So, simultaneous study of risk and inefficiency can lead to more productive production. The method of analysis proposed for this study is consistent with the stochastic frontier approach which was independently proposed by Aigner et al. (1977) and Meeusen and Vanden Broeck (1977). This model proposes inputs have a similar effect on mean and variance outputs. But Just and Pope (1978) production function proposed separate effects of the inputs on the mean and variance outputs whilst Kumbhakar (2002) further incorporates technical inefficiency model.
Technical efficiency of the i-th farm is the ratio of observed output given the values of its inputs and its inefficiency effects to corresponding maximum feasible output if there were no inefficiency effects.
This study used cross-sectional data from 221 rice paddy fields which is a fair representation of the paddy fields in the region. A two-stage cluster random sampling method was employed to fulfill the questioners for obtaining the data on the relevant variables of this study including output and inputs as well as the farmers socio- economic variables.
According to the collected data, the average area under cultivation in the area was 1.33 hectares and rice farmers used on average 98.92 kilograms of rice seed, 29.50 man-days of labor, 258.35 kilograms of nitrate fertilizer, 142.28 kilograms of phosphate fertilizer, 4.51 liters of pesticide and 65.68 hours of agricultural machinery to produce 4.94 tons of output.
Also, according to completed questionnaires, the average age of rice farmers in the sample was 51 years old and more than 97% were married. The average size of the households was 3 people and 92% were male and the rest were female. Rice farming is the main job of more than 53% of respondents and more than 81% of them are the land owners. About the ownership of machinery, only 10% of farmers owned the machinery and the rest used rental machineries. More than 48% of farms were insured and 21% of them had participated in educational programs.
According to the results, the elasticity of land input is positive and equals to 1.04 showing one percent increase in the usage of this input will increase output by 1.04 percent and this input was used in the first stage of production in the studied area. The elasticities of nitrate and phosphate fertilizers, herbicide and machinery inputs had a positive sign, meaning that one percent increase in the usage of these inputs will increase output by 0.258, 0.033, 0.058 and 0.0003 percent respectively. The value of these elasticities is between zero and one indicating that farmers are currently operating in the second stage of production with respect to these inputs. The negative elasticity of production of seed and labour inputs means that one percent increase in the usage of these inputs will lead to the reduction of production, so these inputs are used in the third stage of production. The returns to scale coefficient is estimated at 1.092. This value is greater than one showing the increasing returns to scale structure of rice production in the studied area.
The results of estimating production risk function showed that rice production was significantly affected by land, seed and labour inputs. Also, land, water, age, and gender variables are risk increasing, and seed, herbicides, machinery, farmer’s education, family size, and farming experience are risk reducing inputs. In addition, seed, labour, membership in the agricultural cooperatives and insurance, increase technical inefficiency. Nitrate fertilizer, water, gender, rice cultivating experience and participation in educational and promotional classes reduce technical inefficiency in the studied area. The results of estimating technical efficiency showed that the average technical efficiency of the rice paddy field with risk component was 93.47% and without risk component, it was 96.27%. Therefore, it’s obvious that estimating the model without risk component leads to magnification error in the amount of technical efficiency. In conclusion, it is recommended to consider the risk component in measuring technical efficiency of paddy fields in order to achieve a sound risk management and highly efficient production.
Keywords
- Agricultural Inputs
- Production Risk
- Risk Management
- Stochastic Frontier (SPF) Model
- Technical Efficiency
Main Subjects
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