Production risk and technical efficiency of cotton production in northeast zone of Nigeria

Cotton is among the major cash crop and of considerable importance to Nigeria. Despite the importance of cotton in the nation’s economy, the actual cotton yield in the country was low as its fall within the range of 0.4-0.5 t/ha, compared to the genetic yield range of 2.5-3.0 t/ha in the country, Ogunlela (2004). Cotton farming in Nigeria is naturally operated with risk that mostly emanated from weeds, pests, diseases, inadequate supply of seed quantity, lack of financial backing of the extension staff by the government on the enlightenment of the farmers on how to adapt the modern farming system. other constrains includes low yield that emanated from low-quality seeds, high cost of production and lack of strict adherence to good agronomic practices by the farmers, unable to have access to credit and their inability to farm efficiently, as identified by previous studies Ogunlela (2004). Research has shown that the varieties grown and the yield attainable is between 1.5- 2.5 t/ha contributes to the decline and fluctuation in farmers’ productivity due to increasing rate of diseases, pests and soil fertility. This caused uncertainties in every cropping season of production and has to be examined through the choices of model that shows the effect of inputs on the output variance called production risk in inputs. With the realization of output that is uncertain, the ability of farmers to obtain maximum yield given the set of input factors influenced by their input’s decision as well as environmental factors will be achieved. Though some input factors may contribute positively to the realization farmer’s output, factors that are related to environments such as the incidence of pests and diseases, drought and floods affect the ability of farmers to obtain high yields ultimately. Therefore, the main objective of this study is to determine the production risk and technical efficiency of cotton production in the North-east Zone, Nigeria by engaging the stochastic frontier model with flexible risk specifications, sampling 349 cotton producers in the study area, in an effort to properly understand cotton production technique. The variation of technical inefficiency is explained by the following determinants: 1) demographic socioeconomic and farm characteristics; 2) agricultural extension characteristic; and 3) environmental characteristics. On the other hand, the specific objectives of this study were to: i) determine the production risk behavior with respect to farmers’ inputs; ii) determine the technical efficiency with respect to farmers’ inputs; iii) compare farmers’ technical efficiency using DEA and SFA; and to iv) determine the factors affecting the technical efficiency of cotton farmers. Consequently, the study models technical efficiency with production risk in inputs use as two (2) possible sources of production variability that characterized cotton production in the study area. Data from 349 cotton farmers that are randomly selected from three (3) different states in the study area were used for the analysis which was sourced from the survey conducted for the period of 2016 farming season. The study employed a trans-log stochastic frontier production function model with flexible risk specification. The empirical estimates revealed that the mean output is positively influenced the variable inputs (seed, fertilizer, agrochemicals and labour). Seed, fertilizer, agrochemicals are found to be risk-reducing inputs, while labour is riskincreasing inputs. By implication, it shows that the risk-averse farmer is expected to use more seed, fertilizer and agrochemicals and less labour compared to risk-neutral farmer in the study area. Several characteristics of demographic data like age, education, marital status household size, farming experience, off farm activities, extension visit and credit access were found to have significant effects on technical inefficiency of the cotton farmers in the study area. The estimated technical efficiency indicates that the efficiency score is overstated when the production of cotton farms is modelled without flexible risk component (88.3 percent) while it was found when estimated with risk component to be 83.7 percent. Technical efficiency of cotton farmers in the study area was also compared between DEA and SFA. The result revealed that the average efficiency score of SFA 91 percent implies that, although farmers in the study area are technically efficient. On the other hand, the result of DEA technical efficiency score is 78 percent, meaning that the cotton farmers in the study area are technically efficient in their production. The study concludes that the translog production function model is the best fit for the data for the estimation of farmers’ technical efficiency as the analysis recorded that technical efficiency enhances the variability of cotton production in the study area. Though the model estimates display that production risk contributes considerably to the vitality of cotton production, findings of this research work also shows that output variability is primarily explained by technical inefficiency and production risk. The present study authenticates that there is a needs to extend the theoretical framework for investigating technical efficiency because of the uncertainty that was associated with production processes, generally link production risk with the persistent uncertainty. The estimation of technical efficiency of cotton farms in Nigeria, however, under the assumption of risk neutrality with respect to production risk in inputs, has fundamentally combine biased estimates of the technical efficiency of the farms. Apparently, such estimate might lead to misleading policy recommendations judging from the results of this study. The deviation in output as a result of technical inefficiency are more pronounced than the deviation in output as a result of the pure noise component in output, as explained by the lambda value of (3.4937). The combined effects of farm inefficiency factors are able to explain variation in technical efficiency although some individual variables are not significant. The conventional input factors such as, seed, fertilizer, agrochemicals and labour are essential in the development of cotton production as they increase mean output positively in the production process. The production behaviour as characterized by cotton farmers in the study area display decreasing return to scale (0.533). Seed, fertilizer and agrochemicals are risk reducing variable inputs. Therefore, these variables can be used to alleviate the effect of risk in the production process. The mean technical efficiency for the surveyed using stochastic frontier analysis is 91.34 percent. Cotton farmers in Taraba State are more efficient than their counterpart in Adamawa and Gombe States. Cotton producers that are more educative have access to credit but less efficient. Other factors like farming experience, extension contact, farmers’ age and marital status have positive effect on farmers’ technical efficiency in the study area. On the other hand, household size and off farm activities diminishes farmers’ technical efficiency. Cotton farmers in the study area should be encouraged to consider the demographic data like access to credit, farming experience, educational status very vital activities as they help in increasing their technical efficiency, hence their living standard. It is recommended that government should ease the accessibility to credit and to enlighten the farmers on its advantages towards boosting their livelihood. Consequently, the role of adopting high yield variety in improving cotton seeds that not only reduces the level of inefficiency, production risk and increases efficiency among cotton farmers should be equally encouraged.

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