Influence Of Management Characteristics On Technical Efficiency Of Rice Farming In The Muda Agricultural Development Authority, Kedah, Malaysia
Abdlatif, Ismail (2008) Influence Of Management Characteristics On Technical Efficiency Of Rice Farming In The Muda Agricultural Development Authority, Kedah, Malaysia. PhD thesis, Universiti Putra Malaysia.
Rice is a strategically important crop due to it being a staple food commodity. The rice industry is heavily regulated and completely protected from direct foreign competition. However, technical efficiency remains low. Numerous farm studies have shown the widespread existence of inefficiencies among rice producers. Despite the general adoption of the green revolution technology, enormous differences in farm technical efficiency still exists intra and inter farms, within regions and nations. Even though farms face similar environmental conditions and apply the same production techniques, yield levels still differ between them. Many believe that management skills must be the X factors that contribute to the differences in technical efficiency between farms. This study attempts to examine the farm technical efficiency levels and the effects of management variables on efficiency. The purpose of the study was to analyze the roles of management proxied as a soft technology variable in determining the technical efficiency of paddy farms in MADA, Kedah. Three hundred and seventy five farm records of MADA paddy farmers in Season 1, 2002 were analyzed for the levels and determinants of technical inefficiency. Data collected included a) production variables: paddy output, land, fertilizer, chemicals, labour, and b) management demographic variables: planning, organizing, directing, controlling, age, education and family size. The data were comparatively analysed via the parametric (Stochastic Frontier Analysis) and non parametric (Data Envelopment Analysis) framework according to regions and farm size. The estimated efficiency indexes and the determinants of technical inefficiency yielded by each method were compared and analysed. The empirical results were subsequently examined to ascertain the extent to which they served the needs of policymakers. Determinants of inefficiency which include planning, organizing, directing, coordinating, control, age, education and experience were analyzed using, i) Battesse and Coelli (1995) model, and ii) Tobit model. Results indicated that farmers have an average farm size of 2.04 hectares. Most farmers were generally old with households’ size becoming smaller implying less family labour hours available for farm work. Most farmers have low education levels but are rather well experienced in paddy planting with the knowledge generally being passed down from elders and experienced on the job rather than formal training.Stochastic Frontier Analysis showed a wide variation in the estimated technical efficiencies, ranging from 37 to 98 percent, indicating opportunities for improvement in the technical efficiency of farms. Parameter estimates show systematic technical inefficiency effects do exist. Management variables that exert positive effects on efficiency are organizing, staffing and controlling. Motivational variables that promote efficiency include maintaining way of life, maintaining environment and increasing farm size. Demographic variables that are positively related to efficiency are experience, household size and education levels. Data Envelopment Analysis indicated that efficiency levels, ranging from 26 to 100 percent, vary across farms and within each production regions. Most farms are technically efficient with 60 percent of the sample above the mean efficiency score. Efficient farms that are well managed have proper planning, organizing and control schedules. Most of these farms are managed by rather old, very experienced but lowly educated farmers. Farm objective of maintaining environment was the main characteristics of efficient farms. Personal aspects which include years of experience and age exert positive effects on efficiency. The comparative analysis for the mean technical efficiency for the sample were estimated to be 88 percent for SFA, 72.5 percent for DEA Constant Returns to Scale and 83.1 percent for DEA Variable Returns to Scale. This implies that more work can still be done to increase the efficiency levels. The technical inefficiency effects were found to be significant in explaining the levels of and variation in farm earnings. Organizing, staffing, controlling, environment conscious, maintaining way of life, maintaining farm size, age and experience exert positive factors on farm efficiency. In conclusion, policymakers should not be indifferent to the choice of the frontier models used to score efficiency relationships. They may be well advised to wait until additional research clarifies reasons why DEA and SFA models yield divergent results before they introduce these methods into the policy process. Thus, farm policies should focus more on improving the skills and techniques of farm operations. Correct application and timeliness of farm operation will promote the optimal usage of inputs. Training and extension services can also help farms acquire new technology so that they can be at the frontier of paddy production.
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