埃塞俄比亚奥罗米亚州东阿尔西地区Hetosa wooreda矿区示范小农与非示范小农小麦生产农户的技术效率差异

Meshesha Zewde, Desalegn Shamemo
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引用次数: 0

摘要

埃塞俄比亚的小农农业系统主要以主粮作物为主,这些作物受到管理效率低下和农民无法控制的因素的影响。因此,本研究旨在分析东阿尔西地区Hetosa wooreda模式与非模式小农小麦生产农户的技术效率差异及效率低下的决定因素。采用随机生产边界Cobb-Douglas函数形式。为了分析从700名(350名模型农民和350名非模型农民)收集的2018/19生产年的横截面数据,使用了描述性和计量经济学数据分析技术。描述性分析的结果表明,模范农民每公顷平均生产32.82公达小麦,非模范农民每公顷平均生产29.36公达小麦。差异比(γ)的值表明技术效率低下的可变性分别为89%,82%和84%的模型,非模型和整体农民。模型农户、非模型农户和整体农户的平均技术效率得分分别为81%、79%和80%。土地、肥料和劳动力对示范农户小麦产量的影响具有统计学意义,而所有投入变量对非示范农户小麦产量的影响均具有统计学意义。此外,模范农民的技术无效率受耕作方式、收获方式、冲击、培训和市场营销的影响显著,非模范农民的技术无效率受收获方式、教育水平、土地破碎化程度和市场营销的影响显著。因此,需要考虑培训、市场、教育和战略计划,以减轻农民无法控制的因素,从而提高研究地区农民的小麦生产效率和技术效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Technical Efficiency Difference between Model and Non-Model Smallholder Wheat Producer Farmers in Lode Hetosa Woreda of East Arsi Zone of Oromia Regional State, Ethiopia
The smallholder farming system in Ethiopia is largely dominated by staple food crops which are exposed to managerial inefficiency and factors beyond the control of the farmer. Accordingly, the study aimed to analyze technical efficiency differences between model and non-model smallholder wheat producer farmers and inefficiency determinants in Hetosa Woreda of East Arsi Zone. The Stochastic Production Frontier Cobb-Douglas functional form was used. To analyze cross-sectional data collected from 700(350 model and 350 non-model) farmers for the production year of 2018/19, descriptive and econometrics data analysis techniques were used. The findings of the descriptive analysis showed that model farmers produced an average of 32.82 and non-model farmers produced on average 29.36 quintals of wheat per hectare. The value of the discrepancy ratio (γ) which indicates technical inefficiency variability was 89%, 82%, and 84% for the model, non-model, and overall farmers respectively. The mean technical efficiency score was 81%, 79%, and 80% for the model, non-model, and overall farmers respectively. Land, fertilizer, and labor statistically significantly affected the wheat output of model farmers, whereas all input variables statistically significantly affected the wheat output of non-model farmers. In addition, model farmers' technical inefficiency was statistically significantly determined by the mode of plowing, mode of harvesting, shock, training, and marketing, and that of non-model farmers’ technical inefficiency was statistically significantly affected by mode of harvesting, level of education, land fragmentation, and marketing. Thus, training, market, education, and strategic plan to mitigate factors beyond the control of farmers need to be considered for improvement to make farmers more productive and technically efficient in wheat production in the study area.
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