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基于DEA模型的陕西省粮食生产效率评价及影响因素研究 被引量:23

Studies on Evaluation of Grain Production Efficiency and Influencing Factors in Shaanxi Province Based on DEA Model
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摘要 为提升陕西省粮食生产效率,收集2000~2016年相关数据,构建指标体系,运用数据包络模型测算陕西省粮食生产效率。并在此基础上,运用SPSS软件进行多元线性回归分析影响陕西省粮食生产的主要因素。结果表明:2000~2016年陕西省粮食生产的技术效率为0.986 2,仍有提升空间;其中粮食种植面积、农业生产化肥施用折纯量、农业机械总动力与粮食生产技术呈正相关,粮食生产劳动力与粮食生产效率呈负相关。因此,应扩大粮食种植面积、提升粮食生产劳动效率、合理控制化肥的投入量、增加粮食生产农业机械拥有量、加大粮食生产技术投入力度、改善农业生产条件、推动粮食生产规模化,以提升陕西省粮食生产效率。 T0 improve the grain production efficiency in Shaanxi Province, relevant data from 2000 to 2016 were col-lected and a index system constructed. The grain production efficiency in Shaanxi Province was calculated by dataenvelopment model. On this basis, main factors that affected grain production in Shaanxi Province were analyzed bySPSS software to conduct multiple linear regression. The results showed that the technical efficiency of grain produc-tion in Shaanxi Province from 2000 to 2016 was 0.986 2, and there was still room for improvement. The grain acre-age, the amount of pure fertilizers used in agricultural production and the total power of agricultural machinery werepositively correlated with the grain production technology. The grain production labor force was negatively correlat-ed with the grain production efficiency. Therefore, we should enlarge the area of grain planting, improve the labor ef-ficiency of grain production, control reasonably of the input of chemical fertilizer, increase the quantity of agricultur-al machinery in grain production, increase investment in grain production technology, improve the conditions of agri-cultural production, promote large-scale food production so as to enhance the grain production efficiency in ShaanxiProvince.
作者 尚丽 SHANG Li(School of Economics and Management,Shaanxi Fashion Engineering University,Xianyang 712046,China)
出处 《东北农业科学》 北大核心 2018年第5期47-54,共8页 Journal of Northeast Agricultural Sciences
基金 陕西服装工程学院科研项目(2018KYR22)
关键词 粮食生产效率 技术效率 DEA分析 多元线性回归 陕西省 Grain production efficiency Technical efficiency DEA analysis Multiple linear regression Shaanxi Province
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