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中国农学通报

中国农学通报 ›› 2026, Vol. 42 ›› Issue (15): 27-35.doi: 10.11924/j.issn.1000-6850.casb2025-0800

• 农学·农业基础科学 • 上一篇    下一篇

基于DEA模型的农业生产效率研究进展

张耀文1,2(), 赵宇1, 李洪波3, 安亚明1, 蔡鑫2, 郭玉彬1, 刘猛1(), 刘建军4   

  1. 1 河北省农林科学院谷子研究所/农业农村部特色杂粮遗传改良与利用重点实验室（部省共建）/河北省杂粮研究实验室， 石家庄 050035
    2 河北农业大学农学院， 河北保定 071033
    3 河北省农业科技发展中心， 石家庄 050031
    4 河北省杂粮产业技术研究院， 河北邯郸 057250
  • 收稿日期:2025-09-19 修回日期:2026-03-23 出版日期:2026-08-15 发布日期:2026-08-13
  • 通讯作者:
    刘猛，男，1982年出生，河北沧县人，研究员，主要从事杂粮产业经济研究。通信地址：050000 河北省石家庄市裕华区高新技术开发区恒山街162号 河北省农林科学院谷子研究所，E-mail：。
  • 作者简介:

    张耀文，男，2000年出生，河北保定人，硕士研究生在读。通信地址：050000 河北省石家庄市裕华区高新技术开发区恒山街162号 河北省农林科学院谷子研究所，E-mail：。

  • 基金资助:
    河北省现代农业产业技术创新团队“河北省谷子创新团队”(HBCT2024080301); 财政部和农业农村部：国家现代农业产业技术体系“国家谷子高粱产业技术体系产业经济岗位”(CARS-06-14.5-A33)

Research Progress on Agricultural Production Efficiency Based on DEA Model

ZHANG Yaowen1,2(), ZHAO Yu1, LI Hongbo3, AN Yaming1, CAI Xin2, GUO Yubin1, LIU Meng1(), LIU Jianjun4   

  1. 1 Institute of Millet Crops, Hebei Academy of Agricultural and Forestry Sciences/Key Laboratory of Genetic Improvement and Utilization for Featured Coarse Cereals (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs/The Key Research Laboratory of Minor Cereal Crops of Hebei Province, Shijiazhuang 050035
    2 College of Agronomy, Hebei Agricultural University, Baoding, Hebei 071033
    3 Hebei Agricultural Science and Technology Development Center, Shijiazhuang 050031
    4 Hebei Industry and Technology Academy of Coarse Cereals, Handan, Hebei 057250
  • Received:2025-09-19 Revised:2026-03-23 Published:2026-08-15 Online:2026-08-13

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摘要：

本研究旨在系统综述梳理数据包络分析(DEA)模型在农业生产效率研究领域的演进脉络，总结现有研究的成果与不足，以期为推动农业生产效率的提升和农业可持续发展提供理论依据与方向指引。本研究通过文献综述法，系统分析了CCR、BCC、超效率DEA、SBM、DEA-Malmquist指数、三阶段DEA、网络DEA等不同类型DEA模型在农业生产效率评价中的适配场景。研究聚焦于DEA模型在三大维度上的应用：区域层级（多尺度效率分异）、农业细分领域、效率影响因素。结果显示：DEA模型体系呈多元发展趋势，不断演进以适应更复杂的研究需求。从基础的CCR、BCC模型，到能够区分有效单元效率的超效率DEA模型；从能够动态分析全要素生产率变化的DEA-Malmquist指数模型，到可处理非期望产出的SBM模型；再到能够剔除环境与随机因素干扰的三阶段DEA模型，以及打破“黑箱”测度子阶段效率的网络DEA模型，DEA模型工具箱日益丰富。在应用层面，DEA模型被广泛用于不同区域层级（国家、省、市、县）的农业生产效率评价，揭示了区域农业效率的空间分异特征与动态演变规律；深入应用于种植业、畜牧业、渔业等细分领域，精准识别各领域的效率短板；还常与Tobit回归等模型相结合，以探究影响农业生产效率的关键因素。综上所述，DEA模型在农业领域的应用已取得显著成效。它不仅能够清晰揭示不同区域农业效率的空间分异与动态演变，又能为种植业、畜牧业、渔业等细分领域提供效率瓶颈的诊断，还可结合Tobit回归等方法，明确效率的影响因素。这些研究成果为优化农业资源配置、制定针对性政策、提升农业生产效率及推动农业高质量发展提供了坚实的理论支撑与实践指导。

关键词: 农业生产效率, 数据包络分析, 全要素生产率, Malmquist指数, 效率评价, 研究进展

Abstract:

This study intends to systematically review the research evolution in the field of agricultural production efficiency, summarize existing achievements and limitations, thereby providing theoretical support and directional guidance for enhancing agricultural production efficiency and advancing agricultural sustainable development. This study adopts the literature review method to sort out research on agricultural production efficiency based on the data envelopment analysis (DEA) model. It systematically analyzes the applicable scenarios of different types of DEA models, including the CCR, BCC, Super-efficiency DEA, SBM, DEA-Malmquist Index, Three-stage DEA, and network DEA models, as well as their applications in three dimensions: regional level (multi-scale efficiency differentiation), agricultural sub-sectors, and influencing factors. The results show that the DEA model system presents a trend of diversified development. This model system has been continuously evolving, covering basic CCR and BCC models; super-efficiency DEA models that can distinguish the efficiency of effective units; DEA-Malmquist index models that enable dynamic analysis of total factor productivity changes; SBM models capable of handling undesirable outputs; three-stage DEA models that eliminate the interference of environmental and random factors; and network DEA models that break the "black box" to measure the efficiency of sub-stages. At the application level, DEA models are widely used to evaluate agricultural production efficiency at different regional levels (national, provincial, municipal, and county levels), revealing spatial differentiation and dynamic evolution characteristics. They are also applied in specific sub-sectors such as crop farming, animal husbandry, and fishery to identify efficiency shortcomings. Additionally, DEA models are often combined with Tobit regression to explore the influencing factors of efficiency. The DEA model has been widely applied in the agricultural field with remarkable effects. It can not only reveal the spatial differentiation and dynamic evolution of agricultural efficiency in different regions, but also identify efficiency shortcomings in specific sub-sectors like crop farming, animal husbandry, and fishery. Furthermore, when combined with methods such as Tobit regression, it can clarify the influencing factors of efficiency. Overall, the DEA model provides solid theoretical support and practical guidance for optimizing the allocation of agricultural resources, formulating targeted policies, improving agricultural production efficiency, and promoting the high-quality development of agriculture.

Key words: agricultural production efficiency, data envelopment analysis (DEA), total factor productivity, Malmquist index, efficiency evaluation, research progress

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