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