明晰农业生态效率的时空格局演化及影响机制,对促进农业高质量发展及乡村振兴战略实施具有重要的理论与实践意义。文章以河南省为研究对象,运用超效率SBM模型、变异系数法、自然断裂点法、冷热点分析法,对2010—2022年河南省县域视角下的农业生态效率进行综合评价,并借助GD+GWR模型探析了河南省农业生态效率的时空异质性成因规律。结果表明：①2010—2022年河南省各县市的农业生态效率波动下降,各地区效率差异呈现平稳上升态势,且豫北地区的效率差异明显大于豫南地区。②河南省农业生态效率高值区覆盖淇县、内黄县、濮阳县等地,低值区呈基底状分布在河南省境内;莫兰指数波动上升,空间上异质性与集聚性特征并存;热点区以安阳市为核心区逐渐向南延伸,冷点区呈先缩后扩型分布在登封市附近。③影响农业生态效率的主导因子为科技投入、种植结构和灌溉指数,因子间交互作用强烈且显著大于单一因子解释力;主要驱动因子对农业生态效率空间格局变化的作用方向和强度存在明显空间异质性,其中科技投入的回归系数有正有负,种植结构、产业结构、城镇化进程与农业生态效率分异格局存在显著正相关,人口老龄化程度和灌溉指数则以负反馈作用为主。
Clarifying the spatio-temporal pattern evolution and impact mechanism of agricultural eco-efficiency has important theoretical and practical significance for promoting the high-quality development of agriculture and implementing rural revitalization strategies. Taking Henan Province as the research area, this paper uses the Super-SBM model, Natural Breaks method, and cold-hot spots analysis to analyze the agricultural eco-efficiency at county level from 2010 to 2022. Furthermore, it explores the spatiotemporal heterogeneity of agricultural eco-efficiency in Henan Province by the GD and GWR models. The conclusions are as follows: 1) The agricultural eco-efficiency showed the fluctuated and decreased characteristics in Henan Province from 2010 to 2022. Moreover, the efficiency difference in different counties showed a steady upward trend, and the efficiency difference in northern Henan was significantly greater than that in southern Henan. 2) The high-value areas of agricultural eco-efficiency in Henan Province were Qi County, Neihuang County and Puyang County, while the low-value areas showed the distribution of basal pattern. The Moran index fluctuates and increases with spatial heterogeneity and agglomeration characteristics coexisting. The hot spot areas, with Anyang as the core area, gradually extended southward, while the cold spot areas were distributed near Dengfeng City in a first shrinking and then expanding pattern. 3) The dominant factors of agricultural eco-efficiency are technology investment, planting structure and irrigation index, the interaction between factors is significantly greater than the explanatory power of a single factor. Meanwhile, there is significant spatial heterogeneity in the direction and intensity of the main driving factors on the spatial pattern change of agricultural eco-efficiency. The regression coefficient of technology investment is positive and negative, and there is a significant positive correlation between planting structure, industrial structure, urbanization process and the spatial pattern of agricultural eco-efficiency, but the degree of population aging and irrigation index both have mainly negative feedback effects.
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