2000—2020年苏北地区农业生态效率变化及其影响因素分析

为实现农业生产高效率、减少资源投入、降低环境损失、保障农产品供给和维持生态系统服务功能等多重目标,兼顾经济效益和生态效益评价农业生产绩效,该研究采用基于松弛值测算的数据包络分析模型（slack-based measure data envelopment analysis,SBM-DEA）,对2000—2020年苏北地区乡镇单元的农业生态效率进行评估,解析其时空变化特征,并对相关影响因素进行分析。研究结果表明：1）近20 a苏北地区农业生态效率呈现“升-降-升”的波浪式发展趋势,高值乡镇占比较低,农业生态效率仍有较大的提升潜力;2）研究区农业生态效率空间分布不均衡,整体呈现“南高北低”的分布特征,高值区分布范围有沿西南向东北扩展的趋势。农业生态效率呈现明显的集聚格局,低低型集聚主要分布在徐州市,高高型集聚主要集中于淮安市;3）能源投入、农药投入、农业碳排放是影响农业生态效率空间分异的主导因素,不同影响因素之间的交互作用会增强农业生态效率的空间分异特征。研究结果对于深化农业生态效率研究具有指导意义,可以为解析和优化农业生产方式,推动区域农业绿色发展提供决策支撑。

Process and influencing factors of agricultural eco-efficiency in northern Jiangsu of China from 2000 to 2020

Multiple goals are often required to balance the agricultural productivity for the food supply and the minimum resource input into the environmental damage in the ecological system services. Agricultural eco-efficiency has been used to evaluate the economic and ecological benefits for the better production performance. In this study, an assessment system was established to explore the spatial and temporal development of agricultural eco-efficiency. The panel data was collected from 678 towns in northern Jiangsu Province of China from 2000 to 2020. A data envelopment model was also used to assess the agricultural eco-efficiency using relaxation value measurement. In addition, the spatiotemporal evolution and regional differences of agricultural eco-efficiency were systematically investigated using global- and local-spatial autocorrelation analysis. Finally, the geographical detector model was selected to identify the influence of the component indicators on the spatial pattern of agricultural eco-efficiency. The results showed that: 1) The agricultural eco-efficiency shared a wave-like trend of "rise, fall, and rise". There was the great variation in the agricultural eco-efficiency in each urban area over the time. The descending order was ranked in Huai''an City>Yancheng City>northern Jiangsu Province>Lianyungang City>Suqian City>Xuzhou City. Among them, the maximum increase of agricultural eco-efficiency was found in Yancheng City, with an increase of 22.77%. By contrast, Suqian City decreased from 0.46 in 2000 to 0.35 in 2020, whereas, Huai''an City was basically the same from 2000 to 2020. The number of townships was 78, 98, 67, 57, and 88, respectively, with the high agricultural eco-efficiency (>0.81-1.0) in the study area. The townships with the high agricultural eco-efficiency were accounted for only 12.98% of the total in 2020, indicating the great potential to the improvement. 2) The overall agricultural eco-efficiency showed a spatial pattern of "high in the south and low in the north". The high-quality areas were small and scattered with a tendency to expand along the south-west to the north-east. By contrast, the low-quality areas were concentrated in Xuzhou, Suqian, and Lianyungang City. A clear pattern of agglomeration was observed with the low-low clustering region (23.89%-25.22%) in Xuzhou City, Ganyu County in the northern area of Lianyungang City, and Shuyang County in the northern of Suqian City, indicating the high spatial aggregation. The high-high clustering region (14.9%-24.34%) was shifted from the areas, such as Xuyi County in the north and south-west of Huai''an City to Yancheng City. 3) Energy inputs, pesticide inputs, and agricultural carbon emissions were the dominant factors in the spatial differentiation of agricultural eco-efficiency. The second most important factors were determined as the fertilizer inputs, agricultural film inputs, agricultural non-point source pollution, machinery inputs, and labor inputs in the spatial pattern of agricultural eco-efficiency. There was the different increase in the q-values for the interactions of each indicator. Two factors were also combined to strengthen the influence on the agricultural eco-efficiency. The type of interaction was also the non-linear strengthening (73%). Therefore, the agricultural carbon emissions, energy and machinery inputs were the common dominant factors using the factor detector. The weaker influence was found from the grain yield and ecosystem service function using factor detector. Anyway, the synergistic effects of various factors can be expected to achieved the optimal output on the spatial pattern of agricultural eco-efficiency. The finding can also provide a better guidance for the decision-making on the agricultural eco-efficiency in the environmentally friendly development of regional agriculture.