长江经济带耕地产能变化及土地整治影响分析

耕地产能改善是中国在耕地资源数量约束下确保粮食安全的重要手段。土地整治是提升耕地产能的重要途径,但目前在大尺度区域对不同类型土地整治影响耕地产能的空间分异规律认识仍不充分。因此该研究以长江经济带为例,基于MODIS-EVI遥感数据评估了2001—2017年研究区耕地产能及变化特征,使用地理加权回归模型分析了80 920个土地整治项目对耕地产能影响的空间格局和分区特征,结果表明：1）耕地产能变化趋势有显著空间分异特征,单季作物、双季作物第一和二季产能分别呈减速上升、加速上升和先增后减趋势,长江上游产能增幅最大,下游单季作物和双季作物第二季产能降幅最大;2）土地整治对耕地产能影响机制存在空间分异,长江上游单季作物主要受土地整治建设规模和投资标准的积极影响,长江下游双季作物第二季产能受土地整治消极影响;3）根据耕地产能变化特征及土地整治影响机制差异可以将长江经济带可划分为西南山地整治区、四川盆地整治区、南方丘陵整治区和长江下游平原整治区。该研究可以为各区域土地整治政策优化提供参考。

Analysis of the cropland productivity change and the impact of land consolidation in the Yangtze River Economic Zone

Abstract: Cropland productivity can be greatly improved to ensure national food security in China, particularly under the quantitative constraints of farmland resources. Among them, land consolidation can be an important way for the high cropland productivity in recent years. But, the spatial differentiation patterns of land consolidation can pose a great impact on cropland productivity in large-scale regions. Taking the Yangtze River Economic Zone as an example, this study aims to determine the impact of land consolidation on cropland productivity change using MODIS-EVI remote sensing data. The S-G filtering and threshold were used to extract the cropland productivity (i.e., single-crops productivity, the first and second season productivity of double crops) in the study area from 2001-2017. Then, 80 920 projects of land consolidation were collected (including land development, consolidation, and reclamation projects) with the location and attribute information using the national rural land consolidation monitoring and supervision system. The contribution rate of land consolidation was further analyzed for the cropland productivity in counties using a geographically weighted regression model, together with the spatial differentiation of the impact degree. Finally, the impact of land consolidation was spatially partitioned on the cropland productivity using the hierarchical clustering model. The optimization strategies of land consolidation were also proposed for each zone, according to the cropland utilization characteristics. The results showed that: 1) There was a significant spatial divergence trend of cropland productivity. Specifically, the productivity of single-crops and double-crops in the first and second seasons presented a decelerating increase, an accelerating increase, and an increase followed by a decrease, respectively, with the largest increase in the productivity in the upper reaches of the Yangtze River. There was a significant decrease in the productivity of the single-crops and the second season productivity of double-crops in the lower reaches; (2) The average contribution of land development, allocation, and reclamation to the change of single-crops productivity were 7.38%, 4.44%, and 2.80%, respectively; the average contribution to the change of first season productivity of double-crops were 6.21%, 5.04%, and 3.34%, respectively; and the average contribution to the change of second season productivity of double-crops were 7.10%, 7.15%, and 3.69%, respectively. (3) The Yangtze River economic belt was divided into the consolidation areas of the southwest mountain, the Sichuan basin, the southern hilly, and the lower Yangtze River plain, according to the cropland productivity and the impact mechanism of land consolidation. A better optimization was also provided for the land consolidation of each region. In addition, some findings were also needed to further explain: firstly, the land allocation was the least effective to promote cropland productivity improvement among all types of projects. The reason behind this issue can be expected to continue. Another phenomenon is that the second season productivity of double-crops was generally negatively correlated with the land consolidation indicators, has the implementation of land consolidation accelerated the phenomenon of "double to single"? What is the mechanism behind this phenomenon? Finally, it is very necessary to improve the efficiency of the causal inference using a geographically weighted regression model.