Affiliation:
1. School of Public Affairs, Zhejiang University, Hangzhou 310058, China
2. Hunan Provincial Institute of Land and Resources Planning, Changsha 410007, China
3. Guangzhou South China Institute of Natural Resources Science and Technology, Guangzhou 510642, China
Abstract
The increasing global population is leading to a decline in cropland per person, aggravating food security challenges. The global implementation of land consolidation (LC) has created new farmland and increased productivity. LC is a critical strategy in China for ensuring food security and gaining significant government support. This article investigates the impact of LC on farmland productivity in Sichuan Province in 2020. We utilize time series remote sensing data to analyze LC’s impact on farmland capacity. This study uses Sentinel and Landsat satellite data to calculate CumVI and assesses the LC project’s spatiotemporal evolution. To evaluate LC’s effectiveness, we create indexes for yield level and stability and employ Getis-Ord Gi* to identify spatial differentiation in LC’s impact. GeoDetector and GWR examine the impact of natural factors like elevation, slope, soil organic carbon, and rainfall on the effectiveness of LC. The research results show that: (1) After the implementation of LC, 55.51% of the project areas experienced significant improvements in agricultural productivity; the average increase rate of yield level is 7.74%; and the average increase rate of yield stability is 12.40%. Overall, LC is significant for improving farmland capacity. (2) The effectiveness of LC exhibits spatial differences and correlations in different areas. The main location for high-value agglomeration of yield levels is Nanchong City, while the northern part of Guangyuan City primarily hosts low-value agglomeration areas. (3) Natural conditions influence LC’s effectiveness. In terms of affecting the yield level of LC, the driving factors from high to low are SOC, elevation, slope, and rainfall. In terms of affecting the yield stability of LC, the driving factors, from high to low, are elevation, SOC, slope, and rainfall. LC’s effectiveness is influenced by different natural conditions that have different effects.
Funder
Hunan Provincial Natural Science Foundation of China
Open Fund of Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources
Fundamental Research Funds for the Central Universities
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