Spatial Heterogeneity Characteristics and Driving Mechanisms of Abandoned Farmland in Different Scales and Regions in China

Abstract

Abandonment of farmland has become a widespread land use phenomenon worldwide. However, the spatial pattern and driving factors of abandoned farmland in different scales and regions in China are still unclear, which limits the government in formulating relevant policies. This study is based on the statistical data of abandoned farmland in county level in 2020, and examines the spatial patterns and driving mechanisms of the abandoned farmland over national level and geographical divisions in China. The results show that the abandoned rate of farmland (ARF) in China is 6.4%, but the total area reaches 8.56 million hectares in 2020. Northern region (NR) has the largest abandoned farmland area (AFA) among the various geographical divisions. High ARFs concentrate over hilly, mountainous and plateau regions with complex terrain conditions at the national scale, while plains and basins represent the opposite. The driving mechanisms of abandoned farmland vary widely at national scale and each geographical division. Agricultural added value (AAV) and surface roughness index (SRI) are two key factors that make opposite contributions to the spatial pattern of AFA and ARF across the country. Temperature, population ageing index (PAI), and AAV are the primary factors determining the AFA in NR, Northwest region (NWR) and Qinghai-Tibet region (QTR) respectively, whereas PAI and per capita disposable income (PCDI) are the main negative factors that determine the AFA in Southern region (SR). SRI and AAV are both the most critical factors determining ARF in the country, NR, and QTR, but presenting opposite contributions. Temperature and precipitation have a significant correlation with the ARF in the SR. Economic factors play important roles in controlling the ARF in the NWR. The results can assist the government to formulate scientific policies on farmland utilization at multiple scales over different regions.