Spatial heterogeneity characteristics and driving mechanisms of
abandoned farmland at different scales and regions in China
Abstract
Abandonment of farmland has become a common phenomenon of land use in
the world. However, spatial patterns and the driving factors of farmland
abandonment at different scales and regions remain ambiguous at the
national level, which limits the government to formulating relevant
policies. This study is based on the statistical data of abandoned
farmland in county level units in 2020, and investigates the spatial
patterns and driving mechanisms of abandoned farmland at national and
geographical divisions in China. The results show that the abandoned
rate of farmland (ARF) in China reached 6.4%. The abandoned farmland
area (AFA) of geographical divisions shows northern region (NR)
> southern region (SR) > northwest region
(NWR) > Qinghai-Tibet region (QTR), and the ARF shows NWR
> QTR > NR > SR. There are six
cluster regions with a low ARF and three cluster regions with a high ARF
in China, which are consistent with the geomorphic unit in spatial
distribution. The key driving factors and mechanisms of farmland
abandonment at national and geographical divisions are significant
different. Agricultural added value (AAV) is the primary factor that
controls the pattern of AFA and ARF nationwide, followed by the surface
roughness index (SRI). Temperature, the population ageing index (PAI),
and AAV are the main factors determining the abandonment of farmland for
the NR, NWR and QTR, respectively, while the PAI and per capita
disposable income (PCDI) become the main factors in SR among all
factors. With the exception of a significant impact of temperature and
precipitation on the AFA in the NR, the AFA in the national and other
geographical regions is significantly affected by AAV and PAI. The SRI
is a key factor determining ARF in the whole country, NR, and QTR.
Temperature and precipitation have a significant correlation with the
ARF in the SR. The results can provide support for the formulation of
scientific farmland utilization policies at multiple scales.