Spatial pattern of arable land-use intensity in China

• Published in: Land use policy 2020-12, Vol.99, p.104845, Article 104845
• Main Authors: Ye, Sijing, Song, Changqing, Shen, Shi, Gao, Peichao, Cheng, Changxiu, Cheng, Feng, Wan, Changjun, Zhu, Dehai
• Format: Article
• Language: English
• Subjects: Agricultural land; Agricultural policy; Agricultural productivity; Algorithms; Arable land; Arable land protection; Arable land quality; Cluster analysis; Clustering; Counties; Crop yield; Decision making; Districts; Farming; Grain; K-means; Land computing; Land use; Land-use intensity; Mountains; Plateaus; Policies; Rationality; Regions; Spatial analysis; Spatial distribution; Sustainable use; Vector quantization
• ISSN: 0264-8377; 1873-5754
• DOI: 10.1016/j.landusepol.2020.104845

In recent years, the sustainable utilization of China’s arable land has been confronted with several challenges. The China government has been very strict in arable land protection, and a package of policies and measures have been promulgated. All these endeavors are of great significance for proposing an innovative policy system for sustainable land use in China. However, above stated policies are all designed from the perspective of space control with the purpose of reducing arable land loss or increasing arable land area, few policies have been designed from the perspective of utilization control, namely guide the actual arable land farming in sustainable ways and constraint unreasonable land use behavior such as overuse, rough use, land abandonment. In this paper, we analyze spatial distribution of average land-use intensity (ALUI) at the county-level in Mainland China, which can be used as a significant index for evaluating the rationality of arable land use and providing effective decision-making supporting information for design of regional arable land protection policy. Based on the experimental results, there is still considerable room for yield improvement as the ALUI of ∼73.1 % counties are lower than 0.7 while the 53.60 % counties are lower than 0.6. Furthermore, the ALUI dataset shows significant global spatial autocorrelation characteristic. Boundaries of regions that aggregated by counties with high ALUI are more consistent with that of provincial administrative districts, comparing with that of sub-standard farming system regions. On the other hand, counties with low ALUI are mostly cluster in mountains, hills, or plateaus, where grain yield is mainly limited by regional hydrothermal conditions. In addition, counties with different ALUI status have been divided into six classes, using k-means clustering algorithm. This will facilitate the understanding of appropriate arable land protection and utilization paths for different regions and the rethinking of current support policies on farmland protection. [Display omitted] •Present arable land-use intensity of mainland China at county scale.•Analyze spatial aggregation characteristics of arable land-use intensity.•Key points for regional arable land protection were found among different counties.•Present a new, output-oriented measure for arable land-use intensity. In recent years, the sustainable utilization of China’s arable land has been confronted with several challenges. The China governm