Spatial pattern of arable land-use intensity in China
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Ye, Sijing [1] ; Song, Changqing [1] ; Shen, Shi [1] ; Gao, Peichao [1] ; Cheng, Changxiu [1] ; Cheng, Feng [3] ; Wan, Changjun [1] ; Zhu, Dehai [2]
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[1] Beijing Normal University
Beijing Normal University
China
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[2] China Agricultural University
China Agricultural University
China
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[3] Centre of Land Consolidation, Ministry of Natural Resources, Beijing, 100035, China
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- Localización: Land use policy: The International Journal Covering All Aspects of Land Use, ISSN 0264-8377, ISSN-e 1873-5754, Nº. 99, 2020
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
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Resumen
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.
