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Resumen de Modeling agricultural land use change in a rapid urbanizing town: Linking the decisions of government, peasant households and enterprises

Min Xia, Yanyuan Zhang, Zihong Zhang, Jingjie Liu, Weixin Ou, Wei Zou

  • Analyzing and understanding land use decisions of government, peasant households and enterprises could help modeling agricultural land use change, which has taken place in China during the past thirty years. Taking Guanlin town, Yixing city, China as a case, land use data in 4 periods from 2006 to 2015 were used for analyzing agricultural land use change. With the consideration of land use decisions of government, peasant households and enterprises, agricultural land use change in 2021 and 2030 of the study area were modeled by coupling CA and MAS. The results showed that agricultural land area decreased in 2006–2009 and then increased in 2009–2015, with an overall increase of 5.13 km2. Paddy fields, aquaculture surface and rainfed cropland were converted most frequently. Government was the main planner and key decision maker of agricultural land use, which guides the allocation and use of agricultural land. Peasant households participated in the adjustment of agricultural land with the authorization of the government, and their decisions were influenced by traffic accessibility, irrigation and drainage condition, land quality, land rent and distance to residential land. Enterprises participated in the adjustment of industrial land with the supervision of the government, which indirectly affecting agricultural land use. The main factors affecting their site selection decision were environmental quality, land price, transportation conditions and public facilities conditions. The modeling results indicated that agricultural land area would decrease by 4.74 km2 from 2015 to 2030. Cultivated land had an accelerating decrease trend while aquaculture surface increased continuously. These results provide reference for the research of agricultural land use change and the decision of rural land management.


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