Remote Sensing (Oct 2022)

The Quantitative Impact of the Arable Land Protection Policy on the Landscape of Farmland Abandonment in Guangdong Province

  • Le Li,
  • Siyan Zheng,
  • Kefei Zhao,
  • Kejian Shen,
  • Xiaolu Yan,
  • Yaolong Zhao

DOI
https://doi.org/10.3390/rs14194991
Journal volume & issue
Vol. 14, no. 19
p. 4991

Abstract

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In the past two decades, the Ministry of Agriculture and Rural Affairs of China (MARA) has issued a series of strict cultivated land protection policies to prevent the spread of farmland abandonment and maintain a dynamic balance between the quantity and quality of arable land. However, high-speed economic development, strict arable land protection policies, and ecological security and sustainable development strategies interacting with human activities have brought challenges to quantifying the effectiveness of arable land protection policies. In this study, we proposed a method to quantify the impacts of the arable land protection policies and evaluate the quantitative impacts on farmland abandonment in Guangdong Province after 2014 from the perspective of landscape ecology. The results illustrated that the landscape fragmentation of farmland abandonment in Guangdong Province decreased after the new arable land policies were issued. More annual farmland abandonment (AFA) shifted to seasonal farmland abandonment (SFA), revealing the considerable pronounced effects of farmland abandonment management. The new policies effectively restrained the area increase for AFA in the regions with lower rural population (RPOP) and lower gross domestic product (GDP), and reduced the fragmentation of AFA in the regions with the highest RPOP and lower GDP. Additionally, the new policies effectively restrained the fragmentation increase for SFA in the regions with lower RPOP and lower GDP, and reduced the area increase for SFA in the regions with the highest RPOP and lower GDP. The management effect was not that significant in the regions with higher RPOP and higher GDP. These findings will provide important data references for arable land decision making in southern China.

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