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Landslides and Cropland Abandonment in China’s Mountainous Areas: Spatial Distribution, Empirical Analysis and Policy Implications

Author

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  • Xin Deng

    (College of Economics of Sichuan Agricultural University, Chengdu 611130, China
    These authors contributed equally to this work and should be regarded as co-first authors.)

  • Dingde Xu

    (Sichuan Center for Rural Development Research, College of Management of Sichuan Agricultural University, Chengdu 611130, China
    These authors contributed equally to this work and should be regarded as co-first authors.)

  • Miao Zeng

    (School of Economics of Sichuan University, Chengdu 610065, China
    These authors contributed equally to this work and should be regarded as co-first authors.)

  • Yanbin Qi

    (College of Economics of Sichuan Agricultural University, Chengdu 611130, China)

Abstract

Cropland, as the largest land use type in the human landscape, contributes to not only biodiversity but also global food security. However, cropland abandonment not only is harmful to agricultural cultural landscapes but also threatens food security. Prior studies have suggested that changes in the social environment drive farmers to abandon cropland. In contrast, this study reveals that factors of the natural environment (e.g., landslides) have steadily and significantly affected cropland abandonment after controlling the factors of the social environment. More specifically, based on the survey data of a large sample of 4850 mountainous households in 24 provinces of China and following the theoretical framework of “environment → land use”, this study quantitatively identified the impacts of landslides on cropland abandonment in mountainous areas using the Probit and Tobit models. The results show that: (1) There is a similar spatial agglomeration trend between landslides and cropland abandonment. Namely, an area that has a high incidence of landslides also has a high incidence of cropland abandonment. (2) There is a significant and positive correlation between landslides and cropland abandonment. Namely, compared with peasants who have not suffered from landslides, the probability that peasants suffering from landslides will abandon cropland and the area abandoned increase by 6.8% and 0.064 mu, respectively. (3) Elderly farmers (over 64 years old) and the development of urbanization help curb cropland abandonment in the mountains. The results of this study may provide reference for the government to implement effective policies for managing landslides and revitalizing unused cropland resources.

Suggested Citation

  • Xin Deng & Dingde Xu & Miao Zeng & Yanbin Qi, 2018. "Landslides and Cropland Abandonment in China’s Mountainous Areas: Spatial Distribution, Empirical Analysis and Policy Implications," Sustainability, MDPI, vol. 10(11), pages 1-14, October.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:11:p:3909-:d:178717
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    References listed on IDEAS

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    6. Yuchen Guo & Wei Song, 2019. "Spatial Distribution and Simulation of Cropland Abandonment in Wushan County, Chongqing, China," Sustainability, MDPI, vol. 11(5), pages 1-25, March.
    7. Deng, Xin & Xu, Dingde & Zeng, Miao & Qi, Yanbin, 2019. "Does Internet use help reduce rural cropland abandonment? Evidence from China," Land Use Policy, Elsevier, vol. 89(C).
    8. Raju Rai & Yili Zhang & Basanta Paudel & Narendra Raj Khanal, 2019. "Status of Farmland Abandonment and Its Determinants in the Transboundary Gandaki River Basin," Sustainability, MDPI, vol. 11(19), pages 1-18, September.
    9. Xu, Dingde & Deng, Xin & Huang, Kai & Liu, Yi & Yong, Zhuolin & Liu, Shaoquan, 2019. "Relationships between labor migration and cropland abandonment in rural China from the perspective of village types," Land Use Policy, Elsevier, vol. 88(C).
    10. Dingde Xu & Xin Deng & Shili Guo & Shaoquan Liu, 2019. "Sensitivity of Livelihood Strategy to Livelihood Capital: An Empirical Investigation Using Nationally Representative Survey Data from Rural China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 144(1), pages 113-131, July.
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