TY - JOUR

T1 - How is urbanization shaping agricultural land-use? Unraveling the nexus between farmland abandonment and urbanization in China

AU - Hou, Dawei

AU - Meng, Fanhao

AU - Prishchepov, Alexander V.

N1 - Funding Information: We sincerely thanks for the constructive comments from Prof. Daniel Müller ( IAMO ) and Tzu-Hsin Karen Chen ( Yale University ). We acknowledge funding of the DFF-Danish ERC Support Program (grant number: 116491 , 9127-00001B ), the Overseas Expertise Introduction Project for Discipline Innovation (111 Project) (Grant No. B17024), Research Fund Program of Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology (Grant No. 2018K01 ) and Scientific Research Start-up Fund Projects of Introduced Talents (Grant No. 5909001803 ). Funding Information: We sincerely thanks for the constructive comments from Prof. Daniel M?ller (IAMO) and Tzu-Hsin Karen Chen (Yale University). We acknowledge funding of the DFF-Danish ERC Support Program (grant number: 116491, 9127-00001B), the Overseas Expertise Introduction Project for Discipline Innovation (111 Project) (Grant No. B17024), Research Fund Program of Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology (Grant No. 2018K01) and Scientific Research Start-up Fund Projects of Introduced Talents (Grant No. 5909001803). Publisher Copyright: © 2021 Elsevier B.V.

PY - 2021/10

Y1 - 2021/10

N2 - Urbanization often results in agricultural expansion but can also lead to farmland abandonment. However, it remains unclear about the extent, exact timing and determinants of farmland abandonment in response to ongoing urbanization. Using the example of China's Sunan economic region, we present the spatiotemporal trajectories of farmland abandonment and recultivation from 2001 to 2018. We classified Landsat satellite image time-series with a regression trees classifier in Google Earth Engine (GEE). Further, we analyzed the spatiotemporal patterns and rates of farmland abandonment and recultivation. Spatially-explicit logistic regressions were applied to explore the determinants of farmland abandonment in space and time. Our results show widespread farmland abandonment: approximately 232,700 ha of farmland had ever been abandoned from 2001 to 2018, with the highest annual abandonment rate (8.5%) in 2017. Approximately 66,200 ha of abandoned fields were later recultivated, with the maximum recultivated area (13,600 ha) in 2018. Approximately 92% of abandoned fields were later recultivated or reused as the impervious surface within two years of the first detection of abandonment, suggesting more rapid land transformation. The regressions reveal that locational and socio-economic factors determined farmland abandonment patterns. Specifically, a higher likelihood of farmland abandonment was statistically associated with an increased distance from the nearest settlements; the significantly positive relationship between ‘Non-agricultural GDP’ and farmland abandonment strengthened over time. In contrast, a lower likelihood of farmland abandonment was observed in areas with more cash crops. The statistical results may extend the application of the Ricardian comparative advantage theory along with Alonso's bid rent theory to explain and predict abandonment patterns in response to ongoing urbanization. Our study is the first attempt in China to apply 30-m Landsat imageries to reconstruct abandonment patterns over long time-series. The findings provide important insights into adjusting land-management practices for preventing farmland abandonment due to urbanization.

AB - Urbanization often results in agricultural expansion but can also lead to farmland abandonment. However, it remains unclear about the extent, exact timing and determinants of farmland abandonment in response to ongoing urbanization. Using the example of China's Sunan economic region, we present the spatiotemporal trajectories of farmland abandonment and recultivation from 2001 to 2018. We classified Landsat satellite image time-series with a regression trees classifier in Google Earth Engine (GEE). Further, we analyzed the spatiotemporal patterns and rates of farmland abandonment and recultivation. Spatially-explicit logistic regressions were applied to explore the determinants of farmland abandonment in space and time. Our results show widespread farmland abandonment: approximately 232,700 ha of farmland had ever been abandoned from 2001 to 2018, with the highest annual abandonment rate (8.5%) in 2017. Approximately 66,200 ha of abandoned fields were later recultivated, with the maximum recultivated area (13,600 ha) in 2018. Approximately 92% of abandoned fields were later recultivated or reused as the impervious surface within two years of the first detection of abandonment, suggesting more rapid land transformation. The regressions reveal that locational and socio-economic factors determined farmland abandonment patterns. Specifically, a higher likelihood of farmland abandonment was statistically associated with an increased distance from the nearest settlements; the significantly positive relationship between ‘Non-agricultural GDP’ and farmland abandonment strengthened over time. In contrast, a lower likelihood of farmland abandonment was observed in areas with more cash crops. The statistical results may extend the application of the Ricardian comparative advantage theory along with Alonso's bid rent theory to explain and predict abandonment patterns in response to ongoing urbanization. Our study is the first attempt in China to apply 30-m Landsat imageries to reconstruct abandonment patterns over long time-series. The findings provide important insights into adjusting land-management practices for preventing farmland abandonment due to urbanization.

KW - Farmland abandonment

KW - Google Earth Engine

KW - Landsat imagery

KW - Logistic regression

KW - Recultivation

KW - Urbanization

U2 - 10.1016/j.landurbplan.2021.104170

DO - 10.1016/j.landurbplan.2021.104170

M3 - Journal article

AN - SCOPUS:85110415401

VL - 214

JO - Landscape and Urban Planning

JF - Landscape and Urban Planning

SN - 0169-2046

M1 - 104170

ER -