Examining pathways linking rural labour outflows to the abandonment of arable land in China

Chen, Lili; Meadows, Michael E.; Liu, Ye; Lin, Yanliu

doi:10.1002/psp.2519

Cited by 9 publications

(3 citation statements)

References 57 publications

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“…With the rapid development of the non-agricultural economy, the opportunity cost of farming is increasing continuously, and farmers are obviously apt to abandon their farmland (Luo et al, 2019). Abandoned farmland is not only related to local land use and sustainable development policies but may also cause food insecurity (Chen and Zheng, 2018). pointed out that the rate of farmland abandonment in karst regions is higher than the national average level.…”

Section: Continuously Decreasing Cultivated Land and Abandoned Farmlandmentioning

confidence: 99%

Rocky desertification poverty in Southwest China: Progress, challenges and enlightenment to rural revitalization

Zuo

Zhang

et al. 2022

J. Geogr. Sci.

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Rocky desertification poverty (RDP) refers to rural poverty triggered or exacerbated by rocky desertification, which deprives farmers of opportunities for improving their financial conditions through agricultural production. As China implements strategies including building a moderately prosperous society, targeted poverty alleviation, and rural revitalization, issues concerning RDP have become the work foci for the Chinese government and research hotspots for Chinese academic circles. This paper provides a thorough review of the history, progress, challenges and prospects based on decades of RDP studies in Southwest China. First, we review the origin, definitions and historical development trends of RDP. Over nearly four decades, research on RDP can be framed as four stages: start-up, poverty alleviation and ecological reconstruction, rocky desertification comprehensive control and green development. We find that RDP research progress appears to be directly related to the Chinese government's policy support and decision-making in mitigating RDP. Second, previous findings are reviewed and summarised regarding four research aspects: relationship exploration between rocky desertification and rural poverty, mechanisms of rocky desertification for exacerbating poverty, strategies for eliminating RDP and anti-poverty benefit evaluations of rocky desertification control efforts. Third, the remaining challenges are identified and summarised, including the challenges of conducting theoretical research on RDP and consolidating achievements in eliminating RDP. We find that while many achievements have been made in the study of RDP in recent decades, the completeness and systematism of the theoretical system remain weak, and the guidance for empirical research is still insufficient. Finally, this research is fundamental in achieving rural revitalization in rocky desertification ar-

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Section: Continuously Decreasing Cultivated Land and Abandoned Farmlandmentioning

confidence: 99%

Rocky desertification poverty in Southwest China: Progress, challenges and enlightenment to rural revitalization

Zuo

Zhang

et al. 2022

J. Geogr. Sci.

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“…Since the industrial age, urbanization has been driving people out of the mountains, concentrating farming on the plains (Chen et al, 2021;Tan et al, 2021). Abandonment and withdrawal of cultivated land in mountainous areas is an irreversible global phenomenon (Estel et al, 2015;Song and Zhang, 2019).…”

Section: Introductionmentioning

confidence: 99%

Mechanism, risk, and solution of cultivated land reversion to mountains and abandonment in China

Xie

Fan

et al. 2023

Front. Environ. Sci.

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The cultivated land requisition-compensation balance (CLRB) system in China has been designed to defend cultivated land resources and grain production functions. Nevertheless, since the addition of a new policy, namely the linkage of increase and decrease (LID) of urban and rural construction land in 2008, a mass of cultivated land has been returning to mountains, sometimes resulting in abandonment. The county of Wannian was investigated from the microcosmic perspective, and we attempted to analyze the causes and risks employing the boosted regression trees (BRT) model and the grain productive capacity assessment model. The results indicate that (1) The compensatory cultivated land (CCL) has shifted uphill, with considerable fragmentation, from 2010 to 2020, and the abandonment rate reached 14.77%. (2) The factors of site condition, including elevation, patch area, and continuity of cultivated land, as well as a series of combinations, can explain the causes of abandonment. (3) The abandonment of these cultivated land areas eventually resulted in the risk of 297.48 t grain production capacity loss. The reason for the return of cultivated land to the mountains and its subsequent abandonment is the lack of consideration for the coupling relationship among site condition, use state, and function requirement, resulting in a spatial mismatch. Based on the findings, we propose a solution of the natural resources requisition-compensation balance (NRRB). To make up for the loss and reduce the risk, a spatial replacement was taken between the abandoned CCL in uphill and cultivable and available forest land (CAFL) in submontane areas CCL, optimizing the spatial pattern of land use toward Von Thunen’s agricultural circle.

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“…Until now, the extraction and recognition of abandoned land involved field surveys and questionnaires (Ge et al, 2012), visual interpretation (Gellrich et al, 2007), and normalized difference vegetation index (NDVI) time series (Cheng, 2011; Zhu, Xiao, et al, 2021), which are largely subjective, time‐consuming and laborious, and not conducive to long‐term monitoring (Chen & Zheng, 2018). In addition, feature extraction based on NDVI time series data depends on the temporal and spatial resolution of remote sensing data when applied on a large or national scale (Shi, 2020).…”

Section: Introductionmentioning

confidence: 99%

Abandoned terrace recognition based on deep learning and change detection on the Loess Plateau in China

et al. 2023

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Terraces are an important cultivated land resource. Terrace abandonment affects the soil quality, soil and water conservation benefits, and biodiversity of terraces. Therefore, it is important to quantify the number and spatial distribution of abandoned terraces to protect cultivated land and food security. However, the traditional remote sensing method cannot identify small plots and make accurate assessment of abandoned farmland quickly in mountainous areas. To accurately identifying abandoned terraces, this study used semantic segmentation based on deep learning and change detection to identify abandoned terraces and their spatial distribution in a small watershed on the Loess Plateau in 2021. A comparative analysis of the accuracy of three deep learning models revealed that RefineNet is superior to DeepLabv3+ and DeepLabv3 for identifying abandoned terraces. The user's accuracy, producer's accuracy, overall accuracy, and appa values for RefineNet were 0.817, 0.894, 0.800, and 0.539, respectively. For change detection, the corresponding values were 0.821, 0.753, 0.731, and 0.426, respectively. In addition, semantic segmentation produced better recognition results than change detection in complex terrain and geomorphological areas. The abandoned terraces in the study area were mainly distributed in mountainous areas far from residential areas and more likely at high elevations with large slopes. This study provides a new method for recognizing abandoned terraces and spatial distribution information for managing and utilizing abandoned terraces.

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Examining pathways linking rural labour outflows to the abandonment of arable land in China

Cited by 9 publications

References 57 publications

Rocky desertification poverty in Southwest China: Progress, challenges and enlightenment to rural revitalization

Rocky desertification poverty in Southwest China: Progress, challenges and enlightenment to rural revitalization

Mechanism, risk, and solution of cultivated land reversion to mountains and abandonment in China

Abandoned terrace recognition based on deep learning and change detection on the Loess Plateau in China

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