ORIGINAL RESEARCH article
Front. Environ. Sci.
Sec. Land Use Dynamics
Volume 13 - 2025 | doi: 10.3389/fenvs.2025.1517085
Marginal Land Identification and Grain Production Capacity Prediction of the Coverage Area of Western Route of China's South-to-North Water Diversion Project
Provisionally accepted- 1PowerChina Northwest Engineering Corporation Limited,, Xi'an, Shanxi, China
- 2China Agricultural University, Beijing, China
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The western route of the South-to-North Water Diversion Project (SNWDP) provides opportunities to improve agricultural production by altering regional water availability. This study identifies and evaluates marginal land-defined as undeveloped reserve cultivated land and low-quality and inefficiently-utilized farmland-within provinces along the SNWDP route. Using ecological, topographic, climatic, and soil indicators, we identified 145,062 km² of marginal land, including 3,626 km² of reserve cultivated land and 141,436 km² of low-quality and inefficiently-utilized farmland, mainly concentrated in northwestern Xinjiang, with Qinghai having the least. To assess the grain production potential of these lands, we used maize and wheat as representative crops. Three modeling approaches-random forest regression, gradient boosted regression trees, and two-point machine learning (TPML)-were compared for their predictive accuracy. The TPML model showed the best performance. For maize, the model yielded a root mean square error (RMSE) of 48.94, a mean absolute error (MAE) of 34.01, and a mean absolute percentage error (MAPE) of 7.65%. For wheat, the RMSE was 23.92, MAE 17.67, and MAPE 6.31%. Results reveal that maize has a higher production capacity than wheat, and that grain yields are higher in the west and lower in the east, with Xinjiang showing the highest average yields on marginal land. These findings provide a scientific basis for optimizing land use, improving food self-sufficiency, and supporting regional sustainable development and national food security.
Keywords: marginal land1, grain production capacity2, corn3, wheat4, machine learning 5. the South-to-North Water Diversion Project 6
Received: 25 Oct 2024; Accepted: 02 Jun 2025.
Copyright: © 2025 Zhou, Zhou, Lu, Niu, Wang, Zhang and Kou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jun Zhou, China Agricultural University, Beijing, China
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