Climate change and economic policy dominate soybean cultivation in china in recent decades of the 21st century
気候変動と経済政策が21世紀の中国大豆栽培を支配する (AI 翻訳)
Yulong Lv, Hongchi Zhang, Dailiang Peng, Xuxiang Feng, Pengyang Wang, Hao Peng, Zihang Lou, Xiaoyang Zhang, Jianxi Huang, Le Yu, Kaishan Song, Yaqiong Zhang, Qiaoyun Xie, Zhiyuan Pei, Li Chai, Xuecao Li, Jinkang Hu, Shijun Zheng, Enhui Cheng, Shengyi Liu +1
🤖 gxceed AI 要約
日本語
本研究は、衛星リモートセンシングデータを用いて2000～2022年の中国の大豆作付面積を解明し、ランダムフォレストとSHAP分析により、経済的要因、降水量、生育度日が主要な決定要因であることを明らかにした。地域により支配的要因が異なり、東北では経済的優位性、他地域では気候が重要である。補助金政策は効果的だが、限界効果は逓減する。
English
This study maps soybean planting areas in China at 1 km resolution from 2000-2022 using satellite data. Random forest and SHAP analyses show that comparative economic benefit, precipitation, and growing degree days are the main drivers, with spatial heterogeneity across regions. Subsidy policies are effective but with diminishing marginal returns.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
中国の事例ではあるが、気候変動適応に向けた土地利用政策の設計や、複合的なドライバーの影響を評価する手法は、日本の農業分野におけるGX（グリーントランスフォーメーション）や気候リスク管理にも示唆を与える。
In the global GX context
This paper provides a rigorous framework for disentangling the impacts of economic, climatic, and policy factors on agricultural land use, which is increasingly important for climate adaptation and food security discussions globally.
👥 読者別の含意
🔬研究者:Researchers can adopt the random forest + SHAP methodology and 1-km satellite data approach for studying land-use drivers in other regions.
🏛政策担当者:Policymakers can learn how targeted subsidies and climate factors jointly influence crop area, informing the design of adaptive agricultural policies.
📄 Abstract（原文）
Over the past two decades, despite significant fluctuations in China's soybean planting area, high-resolution assessments quantifying the spatially heterogeneous and nonlinear impacts of compounding economic, climatic, and policy drivers remain scarce. To fill this gap, this study uses satellite remote sensing data (2000–2022) to map 1 km gridded soybean areas across China’s three main producing regions: the Northeast China Plain (NEP), Huang-Huai and Middle-Lower Yangtze Plains (HH-MLYP), and Sichuan Basin (SCB). Integrating yield and cost‒price data, we calculated the comparative economic benefit (CEB) between soybean and maize. A random forest model with SHapley additive explanations (SHAP) quantified the contributions of CEB, climatic, and topographical variables. Results revealed that CEB, growing season precipitation, and growing degree days were the most influential drivers, explaining 14.88%, 14.47%, and 13.70% of area variation, respectively. These impacts exhibit strong spatial heterogeneity: economic factors dominate in the NEP, whereas climate is more critical in the HH-MLYP and SCB. Subsidy policies effectively expanded planting in the NEP, despite diminishing marginal effects. These findings provide a scientific basis for optimizing planting strategies and designing targeted subsidies.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.6084/m9.figshare.32131708.v1first seen 2026-05-17 05:01:32 · last seen 2026-05-30 05:00:44
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