世界のトウモロコシ収量に対する気候影響の地域・文脈依存性の解明
Disentangling the Regional and Contextual Dependencies of Climate Effects on Global Maize Yield (原題)
Yunmeng Zhao, Chiyuan Miao
🤖 gxceed AI 要約
日本語
本研究は、ランダムフォレストとSHAPを用いて、1982〜2016年の世界のトウモロコシ収量に対する気候変動の影響を分析。気候帯ごとに収量変動の23〜45%が気候変動で説明され、技術進歩の効果を相殺する傾向がある。収量増加と減少で主要な気候要因が異なり、極端な高温の影響が増大していることを示した。
English
This study uses random forests and SHAP to analyze the impact of climate variability on global maize yield from 1982-2016. Climate variability explains 23-45% of yield variability across climate zones, increasingly offsetting technological gains. Key drivers differ between yield-gain and loss scenarios, with extreme heat playing a growing role.
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
日本の農業分野における気候変動適応策の科学的根拠を提供する。ただし、直接的なGX政策や開示には関連しないが、気候リスク評価の手法として参考になる。
In the global GX context
This paper contributes to global climate risk assessment in agriculture, offering methods applicable to climate adaptation planning. It aligns with broader climate resilience discussions but is not directly tied to disclosure frameworks.
👥 読者別の含意
🔬研究者:気候変動が農業収量に与える影響を分析するためのML手法と知見を提供。
🏛政策担当者:農業分野の気候適応戦略を策定する際の科学的根拠として活用可能。
📄 Abstract（原文）
Abstract Maize is a cornerstone of global food security, yet the effects of interannual climate variability on its yield under varying production contexts remain not fully understood. Using Köppen climate zones, we coupled random forests with Shapley additive explanations to disentangle water‐heat drivers from three perspectives: yield state, cropping calendar, and their combined scenarios. Between 1982 and 2016, interannual climate variability explained 23%–45% of maize yield variability across zones and increasingly offset the yield benefits from technological advancements. From the yield state perspective, except in arid regions, the dominant climatic drivers differed between yield‐gain and yield‐loss scenarios. Notably, across all zones, key climatic drivers exhibited stronger strength in enhancing yield loss than in enhancing yield gain. From the cropping calendar perspective, compared to within‐year, cross‐year cropping in arid and warm temperate zones shifted dominant drivers from heat to water indices and triggered interactions among key drivers, amplifying yield risk under unfavorable conditions. Moreover, the contribution of extreme temperature indicators generally increased across all combined scenarios. In warm temperate zones, interannual variations in heat stress degree days became the dominant driver in cross‐year yield‐gain and yield‐loss scenarios, contributing 27% and 25%, respectively. Furthermore, two effect patterns were identified: one where main effects and interaction effects jointly amplified yield variability, and another where main effects were suppressed while interaction effects still amplified. Our findings enhance the comprehensive understanding of the climate–yield relationship and provide a scientific basis for developing targeted adaptation strategies.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1029/2026wr043486first seen 2026-09-01 05:16:14
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