再生農業・気候レジリエント食料システム・持続可能な栄養を統合する説明可能デジタルツインフレームワーク

An Explainable Digital Twin Framework for Integrating Regenerative Agriculture, Climate-Resilient Food Systems, and Sustainable Nutrition (原題)

Wida SİMZARİ, Ali Güneş, Farshad Ganji, Hamed Kioumarsi, Şerafettin Sevgili

Sustainability📚 査読済 / ジャーナル2026-09-20#気候科学Origin: Global対象セクター: agriculture

DOI: 10.3390/su18189645

原典: https://doi.org/10.3390/su18189645

🤖 gxceed AI 要約

日本語

本論文は、再生農業と気候レジリエントな食料システムを統合する説明可能なデジタルツイン（SEDT）と進化的最適化（SEEFO）を提案する。食料・水・エネルギー・炭素・栄養（FWEC-N）ネクサスを明示的に扱い、作物の微量栄養素密度を意思決定に組み込む。約60カ国・15気候帯の2000〜2026年データで検証し、高精度（R²=0.972）と最適化性能を確認した。XAIによる特徴量寄与で透明な意思決定支援を実現する。

English

This paper proposes an explainable digital twin (SEDT) and evolutionary optimizer (SEEFO) integrating regenerative agriculture, climate-resilient food systems, and sustainable nutrition. It operationalizes the food–water–energy–carbon–nutrition nexus, embedding crop micronutrient density into decision-making. Validated on ~60 countries across 15 climate zones (2000–2026), it achieved high accuracy (R²=0.972) and strong optimization performance, with XAI feature attributions ensuring transparency.

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

日本では食料安全保障・農業の脱炭素化がGX政策の重要課題であり、本枠組みはスマート農業や環境負荷低減の意思決定支援に応用可能。ただしSSBJ・有報開示やScope算定との直接接続は薄く、企業開示実務への示唆は限定的。

In the global GX context

Globally, agriculture accounts for roughly a quarter of GHG emissions, and frameworks linking regenerative practices to carbon and nutrition outcomes inform emerging nature and land-sector disclosure (TNFD, SBTN, CSRD). This work's XAI-based transparency aligns with growing demands for verifiable, explainable sustainability metrics, though it does not directly address TCFD/ISSB reporting.

👥 読者別の含意

🔬研究者:AI×農業・気候レジリエンスの統合モデリングとXAI適用の方法論的参考になる。

🏢実務担当者:食品・農業企業が再生農業の炭素・栄養・水収支を評価する際の意思決定支援ツールとして参考。

🏛政策担当者:食料・水・エネルギー・炭素・栄養ネクサスを統合評価する政策設計の枠組みとして示唆。

📄 Abstract（原文）

Sustainable agricultural management faces growing challenges from climate change, water scarcity, energy constraints, soil degradation, and the need to ensure both food production and nutritional security. This study proposes an integrated computational framework combining a Global Agricultural Foundation Representation Model (GAFRM), Self-Evolving Explainable Digital Twin (SEDT), Self-Evolving Evolutionary Foundation Optimizer (SEEFO), and an enhanced Green Regenerative Agriculture Sustainability Index (GRASI). The framework operationalizes the food–water–energy–carbon–nutrition (FWEC-N) nexus by explicitly incorporating crop micronutrient density into the agricultural decision architecture and modeling its relationship with regenerative practices such as cover cropping, biochar application, and zero tillage. Using multi-source global datasets, GAFRM learns transferable agricultural representations, SEDT enables adaptive prediction under climate uncertainty, and SEEFO performs five-objective optimization of agricultural productivity, irrigation water use, energy demand, net carbon balance, and overall sustainability, while nutritional quality is evaluated through the MODI outcome indicator. The enhanced GRASI further evaluates nutrient output, soil restoration, carbon storage, and climate resilience within a unified sustainability framework. The framework was evaluated using a global agricultural dataset covering approximately 60 representative countries across six continents and 15 climate zones over the 2000–2026 period. SEDT achieved an RMSE of 3.18, MAE of 2.29, R2 of 0.972, and NSE of 0.968, while SEEFO achieved the highest Hypervolume (0.956) and the lowest GD (0.028), IGD (0.039), and Spread (0.162) among the benchmark optimization algorithms. The observed performance differences were statistically significant according to the Wilcoxon signed-rank and Friedman tests (p < 0.05). The findings indicate that integrating nutritional quality with resource efficiency, carbon balance, soil regeneration, and climate resilience provides a more comprehensive basis for evaluating regenerative agricultural strategies. The architecture establishes a fully transparent, explainable decision-support environment through explainable AI (XAI) feature attributions, bridging the gap between digital precision farming, regenerative ecosystem restoration, and sustainable human nutrition under increasing environmental uncertainty.

🔗 Provenance — このレコードを発見したソース

openalex https://doi.org/10.3390/su18189645first seen 2026-10-09 04:42:57

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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。