Hybrid Process and Machine Learning Framework for Greenhouse Gas Mitigation in Legume Cropping Systems
マメ科作物栽培システムにおける温室効果ガス削減のためのハイブリッドプロセス・機械学習フレームワーク (AI 翻訳)
Arnima Pathak, Monika Sharma, Devendra Kumar
🤖 gxceed AI 要約
日本語
農業由来の温室効果ガス排出削減に向け、マメ科作物システムにおける亜酸化窒素排出を予測・分類するハイブリッド機械学習フレームワークを提案。22年間の圃場データを用い、アンサンブルモデルで高い予測精度（R²=0.91）を達成。マメ科ベースのシステムで排出量が約45%削減されることを示し、気候スマート農業への戦略的アプローチを提供する。
English
This paper proposes a hybrid process and machine learning framework to predict and classify nitrous oxide emissions in legume-based cropping systems. Using 22 years of field data, the ensemble model achieved high predictive accuracy (R²=0.91) and showed that legume-based systems reduce emissions by nearly 45%, supporting climate-smart agriculture.
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、農業分野のGHG排出削減が政策課題となっており、みどりの食料システム戦略とも関連。本フレームワークは、日本の農業現場での排出モニタリングや持続可能な農業実践の評価に応用可能で、データ駆動型の農業環境政策の基盤となり得る。
In the global GX context
Globally, this work contributes to climate-smart agriculture by providing a robust ML-based tool for predicting agricultural GHG emissions. It aligns with international efforts to quantify and reduce emissions from agriculture, supporting sustainability reporting and climate mitigation strategies.
👥 読者別の含意
🔬研究者:Provides a validated ML framework for predicting N2O emissions in agricultural systems, useful for further research in climate-smart agriculture.
🏢実務担当者:Offers a practical tool for farmers and agribusinesses to monitor and reduce GHG emissions, potentially supporting sustainability certifications and supply chain requirements.
🏛政策担当者:Demonstrates the effectiveness of legume-based systems in reducing emissions, informing agricultural climate policies and incentive programs.
📄 Abstract（原文）
Agricultural soils provide around 25% of worldwide anthropogenic greenhouse gas emissions, with synthetic nitrogen fertilisers playing a substantial role in nitrous oxide emissions via nitrification and denitrification processes. Legume-based farming systems provide a sustainable solution by organically fixing atmospheric nitrogen and decreasing reliance on fertilisers. Nonetheless, their efficacy in reducing emissions across various management approaches remains inadequately investigated. This paper presents a Hybrid Process and Machine Learning Framework designed to anticipate nitrous oxide emissions and categorise emission severity levels in legume-based agricultural systems. Data from twenty-two years of field observations at the Kellogg Biological Station Long-Term Ecological Research Main Cropping System Experiment were examined. The dataset comprised 3,696 observations across four management treatments: conventional, no-till, reduced-input with legume cover crops, and biologically based organic systems within a corn–soybean–winter wheat rotation. Five machine learning algorithms were developed, and the three most effective models were integrated into an ensemble framework. The ensemble model demonstrated robust predictive ability, evidenced by an R-squared value of 0.91 and a root mean square error of 0.51 g N ha⁻¹ day⁻¹. The accuracy and F1-score of the framework were 0.83 for the classification of emissions to low, medium and high categories. Emissions reduced by nearly 45% below the traditional systems in legume-based systems on average. The proposed framework provides a strategic and effective approach to support climate-smart agriculture and reduce GHGs.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.70917/ijcisim-2026-4752first seen 2026-08-18 05:24:52
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