農業における炭素排出量予測の第一歩となる新しい研究(New study is first step in predicting carbon emissions in agriculture)

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2024-02-21 ミネソタ大学

ミネソタ大学ツインシティー校(UMN)とイリノイ大学アーバナ・シャンペーン校(UIUC)の研究者らが初めて、農業生態系の炭素循環に関する正確で高解像度の予測を提供できることを実証しました。これは、気候変動の影響を緩和するのに役立つ可能性があります。この研究は、Nature Communications誌に掲載されました。この研究結果は、農業排出量の信頼性のある測定、監視、報告、検証(MMRV)の開発の重要な第一歩であり、これにより気候スマートな実践の実施が奨励され、地域経済が促進されます。

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知識誘導型機械学習により、農業生態系における炭素循環定量化を改善できる Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems

Licheng Liu,Wang Zhou,Kaiyu Guan,Bin Peng,Shaoming Xu,Jinyun Tang,Qing Zhu,Jessica Till,Xiaowei Jia,Chongya Jiang,Sheng Wang,Ziqi Qin,Hui Kong,Robert Grant,Symon Mezbahuddin,Vipin Kumar & Zhenong Jin
Nature Communications  Published:08 January 2024
DOI:https://doi.org/10.1038/s41467-023-43860-5

農業における炭素排出量予測の第一歩となる新しい研究(New study is first step in predicting carbon emissions in agriculture)

Abstract

Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the above challenges by integrating knowledge embedded in a process-based model, high-resolution remote sensing observations, and machine learning (ML) techniques. Using the U.S. Corn Belt as a testbed, we demonstrate that KGML can outperform conventional process-based and black-box ML models in quantifying carbon cycle dynamics. Our high-resolution approach quantitatively reveals 86% more spatial detail of soil organic carbon changes than conventional coarse-resolution approaches. Moreover, we outline a protocol for improving KGML via various paths, which can be generalized to develop hybrid models to better predict complex earth system dynamics.

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