Tracking the Dynamics and Uncertainties of Soil Organic Carbon in Agricultural Soils Based on a Novel Robust Meta-Model Framework Using Multisource Data

Tatiana Ermolieva (), Peter Havlik, Andrey Lessa-Derci-Augustynczik, Stefan Frank, Juraj Balkovic, Rastislav Skalsky, Andre Deppermann, Mahdi (Andrè) Nakhavali, Nadejda Komendantova, Taher Kahil, Gang Wang, Christian Folberth and Pavel S. Knopov
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Tatiana Ermolieva: International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria
Andrey Lessa-Derci-Augustynczik: International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria
Stefan Frank: International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria
Juraj Balkovic: International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria
Rastislav Skalsky: International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria
Andre Deppermann: International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria
Mahdi (Andrè) Nakhavali: International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria
Nadejda Komendantova: International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria
Taher Kahil: International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria
Gang Wang: Department of Soil and Water Sciences, China Agricultural University, Beijing 100193, China
Christian Folberth: International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria
Pavel S. Knopov: Institute of Cybernetics, National Academy of Sciences of Ukraine, 03187 Kyiv, Ukraine

Sustainability, 2024, vol. 16, issue 16, 1-23

Abstract: Monitoring and estimating spatially resolved changes in soil organic carbon (SOC) stocks are necessary for supporting national and international policies aimed at assisting land degradation neutrality and climate change mitigation, improving soil fertility and food production, maintaining water quality, and enhancing renewable energy and ecosystem services. In this work, we report on the development and application of a data-driven, quantile regression machine learning model to estimate and predict annual SOC stocks at plow depth under the variability of climate. The model enables the analysis of SOC content levels and respective probabilities of their occurrence as a function of exogenous parameters such as monthly temperature and precipitation and endogenous, decision-dependent parameters, which can be altered by land use practices. The estimated quantiles and their trends indicate the uncertainty ranges and the respective likelihoods of plausible SOC content. The model can be used as a reduced-form scenario generator of stochastic SOC scenarios. It can be integrated as a submodel in Integrated Assessment models with detailed land use sectors such as GLOBIOM to analyze costs and find optimal land management practices to sequester SOC and fulfill food–water–energy–-environmental NEXUS security goals.

Keywords: food–water–energy–environmental NEXUS; soil health; climate variability; SOC dynamics; uncertainty ranges; robust estimation; machine learning; quantile regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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