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

Ermolieva, T., Havlik, P. ORCID: https://orcid.org/0000-0001-5551-5085, Derci Augustynczik, A.L., Frank, S. ORCID: https://orcid.org/0000-0001-5702-8547, Balkovič, J. ORCID: https://orcid.org/0000-0003-2955-4931, Skalský, R. ORCID: https://orcid.org/0000-0002-0983-6897, Deppermann, A., Nakhavali, A., Komendantova, N. ORCID: https://orcid.org/0000-0003-2568-6179, Kahil, T. ORCID: https://orcid.org/0000-0002-7812-5271, Wang, G., Folberth, C. ORCID: https://orcid.org/0000-0002-6738-5238, & Knopov, P.S. (2024). Tracking the Dynamics and Uncertainties of Soil Organic Carbon in Agricultural Soils Based on a Novel Robust Meta-Model Framework Using Multisource Data. Sustainability 16 (16) e6849. 10.3390/su16166849.

[thumbnail of sustainability-16-06849.pdf]
Preview
Text
sustainability-16-06849.pdf - Published Version
Available under License Creative Commons Attribution.

Download (79MB) | Preview
Project: Exploring National and Global Actions to reduce Greenhouse gas Emissions (ENGAGE, H2020 821471), CO-designing the Assessment of Climate CHange costs (COACCH, H2020 776479), Accelerating collection and use of soil health information using AI technology to support the Soil Deal for Europe and EU Soil Observatory (AI4SoilHealth, HE 101086179)

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.

Item Type: Article
Uncontrolled Keywords: food–water–energy–environmental NEXUS; soil health; climate variability; SOC dynamics; uncertainty ranges; robust estimation; machine learning; quantile regression
Research Programs: Advancing Systems Analysis (ASA)
Advancing Systems Analysis (ASA) > Cooperation and Transformative Governance (CAT)
Biodiversity and Natural Resources (BNR)
Biodiversity and Natural Resources (BNR) > Agriculture, Forestry, and Ecosystem Services (AFE)
Biodiversity and Natural Resources (BNR) > Integrated Biosphere Futures (IBF)
Biodiversity and Natural Resources (BNR) > Water Security (WAT)
Depositing User: Luke Kirwan
Date Deposited: 28 Aug 2024 10:44
Last Modified: 28 Aug 2024 10:45
URI: https://pure.iiasa.ac.at/19960

Actions (login required)

View Item View Item