RT Journal Article
T1 Analysing the different interrelationships of soil organic carbon using machine learning approaches: Assessing the specific case of Portugal. The specific case of Portuguese land
T2 Utilização de abordagens machine learning para analisar as diferentes inter‑relações do carbono orgânico do solo: Avaliação do caso específico de Portugal. The specific case of Portuguese land
A1 Martinho, Vítor João Pereira Domingues
A1 Ramos, Tiago C. B.
A1 Castanheira, Nádia L.
A1 Cunha, Carlos
A1 Ferreira, António J. D.
A1 Pereira, José L. S.
A1 Sánchez Carreira, María del Carmen
K1 INFOSOLO Database
K1 Artificial Intelligence
K1 Cross‑Sectional Regressions
K1 Optimisation Approaches
AB Given the importance of soil organic carbon (SOC) for sustainability, policymakers and researchers are particularly concerned with identifying the conditions that promote carbon storage in the soil. These assessments provide relevant support for the design of policy instruments aimed at increasing soil quality and its carbon sequestration capacity. The new technologies associated with the digital transition can bring relevant added value, namely through artificial intelligence methodologies, where machine learning approaches are important. In this context, this research aims to analyse the several interrelationships of SOC in the specific Portuguese context, with a focus on highlighting its main predictors and providing proposals for stakeholders (including policymakers). To achieve these objectives, statistics from the INFOSOLO database were considered and evaluated using machine learning algorithms to select the most important SOC predictors and identify accurate models. These interrelationships were quantified with cross-sectional regressions and optimisation models. The results obtained provide relevant information for the design of adjusted policy measures that promote sustainable practices and increase soil quality.
PB Sociedade de Ciências Agrárias de Portugal (SCAP)
SN 2183-041X
YR 2025
FD 2025
LK https://hdl.handle.net/10347/43512
UL https://hdl.handle.net/10347/43512
LA eng
NO Martinho, V. J. P. D., Ramos, T. C. B., Castanheira, N. L., Cunha, C., Ferreira, A. J. D., da Silva Pereira, J. L., & Carreira, M. D. C. S. (2025). Analysing the different interrelationships of soil organic carbon using machine learning approaches: The specific case of Portuguese land. Revista de Ciências Agrárias, 48(1).
NO This work is funded by National Funds through the FCT ‑ Foundation for Science and Technology, I.P., within the scope of the project Refª UIDB/00681 (https://doi.org/10.54499/UIDP/00681/2020).   Furthermore we would like to thank the CERNAS Research Centre and the Polytechnic Institute of Viseu for their support. This work was developed under the Science4Policy 2023 (S4P‑23): annual science for policy project call, an initiative by PlanAPP  ‑ Competence Centre for Planning, Policy and Foresight in Public Administration in partnership with the Foundation for Science and Technology, financed by Portugal’s Recovery and Resilience Plan.
DS Minerva
RD 24 abr 2026