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dc.contributor.authorBARIONI, L. G.
dc.contributor.authorVALLADÃO, B. A.
dc.contributor.authorMOURÃO, V. H. M.
dc.contributor.authorEWING, R. P.
dc.contributor.authorKARATAY, Y. N.
dc.contributor.authorDAMIAN, J. M.
dc.contributor.authorMELÍCIO, V. C.
dc.contributor.authorREJAILI, R. P. A.
dc.contributor.authorSILVA, R. O.
dc.date.accessioned2026-06-22T12:48:51Z-
dc.date.available2026-06-22T12:48:51Z-
dc.date.created2026-06-22
dc.date.issued2026
dc.identifier.citationSoil Science Society of America Journal, v. 90, n. 3, e70218, 2026.
dc.identifier.issn1435-0661
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1187720-
dc.descriptionCarbon farming is a nature-based solution to capture atmospheric CO2 and store it as soil organic carbon (SOC). Carbon farming trading schemes (CFTS) incentivize farmers to adopt these practices. Integral to CFTS is forecasting SOC changes, typically achieved using traditional multicompartmental soil carbon models (mSCM), and monitoring total SOC stocks. However, traditional mSCM simulate unmeasurable compartments, leading to overparameterization and indeterminable partitioning among carbon compartments, suggesting a need for structural improvements. The ProCarbon-Soil (PROCS) model addresses this need by abstracting fundamental principles of mSCM, reducing SOC state variables to two (total carbon and decomposability), and employing only one stabilization parameter, compared to the four to eight state variables and 7–20 parameters typically required by mSCM. We mathematically derive methods that can use successive carbon measurements to estimate decomposability and initialize the model. PROCS can handle environmental modifiers and events such as crop rotations, tillage, and manuring events, and respond to soil characteristics and weather conditions. Tests show that PROCS can accurately reproduce synthetic SOC trajectories generated by an mSCM with perturbed parameters using short-term data (12 years) with acceptable accuracy (median root mean square error <1.03 Mg ha−1 and absolute median of mean bias <0.55 Mg ha−1). In a cross-validation test, the mean normalized root mean square error (NRMSE) closely aligns with the coefficient of variation of white noise introduced in the synthetic data (4.15% vs. 4.00%, respectively) for augmented carbon inflow scenarios, whereas the model exhibits higher errors for the no-carbon-inflow scenario (NRMSE = 5.48%, 7.25%, and 8.99% for 12, 24, and 50 years, respectively).
dc.language.isoeng
dc.rightsopenAccess
dc.subjectAgricultura de carbono
dc.subjectSistema de carbono do solo
dc.subjectCarbono orgânico do solo
dc.subjectModelos de carbono do solo
dc.subjectCarbon farming
dc.subjectSoil carbon system
dc.subjectSoil carbon models
dc.titleProCarbon‐Soil: a dynamic model for improved model‐data compatibility in carbon farming.
dc.typeArtigo de periódico
dc.subject.nalthesaurusSoil organic carbon
riaa.ainfo.id1187720
riaa.ainfo.lastupdate2026-06-22
dc.identifier.doihttps://doi.org/10.1002/saj2.70218
dc.contributor.institutionLUIS GUSTAVO BARIONI, CNPTIA; BEATRIZ ARIA VALLADÃO; VITOR H. M. MOURÃO; ROBERT P. EWING, CLIMATE LLC; YUSUF NADI KARATAY; JÚNIOR MELO DAMIAN; VINÍCIUS DO CARMO MELÍCIO; RODRIGO P. A. REJAILI, BAYER; RAFAEL DE OLIVEIRA SILVA, THE UNIVERSITY OF EDINBURGH.
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