Calibration and evaluation of a carbon net primary productivity module based on the SIMPLE model.

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dc.contributorMICHEL ANDERSON ALMEIDA COLMANETTI
dc.contributorHENRIQUE OLDONI
dc.contributorLEANDRO ANNIBAL
dc.contributorWAGNER WOLFF, BAYER
dc.contributorRODRIGO PEREIRA ABOU REJAILI, BAYER
dc.contributorLUIS GUSTAVO BARIONI, CNPTIA
dc.contributorIVAN RODRIGUES DE ALMEIDA, CNPTIA
dc.contributorROBERT P. EWING, BAYER
dc.contributorSANTIAGO VIANNA CUADRA, CNPTIA.
dc.creatorCOLMANETTI, M. A. A.
dc.creatorOLDONI, H.
dc.creatorANNIBAL, L.
dc.creatorWOLFF, W.
dc.creatorREJAILI, R. P. A.
dc.creatorBARIONI, L. G.
dc.creatorALMEIDA, I. R. de
dc.creatorEWING, R. P.
dc.creatorCUADRA, S. V.
dc.date2026-04-06T16:48:43Z
dc.date2026-04-06T16:48:43Z
dc.date2026-04-06
dc.date2026
dc.date.accessioned2026-07-07T04:18:44Z
dc.descriptionThis study presents the carbon net primary productivity (CNPP) module, an imple-mentation of the simple generic crop model (SIMPLE model) that we have extendedto predict aboveground and belowground biomass production and carbon assim-ilation. The goal for this module is to predict biomass inputs at and below thesoil surface, during and at the end of crop cycles, while integrated into a broaderenvironmental carbon simulation framework, ProCarbon-Soil. CNPP was initiallyparameterized for soybean [Glycine max (L.) Merr.] and maize (Zea mays L.) usingmicrometeorological data, and for wheat (Triticum aestivum L.), bean (Phaseolusvulgaris L.), and perennial forage [Urochloa (syn. Brachiaria) brizantha (Hochstex A. Rich.) Stapf cv. Marandu] using agrometeorological experimental data and/ordata from the literature. Subsequently, calibrations for reference cultivars were per-formed, grouping cultivars by crop phenological characteristics and edaphoclimaticregions using farm-level data. CNPP accurately simulated leaf area index, evapotran-spiration, and biomass dry matter production and allocation for soybean and maizewhen evaluated at the sites with micrometeorological data (R2 > 0.76, Nash–Sutcliffeefficiency > 0.56, and relative root mean square error < 38% for all variables).Simulations for wheat, bean, and perennial forage exhibited lower performanceowing to lower availability of yield data. Nonetheless, the resulting statistics supportthis module’s efficacy in predicting crop productivity in major Brazilian agricul-tural areas. By employing a reduced and efficient parameter set, the CNPP moduleachieves enhanced performance and enables robust calibration across diverse crops,genotypes, and management schemes in multiple regions.
dc.identifierAgronomy Journal, v. 118, n. 2, e70309, Mar. 2026.
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1186080
dc.identifier10.1002/agj2.70309
dc.identifier.urihttp://hdl.handle.net/123456789/457452
dc.languageeng
dc.rightsopenAccess
dc.subjectCalibração
dc.subjectSimulação de carbono
dc.subjectCrop model
dc.subjectBalanço Hídrico
dc.subjectSimulação
dc.subjectTrigo
dc.subjectFeijão
dc.subjectMilho
dc.subjectSoja
dc.subjectBiomassa
dc.subjectWater balance
dc.subjectCalibration
dc.subjectComputer simulation
dc.subjectSimulation models
dc.titleCalibration and evaluation of a carbon net primary productivity module based on the SIMPLE model.
dc.typeArtigo de periódico

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