Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1186080
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dc.contributor.authorCOLMANETTI, M. A. A.
dc.contributor.authorOLDONI, H.
dc.contributor.authorANNIBAL, L.
dc.contributor.authorWOLFF, W.
dc.contributor.authorREJAILI, R. P. A.
dc.contributor.authorBARIONI, L. G.
dc.contributor.authorALMEIDA, I. R. de
dc.contributor.authorEWING, R. P.
dc.contributor.authorCUADRA, S. V.
dc.date.accessioned2026-04-06T16:48:43Z-
dc.date.available2026-04-06T16:48:43Z-
dc.date.created2026-04-06
dc.date.issued2026
dc.identifier.citationAgronomy Journal, v. 118, n. 2, e70309, Mar. 2026.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1186080-
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.language.isoeng
dc.rightsopenAccess
dc.subjectCalibração
dc.subjectSimulação de carbono
dc.subjectCrop model
dc.titleCalibration and evaluation of a carbon net primary productivity module based on the SIMPLE model.
dc.typeArtigo de periódico
dc.subject.thesagroBalanço Hídrico
dc.subject.thesagroSimulação
dc.subject.thesagroTrigo
dc.subject.thesagroFeijão
dc.subject.thesagroMilho
dc.subject.thesagroSoja
dc.subject.thesagroBiomassa
dc.subject.nalthesaurusWater balance
dc.subject.nalthesaurusCalibration
dc.subject.nalthesaurusComputer simulation
dc.subject.nalthesaurusSimulation models
riaa.ainfo.id1186080
riaa.ainfo.lastupdate2026-04-06
dc.identifier.doi10.1002/agj2.70309
dc.contributor.institutionMICHEL ANDERSON ALMEIDA COLMANETTI
dc.contributor.institutionHENRIQUE OLDONIeng
dc.contributor.institutionLEANDRO ANNIBALeng
dc.contributor.institutionWAGNER WOLFF, BAYEReng
dc.contributor.institutionRODRIGO PEREIRA ABOU REJAILI, BAYEReng
dc.contributor.institutionLUIS GUSTAVO BARIONI, CNPTIAeng
dc.contributor.institutionIVAN RODRIGUES DE ALMEIDA, CNPTIAeng
dc.contributor.institutionROBERT P. EWING, BAYEReng
dc.contributor.institutionSANTIAGO VIANNA CUADRA, CNPTIA.eng
Appears in Collections:Artigo em periódico indexado (CNPTIA)

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