Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1185927
Full metadata record
DC FieldValueLanguage
dc.contributor.authorNORONHA, M. V. O.
dc.contributor.authorSILVA, J. P.
dc.contributor.authorPLACHI, M. A.
dc.contributor.authorSANTANA, D. M. de
dc.contributor.authorRODRIGUES, M. A. T.
dc.contributor.authorFAGUNDES, P. R. S.
dc.contributor.authorROMANI, L. A. S.
dc.contributor.authorMASSRUHÁ, S. M. F. S.
dc.contributor.authorDOURADO NETO, D.
dc.date.accessioned2026-03-30T19:52:14Z-
dc.date.available2026-03-30T19:52:14Z-
dc.date.created2026-03-30
dc.date.issued2025
dc.identifier.citationIn: WORKSHOP CIENTÍFICO DO CENTRO DE CIÊNCIA PARA O DESENVOLVIMENTO EM AGRICULTURA DIGITAL – SEMEAR DIGITAL, 2., 2025, Campinas. Anais [...]. Piracicaba: ESALQ/USP, 2025. p. 306-312.
dc.identifier.isbn978-85-86481-94-9
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1185927-
dc.descriptionThis study aimed to validate the agro-meteorological model developed by Santos and Camargo (2006) for predicting Arabica coffee yield, using 14 years of data (2011–2024) from Agrotechnological District (DAT) Caconde and the Vulcanic Region of Poços de Caldas. The model, based on climatic variables and phenological sensitivity coefficients (Ky), was tested under 16 scenarios combining genotypes, productivity ranges, and two spatial levels: plot and regional. Results indicated better performance at the regional scale, with lower errors (MAE, RMSE) and high R² values (> 0.9), especially in areas with homogeneous productivity. In contrast, plot-level scenarios, such as those for the Bourbon cultivar, showed high variability and lower predictive accuracy. Water deficit was identified as the main factor associated with yield losses. The findings highlight the model’s potential for strategic planning at broader spatial scales and the need for local calibration to improve accuracy in heterogeneous environments.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectProdutividade agricola
dc.subjectModelos agrometeorológicos
dc.subjectVariabilidade climática
dc.subjectDistrito agrotecnológico de Caconde
dc.subjectProjeto Semear Digital
dc.subjectAgricultural productivity
dc.subjectAgro-meteorological models
dc.subjectClimate variability
dc.titleAssessing coffee yield: predictive modeling based on phenological sensitivity to climate variability.
dc.typeArtigo em anais e proceedings
dc.subject.thesagroProdutividade
dc.subject.nalthesaurusModels
dc.description.notesOrganização: Silvia Maria Fonseca Silveira Massruhá, Durval Dourado Neto, Luciana Alvim Santos Romani, Jayme Garcia Arnal Barbedo, Édson Luis Bolfe, Ivan Bergier, Maria Angelica de Andrade Leite, Vitor Del Alamo Guarda, Catarina Barbosa Careta.
riaa.ainfo.id1185927
riaa.ainfo.lastupdate2026-03-30
dc.contributor.institutionMARCUS VINICIUS OLIVEIRA NORONHA, UNIVERSIDADE DE SÃO PAULO; JOÃO PAULO SILVA, UNIVERSIDADE ESTADUAL DE CAMPINAS; MARCOS AUGUSTO PLACHI, FAZENDA BELA VISTA DA FUMAÇA; DOUGLAS MARTINS DE SANTANA, UNIVERSIDADE DE SÃO PAULO; MARCO ANTONIO TAVARES RODRIGUES, UNIVERSIDADE DE SÃO PAULO; PRISCILLA ROCHA SILVA FAGUNDES, UNIVERSIDADE DE SÃO PAULO; LUCIANA ALVIM SANTOS ROMANI, CNPTIA; SILVIA MARIA FONSECA SILVEIRA MASSRUHÁ, PR; DURVAL DOURADO NETO, UNIVERSIDADE DE SÃO PAULO.
Appears in Collections:Artigo em anais de congresso (CNPTIA)

Files in This Item:
File Description SizeFormat 
AA-Assessing-Coffee-Workshop-2025.pdf488,88 kBAdobe PDFThumbnail
View/Open
Show simple item record

FacebookTwitterDeliciousLinkedInGoogle BookmarksMySpace