GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorFederal University of Viçosa-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorLimagrain Brazil S.A.-
Autor(es): dc.contributorCornell University-
Autor(es): dc.contributorEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA)-
Autor(es): dc.contributorIowa State University-
Autor(es): dc.creatorAraújo, Maurício S.-
Autor(es): dc.creatorChaves, Saulo F. S.-
Autor(es): dc.creatorDias, Luiz A. S.-
Autor(es): dc.creatorFerreira, Filipe M.-
Autor(es): dc.creatorPereira, Guilherme R.-
Autor(es): dc.creatorBezerra, André R. G.-
Autor(es): dc.creatorAlves, Rodrigo S.-
Autor(es): dc.creatorHeinemann, Alexandre B.-
Autor(es): dc.creatorBreseghello, Flávio-
Autor(es): dc.creatorCarneiro, Pedro C. S.-
Autor(es): dc.creatorKrause, Matheus D.-
Autor(es): dc.creatorCosta-Neto, Germano-
Autor(es): dc.creatorDias, Kaio O. G.-
Data de aceite: dc.date.accessioned2025-08-21T19:48:14Z-
Data de disponibilização: dc.date.available2025-08-21T19:48:14Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-04-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s00122-024-04579-z-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/307763-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/307763-
Descrição: dc.descriptionKey message: We propose an “enviromics” prediction model for recommending cultivars based on thematic maps aimed at decision-makers. Abstract: Parsimonious methods that capture genotype-by-environment interaction (GEI) in multi-environment trials (MET) are important in breeding programs. Understanding the causes and factors of GEI allows the utilization of genotype adaptations in the target population of environments through environmental features and factor-analytic (FA) models. Here, we present a novel predictive breeding approach called GIS-FA, which integrates geographic information systems (GIS) techniques, FA models, partial least squares (PLS) regression, and enviromics to predict phenotypic performance in untested environments. The GIS-FA approach enables: (i) the prediction of the phenotypic performance of tested genotypes in untested environments, (ii) the selection of the best-ranking genotypes based on their overall performance and stability using the FA selection tools, and (iii) the creation of thematic maps showing overall or pairwise performance and stability for decision-making. We exemplify the usage of the GIS-FA approach using two datasets of rice [Oryza sativa (L.)] and soybean [Glycine max (L.) Merr.] in MET spread over tropical areas. In summary, our novel predictive method allows the identification of new breeding scenarios by pinpointing groups of environments where genotypes demonstrate superior predicted performance. It also facilitates and optimizes cultivar recommendations by utilizing thematic maps.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)-
Descrição: dc.descriptionDepartment of Agronomy Federal University of Viçosa, Minas Gerais-
Descrição: dc.descriptionDepartment of Crop Science - College of Agricultural Sciences São Paulo State University, São Paulo-
Descrição: dc.descriptionLimagrain Brazil S.A., Goiás-
Descrição: dc.descriptionDepartment of General Biology Federal University of Viçosa, Minas Gerais-
Descrição: dc.descriptionInstitute for Genomics Diversity Cornell University-
Descrição: dc.descriptionBrazilian Agricultural Research Corporation (Embrapa Rice and Beans), Goiás-
Descrição: dc.descriptionDepartment of Agronomy Iowa State University-
Descrição: dc.descriptionDepartment of Crop Science - College of Agricultural Sciences São Paulo State University, São Paulo-
Idioma: dc.languageen-
Relação: dc.relationTheoretical and Applied Genetics-
???dc.source???: dc.sourceScopus-
Título: dc.titleGIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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