Data/hora: 21/09/2024 06:01:39

Biblioteca(s): 	Embrapa Arroz e Feijão.	
Data corrente: 	19/03/2024	
Data da última atualização: 	19/03/2024	
Autoria: 	ARAÚJO, M. S.; CHAVES, S. F. S.; DIAS, L. A. S.; FERREIRA, F. M.; PEREIRA, G. R.; BEZERRA, A. R. G.; ALVES, R. S.; HEINEMANN, A. B.; BRESEGHELLO, F.; CARNEIRO, P. C. S.; KRAUSE, M. D.; COSTA-NETO, G.; DIAS, K. O. G.	
Afiliação: 	MAURICIO S. ARAUJO, UFV; SAULO F. S. CHAVES, UFV; LUIZ A. S. DIAS, UFV; FILIPE M. FERREIRA, UNESP, Botucatu-SP; GUILHERME R. PEREIRA, UFV; ANDRE R. G. BEZERRA, LIMAGRAIN BRAZIL, Jataí-GO; RODRIGO S. ALVES, UFV; ALEXANDRE BRYAN HEINEMANN, CNPAF; FLAVIO BRESEGHELLO, CNPAF; PEDRO C. S. CARNEIRO, UFV; MATHEUS D. KRAUSE, IOWA STATE UNIVERSITY; GERMANO COSTA-NETO, CORNELL UNIVERSITY; KAIO O. G. DIAS, UFV.	
Título: 	GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting.	
Ano de publicação: 	2024	
Fonte/Imprenta: 	Theoretical and Applied Genetics, v. 137, 80, Mar. 2024.	
ISSN: 	0040-5752	
DOI: 	https://doi.org/10.1007/s00122-024-04579-z	
Idioma: 	Inglês	
Conteúdo: 	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.	
Thesagro: 	Sistema de Informação Geográfica.	
Thesaurus Nal: 	Cultivars; Environmental indicators.	
Categoria do assunto: 	X Pesquisa, Tecnologia e Engenharia	
Registro original: 	Embrapa Arroz e Feijão (CNPAF)	
Exemplares: 	CNPAF - (36945-1) - UPC - AP - DD - 2024 -  - 2024 - DIS