Data/hora: 24/09/2024 08:16:10

Biblioteca(s): 	Embrapa Milho e Sorgo.	
Data corrente: 	21/03/2022	
Data da última atualização: 	12/07/2022	
Autoria: 	DIAS, K. O. G.; SANTOS, J. P. R. dos; KRAUSE, M. D.; PIEPHO, H.-P.; GUIMARAES, L. J. M.; PASTINA, M. M.; GARCIA, A. A. F.	
Afiliação: 	KAIO O. G. DIAS, Universidade Federal de Viçosa; JHONATHAN P. R. DOS SANTOS, Escola Superior de Agricultura Luiz de Queiroz; MATHEUS D. KRAUSE, Iowa State University; HANS-PETER PIEPHO, University of Hohenheim; LAURO JOSE MOREIRA GUIMARAES, CNPMS; MARIA MARTA PASTINA, CNPMS; ANTONIO A. F. GARCIA, Escola Superior de Agricultura Luiz de Queiroz.	
Título: 	Leveraging probability concepts for cultivar recommendation in multi-environment trials.	
Ano de publicação: 	2022	
Fonte/Imprenta: 	Theoretical and Applied Genetics, v. 135, n. 4, p. 1385-1399, 2022.	
DOI: 	https://doi.org/10.1007/s00122-022-04041-y	
Idioma: 	Inglês	
Conteúdo: 	Statistical models that capture the phenotypic plasticity of a genotype across environments are crucial in plant breeding programs to potentially identify parents, generate ofspring, and obtain highly productive genotypes for target environments. In this study, our aim is to leverage concepts of Bayesian models and probability methods of stability analysis to untangle genotype-by-environment interaction (GEI). The proposed method employs the posterior distribution obtained with the NoU-Turn sampler algorithm to get Hamiltonian Monte Carlo estimates of adaptation and stability probabilities. We applied the proposed models in two empirical tropical datasets. Our fndings provide a basis to enhance our ability to consider the uncertainty of cultivar recommendation for global or specifc adaptation. We further demonstrate that probability methods of stability analysis in a Bayesian framework are a powerful tool for unraveling GEI given a defned intensity of selection that results in a more informed decision-making process toward cultivar recommendation in multi-environment trials.	
Palavras-Chave: 	Modelo Bayesiano; Modelo misto; Previsão genômica; Regressão de parâmetro.	
Thesagro: 	Genótipo; Melhoramento Genético Vegetal; Variedade.	
Categoria do assunto: 	G Melhoramento Genético	
Registro original: 	Embrapa Milho e Sorgo (CNPMS)	
Exemplares: 	CNPMS - (29787-1) - UPC - AP - DD -  -  -  - DIS