Consulta: creatorFacets:"Rosado,Renato Domiciano Silva"
Registros recuperados: 2
Data/hora: 12/06/2024 00:43:06

Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee
Provedor de dados: 86
Autores: Silva,Gabi Nunes; Nascimento,Moysés; Sant’Anna,Isabela de Castro; Cruz,Cosme Damião; Caixeta,Eveline Teixeira; Carneiro,Pedro Crescêncio Souza; Rosado,Renato Domiciano Silva; Pestana,Kátia Nogueira; Almeida,Dênia Pires de; Oliveira,Marciane da Silva.
Abstract: The objective of this work was to evaluate the use of artificial neural networks in comparison with Bayesian generalized linear regression to predict leaf rust resistance in Arabica coffee (Coffea arabica). This study used 245 individuals of a F2 population derived from the self-fertilization of the F1 H511-1 hybrid, resulting from a crossing between the susceptible cultivar Catuaí Amarelo IAC 64 (UFV 2148-57) and the resistant parent Híbrido de Timor (UFV 443-03). The 245 individuals were genotyped with 137 markers. Artificial neural networks and Bayesian generalized linear regression analyses were performed. The artificial neural networks were able to identify four important markers belonging to linkage groups that have been recently mapped,...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: Coffea arabica; Hemileia vastatrix; Artificial intelligence; Molecular markers; Prediction.
Ano: 2017
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2017000300186

Half a century of studying adaptability and stability in maize and soybean in Brazil
Provedor de dados: 63
Autores: Rezende,Wender Santos; Cruz,Cosme Damião; Borém,Aluízio; Rosado,Renato Domiciano Silva.
ABSTRACT: The study of adaptability and stability underlies the cultivar recommendation process for all crops. There is a considerable number of statistical methods available for this purpose, but little is known about their actual adoption by the Brazilian scientific community. The objective of this study was to carry out a systematic review of the scientific literature on the adaptability and stability methods used in maize and soybean in Brazil from scientific articles published between 1970 and 2017 in Brazilian journals. Article searches were carried out in journals indexed through the SciELO database. The articles were classified according to the year of publication and the adaptability and stability methods used. We also evaluated the pattern of...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: Zea mays L.; Glycine max (L.) Merril; Genotype × environment interaction; Adaptation; Cultivar recommendation.
Ano: 2021
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162021000300501