Antônio Carlos da Silva Júnior, W. M. Moura, L. Torres, I. G. Santos, Michele Jorge da Silva, C. F. Azevedo, C. Cruz
{"title":"贝叶斯多性状模型在低氮营养条件下环境高效阿拉比卡咖啡中的应用","authors":"Antônio Carlos da Silva Júnior, W. M. Moura, L. Torres, I. G. Santos, Michele Jorge da Silva, C. F. Azevedo, C. Cruz","doi":"10.1590/1678-4499.20220157","DOIUrl":null,"url":null,"abstract":": Identifying Coffea arabica cultivars that are more efficient in the use of nitrogen is an important strategy and a necessity in the context of environmental and economic impacts attributed to excessive nitrogen fertilization. Although Coffea arabica breeding data have a multi-trait structure, they are often analyzed under a single trait structure. Thus, the objectives of this study were to use a Bayesian multitrait model, to estimate heritability in the broad sense, and to select arabica coffee cultivars with better genetic potential (desirable agronomic traits) in nitrogen-restricted cultivation. The experiment was carried out in a greenhouse with 20 arabica coffee cultivars grown in a nutrient solution with low-nitrogen content (1.5 mM). The experimental design used was in randomized blocks with three replications. Six agromorphological traits of the arabica coffee breeding program and five nutritional efficiency indices were used. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. The agromorphological traits were considered highly heritable, with a credibility interval (95% probability):〖 H 2 = 0.9538 – 5.89E-01. The Bayesian multitrait model presents an adequate strategy for the genetic improvement of arabica coffee grown in low-nitrogen concentrations. 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Multiple-trait model by Bayesian inference applied to environment efficient Coffea arabica with low-nitrogen nutrient
: Identifying Coffea arabica cultivars that are more efficient in the use of nitrogen is an important strategy and a necessity in the context of environmental and economic impacts attributed to excessive nitrogen fertilization. Although Coffea arabica breeding data have a multi-trait structure, they are often analyzed under a single trait structure. Thus, the objectives of this study were to use a Bayesian multitrait model, to estimate heritability in the broad sense, and to select arabica coffee cultivars with better genetic potential (desirable agronomic traits) in nitrogen-restricted cultivation. The experiment was carried out in a greenhouse with 20 arabica coffee cultivars grown in a nutrient solution with low-nitrogen content (1.5 mM). The experimental design used was in randomized blocks with three replications. Six agromorphological traits of the arabica coffee breeding program and five nutritional efficiency indices were used. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. The agromorphological traits were considered highly heritable, with a credibility interval (95% probability):〖 H 2 = 0.9538 – 5.89E-01. The Bayesian multitrait model presents an adequate strategy for the genetic improvement of arabica coffee grown in low-nitrogen concentrations. Coffee arabica cultivars Icatu Precoce 3282, Icatu Vermelho IAC 4045, Acaiá Cerrado MG 1474, Tupi IAC 1669-33, Catucaí 785/15, Caturra Vermelho and Obatã IAC 1669/20 demonstrated greater potential for cultivation in low-nitrogen concentration.
期刊介绍:
Bragantia é uma revista de ciências agronômicas editada pelo Instituto Agronômico da Agência Paulista de Tecnologia dos Agronegócios, da Secretaria de Agricultura e Abastecimento do Estado de São Paulo, com o objetivo de publicar trabalhos científicos originais que contribuam para o desenvolvimento das ciências agronômicas.
A revista é publicada desde 1941, tornando-se semestral em 1984, quadrimestral em 2001 e trimestral em 2005.
É filiada à Associação Brasileira de Editores Científicos (ABEC).