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 context environmental economic impacts attributed to excessive fertilization. Although breeding data have multi-trait structure, they often analyzed under single trait structure. Thus, objectives this study were Bayesian multitrait model, estimate heritability broad sense, select coffee with better genetic potential (desirable agronomic traits) nitrogen-restricted cultivation. The experiment was carried out greenhouse 20 grown nutrient solution low-nitrogen content (1.5 mM). experimental design used randomized blocks three replications. Six agromorphological traits program five nutritional efficiency indices used. Markov Chain Monte Carlo algorithm parameters values. considered highly heritable, credibility interval (95% probability): H2 = 0.9538 – 5.89E-01. model presents adequate for improvement concentrations. Coffee Icatu Precoce 3282, Vermelho IAC 4045, Acaiá Cerrado MG 1474, Tupi 1669-33, Catucaí 785/15, Caturra Obatã 1669/20 demonstrated greater cultivation concentration.