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URI permanente desta comunidadehttps://thoth.dti.ufv.br/handle/123456789/3352
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Item Multiple-trait model by Bayesian inference applied to environment efficient Coffea arabica with low-nitrogen nutrient(Instituto Agronômico (IAC), 2023-04-14) Silva Júnior, Antônio Carlos da; Moura, Waldênia de Melo; Torres, Lívia Gomes; Santos, Iara Gonçalves dos; Silva, Michele Jorge da; Azevedo, Camila Ferreira; Cruz, Cosme DamiãoIdentifying 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): H2 = 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.Item Genetic diversity in arabica coffee grown in potassium-constrained environment(Editora UFLA, 2015-01) Moura, Waldênia de Melo; Soares, Yaska Janaína Bastos; Amaral Júnior, Antônio Teixeira do; Lima, Paulo César de; Martinez, Hermínia Emília Prieto; Gravina, Geraldo de AmaralPotassium is a source of non-renewable natural resource, and is used in large quantities in coffee fertilization through basically imported formulations in the form of potassium chloride. An alternative to make production systems more sustainable would be obtaining cultivars more efficient in the use of this nutrient. This study aimed to evaluate the genetic diversity among 20 cultivars of coffee, in conditions of low availability of potassium to identify the best combinations for composing future populations to be used in breeding programs. The experiment was arranged in a randomized block design with three replications of nutrient solution. Agronomic characteristics and efficiencies of rooting, absorption, translocation, biomass production and potassium utilization were evaluated. The clustering analysis was based on the unweighted pair group method with arithmetic mean clustering algorithm (UPGMA) and canonical variables. Variability was observed for most treatments. The multivariate procedures produced similar discrimination of genotypes, with the formation of five groups. Hybridizations between the cultivar Icatu Precoce IAC 3283 with cultivars Catuaí Amarelo IAC 62, Araponga MG1, Caturra Vermelho IAC 477, Catuaí Vermelho IAC 15, Rubi MG 1192 and Catucaí 785/15, and between the cultivar Tupi IAC 1669-33 with cultivars Icatu Vermelho IAC 4045, Acaiá Cerrado MG 1474 and Oeiras MG 6851 are the most promising for obtaining segregating populations or heterotic hybrids in breeding programs aiming more efficiency in potassium utilization.