Navegando por Autor "Rosado, Renato Domiciano Silva"

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Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee
(Empresa Brasileira de Pesquisa Agropecuária - Embrapa, 2017-03) 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
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 F 2 population derived from the self-fertilization of the F 1 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, while the Bayesian generalized model identified only two markers belonging to these groups. Lower prediction error rates (1.60%) were observed for predicting leaf rust resistance in Arabica coffee when artificial neural networks were used instead of Bayesian generalized linear regression (2.4%). The results showed that artificial neural networks are a promising approach for predicting leaf rust resistance in Arabica coffee.
Sensory quality of Coffea arabica L. genotypes influenced by postharvest processing
(Crop Breeding and Applied Biotechnology, 2019) Barbosa, Ivan de Paiva; Oliveira, Antonio Carlos Baião de; Rosado, Renato Domiciano Silva; Sakiyama, Ney Sussumu; Cruz, Cosme Damião; Pereira, Antônio Alves
The specialty coffee market has grown significantly in the past decades and has several cultivars with productive potential. The objective of this study was to evaluate the sensory profile of the beverage produced from Coffea arabica L. genotypes based on postharvest processing and to identify cultivars with the greatest genetic potential for coffee cultivation in the city of Araponga, Minas Gerais, Brazil. The experiment was a randomized complete block design with two replicates and 11 genotypes with different levels of resistance to rust. The sensory profile demonstrated an interaction between the genotype and the processing technique. Five of the genotypes presented total scores above 85 points according to the SCAA protocol. Moreover, two of these genotypes yielded heightened sensory notes after undergoing dry processing. The selection of coffee genotypes should consider the level of technology involved in the drying of the coffee beans, which preserves the potential quality of the beverage.
Trait selection using procrustes analysis for the study of genetic diversity in Conilon coffee
(Editora da Universidade Estadual de Maringá - EDUEM, 2020) Pontes, Daiana Salles; Rosado, Renato Domiciano Silva; Cruz, Cosme Damião; Nascimento, Moysés; Oliveira, Ana Maria Cruz; Pensky, Scott Michael
Trait selection is occasionally necessary to save money and time, as well as accelerate breeding program processes. This study aimed to propose two criteria to select traits based on a Procrustes analysis that are poorly explored in genetic breeding: Criterion 1 (backward algorithm) and Criterion 2 (exhaustive algorithm). Then, these two criteria were further compared with Jolliffe’s criterion, which has often been used to select traits in genetic diversity studies. Sixteen agronomic traits were considered, and 40 Conilon coffee (Coffea canephora) accessions were evaluated. This study showed that the flexibility in selecting traits by researcher preference, graphical visualization, and Procrustes statistic through criteria 1 and 2 is a fast and reliable alternative for decision-making. These decisions are based on the removal and addition of traits for phenotyping in studies of Conilon coffee diversity that can be applied to other crops. Other relevant aspects of selection traits criteria were also discussed.