| Revista: | Scientia Agricola |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000455445 |
| ISSN: | 0103-9016 |
| Autores: | Souza, André Oliveira1 Viana, Alexandre Pio2 Silva, Fabyano Fonseca e3 Azevedo, Camila Ferreira4 Silva, Flavia Alves da2 Silva, Fernando Higino Lima e5 |
| Instituciones: | 1Instituto Federal do Espirito Santo, Alegre, Espirito Santo. Brasil 2Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, Rio de Janeiro. Brasil 3Universidade Federal de Vicosa, Departamento de Zootecnia, Vicosa, Minas Gerais. Brasil 4Universidade Federal de Vicosa, Departamento de Estatistica, Vicosa, Minas Gerais. Brasil 5Instituto Federal de Educacao, Ciencia e Tecnologia de Goias, Rio Verde, Goias. Brasil |
| Año: | 2022 |
| Volumen: | 79 |
| Número: | 4 |
| País: | Brasil |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Experimental, analítico |
| Resumen en inglés | Methods for genetic improvement of semi–perennial species, such as passion fruit, often involve large areas, unbalanced data, and lack of observations. Some strategies can be applied to solve these problems. In this work, different models and approaches were tested to improve the precision of estimates of genetic evaluation models for several characteristics of the passion fruit. A randomized block design (RBD) model was compared to a posteriori correction, adding two factors to the model (post–hoc blocking Row–Col). These models were also combined with the frequentist and Bayesian approaches to identify which combination yields the most accurate results. These approaches are part of a strategic plan in a perennial plant breeding program to select promising genitors of passion to compose the next selection cycle. For Bayesian, we tested two priors, defining different values for the distribution parameters of effect variances of the model. We also performed a cross–validation test to choose a priori values and compare the frequentist and Bayesian approaches using the root mean square error (RMSE) and the correlation between the predicted and observed values, called Predictive capacity of the model (PC). The model with the post–hoc blocking Row–Col design captured the spatial variability for productivity and number of fruits, directly affecting the experimental precision. Both approaches applied to the models showed a similar performance, with predictive capacity and selective efficiency leading to the selection of the same individuals |
| Disciplinas: | Agrociencias, Biología |
| Palabras clave: | Frutales, Genética, Modelos biológicos, Mejoramiento genético, Fruta de la pasión, Passiflora maliformis, Análisis bayesiano |
| Keyword: | Fruit trees, Genetics, Biological models, Passion fruit, Passiflora maliformis, Bayesian analysis |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
