1. Genetic parameters, prediction of selection gains and genetic diversity in Andropogon lateralis Nees ecotypes
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Diógenes Cecchin Silveira, Rodrigo Sampaio, Arthur Valentini, Weliton Menezes dos Santos, Júlia Longhi, Carla Nauderer, Juliana Medianeira Machado, Annamaria Mills, Carine Simioni, André Pich Brunes, Roberto Luis Weiler, and Miguel Dall’Agnol
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BLUP ,genetic distance ,multivariate analysis ,REML ,Animal culture ,SF1-1100 - Abstract
ABSTRACT The objective of this study was to estimate the genetic parameters and predict selection of genetic gains and genetic diversity of 12 Andropogon lateralis ecotypes collected in the State of Rio Grande do Sul, Brazil. To estimate genetic parameters and predict selection gains, the REML/BLUP technique was applied. Genetic diversity among the ecotypes was evaluated by two clustering methods (optimization and hierarchical) and principal component (PC) analysis, the latter method also used to discard variables. The genetic parameters studied showed high potential for selection of important agronomic forage traits for livestock production. Results showed that the 12 A. lateralis Nees ecotypes exhibited high genetic variability for the studied forage characters and indicated parental prosperity for crosses within the genetic breeding program. Principal component analysis showed that number of total vegetative tillers, leaf:stem ratio, number of reproductive tillers, and leaf dry matter yield accounted for 80.6% of the observed variation in PC1. These variables are important characteristics for quantifying the dry matter production and nutritional value of forage plants, and they can help to discriminate amongst ecotypes. Ecotypes sourced from the Pelotas, Piratini, Passo Fundo, Bagé, and Montenegro regions showed superior forage production when evaluated by the BLUP methodology. Therefore, this group was identified as the most suitable for selection and crossing purposes. Tocher’s cluster analysis grouped the ecotypes into five divergent groups. Principal component and UPGMA hierarchical methods were also efficient at separating the ecotypes.
- Published
- 2024
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