Statistical Modeling of Vigor Ratings in Ruzigrass Breeding

Abstract

Abstract Ruzigass (Urochloa ruziziensis) is a forage crop with high agronomic and nutritional value. Plant breeders often assess ruzigrass phenotypic traits using vigor ratings. The analyses of these categorical data often fail to meet usual statistical assumptions. In this study we compared four fittings of linear models for vigor rating analyses: i) a mixed model for the original scale (LMM), ii) a mixed model for a Box-Cox transformed scale (BCLMM), iii) a multinomial generalized mixed model (GLMM), and iv) a hierarchical Bayesian model (HBM). Additionally, biomass yield was assessed, and indirect selection of high-performing genotypes was evaluated. The experimental design had 2,204 ruzigrass genotypes randomized to augmented blocks. Six graders visually assessed each plot using a rating scale. Fitting methods were sampled from three scenarios, using just one, three, or six graders. A non-null genetic variance component was detected for both traits. Except for BCLMM, methods for analyzing vigor ratings were correlated. The correlations and coincidence indexes for selecting genotypes increased with the number of graders. The analysis of vigor ratings under gaussian approximations is riskier when a single grader evaluates genotypes. GLMM and HBM are more recommendable and suitable analyses of vigor ratings to select high-performing ruzigrass genotypes.