Statistical modeling of vigor ratings in ruzigrass breeding

Ruzigass (Urochloa ruziziensis) is a forage crop with high agronomic and nutritional value. Plant breeders often assess ruzigrass phenotypic traits via vigor ratings. The analyses of these categorical data often fail to meet the usual statistical assumptions. In this study, we compared four fittings of linear models for vigor rating analyses: i) a linear mixed model for the original scale (LMM), ii) a linear mixed model for a Box–Cox transformed scale (BCLMM), iii) a multinomial generalized mixed model using a probit link function, also known as threshold model (GLMM), and iv) a hierarchical Bayesian model, also referred to as a Bayesian threshold model (HBM). Additionally, biomass yield was assessed, and the indirect selection of high-performing genotypes was evaluated. The experimental design included 2,204 ruzigrass genotypes randomized into augmented blocks. Six graders visually assessed each plot using a rating scale. Fitting methods were sampled from three scenarios, employing one, three, or six graders. A nonnull genetic variance component was detected for vigor and biomass yield traits. Except for BCLMM, the methods for analyzing vigor ratings were correlated. The correlations and coincidence indices for selecting genotypes increased with the number of graders. The analysis of vigor ratings under Gaussian approximations is riskier when a single grader is used to evaluate genotypes. The GLMM and HBM perform similarly and are more recommended and suitable analyses of vigor ratings when selecting high-performing ruzigrass genotypes.