Abstract The Paspalum genus has potential for further genetic improvement because of its adaptability to different ecosystems and production of high yields for grazing livestock. We estimate the genetic parameters of 195 intraspecific P. notatum hybrids using Restricted Maximum Likelihood (REML), followed by selection based on Best Linear Unbiased Prediction (BLUP) through multivariate analysis. The intraspecific hybrids studied showed considerable genetic variability in the evaluated forage traits, displaying their potential for progression in subsequent stages of the genetic improvement program. Notably, plant height emerged as an important trait for indirect selection to enhance forage production. The use of the REML/BLUP procedure proves to be a robust tool for data analysis, particularly for perennial species. Furthermore, multivariate analysis based on BLUPs should be used in the selection process within breeding programs. Based on the BLUP values, hybrids D3, D16, C17, C2 and B17 were identified as superior for forage production, and they hold promise for future breeding programs for future breeding initiatives aimed at direct selection to improve yield.

ABSTRACT The objective of this study was to verify the repeatability of the expression of forage characters in intraspecific hybrids of Paspalum notatum Flügge to aid early selection. Across five harvests, plant height, tiller population density, leaf dry matter, stem dry matter, inflorescence dry matter, total dry matter, and growth habit were quantified for five parents, 189 hybrids, and a commercially available cultivar as a control (n = 195). Analysis of variance, principal components analysis, and structural analysis methods were used to determine the repeatability coefficients. The repeatability coefficients ( ρ ^) for all evaluated characteristics generated by the different methods were between 0.05 (ANOVA II) and 0.95 (PCACov). For most of the characteristics studied, repeatability coefficients and determination coefficients were considered high. The repeatability coefficients estimates obtained for the eight characteristics evaluated with the ANOVA I and II methods were almost always lower than those obtained by PCA and structural analysis methods. Based on the covariance matrix, the principal component method generated higher estimates than those produced by ANOVA or structural analysis. Assuming a minimum 80% reliability to verify the relative superiority of the hybrids across all assessed traits, the five harvests proved adequate for selecting the optimal plant materials to advance to the next phase of the breeding program. However, reliable early selection for leaf dry matter, leaf:stem ratio, and total dry matter required a minimum of two harvests. The genetic parameters (h2 and CVg) showed a favorable scenario for direct selection to increase forage production.