Application of the multivariate and univariate analyses to estimate the feed efficiency in beef cattle

This study aimed to compare the univariate analyses (relationship between dry matter intake (DMI) and average daily gain (ADG), Kleiber ratio, and residual feed intake) and multivariate analysis (bionutritional index [BNI]) to determine feed efficiency. Were used a total of 160 cattle (individual data) and the analyzed variables were dry matter intake, weight gain, and body weight of the animals. We used five methods to evaluate feed efficiency, the BNI, FE, corrected feeding efficiency (corFE), Kleiber ratio (KR), and residual feed intake (RFI). Study 1 demonstrated that only the FE (p=0.0472) was significant, although the FE after the transformation of Box-Cox (corFE) (p=0.0642) showed a statistical trend. In studies 2, 3, and 5, we observed that BNI was the best biological efficiency indicator. In the study 4, we observed that the best indicators were FE (0.110; p=0.0281), corFE (0.380; p=0.0429), and RFI (0.465; p=0.0340) for the genders. However, corFE decreased the coefficient of variation in all studies. In conclusion, the use of the Box-Cox transformation is as efficient as the multivariate analysis in discriminating experimental groups (genetic groups, different levels of concentrate in the diet, and genders) concerning the other univariate analyzes.

A two-location trial for selecting corn silage hybrids for the humid tropic: forage and grain yields and in vitro fermentation characteristics

The goal of our study was to evaluate the nutritional potential of dented corn hybrids for silage production. We performed a two-location trial in which 19 dented corn hybrids and five corn controls grew in four randomized blocks within two experimental areas located in the Northern (Campos dos Goytacazes) and Northwestern (Itaocara) Rio de Janeiro State, Brazil. We recorded yields of fresh and dry forage matter and yields of fresh and dry grain matter, as well as chemical composition variables. We interpreted variables by assuming a Normal distribution for yield variables and a Beta distribution for chemical composition and ratios. The SAS GLIMMIX procedure fitted the linear model under those assumptions. Dual-pool models fitted the gas production profiles generated by in vitro anaerobic fermentations. We used the nlme of R software to fit the dual-pool models and the information-theoretic approach to evaluate their quality of fit. We did a cluster analysis (NbClust of R) to group corn hybrids based on fresh and DM yields and kinetic parameters of in vitro gas production. Three clusters of corn hybrids stood out, their basic differences relied on fresh and DM yields. Nonetheless, the least-squares means for gas production characteristics among groups did not present disjoint confidence intervals. Therefore, we can infer that dented corn hybrids rank by forage yield, but not by forage quality, and recommend the most productive ones that consistently outstand in both locations (hybrids UENF-2203, UENF-2192, UENF-2193, and UENF-506-11).