ROBUSTNESS CLUSTERS AS SELECTION CRITERIUM IN GENETIC IMPROVEMENT FOR MITIGATION OF THE IMPACTS OF CLIMATE CHANGE - Pereira, R. N.; Serodio, R. L.; Ventura, H. T.; Araújo Neto, F. R.; Pegolo, N. T. (Revista Brasileira de Engenharia de Biossistemas, 2018)
Climate changes are expected for the next decades and, consequently, their impacts on cattle breeding, with artificial selection a possible method to mitigate them. This work aimed to develop a selection system based on the genetic parameters generated by adaptive reaction norm models in Nellore cattle. Genetic and growth data were provided by the Brazilian Association of Cattle Breeders. An environmental gradient was created using contemporary group averages standardized to a mean of zero and standard deviation of one. For the prediction of coefficients of the adaptive reaction norms, the random regression model was used, considering cubic polynomials for weights at 450 days with analysis of separated sexes. The genetic values ​​of the different individuals were calculated as a function of the environmental gradient, using the BLUPF90 software. The individuals were classified considering coefficients that generated norms with high genetic values ​​and with lower variation along the environmental gradient. The increase in genetic value and its robustness were then compensated by creating robustness clusters (CRs) based on the direct comparison between the coefficients. The results of the classification showed that the selection of individuals of the classes of greater robustness should generate progenies with less environmental sensitivity, since the coefficients are additive genetic components. It is concluded that the cluster selection of robustness showed to be an effective method of mitigating the impacts produced in the production systems by changes in the breeding environments.