Phenotypic correlation of characteristics in the gilt performance test with an average number of live-born piglets through all achieved births, during the reproductive exploitation

Abstract

ABSTRACT Reproductive efficacy of sows dictates the level of profitability of the production itself, and the moment of testing and selection of gilts for breeding (as the parents of a future generation), represents a very important moment. Given the fact that the selection of gilts for breeding is partly based on the weight-gain in the test, in this study we examined the influence of a daily weight-gain of gilts in the test of the manifestation of important reproductive characteristics - the size of the litter of the first and the other parities, as well as the percentage of sows that gave birth in relation to previous breeding process. Data of the 11637 tested gilts that completed the performance test have been used for the test. The studied characteristics were: the weight-gain at the end of the test, the number of live-born piglets, the number of stillborn piglets and the number of grown-weaned piglets in the first litter and the total number of live-born piglets, the total number of stillborn piglets and the total number of grown-weaned piglets. The results obtained in this study showed the effect of weight-gain in the reproductive efficacy test in the number of the live-born piglets in the first and from the 2nd to the 9th parity, as well as the percentage of the sows included in the next breeding process. By including all selected independent variables that showed the adequate statistical significance in correlation with the dependent variable, the determination coefficient rises to a value of 0.098 which represents 9.80% of the influence on the dependent variable, that can be explained by the independent variable, which leads to the conclusion that the binding strength between the variable "average live" and all the independent variables: “the number of litters”, the MLD depth” and “the back-fat thickness”, is very weak, which is not a rare case in multiple regression.