The dependence of the growth rate and meat content of young boars on semen parameters and conception rate

Boars have a decisive impact on the progress in pig production, however, there is no recent information about the optimal growth parameters during the rearing period for modern breed later used in artificial insemination (AI) stations. Therefore, the objective of the research was to conduct semen parameter and conception rate analyses on the basis of growth rate and meat content assessments made during the rearing of AI boars of different genotypes. The study was carried out between 2010 and 2014 and included 184 boars in five breed combinations: 46 Polish Large White, 50 Polish Landrace, 27 Pietrain, 36 Duroc×Pietrain and 25 Hampshire×Pietrain. Boars were qualified by daily gains and meat content assessment (between 170 and 210 days of life). A total number of 38 272 ejaculates were examined (semen volume (ml), spermatozoa concentration (×106 ml−1), total number of spermatozoa (×109) and number of insemination doses from one ejaculate (n)). The fertility was determined by the conception rate (%). Semen volume, spermatozoa concentration and conception rate (P<0.01), followed by the total number of spermatozoa and insemination doses (P<0.05) were characterized by the highest variability in relation to breed of boars. The effect of daily gains was reported for spermatozoa concentration, number of insemination doses, conception rate (all P<0.01) and total number of spermatozoa (P<0.05). The peak of growth for spermatozoa concentration, total number of spermatozoa, insemination doses and conception rate was achieved for 800 to 850 g gains. Meat content affected semen volume, number of insemination doses and conception rate (P<0.05). Rearing boars while maintaining daily gains at the 800 to 850 g level and 62.5% to 65% meat content helps AI stations to increase the efficiency and economic profitability, and the number of insemination doses to increase by up to 300 doses/boar within a year. The analyses of growth parameters may help increase the efficiency and economic viability of AI stations.