Plateau-linear reaction norm model analysis of number born alive in purebred Landrace pigs using meteorological data in Japan

Abstract

We performed a plateau-linear reaction norm model (RNM) analysis of number born alive (NBA) in purebred Landrace pigs, where breeding value changes according to maximum temperature at mating day, using public meteorological observation data in Japan. We analysed 52,668 NBA records obtained from 10,320 Landrace sows. Pedigree data contained 99,201 animals. Off-farm daily temperature data at the nearest weather station from each of the farms were downloaded from the Japan Meteorological Agency website. A plateau-linear RNM analysis based on daily maximum temperature on mating day (threshold temperature of 16.6°C) was performed. The percentage of the records with daily maximum temperatures at mating days of ≤16.6, ≥25.0, ≥30.0 and ≥35.0°C were 34.3%, 33.6%, 14.0% and 0.8%, respectively. The value of Akaike's information criterion for the plateau-linear RNM was lower than that for a simple repeatability model (RM). With the plateau-linear RNM, estimated value of heritability ranged from 0.14 to 0.15, while that from the RM analysis was 0.15. Additive genetic correlation between intercept and slope terms was estimated to be −0.52 from the plateau-linear RNM analysis. Estimated additive genetic correlations were >0.9 between NBA at different temperatures ranging from 16.6 to 37.6°C. For the 10,320 sows, average values of prediction reliability of the intercept and slope terms for breeding values in the plateau-linear RNM were 0.47 and 0.16, respectively. Increasing weight for slope term in linear selection index could bring positive genetic gain in the slope part, but prediction accuracy would decrease. Our results imply that genetically improving heat tolerance in sows reared in Japan focusing on NBA using RNM is possible, while RNM is more complex to implement and interpret. Therefore, further study should be encouraged to make genetic improvement for heat tolerance in sows more efficient.