Contemporary group estimates adjusted for climatic effects provide a finer definition of the unknown environmental challenges experienced by growing pigs

• Published in: Journal of animal breeding and genetics (1986) Vol. 134; no. 6; pp. 520 - 530
• Main Authors: Guy, S.Z.Y., Li, L., Thomson, P.C., Hermesch, S.
• Format: Journal Article
• Language: English
• Published: Germany Blackwell Publishing Ltd 01.12.2017
• Subjects: Air temperature; Animal models; Animals; Australia; Body Fat Distribution - veterinary; Breeding; Climate Change; disease resilience; Environment; environmental descriptor; Estimates; Female; Genetic improvement; genotype by environment interaction; health; Infections; Male; Models, Statistical; Pigs; Progeny; Rainfall; Resilience; Splines; Swine - genetics; Swine - growth & development; Variance; environmental descriptor; health; genotype by environment interaction; disease resilience; splines
• ISSN: 0931-2668; 1439-0388
• DOI: 10.1111/jbg.12282

Summary Environmental descriptors derived from mean performances of contemporary groups (CGs) are assumed to capture any known and unknown environmental challenges. The objective of this paper was to obtain a finer definition of the unknown challenges, by adjusting CG estimates for the known climatic effects of monthly maximum air temperature (MaxT), minimum air temperature (MinT) and monthly rainfall (Rain). As the unknown component could include infection challenges, these refined descriptors may help to better model varying responses of sire progeny to environmental infection challenges for the definition of disease resilience. Data were recorded from 1999 to 2013 at a piggery in south‐east Queensland, Australia (n = 31,230). Firstly, CG estimates of average daily gain (ADG) and backfat (BF) were adjusted for MaxT, MinT and Rain, which were fitted as splines. In the models used to derive CG estimates for ADG, MaxT and MinT were significant variables. The models that contained these significant climatic variables had CG estimates with a lower variance compared to models without significant climatic variables. Variance component estimates were similar across all models, suggesting that these significant climatic variables accounted for some known environmental variation captured in CG estimates. No climatic variables were significant in the models used to derive the CG estimates for BF. These CG estimates were used to categorize environments. There was no observable sire by environment interaction (Sire×E) for ADG when using the environmental descriptors based on CG estimates on BF. For the environmental descriptors based on CG estimates of ADG, there was significant Sire×E only when MinT was included in the model (p = .01). Therefore, this new definition of the environment, preadjusted by MinT, increased the ability to detect Sire×E. While the unknown challenges captured in refined CG estimates need verification for infection challenges, this may provide a practical approach for the genetic improvement of disease resilience.