Herrero-Medrano J M, Mathur P K, ten Napel J, Rashidi H, Alexandri P, Knol E F, Mulder H A
J Anim Sci. 2015 Apr;93(4):1494-502. doi: 10.2527/jas.2014-8583.
Robustness is an important issue in the pig production industry. Since pigs from international breeding organizations have to withstand a variety of environmental challenges, selection of pigs with the inherent ability to sustain their productivity in diverse environments may be an economically feasible approach in the livestock industry. The objective of this study was to estimate genetic parameters and breeding values across different levels of environmental challenge load. The challenge load (CL) was estimated as the reduction in reproductive performance during different weeks of a year using 925,711 farrowing records from farms distributed worldwide. A wide range of levels of challenge, from favorable to unfavorable environments, was observed among farms with high CL values being associated with confirmed situations of unfavorable environment. Genetic parameters and breeding values were estimated in high- and low-challenge environments using a bivariate analysis, as well as across increasing levels of challenge with a random regression model using Legendre polynomials. Although heritability estimates of number of pigs born alive were slightly higher in environments with extreme CL than in those with intermediate levels of CL, the heritabilities of number of piglet losses increased progressively as CL increased. Genetic correlations among environments with different levels of CL suggest that selection in environments with extremes of low or high CL would result in low response to selection. Therefore, selection programs of breeding organizations that are commonly conducted under favorable environments could have low response to selection in commercial farms that have unfavorable environmental conditions. Sows that had experienced high levels of challenge at least once during their productive life were ranked according to their EBV. The selection of pigs using EBV ignoring environmental challenges or on the basis of records from only favorable environments resulted in a sharp decline in productivity as the level of challenge increased. In contrast, selection using the random regression approach resulted in limited change in productivity with increasing levels of challenge. Hence, we demonstrate that the use of a quantitative measure of environmental CL and a random regression approach can be comprehensively combined for genetic selection of pigs with enhanced ability to maintain high productivity in harsh environments.
健壮性是生猪生产行业中的一个重要问题。由于来自国际育种组织的猪必须经受各种环境挑战，选择具有在不同环境中维持其生产性能内在能力的猪，对于畜牧业来说可能是一种经济可行的方法。本研究的目的是估计不同环境挑战负荷水平下的遗传参数和育种值。挑战负荷（CL）通过使用来自全球各地农场的925,711条产仔记录，估计为一年中不同周期间繁殖性能的下降。在各农场中观察到了广泛的挑战水平，从有利环境到不利环境，高CL值与不利环境的确认情况相关。使用二元分析在高挑战和低挑战环境中估计遗传参数和育种值，并使用勒让德多项式的随机回归模型，估计随着挑战水平增加的遗传参数和育种值。尽管在极端CL环境中出生仔猪数的遗传力估计值略高于中等CL水平环境，但随着CL增加，仔猪损失数的遗传力逐渐增加。不同CL水平环境之间的遗传相关性表明，在极低或极高CL环境中进行选择将导致选择反应较低。因此，通常在有利环境下进行的育种组织选择计划，在环境条件不利的商业农场中可能对选择反应较低。根据其估计育种值（EBV）对在其生产寿命中至少经历过一次高挑战水平的母猪进行排名。使用EBV而忽略环境挑战或仅基于有利环境记录来选择猪，随着挑战水平的增加，生产力会急剧下降。相比之下，使用随机回归方法进行选择，随着挑战水平的增加，生产力变化有限。因此，我们证明，将环境CL的定量测量与随机回归方法全面结合，可用于对在恶劣环境中维持高生产力能力增强的猪进行遗传选择。
