INRA, UMR1348 Pegase, F-35590 Saint-Gilles, France.
Animal. 2013 Aug;7(8):1265-73. doi: 10.1017/S1751731113000554. Epub 2013 Apr 4.
Inclusion of variation in deterministic nutritional models for growth by repeating simulations using different sets of parameters has been performed in literature without or with only hypothetic consideration of the covariance structure among parameters. However, a description of the structure of links among parameters describing individuals is required to generate realistic sets of parameters. In this study, the mean and covariance structure of model parameters describing feed intake and growth were analyzed from 10 batches of crossbred gilts and barrows. Data were obtained from different crossbreeds, originating from Large White × Landrace sows and nine sire lines. Pigs were group-housed (12 pigs/pen) and performance testing was carried out from 70 days of age to ∼110 kg BW. Daily feed intake (DFI) was recorded using automatic feeding stations and BW was measured at least every 3 weeks. A growth model was used to characterize individual pigs based on the observed DFI and BW. In this model, a Gompertz function was used to describe protein deposition and the resulting BW gain. A gamma function (expressing DFI as multiples of maintenance) was used to express the relationship between DFI and BW. Each pig was characterized through a set of five parameters: BW₇₀ (BW at 70 days of age), B(Gompertz) (a precocity parameter) PDm (mean protein deposition rate) and DFI₅₀ and DFI₁₀₀ (DFI at 50 and 100 kg BW, respectively). The data set included profiles for 1288 pigs for which no eating or growth disorders were observed (e.g. because of disease). All parameters were affected by sex (except for BW₇₀) and batch, but not by the crossbreed (except for PDm). An interaction between sex and crossbreed was observed for PDm (P < 0.01) and DFI₁₀₀ (P = 0.05). Different covariance matrices were computed according to the batch, sex, crossbreed, or their combinations, and the similarity of matrices was evaluated using the Flury hierarchy. As covariance matrices were all different, the unit of covariance (subpopulation) corresponded to the combination of batch, sex and crossbreed. Two generic covariance matrices were compared afterwards, with (median matrix) or without (raw matrix) taking into account the size of subpopulations. The most accurate estimation of observed covariance was obtained with the median covariance matrix. The median covariance matrix can be used, in combination with average parameters obtained on-farm, to generate virtual populations of pigs that account for a realistic description of mean performances and their variability.
在文献中，已经通过使用不同参数集重复模拟来实现确定性营养模型中变异的纳入，而没有或仅假设考虑了参数之间的协方差结构。然而，需要描述描述个体的参数之间的联系结构，才能生成现实的参数集。在这项研究中，分析了来自 10 批杂交母猪和公猪的描述饲料采食量和生长的模型参数的均值和协方差结构。数据来自不同的杂交品种，源自大白猪×长白猪母猪和九条父系。猪群饲养（每栏 12 头猪），从 70 日龄到约 110 公斤体重进行性能测试。使用自动给料站记录每日采食量（DFI），并且至少每 3 周测量一次体重。使用生长模型根据观察到的 DFI 和 BW 来描述个体猪。在该模型中，使用 Gompertz 函数来描述蛋白质沉积和由此产生的 BW 增加。使用伽马函数（将 DFI 表示为维持量的倍数）来表达 DFI 与 BW 的关系。每头猪都通过一组五个参数来描述：BW₇₀（70 日龄 BW）、B（Gompertz）（早熟参数）、PDm（平均蛋白质沉积率）和 DFI₅₀ 和 DFI₁₀₀（50 和 100 公斤 BW 时的 DFI）。数据集包括 1288 头猪的剖面，这些猪没有观察到进食或生长障碍（例如，由于疾病）。所有参数均受性别（除 BW₇₀ 外）和批次的影响，但不受杂交品种的影响（除 PDm 外）。PDm（P < 0.01）和 DFI₁₀₀（P = 0.05）观察到性别与杂交品种之间的相互作用。根据批次、性别、杂交品种或它们的组合计算了不同的协方差矩阵，并使用 Flury 层次结构评估了矩阵的相似性。由于协方差矩阵均不同，协方差的单位（亚群）对应于批次、性别和杂交品种的组合。随后比较了两个通用协方差矩阵，一个考虑了亚群的大小（中位数矩阵），另一个不考虑亚群的大小（原始矩阵）。使用中位数协方差矩阵可以获得观察到的协方差的最准确估计。中位数协方差矩阵可与在现场获得的平均参数结合使用，以生成考虑到平均性能及其变异性的现实描述的虚拟猪群。
