1Department of Veterinary and Animal Sciences,University of Copenhagen,Grønnegårdsvej 2,DK-1870 Frederiksberg C,Denmark.
2Department of Animal and Food Sciences,University of Kentucky,Lexington,KY 40546,USA.
Animal. 2018 Feb;12(2):295-302. doi: 10.1017/S1751731117001690. Epub 2017 Jul 24.
Frequent BW monitoring of growing pigs can be useful for identifying production (e.g. feeding), health and welfare problems. However, in order to construct a tool which will properly recognize abnormalities in pigs' growth a precise description of the growth process should be used. In this study we proposed a new model of pig growth accounting for daily fluctuations in BW. Body weight measurements of 1710 pigs (865 gilts and 843 barrows) originating from five consecutive batches from a Danish commercial farm were collected. Pigs were inserted into a large pen (maximum capacity=400) between November 2014 and September 2015. On average, each pig was observed for 42 days and weighed 3.6 times a day when passing from the resting to feeding area. Altogether, 243,160 BW measurements were recorded. A multilevel model of pig growth was constructed and fitted to available data. The BW of pigs was modeled as a quadratic function of time. A diurnal pattern was incorporated into the model by a cosine wave with known length (24 h). The model included pig effect which was defined as a random autoregressive process with exponential correlation. Variance of within-pigs error was assumed to increase with time. Because only five batches were observed, it was not possible to obtain the random effect for batch. However, in order to account for the batch effect the model included interactions between batch and fixed parameters: intercept, time, square value of time and cosine wave. The gender effect was not significant and was removed from the final model. For all batches, morning and afternoon peaks in the frequency of visits to the feeding area could be distinguished. According to results, pigs were lighter in the morning and heavier in the evening (minimum BW was reached around 1000 h and maximum around 2200 h). However, the exact time of obtaining maximum and minimum BW during the day differed between batches. Pigs had access to natural light and, therefore, existing differences could be explained by varying daylight level during observations periods. Because the diurnal amplitude for pig growth varied between batches from 0.9 to 1.4 kg, BW monitoring tools based on frequent measurements should account for diurnal variation in BW of pigs. This proposed description of growth will be built into a monitoring tool (a dynamic linear model) and applied to farm data in future studies.
频繁监测生长猪的体重可以帮助识别生产（例如喂养）、健康和福利问题。然而，为了构建一个能够正确识别猪生长异常的工具，需要使用精确描述生长过程的方法。在这项研究中，我们提出了一种新的猪生长模型，该模型考虑了 BW 的日常波动。我们收集了来自丹麦一个商业农场的五批连续批次的 1710 头猪（865 头母猪和 843 头公猪）的体重测量数据。猪在 2014 年 11 月至 2015 年 9 月期间被放入一个大型围栏（最大容量=400）中。平均而言，每头猪在从休息区到喂食区的过程中每天观察 42 天并称重 3.6 次。总共记录了 243160 次 BW 测量值。我们构建了一个猪生长的多水平模型，并将其拟合到可用数据上。猪的 BW 被建模为时间的二次函数。通过具有已知长度（24 小时）的余弦波将昼夜模式纳入模型中。该模型包括猪的影响，定义为具有指数相关性的随机自回归过程。假设猪内误差的方差随时间增加而增加。由于只观察了五个批次，因此无法获得批次的随机效应。然而，为了考虑批次效应，该模型包括批次与固定参数（截距、时间、时间的平方值和余弦波）之间的交互作用。性别效应不显著，因此从最终模型中删除。对于所有批次，都可以区分早上和下午进入喂食区的频率峰值。结果表明，猪在早上较轻，在晚上较重（最低 BW 约在 1000 时达到，最高约在 2200 时达到）。然而，一天中获得最大和最小 BW 的时间在批次之间有所不同。猪可以接触自然光，因此，现有的差异可以通过观察期间不同的日光水平来解释。由于猪生长的昼夜幅度在不同批次之间从 0.9 到 1.4 公斤不等，因此基于频繁测量的 BW 监测工具应该考虑猪 BW 的昼夜变化。这种生长的描述将被构建到一个监测工具（动态线性模型）中，并在未来的研究中应用于农场数据。
