Wageningen University and Research, Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands.
J Anim Sci. 2019 Oct 3;97(10):4152-4159. doi: 10.1093/jas/skz274.
In pig production, efficiency is benefiting from uniform growth in pens resulting in single deliveries from a pen of possibly all animals in the targeted weight range. Abnormalities, like pneumonia or aberrant growth, reduce production efficiency as it reduces the uniformity and might cause multiple deliveries per batch and pigs delivered with a low meat yield or outside the targeted weight range. Early identification of pigs prone to develop these abnormalities, for example, at the onset of the growing-finishing phase, would help to prevent heterogeneous pens through management interventions. Data about previous production cycles at the farm combined with data from the piglet's own history may help in identifying these abnormalities. The aim of this study, therefore, was to predict at the onset of the growing-finishing phase, that is, at 3 mo in advance, deviant pigs at slaughter with a machine-learning technique called boosted trees. The dataset used was extracted from the farm management system of a research center. It contained over 70,000 records of individual pigs born between 2004 and 2016, including information on, for example, offspring, litter size, transfer dates between production stages, their respective locations within the barns, and individual live-weights at several production stages. Results obtained on an independent test set showed that at a 90% specificity rate, the sensitivity was 16% for low meat percentage, 20% for pneumonia and 36% for low lifetime growth rate. For low lifetime growth rate, this meant an almost three times increase in positive predictive value compared to the current situation. From these results, it was concluded that routine performance information available at the onset of the growing-finishing phase combined with data about previous production cycles formed a moderate base to identify pigs prone to develop pneumonia (AUC > 0.60) and a good base to identify pigs prone to develop growth aberrations (AUC > 0.70) during the growing-finishing phase. The mentioned information, however, was not a sufficient base to identify pigs prone to develop low meat percentage (AUC < 0.60). The shown ability to identify growth aberrations and pneumonia can be considered a good first step towards the development of an early warning system for pigs in the growing-finishing phase.
在养猪生产中，效率得益于猪圈中均匀的生长，从而实现一笼中可能所有动物均达到目标体重范围的单批分娩。异常情况，如肺炎或生长异常，会降低生产效率，因为它降低了均匀性，并可能导致每批多次分娩，以及产肉率低或不在目标体重范围内的猪只被分娩。例如，在生长-育肥阶段开始时，早期识别易发生这些异常的猪，将有助于通过管理干预来防止猪圈不均匀。将农场前几个生产周期的数据与仔猪自身历史数据相结合，可能有助于识别这些异常情况。因此，本研究的目的是使用一种称为提升树的机器学习技术，在生长-育肥阶段开始时（即提前 3 个月）预测屠宰时异常的猪。所使用的数据集是从一个研究中心的农场管理系统中提取的。它包含了 70,000 多只个体猪的记录，这些猪出生于 2004 年至 2016 年之间，包括后代、窝产仔数、在生产阶段之间的转移日期、在畜舍中的各自位置以及在几个生产阶段的个体活重等信息。在独立测试集中获得的结果表明，在特异性为 90%的情况下，低肉率的敏感性为 16%，肺炎为 20%，低终生生长率为 36%。对于低终生生长率，这意味着与当前情况相比，阳性预测值几乎增加了三倍。根据这些结果可以得出结论，在生长-育肥阶段开始时可获得的常规性能信息与前几个生产周期的数据相结合，为识别易发生肺炎的猪（AUC > 0.60）和易发生生长异常的猪（AUC > 0.70）提供了一个中等基础。然而，上述信息不足以识别易发生低肉率的猪（AUC < 0.60）。所展示的识别生长异常和肺炎的能力，可以被视为为生长-育肥阶段的猪开发早期预警系统的良好第一步。
