Adaptation Physiology Group, Department of Animal Sciences, Wageningen University and Research, PO Box 338, 6700 AH, Wageningen, the Netherlands; Laboratory of Biochemistry, Wageningen University and Research, PO Box 338, 6700 AH, Wageningen, the Netherlands.
Adaptation Physiology Group, Department of Animal Sciences, Wageningen University and Research, PO Box 338, 6700 AH, Wageningen, the Netherlands.
J Dairy Sci. 2019 Nov;102(11):10186-10201. doi: 10.3168/jds.2018-15791. Epub 2019 Aug 30.
Metabolic status of dairy cows in early lactation can be evaluated using the concentrations of plasma β-hydroxybutyrate (BHB), free fatty acids (FFA), glucose, insulin, and insulin-like growth factor 1 (IGF-1). These plasma metabolites and metabolic hormones, however, are difficult to measure on farm. Instead, easily obtained on-farm cow data, such as milk production traits, have the potential to predict metabolic status. Here we aimed (1) to investigate whether metabolic status of individual cows in early lactation could be clustered based on their plasma values and (2) to evaluate machine learning algorithms to predict metabolic status using on-farm cow data. Through lactation wk 1 to 7, plasma metabolites and metabolic hormones of 334 cows were measured weekly and used to cluster each cow into 1 of 3 clusters per week. The cluster with the greatest plasma BHB and FFA and the lowest plasma glucose, insulin, and IGF-1 was defined as poor metabolic status; the cluster with the lowest plasma BHB and FFA and the greatest plasma glucose, insulin, and IGF-1 was defined as good metabolic status; and the intermediate cluster was defined as average metabolic status. Most dairy cows were classified as having average or good metabolic status, and a limited number of cows had poor metabolic status (10-50 cows per lactation week). On-farm cow data, including dry period length, parity, milk production traits, and body weight, were used to predict good or average metabolic status with 8 machine learning algorithms. Random Forest (error rate ranging from 12.4 to 22.6%) and Support Vector Machine (SVM; error rate ranging from 12.4 to 20.9%) were the top 2 best-performing algorithms to predict metabolic status using on-farm cow data. Random Forest had a higher sensitivity (range: 67.8-82.9% during wk 1 to 7) and negative predictive value (range: 89.5-93.8%) but lower specificity (range: 76.7-88.5%) and positive predictive value (range: 58.1-78.4%) than SVM. In Random Forest, milk yield, fat yield, protein percentage, and lactose yield had important roles in prediction, but their rank of importance differed across lactation weeks. In conclusion, dairy cows could be clustered for metabolic status based on plasma metabolites and metabolic hormones. Moreover, on-farm cow data can predict cows in good or average metabolic status, with Random Forest and SVM performing best of all algorithms.
奶牛在泌乳早期的代谢状态可以通过血浆中β-羟丁酸（BHB）、游离脂肪酸（FFA）、葡萄糖、胰岛素和胰岛素样生长因子 1（IGF-1）的浓度来评估。然而，这些血浆代谢物和代谢激素在农场中很难测量。相反，在农场中容易获得的牛数据，如产奶量性状，有可能预测代谢状态。在这里，我们的目标是：（1）研究个体奶牛在泌乳早期是否可以根据其血浆值进行聚类；（2）评估使用农场牛数据预测代谢状态的机器学习算法。通过泌乳周 1 到 7，每周测量 334 头奶牛的血浆代谢物和代谢激素，并将每头奶牛每周分为 1 个聚类。具有最高血浆 BHB 和 FFA 以及最低血浆葡萄糖、胰岛素和 IGF-1 的聚类被定义为代谢不良状态；具有最低血浆 BHB 和 FFA 以及最高血浆葡萄糖、胰岛素和 IGF-1 的聚类被定义为代谢良好状态；中间聚类被定义为代谢平均状态。大多数奶牛被归类为具有平均或良好的代谢状态，只有少数奶牛存在代谢不良状态（每个泌乳周 10-50 头奶牛）。使用 8 种机器学习算法，利用干奶期长度、胎次、产奶量性状和体重等农场牛数据来预测良好或平均的代谢状态。随机森林（错误率范围为 12.4%至 22.6%）和支持向量机（SVM；错误率范围为 12.4%至 20.9%）是使用农场牛数据预测代谢状态的前 2 个表现最好的算法。随机森林具有更高的敏感性（第 1 周到第 7 周的范围为 67.8%-82.9%）和负预测值（范围为 89.5%-93.8%），但特异性较低（范围为 76.7%-88.5%）和阳性预测值（范围为 58.1%-78.4%）低于 SVM。在随机森林中，产奶量、脂肪产量、蛋白质百分比和乳糖产量在预测中具有重要作用，但它们的重要性排名在不同的泌乳周有所不同。总之，奶牛可以根据血浆代谢物和代谢激素进行代谢状态聚类。此外，农场牛数据可以预测处于良好或平均代谢状态的奶牛，随机森林和 SVM 在所有算法中表现最好。
