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.
Laboratory of Systems and Synthetic Biology, Wageningen University and Research, Stippeneng 4, 6708 WE, Wageningen, the Netherlands.
J Dairy Sci. 2020 Jul;103(7):6576-6582. doi: 10.3168/jds.2019-17284. Epub 2020 May 21.
The objectives of this study were (1) to evaluate if hyperketonemia in dairy cows (defined as plasma β-hydroxybutyrate ≥1.0 mmol/L) can be predicted using on-farm cow data either in current or previous lactation week, and (2) to study if adding individual net energy intake (NEI) can improve the predictive ability of the model. Plasma β-hydroxybutyrate concentration, on-farm cow data (milk yield, percentage of fat, protein and lactose, fat- and protein-corrected milk yield, body weight, body weight change, dry period length, parity, and somatic cell count), and NEI of 424 individual cows were available weekly through lactation wk 1 to 5 postpartum. To predict hyperketonemia in dairy cows, models were first trained by partial least square discriminant analysis, using on-farm cow data in the same or previous lactation week. Second, NEI was included in models to evaluate the improvement of the predictability of the models. Through leave-one trial-out cross-validation, models were evaluated by accuracy (the ratio of the sum of true positive and true negative), sensitivity (68.2% to 84.9%), specificity (61.5% to 98.7%), positive predictive value (57.7% to 98.7%), and negative predictive value (66.2% to 86.1%) to predict hyperketonemia of dairy cows. Through lactation wk 1 to 5, the accuracy to predict hyperketonemia using data in the same week was 64.4% to 85.5% (on-farm cow data only), 66.1% to 87.0% (model including NEI), and using data in the previous week was 58.5% to 82.0% (on-farm cow data only), 59.7% to 85.1% (model including NEI). An improvement of the accuracy of the model due to including NEI ranged among lactation weeks from 1.0% to 4.4% when using data in the same lactation week and 0.2% to 6.6% when using data in the previous lactation week. In conclusion, trained models via partial least square discriminant analysis have potential to predict hyperketonemia in dairy cows not only using data in the current lactation week, but also using data in the previous lactation week. Net energy intake can improve the accuracy of the model, but only to a limited extent. Besides NEI, body weight, body weight change, milk fat, and protein content were important variables to predict hyperketonemia, but their rank of importance differed across lactation weeks.
（1）评估奶牛的高酮血症（定义为血浆β-羟丁酸≥1.0mmol/L）是否可以通过当前或前泌乳周的农场数据来预测；（2）研究是否可以通过添加个体净能摄入（NEI）来提高模型的预测能力。每周通过泌乳周 1 到 5 次收集 424 头奶牛的个体血浆β-羟丁酸浓度、农场数据（产奶量、脂肪百分比、蛋白质和乳糖、脂肪和蛋白质校正奶产量、体重、体重变化、干奶期长度、胎次和体细胞计数）和 NEI。为了预测奶牛的高酮血症，模型首先通过偏最小二乘判别分析进行训练，使用同一泌乳周或前一泌乳周的农场数据。其次，将 NEI 纳入模型，以评估模型预测能力的提高。通过留一法外验证，通过准确性（真阳性和真阴性之和的比例）、敏感性（68.2%至 84.9%）、特异性（61.5%至 98.7%）、阳性预测值（57.7%至 98.7%）和阴性预测值（66.2%至 86.1%）来评估模型对奶牛高酮血症的预测效果。通过泌乳周 1 到 5，使用同一周的数据预测高酮血症的准确性为 64.4%至 85.5%（仅使用农场数据）、66.1%至 87.0%（包括 NEI 的模型），使用前一周的数据为 58.5%至 82.0%（仅使用农场数据）、59.7%至 85.1%（包括 NEI 的模型）。在使用同一泌乳周的数据时，由于包含 NEI，模型准确性的提高范围在 1.0%至 4.4%之间，而在使用前一泌乳周的数据时，提高范围在 0.2%至 6.6%之间。总之，通过偏最小二乘判别分析训练的模型不仅可以使用当前泌乳周的数据，还可以使用前一泌乳周的数据来预测奶牛的高酮血症。净能摄入可以提高模型的准确性，但提高程度有限。除 NEI 外，体重、体重变化、乳脂和蛋白质含量也是预测高酮血症的重要变量，但它们在不同泌乳周的重要性排名不同。
