Thompson Jake S, Green Martin J, Hyde Robert, Bradley Andrew J, O'Grady Luke
School of Veterinary Medicine and Science, University of Nottingham, Nottingham, United Kingdom.
Quality Milk Management Services Ltd., Easton Hill, United Kingdom.
Front Vet Sci. 2023 Dec 8;10:1297750. doi: 10.3389/fvets.2023.1297750. eCollection 2023.
Udder health remains a priority for the global dairy industry to reduce pain, economic losses, and antibiotic usage. The dry period is a critical time for the prevention of new intra-mammary infections and it provides a point for curing existing intra-mammary infections. Given the wealth of udder health data commonly generated through routine milk recording and the importance of udder health to the productivity and longevity of individual cows, an opportunity exists to extract greater value from cow-level data to undertake risk-based decision-making. The aim of this research was to construct a machine learning model, using routinely collected farm data, to make probabilistic predictions at drying off for an individual cow's risk of a raised somatic cell count (hence intra-mammary infection) post-calving. Anonymized data were obtained as a large convenience sample from 108 UK dairy herds that undertook regular milk recording. The outcome measure evaluated was the presence of a raised somatic cell count in the 30 days post-calving in this observational study. Using a 56-farm training dataset, machine learning analysis was performed using the extreme gradient boosting decision tree algorithm, . External validation was undertaken on a separate 28-farm test dataset. Statistical assessment to evaluate model performance using the external dataset returned calibration plots, a Scaled Brier Score of 0.095, and a Mean Absolute Calibration Error of 0.009. Test dataset model calibration performance indicated that the probability of a raised somatic cell count post-calving was well differentiated across probabilities to allow an end user to apply group-level risk decisions. Herd-level new intra-mammary infection rate during the dry period was a key driver of the probability that a cow had a raised SCC post-calving, highlighting the importance of optimizing environmental hygiene conditions. In conclusion, this research has determined that probabilistic classification of the risk of a raised SCC in the 30 days post-calving is achievable with a high degree of certainty, using routinely collected data. These predicted probabilities provide the opportunity for farmers to undertake risk decision-making by grouping cows based on their probabilities and optimizing management strategies for individual cows immediately after calving, according to their likelihood of intra-mammary infection.
乳房健康仍然是全球乳制品行业的首要任务，以减轻疼痛、减少经济损失并降低抗生素使用量。干奶期是预防新的乳房内感染的关键时期，也是治愈现有乳房内感染的时机。鉴于通过常规牛奶记录通常会产生大量乳房健康数据，且乳房健康对个体奶牛的生产力和寿命至关重要，因此存在从奶牛层面数据中提取更大价值以进行基于风险的决策的机会。本研究的目的是构建一个机器学习模型，利用常规收集的农场数据，对个体奶牛产犊后体细胞计数升高（即乳房内感染）的风险进行干奶期概率预测。作为一个大型便利样本，从108个进行常规牛奶记录的英国奶牛群中获取了匿名数据。在这项观察性研究中，评估的结果指标是产犊后30天内体细胞计数升高的情况。使用一个包含56个农场的训练数据集，采用极端梯度提升决策树算法进行机器学习分析。在一个单独的包含28个农场的测试数据集上进行了外部验证。使用外部数据集评估模型性能的统计评估得出了校准图、缩放布里尔评分为0.095以及平均绝对校准误差为0.009。测试数据集模型校准性能表明，产犊后体细胞计数升高的概率在不同概率水平上有很好的区分度，使最终用户能够应用群体层面的风险决策。干奶期牛群层面新的乳房内感染率是奶牛产犊后体细胞计数升高概率的关键驱动因素，突出了优化环境卫生条件的重要性。总之，本研究确定，使用常规收集的数据，可以高度确定地实现产犊后30天内体细胞计数升高风险的概率分类。这些预测概率为奶农提供了机会，根据奶牛乳房内感染的可能性，按概率对奶牛进行分组，并在产犊后立即针对个体奶牛优化管理策略，从而进行风险决策。
