In this study, we developed a calving prediction model based on continuous measurements of ventral tail base skin temperature (ST) with supervised machine learning and evaluated the predictive ability of the model in 2 dairy farms with distinct cattle management practices. The ST data were collected at 2- or 10-min intervals from 105 and 33 pregnant cattle (mean ± standard deviation: 2.2 ± 1.8 parities) reared in farms A (freestall barn, in a temperate climate) and B (tiestall barn, in a subarctic climate), respectively. After extracting maximum hourly ST, the change in values was expressed as residual ST (rST = actual hourly ST − mean ST for the same hour on the previous 3 d) and analyzed. In both farms, rST decreased in a biphasic manner before calving. Briefly, an ambient temperature–independent gradual decrease occurred from around 36 to 16 h before calving, and an ambient temperature-dependent sharp decrease occurred from around 6 h before until calving. To make a universal calving prediction model, training data were prepared from pregnant cattle under different ambient temperatures (10 data sets were randomly selected from each of the 3 ambient temperature groups: <15°C, ≥15°C to <25°C, and ≥25°C in farm A). An hourly calving prediction model was then constructed with the training data by support vector machine based on 15 features extracted from sensing data (indicative of pre-calving rST changes) and 1 feature from non-sensor-based data (days to expected calving date). When the prediction model was applied to the data that were not part of the training process, calving within the next 24 h was predicted with sensitivities and precisions of 85.3% and 71.9% in farm A (n = 75), and 81.8% and 67.5% in farm B (n = 33), respectively. No differences were observed in means and variances of intervals from the calving alerts to actual calving between farms (12.7 ± 5.8 and 13.0 ± 5.6 h in farms A and B, respectively). Above all, a calving prediction model based on continuous measurement of ST with supervised machine learning has the potential to achieve effective calving prediction, irrespective of the rearing condition in dairy cattle.

在这项研究中，我们基于有监督的机器学习，在连续测量腹侧尾巴基部皮肤温度（ST）的基础上，开发了产犊预测模型，并在具有不同养牛管理规范的2个奶牛场中评估了该模型的预测能力。ST数据以2或10分钟为间隔，分别从A和F牛场（温带气候下的保育牛舍）饲养的105和33头怀孕牛（平均±标准差：2.2±1.8平价）采集。亚北极气候）。提取最大小时ST之后，将值的变化表示为残余ST（rST =实际小时ST-前3天同一小时的平均ST）并进行分析。在两个农场，产犊前rST呈双相下降。简而言之，产犊前36到16 h左右，与环境温度无关的逐渐下降，产犊前6 h左右与环境温度有关的急剧下降发生。为了建立通用的产犊预测模型，在不同环境温度下从怀孕的牛准备了训练数据（从3个环境温度组（<15°C，≥15°C至<25°C，且农场A中的温度≥25°C）。然后，通过支持向量机，基于从感测数据中提取的15个特征（指示产犊前rST变化）和从非基于传感器的数据中提取1个特征（从天数到预期产犊日期），利用训练数据构建每小时产犊预测模型。 。当将预测模型应用于训练过程之外的数据时，预计接下来24小时内产犊的敏感性和精确度在A农场（n = 75）分别为85.3％和71.9％，在B农场（n = 33）分别为81.8％和67.5％。从产犊警报到实际产犊之间的时间间隔和均值差异没有观察到（农场A和B分别为12.7±5.8和13.0±5.6 h）。最重要的是，基于带监督的机器学习对ST的连续测量的产犊预测模型有可能实现有效的产犊预测，而与奶牛的饲养条件无关。A和B场分别为0±5.6小时）。最重要的是，基于带监督的机器学习的ST连续测量的产犊预测模型有可能实现有效的产犊预测，而与奶牛的饲养条件无关。A和B场分别为0±5.6小时）。最重要的是，基于带监督的机器学习对ST的连续测量的产犊预测模型有可能实现有效的产犊预测，而与奶牛的饲养条件无关。