Identifying and predicting heat stress events for grazing dairy cows using rumen temperature boluses

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Abstract

Heat stress events in dairy cows are associated with behavioral and physiological changes such as seeking shade, increased respiration rate and body temperature, reduced milk production, and psychological distress. Knowledge of the relationship between weather and animal responses to heat stress enables automated alerts using forecast weather, aiding early provision of shade or other mitigation practices. While numerous heat stress indices for cattle have been developed, these have limitations for cows exposed to wind and solar radiation (i.e., predominantly grazing outdoors or managed on pasture). To develop a predictive model for heat stress events in pasture-based dairy systems, rumen temperature data from smaXtec (smaXtec animal care GmbH, Graz, Austria) rumen boluses in 443 cows on 3 dairy farms in Northland, New Zealand, were used to identify heat stress events and these were matched with automated weather station data collected on or near the farm. Heat stress rate (HSR) was defined as the percentage of cows within an age-breed group having a rumen temperature greater than 3 standard deviations above an individual cow's mean and heat stress events were defined as HSR >25%. Single and multiple linear regression models, including published heat stress indices, were generally able to predict a high proportion of heat stress events (sensitivity 34%–68%), but were insufficiently discriminating, predicting also a high number of false positives (precision only 9%–27%). A machine learning algorithm, cubist, was the best performing model, predicting 79% of heat stress events with a precision of 52% for this dataset. Our proof-of-concept study demonstrates the potential of this approach, using climate data to predict and forecast heat stress events in pasture-based dairy systems. Further work should test the cubist model using independent data, refine dataset construction, investigate the value of including known animal variables such as cow age or breed, and incorporate other measures of heat stress such as respiration rate.

利用瘤胃温度栓识别和预测放牧奶牛的热应激事件
奶牛的热应激事件与行为和生理变化有关,如寻找阴凉处、呼吸频率和体温升高、产奶量下降和心理压力。通过了解天气与动物热应激反应之间的关系,可利用天气预报自动发出警报,帮助及早提供遮荫处或采取其他缓解措施。虽然已经开发了许多牛的热应激指数,但这些指数对于暴露在风和太阳辐射下的奶牛(即主要在户外放牧或在牧场管理的奶牛)有一定的局限性。为了开发牧场型奶牛系统热应激事件的预测模型,我们使用了来自 smaXtec(smaXtec 动物保健有限公司,奥地利格拉茨)的瘤胃温度数据,这些数据来自新西兰北地 3 个奶牛场 443 头奶牛的瘤胃栓,用于识别热应激事件,并与在牧场或牧场附近收集的自动气象站数据相匹配。热应激率(HSR)被定义为某一年龄品种组中瘤胃温度高于单头奶牛平均值 3 个标准差以上的奶牛百分比,热应激事件被定义为 HSR >25%。包括已公布的热应激指数在内的单线性和多元线性回归模型通常能够预测较高比例的热应激事件(灵敏度为 34%-68% ),但辨别能力不足,预测的假阳性结果也较多(精确度仅为 9%-27% )。机器学习算法 cubist 是性能最好的模型,在该数据集上可预测 79% 的热应激事件,精确度为 52%。我们的概念验证研究证明了这种利用气候数据预测和预报牧场奶牛系统热应激事件的方法的潜力。下一步工作应使用独立数据测试立方体模型,完善数据集的构建,研究纳入已知动物变量(如奶牛年龄或品种)的价值,并纳入其他热应激测量指标(如呼吸速率)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
JDS communications
JDS communications Animal Science and Zoology
CiteScore
2.00
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