英国夏季牛棚内外环境条件的关系:能否通过外界条件预测温度湿度指数?

A.T. Chamberlain , C.D. Powell , E. Arcier , N. Aldenhoven
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引用次数: 7

摘要

热应激在奶牛中是一个日益严重的问题,人们对不使用特定农场数据计算热应激风险(温度湿度指数- THI)和提前几天预测THI变化的兴趣正在发展。以前的工作人员已经证明,使用当地气象站的数据计算牛棚内的THI值不够准确。天气预报正变得更加本地化,可以预测农场的温度和湿度。这项研究着眼于从英国农场牛棚外收集的数据中预测牛棚内THI的效果。从2021年5月1日到2021年9月30日,使用定制的数据监测器对六个农场进行了监测,这些监测器通过蜂窝网络将数据实时上传到云端。牛棚内外THI计算值高度相关(P <0.001),预测棚内THI与棚外THI的回归非常显著(P <0.001)。然而,特定农场的回归具有显著不同的回归截距。将产犊模式类型(秋季或全年)和自然月份单独或共同考虑,均能改善回归。简单单组分模型(THIoutside)预测的95%置信区间(CI)为10.8 THI单位,三组分模型(THIoutside、日历月、产羔模式类型)预测的95%置信区间(CI)为7.8。特定农场回归的CI值最低,表明存在未被捕获的特定农场影响THI的因素。作为预测模型,简单的单组分回归将是最适用的,但相对较高的CI意味着,在不同农场的热应激风险被低估或过度强调的情况下,预测将不那么准确。在热应激奶牛中,一个THI单位相当于大约200毫升的产奶量下降,这样的误差将具有生物学和商业意义。这在一定程度上可能是由于THI方程只考虑了温度和湿度,而忽略了太阳辐射、阴影、风和动物因素,如产奶量、怀孕阶段、体重和遗传变异。进一步的工作正在进行中,以开发一个指数来量化奶牛对综合热负荷因素的反应,这可能会改善热应激的预测。
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The relationship between on-farm environmental conditions inside and outside cow sheds during the summer in England: can Temperature Humidity Index be predicted from outside conditions?

Heat stress is a growing problem in dairy cows, and interest is developing in calculating heat stress risk (Temperature Humidity Index – THI) without using specific farm data and in forecasting THI changes a few days in advance. Previous workers have shown that calculating THI values inside cattle sheds using data from local Meteorological Stations is not sufficiently accurate. Weather forecasting is becoming more local and can forecast on-farm temperature and humidity. This work looked at how well THI inside a cow shed could be predicted from data collected outside the cow shed on British farms. Six farms were monitored from 1 May 2021 to 30 Sept 2021 using bespoke data monitors that uploaded the data to the cloud in real time through the cellular network. Calculated THI values for inside and outside the cow shed were highly correlated (P < 0.001), and a regression predicting THI inside the shed from the THI outside the shed was highly significant (P < 0.001). However, farm-specific regressions had significantly different regression intercepts. Including calving pattern type (autumn or all year round) and calendar month separately or together improved the regression. The 95% confidence interval (CI) of the prediction was 10.8 THI units for the simple one-component model (THIoutside) and 7.8 for the three-component model (THIoutside, calendar month, calving pattern type). Farm-specific regressions had the lowest CI values suggesting there are farm-specific factors affecting THI that had not been captured. As a predictive model, the simple single component regression would be the most applicable but the relatively high CI means that predictions will not be that accurate with the risk of heat stress either under- or overemphasised on different farms. With one THI unit equating to approximately a 200 ml drop in milk yield in heat-stressed cows, such errors will be of biological and commercial significance. This in part may be due to the THI equation only considering temperature and humidity and ignoring solar radiation, shade, wind and animal factors such as milk yield, stage of pregnancy, weight and genetic variability. Further work is underway to develop an index that quantifies how the cow is responding to the combined heat-loading factors which may improve the prediction of heat stress.

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Corrigendum to “The role of anti-E. coli antibody from maternal colostrum on the colonization of newborn dairy calves gut with Escherichia coli and the development of clinical diarrhea” [Animal Open Space 2 (2023) 100037] Method: Body composition assessment of sows using dual-energy X-ray absorptiometry Data paper: Dataset describing the effects of environmental enrichment and sows’ characteristics on the peripheral blood mononuclear cell transcriptome Method: Protocol for in-ovo stimulation with selected pro-/prophy-biotics to mitigate Campylobacter jejuni in broiler chickens Method: Standard operating procedure for the administration of swallowable devices to study pig’s gut content in a non-invasive way
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