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?

Abstract

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 (THI_outside) and 7.8 for the three-component model (THI_outside, 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.