Prediction models, assessment methodologies and biotechnological tools to quantify heat stress response in ruminant livestock

Livestock industries have an important role in ensuring global food security. This review discusses the importance of quantifying the heat stress response of ruminants, with an emphasis on identifying thermo-tolerant breeds. There are numerous heat stress prediction models that have attempted to quantify the response of ruminant livestock to hot climatic conditions. This review highlights the importance of investigating prediction models beyond the temperature-humidity index (THI). Furthermore, this review highlights the importance of incorporating other climatic variables when developing prediction indices to ensure the accurate prediction of heat stress in ruminants. Prediction models, particularly the heat load index (HLI) were developed to overcome the limitations of the THI by incorporating ambient temperature (AT), relative humidity (RH), solar radiation (SR) and wind speed (WS). Furthermore refinements to existing prediction models have been undertaken to account for the interactions between climatic variables and physiological traits of livestock. Specifically, studies have investigated the relationships between coat characteristics, respiration rate (RR), body temperature (BT), sweating rate, vasodilation, body weight (BW), body condition score (BCS), fatness and feed intake with climatic conditions. While advancements in prediction models have been occurring, there has also been substantial advancement in the methodologies used to quantify animal responses to heat stress. The most recent development in this field is the application of radio frequency identification (RFID) technology to record animal behaviour and various physiological responses. Rumen temperature measurements using rumen boluses and skin temperature recording using infrared thermography (IRT) are making inroads to redefine the quantification of the heat stress response of ruminants. Further, this review describes several advanced biotechnological tools that can be used to identify climate resilient breeds of ruminant livestock.

Evaluating rumen temperature as an estimate of core body temperature in Angus feedlot cattle during summer

This study was conducted to determine the relationship between rectal temperature (T_REC) and rumen temperature (T_RUM) and to assess if T_RUM could be used as a proxy measure of core body temperature (T_CORE) in feedlot cattle. Eighty Angus steers (388.8 ± 2.1 kg) were orally administered with rumen temperature boluses. Rumen temperatures were recorded at 10-min intervals over 128 days from all 80 steers. To define the suitability of T_RUM as an estimation of T_CORE, T_REC were obtained from all steers at 7-day intervals (n = 16). Eight feedlot pens were used where there were 10 steers per pen (162 m²). Shade was available in each pen (1.8 m²/animal; 90% solar block). Climatic data were recorded at 30-min intervals, including ambient temperature (T_A; °C); relative humidity (RH; %); wind speed (WS; m/s) and direction; solar radiation (SR; W/m²); and black globe temperature (BGT; °C). Rainfall (mm) was recorded daily at 0900 h. From these data, temperature humidity index (THI), heat load index (HLI) and accumulated heat load (AHL) were calculated. Individual 10-min T_RUM data were converted to an individual hourly average. Pooled mean hourly T_RUM data from the 128-day data were used to establish the diurnal rhythm of T_RUM where the mean minimum (39.19 ± 0.01 °C) and mean maximum (40.04 ± 0.01 °C) were observed at 0800 h and 2000 h respectively. A partial correlation coefficient indicated that there were moderate to strong relationships between T_RUM and T_REC using both real-time (r = 0.55; P < 0.001) and hourly mean (r = 0.51; P < 0.001) T_RUM data. The mean difference between T_REC and T_RUM was small using both real-time (0.16 ± 0.02 °C) and hourly mean T_RUM (0.13 ± 0.02 °C) data. Data from this study supports the hypothesis that T_RUM can be used as an estimate of T_CORE, suggesting that T_RUM can be used to measure and quantify heat load in feedlot cattle.