Livestock plays an important role in the global economy. Climate change effects are not only limited to crop production, but also affect livestock production, for example reduced milk yields and milk quality, reduced meat production and reduced fertility. Therefore, livestock-based food security is threatened in many parts of the world. Furthermore, multiple stressors are a common phenomenon in many environments, and are likely to increase due to climate change. Among these stresses, heat stress appears to be the major factor which negatively influences livestock production. Hence, it is critical to identify agro-ecological zone-specific climate resilient thermo-tolerant animals to sustain livestock production. Livestock responds to the changing environments by altering their phenotypic and physiological characters. Therefore, survivability of the animal often depends on its ability to cope with or adapt to the existing conditions. So to sustain livestock production in an environment challenged by climate change, the animals must be genetically suitable and have the ability to survive in diversified environments. Biological markers or biomarkers indicate the biological states or alterations in expression pattern of genes or state of protein that serve as a reference point in breeding for the genetic improvement of livestock. Conventionally, identification of animals with superior genetic traits that were economically beneficial was the fundamental reason for identifying biomarkers in animals. Furthermore, compared with the behavioural, morphological or physiological responses in animals, the genetic markers are important because of the possibility of finding a solution to animal adaptability to climate change.

The ability to predict the effects of extreme climatic variables on livestock is important in terms of welfare and performance. An index combining temperature and humidity (THI) has been used for more than 4 decades to assess heat stress in cattle. However, the THI does not include important climatic variables such as solar load and wind speed (WS, m/s). Likewise, it does not include management factors (the effect of shade) or animal factors (genotype differences). Over 8 summers, a total of 11,669 Bos taurus steers, 2,344 B. taurus crossbred steers, 2,142 B. taurus x Bos indicus steers, and 1,595 B. indicus steers were used to develop and test a heat load index (HLI) for feedlot cattle. A new HLI incorporating black globe (BG) temperature ( degrees C), relative humidity (RH, decimal form), and WS was initially developed by using the panting score (PS) of 2,490 Angus steers. The HLI consists of 2 parts based on a BG temperature threshold of 25 degrees C: HLI(BG>25) = 8.62 + (0.38 x RH) + (1.55 x BG) - (0.5 x WS) + e((2.4-WS)), and HLI(BG<25) = 10.66 + (0.28 x RH) + (1.3 x BG) - WS, where e is the base of the natural logarithm. A threshold HLI above which cattle of different genotypes gain body heat was developed for 7 genotypes. The threshold for unshaded black B. taurus steers was 86, and for unshaded B. indicus (100%) the threshold was 96. Threshold adjustments were developed for factors such as coat color, health status, access to shade, drinking water temperature, and manure management. Upward and downward adjustments are possible; upward adjustments occur when cattle have access to shade (+3 to +7) and downward adjustments occur when cattle are showing clinical signs of disease (-5). A related measure, the accumulated heat load (AHL) model, also was developed after the development of the HLI. The AHL is a measure of the animal's heat load balance and is determined by the duration of exposure above the threshold HLI. The THI and THI-hours (hours above a THI threshold) were compared with the HLI and AHL. The relationships between tympanic temperature and the average HLI and THI for the previous 24 h were R(2) = 0.67, P < 0.001, and R(2) = 0.26, P < 0.001, respectively. The R(2) for the relationships between HLI or AHL and PS were positive (0.93 and 0.92 for HLI and AHL, respectively, P < 0.001). The R(2) for the relationship between THI and PS was 0.61 (P < 0.001), and for THI-hours was 0.37 (P < 0.001). The HLI and the AHL were successful in predicting PS responses of different cattle genotypes during periods of high heat load.

Heat stress and cold stress have a negative influence on cattle welfare and productivity. There have been some studies investigating the influence of cold stress on cattle, however the emphasis within this review is the influence of heat stress on cattle. The impact of hot weather on cattle is of increasing importance due to the changing global environment. Heat stress is a worldwide phenomenon that is associated with reduced animal productivity and welfare, particularly during the summer months. Animal responses to their thermal environment are extremely varied, however, it is clear that the thermal environment influences the health, productivity, and welfare of cattle. Whilst knowledge continues to be developed, managing livestock to reduce the negative impact of hot climatic conditions remains somewhat challenging. This review provides an overview of the impact of heat stress on production and reproduction in bovines.

Numerous models and indices exist that attempt to characterize the effect of environmental factors on the comfort of animals and humans. Heat and cold indices have been utilized to adjust ambient temperature (Ta) for the effects of relative humidity (RH) or wind speed (WS) or both for the purposes of obtaining a “feels-like” or apparent temperature. However, no model has been found that incorporates adjustments for RH, WS, and radiation (RAD) over conditions that encompass hot and cold environmental conditions. The objective of this study was to develop a comprehensive climate index (CCI) that has application under a wide range of environmental conditions and provides an adjustment to Ta for RH, WS, and RAD. Environmental data were compiled from 9 separate summer periods in which heat stress events occurred and from 6 different winter periods to develop and validate the CCI. The RH adjustment is derived from an exponential relationship between Ta and RH with temperature being adjusted up or down from an RH value of 30%. At 45°C, the temperature adjustment for increasing RH from 30 to 100% equals approximately 16°C, whereas at −30°C temperature adjustments due to increasing RH from 30 to 100% equal approximately −3.0°C, with greater RH values contributing to a reduced apparent temperature under cold conditions. The relationship between WS and temperature adjustments was also determined to be exponential with a logarithmic adjustment to define appropriate declines in apparent temperature as WS increases. With this index, slower WS results in the greatest change in apparent temperature per unit of WS regardless of whether hot or cold conditions exist. As WS increases, the change in apparent temperature per unit of WS becomes less. Based on existing windchill and heat indices, the effect of WS on apparent temperature is sufficiently similar to allow one equation to be utilized under hot and cold conditions. The RAD component was separated into direct solar radiation and ground surface radiation. Both of these were found to have a linear relationship with Ta. This index will be useful for further development of biological response functions, which are associated with energy exchange, and improving decision-making processes, which are weather-dependent. In addition, the defined thresholds can serve as management and environmental mitigation guidelines to protect and ensure animal comfort.

Climate change presents a range of challenges for animal agriculture in Australia. Livestock production will be affected by changes in temperature and water availability through impacts on pasture and forage crop quantity and quality, feed-grain production and price, and disease and pest distributions. This paper provides an overview of these impacts and the broader effects on landscape functionality, with a focus on recent research on effects of increasing temperature, changing rainfall patterns, and increased climate variability on animal health, growth, and reproduction, including through heat stress, and potential adaptation strategies. The rate of adoption of adaptation strategies by livestock producers will depend on perceptions of the uncertainty in projected climate and regional-scale impacts and associated risk. However, management changes adopted by farmers in parts of Australia during recent extended drought and associated heatwaves, trends consistent with long-term predicted climate patterns, provide some insights into the capacity for practical adaptation strategies. Animal production systems will also be significantly affected by climate change policy and national targets to address greenhouse gas emissions, since livestock are estimated to contribute ~10% of Australia’s total emissions and 8–11% of global emissions, with additional farm emissions associated with activities such as feed production. More than two-thirds of emissions are attributed to ruminant animals. This paper discusses the challenges and opportunities facing livestock industries in Australia in adapting to and mitigating climate change. It examines the research needed to better define practical options to reduce the emissions intensity of livestock products, enhance adaptation opportunities, and support the continued contribution of animal agriculture to Australia’s economy, environment, and regional communities.