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Climate change is a major global threat to the sustainability of livestock systems. Climatic factors such as ambient temperature, relative humidity, direct and indirect solar radiation and wind speed influence feed and water availability, fodder quality and disease occurrence, with production being most efficient in optimal environmental conditions. Among these climatic variables, ambient temperature fluctuations have the most impact on livestock production and animal welfare. Continuous exposure of the animals to heat stress compromises growth, milk and meat production and reproduction. The capacity of an animal to mitigate effects of increased environmental temperature, without progressing into stress response, differs within and between species. Comparatively, small ruminants are better adapted to hot environments than large ruminants and have better ability to survive, produce and reproduce in harsh climatic regions. Nevertheless, the physiological and behavioral changes in response to hot environments affect small ruminant production. It has been found that tropical breeds are more adaptive to hot climates than high-producing temperate breeds. The growing body of knowledge on the negative impact of heat stress on small ruminant production and welfare will assist in the development of suitable strategies to mitigate heat stress. Selection of thermotolerant breeds, through identification of genetic traits for adaption to extreme environmental conditions (high temperature, feed scarcity, water scarcity), is a viable strategy to combat climate change and minimize the impact on small ruminant production and welfare. This review highlights such adaption within and among different breeds of small ruminants challenged by heat stress.

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The objective of this experiment was to evaluate the influence of summer heat stress on physiological and behavioral responses of Osmanabadi, Salem Black, and Malabari goats. The study also evaluated the differences in heat shock protein 70 (HSP70) expression pattern between these breeds. The study was conducted over 45 days during summer (April-May) using 36 1-year-old female goats by randomly allocating them into six groups with six animals in each group: Osmanabadi control (Osmanabadi CON), Osmanabadi heat stress (Osmanabadi HS), Malabari control (Malabari CON), Malabari heat stress (Malabari HS), Salem Black control (Salem Black CON), and Salem Black heat stress (Salem Black HS). The Osmanabadi CON, Malabari CON, and Salem Black CON animals were housed in a shed while the Osmanabadi HS, Malabari HS, and Salem Black HS groups were subjected to heat stress by exposing them to outside environment between 1000 and 1600 h during the experimental period. All 36 animals were provided with ad libitum feed and water. The data generated were analyzed by general linear model (GLM) repeated measurement analysis of variance. Results indicated that the drinking frequency (DF) was higher (p < 0.01) in heat stress groups (12.58, 12.25, and 10.75 times for the Osmanabadi HS, Malabari HS, and Salem Black HS, respectively) as compared to their respective control groups (5.67, 6.25, 5.58 times for the Osmanabadi CON, Malabari CON, and Salem Black CON, respectively). Water intake (WI) also showed similar trend to DF. The urinating frequency also (UF) differed between breeds with lower value (p < 0.05) recorded in the Salem Black HS (1.5 times) compared to the Malabari HS (2.92 times). The highest (p < 0.05) rumination time (RuT) was recorded in the Malabari HS (48.00 min) than both the Osmanabadi HS (20.91 min) and Salem Black HS (23.67 min). The heat stress increased (p < 0.05) all physiological variables at 1400 h. The findings of this study suggest RR, RT, and PBMC HSP70 are reliable biological markers for evaluating thermo-tolerance capacity of indigenous goat breeds.

The objective of this study was to measure the impacts of summer heat events on physiological parameters (body temperature, respiratory rate and panting scores), grazing behaviour and production parameters of lactating Holstein Friesian cows managed on an Automated Robotic Dairy during Australian summer. The severity of heat stress was measured using Temperature-Humidity Index (THI) and impacts of different THIs—low (≤72), moderate (73–82) and high (≥83)—on physiological responses and production performance were measured. There was a highly significant (p ≤ 0.01) effect of THI on respiratory rate (66.7, 84.7 and 109.1/min), panting scores (1.4, 1.9 and 2.3) and average body temperature of cows (38.4, 39.4 and 41.5 °C), which increased as THI increased from low to moderate to high over the summer. Average milk production parameters were also significantly (p ≤ 0.01) affected by THI, such that daily milk production dropped by 14% from low to high THI, milk temperature and fat% increased by 3%, whilst protein% increased by 2%. The lactation stage of cow had no significant effect on physiological parameters but affected (p ≤ 0.05) average daily milk yield and milk solids. Highly significant (p ≤ 0.01) positive correlations were obtained between THI and milk temperature, fat% and protein% whilst the reverse was observed between THI and milk yield, feed intake and rumination time. Under moderate and high THI, most cows sought shade, spent more time around watering points and showed signs of distress (excessive salivation and open mouth panting). In view of the expected future increase in the frequency and severity of heat events, additional strategies including selection and breeding for thermotolerance and dietary interventions to improve resilience of cows need to be pursued.

Heat stress affects the fertility and reproductive livestock performance by compromising the physiology reproductive tract, through hormonal imbalance, decreased oocyte quality and poor semen quality, and decreased embryo development and survival. Heat stress decreases the secretion of luteinizing hormone and estradiol resulting in reduced length and intensity of estrus expression, increased incidence of anoestrus and silent heat in farm animals. Oocytes exposed to thermal stress lose its competence for fertilization and development into the blastocyst stage, which results in decreased fertility because of the production of poor quality oocytes and embryos. Furthermore, low progesterone secretion limits the endometrial functions, and subsequently embryo development. In addition, the increased secretion of endometrial prostaglandin F2 alpha during heat stress threatens the maintenance of pregnancy. In general, the percentage of conception rate was found to be reduced by 4.6% for each unit increase in temperature humidity index (THI) above 70, and heat stress during pregnancy further slows down the growth of the foetus and results in lower birth weight. In tropical and subtropical regions, during hot days, the testicular temperature may increase and impair both the spermatogenic cycle and semen quality, which culminates in decreased bull fertility. The effects of heat stress on livestock can be minimized via adapting suitable scientific strategies comprising physical modifications of the environment, nutritional management and genetic development of breeds that are less sensitive to heat stress. In addition, the summer infertility may be countered through advanced reproductive technologies involving hormonal treatments, timed artificial insemination and embryo transfer, which may enhance the chances for establishing pregnancy in farm animals.

Live sheep export has become a public concern. This study aimed to test a non-contact biometric system based on artificial intelligence to assess heat stress of sheep to be potentially used as automated animal welfare assessment in farms and while in transport. Skin temperature (°C) from head features were extracted from infrared thermal videos (IRTV) using automated tracking algorithms. Two parameter engineering procedures from RGB videos were performed to assess Heart Rate (HR) in beats per minute (BPM) and respiration rate (RR) in breaths per minute (BrPM): (i) using changes in luminosity of the green (G) channel and (ii) changes in the green to red (a) from the CIELAB color scale. A supervised machine learning (ML) classification model was developed using raw RR parameters as inputs to classify cutoff frequencies for low, medium, and high respiration rate (Model 1). A supervised ML regression model was developed using raw HR and RR parameters from Model 1 (Model 2). Results showed that Models 1 and 2 were highly accurate in the estimation of RR frequency level with 96% overall accuracy (Model 1), and HR and RR with R = 0.94 and slope = 0.76 (Model 2) without statistical signs of overfitting

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