This review attempted to collate and synthesize information on goat welfare and production constraints during heat stress exposure. Among the farm animals, goats arguably are considered the best-suited animals to survive in tropical climates. Heat stress was found to negatively influence growth, milk and meat production and compromised the immune response, thereby significantly reducing goats’ welfare under extensive conditions and transportation. Although considered extremely adapted to tropical climates, their production can be compromised to cope with heat stress. Therefore, information on goat adaptation and production performance during heat exposure could help assess their welfare. Such information would be valuable as the farming communities are often struggling in their efforts to assess animal welfare, especially in tropical regions. Broadly three aspects must be considered to ensure appropriate welfare in goats, and these include (i) housing and environment; (ii) breeding and genetics and (iii) handling and transport. Apart from these, there are a few other negative welfare factors in goat rearing, which differ across the production system being followed. Such negative practices are predominant in extensive systems and include nutritional stress, limited supply of good quality water, climatic extremes, parasitic infestation and lameness, culminating in low production, reproduction and high mortality rates. Broadly two types of methodologies are available to assess welfare in goats in these systems: (i) animal-based measures include behavioral measurements, health and production records and disease symptoms; (ii) resources based and management-based measures include stocking density, manpower, housing conditions and health plans. Goat welfare could be assessed based on several indicators covering behavioral, physical, physiological and productive responses. The important indicators of goat welfare include agonistic behavior, vocalization, skin temperature, body condition score (BCS), hair coat conditions, rectal temperature, respiration rate, heart rate, sweating, reduced growth, reduced milk production and reduced reproductive efficiency. There are also different approaches available by which the welfare of goats could be assessed, such as naturalistic, functional and subjective approaches. Thus, assessing welfare in goats at every production stage is a prerequisite for ensuring appropriate production in this all-important species to guarantee optimum returns to the marginal and subsistence farmers.

Non-invasive methods of detecting heat stress magnitude for livestock is gaining momentum in the context of global climate change. Therefore, the objective of this review is to focus on the synthesis information pertaining to recent efforts to develop heat stress detection systems for livestock based on multiple behavioral and physiological responses. There are a number of approaches to quantify farm animal heat stress response, and from an animal welfare point of view, these can be categorized as invasive and non-invasive approaches. The concept of a non-invasive approach to assess heat stress primarily looks into behavioral and physiological responses which can be monitored without any human interference or additional stress on the animal. Bioclimatic thermal indices can be considered as the least invasive approach to assess and/or predict the level of heat stress in livestock. The quantification and identification of the fecal microbiome in heat-stressed farm animals is one of the emerging techniques which could be effectively correlated with animal adaptive responses. Further, tremendous progress has been made in the last decade to quantify the classical heat stress endocrine marker, cortisol, non-invasively in the feces, urine, hair, saliva and milk of farm animals. In addition, advanced technologies applied for the real-time analysis of cardinal signs such as sounds through microphones, behavioral images, videos through cameras, and data stalking body weight and measurements might provide deeper insights towards improving biological metrics in livestock exposed to heat stress. Infrared thermography (IRT) can be considered another non-invasive modern tool to assess the stress response, production, health, and welfare status in farm animals. Various remote sensing technologies such as ear canal sensors, rumen boluses, rectal and vaginal probes, IRT, and implantable microchips can be employed in grazing animals to assess the quantum of heat stress. Behavioral responses and activity alterations to heat stress in farm animals can be monitored using accelerometers, Bluetooth technology, global positioning systems (GPSs) and global navigation satellite systems (GNSSs). Finally, machine learning offers a scalable solution in determining the heat stress response in farm animals by utilizing data from different sources such as hardware sensors, e.g., pressure sensors, thermistors, IRT sensors, facial recognition machine vision sensors, radio frequency identification, accelerometers, and microphones. Thus, the recent advancements in recording behavior and physiological responses offer new scope to quantify farm animals’ heat stress response non-invasively. These approaches could have greater applications in not only determining climate resilience in farm animals but also providing valuable information for defining suitable and accurate amelioration strategies to sustain their production.

Heat stress impacts ruminant livestock production on varied levels in this alarming climate breakdown scenario. The drastic effects of the global climate change-associated heat stress in ruminant livestock demands constructive evaluation of animal performance bordering on effective monitoring systems. In this climate-smart digital age, adoption of advanced and developing Artificial Intelligence (AI) technologies is gaining traction for efficient heat stress management. AI has widely penetrated the climate sensitive ruminant livestock sector due to its promising and plausible scope in assessing production risks and the climate resilience of ruminant livestock. Significant improvement has been achieved alongside the adoption of novel AI algorithms to evaluate the performance of ruminant livestock. These AI-powered tools have the robustness and competence to expand the evaluation of animal performance and help in minimising the production losses associated with heat stress in ruminant livestock. Advanced heat stress management through automated monitoring of heat stress in ruminant livestock based on behaviour, physiology and animal health responses have been widely accepted due to the evolution of technologies like machine learning (ML), neural networks and deep learning (DL). The AI-enabled tools involving automated data collection, pre-processing, data wrangling, development of appropriate algorithms, and deployment of models assist the livestock producers in decision-making based on real-time monitoring and act as early-stage warning systems to forecast disease dynamics based on prediction models. Due to the convincing performance, precision, and accuracy of AI models, the climate-smart livestock production imbibes AI technologies for scaled use in the successful reducing of heat stress in ruminant livestock, thereby ensuring sustainable livestock production and safeguarding the global economy.

A comprehensive study was conducted to assess the effects of seasonal transition and temperature humidity index (THI) on the adaptive responses in crossbred dairy cows reared in a tropical savanna region. A total of 40 lactating dairy cattle reared by small-scale dairy farmers in Bengaluru, India, were selected for this study. The research period comprised the transitioning season of summer to monsoon, wherein all traits were recorded at two points, one representing late summer (June) and the other early monsoon (July). A set of extensive variables representing physiological responses (pulse rate, respiration rate, rectal temperature, skin surface temperature), hematological responses (hematological profile), production (test day milk yield, milk composition) and molecular patterns (PBMC mRNA relative expression of selective stress response genes) were assessed. A significant effect of seasonal transition was identified on respiration rate (RR), skin surface temperature, mean platelet volume (MPV), platelet distribution width (PDWc), test day milk yield and on milk composition variables (milk density, lactose, solids-not-fat (SNF) and salts). The THI had a significant effect on RR, skin surface temperature, platelet count (PLT), plateletcrit (PCT) and PDWc. Lastly, THI and/or seasonal transition significantly affected the relative PBMC mRNA expression of heat shock protein 70 (HSP70), interferon beta (IFNβ), IFNγ, tumor necrosis factor alpha (TNFα), growth hormone (GH) and insulin-like growth factor-1 (IGF-1) genes. The results from this study reveal environmental sensitivity of novel physiological traits and gene expressions to climatic stressors, highlighting their potential as THI-independent heat stress biomarkers.

A novel study was conducted to elucidate heat-stress responses on a number of hair- and skin-based traits in two indigenous goat breeds using a holistic approach that considered a number of phenotypic and genomic variables. The two goat breeds, Kanni Aadu and Kodi Aadu, were subjected to a simulated heat-stress study using the climate chambers. Four groups consisting of six goats each (KAC, Kaani Aadu control; KAH, Kanni Aadu heat stress; KOC, Kodi Aadu control; and KOH, Kodi Aadu heat stress) were considered for the study. The impact of heat stress on caprine skin tissue along with a comparative assessment of the thermal resilience of the two goat breeds was assessed. The variables considered were hair characteristics, hair cortisol, hair follicle quantitative PCR (qPCR), sweating (sweating rate and active sweat gland measurement), skin histometry, skin-surface infrared thermography (IRT), skin 16S rRNA V3-V4 metagenomics, skin transcriptomics, and skin bisulfite sequencing. Heat stress significantly influenced the hair fiber characteristics (fiber length) and hair follicle qPCR profile (Heat-shock protein 70 (HSP70), HSP90, and HSP110). Significantly higher sweating rate, activated sweat gland number, skin epithelium, and sweat gland number (histometry) were observed in heat stressed goats. The skin microbiota was also observed to be significantly altered due to heat stress, with a relatively higher alteration being noticed in Kanni Aadu goats than in Kodi Aadi goats. Furthermore, the transcriptomics and epigenetics analysis also pointed towards the significant impact of heat stress at the cellular and molecular levels in caprine skin tissue. The higher proportion of differentially expressed genes (DEGs) along with higher differentially methylated regions (DMRs) in Kanni Aadu goats due to heat stress when compared to Kodi Aadu goats pointed towards the better resilience of the latter breed. A number of established skin, adaptation, and immune-response genes were also observed to be significantly expressed/methylated. Additionally, the influence of heat stress at the genomic level was also predicted to result in significant functional alterations. This novel study thereby highlights the impact of heat stress on the caprine skin tissue and also the difference in thermal resilience exhibited by the two indigenous goat breeds, with Kodi Aadu goats being more resilient.

The livestock sector, essential for maintaining food supply and security, encounters numerous obstacles as a result of climate change. Rising global populations exacerbate competition for natural resources, affecting feed quality and availability, heightening livestock disease risks, increasing heat stress, and contributing to biodiversity loss. Although various management and dietary interventions exist to alleviate these impacts, they often offer only short-lived solutions. We must take a more comprehensive approach to understanding how animals adapt to and endure their environments. One such approach is quantifying transcriptomes under different environments, which can uncover underlying pathways essential for livestock adaptation. This review explores the progress and techniques in studies that apply gene expression analysis to livestock production systems, focusing on their adaptation to climate change. We also attempt to identify various biomarkers and transcriptomic differences between species and pure/crossbred animals. Looking ahead, integrating emerging technologies such as spatialomics could further accelerate genetic improvements, enabling more thermoresilient and productive livestock in response to future climate fluctuations. Ultimately, insights from these studies will help optimize livestock production systems by identifying thermoresilient/desired animals for use in precise breeding programs to counter climate change.