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.

The aim of the present study was to quantify the effect of heat stress (HS) from different points in time on production, female fertility, and health traits. In this regard, on-farm measurements for temperature and relative humidity were combined into temperature-humidity indexes (THI), and merged with longitudinal cow traits from electronic recording systems. The study included traits from 22,212 Holstein cows kept in 15 large-scale dairy co-operator herds. Trait and meteorological data recording spanned a period between May 2013 and November 2015. Longitudinal production traits considered 191,911 test-day records for protein yield, protein percentage, and milk urea nitrogen (MUN). Female fertility traits were the pregnancies per AI (P/AI) and the number of daily inseminations per herd cow (INS/HCOW). Health traits considered clinical mastitis (MAST), retained placenta, puerperal disorders (PD) from d 0 to 10 postpartum, and the claw disorders digital phlegmona, digital dermatitis (DD), and interdigital hyperplasia from d 0 to 360 postpartum. For all traits, we analyzed the THI influence from the trait-recording day. In addition, we studied the time-lagged THI effect from the previous week. Linear mixed models were applied to estimate THI effects on Gaussian distributed production traits. For binary health and fertility traits, generalized linear mixed models with a logit link function were used. The continuous THI effect was either modeled linear, or via Legendre polynomials of order 4. Regression models for THI were validated via THI class effects (i.e., 5% percentiles for THI). Protein percentage decreased with increasing test-day THI, and with increasing THI from the previous week. Protein yield obviously decreased beyond THI 68 for both THI measurements (test-day THI and THI from previous week). For MUN, the visually identified test-day HS threshold was THI 70. Time-lagged THI effects on MUN were less obvious. For both THI measuring dates, INS/HCOW was highest at THI 57. Beyond THI 57, INS/HCOW substantially decreased. For P/AI, the visually identified HS threshold at the insemination date was THI 65. Temperature-humidity indexes from the previous week had a moderate detrimental effect on P/AI. Incidences for MAST, retained placenta, and PD during d 0 to 10 postpartum increased with increasing average THI from this period. Studying the whole lactation period, incidences for interdigital hyperplasia also increased with increasing THI from the previous week. An opposite THI response was identified for DD: DD decreased with increasing THI. For all health traits, associations between disease incidences and THI were almost linear. Hence, for health traits, no obvious HS thresholds were detected. Especially in early lactation, HS had a detrimental effect on cow productivity and female fertility. The influence of HS on cow health differed, depending on the disease pathogenesis.

In dairy cattle, resistance, tolerance and resilience refer to the adaptation ability to a broad range of environmental conditions, implying stable performances (e.g. production level, fertility status) independent from disease or infection pressure. All three mechanisms resistance, tolerance and resilience contribute to overall robustness, implying the evaluation of phenotyping and breeding strategies for improved robustness in dairy cattle populations. Classically, breeding approaches on improved robustness rely on simple production traits, in combination with detailed environmental descriptors and enhanced statistical modelling to infer possible genotype by environment interactions. In this regard, innovative environmental descriptors were heat stress indicators, and statistical modelling focussed on random regression or reaction norm methodology. A robust animal has high breeding values over a broad spectra of environmental levels. During the last years, direct health traits were included into selection indices, implying advances in genetic evaluations for traits being linked to resistance or tolerance against infectious and non-infectious diseases. Up to now, genetic evaluation for health traits is primarily based on subjectively measured producer-recorded data, with disease trait heritabilities in a low-to-moderate range. Thus, it is imperative to identify objectively measurable phenotypes as suitable biomarkers. New technologies (e.g. mid-infrared spectrometry) offer possibilities to determine potential biomarkers via laboratory analyses. Novel biomarkers include measurable physiological traits (e.g. serum metabolites, hormone levels) as indicators for a current infection, or the host’s reaction to environmental stressors. The rumen microbiome composition is proposed as a biomarker to detect interactions between host genotype and environmental effects. The understanding of host genetic variation in disease resistance and individual expression of robustness encourages analyses on the underlying immune response (IR) system. Recent advances have been made in order to infer the genetic background of IR traits and cows immunological competence in relation to functional and production traits. Thus, a last aspect of this review addresses the genetic background and current state of genetic control for resistance to economically relevant infectious and non-infectious dairy cattle diseases by considering immune-related factors.

Heat stress implies unfavorable effects on primary and functional traits in dairy cattle and, in consequence, on the profitability of the whole production system. The increasing number of days with extreme hot temperatures suggests that it is imperative to detect the heat stress status of animals based on adequate measures. However, confirming the heat stress status of an individual is still challenging, and, in consequence, the identification of novel heat stress biomarkers, including molecular biomarkers, remains a very relevant issue. Currently, it is known that heat stress seems to have unfavorable effects on immune system mechanisms, but this information is of limited use in the context of heat stress phenotyping. In addition, there is a lack of knowledge addressing the molecular mechanisms linking the relevant genes to the observed phenotype. In this review, we explored the potential molecular mechanisms explaining how heat stress affects the immune system and, therefore, increases the occurrence of immune-related diseases in cattle. In this regard, 2 relatively opposite hypotheses are under focus: the immunosuppressive action of cortisol, and the proinflammatory effect of heat stress. In both hypotheses, the modulation of the immune response during heat stress is highlighted. Moreover, it is possible to link candidate genes to these potential mechanisms. In this context, immune markers are very valuable indicators for the detection of heat stress in dairy cattle, broadening the portfolio of potential biomarkers for heat stress.

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.

Background: Specific adaptive features including disease resistance and growth abilities in harsh environments are attributed to indigenous cattle breeds of Benin, but these breeds are endangered due to crossbreeding. So far, there is a lack of systematic trait recording, being the basis for breed characterizations, and for structured breeding program designs aiming on conservation. Bridging this gap, own phenotyping for morphological traits considered measurements for height at withers (HAW), sacrum height (SH), heart girth (HG), hip width (HW), body length (BL) and ear length (EL), including 449 cattle from the four indigenous Benin breeds Lagune, Somba, Borgou and Pabli. In order to utilize recent genomic tools for breed characterizations and genetic evaluations, phenotypes for novel traits were merged with high-density SNP marker data. Multi-breed genetic parameter estimations and genome-wide association studies (GWAS) for the six morphometric traits were carried out. Continuatively, we aimed on inferring genomic regions and functional loci potentially associated with conformation, carcass and adaptive traits. Results: SNP-based heritability estimates for the morphometric traits ranged between 0.46 ± 0.14 (HG) and 0.74 ± 0.13 (HW). Phenotypic and genetic correlations ranged from 0.25 ± 0.05 (HW-BL) to 0.89 ± 0.01 (HAW-SH), and from 0.14 ± 0.10 (HW-BL) to 0.85 ± 0.02 (HAW-SH), respectively. Three genome-wide and 25 chromosome-wide significant SNP positioned on different chromosomes were detected, located in very close chromosomal distance (±25 kb) to 15 genes (or located within the genes). The genes PIK3R6 and PIK3R1 showed direct functional associations with height and body size. We inferred the potential candidate genes VEPH1, CNTNAP5, GYPC for conformation, growth and carcass traits including body weight and body fat deposition. According to their functional annotations, detected potential candidate genes were associated with stress or immune response (genes PTAFR, PBRM1, ADAMTS12) and with feed efficiency (genes MEGF11 SLC16A4, CCDC117). Conclusions: Accurate measurements contributed to large SNP heritabilities for some morphological traits, even for a small mixed-breed sample size. Multi-breed GWAS detected different loci associated with conformation or carcass traits. The identified potential candidate genes for immune response or feed efficiency indicators reflect the evolutionary development and adaptability features of the breeds.