Quantify Heat Stress Response in Farm Animals: History
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Contributor:
Veerasamy Sejian
,
Chikamagalore Gopalakrishna Shashank
,
Mullakkalparambil Velayudhan Silpa
,
Aradotlu Parameshwarappa Madhusoodan
,
Chinnasamy Devaraj
,
Sven Koenig

Non-invasive methods of detecting heat stress magnitude for livestock is gaining momentum in the context of global climate change. 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. Assessing these responses can prove beneficial to quantifying heat stress and thereby enforcing suitable amelioration and mitigation strategies. There are a number of approaches to quantify heat stress, which in the current scenario with increasing animal welfare concern, can be considered as invasive and non-invasive approaches.

  • heat stress
  • animal welfare
  • non-invasive
  • IRT
  • sensors

1. Methods to Quantify Heat Stress Response

1.1. Invasive Approaches to Quantify Heat Stress

When exposed to hot and/or stressful climatic conditions, animals exhibit a number of metabolic, cellular and molecular changes which can also be accompanied with production-related losses [1]. Assessing these changes is considered to provide a vital indication of heat stress impact in livestock. Invasive approaches to assess heat stress involves interference with tissues, blood, or any structures of an animal to gain an in-depth insight into the mechanisms involved. The conventional methods of recording classical physiological responses in farm animals such as the use of a rectal thermometer to measure body temperature and the use of a stethoscope to measure heart rate and respiration rate can be grouped as invasive methodologies.
Hematological profiles can depict health statuses in animals and can serve as good indicators to assess heat stress, as it can reflect a number of metabolic activities [2]. In a study led by Morar et al. [3], heat stress was reported to have a significant impact on the hematological profile in Holstein dairy cows. The significantly lower hemoglobin (Hg) concentration and hematocrit (Ht) along with higher reticulocyte and white blood cell (WBC) counts were associated with heat stress. However, Attia [2] reported significantly increased RBC counts, Hb concentrations, and packed cell volumes (PCVs) in heat-stressed Zarabi goats in Egypt. The decreased Hb concentration and RBC count were associated with hemodilution and/or erythrocyte destruction, which were associated with increased water intake in animals during heat stress [4], while the increased PCV and hemoconcentration were hypothesized to be consequences of higher evaporative cooling [2]. Thus, the hematological profile in livestock was proved to be influenced by heat stress; however, its use to assess the degree of heat stress still remains questionable [3].
The evaluation of biochemical profiles is another widely used methodology to assess the impact of heat stress on livestock. A number of biochemical variables have been reported to be altered during heat stress. Chaudhary et al. [5] studied the blood biochemical profile of Surti buffaloes exposed to various seasons. The authors stated that an increase in the Temperature Humidity Index (THI) resulted in significant declines in serum glucose and cholesterol levels, while the same increase significantly increased serum alanine aminotransferase (ALT), creatinine, blood urea nitrogen, sodium (Na), potassium (K), manganese (Mn), copper (Cu), and zinc (Zn). Further, Aleena et al. [6] assessed the adaptability of three indigenous goat breeds, Malabari, Osmanabadi, and Salem Black, on exposure to heat stress based on their blood biochemical profile. Alterations in blood biochemical variables such as total protein and serum glucose have also been reported in broiler chicks [7]. Heat-stress-induced factors such as reductions in feed intake, altered energy metabolism, metabolism disorders, and altered liver function have been stated to be the major causes for alterations in the biochemical profiles of livestock [5].
The estimation of the hormonal profile in heat-stressed animals is another vital method to assess the stress impact for their significant role in neuro-endocrine responses [8]. This invasive approach to quantify heat stress is again of high importance, as it considers certain classical heat stress markers such as cortisol, triiodothyronine (T3), and thyroxine (T4). In a study to assess the impact of heat stress, nutritional stress, and their combination (combined stresses) in Malpura sheep, cortisol, T3, and T4 were proposed to be potential biomarkers [9]. Similarly in goats, cortisol levels were found to be significantly increased due to heat stress [2]. Apart from these, alterations in hormones such as thyroid-stimulating hormone (TSH), progesterone, estradiol, follicle-stimulating hormone (FSH), inhibin, luteinizing hormone (LH), growth hormone, etc., have also been reported to be associated with heat stress in livestock [10][11].
Another vital approach to assess heat stress in livestock is by looking into the changes occurring at the cellular and molecular level. A number of advanced biotechnological applications and tools have been used to assess the impact of heat stress on the cellular and molecular level. Gene expression studies to compare the relative expression profile of a number of adaptive, metabolic, productions-related and immune-response-associated genes have been screened in livestock [12]. Most of these studies are conducted on tissues such as PBMC (Tarai buffalo, [13]), liver (Malabari goat, [14]), meat (Osmanabadi and Salem Black goat, [15]), lymph nodes (Malabari goat, [16]), and other organs (thymus, bursa of Fabricius, and spleen in broilers, [17]). Several approaches using next-generation sequencing technologies have also aided the assessment of the impact of heat stress in animals. The study led by Garner et al. [18] revealed new insights into cellular adaptations in Holstein Friesian cows exposed to heat stress. The authors adopted the transcriptomics approach to identify differentially expressed genes that were altered due to heat stress in PBMC and milk somatic cells. In another study, Halli et al. [19] identified 31 suggestive single-nucleotide polymorphisms that were closely associated to 62 potential candidate genes using genomic animal models from genotyped Holstein cows on the basis of time-lagged heat stress interactions for milk production traits. All these stated approaches aid in assessing the impact of heat stress in animals and also in comparing their adaptability to heat stress.

1.2. Non-Invasive Approaches

With the rising concern for animal welfare, researchers are encouraged to opt for methodologies that are non-invasive, which would additionally reduce the stress caused to the animal. This area is slowly gaining attention, with several innovations already being brought about. Most of the behavioral (postural adjustments, rumination time, drinking frequency, etc.) and physiological (respiration rate, body temperature, heart rate, pulse rate, etc.) responses exhibited by animals have good correlation with heat stress. The concept of a non-invasive approach to assess heat stress primarily looks into responses which can be monitored without any human interference or additional stress on the animal [1].
Bioclimatic thermal indices can be considered as the least invasive approach to assess and/or predict the level of heat stress in livestock [20]. This is also the most widely implemented tool; rather, any heat stress study would be incomplete without considering any one of the established bioclimatic indices. The incorporation of such indices to measure the impact of heat stress on cattle began way back in the 1940s [21]. Since then, a number of indices based on meteorological variables have been developed in livestock and poultry, which are widely associated with physiological and/or production related responses [20][22]. The Temperature Humidity Index (THI) is one such index that has been widely used by researchers. This index is formulated by incorporating air temperature and relative humidity [20].
Behavioral responses, being among the first response exhibited by animals to combat heat stress, are among the prime non-invasive indicators to estimate the impact of heat stress in animals. Behavioral coping strategies are stated to be altered based on the level of heat stress experienced by an animal [23]. In a study conducted to assess the impact of heat stress in three indigenous goat breeds, Aleena et al. [24] observed that the drinking frequency of heat-stressed goats was significantly higher that of their respective controls. Likewise, heat-stressed cattle were found to have increased standing bouts along with drinking more water [25]. This was also accompanied with altered eating, decreased lying bouts, and agonistic behaviors [26]. This behavior might be due to the fact that upon standing, the body surface area is increased for heat loss through convection [27].
The respiratory dynamics act as a critical source to dissipate heat in livestock. Increased respiration rate and panting score are the classical physiological indicators of heat stress in animals [8]. Upon exposure to heat stress where the core body temperature increases beyond dissipation, animals, particularly sheep and poultry, adopt panting to efficiently dissipate the heat from the body [28]. These indicators could be recorded manually by recording the flank movements [2][24] or with the use of several advanced monitoring tools [29][30].
Another widely adopted non-invasive methodology to assess heat stress impact in livestock is body surface temperature. Infrared thermometry and infrared thermography (IRT) are the two technologies that are used to record body surface temperatures [20]. Though these methodologies have been well established, their applicability in the area of heat stress assessment in livestock has gradually been being established in recent years. The adoption of certain classical heat stress biomarkers using biological samples that are non-invasive is also another alternative. Fecal and hair cortisol estimations are the best-suited example for this approach. Based on the experiment conducted by Rees et al. [31], an increased concentration of the glucocorticoid metabolite 11,17-dioxoandrostanes (11,17-DOA) was stated to be associated with heat stress in Holstein Friesian cows. Likewise, Broin et al. [32] also concluded, based on their study on Rocky mountain goats, that fecal glucocorticoid metabolites and hair cortisol can be used as valid biomarkers for HPA-axis activity.
The assessment of heat stress in livestock has advanced to the nest level with the incorporation of automated monitoring systems. These are broadly composed of ‘on-animal sensors’ and ‘off-animal devices’ [26]. These sensors, being of varied types, can record a number of behavioral responses (eating, drinking and rumination activity, resting time, rumination time, and locomotory activity) and physiological responses (respiration rate, heart rate, body temperature, and rumen temperature) [26]. Further, these technologies can also enable automatic, continuous, and real-time heat stress monitoring. Although applications of these advanced technologies in heat-stressed animals, especially in species other than cattle, are limited, they are, however, gaining more reach due to their importance, especially in the current scenario wherein animal welfare is considered of equal importance.

2. Sensor-Based Applications in Assessing Heat Stress Response in Grazing Animals

The sensor-based, non-invasive, and real-time automated measurement of physiological and behavioral traits in livestock is the new arena to explore for researchers and academicians. These promising technologies allows one to closely monitor body temperature and aids in the early prediction of heat stress and diseases without human interference, keeping animal welfare standards at the highest level. Thus, the accurate thermal status level of livestock can be determined though sensor-based technologies without the risk of handling and restraint [33]. Various remote sensing technologies such as ear canal sensors, rumen boluses, rectal and vaginal probes, infrared thermography (IRT) or thermal imaging, and implantable microchips [34][35][36] can be employed in grazing animals to assess the heat stress. Behavioral responses and activity alterations to heat stress can be monitored using accelerometers, Bluetooth technology, GPS, or GNSS [37]. Animal welfare is of paramount importance in employing remote sensing technologies to measure and monitor physiological and behavioral responses to heat stress in grazing animals.

2.1. Rumen/Reticular Boluses

Rumen/reticular boluses consist of temperature sensors, a battery, chip, and antenna. They enable the real-time collection of rumen temperature (RuT) data via wireless transmission [38]. These boluses are orally administered and are lodged in the reticulum or at the rumeno-reticluar junction through gravity. They are commonly employed in cattle for the remote measurement of core body temperature and thus in turn can be used to assess heat stress. As RuT is affected by feed and water intake as well as by their frequency, correlating these parameters with heat stress events in grazing animals paves the way for management [39].

2.2. Subcutaneous Implantable Devices

The continuous measurement of body temperature in grazing livestock can be accomplished through subcutaneous implantable devices such as microchips. These devices are placed under the skin, and real-time temperature measurement is conveyed to a handheld receiver. These are exclusively used in sheep to measure the core body temperature and to relate it with the environmental temperature [40].

2.3. Rectal and Vaginal Probes

Thermal sensors such as rectal and vaginal probes facilitate the accurate measurement of core body temperature in heat-stressed animals without affecting their grazing behavioral pattern [41]. Rectal and vaginal areas are well insulated; these probes are minimally invasive and record core body temperature accurately and consistently [42]. These are extensively used in sheep husbandry [43]. The expulsion of probes during defecation, micturition, and parturition needs to be considered.

2.4. GPS Technology

The spatial behavioral patterns of grazing livestock can be sensed remotely through the monitoring of animal movement via GPS technology. Shade-seeking preferences in grazing livestock during heat stress can be mapped through GPS data [44]. With the aid of GPS data, shade and water provision management can be accomplished for grazing animals during heat stress periods.

2.5. Accelerometer

Behavioral monitoring in grazing animals aids in the early detection of heat stress and related illness, which can be used to modify management strategies, and thus, the efficiency of farm production can be improved. To monitor behavior in grazing animals, “accelerometers” come in handy. Accelerometers are minute and lightweight apparatus with the least interference to the natural behavior of grazing animals [45]. Accelerometers measure linear acceleration along the axes and thus precisely determine animals’ movement. Accelerometers are used in conjunction with Global Positioning Systems (GPSs) to track various behaviors such as grazing, lying down, foot movement, standing, rumination, kicking, and running [46]. Thus, accelerometers could be a promising remote sensing technology for the identification of heat-stress-related behavioral alterations in extensive animal production systems.

2.6. Bioacoustics

Bioacoustics technology can be employed to analyze vocalization patterns in grazing animals as well as in broiler production. This is the best non-invasive remote sensing technology for the determination of the welfare of animals and birds during heat stress [47]. The research related to bioacoustics technology applications in the livestock sector is at the primordial stage and has huge scope for exploration in heat stress studies.
To conclude, sensor-based technologies aim to provide the accurate and real-time measurement of grazing animals’ behavioral and physiological alterations to heat stress. These technologies are non-invasive and provide precise, continuous, remote, and real-time data. Animal welfare takes the highest priority, as handling and restraining is negligible while applying these technologies at the farm level. These pools of technologies along with automation and big data analytics are the future perspectives of heat stress studies. Figure 1 depicts an overview of some of the non-invasive methodologies to quantify heat stress responses in farm animals.
Figure 1. Different non-invasive methods to quantify heat stress response in farm animals.

3. Applications of Machine Learning in Heat Stress Assessment in Farm Animals

Machine learning (ML) is a subset of artificial intelligence (AI) and computer science that is concerned with the use of data and algorithms to improve performance or to make accurate predictions [48]. Data science is a rapidly expanding area that relies heavily on ML. In data mining projects, data inputs and statistical methods are used to train algorithms to make proper divisions, characterizations, and predictions, uncovering key insights within projects. Decisions made based on these insights can then have a direct impact on key growth indicators in fields such as agriculture and allied sectors. The key outcome of ML is generalizability; the algorithm’s ability to correctly anticipate new data based on previously acquired rules [49].
Recently, a concept named Precision Livestock Farming (PLF) has been implemented to improve the farming process. In traditional livestock farming, the producer decides a factor based on his experiences, whereas in PLF, such decisions are based on quantitative data, such as liters of milk per milking, respiratory rate during the afternoon, behavior during heat stress, methane production, and many more aspects. In addition, quantitative data can be obtained in real time [50]. To study real-time complex and huge data, PLF depends on systems such as ML, control systems, and information and communication technologies [51]. ML has been used in different fields due to its versatility and ability to derive a model from available data [52]; however, because ML is not always the best match, traditional linear models, such as logistic regression, are still used to make predictions in some cases [53]. In some cases, different ML alternatives can be compared, but it can be difficult to predict which strategy will yield the best results [54]. Many ML models exist and may be suitable to predict the variable of interest. In nutshell, a trial-and-error strategy can be utilized to determine the most appropriate procedure for each prediction [55]. As mentioned previously, to process and discover abnormalities in the data that impact the production of animals, such as the effects of heat stress on livestock production, ML plays a major role in extracting huge data from various sensors put on a large population of animals and further processes it. ML can offer a scalable solution, utilizing data from different sources such as hardware sensors, e.g., pressure sensors, thermistors, infrared thermal imaging sensors, facial recognition machine vision sensors, Radio Frequency Identification, accelerometers, microphones, etc. [56]; functional data such as climate variables, weight estimates, physiological parameters (respiration rate, pulse rate, rectal temperature, and skin temperature), animal behavior, feed intake, and water intake assisted with biochemical and endocrine parameters.
The idea of introducing sensors on the farm is to provide decision-making information to the farmer. Through the GPS, in pasture-based dairy cows, it is easy to distinguish between grazing, resting, and walking through temporal positioning. Different models such as JRip, J48, and random forest all classified resting with an accuracy of 0.85 or more, while all models failed to accurately categorize grazing behavior (accuracy: 0.16–0.72). Additionally, researchers used GPS locations to forecast cow behavior and effectively detected transition moments between walking, grazing, and resting [57]. Similar behavior analysis studies were carried out using accelerometers mounted on the cow’s neck and leg [58]. Further, a neural network model was used on data from radiofrequency identification (RFID) sensors along with automatic milking system data to track cows. Recently, by combining background reduction and inter-frame difference models, Guo et al. [59] built a machine vision model for calf behavior recognition, and the detection rate was over 90%. These behavioral models can be implemented in heat stress studies to predict the changes in behaviors when animals are exposed to heat stress.
Several climate variables play a major role in causing heat stress, such as air temperature, solar radiation, relative humidity, and wind speed. Ranking these variables according to their impact on animals can be achieved through ML. In a study carried out by Gorczyca [60], where four models (penalized linear regression, random forests, gradient boosted machines, and neural network models) were utilized, a random forest model was accurate in predicting physiological responses (core temperature, skin temperature, and respiration rate) during heat stress. Further, these authors stated that air temperature has the largest effect on physiological responses, followed by solar radiation and by the interaction of air temperature and relative humidity during heat stress; wind speed and relative humidity were negligible heat stressors to cows. To assess the physiological responses, Gorczyca and Gebremedhin [61] used non-linear models such as neural networks and random forest and claimed these models are top predicting models for respiration rate, skin temperature, and vaginal temperature (R2: 0.61, 0.85, and 0.472, respectively).
In another study by Kim and Hidaka [62], infrared thermography (IRT) was used along with RGB (red, green, blue) images to observe breathing patterns in cattle. The Mask R-CNN algorithm was used to determine breathing rates and breathing patterns and to detect the region of interest, in this case, the nose. Temperature fluctuations around the nose during respiration were monitored through IRT to determine the respiration rate per minute in animals. The author proposed that the R-CNN algorithm has an accuracy of 76% in detecting cattle nose and infrared thermography, and a deep learning algorithm can be used to determine breathing patterns whenever animals are exposed to different stressors. Using these algorithms, farmers may determine when to deploy heat stress mitigation techniques by computing thresholds for environmental factors. One of the subsets of ML, neural networks were applied to assess the level of thermal stress in feedlot cattle, considering both weather and animal factors, by Sousa et al. [63] in Nellore cattle. The results suggested that the neural model has a good predictive ability, which had an R2 of 0.72, while the normal regression model had an R2 of 0.57. The results suggested that the neural model has a good predictive ability, with an R2 of 0.72, while the regression model yielded an R2 of 0.57. The neural model predicted thermal stress and was well correlated with the measured rectal temperature (94.35%), and it was significantly better than the THI method’s performance. Another non-invasive method of recognizing the stress in animals is through livestock vocalization.
Milk yield decreases particularly during heat stress, especially in high-producing dairy cattle, which have higher metabolic turnovers [64]. Benni et al. [65] predicted the response of milch cattle to heat stress, where they proposed a generalized additive model with mixed effects that was suitable for describing each cow’s response to crucial THI circumstances on milk production. Furthermore, they asserted that this analysis can contribute to improving herd management during heat stress conditions and identify the cows most likely to be suffering during heat stress to implement some specific actions for these animals in terms of cooling treatments, the enhancement of the feeding strategies, and attention to their specific welfare conditions. Another study by Bovo et al. [66] revealed that the Random Forest Model can detect a decrease in milk yield in cows due to heat stress effects caused by extremely hot temperatures. Indeed, the model’s average relative error in the forecasts is approximately 18% when considering a single daily yield but drops to just 2% when considering the total milk output during the test days.

This entry is adapted from the peer-reviewed paper 10.3390/atmos13101642

References

  1. Idris, M.; Uddin, J.; Sullivan, M.; McNeill, D.M.; Phillips, C.J.C. Non-Invasive Physiological Indicators of Heat Stress in Cattle. Animals 2021, 11, 71.
  2. Attia, N. Physiological, Hematological and Biochemical Alterations in Heat Stressed Goats. Benha Vet. Med. J. 2016, 31, 56–62.
  3. Morar, D.; Ciulan, V.; Simiz, F.; Moț, T.; Hutu, I.; Văduva, C. Effect of Heat Stress on Haematological Parameters in Dairy Cows. In Proceedings of the Lucrari Stiintifice: Medicina Veterinara Timisoara (Scientifical Papers: Veterinary Medicine Timisoara), Timisoara, Romania, 29 May 2018; Imprimeria Mirton Timisoara: Timisoara, Romania, 2018; Volume LI, pp. 65–70.
  4. Omran, F.I.; Ashour, G.; Youssef, M.M.; Shafie, M.M. Responses of Hematology, Blood Metabolites, Mineral Ions and Hormonal Profile to Heat Stress for Egyptian Buffalo-Calves. Egypt. J. Agric. Res. 2011, 89, 1129–1140.
  5. Chaudhary, S.S.; Singh, V.K.; Upadhyay, R.C.; Puri, G.; Odedara, A.B.; Patel, P.A. Evaluation of Physiological and Biochemical Responses in Different Seasons in Surti Buffaloes. Vet. World 2015, 8, 727–731.
  6. Aleena, J.; Sejian, V.; Krishnan, G.; Bagath, M.; Pragna, P.; Bhatta, R. Heat Stress Impact on Blood Biochemical Response and Plasma Aldosterone Level in Three Different Indigenous Goat Breeds. J. Anim. Behav. Biometeorol. 2020, 8, 266–275.
  7. Feizi, A.; Dadian, F.; Asadzadehmajdi, S. The Effect of Heat Stress on Some Blood Parameters, Biochemical Values and Humoral Immunity in Broiler Chickens. Vet. Clin. Pathol. Q. Sci. J. 2012, 6, 1621–1627.
  8. Sejian, V.; Bhatta, R.; Gaughan, J.B.; Dunshea, F.R.; Lacetera, N. Review: Adaptation of Animals to Heat Stress. Animal 2018, 12, s431–s444.
  9. Maurya, V.P.; Sejian, V.; Kumar, D.; Naqvi, S.M.K. Impact of Heat Stress, Nutritional Stress and Their Combinations on the Adaptive Capability of Malpura Sheep under Hot Semi-Arid Tropical Environment. J. Anim. Behav. Biometeorol. 2019, 7, 31–38.
  10. Bernabucci, U.; Lacetera, N.; Baumgard, L.H.; Rhoads, R.P.; Ronchi, B.; Nardone, A. Metabolic and Hormonal Acclimation to Heat Stress in Domesticated Ruminants. Animal 2010, 4, 1167–1183.
  11. Pragna, P.; Sejian, V.; Soren, N.M.; Bagath, M.; Krishnan, G.; Beena, V.; Devi, P.I.; Bhatta, R. Summer Season Induced Rhythmic Alterations in Metabolic Activities to Adapt to Heat Stress in Three Indigenous (Osmanabadi, Malabari and Salem Black) Goat Breeds. Biol. Rhythm Res. 2018, 49, 551–565.
  12. Sejian, V.; Bagath, M.; Krishnan, G.; Rashamol, V.P.; Pragna, P.; Devaraj, C.; Bhatta, R. Genes for Resilience to Heat Stress in Small Ruminants: A Review. Small Rumin. Res. 2019, 173, 42–53.
  13. Manjari, R.; Yadav, M.; Ramesh, K.; Uniyal, S.; Rastogi, S.K.; Sejian, V.; Hyder, I. HSP70 as a Marker of Heat and Humidity Stress in Tarai Buffalo. Trop. Anim. Health Prod. 2015, 47, 111–116.
  14. Angel, S.P.; Bagath, M.; Sejian, V.; Krishnan, G.; Bhatta, R. Expression Patterns of Candidate Genes Reflecting the Growth Performance of Goats Subjected to Heat Stress. Mol. Biol. Rep. 2018, 45, 2847–2856.
  15. Archana, P.R.; Sejian, V.; Ruban, W.; Bagath, M.; Krishnan, G.; Aleena, J.; Manjunathareddy, G.B.; Beena, V.; Bhatta, R. Comparative Assessment of Heat Stress Induced Changes in Carcass Traits, Plasma Leptin Profile and Skeletal Muscle Myostatin and HSP70 Gene Expression Patterns between Indigenous Osmanabadi and Salem Black Goat Breeds. Meat Sci. 2018, 141, 66–80.
  16. Rashamol, V.P.; Sejian, V.; Bagath, M.; Krishnan, G.; Beena, V.; Bhatta, R. Effect of Heat Stress on the Quantitative Expression Patterns of Different Cytokine Genes in Malabari Goats. Int. J. Biometeorol. 2019, 63, 1005–1013.
  17. Hirakawa, R.; Nurjanah, S.; Furukawa, K.; Murai, A.; Kikusato, M.; Nochi, T.; Toyomizu, M. Heat Stress Causes Immune Abnormalities via Massive Damage to Effect Proliferation and Differentiation of Lymphocytes in Broiler Chickens. Front. Vet. Sci. 2020, 7, 46.
  18. Garner, J.B.; Chamberlain, A.J.; Vander Jagt, C.; Nguyen, T.T.T.; Mason, B.A.; Marett, L.C.; Leury, B.J.; Wales, W.J.; Hayes, B.J. Gene Expression of the Heat Stress Response in Bovine Peripheral White Blood Cells and Milk Somatic Cells in Vivo. Sci. Rep. 2020, 10, 19181.
  19. Halli, K.; Vanvanhossou, S.F.; Bohlouli, M.; König, S.; Yin, T. Identification of Candidate Genes on the Basis of SNP by Time-Lagged Heat Stress Interactions for Milk Production Traits in German Holstein Cattle. PLoS ONE 2021, 16, e0258216.
  20. Wijffels, G.; Sullivan, M.; Gaughan, J. Methods to Quantify Heat Stress in Ruminants: Current Status and Future Prospects. Methods 2021, 186, 3–13.
  21. Rhoad, A.O. The Iberia Heat Tolerance Test for Cattle. Trop. Agric. 1944, 21, 162–164.
  22. Rashamol, V.P.; Sejian, V.; Pragna, P.; Lees, A.M.; Bagath, M.; Krishnan, G.; Gaughan, J.B. Prediction Models, Assessment Methodologies and Biotechnological Tools to Quantify Heat Stress Response in Ruminant Livestock. Int. J. Biometeorol. 2019, 63, 1265–1281.
  23. Herbut, P.; Hoffmann, G.; Angrecka, S.; Godyń, D.; Vieira, F.M.C.; Adamczyk, K.; Kupczyński, R. The Effects of Heat Stress on the Behaviour of Dairy Cows—A Review. Ann. Anim. Sci. 2021, 21, 385–402.
  24. Aleena, J.; Sejian, V.; Bagath, M.; Krishnan, G.; Beena, V.; Bhatta, R. Resilience of Three Indigenous Goat Breeds to Heat Stress Based on Phenotypic Traits and PBMC HSP70 Expression. Int. J. Biometeorol. 2018, 62, 1995–2005.
  25. Allen, J.D.; Hall, L.W.; Collier, R.J.; Smith, J.F. Effect of Core Body Temperature, Time of Day, and Climate Conditions on Behavioral Patterns of Lactating Dairy Cows Experiencing Mild to Moderate Heat Stress. J. Dairy Sci. 2015, 98, 118–127.
  26. Islam, M.A.; Lomax, S.; Doughty, A.; Islam, M.; Jay, O.; Thomson, P.; Clark, C. Automated Monitoring of Cattle Heat Stress and Its Mitigation. Anim. Sci. 2021, 2, 737213.
  27. Herbut, P.; Angrecka, S. The Effect of Heat Stress on Time Spent Lying by Cows in a Housing System. Ann. Anim. Sci. 2018, 18, 825–833.
  28. Habibu, B.; Yaqub, L.S.; Dzenda, T.; Kawu, M.U. Sensitivity, Impact and Consequences of Changes in Respiratory Rate During Thermoregulation in Livestock—A Review. Ann. Anim. Sci. 2019, 19, 291–304.
  29. Pastell, M.; Kaihilahti, J.; Aisla, A.-M.; Hautala, M.; Poikalainen, V.; Ahokas, J. A System for Contact-Free Measurement of Respiration Rate of Dairy Cows. J. Prec. Livest. Farm 2007, 7, 105–109.
  30. Milan, H.F.M.; Maia, A.S.C.; Gebremedhin, K.G. Technical Note: Device for Measuring Respiration Rate of Cattle under Field Conditions1. J. Anim. Sci. 2016, 94, 5434–5438.
  31. Rees, A.; Fischer-Tenhagen, C.; Heuwieser, W. Effect of Heat Stress on Concentrations of Faecal Cortisol Metabolites in Dairy Cows. Reprod. Domest. Anim. 2016, 51, 392–399.
  32. Dulude-de Broin, F.; Côté, S.D.; Whiteside, D.P.; Mastromonaco, G.F. Faecal Metabolites and Hair Cortisol as Biological Markers of HPA-Axis Activity in the Rocky Mountain Goat. Gen. Comp. Endocrinol. 2019, 280, 147–157.
  33. Lewis Baida, B.E.; Swinbourne, A.M.; Barwick, J.; Leu, S.T.; van Wettere, W.H.E.J. Technologies for the Automated Collection of Heat Stress Data in Sheep. Anim. Biotelemetry 2021, 9, 4.
  34. Burdick, N.C.; Carroll, J.A.; Dailey, J.W.; Randel, R.D.; Falkenberg, S.M.; Schmidt, T.B. Development of a Self-Contained, Indwelling Vaginal Temperature Probe for Use in Cattle Research. J. Therm. Biol. 2012, 37, 339–343.
  35. Lees, A.M.; Lees, J.C.; Lisle, A.T.; Sullivan, M.L.; Gaughan, J.B. Effect of Heat Stress on Rumen Temperature of Three Breeds of Cattle. Int. J. Biometeorol. 2018, 62, 207–215.
  36. Torrao, N.A.; Hetem, R.S.; Meyer, L.C.R.; Fick, L.G. Assessment of the Use of Temperature-Sensitive Microchips to Determine Core Body Temperature in Goats. Vet. Rec. 2011, 168, 328.
  37. Taylor, D.B.; Schneider, D.A.; Brown, W.Y.; Price, I.R.; Trotter, M.G.; Lamb, D.W.; Hinch, G.N.; Taylor, D.B.; Schneider, D.A.; Brown, W.Y.; et al. GPS Observation of Shelter Utilisation by Merino Ewes. Anim. Prod. Sci. 2011, 51, 724–737.
  38. Koltes, J.E.; Koltes, D.A.; Mote, B.E.; Tucker, J.; Hubbell, D.S., III. Automated Collection of Heat Stress Data in Livestock: New Technologies and Opportunities. Transl. Anim. Sci. 2018, 2, 319–323.
  39. AlZahal, O.; AlZahal, H.; Steele, M.A.; Van Schaik, M.; Kyriazakis, I.; Duffield, T.F.; McBride, B.W. The Use of a Radiotelemetric Ruminal Bolus to Detect Body Temperature Changes in Lactating Dairy Cattle. J. Dairy Sci. 2011, 94, 3568–3574.
  40. Beatty, D.T.; Barnes, A.; Fleming, P.A.; Taylor, E.; Maloney, S.K. The Effect of Fleece on Core and Rumen Temperature in Sheep. J. Therm. Biol. 2008, 33, 437–443.
  41. Taylor, N.A.S.; Tipton, M.J.; Kenny, G.P. Considerations for the Measurement of Core, Skin and Mean Body Temperatures. J. Therm. Biol. 2014, 46, 72–101.
  42. Vickers, L.A.; Burfeind, O.; von Keyserlingk, M.A.G.; Veira, D.M.; Weary, D.M.; Heuwieser, W. Technical Note: Comparison of Rectal and Vaginal Temperatures in Lactating Dairy Cows. J. Dairy Sci. 2010, 93, 5246–5251.
  43. Pent, G.J.; Fike, J.H.; Kim, I. Ewe Lamb Vaginal Temperatures in Hardwood Silvopastures. Agrofor. Syst. 2021, 95, 21–32.
  44. Bailey, D.W.; Trotter, M.G.; Knight, C.W.; Thomas, M.G. Use of GPS Tracking Collars and Accelerometers for Rangeland Livestock Production Research1. Transl. Anim. Sci. 2018, 2, 81–88.
  45. Yang, C.-C.; Hsu, Y.-L. A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring. Sensors 2010, 10, 7772–7788.
  46. Umstätter, C.; Waterhouse, A.; Holland, J.P. An Automated Sensor-Based Method of Simple Behavioural Classification of Sheep in Extensive Systems. Comput. Electron. Agric. 2008, 64, 19–26.
  47. Corkery, G.; Ward, S.; Kenny, C.; Hemmingway, P. Incorporating Smart Sensing Technologies into the Poultry Industry. J. Worlds Poult. Res. 2013, 3, 106–128.
  48. IBM. What Is Machine Learning? Available online: https://www.ibm.com/in-en/cloud/learn/machine-learning (accessed on 16 September 2022).
  49. Domingos, P. A Few Useful Things to Know about Machine Learning. Commun. ACM 2012, 55, 78–87.
  50. García, R.; Aguilar, J.; Toro, M.; Pinto, A.; Rodríguez, P. A Systematic Literature Review on the Use of Machine Learning in Precision Livestock Farming. Comput. Electron. Agric. 2020, 179, 105826.
  51. Banhazi, T.M.; Lehr, H.; Black, J.L.; Crabtree, H.; Schofield, P.; Tscharke, M.; Berckmans, D. Precision Livestock Farming: An International Review of Scientific and Commercial Aspects. Int. J. Agric. Biol. Eng. 2012, 5, 1–9.
  52. Kotsiantis, S.B. Supervised Machine Learning: A Review of Classification Techniques. In Proceedings of the 2007 Conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies, 10 June 2007; IOS Press: Amsterdam, The Netherlands; pp. 3–24.
  53. Ghafouri-Kesbi, F.; Rahimi-Mianji, G.; Honarvar, M.; Nejati-Javaremi, A.; Ghafouri-Kesbi, F.; Rahimi-Mianji, G.; Honarvar, M.; Nejati-Javaremi, A. Predictive Ability of Random Forests, Boosting, Support Vector Machines and Genomic Best Linear Unbiased Prediction in Different Scenarios of Genomic Evaluation. Anim. Prod. Sci. 2016, 57, 229–236.
  54. White, B.J.; Amrine, D.E.; Larson, R.L. Big data analytics and precision animal agriculture symposium: Data to Decisions. J. Anim. Sci. 2018, 96, 1531–1539.
  55. Libbrecht, M.W.; Noble, W.S. Machine Learning Applications in Genetics and Genomics. Nat. Rev. Genet. 2015, 16, 321–332.
  56. Neethirajan, S. Transforming the Adaptation Physiology of Farm Animals through Sensors. Animals 2020, 10, 1512.
  57. Williams, M.L.; Mac Parthaláin, N.; Brewer, P.; James, W.P.J.; Rose, M.T. A Novel Behavioral Model of the Pasture-Based Dairy Cow from GPS Data Using Data Mining and Machine Learning Techniques. J. Dairy Sci. 2016, 99, 2063–2075.
  58. Benaissa, S.; Tuyttens, F.A.M.; Plets, D.; de Pessemier, T.; Trogh, J.; Tanghe, E.; Martens, L.; Vandaele, L.; Van Nuffel, A.; Joseph, W.; et al. On the Use of On-Cow Accelerometers for the Classification of Behaviours in Dairy Barns. Res. Vet. Sci. 2019, 125, 425–433.
  59. Guo, Y.; He, D.; Chai, L. A Machine Vision-Based Method for Monitoring Scene-Interactive Behaviors of Dairy Calf. Animals 2020, 10, 190.
  60. Gorczyca, M.T. Machine Learning Applications for Monitoring Heat Stress in Livestock. Master’s Thesis, Cornell University, Ithaca, NY, USA, 2019.
  61. Gorczyca, M.T.; Gebremedhin, K.G. Ranking of Environmental Heat Stressors for Dairy Cows Using Machine Learning Algorithms. Comput. Electron. Agric. 2020, 168, 105124.
  62. Kim, S.; Hidaka, Y. Breathing Pattern Analysis in Cattle Using Infrared Thermography and Computer Vision. Animals 2021, 11, 207.
  63. De Sousa, R.V.; da Rodrigues, A.V.S.; de Abreu, M.G.; Tabile, R.A.; Martello, L.S. Predictive Model Based on Artificial Neural Network for Assessing Beef Cattle Thermal Stress Using Weather and Physiological Variables. Comput. Electron. Agric. 2018, 144, 37–43.
  64. Nardone, A.; Ronchi, B.; Lacetera, N.; Ranieri, M.S.; Bernabucci, U. Effects of Climate Changes on Animal Production and Sustainability of Livestock Systems. Livest. Sci. 2010, 130, 57–69.
  65. Benni, S.; Pastell, M.; Bonora, F.; Tassinari, P.; Torreggiani, D. A Generalised Additive Model to Characterise Dairy Cows’ Responses to Heat Stress. Animal 2020, 14, 418–424.
  66. Bovo, M.; Agrusti, M.; Benni, S.; Torreggiani, D.; Tassinari, P. Random Forest Modelling of Milk Yield of Dairy Cows under Heat Stress Conditions. Animals 2021, 11, 1305.
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