Open Veterinary Journal, (2026), Vol. 16(4): 2539-2539

Research Article

10.5455/OVJ.2026.v16.i4.53

Assessment of the relationship between body surface temperature and behavioral responses in rams using infrared thermography

Ali Faisal Washam^*, Ali Hussein Mohammedand Atheer Salih Mahdi

Department of Animal Production, Faculty of Agriculture, University of Kufa, Najaf, Iraq

*Corresponding Author: Ali Faisal Washam. Department of Animal Production, Faculty of Agriculture, University of Kufa, Najaf, Iraq. Email: alif.altai [at] uokufa.edu.iq

Submitted: 14/01/2026 Revised: 15/03/2026 Accepted: 26/03/2026 Published: 30/04/2026

---

ABSTRACT

Background: Animals under heat stress change their behavior to regulate their body temperature, as higher temperatures lead to a decrease in feed intake, thereby affecting production. 

Aim: This study aimed to evaluate the relationship between the temperature of different body parts measured by a thermal camera as a non-invasive indicator and the behavioral responses shown by the animal under different environmental conditions to predict these responses and understand the physiological and emotional state of rams.

Methods: The temperature–humidity index (THI) was used to define the environmental conditions. Infrared thermography was used to measure body surface temperatures (forehead, eye, nose, ear, and back), while concurrent behavioral observations were recorded.

Results: A higher THI was associated (p ≤ 0.01) with significant increases in fear, pushing, and panting behaviors (reaching 12.79, 7.59, and 66.48 events/hour, respectively) and a rise (p ≤ 0.05) in water consumption (4.41 times/hour). Conversely, lower THI was correlated (p ≤ 0.01) with increased feed consumption, huddling, resting, and active movement. The surface temperatures of the forehead, nose, ear, and back increased significantly (p ≤ 0.01) with higher THI (reaching 39.44°C, 38.45°C, 37.30°C, and 38.38°C), while the eye temperature also increased (p ≤ 0.05; 36.87°C). Thermographic measures of the forehead, eye, nose, ear, and back showed positive correlations with fear, isolation, pushing, and panting behaviors but negative correlations with aggressive behavior.

Conclusion: These findings indicate that body surface temperature, measured via thermal imaging, is sensitive to environmental stress and correlates with specific behavioral responses in rams. This supports the potential use of infrared thermography as a non-invasive tool for predicting behavioral changes and assessing welfare states.

Keywords: Behavior, Body surface temperature, Rams, Thermal camera, Thermography.

---

Introduction

Animals in heat-stress (HS) environments modify their behavior to regulate body temperature, where high temperatures lead to increased standing periods and decreased feed intake, thereby affecting production (Allen et al., 2015; Guo et al., 2018). HS occurs when an animal’s thermoregulation fails to maintain a normal body temperature, significantly impacting the health and productivity of farm animals, such as sheep, in subtropical and tropical environments (Lewis Baida et al., 2021). This condition adversely affects animal behavior, production, and overall performance (Vieira et al., 2022; Xu et al., 2022). Environmental temperature and humidity fluctuations can cause stress in animals, but they typically adapt to moderate changes. Animals must employ behavioral and physiological strategies to cope in extreme cases (Collier et al., 2019; Ahmed et al., 2024). For ruminants, heat overload results in an increased body temperature and respiratory rate; the normal temperature range for sheep is 38.1°C–39.9°C, and the normal respiratory rate is 12–30 beats per minute, although these values can vary based on factors such as activity, diet, breed, and age (Joy et al., 2020).

Infrared thermography (IRT) offers a non-invasive and efficient alternative to traditional body temperature measurements, which are often labor-intensive and can compromise animal welfare (Macmillan et al., 2019). IRT assesses surface temperature from thermal radiation emissions, revealing variations linked to blood flow and stress (Daltro et al., 2017). Measurements taken from sites such as the eyes, forehead, and nose are useful in evaluating physiological parameters across livestock species (Peng et al., 2019; Fuentes et al., 2020). The proposed method is rapid, reliable, and capable of monitoring multiple animals simultaneously, while also presenting greater automation potential than conventional techniques (Idris et al., 2021).

The variability in behavioral adaptations is a challenge in assessing animal responses to climate-related stress through behavior. Extreme temperature changes yield more noticeable adaptations, whereas gradual shifts make detection difficult. Animals are conscious beings capable of experiencing emotions (Chincarini et al., 2020). Emotions are short-term, intense affective responses characterized by facial expressions, behaviors, physiological activation, and unconscious appraisal mechanisms (Cannas et al., 2018; Travain et al., 2021). They may have evolved to help organisms avoid harm or pursue rewards, influencing survival strategies in all mammals and birds (Stewart et al., 2017; Bonelli et al., 2019). Emotions are classified as either negative or positive. Negative emotions, such as fear and anger, activate the sympathetic nervous system for threat response, whereas positive emotions foster social bonding through the parasympathetic nervous system (Sutherland et al., 2020). Accurately determining the emotions of farm animals is challenging due to their non-verbal nature, and relying on a single emotional expression indicator is insufficient for capturing their emotional states (Kitajima et al., 2021; Thornton et al., 2022).

Although heat stress physiology and infrared thermography have been widely studied in livestock, their integration within outcome-based animal welfare assessment remains limited. Most investigations have examined physiological indices or behavioral responses separately, often under controlled conditions and predominantly in cattle. The quantitative linkage between regional thermal windows and validated behavioral indicators of affective state in mature rams under naturally fluctuating seasonal THI conditions has received little attention. Moreover, the potential of thermographic signatures as non-invasive biomarkers of autonomic activation and welfare compromise in small ruminants remains underexplored. Therefore, this study integrates IR thermography with a comprehensive welfare-relevant ethogram across seasonal THI gradients in Awassi rams. By elucidating physiological–behavioral coupling under field conditions, the existing study advances objectives for non-invasive welfare assessment frameworks in sheep production systems.

---

Materials and Methods

Animals

The study was conducted at the Khairat Al-Ittihad Private Station, located in Al-Shomali District (Babil Governorate, Iraq), during winter (January 1 to February 1, 2025), spring (March 1 to April 1, 2025), and summer (July 1 to August 1, 2025). Thirty Awassi rams (24 months of age) with an initial body weight of 54.7 ± 5.8kg were used in this study. Moreover, all the rams were in outstanding health, free from injuries, infectious diseases, or deformities that could negatively affect their body temperature regulation or behavior. A veterinarian performed health checks before and during the study period. All rams were maintained and raised under the same feeding and management practices as in the field. Every 10 rams were allocated to a pen (8 × 10 m), which goes beyond the recommended space allowances to minimize the impact of overcrowding on behavior. The rams were fed a balanced total mixed diet formulated to meet the maintenance requirements for adult rams. The diet was provided twice daily (08:00 and 16:00 hours) in feeders with sufficient space (0.6 m per animal) to minimize competition. In addition, water was made available ad libitum in troughs that had enough linear space (0.6 m per animal) to keep competition to a minimum. The troughs were cleaned and filled again twice daily to ensure that animals could always access them. In this way, the frequency of drinking depends on physiological requirements rather than limited access.

Animal acclimation

Before data collection, all rams underwent a 7-day acclimatization period to acclimate to the experimental environment and the protocols of infrared thermography. During these periods, the rams were kept in the research pens to acclimate to spatial configurations and social groupings. They were subjected to consistent human interaction and progressively acclimatized to the thermal camera via a systematic protocol: days 1–2 entailed the observer’s presence without equipment; days 3–4 incorporated the camera (powered off) at a distance; days 5–6 featured simulated imaging approaches at the standard 25 cm distance without recording; and day 7 comprised a full practice imaging session. No physical constraint was used through thermal imaging. Images were taken solely when the rams remained tranquil in the shadowed region, approached gently by the researcher. Should an animal show signs of excitement, the approach was discontinued, and the animal was subsequently reattempted. At the conclusion of the acclimation period, all rams showed tolerance to the process, devoid of any indications of fear or avoidance, indicating that the thermal data accurately represented the baseline physiological conditions rather than the stress responses caused by handling (Obernier and Baldwin, 2006; O’Neill et al., 2018; Comin et al., 2023).

Measurements of body temperature

An infrared thermal imaging camera (HT-02D, Dongguan Xintai Instrument Co., China) was used for all thermographic measurements. The camera has an infrared resolution of 1024 pixels, a temperature sensitivity of 0.3°C, an accuracy of ±2% or ±2°C (whichever is greater), a refresh rate of 6 Hz, and a spectral range from 8–14 μm. Additionally, the camera was factory-standardized before the study. The same operator (the first author) made all thermal images and behavioral observations to minimize inter-worker variability and confirm measurement consistency throughout the study period.

All imaging was conducted in a shaded environment between 08:00 and 10:00 hours to avoid direct sun radiation. The camera’s emissive setting was altered to ε=0.95 for all measurements, which is the standard value for biological tissues, including animal skin and wool (Stewart et al., 2017; Soerensen and Pedersen, 2015). This value is usually employed in sheep thermography investigations (Cannas et al., 2018; Joy et al., 2020) and provides accurate temperature readings in the range of 8–14 μm, where biological tissues have high and stable emissivity. Though there are minor variations in emissivity in many areas of the rams’ bodies (0.95–0.98), employing ε=0.95 leads to temperature errors of less than 0.3°C (McCafferty, 2007), which is appropriate according to the camera's accuracy.

Images were taken at a standardized distance of 25 cm (confirmed using a flexible tape) from the animal’s body, with the camera positioned perpendicular to the body surface. Three successive thermal images were taken for each body area (such as forehead, eye, nose, ear, and back) within 30 seconds. For each image, the highest temperature within the region of interest was documented, and the means of the three measurements were used for the statistical analysis procedure as described by Cannas et al. (2018) and Joy et al. (2020). Figure 1 shows representative thermal images of different body areas.

Fig. 1. Various images of different areas of the bodies of rams.

Temperature–humidity index

Environmental conditions were continuously observed during each study period using an automatic weather station (WS-1080, Fine Offset Electronics) positioned approximately 50 m from the animal pens. The dry bulb temperature (Tdb, °C) and relative humidity (RH, %) were recorded hourly, and the daily mean values were calculated from the 24-hour measurements. Values were measured for three periods/seasons (winter, spring, and summer), and the THI was calculated according to the method described by Bhateshwar et al. (2023).

THI=(1.8 × Tdb + 32) [0.55–0.0055 × RH] × (1.8 × Tdb–26.8)

where: RH represents relative humidity (%), and Tdb represents dry bulb temperature (°C).

The mean THI values were estimated for each period based on daily means. The peak afternoon THI values (14:00–16:00 hour) were also documented to describe the maximum daily heat load. Based on the categorization system of Bhateshwar et al. (2023), THI < 70 suggests normal conditions (no stress), THI (70–74) suggests mild heat stress, THI (75–78) suggests moderate heat stress, and THI (79–82) suggests critical heat stress.

The THI values were:

Winter (January 1–1 February): THI Mean=69 (range, 64–71), indicating no stress. Spring (March 1–1 April): Mean THI=72 (range, 66–76), categorized as mild heat stress. Summer (1 July–1 August): THI mean=80 (range, 72–84), categorized as severe heat stress. Moreover, behavioral observations and thermal imaging processes were performed through morning hours (08:00–10:00 hour) to reduce the confounding impacts of acute diurnal heat peaks while still capturing seasonal differences in baseline conditions (Marai et al., 2007; Pinto-Santini et al., 2023).

Behavioral measurements

Behavioral measurements were calculated using visual observation during the morning hours (08:00–11:00 hour) to coincide with thermographic imaging and avoid peak diurnal heat loads, using continuous recording during focal animal sampling (Marai et al., 2007). Each of the 30 rams was observed for a total of 60 minutes per seasonal period (winter, spring, and summer), divided into six 10-minute focal observation sessions distributed across three non-consecutive days within each 30-day collection period. Based on established behavioral categories in sheep, a comprehensive ethogram with precise operational definitions was developed (Cannas et al., 2018; Tufekci and Sejian, 2023). Fear behavior was measured by counting the number of times the rams displayed signs of fear, such as running away, jumping, startling, alarm vocalization, avoidance of stimuli, or freezing in response to environmental stimuli (Forkman et al., 2007). Active movement is the number of times each movement, such as walking, running, and turning, occurs (Shi et al., 2022). The number of times the rams opened their mouths and breathed rapidly (exceeding 40 breaths per minute) was recorded as panting, with each panting episode counted separately (Heal et al., 2022; Joy et al., 2020). Pushing behavior was defined as the number of times violent friction or jostling occurred over feeders and drinkers during resource competition, specifically involving forceful physical contact (Borthwick et al., 2024).

Aggression behavior was measured by counting the number of times the rams exhibited aggressive behavior, such as stamping the ground with their feet, butting, attempting to bite, or chasing conspecifics (Menchetti et al., 2021). Resting behavior was measured as the time the rams spent lying down without moving (minutes per hour), including sternal or lateral recumbency (Herbut et al., 2021). Isolation behavior was measured as the number of times the rams spent more than 5 m away from the herd for at least 30 seconds (Tufekci and Sejian, 2023). Huddling behavior was defined as the time the rams spent together without a clear defensive or feeding reason, recorded in minutes per hour, which represents social thermoregulation through body-to-body contact (Ibanez et al., 2023). Water drinking behavior was measured as the number of times the rams approached and consumed water from troughs (Herbut et al., 2021). Feed intake behavior was recorded as the time the head entered the feeder and the ram actively consumed feed (minutes per hour) (Goma and Phillips, 2022).

All observation sessions were video recorded, and a handheld digital voice recorder (Olympus VP-10) was used for continuous event recording, with subsequent transcription to standardized data sheets. Duration behaviors (feeding, resting, and huddling) were timed and recorded to the nearest second using a digital stopwatch (Casio HS-80TW). To minimize expectation bias (Travain and Valsecchi, 2021), observers were blinded to THI levels during all behavioral observations. Observation sessions were conducted in random order across seasons, animals were identified only by ear tag numbers, and environmental data were recorded separately by a different researcher. Behavioral data were entered using animal ID and date only, with THI values added later by a separate researcher after all behavioral recording was completed.

Statistical analysis

Data were statistically analyzed using the Statistical Analysis System (SAS, 1996) software. Because the same animals were repeatedly assessed across different environmental conditions, data were analyzed using a linear mixed model for repeated measures (PROC MIXED, SAS Institute). In this model, THI was entered as a continuous covariate rather than a categorical factor to better describe the dose–response relationship between the environmental heat load and physiological and behavioral variables. Rams were involved as a random effect to account for within-subject variability, and each individual ram was subjected to repeated observations over time. The statistical model used was as follows:

Y_ij=μ + β (THI _ij) +A_j+ e_ij

where, Y_ij=observed values of the dependent variable, μ=overall mean, β=regression coefficient referring to the THI effects, THI_ij=continuous THI, A_j=rams’ random effect, and e_ij=the residual error.

Model assumptions, involving normality of residuals and homogeneity of variance, were confirmed using diagnostic plots. When needed, the data were log- or square-root transformed before analysis. Pearson correlation coefficients between body surface temperatures and behavioral responses were estimated using PROC CORR. As described by Hinkle et al. (2003), correlation coefficients were translated as follows: 0.90–1.00 (very high), 0.70–0.89 (high), 0.50–0.69 (moderate), 0.30–0.49 (low), and 0.00–0.29 (negligible).

Ethical approval

All experimental procedures and protocols were approved by the Animal Care Committee of the University of Kufa, Iraq (Directive 37426-11/6/2024/ Kufa University).

---

Results

Effects of temperature–humidity index on behavioral responses

The THI significantly influenced several behavioral responses in rams (Table 1). Fear behavior increased with increasing THI (p ≤ 0.01), reaching 12.79 events/hour under high THI conditions compared with 8.86 and 5.67 events/hour under moderate and low THI levels, respectively. Pushing behavior also increased significantly with increasing THI (p ≤ 0.01), reaching 7.59 events/hour under high THI conditions. Panting frequency increased markedly as THI increased (p ≤ 0.01), reaching 66.48 breaths/hour under high THI conditions. In contrast, aggressive behavior showed an opposite trend, increasing significantly under low THI conditions (p ≤ 0.01) with a value of 4.73 events/hour. Isolation behavior did not differ significantly across THI levels.

Table 1. Effect of temperature–humidity index levels on ram behavior.

Effects of THI levels on the behavioral responses and physiological adaptations of rams

The THI significantly affected several activity-related behaviors (Table 2). Drinking frequency increased significantly by increasing THI (p ≤ 0.05), reaching 4.41 times/hour under high THI conditions. In contrast, feeding behavior, huddling behavior, active movement, and resting time decreased significantly as THI increased (p ≤ 0.01). Under low THI conditions, feeding behavior reached 7.15 minute/hour, huddling behavior 16.70 minute/hour, active movement 9.94 events/hour, and resting time 33.58 minute/hour.

Table 2. Effect of temperature–humidity index levels on ram behavior.

Effect of THI on body surface temperature

Body surface temperatures of all measured anatomical regions were significantly affected by THI (Table 3). Forehead, nose, ear, and back temperatures increased significantly as THI increased (p ≤ 0.01), reaching 39.44°C, 38.45°C, 37.30°C, and 38.38°C, respectively, under high THI conditions. Eye temperature also increased significantly with increasing THI (p ≤ 0.05), reaching 36.87°C.

Table 3. Effect of temperature–humidity index on body surface temperature (°C).

Correlations between thermographic temperature and behavioral responses in rats

Significant correlations were observed between body surface temperatures and several behavioral responses (Table 4, Fig. 2). Positive correlations were detected between the temperatures of the forehead, eye, nose, ear, and back and fear behavior (r=0.631–0.749, p < 0.01), pushing behavior (r=0.668–0.720, p < 0.01), and panting frequency (r=0.468–0.581, p < 0.05). Isolation behavior showed low to moderate positive correlations with body surface temperatures (r=0.411–0.497, p < 0.05). Aggressive behavior showed negative correlations with body surface temperatures (r=−0.560 to −0.701, p < 0.01).

Table 4. Relationship between body surface temperature (°C) and ram behavioral responses.

Fig. 2. Relationship between body surface temperatures and ram behavioral responses. The correlation heatmap shows the relationships between rams’ body surface temperatures (forehead, eye, nose, ear, and back) and behavioral responses (fear, aggression, isolation, pushing, and panting). A heatmap figure uses a color scale from blue (negative correlations) to red (positive correlations), with the x-axis representing behaviors and the y-axis representing temperatures.

Correlations among body temperature, feed, water consumption, and behavioral responses

Moderate positive correlations were observed between body surface temperatures and drinking frequency (r=0.434–0.492, p < 0.05) (Table 5, Fig. 3). Conversely, moderate negative correlations were observed between body surface temperatures and huddling behavior (r=−0.653 to −0.729, p < 0.01) and feeding behavior (r=−0.469 to −0.555, p < 0.05). Weak negative correlations were detected between body surface temperatures and active movement (r=−0.102 to −0.221, p > 0.05). Strong negative correlations were observed between body surface temperatures and resting behavior (r=−0.722 to −0.838, p < 0.01).

Table 5. Relationship between body surface temperature (°C) and ram behavioral responses.

Fig. 3. Relationship between body surface temperatures and ram behavioral responses. The correlation heatmap contains a correlation matrix between the body surface temperatures and behavioral responses of rams. The heatmap displays temperatures on the y-axis, behaviors on the x-axis, and a blue-to-red color scale (blue for negative, red for positive).

---

Discussion

The present study provides evidence that thermal load, quantified through the THI, significantly modulates both physiological and behavioral responses in Awassi rams. By integrating infrared thermography with behavioral observations under naturally fluctuating seasonal conditions, this work demonstrates that body surface temperature (BST) measured at specific anatomical regions can serve as a sensitive indicator of heat stress and associated welfare changes. The results indicate that increases in THI were accompanied by elevations in peripheral surface temperatures and distinct behavioral adjustments, suggesting a coordinated thermoregulatory and behavioral response aimed at maintaining homeostasis under environmental heat challenge. One of the most prominent responses observed in the present study was the substantial increase in panting frequency under elevated THI conditions. Panting represents a primary evaporative cooling mechanism in small ruminants, enabling heat dissipation when ambient temperatures approach or exceed the thermoneutral zone. Physiologically, heat exposure activates hypothalamic thermoregulatory centers that stimulate respiratory evaporation through increased respiratory rate and tidal volume. This response is mediated by sympathetic activation and endocrine adjustments, including increased circulating cortisol and catecholamines, which together facilitate thermoregulatory adaptation (Ayoola et al., 2025; Zhang et al., 2025). Similar thermoregulatory respiratory responses have been reported in sheep and goats exposed to high environmental temperatures, where increased panting frequency serves as an early indicator of thermal stress before more severe physiological disturbances occur (Bhateshwar et al., 2023; Tufekci and Sejian, 2023).

Behavioral changes observed in the present study further support the interpretation that rams actively modify their activity patterns to minimize heat load. Reduced feeding behavior under high THI conditions is consistent with the well-established phenomenon of heat-induced hypophagia. Digestion and metabolic processing of nutrients generate substantial endogenous heat through fermentation and metabolic oxidation. Consequently, animals exposed to heat stress often reduce feed intake as an adaptive strategy to limit metabolic heat production. At the neuroendocrine level, thermal stress affects hypothalamic appetite centers through alterations in leptin signaling, ghrelin activity, and glucocorticoid secretion, leading to suppression of feeding motivation (Thornton et al., 2022; Tufekci and Sejian, 2023). The concurrent increase in water intake observed in the present study represents a complementary physiological response aimed at maintaining hydration status and supporting evaporative cooling processes. Another notable behavioral adaptation was the reduction in resting and huddling behavior under high THI conditions. Huddling is typically observed in cold environments as a form of social thermoregulation that reduces convective heat loss. However, under heat stress conditions, aggregation may exacerbate thermal load by increasing conductive heat transfer between animals. Therefore, the decline in huddling behavior observed in the current study likely reflects an adaptive response aimed at increasing heat dissipation through enhanced airflow around the body surface. Similar behavioral thermoregulation strategies have been documented in small ruminants exposed to elevated temperatures, where animals reduce social contact and increase spatial dispersion to mitigate heat accumulation (Borthwick et al., 2024).

Infrared thermography results revealed that surface temperatures at the forehead, nose, ear, and back increased significantly with increasing THI. These anatomical regions function as peripheral thermal windows due to their relatively high vascularization and reduced insulation compared with heavily fleeced body areas. During heat stress, peripheral vasodilation redistributes blood flow toward superficial tissues, facilitating heat transfer from the body core to the environment through radiation and convection. This vasodilatory response is regulated by autonomic nervous system activity and is considered a key component of thermoregulatory physiology in mammals (Collier et al., 2019; Samara et al., 2025). The pronounced temperature changes detected in the frontal and nasal regions in the present study, therefore, likely reflect increased peripheral blood perfusion associated with active thermoregulation.

Particularly noteworthy is the increase in ocular temperature with rising THI levels. The ocular region has increasingly been recognized as a reliable thermal window for monitoring physiological stress because it is closely associated with the lacrimal caruncle and surrounding vascular structures supplied by branches of the carotid artery. Changes in ocular surface temperature are therefore strongly influenced by autonomic regulation of blood flow and can reflect sympathetic nervous system activation (Ghezzi et al., 2024). Recent studies have highlighted ocular thermography as a sensitive indicator of both thermal and emotional stress in livestock species (Comin et al., 2023; Ibanez et al., 2023). The findings of the present study further support the use of eye temperature as a non-invasive proxy for physiological stress in sheep.

The correlation analysis revealed strong positive relationships between body surface temperatures and several stress-related behaviors, particularly fear, pushing behavior, and panting frequency. These associations suggest that thermographic indicators may capture not only thermoregulatory responses, but also affective states associated with environmental stressors (Guerrero-Gutiérrez et al., 2025). Activation of the hypothalamic-pituitary-adrenal axis during heat stress leads to increased cortisol secretion, which can influence behavioral reactivity and emotional responses. Elevated cortisol levels have been associated with increased vigilance, heightened sensitivity to environmental stimuli, and changes in social behavior in livestock animals (Kitajima et al., 2021; Travain and Valsecchi, 2021). Consequently, the strong correlations observed in this study between thermographic measures and fear-related behaviors may reflect the integrated physiological and behavioral response to thermal stress. Conversely, negative correlations were observed between body surface temperatures and behaviors associated with comfort or low activity levels, including feeding, resting, and huddling. These relationships highlight the trade-offs animals face when coping with heat stress: behaviors that normally support energy balance and social stability may be suppressed to prioritize thermoregulation (Oke et al., 2025). Similar behavioral shifts have been reported in cattle and sheep exposed to heat stress, where animals reduce physical activity and feeding time while increasing behaviors associated with heat dissipation (Herbut et al., 2021).

From an applied perspective, the integration of thermographic monitoring with behavioral indicators offers considerable potential for improving heat stress management in small ruminant production systems. Traditional measures of thermal stress, such as rectal temperature or respiration rate, often require physical restraint and can induce additional stress in animals (Shephard and Maloney, 2023). In contrast, infrared thermography allows rapid, non-invasive assessment of physiological status without disturbing the animals. Advances in precision livestock farming technologies are increasingly enabling automated thermal imaging systems capable of continuous monitoring at the herd level. When combined with environmental sensors and machine learning algorithms, such systems could provide real-time detection of heat stress and support early management interventions (Lewis Baida et al., 2021). In regions where climate change is expected to increase the frequency and intensity of heat waves, the ability to detect early signs of thermal stress will become increasingly important for maintaining animal welfare and productivity. Integrating thermographic monitoring into routine management practices could enable farmers to implement timely mitigation strategies, such as improving shade availability, adjusting feeding schedules, enhancing ventilation, or optimizing water access. Such proactive management approaches may help reduce the negative impacts of heat stress on animal performance while supporting more resilient and welfare-oriented livestock production systems.

Overall, the present study contributes to a growing body of evidence supporting infrared thermography as a valuable tool for non-invasive assessment of thermal stress in livestock. By demonstrating clear associations between regional body surface temperatures and behavioral responses in rams under field conditions, this work highlights the potential of thermographic indicators to serve as practical biomarkers of both physiological and behavioral stress responses in small ruminants.

Practical implications and field-level applications

From a translational and systems-level perspective, the present results expand beyond experimental observation and support a mechanistically grounded framework for on-farm heat stress surveillance in sheep production systems. The strong correlations identified between regional surface temperatures, principally at vascularized thermal windows such as the ocular, nasal, and frontal areas, and behavioral indicators of fear, panting, and social disruption suggest that infrared thermographic signatures mirrored peripheral vasodilation and autonomic activation. This physiological–behavioral coupling implies that thermal imaging could be employed as an immediate biomarker of sympathetic activation and thermoregulatory strain, preceding obvious productivity losses. At the farm level, this assists a shift from reactive to preventive management, whereby farmers could implement targeted mitigation strategies (such as microclimate modification, dynamic shade allocation, ventilation improvement, water system optimization, and temporal adjustment of feeding regimes) once defined thermal thresholds are exceeded. Notably, embedding TGS within precision animal farming architectures, such as fixed automated cameras incorporated with environmental sensors and THI-based decision algorithms, would allow continuous, non-invasive welfare assessment at herd scales. This integration confirms actual welfare assessment frameworks and aligns with emerging global standards, highlighting outcome-based welfare markers and climate-adaptive animal systems. In areas increasingly exposed to repeated heat stress waves, incorporating thermography into repetitive husbandry practices may decrease subclinical heat stress, alleviate dietary efficiency, and enhance the resilience of small ruminant enterprises. Consequently, infrared thermography should be considered not only a diagnostic tool but also a component of evidence-based policy strategies aimed at improving animal welfare, production sustainability, and climate change adaptation in the animal husbandry sector.

Limitations of the study

When interpreting the results of the current study, several limitations must be considered:

I. The study was performed at a single site, applying one breed (Awassi rams) across three distinct seasonal intervals, possibly constraining the applicability of results to other breeds, management practices, or geographical zones with differing climatic conditions.

II. The sample size (30 rams) is sufficient to find significant differences, but it might not show all the different responses of individual rams to temperature and behavior.

III. Watching and counting the animals’ behavior by eye might lead to mistakes and might not capture details as well as automated systems would.

IV. Although the infrared thermography measurements followed guidelines with a standard distance (25 cm) and in shaded conditions, factors such as wind speed, emissivity settings, and small changes in the image's angle could affect the surface temperature readings.

V. The emotional states predicted from watching behavior (fear or aggression) are not direct measures; future studies should use physical stress indicators such as plasma or salivary cortisol levels or heart rate variability to enhance our understanding of these emotional states. Despite these limitations, the current study offers compelling evidence that infrared thermography is a valuable, noninvasive technique for measuring thermal stress and forecasting ram behavior, thereby establishing a foundation for future research and practical applications in sheep welfare management.

---

Conclusion

This study demonstrates that BST is a reliable noninvasive indicator of heat stress in sheep, as it shows strong associations with behavioral responses. These findings provide further evidence that monitoring thermal windows in combination with behavioral patterns can serve as an effective strategy for assessing and managing heat stress in sheep, thereby contributing to improved husbandry practices under challenging environmental conditions.

---

Acknowledgments

The authors would like to express their sincere appreciation to Khairat Al-Ittihad Station for providing the facilities and support that enabled us to conduct this experiment successfully. We are deeply grateful for the assistance and cooperation of the staff, whose efforts significantly contributed to the completion of this research.

Conflict of interest

All authors declare no conflicts of interest that could inappropriately influence this manuscript.

Funding

This research has not received any external funding.

Authors' contributions

AFW, AHM, and ASM drafted, revised, and edited the manuscript. All authors have read and approved the final version of the manuscript.

Data availability

The data supporting the findings of this study are available upon reasonable request from the corresponding author.

---

References

Ahmed, A.K., Khudhair, N., Melak, S. and Peng, Z. 2024. Selenium with feed and/or water upregulates gene expression in Liver tissues related to immune response, programmed cell death, antioxidant and metabolism in broilers. Tikrit J. Agric. Sci. 24(4), 266–279.

Allen, J.D., Hall, L.W., Collier, R.J. and Smith, J.F. 2015. 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. 98(1), 118–127.

Ayoola, M.O., Akinmoladun, O.F., Esan, V.I., Ogunbode, T.O., Afolabi, C.O. and Lawal, T.E. 2025. Managing heat stress in goats for sustainable climate-resilient production. J. Anim. Sci. Vet. Med. 10(2), 142–157.

Bhateshwar, V., Rai, D.C. and Datt, M. 2023. Heat stress responses in small ruminants under arid and semiarid regions of western India: a review. Agric. Rev. 44(2), 164–172.

Bonelli, F., Rota, A., Aurich, C., Ille, N., Camillo, F., Panzani, D. and Sgorbini, M. 2019. Determination of salivary cortisol in donkey stallions. J. Equine Vet. Sci. 77, 68–71.

Borthwick, Z., Quiring, K., Griffith, S.C. and Leu, S.T. 2024. Heat stress conditions affect the social network structure of free-ranging sheep. Ecol. Evol. 14(2), e10996.

Cannas, S., Palestrini, C., Canali, E., Cozzi, B., Ferri, N., Heinzl, E., Minero, M., Chincarini, M., Vignola, G. and Dalla Costa, E. 2018. Thermography as a non-invasive measure of stress and fear of humans in sheep. Animals 8(9), 146.

Chincarini, M., Dalla Costa, E., Qiu, L., Spinelli, L., Cannas, S., Palestrini, C., Canali, E., Minero, M., Cozzi, B., Ferri, N., Ancora, D., De Pasquale, F., Vignola, G. and Torricelli, A. 2020. Reliability of fNIRS for noninvasive monitoring of brain function and emotion in sheep. Sci. Rep. 10(1), 14726.

Collier, R.J., Baumgard, L.H., Zimbelman, R.B. and Xiao, Y. 2019. Heat stress: physiology of acclimation and adaptation. Anim. Front. 9(1), 12–19.

Comin, M., Atallah, E., Chincarini, M., Mazzola, S.M., Canali, E., Minero, M., Cozzi, B., Rossi, E., Vignola, G. and Dalla Costa, E. 2023. Events with Different Emotional Valence Affect the Eye’s Lacrimal Caruncle Temperature Changes in Sheep. Animals 14(1), 50.

Daltro, D.D.S., Fischer, V., Alfonzo, E.P.M., Dalcin, V.C., Stumpf, M.T., Kolling, G.J., Silva, M.V.G.B.D. and Mcmanus, C. 2017. Infrared thermography as a method for evaluating the heat tolerance in dairy cows. Rev. Bras. Zootec. 46, 374–383.

Forkman, B., Boissy, A., Meunier-Salaün, M.C., Canali, E. and Jones, R.B. 2007. A critical review of fear tests used on cattle, pigs, sheep, poultry and horses. Physiol. Behav. 92(3), 340–374.

Fuentes, A., Yoon, S., Park, J. and Park, D.S. 2020. Deep learning-based hierarchical cattle behavior recognition with spatio-temporal information. Comput. Electron. Agric. 177, 105627.

Ghezzi, M.D., Napolitano, F., Casas-Alvarado, A., Hernández-Ávalos, I., Domínguez-Oliva, A., Olmos-Hernández, A. and Pereira, A.M.F. 2024. Utilization of infrared thermography in assessing thermal responses of farm animals under heat stress. Animals 14(4), 616.

Goma, A.A. and Phillips, C.J.C. 2022. 'Can they take the heat?'—The Egyptian climate and its effects on livestock. Animals 12(15), 1937.

Guerrero-Gutiérrez, M., Freitas-De-Melo, A., Pinto-Santini, L., Giriboni, J. and Ungerfeld, R. 2025. Short and long-term thermoregulatory, metabolic, and stress responses to winter shearing in rams. Int. J. Biometeorol. 69(12), 3669–3683.

Guo, L., Welch, M., Dobos, R., Kwan, P. and Wang, W. 2018. Comparison of grazing behaviour of sheep on pasture with different sward surface heights using an inertial measurement unit sensor. Comput. Electron. Agric. 150, 394–401.

Heal, C., Harvey, A., Brown, S., Rowland, A.G. and Roland, D. 2022. The association between temperature, heart rate, and respiratory rate in children aged under 16 years attending urgent and emergency care settings. Eur. J. Emerg. Med. 29(6), 413–416.

Herbut, P., Hoffmann, G., Angrecka, S., Godyń, D., Vieira, F.M.C., Adamczyk, K. and Kupczyński, R. 2021. The effects of heat stress on the behaviour of dairy cows–a review. Ann. Anim. Sci. 21(2), 385–402.

Hinkle, K.H., Blum, R.D., Joyce, R.R., Sharp, N., Ridgway, S.T., van der Bliek, N.S. and Valenti, J. 2003. Phoenix spectrograph at Gemini South. In Discoveries and Research Prospects from 6- to 10-Meter-Class Telescopes II 4834. Ed., Guhathakurta, P. Bellingham, WA: SPIE—International Society for Optics and Photonics, 353–363.

Ibáñez, C., Moreno-Manrique, M., Villagrá, A., Bueso-Ródenas, J. and Mínguez, C. 2023. Evaluation of non-contact device to measure body temperature in sheep. Animals 14(1), 98.

Idris, M., Uddin, J., Sullivan, M., Mcneill, D.M. and Phillips, C.J.C. 2021. Non-invasive physiological indicators of heat stress in cattle. Animals 11(1), 71.

Joy, A., Dunshea, F.R., Leury, B.J., Digiacomo, K., Clarke, I.J., Zhang, M.H., Abhijith, A., Osei-Amponsah, R. and Chauhan, S.S. 2020. Comparative assessment of thermotolerance in Dorper and second-cross (poll Dorset/merino× Border Leicester) lambs. Animals 10(12), 2441.

Kitajima, K., Oishi, K., Miwa, M., Anzai, H., Setoguchi, A., Yasunaka, Y., Himeno, Y., Kumagai, H. and Hirooka, H. 2021. Effects of heat stress on heart rate variability in free-moving sheep and goats assessed with correction for physical activity. Front. Vet. Sci. 8, 658763.

Lewis Baida, B.E., Swinbourne, A.M., Barwick, J., Leu, S.T. and Van Wettere, W.H.E.J. 2021. Technologies for the automated collection of heat stress data in sheep. Anim. Biotelemetry. 9(1), 4.

Macmillan, K., Colazo, M.G. and Cook, N.J. 2019. Evaluation of infrared thermography compared to rectal temperature to identify illness in early postpartum dairy cows. Res. Vet. Sci. 125, 315–322.

Marai, I.F.M., El-Darawany, A.A., Fadiel, A. and Abdel-Hafez, M.A.M. 2007. Physiological traits as affected by heat stress in sheep—a review. Small Ruminant Res. 71(1-3), 1–12.

Mccafferty, D.J. 2007. The value of infrared thermography for research on mammals: previous applications and future directions. Mamm. Rev. 37(3), 207–223.

Menchetti, L., Nanni Costa, L., Zappaterra, M. and Padalino, B. 2021. Effects of reduced space allowance and heat stress on behavior and eye temperature in unweaned lambs: a pilot study. Animals 11(12), 3464.

O’Neill, S.J., Williamson, J.E., Tosetto, L. and Brown, C. 2018. Effects of acclimatisation on behavioural repeatability in two behaviour assays of the guppy Poecilia reticulata. Behav. Ecol. Sociobiol. 72(10), 166.

Obernier, J.A. and Baldwin, R.L. 2006. Establishing an appropriate period of acclimatization following transportation of laboratory animals. ILAR J. 47(4), 364–369.

Oke, O.E., Eletu, T.A., Akosile, O.A., Fasasi, L.O., Adeniji, O.E., Ojedokun, M.Z. and Oni, A.I. 2025. Behavioural adaptations of livestock to environmental stressors: implications for welfare and productivity. J. App. Ani. Res. 53(1), 2583108.

Peng, D., Chen, S., Li, G., Chen, J., Wang, J. and Gu, X. 2019. Infrared thermography measured body surface temperature and its relationship with rectal temperature in dairy cows under different temperature-humidity indexes. Int. J. Biometeorol. 63(3), 327–336.

Pinto-Santini, L., Pérez-Clariget, R. and Ungerfeld, R. 2023. Does the metabolic and behavior daily variation pattern on rams differ in summer and winter? Biol. Rhythm Res. 54(2), 213-231.

Samara, E.M., Al-Badwi, M.A., Abdoun, K.A., Abdelrahman, M.M., Okab, A.B., Bahadi, M.A. and Al-Haidary, A.A. 2025. The interrelationship between macrominerals and heat stress in ruminants: current perspectives and future directions-a review. Vet. Res. Commun. 49(1), 29.

SAS. 1996. SAS Institute. SAS/STAT Software: Changes and Enhancements for release 6.12. SAS Inst, USA.

Shephard, R. and Maloney, S. 2023. A review of thermal stress in cattle. Aust. Vet. J. 101(11), 417–429.

Shi, L., Xu, Y., Jin, X., Wang, Z., Mao, C., Guo, S., Yan, S. and Shi, B. 2022. Influence of cold environments on growth, antioxidant status, immunity and expression of related genes in lambs. Animals 12(19), 2535.

Soerensen, D.D. and Pedersen, L.J. 2015. Infrared skin temperature measurements for monitoring health in pigs: a review. Acta. Vet. Scand. 57(1), 5.

Stewart, M., Wilson, M.T., Schaefer, A.L., Huddart, F. and Sutherland, M.A. 2017. The use of infrared thermography and accelerometers for remote monitoring of dairy cow health and welfare. J. Dairy. Sci. 100(5), 3893–3901.

Sutherland, M.A., Worth, G.M., Dowling, S.K., Lowe, G.L., Cave, V.M. and Stewart, M. 2020. Evaluation of infrared thermography as a non-invasive method of measuring the autonomic nervous response in sheep. PLos One 15(5), e0233558.

Thornton, P., Nelson, G., Mayberry, D. and Herrero, M. 2022. Impacts of heat stress on global cattle production during the 21st century: a modelling study. Lancet. Planet. Health. 6(3), e192–e201.

Travain, T. and Valsecchi, P. 2021. Infrared thermography in the study of animals' emotional responses: a critical review. Animals 11(9), 2510.

Tüfekci, H. and Sejian, V. 2023. Stress factors and their effects on productivity in sheep. Animals 13(17), 2769.

Vieira, R., Louvandini, H., Barcellos, J., Martins, C.F. and Mcmanus, C. 2022. Path and logistic analysis for heat tolerance in adapted breeds of cattle in Brazil. Livestock. Sci. 258, 104888.

Xu, H.L., Li, H., Bao, R.K., Tang, Y.X., Elsherbeni, A.I.A., Gharib, H.B.A. and Li, J.L. 2022. Transport stress induced cardiac NO-NOS disorder is mitigated by activating Nrf2/HO-1/NQO1 antioxidant defense response in newly hatched chicks. Front. Vet. Sci. 9, 938826.

Zhang, Y., Jiang, F., Song, Z., Lian, J. and Han, L. 2025. Changes in physiological parameters and thermal comfort when wearing protective clothing in long-range aeromedical evacuation: a prospective, non-blinded, two-stage crossover self-controlled study. J. Physiol. Anthropol. 44(1), 28.