Automated Monitoring of Panting for Feedlot Cattle: Sensor System Accuracy and Individual Variability

Artificial Intelligence for Climate Change Biology: From Data Collection to Predictions

In the era of big data, ecological research is experiencing a transformative shift, yet big-data advancements in thermal ecology and the study of animal responses to climate conditions remain limited. This review discusses how big data analytics and artificial intelligence (AI) can significantly enhance our understanding of microclimates and animal behaviors under changing climatic conditions. We explore AI's potential to refine microclimate models and analyze data from advanced sensors and camera technologies, which capture detailed, high-resolution information. This integration can allow researchers to dissect complex ecological and physiological processes with unprecedented precision. We describe how AI can enhance microclimate modeling through improved bias correction and downscaling techniques, providing more accurate estimates of the conditions that animals face under various climate scenarios. Additionally, we explore AI's capabilities in tracking animal responses to these conditions, particularly through innovative classification models that utilize sensors such as accelerometers and acoustic loggers. For example, the widespread usage of camera traps can benefit from AI-driven image classification models to accurately identify thermoregulatory responses, such as shade usage and panting. AI is therefore instrumental in monitoring how animals interact with their environments, offering vital insights into their adaptive behaviors. Finally, we discuss how these advanced data-driven approaches can inform and enhance conservation strategies. In particular, detailed mapping of microhabitats essential for species survival under adverse conditions can guide the design of climate-resilient conservation and restoration programs that prioritize habitat features crucial for biodiversity resilience. In conclusion, the convergence of AI, big data, and ecological science heralds a new era of precision conservation, essential for addressing the global environmental challenges of the 21st century.

Impacts of heat stress on the accuracy of a noseband sensor for detection of eating and rumination behavior in confined cattle

Precision monitoring of feeding behaviors can aid in dairy herd management. Noseband sensors (RumiWatch System [RW]; Itin + Hoch GmbH) have been established as an automated gold standard for evaluating precision technologies in grazing cows, but more advanced algorithms have not been validated in confinement settings. Additionally, little is known regarding effects of environmental conditions on sensor performance. Therefore, accuracy of RW in quantifying eating and rumination time in confinement was evaluated using 2 versions of the analysis software algorithms (RW Converter V.7.3.2 and V.7.3.36) under thermoneutral (TN; 21.0°C, 64.0% relative humidity [RH], temperature-humidity index [THI] = 67) and heat stress conditions (HS; cyclical daily temperatures to mimic diurnal patterns; 0700-1900 h: 33.6°C, 40.0% RH, THI = 83.5; 1900-0700 h: 23.2°C, 70.0% RH; THI = 70.3). Nine individually housed Holstein × Simmental cross steers were fitted with RW noseband sensors. Agreement for eating time reported by RW and visual observations (1-min scan sampling) was very high in TN regardless of software version (concordance correlation coefficient [CCC]: V.7.3.2 = 0.91; V.7.3.36 = 0.94), and remained high to very high (CCC: V.7.3.2 = 0.89; V.7.3.36 = 0.95) during HS. Agreement for rumination time was very high regardless of software version in both TN (CCC: V.7.3.2 = 0.93; V.7.3.36 = 0.99) and HS (CCC: V.7.3.2 = 0.91; V.7.3.36 = 0.99). Overall, RW accurately quantified eating and ruminating time in confined cattle, and noseband sensors retained accuracy during heat stress. These results indicate RW may serve as a benchmark for future precision technology validations in dairy cattle managed in confinement systems.

Heat Stress Effects on Physiological and Milk Yield Traits of Lactating Holstein Friesian Crossbreds Reared in Tanga Region, Tanzania

Global warming caused by climate change is a challenge for dairy farming, especially in sub-Saharan countries. Under high temperatures and relative humidity, lactating dairy cows suffer from heat stress. The objective of this study was to investigate the effects and relationship of heat stress (HS) measured by the temperature-humidity index (THI) regarding the physiological parameters and milk yield and composition of lactating Holstein Friesian crossbred dairy cows reared in the humid coastal region of Tanzania. A total of 29 lactating Holstein Friesian x Zebu crossbred dairy cows with 50% (HF50) and 75% (HF75) Holstein Friesian gene levels in the second and third months of lactation were used. The breed composition of Holstein Friesians was determined based on the animal recording system used at the Tanzania Livestock Research Institute (TALIRI), Tanga. The data collected included the daily temperature, relative humidity, daily milk yield, and physiological parameters (core body temperature, rectal temperature, respiratory rate, and panting score). THI was calculated using the equation of the National Research Council. The THI values were categorized into three classes, i.e., low THI (76-78), moderate THI (79-81), and high THI (82-84). The effects of THI on the physiological parameters and milk yield and composition were assessed. The effects of the genotype, the parity, the lactation month, and the interaction of these parameters with THI on the milk yield, milk composition, and physiological parameters were also investigated. The results show that THI and its interaction with genotypes, parity, and the lactation month had a highly significant effect on all parameters. THI influenced (p ˂ 0.05) the average daily milk yield and milk fat %, protein %, lactose %, and solids-not-fat %. As the THI increased from moderate to high levels, the average daily milk yield declined from 3.49 ± 0.04 to 3.43 ± 0.05 L/day, while the fat % increased from 2.66 ± 0.05% to 3.04 ± 0.06% and the protein decreased from 3.15 ± 0.02% to 3.13 ± 0.03%. No decline in lactose % was observed, while the solid-not-fat % declined from 8.56 ± 0.08% to 8.55 ± 0.10% as the THI values increased from moderate to high. Also, the THI influenced physiological parameters (p ˂ 0.05). The core body temperature (CBT), rectal temperature (RT), respiratory rate (RR) and panting score (PS) increased from 35.60 ± 0.01 to 36.00 ± 0.01 °C, 38.03 ± 0.02 to 38.30 ± 0.02 °C, 62.53 ± 0.29 to 72.35 ± 0.28 breaths/min, and 1.35 ± 0.01 to 1.47 ± 0.09, respectively, as the THI increased from low to high. The THI showed a weak positive correlation with the average daily milk yield and fat percentage, whereas the protein, lactose, and solids-not-fat percentages showed negative relationships with THI (p ≤ 0.05). CBT, RT, RR, and PS showed positive relationships (p ≤ 0.05) with THI. These negative relationships indicate that there is an antagonistic correlation between sensitivity to HS and the level of production. It is concluded that the THI, the genotype, the parity, and the lactation month, along with their interactions with THI, significantly influenced the milk yield, milk composition, and physiological parameters of lactating Holstein Friesian dairy crosses at THI thresholds ranging from 77 to 84.

The Impact of Abrupt and Fenceline-Weaning Methods on Cattle Stress Response, Live Weight Gain, and Behaviour

The impact of abrupt (AB) and fenceline (FL) weaning methods on cattle stress response, live weight gain, and behaviour were determined across 14 days. Thirty-two cow-calf pairs were fitted with ear tag sensors (to continuously record behaviour) and allocated to two weaning treatments. After separation, FL calves were maintained in a pen adjacent to the FL cow paddock. The AB calves were transported to a pen removing all contact with the cows. After 7 d, FL cows were transported away from all calf pens. Body weights and salivary samples were collected for all animals on experimental days 0, 7, and 14. Fenceline-weaned calves had a greater duration of rest and rumination with reduced high activity across the first 3 days after separation as compared to abruptly weaned calves in line with the greater occurrences of pacing observed for AB calves. Fenceline-separated cows had greater levels of rest across the first 7 days but similar levels of rumination compared to abruptly separated cows. Fenceline-separated cow activity levels tended to be greater and eating levels were similar across the first three days. Body weight (BW) and cortisol concentrations were similar for AB and FL cattle, but FL cows had lower overall weight gain than the abrupt cows likely due to reduced eating time on days four to seven. Together, these results suggest that calves be fenceline-weaned for three days followed by total separation.

Application of Precision Technologies to Characterize Animal Behavior: A Review

This study aims to evaluate the state of precision livestock farming (PLF)'s spread, utilization, effectiveness, and evolution over the years. PLF includes a plethora of tools, which can aid in a number of laborious and complex tasks. These tools are often used in the monitoring of different animals, with the objective to increase production and improve animal welfare. The most frequently monitored attributes tend to be behavior, welfare, and social interaction. This study focused on the application of three types of technology: wearable sensors, video observation, and smartphones. For the wearable devices, the focus was on accelerometers and global positioning systems. For the video observation, the study addressed drones and cameras. The animals monitored by these tools were the most common ruminants, which are cattle, sheep, and goats. This review involved 108 articles that were believed to be pertinent. Most of the studied papers were very accurate, for most tools, when utilized appropriate; some showed great benefits and potential.

Effects of cow reproductive status, parity and lactation stage on behaviour and heavy breathing indications of a commercial accelerometer during hot weather conditions

Heat stress presents one of the most urgent challenges to modern dairy farming, having major detrimental impacts on cow welfare, health, and production. Understanding the effect of cow factors (reproductive status, parity, and lactation stage) on the physiological and behavioural response to hot weather conditions is essential for the accurate detection and practical application of heat mitigation strategies. To study this, collars with commercial accelerometer-based sensors were fitted on 48 lactation dairy cows to record behaviour and heavy breathing from late spring to late summer. The temperature-humidity index (THI) was calculated from measurements of 8 barn sensors. We found that, above a THI of 84, cows in advanced pregnancy (>90 days) spent more time breathing heavily and less time eating and in low activity than other cows, while cows in early pregnancy (≤90 days) spent less time breathing heavily, more time eating and in low activity. Cows with 3+ lactations showed less time breathing heavily and in high activity and more time ruminating and in low activity than cows with fewer lactations. Although lactation stage interacted significantly with THI on time spent breathing heavily, ruminating, eating, and in low activity, there was no clear indication at which lactation stage cows were more sensitive to heat. These findings show that cow factors affect the cow's physiological and behavioural response to heat, which could be used to provide group-specific heat abatement strategies, thereby improving heat stress management.

Welfare of calves

This Scientific Opinion addresses a European Commission request on the welfare of calves as part of the Farm to Fork strategy. EFSA was asked to provide a description of common husbandry systems and related welfare consequences, as well as measures to prevent or mitigate the hazards leading to them. In addition, recommendations on three specific issues were requested: welfare of calves reared for white veal (space, group housing, requirements of iron and fibre); risk of limited cow–calf contact; and animal‐based measures (ABMs) to monitor on‐farm welfare in slaughterhouses. The methodology developed by EFSA to address similar requests was followed. Fifteen highly relevant welfare consequences were identified, with respiratory disorders, inability to perform exploratory or foraging behaviour, gastroenteric disorders and group stress being the most frequent across husbandry systems. Recommendations to improve the welfare of calves include increasing space allowance, keeping calves in stable groups from an early age, ensuring good colostrum management and increasing the amounts of milk fed to dairy calves. In addition, calves should be provided with deformable lying surfaces, water via an open surface and long‐cut roughage in racks. Regarding specific recommendations for veal systems, calves should be kept in small groups (2–7 animals) within the first week of life, provided with ~ 20 m²/calf and fed on average 1 kg neutral detergent fibre (NDF) per day, preferably using long‐cut hay. Recommendations on cow–calf contact include keeping the calf with the dam for a minimum of 1 day post‐partum. Longer contact should progressively be implemented, but research is needed to guide this implementation in practice. The ABMs body condition, carcass condemnations, abomasal lesions, lung lesions, carcass colour and bursa swelling may be collected in slaughterhouses to monitor on‐farm welfare but should be complemented with behavioural ABMs collected on farm.

Revealing the diversity of internal body temperature and panting response for feedlot cattle under environmental thermal stress

Core body temperature (CBT) regulation is crucial for mammalian wellbeing and survival. Cattle pant to dissipate excess heat to regulate CBT when ambient conditions exceed thermoneutral zones. However, to date, neither the variability in cattle heat response, the lagged response of CBT to thermal indices, nor the diurnal patterns of thermal indices, CBT and panting have been reported in the literature. We decomposed thermal indices, CBT and panting time-series data for 99 feedlot heifers across three discrete heat events into diurnal, trend and residual components. Both raw and decomposed data were analysed to explore the lagged CBT and panting responses and the association between series. We show ambient thermal conditions impact CBT with a 1-h lag despite a lag of between 1.5 to 3 h from raw data. Average individual panting scores were used to identify heat-susceptible and heat-tolerant cattle. Heat-susceptible cattle showed greater CBT (P < 0.01) between 8:00 and 23:00 and greater panting duration (P < 0.05) between 10:00 and 18:00 than heat-tolerant cattle under the same thermal conditions and these variations followed a similar pattern despite differences in cattle breed. This new information enables targeted amelioration and selection of individuals against heat susceptibility.

Automated Monitoring of Cattle Heat Stress and Its Mitigation

Climate change related global warming is likely to continue, despite all mitigation measures taken by humans, due to the lag effect of long-term anthropogenic activities. Warming of the atmosphere can impact worldwide cattle production directly by compromising health, welfare and productivity, and indirectly by reducing the quality and quantity of animal feed. Under warm thermal conditions, cattle adjust their physiological and behavioural responses as an integral part of thermoregulation to maintain internal body temperature within a safe range. However, a greater intensity and duration of heat exposure can exceed thermoregulatory capacity leading to an increase in internal body temperature beyond the normal limit that ultimately evokes different animal responses to heat. In cattle, response to heat stress can be visually observed as elevated respiration rate or panting, but continuous visual monitoring is labour intensive, time consuming and subjective. Therefore, different weather-based indices have been developed such as the temperature humidity index (THI) and heat load index (HLI) which are commonly used weather-based indices for monitoring cattle heat stress at commercial level. However, the thermal comfort level of cattle based on weather-based indices has limited use at a microclimatic and individual animal level. Varying sensor-based approaches have shown promise to shift the focus of heat stress management to the individual level. Monitoring individual animal response and mitigation strategies for isolated heat-susceptible cattle could save on heat management costs whilst improving animal welfare and productivity. Here we review the technologies that enable automatic, continuous, and real-time cattle heat stress monitoring and mitigation under commercial conditions. Future platforms for autonomous monitoring and mitigation of heat stress in cattle are likely to be based on minimally-invasive smart technologies either singly, or in an integrated system, enabling real-time solutions to animal responses under various production systems and environmental conditions.

Biometric Physiological Responses from Dairy Cows Measured by Visible Remote Sensing Are Good Predictors of Milk Productivity and Quality through Artificial Intelligence

New and emerging technologies, especially those based on non-invasive video and thermal infrared cameras, can be readily tested on robotic milking facilities. In this research, implemented non-invasive computer vision methods to estimate cow's heart rate, respiration rate, and abrupt movements captured using RGB cameras and machine learning modelling to predict eye temperature, milk production and quality are presented. RGB and infrared thermal videos (IRTV) were acquired from cows using a robotic milking facility. Results from 102 different cows with replicates (n = 150) showed that an artificial neural network (ANN) model using only inputs from RGB cameras presented high accuracy (R = 0.96) in predicting eye temperature (°C), using IRTV as ground truth, daily milk productivity (kg-milk-day^-1), cow milk productivity (kg-milk-cow^-1), milk fat (%) and milk protein (%) with no signs of overfitting. The ANN model developed was deployed using an independent 132 cow samples obtained on different days, which also rendered high accuracy and was similar to the model development (R = 0.93). This model can be easily applied using affordable RGB camera systems to obtain all the proposed targets, including eye temperature, which can also be used to model animal welfare and biotic/abiotic stress. Furthermore, these models can be readily deployed in conventional dairy farms.

Evaluation of Wearable Cameras for Monitoring and Analyzing Calf Behavior: A Preliminary Study

Simple Summary

Owing to the small size and lightweight of wearable cameras, they do not affect cattle behavior when attached to their bodies. Thus, this study aimed to evaluate the suitability of wearable cameras for monitoring and analyzing calf behavior. We conclude that wearable cameras are suitable for observing calf behavior, particularly their posture (standing or lying), as well as their ruminating and feeding behaviors.

Abstract

Understanding cattle behavior is important for discerning their health and management status. However, manual observations of cattle are time-consuming and labor-intensive. Moreover, during manual observations, the presence or position of a human observer may alter the normal behavior of the cattle. Wearable cameras are small and lightweight; therefore, they do not disturb cattle behavior when attached to their bodies. Thus, this study aimed to evaluate the suitability of wearable cameras for monitoring and analyzing cattle behavior. From December 18 to 27, 2017, this study used four 2-month-old, group-housed Holstein calves at the Field Science Center of the Obihiro University of Agriculture and Veterinary Medicine, Japan. Calf behavior was recorded every 30 s using a wearable camera (HX-A1H, Panasonic, Japan) from 10:00 to 15:30 and observed directly from 11:00 to 12:00 and 14:00 to 15:00. In addition, the same observer viewed the camera recordings corresponding to the direct observation periods, and the results were compared. The correlation coefficients of all behavioral data from direct and wearable camera video observations were significant (p < 0.01). We conclude that wearable cameras are suitable for observing calf behavior, particularly their posture (standing or lying), as well as their ruminating and feeding behaviors.

Back to Nature With Fenceless Farms—Technology Opportunities to Reconnect People and Food

The development and application of the fence was one of the earliest forms of agricultural technology in action. Managing the supply of animal protein required hunter gatherer communities to be able to domesticate and contain wild animals. Over the ages the fence has become ingrained in the very fabric of society and created a culture of control and ownership. Garett Hardin's article titled “The Tragedy of the Commons” suggested that shared land, typified by access to a fenceless common resource, was doomed to failure due to a human instinct for mistrust and exploitation. Perhaps the fence has created an ingrained societal cultural response. While natural ecosystems do have physical boundaries, these are based on natural environmental zones. Landscapes are more porous and resilience is built up through animal's being able to respond to dynamic changes. This paper explores the opportunity for remote monitoring technologies to create open fenceless landscapes and how this might be integrated into the growing need for humans to access animal protein.

Recent Advances on Early Detection of Heat Strain in Dairy Cows Using Animal-Based Indicators: A Review

In pursuit of precision livestock farming, the real-time measurement for heat strain-related data has been more and more valued. Efforts have been made recently to use more sensitive physiological indicators with the hope to better inform decision-making in heat abatement in dairy farms. To get an insight into the early detection of heat strain in dairy cows, the present review focuses on the recent efforts developing early detection methods of heat strain in dairy cows based on body temperatures and respiratory dynamics. For every candidate animal-based indicator, state-of-the-art measurement methods and existing thresholds were summarized. Body surface temperature and respiration rate were concluded to be the best early indicators of heat strain due to their high feasibility of measurement and sensitivity to heat stress. Future studies should customize heat strain thresholds according to different internal and external factors that have an impact on the sensitivity to heat stress. Wearable devices are most promising to achieve real-time measurement in practical dairy farms. Combined with internet of things technologies, a comprehensive strategy based on both animal- and environment-based indicators is expected to increase the precision of early detection of heat strain in dairy cows.