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Simple Summary: The use of the compost barn system has increased in dairy farms as it provides greater well-being to animals, favoring productivity. Thus, studies related to the thermal environment and behavior are paramount to assessing animal welfare and optimizing management. The objective of this work was to characterize the thermal environment inside a compost barn and to evaluate the standing and lying behavior of cows through images covering the four seasons. Dry bulb temperature, dew point temperature, and relative humidity data were collected every 10 minutes during the analyzed days, calculating the temperature and humidity index (THI). Filming was performed inside the barn, which was analyzed visually and in an automated way to assess the behavior of these animals. For the automated analysis, an algorithm was developed using Artificial Intelligence tools, YOLOv3. It was observed that in the experimental period the highest mean values of THI were observed during the afternoon and autumn. The animals' preference to lie down on the bed for most of the day was verified. Regarding the developed algorithm, it was observed that it could detect cow behavior (lying down or standing), concluding that artificial intelligence was successfully applied and can be recommended for such use. The compost barn system has become popular in recent years for providing greater animal well-being and quality of life, favoring productivity and longevity. With the increase in the use of compost barn in dairy farms, studies related to the thermal environment and behavior are of paramount importance to assess the well-being of animals and improve management, if necessary. This work aimed to characterize the thermal environment inside a compost barn during the four seasons of a year and to evaluate the standing and lying behavior of the cows through images. The experiment was carried out during March (summer), June (autumn), August (winter), and November (spring). Dry bulb temperature (tdb, °C), dew point temperature (tdp, °C), and relative humidity (RH,%) data were collected every 10 minutes during all analyzed days, and the temperature and humidity index (THI) was subsequently calculated. In order to analyze the behavior of the cows, filming of the barn interior was carried out during the evaluated days. Subsequently, these films were analyzed visually, and in an automated way to evaluate the behavior of these animals. For the automated analysis, an algorithm was developed using artificial intelligence tools, YOLOv3, so that the evaluation process could be automated and fast. It was observed that during the experimental period, the highest mean values of THI were observed during the afternoon and the autumn. The animals' preference to lie down on the bed for most of the day was verified. It was observed that the algorithm was able to detect cow behavior (lying down or standing). It can be concluded that the behavior of the cows was defined, and the artificial intelligence was successfully applied and can be recommended for such use. [ABSTRACT FROM AUTHOR]

Simple Summary: A rabbit house's microclimate has a strong influence on the physiological responses of rabbits, and thermal discomfort may damage their development. The primary aim of this study was to develop a spatial distribution of temperature and humidity index (THI) maps via kriging interpolation, as well as to characterize and evaluate its relationship with physiological responses (i.e., the respiratory frequency (RF) and ear surface temperature (EST) of New Zealand white (NZW) rabbits kept in a rabbit house. The spatial distribution maps of THI allowed us to visualize the heterogeneity in the distribution space of the variables, as well as to identify regions where NZW rabbits were exposed to unfavorable developmental conditions. The relationship between THI, RF, and EST was evident, and the maps showed that an increase in THI led to an acceleration of respiratory movements and an elevation of surface temperature in rabbit ears. This led to an attempt to dissipate excess heat acquired from the environment. Thus, the spatial distribution maps of THI overlaid with RF and EST data was found to provide useful information to assist the producer in making decisions to improve the production environment for NZW rabbits. The objective of this study was to characterize and evaluate the temperature and humidity index (THI) of New Zealand white (NZW) rabbits kept in a rabbit house using geostatistical techniques. Furthermore, we sought to evaluate its relationship with respiratory frequency (RF) and ear surface temperature (EST). The experiment was conducted at the Federal University of Lavras, Brazil. A total of 52 NZW rabbits were used. For the characterization of the thermal environment, the dry bulb temperature (tdb, °C), relative humidity (RH, %), and dew point temperature (tdp, °C) were collected at 48 points in the rabbit house at 6:00 a.m., 12:00 p.m., and 6:00 p.m. for seven days. The RF and EST of the animals was monitored. Subsequently, the THI was calculated and the data were analyzed using geostatistical tools and kriging interpolation. In addition, the RF and EST data were superimposed on the rabbit house's THI data maps. The magnitude of the variability and structure of the THI inside the rabbit house were characterized and the heterogeneity was visualized. Critical THI points inside the rabbit house and in locations where animals with high RF and ESTs were housed were identified, thus providing information about improving the production environment. [ABSTRACT FROM AUTHOR]