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Simple Summary: The aim of the present study was to develop predictive models for the potential generation and emission of ammonia from Brazilian broiler chicken production. For this study, samples of poultry litter (shavings and coffee husks) from thirty commercial poultry houses located in the Zona da Mata Mineira region were analyzed. The poultry litter samples were subjected to different air temperatures in climatic chambers. The models developed and validated showed high accuracy, indicating that they can be used to estimate the potential for generation and emission of ammonia in poultry production, enabling its quantification when its measurement is not possible. Air quality is one of the main factors that must be guaranteed in animal production. However, the measurement of pollutants is still a problem in several countries because the available methods are costly and do not always apply to the reality of the constructive typology adopted, as in countries with a hot climate, which adopt predominantly open facilities. Thus, the objective of the present study was to develop predictive models for the potential generation and emission of ammonia in the production of broiler chickens with different types of litter, different reuse cycles and under different climatic conditions. Samples of poultry litter from thirty commercial aviaries submitted to different air temperatures were analyzed. The experiment was conducted and analyzed in a completely randomized design, following a factorial scheme. Models were developed to predict the potential for generation and emission of ammonia, which can be applied in facilities with ambient conditions of air temperature between 25 and 40 °C and with wood shaving bed with up to four reuse cycles and coffee husks bed with up to six reuse cycles. The developed and validated models showed high accuracy indicating that they can be used to estimate the potential for ammonia generation and emission. [ABSTRACT FROM AUTHOR]

The present work aims to develop a mathematical model, based on fuzzy set theory, for predicting performance and the physiological responses of piglets raised in the farrowing house with different solar heating systems. To do this, a solar heater prototype was constructed using alternative materials and the heating efficiency was compared with a commercial solar heater system. In order to thermally evaluate the heaters, temperature sensors were installed in the inlet and outlet pipes of each floor and thermal reservoir. The fuzzy system was developed and the variables dry air bulb temperature (Tbs) and relative humidity (RH) of the air were defined as inputs. Based on the input variables, the fuzzy system predicts the productive performance (weight gain - WG) and physiological responses (respiratory rate - RR, rectal temperature - RT, and skin temperature - ST) of piglets raised in an environment with solar heating. Based on the results, the fuzzy model was adequate for predicting the physiological responses and productive performance of piglets, presenting low standard deviation and high correlation with the validation data. This model can be used to assist producers in decision making, especially regarding maintaining animal welfare while the thermal environment changes. [ABSTRACT FROM AUTHOR]

Based on the necessity for enclosure protection of temperature and relative humidity sensors installed in a hostile environment, a wind tunnel was used to quantify the time that the sensors take to reach equilibrium in the environmental conditions to which they are exposed. Two treatments were used: (1) sensors with polyvinyl chloride (PVC) enclosure protection, and (2) sensors with no enclosure protection. The primary objective of this study was to develop and validate a 3-D computational fluid dynamics (CFD) model for analyzing the temperature and relative humidity distribution in a wind tunnel using sensors with PVC enclosure protection and sensors with no enclosure protection. A CFD simulation model was developed to describe the temperature distribution and the physics of mass transfer related to the airflow relative humidity. The first results demonstrate the applicability of the simulation. For verification, a sensor device was successfully assembled and tested in an environment that was optimized to ensure fast change conditions. The quantification setup presented in this paper is thus considered to be adequate for testing different materials and morphologies for enclosure protection. The results show that the boundary layer flow regime has a significant impact on the heat flux distribution. The results indicate that the CFD technique is a powerful tool which provides a detailed description of the flow and temperature fields as well as the time that the relative humidity takes to reach equilibrium with the environment in which the sensors are inserted. [ABSTRACT FROM AUTHOR]

Simple Summary: The primary aim of this paper is to develop decision trees to predict rabbits' physiological responses, such as the respiratory rate or ear temperature, based on environmental variables (dry bulb temperature and relative humidity). The decision tree for ear temperature exhibited better statistical indices, indicating the benefits of using the ear temperature as an indicator of thermal stress. Our findings confirm that the resulting decision trees are powerful classifiers, and the results can be easily understood. Hence, the proposed decisions trees can aid in investigating the influence of environmental conditions on physiological responses and, consequently, the rabbits' welfare. These results can be used in practical situations and can be obtained in real time to support rabbit breeders in decision-making to improve environmental conditions for rabbits. The thermal environment inside a rabbit house affects the physiological responses and consequently the production of the animals. Thus, models are needed to assist rabbit producers in decision-making to maintain the production environment within the zone of thermoneutrality for the animals. The aim of this paper is to develop decision trees to predict the physiological responses of rabbits based on environmental variables. The experiment was performed in a rabbit house with 26 rabbits at eight weeks of age. The experimental database is composed of 546 observed data points. Sixty decision tree models for the prediction of respiratory rate (RR, mov.min−1) and ear temperature (ET, °C) of rabbits exposed to different combinations of dry bulb temperature (tdb, °C) and relative humidity (RH, %) were developed. The ET model exhibited better statistical indices than the RR model. The developed decision trees can be used in practical situations to provide a rapid evaluation of rabbit welfare conditions based on environmental variables and physiological responses. This information can be obtained in real time and may help rabbit breeders in decision-making to provide satisfactory environmental conditions for rabbits. [ABSTRACT FROM AUTHOR]