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Ammonia (NH3) concentration has seldom been used for environmental control of weaner buildings despite its impact on environment, animal welfare, and workers’ health. This paper aims to determine the effects of setpoint temperature (ST) on the daily evolution of NH3 concentration in the animal-occupied zone. An experimental test was conducted on a conventional farm, with ST between 23 °C and 26 °C. NH3 concentrations in the animal-occupied zone were dependent on ST insofar as ST controlled the operation of the ventilation system, which effectively removed NH3 from the building. The highest NH3 concentrations occurred at night and the lowest concentrations occurred during the daytime. Data were fitted to a sinusoidal model using the least squares setting (LSS) and fast Fourier transform (FFT), which provided R2 values between 0.71 and 0.93. FFT provided a better fit than LSS, with root mean square errors (RMSEs) between 0.09 ppm for an ST of 23 °C and 0.55 ppm for an ST of 25 °C. A decrease in ST caused a delay in the wave and a decrease in wave amplitude. The proposed equations can be used for modeling NH3 concentrations and implemented in conventional controllers for real-time environmental control of livestock buildings to improve animal welfare and productivity.

An AutoRegressive Integrated Moving Average model was validated for the prediction of temperatures in the animal zone of conventional weaned piglet barn. The validation period covered seven cycles and recorded values at 10-min intervals for 292 days. Average weight was 5.75±0.86 kg at the beginning of the production cycle and 18.41±2.12 kg at the end of the cycle. Mean outdoor air temperatures ranged 6.14 to 17.85ºC with deviations in the range 2.49ºC to 5.24ºC, which involved marked differences in the operation of the ventilation system. The Mean Average Percentage Error was below 4%, with a mean error of 1ºC. The Root Mean Square Error was in the range 0.77ºC to 1.19ºC, whereas the coefficient of determination ranged between 0.52 and 0.81. Despite the changes in environmental conditions and in animal weight and management, the accuracy of the model remained stable with low dispersion of values. Tao outdoor air temperature, ºC Taz animal zone temperature, ºC Vao volume of extracted air by the fan, m 3

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Abstract. Predictive models provide an efficient tool for improving environmental control in livestock buildings. In this article, a robust and accurate ARIMA model for forecasting temperature inside a building for weaned piglets in the range 6 to 20 kg live weight was built. The candidate models presented in this article predict 10 min values during a complete production cycle, which makes them suitable as predictive models for improving control strategies. The accuracy of the base model, which used outdoor temperature as a predictor variable, can be improved by appropriately replacing the outliers in the time series. Because accuracy increases with the increase in the number of predictor variables, the model that used four variables (temperature at the air outlet, area of the air outlet through the fan, volume of air extracted, and animal live weight) provided the best results, with a maximum absolute error of 0.840°C, a root mean square error of 0.204°C, and random residuals according to the Ljung-Box statistic. This model used only the values of the last 20 min for the forecast, which suggests low thermal inertia in the animal zone. In addition, the model includes predictor variables that are representative of outdoor conditions, operation of the systems, and animal health status. Keywords: ARIMA, Forecast, Model, Piglet, Temperature.