Artificial Neural Network Models for the Prediction of Ammonia Concentrations in a Mediterranean Dairy Barn

Understanding the relationship between environmental variables and gas concentrations from livestock production is essential for evaluating the impact of pollutants on animal housing and surrounding areas. This study investigates the use of ANNs to predict NH₃ concentrations in a Mediterranean dairy barn under seasonal conditions-namely, hot, cold, and transitional weather. A Multi-Layer Perceptron (MLP) structure was employed, trained using Levenberg-Marquardt and Bayesian Regularization algorithms. The input dataset included ten variables related to internal and external environmental conditions, NH₃ concentrations, and time of day. The models were evaluated using R², R, MAE, MSE, and RMSE as performance metrics. Results showed strong predictive capabilities, with R² values ranging from 0.75 to 0.96 and RMSE values between 0.47 and 0.80 due to the number of input data (different days) and environmental conditions. These findings highlight the potential of ANNs as effective tools for real-time pollutant prediction, supporting Precision Livestock Farming (PLF) strategies.