Data-Driven Prediction of Ammonia and Methane Concentrations and Emissions in Dairy Barns Using Artificial Neural Networks

Ammonia (NH3) and methane (CH4) emissions from dairy farming pose significant environmental and climatic concerns. Several factors influence these emissions, including housing systems, diet, environmental conditions, and animal activity. Previous studies have mostly applied classical statistical methods to analyse the effect of these variables on gas emissions. Recent applications of artificial neural networks (ANNs) have not yet incorporated animal activity and diet as input variables. This study assessed the influence of climatic variables, animal activity and dietary intake on NH3 and CH4 concentrations and emissions from a naturally ventilated dairy barn under a Mediterranean climate. Multilayer perceptron (MLP) models were applied using environmental, activity, and dietary inputs. Model performance was evaluated using R, R2, MAE, MSE, SD, and RMSE. The results demonstrate that MLP models achieved accurate predictions, with R2 values of 0.93 and 0.96 for NH3 and CH4, respectively. Predictions incorporating climatic, diet and activity variables achieved the best performance. These findings suggest that ANN models, integrating these variables, represent effective tools for emission prediction, contributing to improved environmental management in dairy farming.