Predicting Winter Ammonia and Methane Emissions from a Naturally Ventilated Dairy Barn in a Cold Region Using an Adaptive Neural Fuzzy Inference System

This study aims to characterize the emissions of ammonia (NH 3 ) and methane (CH 4 ) from naturally ventilated dairy barns located in cold regions during the winter season, thereby providing a scientific basis for optimizing dairy barn environmental management. The target barn was selected at a commercial dairy farm in Ulanchab, Inner Mongolia, China. Environmental factors, including temperature, humidity, wind speed, and concentrations of NH 3 , CH 4 , and CO 2 , were monitored both inside and outside the barn. The ventilation rate and emission rate were calculated using the CO 2 mass balance method. Additionally, NH 3 and CH 4 emission prediction models were developed using the adaptive neural fuzzy inference system (ANFIS). Correlation analyses were conducted to clarify the intrinsic links between environmental factors and NH 3 and CH 4 emissions, as well as the degree of influence of each factor on gas emissions. The ANFIS model with a Gaussian membership function (gaussmf) achieved the highest performance in predicting NH 3 emissions (R 2 = 0.9270), while the model with a trapezoidal membership function (trapmf) was most accurate for CH 4 emissions (R 2 = 0.8977). The improved ANFIS model outperformed common models, such as multilayer perceptron (MLP) and radial basis function (RBF). This study revealed the significant effects of environmental factors on NH 3 and CH 4 emissions from dairy barns in cold regions and provided reliable data support and intelligent prediction methods for realizing the precise control of gas emissions.