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

Hualong Liu, Xin Wang, Tana (), Tiezhu Xie, Hurichabilige, Qi Zhen and Wensheng Li
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Hualong Liu: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Xin Wang: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Tana: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Tiezhu Xie: HAI GAO MU YE Co., Ltd., Ulanqab 012000, China
Hurichabilige: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Qi Zhen: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Wensheng Li: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China

Agriculture, 2025, vol. 15, issue 14, 1-17

Abstract: 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.

Keywords: dairy barn; ammonia; methane; gaseous emissions; machine learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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