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<sub>3</sub>) and methane (CH<sub>4</sub>) from naturally ventilated dairy barns located in cold regions during the winter season, thereby providing a scientific basis for optimizing dairy barn environmental manageme...
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MDPI AG
2025-07-01
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| author | Hualong Liu Xin Wang Tana Tiezhu Xie Hurichabilige Qi Zhen Wensheng Li |
| author_facet | Hualong Liu Xin Wang Tana Tiezhu Xie Hurichabilige Qi Zhen Wensheng Li |
| author_sort | Hualong Liu |
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| description | This study aims to characterize the emissions of ammonia (NH<sub>3</sub>) and methane (CH<sub>4</sub>) 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<sub>3</sub>, CH<sub>4</sub>, and CO<sub>2</sub>, were monitored both inside and outside the barn. The ventilation rate and emission rate were calculated using the CO<sub>2</sub> mass balance method. Additionally, NH<sub>3</sub> and CH<sub>4</sub> 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<sub>3</sub> and CH<sub>4</sub> 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<sub>3</sub> emissions (R<sup>2</sup> = 0.9270), while the model with a trapezoidal membership function (trapmf) was most accurate for CH<sub>4</sub> emissions (R<sup>2</sup> = 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<sub>3</sub> and CH<sub>4</sub> emissions from dairy barns in cold regions and provided reliable data support and intelligent prediction methods for realizing the precise control of gas emissions. |
| format | Article |
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| issn | 2077-0472 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Agriculture |
| spelling | doaj-art-3bb7b02f0baf4d41bfdaace1ac34a46a2025-07-25T13:09:29ZengMDPI AGAgriculture2077-04722025-07-011514156010.3390/agriculture15141560Predicting Winter Ammonia and Methane Emissions from a Naturally Ventilated Dairy Barn in a Cold Region Using an Adaptive Neural Fuzzy Inference SystemHualong Liu0Xin Wang1Tana2Tiezhu Xie3Hurichabilige4Qi Zhen5Wensheng Li6College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaHAI GAO MU YE Co., Ltd., Ulanqab 012000, ChinaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaThis study aims to characterize the emissions of ammonia (NH<sub>3</sub>) and methane (CH<sub>4</sub>) 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<sub>3</sub>, CH<sub>4</sub>, and CO<sub>2</sub>, were monitored both inside and outside the barn. The ventilation rate and emission rate were calculated using the CO<sub>2</sub> mass balance method. Additionally, NH<sub>3</sub> and CH<sub>4</sub> 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<sub>3</sub> and CH<sub>4</sub> 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<sub>3</sub> emissions (R<sup>2</sup> = 0.9270), while the model with a trapezoidal membership function (trapmf) was most accurate for CH<sub>4</sub> emissions (R<sup>2</sup> = 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<sub>3</sub> and CH<sub>4</sub> emissions from dairy barns in cold regions and provided reliable data support and intelligent prediction methods for realizing the precise control of gas emissions.https://www.mdpi.com/2077-0472/15/14/1560dairy barnammoniamethanegaseous emissionsmachine learning |
| spellingShingle | Hualong Liu Xin Wang Tana Tiezhu Xie Hurichabilige Qi Zhen Wensheng Li Predicting Winter Ammonia and Methane Emissions from a Naturally Ventilated Dairy Barn in a Cold Region Using an Adaptive Neural Fuzzy Inference System Agriculture dairy barn ammonia methane gaseous emissions machine learning |
| title | Predicting Winter Ammonia and Methane Emissions from a Naturally Ventilated Dairy Barn in a Cold Region Using an Adaptive Neural Fuzzy Inference System |
| title_full | Predicting Winter Ammonia and Methane Emissions from a Naturally Ventilated Dairy Barn in a Cold Region Using an Adaptive Neural Fuzzy Inference System |
| title_fullStr | Predicting Winter Ammonia and Methane Emissions from a Naturally Ventilated Dairy Barn in a Cold Region Using an Adaptive Neural Fuzzy Inference System |
| title_full_unstemmed | Predicting Winter Ammonia and Methane Emissions from a Naturally Ventilated Dairy Barn in a Cold Region Using an Adaptive Neural Fuzzy Inference System |
| title_short | Predicting Winter Ammonia and Methane Emissions from a Naturally Ventilated Dairy Barn in a Cold Region Using an Adaptive Neural Fuzzy Inference System |
| title_sort | predicting winter ammonia and methane emissions from a naturally ventilated dairy barn in a cold region using an adaptive neural fuzzy inference system |
| topic | dairy barn ammonia methane gaseous emissions machine learning |
| url | https://www.mdpi.com/2077-0472/15/14/1560 |
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