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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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
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| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/15/14/1560 |
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| Summary: | 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. |
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| ISSN: | 2077-0472 |