Estimation of Manure Emissions Issued from Different Chinese Livestock Species: Potential of Future Production

Tao He, Wenya Zhang, Hanwen Zhang and Jinliang Sheng ()
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Tao He: College of Animal Science & Technology, Shihezi University, Shihezi 832000, China
Wenya Zhang: College of Animal Science & Technology, Shihezi University, Shihezi 832000, China
Hanwen Zhang: College of Animal Science & Technology, Shihezi University, Shihezi 832000, China
Jinliang Sheng: College of Animal Science & Technology, Shihezi University, Shihezi 832000, China

Agriculture, 2023, vol. 13, issue 11, 1-17

Abstract: In this study, mathematical models are used to estimate the emissions of livestock excreta (LE) generated by China’s livestock industry more accurately. Also, the spatial relationship between provinces is analyzed. LE emissions are predicted for the next decade through appropriate parameters and non-parametric models. Additionally, a literature review is conducted to propose two hypotheses. As revealed by the research, there are four stages that LE emissions experience over time. From 2017 to 2021, LE emissions showed a trend of steady increase, suggesting a stronger awareness of the issue and the enforcement of more measures related to management and emission reduction. According to the results of a spatial analysis, there was no significant positive or negative correlation present between LE emissions in different provinces of China. In the selection of the prediction model, the BP-RE model achieved the best predictive performance. According to the prediction results, the fresh weight emissions from China’s livestock industry will increase by 24.53% by 2031, while dry weight emissions will decrease by 28.06%. Large-scale aquaculture farms show an upward trend, with fresh weight and dry weight emissions rising by 11.16% and 2.05%, respectively. Therefore, in light of this study’s findings, it is crucial for China to pursue additional measures in reducing LE emissions, despite the implementation of existing management policies. These insights can inform the development of livestock and poultry manure management policies and resource utilization strategies for the coming decade.

Keywords: livestock excreta; spatial autocorrelation; ARIMA; BP neural network time series prediction model; BP neural network regression prediction model (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: 2023
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