Forecasting N₂O emission and nitrogen loss from swine manure composting based on BP neural network

College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China

^* Corresponding author: baoli@cau.edu.cn

Abstract

Nitrogen loss and greenhouse gas emission during compost will cause secondary pollution and waste nutrients. To address this issue, a predictive model was set up to obtain a clear knowledge of the N₂O emission and nitrogen loss from swine manure composting. This paper collected 68 group data from 11 published papers about pig manure composting N₂O emission and total nitrogen loss. Select 4 indexes were taken as predicted indexes include aeration rate, moisture content, C/N, and the amount of superphosphate to establish a BP neural network for forecasting the N₂O emission and total nitrogen loss from composting. The analyses show that the mean error of N₂O emission forecasting model is 1.17; the value of MAPE is 138.85%. As for nitrogen loss, the mean error is 24.72 and the mean absolute percentage error is 11.06%. Compare to the traditional linear regression, the BP neural network model has good accuracy on forecasting N₂O emission and TN loss from manure composting. BP neural network has considerable application prospect in forecast nitrogen loss and greenhouse gas emission from composting.