Dynamic Prediction of Total N and P Contents in Slurry from Dairy Farms under Different Treatment Processes Using Near-Infrared Spectroscopy

Abstract

Nutrient content fluctuation in dairy production slurry is highly influenced by the various treatment processes applied in the Chinese dairy sector. The dynamic measurement of these contents is critical for the practical and efficient field application of slurry subjected to various processes. In the study, a total of 715 slurry samples were collected from 24 intensive dairy farms in Tianjin subjected to three typical treatment processes. Descriptive statistical analysis, principal component analysis, and partial least square regression were used to investigate the variation in total nitrogen (TN) and total phosphorus (TP) contents, spectral characteristics, and the performance of the prediction model of the slurry under the processes, respectively. Results revealed significant differences in both TN and TP contents along with the spectra for the slurry subjected to different treatment processes. All the inter-process models showed poor performance, and the results were worse compared to the intra-process models. Among the intra-process models for TN, the optimally performing models were the Pac fusion model (R2pred = 0.82; RPD = 2.38) and the single model Pa (R2pred = 0.83; RPD = 2.31). Among the intra-process models for TP, the optimum results were seen for Pab (R2pred = 0.77; RPD = 2.07) and Pa (R2pred = 0.79; RPD = 2.30). Taking different treatment processes into consideration is essential to establish flexible models that can be adaptive for diversified scenarios. This would be helpful to improve the tracking monitor measures, efficiently guide the land application of slurry, and support the sustainable development of animal farming and environmental conversation.