Real-Time Monitoring of Fecal Nitrogen Excretion to the Environment Using Near-Infrared Reflectance Spectroscopy: A Preliminary Study in Rabbits

Abstract

The excessive excretion of nitrogen (N) by farm animals can pose severe environmental risks. In this study, near-infrared reflectance (NIR) spectroscopy (NIRS) was used to explore the feasibility of developing a real-time in situ monitoring tool for fecal N excretion in rabbits. A total of 70 feed and 282 fecal samples from an in vivo digestibility experiment were used. Feed and fecal NIR spectra were employed to develop chemometric models using modified partial least squares (MPLS) for feed and feces, and artificial neural networks (ANN) for feces to predict dietary and fecal N content and fecal N digestibility. Very good accuracy was achieved in predicting feed N (R2val = 0.96; standard error of prediction, SEP = 0.15) and fecal N (R2val = 0.88; SEP = 0.44) content, whereas N digestibility models from wet fecal spectra had a relatively low precision (R2val = 0.70; SEP = 0.018) with MPLS methodology. In contrast, ANNs yielded more robust prediction models for fecal (R2val = 0.98; SEP = 0.25) N content and N digestibility (R2val = 0.91; SEP = 0.012) using wet feces. In conclusion, NIRS calibration with ANNs can be a suitable tool for monitoring the environmental load of N with high precision and accuracy.