Influences of season on cattle slurry compositions predicted by micro-NIR spectrometry

Abstract

In this study, we used a low-cost micro-near-infrared (NIR) spectrometer to analyze the composition of cattle slurries and examined the influence of the season (spring vs. winter) and characteristic wavelength on the performance of the corresponding quantitative models. One hundred and twenty-seven cattle slurry samples collected from five farms in spring and winter (Heilongjiang, China) were characterized in terms of total nitrogen (TN), total phosphorus (TP), total potassium (TK), available phosphorus (AP), available potassium (AK), organic matter (OM), and pH using reference methods. The partial least squares (PLS) method was used to establish seasonal models that involved winter and a mixed model to quantify cattle slurry composition. The characteristic wavelengths were selected using a competitive adaptive reweighted-sampling (CARS) algorithm. The results showed that the winter model successfully predicted the TP content using the full band. The coefficient of determination for prediction (R2p) and ratio of prediction to deviation (RPD) were 0.846 and 3.077, respectively. This model was useful for predicting TK and AK contents and pH (R2p = 0.551–0.773, RPD = 2.251–2.727) and moderately useful for predicting TN and OM contents (R2p = 0.517 and 0.498, respectively; RPD = 1.892 and 2.094, respectively). For all compositions, the prediction performance was better for the winter models than for the mixed models. Although the use of characteristic wavelengths improved the efficiency of model construction, it had no positive effect on the performance of the mixed models. This study enables the setup of a rapid testing method for safely returning slurry to the field.