Evaluation of potential nirs to predict pastures nutritive value

Abstract

This paper describes the capability of near infra-reflectance (NIRS) to predict the nutritional quality of pastures from southern Chile (39°-40°S). A Fourier transformed near-infrared (FT-NIR) method for rapid determination of dry matter (DM), crude protein (CP), in vitro digestibility (IVD) and metabolizable energy (ME) was used. Calibration models were developed between chemical and NIRS spectral data using partial least squares (PLS) regression and external validation. The coefficients of determination in calibration (R2c) were high varying between 0.89-0.99 and the root mean square errors of calibration (RMSEC) were low, ranging between 0.46-2.55 for the parameters analysed. The Residual Prediction Deviation (RPD) was higher than 2.5. Our results confirmed the convenience of using a wide range of samples applicability in the calibration set. Data also showed that the use of an independent set of samples for external validation increases the robustness of the models to predict unknown samples. Our results indicated RPD values higher than 2.5 which is the minimum recommended for this type of prediction. Thus, the result showed that NIRS was useful to estimate the nutritional quality of permanent pastures, and has a great potential to be used as a rapid decision tool for the studied analysis.