Calibration models for the nutritional quality of fresh pastures by near-infrared reflectance spectroscopy

Abstract High levels of animal performance and health depend on high-quality nutrition. Determining forage quality both reliably and quickly is essential for improving animal production. The present study describes the use of near infrared reflectance spectroscopy (NIRS) for the quantification of nutritional quality (dry matter (DM), water-soluble carbohydrates (WSC), crude protein (CP), in vitro dry matter digestibility (DMD), organic matter digestibility (OMD), neutral detergent fiber (NDF) and the WSC/CP ratio) in samples from fresh pastures in southern Chile (39° to 40° S). Calibration models were developed with wet chemistry and NIRS spectral data using partial least squares regression (PLSR). The coefficients of determination in the validation set ranged between 0.69 and 0.93, and the error of prediction varied from 0.064 to 2.89. The evaluation of the model confirmed the high predictive ability of NIRS for DM and CP and its low predictive ability for DMD, OMD, NDF and the WSC/CP ratio. It was not possible to obtain a model for WSC because it would have required an increased number of samples to improve the spectral variability and the R2 value (> 80%).