Prediction of the composition of fresh pastures by near infrared reflectance or interactance-reflectance spectroscopy

Fast and precise analytical tools can contribute to optimize pasture management decisions. This work was carried out to evaluate the potential of one such technique, near infrared spectroscopy (NIRS), to predict the nutritional value of pastures without previous drying of the samples, comparing two forms of collecting the spectra: reflectance, or interactance-reflectance (fiber optic probe). Samples (n = 107) from different swards were taken across the humid and temperate regions (Los Ríos and Los Lagos) of southern Chile. Once their spectra were collected, dry matter (DM) and several chemical constituents, such as crude protein (CP), metabolizable energy (ME), neutral (NDF) and acid detergent fiber (ADF), soluble carbohydrates (SC), soluble crude protein (SCP) and neutral detergent insoluble N (NDFIN), were determined as reference data. Calibrations were developed and the best ranked were selected (by cross-validation) according to a lower standard error of cross validation (SE_CV ) and a higher determination coefficient of cross validation (R^2CV ). Calibrations in the reflectance mode, for DM and CP, reached a high R^2CV (0.99 and 0.91, respectively) and a SE_CV (6.5 and 18.4 g kg^-1 ). Equations for ADF, SCP and ME were ranked next, with R^2CV of 0.87, 0.84 and 0.82, respectively, and SE_CV of 15.88 g kg^-1 , 15.45 g kg^-1 and 0.34 Mj kg^-1 . Equations for NDF, SC and NDFIN, with R^2CV of 0.78, 0.77 and 0.61, respectively, and SE_CV of 35.57, 94.54 and 1.89 g kg^-1 , respectively, are considered unreliable for prediction purposes. Interactance-reflectance, on the other hand, resulted in poorer equations for all fractions.

Keywords
pasture composition; NIR prediction; near infrared reflectance spectroscopy, fresh pastures, fiber optics.