Near infrared spectroscopy calibration strategies to predict multiple nutritional parameters of pasture species from different functional groups

Near infrared reflectance (NIR) spectroscopy has been used by the agricultural industry as a rapid and inexpensive technique to quantify nutritional chemistry in plants. The aim of this study was evaluate performance NIR calibrations predicting composition ten pasture species that underpin livestock industries many countries. comprised range functional diversity (C 3 legumes; C /C 4 grasses; annuals/perennials) origins (tropical/temperate; introduced/native) grew under varied environmental conditions (control experimentally induced warming drought) over period more than two years ( n = 2622). A maximal calibration set including 391 samples develop for all (global calibrations), well subsets plant groups. This found global were appropriate predict six key quality parameters studied species, with highest estimation ash (ASH), crude protein (CP), amylase-treated neutral detergent fibre (aNDF) acid (ADF), lowest ether extract (EE) lignin (ADL) parameters. group grasses performed better ASH, CP, ADF EE parameters, whereas legumes less these Additionally, able capture variation forage caused future climate scenarios severe drought.