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

Abstract

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 to evaluate the performance of NIR calibrations in predicting the nutritional composition of ten pasture species that underpin livestock industries in many countries. The species comprised a range of functional diversity (C3 legumes; C3/C4 grasses; annuals/perennials) and origins (tropical/temperate; introduced/native) that grew under varied environmental conditions (control and experimentally induced warming and drought) over a period of more than two years (n = 2622). A maximal calibration set including 391 samples was used to develop and evaluate calibrations for all ten pasture species (global calibrations), as well as for subsets comprised of the plant functional groups. This study found that the global calibrations were appropriate to predict the six key nutritional quality parameters for the studied pasture species, with the highest estimation quality found for ash (ASH), crude protein (CP), amylase-treated neutral detergent fibre (aNDF) and acid detergent fibre (ADF), and the lowest for ether extract (EE) and acid detergent lignin (ADL) parameters. The plant functional group calibrations for C3 grasses performed better than the global calibrations for ASH, CP, ADF and EE parameters, whereas for C3 legumes and C4 grasses the functional group calibrations performed less well than the global calibrations for all nutritional parameters of these groups. Additionally, the calibrations were able to capture the range of variation in forage nutritional quality caused by future climate scenarios of warming and severe drought.