Use of FT-NIRS for determination of chemical components and nutritional value of natural pasture

Abstract

This study evaluates the potential of Fourier transformation near-infrared reflectance spectroscopy to estimate the nutritional value and the chemical composition of natural pastures. Variability from all samples of pastures available is considered in order to assess the applicability of the calibration models in the future predictions. Chemical components (dry matter, crude protein, ash, ether extract, crude fibre, fibrous fractions) of grass samples were determined by applying official methods, and milk and meat forage units were calculated. Calibration and validation models were developed between chemical-nutritional parameters and NIRS spectral data using partial least square regression (PLS). The capacity of methods has been achieved using two validation approaches: the first using an independent dataset for prediction and the second by crossvalidation process. The results are evaluated in term of coefficient of determination, root-meansquare error and residual prediction deviation. Despite the wide variability of the data set, the results of FT-NIRS have been able to estimate the chemical composition of natural and naturalised pasture with good accuracy and precision, while for nutritional value parameters, a further evaluation may be useful.

Broad near-infrared spectroscopy calibrations can predict the nutritional value of >100 forage species within the Australian feedbase

“…3.5 and 3.4; Hsu et al 2000;Stubbs et al 2010) and lower than others (e.g. 4.5; Rothman et al 2009;Parrini et al 2018). For perennial legumes and grasses, we were able to achieve higher RPD values for NDF after restricting the calibration set to just perennial legumes and grasses.…”

“…In southern Chile, a calibration was successfully developed for mixed swards, comprising eight perennial grass and legume species by using nearly 300 spectra/chemistry pairs (Lobos et al 2013). In Italy, calibrations have been developed for 13 species that are endemic to native grasslands, including grasses and legumes (Parrini et al 2018). In southern Australia, calibrations were successfully developed for eight woody shrub species (Norman and Masters 2010).…”

Use of near infrared spectroscopy for the evaluation of forage for ruminants

“…Durmic et al (2017) used 1231 samples for a calibration equation predicting nitrogen, 427 samples for a neutral detergent fiber (NDF) calibration, 402 samples for an acid detergent fiber (ADF) calibration, and 405 samples for an in vitro dry matter loss calibration. Parrini et al (2018) used 105 samples of natural and naturalized pastures from Tuscany (Italy) and affirmed that NIRS was able to precisely and accurately estimate the chemical composition of the pastures, even with a reduced number of samples.…”

“…Calibration equations can be calculated from the relationship between the spectral properties of samples and the results obtained by a reference laboratory method (Marten et al, 1989); the development of robust and accurate NIRS predictions depends on a database of samples that represents the predicted forage characteristics (Parrini et al, 2018).…”

“…Forage can be dried or oven-dried and afterwards ground to 1 mm (Bezada et al, 2017;Simeone et al, 2015;Andueza et al, 2016;Molano et al, 2016;Durmic et al, 2017;Ullmann et al, 2017;Parrini et al, 2018) or 2 mm (Zhang et al, 2017). This is done for the sake of standardization, especially because the quality of the calibration can be increased with sample preparation and measurement standardization (Reddersen et al, 2013).…”