Prediction of nutritive values, morphology and agronomic characteristics in forage maize using two applications of NIRS spectrometry

Abstract

This study evaluates nutritive, morphological and agronomic characteristics of forage maize predicted by using a high-quality near-infrared (NIR) spectrometer and an NIR hyperspectral-imaging technique using partial least squares (PLS) regression models. The study includes 132 samples of dried milled whole-plant homogenates of forage maize with variation in maturity, representing two growing seasons, three locations in Sweden and three commercial maize hybrids. The samples were measured by a classical sample cup NIR spectrometer and by a pushbroom hyperspectral-imaging instrument. The spectra and a number of variables (crude protein, CP, neutral detergent fibre, starch, water soluble carbohydrates (WSC) and organic matter digestibility), morphological variables (leaves, stems & ears) and crop yield were used to make PLS calibration models. Using PLS modelling allowed the determination of how well maize variables can be predicted from NIR spectra and a comparison of the two types of instruments. Most examined variables could be determined equally well, by both instruments, but the pushbroom technique gave slightly better predictions and had higher analytical capacity. Predictions of CP, starch, WSC and the proportions of ears in the maize gave robust. The findings open new possibilities to further utilise the technology in plant breeding, crop management, modelling and forage evaluation.

Keywords

• morphological proportions
• chemical composition
• multivariate calibration
• agronomic performance
• robustified rer
• robustified rpd
• starch
• neutral detergent fibre