Abstract

The aim of this study was to investigate the potential use of near infrared (NIR) reflectance spectroscopy to predict chemical composition in both sunflower whole plant (WPSun) and sunflower silage (SunS). Samples of both WPSun (n = 73) and SunS (n = 50) were analysed by reference method and scanned in reflectance using a NIR monochromator instrument (400–2500 nm). Calibration models were developed between NIR data and reference values for dry matter (DM), crude protein (CP), ash, acid detergent fibre (ADFom), neutral detergent fibre (aNDFom), in vitro organic matter digestibility (OMD), ether extract (EE) and pH using partial least squares regression (PLS). Due to the limited number of samples full cross-validation was used to test the calibration models. The best correlations (R²_cal) and lowest standard errors in cross-validation (SECV) were obtained for DM (R²_cal> 0.82, SECV: 27.0 and 35.8 g kg⁻¹), CP (R²_cal> 0.85, SECV: 9.9 and 10.1 g kg⁻¹) and ash (R²_cal> 0.85, SECV 11.2 and 8.2 g kg⁻¹) in both WPSun and SunS samples, respectively. For ADFom, aNDFom and OMD the calibrations were considered to be poor (R²_cal< 0.85). In SunS samples a good correlation was found for EE (R²_cal = 0.94, SECV: 15.3 g kg⁻¹).

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