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FORAGE & GRAZINGLANDS

Development of Canopy Reflectance Models to Predict Forage Quality of Legume–Grass Mixtures

Dep. of Grassland Science and Renewable Plant Resources, Univ. of Kassel, Steinstr. 19, D-37213 Witzenhausen, Germany

^* Corresponding author (mwach{at}uni-kassel.de).

Timely assessments of nutritive values of legume-based swards during the growing season can facilitate a targeted and site-specific forage management. This study was undertaken to explore the potential of field spectral measurements for a nondestructive prediction of metabolizable energy, ash content, crude protein (CP), and acid detergent fiber of legume–grass mixtures. A population of 200 legume–grass swards (Lolium perenne L., Trifolium repens L., Trifolium pratense L.) representing a wide range of legume proportion (0–100% of dry matter), and growth stages (beginning of tillering to end of flowering) were used in this investigation. The paper examines three techniques for analysis of the hyperspectral data set (350–2500 nm): two-waveband reflectance ratios, modified partial least squares (MPLS) regression, and stepwise multiple linear regression (SMLR). Forage quality variables had weak relationships with the developed reflectance ratios, whereas hyperspectral analysis by MPLS and SMLR resulted in high precision (0.70 R² 0.94). Even with a reduced spectral data set (630–1000 nm), estimates of MPLS and SMLR models were still acceptable for forage ash (0.62 R² 0.78) and CP (0.83 R² 0.86), a finding that could facilitate an application of field spectroscopy with more simple sensors. Estimates of ash and CP were further improved by legume-specific calibrations.

Abbreviations: ADF, acid detergent fiber • CP, crude protein • DM, dry matter • ME, metabolizable energy • MPLS, modified partial least squares • NIRS, near-infrared spectroscopy • RPD, residual predictive value • SECV, standard error of cross-validation • SMLR, stepwise multiple linear regression • VI, vegetation index • 1-VR, coefficient of determination of cross-validation