Multi-spectral radiometry to estimate pasture quality components

Multi-spectral remote sensing of green vegetation provides an opportunity for assessing biophysical and biochemical properties. This technique could play a crucial role in pasture management by providing the means to evaluate pasture quality in situ. In this study, the potential of a 16-channel multi-spectral radiometer (MSR) for predicting pasture quality, crude protein (CP), acid detergent fibre (ADF), neutral detergent fibre (NDF), ash, dietary cation-anion difference (DCAD), lignin, lipid, metabolisable energy (ME) and organic matter digestibility (OMD) was evaluated. In situ canopy spectral reflectance was acquired from mixed pastures, under commercial farm conditions in New Zealand. The multi-spectral data were evaluated by single wavelength, linear and non-linear renormalized difference vegetation index (RDVI), and stepwise multiple linear regression (SMLR) models. The selected non-linear, exponential fit, RDVI index models described (0.65 a parts per thousand currency sign r (2) a parts per thousand currency sign 0.85) of the variation of pasture quality components (CP, DCAD, ME and OMD), while CP, ash, DCAD, lipid, ME and OMD were estimated with moderate accuracy (0.60 a parts per thousand currency sign r (2) a parts per thousand currency sign 0.80) by the SMLR model. The remaining pasture quality components ADF, NDF and lignin were poorly explained (0.40 a parts per thousand currency sign r (2) a parts per thousand currency sign 0.58) by the models. This experiment concluded that the MSR has potential to rapidly estimate pasture quality in the field using non-destructive sampling.