Potential of imaging spectroscopy as tool for pasture management

Abstract

The use of imaging spectroscopy to predict the herbage mass of dry matter (DM), DM content of herbage and crude fibre, ash, total sugars and mineral (N, P, K, S, Ca, Mg, Mn, Zn and Fe) concentrations was evaluated. The experimental system used measured reflectance between 404 and 1650 nm at high spatial (0AE28-1AE45 mm 2 ) and spectral resolution. Data from two experiments with Lolium perenne L. mini-swards were used where the degree of sward damage or N-fertilizer application varied. Regression models were calibrated and validated and the potential reduction in prediction error with multiple observations was estimated. The mean prediction errors for DM mass, DM content and N, total sugars, ash and crude fibre concentrations were 235-268 kg ha )1 , 9AE6-16AE8 g kg )1 , 2AE4-3AE4 g kg DM )1 , 16AE2-27AE7 g kg DM )1 , 5AE8-6AE5 g kg DM )1 and 8AE4-10AE4 g kg DM )1 respectively. The predictions for concentrations of P, K, S and Mg allowed identification of deficiency levels, in contrast to the concentrations of Na, Zn, Mn and Ca which could not be predicted with adequate precision. Prediction errors of DM mass may be maximally reduced to 95-142 kg ha )1 with 25 replicate measurements per field. It is concluded that imaging spectroscopy can provide an accurate means for assessment of DM mass of standing grass herbage. Predictions of macronutrient content and feeding value were satisfactory. The methodology requires further evaluation under field conditions.

Herbage mass, nutritive value and canopy spectral reflectance of bermudagrass pastures

“…(2002) investigated canopy reflectance of a tropical grass grown under different nitrogen treatments and concluded that remote sensing of canopy chemistry in the visible region might be used to map variation in pasture quality. With herbage grown in pots, Schut et al. (2005) found that imaging spectroscopy can provide a means for assessment of herbage mass of DM and for predicting concentrations of several mineral nutrients in herbage.…”

“…canopy chemistry in the visible region might be used to map variation in pasture quality. With herbage grown in pots, Schut et al (2005) found that imaging spectroscopy can provide a means for assessment of herbage mass of DM and for predicting concentrations of several mineral nutrients in herbage. These findings and methodologies are still pending further evaluation under field conditions.…”

“…Based on linear regression of each measure of nutritive value and the corresponding canopy reflectance ratio that has the greatest |r| value ( Figure 6), CP concentration of herbage and herbage mass of CP can be estimated using R (blue) /R (red) and R (NIR) /R (red) although the best broad waveband reflectance ratios explained only 0AE21-0AE23 of the variation in NDF and ADF concentrations. Narrow waveband reflectance and other data analysis methods, such as the band-depth analysis (Kokaly and Clark, 1999) and partial least square regression (Starks et al, 2004;Schut et al, 2005), should be investigated as possible ways to improve relationships between pasture canopy reflectance and nutritive value of herbage and to more accurately estimate the nutritive value of live pastures using canopy reflectance data.…”

Estimating forage biomass and quality in a mixed sown pasture based on partial least squares regression with waveband selection

“…Moreover, spectral data efficiency would be improved by optimization of a wavelength subset in the PLS model. This trial might be the first to use ground-based canopy reflectance data acquisition for forage BM and quality prediction using PLS with selected wavebands, although some similar studies have used fullspectrum data applied to PLS regression (Starks et al 2004;Schut et al 2005;Zhao et al 2007).…”

The use of pasture reflectance characteristics and arbuscular mycorrhizal root colonization to predict pasture characteristics of tallgrass prairie grazed by cattle and bison

“…Ustin et al (2004) also suggested that the use of more spectral information may be desirable when measuring heterogeneous vegetation as was the case in this study. Use of different sensor-detecting reflectance between 404 and 709 nm, and 960 and 1650 nm, were useful in models to predict standing grass herbage, macronutrient content and feeding value suggesting that different parts of the spectra contain additional and valuable information (Schut et al, 2005). However, that work was carried out on mini-swards of Lolium perenne, without grazing, and using a procedure by which pixels with soil and dead material were deleted.…”