Predicting pasture root density from soil spectral reflectance: field measurement

Abstract

This paper reports the development and evaluation of a field technique for in situ measurement of root density using a portable spectroradiometer. The technique was evaluated at two sites in permanent pasture on contrasting soils (an Allophanic and a Fluvial Recent soil) in the Manawatu region, New Zealand. Using a modified soil probe, reflectance spectra (350-2500 nm) were acquired from horizontal surfaces at three depths (15, 30 and 60 mm) of an 80-mm diameter soil core, totalling 108 samples for both soils. After scanning, 3-mm soil slices were taken at each depth for root density measurement and soil carbon (C) and nitrogen (N) analysis. The two soils exhibited a wide range of root densities from 1.53 to 37.03 mg dry root g −1 soil. The average root density in the Fluvial soil (13.21 mg g −1 ) was twice that in the Allophanic soil (6.88 mg g −1 ). Calibration models, developed using partial least squares regression (PLSR) of the first derivative spectra and reference data, were able to predict root density on unknown samples using a leave-one-out cross-validation procedure. The root density predictions were more accurate when the samples from the two soil types were separated (rather than grouped) to give sub-populations (n = 54) of spectral data with more similar attributes. A better prediction of root density was achieved in the Allophanic soil (r 2 = 0.83, ratio prediction to deviation (RPD) = 2.44, root mean square error of cross-validation (RMSECV) = 1.96 mg g −1 ) than in the Fluvial soil (r 2 = 0.75, RPD = 1.98, RMSECV = 5.11 mg g −1 ). It is concluded that pasture root density can be predicted from soil reflectance spectra acquired from field soil cores. Improved PLSR models for predicting field root density can be produced by selecting calibration data from field data sources with similar spectral attributes to the validation set. Root density and soil C content can be predicted independently, which could be particularly useful in studies examining potential rates of soil organic matter change.

Measuring carbon dynamics in field soils using soil spectral reflectance: prediction of maize root density, soil organic carbon and nitrogen content

“…Moderate accuracy in predicting grass root density in permanent pastures using Vis-NIRS was also reported by Kusumo et al (2009b). This can be a useful when information is required about the potential for deeper rooting species to synthesize organic matter and sequester atmospheric CO 2 (Rees et al 2005); particularly as the technique has been proven able to simultaneously predict root density, soil C and soil N, both in permanent pasture (Kusumo et al 2009b) and arable land (in this study). In addition, NIRS has been reported to be able to measure live and dead grass roots (Picon-Cochard et al 2009).…”

“…A soil core was sectioned at 5 depths (7.5, 15, 30, 45, and 60 cm) and at each depth the soil reflectance spectra was acquired in situ from the freshly cut surface using a purpose built soil probe ( Fig. 1b and c, and described in detail by Kusumo et al 2008aKusumo et al , 2009b attached by fibre optic cable to a field spectroradiometer (ASD FieldSpecPro, Boulder, Colorado, USA). The soil core was rotated 360°, to give a field of view (collection area) of 561 mm 2 (Fig.…”

“…Changes in the reflectance spectra of a soil sample may therefore be correlated to the change in the intrusive property, such as root density. In a previous study, Kusumo et al (2009b) demonstrated that variations in pasture root density could be predicted independently from variations in soil C in an Allophanic soil, but not in a Fluvial Recent soil, in which measured root densities were co-correlated with measured soil C values.…”

“…Vis-NIR reflectance spectroscopy has been successfully used to predict soil C (e.g. Chang and Laird 2002;Kusumo et al 2008a), ryegrass root density in a glasshouse experiment (Kusumo et al 2009a) and in field trials (Kusumo et al 2009b), despite variations in soil texture, structure (Udelhoven et al 2003) and other chromophores (e.g. clay and non-clay contents, iron oxides, water, decomposed organic matter) (Baumgardner et al 1985;Ben-Dor 2002).…”

Sorting of crop residues and fossil bones from soil by NIR Hyperspectral Imaging

“…In the field of soil analyses, hyperspectral imaging has been applied to analyse soil organic matter and phosphorus [3][4][5][6][7], to determine maize and pasture root density [8][9], or to classify archaeological soils [10].…”