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The use of Vis-NIR spectral reflectance for determining root density: evaluation of ryegrass roots in a glasshouse trial


Kusumo, B H; Hedley, M J; Hedley, C B; Hueni, A; Arnold, G C; Tuohy, M P (2009). The use of Vis-NIR spectral reflectance for determining root density: evaluation of ryegrass roots in a glasshouse trial. European Journal of Soil Science, 60(1):22-32.

Abstract

This paper reports the use of visible/near-infrared reflectance spectroscopy (Vis-NIRS) to predict pasture root density. A population of varying grass root densities was created by growing Moata ryegrass (Lolium multiflorum Lam.) for 72 days in pots of Ramiha silt loam (Allophanic) and Manawatu fine sandy loam (Recent Fluvial) (60 pots for each soil) differentially fertilized with nitrogen (N) and phosphorus (P) in a glass house experiment. At harvest, the reflectance spectra (350–2500 nm) from flat sectioned horizontal soil slices (1.3 cm depth), taken from 57 selected pots, were recorded using a portable spectroradiometer (ASD FieldSpec Pro, Boulder, CO). Root densities within each of the soil slices were measured using a wet sieving technique. A large variation in root densities (0.46–5.02 mg dry root cm−3) was obtained from the glass house experiment as plant growth responded to the different soils and rates of N and P fertilizer treatment. Pots of the Manawatu soil contained greater ryegrass root densities (1.76–5.02 mg dry root cm−3) than pots of the Ramiha soil (0.46–3.84 mg dry root cm−3). Each soil had visually distinct reflectance spectra in the range 470–2440 nm, but different root masses produced relatively small differences in reflectance spectra. The first two principal components (PC1 and PC2) of a principal component analysis of the first derivative of the spectral reflectance accounted for 71.3% of the spectral variance and clearly separated the Ramiha and Manawatu soils. PC1, which accounted for 58.4% of the spectral variance, was also well correlated to root density. Partial least squares regression (PLSR) of the first derivative of the 10 nm spaced spectral data against measured root densities produced calibration models that allowed quantitative estimates of root densities (without removing outlier, r2 cross-validation = 0.78, ratio of prediction to deviation (RPD) = 2.14, root mean squares error of cross-validation (RMSECV) = 0.60 mg cm−3; with removing outliers, r2 cross-validation = 0.85, RPD = 2.63, RMSECV = 0.47 mg cm−3). The study indicated that spectral reflectance measurement has the potential to quantify root density in soils.

Abstract

This paper reports the use of visible/near-infrared reflectance spectroscopy (Vis-NIRS) to predict pasture root density. A population of varying grass root densities was created by growing Moata ryegrass (Lolium multiflorum Lam.) for 72 days in pots of Ramiha silt loam (Allophanic) and Manawatu fine sandy loam (Recent Fluvial) (60 pots for each soil) differentially fertilized with nitrogen (N) and phosphorus (P) in a glass house experiment. At harvest, the reflectance spectra (350–2500 nm) from flat sectioned horizontal soil slices (1.3 cm depth), taken from 57 selected pots, were recorded using a portable spectroradiometer (ASD FieldSpec Pro, Boulder, CO). Root densities within each of the soil slices were measured using a wet sieving technique. A large variation in root densities (0.46–5.02 mg dry root cm−3) was obtained from the glass house experiment as plant growth responded to the different soils and rates of N and P fertilizer treatment. Pots of the Manawatu soil contained greater ryegrass root densities (1.76–5.02 mg dry root cm−3) than pots of the Ramiha soil (0.46–3.84 mg dry root cm−3). Each soil had visually distinct reflectance spectra in the range 470–2440 nm, but different root masses produced relatively small differences in reflectance spectra. The first two principal components (PC1 and PC2) of a principal component analysis of the first derivative of the spectral reflectance accounted for 71.3% of the spectral variance and clearly separated the Ramiha and Manawatu soils. PC1, which accounted for 58.4% of the spectral variance, was also well correlated to root density. Partial least squares regression (PLSR) of the first derivative of the 10 nm spaced spectral data against measured root densities produced calibration models that allowed quantitative estimates of root densities (without removing outlier, r2 cross-validation = 0.78, ratio of prediction to deviation (RPD) = 2.14, root mean squares error of cross-validation (RMSECV) = 0.60 mg cm−3; with removing outliers, r2 cross-validation = 0.85, RPD = 2.63, RMSECV = 0.47 mg cm−3). The study indicated that spectral reflectance measurement has the potential to quantify root density in soils.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Date:2009
Deposited On:15 Apr 2009 19:49
Last Modified:05 Apr 2016 13:12
Publisher:Wiley-Blackwell
ISSN:1351-0754
Publisher DOI:https://doi.org/10.1111/j.1365-2389.2008.01093.x

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