Application of fractional-order derivative in the quantitative estimation of soil organic matter content through visible and near-infrared spectroscopy

Soil fertility diagnostics rely not only upon measurement of available nutrients but also upon the ability of the soil to retain these nutrients. Near infrared (NIR) reflectance spectroscopy is a rapid and non-destructive analytical technique which allows the simultaneous estimation of standard soil characteristics and does not require the use of chemicals. Previous studies showed that NIR spectroscopy could be used in local contexts to predict soil properties. The main goal of our research was to build a methodological framework for the use of NIR spectroscopy on a more global scale. The specific goals of this study were (i) to identify the best spectral treatment and processing—LOCAL versus GLOBAL—regression methods, Nil to compare the performance of NIR to standard chemical protocols and (iii) to evaluate the ability of NIR spectroscopy to predict soil total organic carbon (TOC), total nitrogen (TN), clay content and cationic exchange capacity (CEC) for a wide range of soil conditions. We scanned 1300 ...

Near Infrared Reflectance Spectroscopy for Estimating Soil Characteristics Valuable in the Diagnosis of Soil Fertility

A possible fractional order derivative and optimized spectral indices for assessing total nitrogen content in cotton

This paper reports the development of a proximal sensing technique used to predict maize root density, soil carbon (C) and nitrogen (N) content from the visible and near-infrared (Vis-NIR) spectral reflectance of soil cores. Eighteen soil cores (0–60 cm depth with a 4.6 cm diameter) were collected from two sites within a field of 90-day-old maize silage; Kairanga silt loam and Kairanga fine sandy loam (Gley Soils). At each site, three replicate soil cores were taken at 0, 15 and 30 cm distance from the row of maize plants (rows were 60 cm apart). Each soil core was sectioned at 5 depths (7.5, 15, 30, 45, and 60 cm) and soil reflectance spectra were acquired from the freshly cut surface at each depth. A 1.5 cm soil slice was taken at each surface to obtain root mass and total soil C and N reference (measured) data. Root densities decreased with depth and distance from plant and were lower in the silt loam, which had the higher total C and N contents. Calibration models, developed using partial least squares regression (PLSR) between the first derivative of soil reflectance and the reference data, were able to predict with moderate accuracy the soil profile root density (r
 2 = 0.75; ratio of prediction to deviation [RPD] = 2.03; root mean square error of cross-validation [RMSECV] = 1.68 mg/cm3), soil% C (r
 2 = 0.86; RPD = 2.66; RMSECV = 0.48%) and soil% N (r
 2 = 0.81; RPD = 2.32; RMSECV = 0.05%) distribution patterns. The important wavelengths chosen by the PLSR model to predict root density were different to those chosen to predict soil C or N. In addition, predicted root densities were not strongly autocorrelated to soil C (r = 0.60) or N (r = 0.53) values, indicating that root density can be predicted independently from soil C. This research has identified a potential method for assessing root densities in field soils enabling study of their role in soil organic matter synthesis.

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

The nutritive value of pasture is an important determinant of the performance of grazing livestock. Proximal sensing of in situ pasture is a potential technique for rapid prediction of nutritive value. In this study, multispectral radiometry was used to obtain pasture spectral reflectance during different seasons (autumn, spring and summer) in 2009–2010 from commercial farms throughout New Zealand. The analytical data set (n = 420) was analysed to develop season-specific and combined models for predicting pasture nutritive-value parameters. The predicted parameters included crude protein (CP), acid detergent fibre (ADF), neutral detergent fibre (NDF), ash, lignin, lipid, metabolizable energy (ME) and organic matter digestibility (OMD) using a partial least squares regression analysis. The calibration models were tested by internal and external validation. The results suggested that the global models can predict the pasture nutritive value parameters (CP, ADF, NDF, lignin, ME and OMD) with moderate accuracy (0·64 � r 2 � 0·70) while ash and lipid are poorly predicted (0·33 � r 2 � 0·40). However, the seasonspecific models improved the prediction accuracy, in autumn (0·73 � r 2 � 0·83) for CP, ADF, NDF and lignin; in spring (0·61 � r 2 � 0·78) for CP and ash; in summer (0·77 � r 2 � 0·80) for CP and ash, indi

Proximal sensing of the seasonal variability of pasture nutritive value using multispectral radiometry

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.

/pdf/the-use-of-vis-nir-spectral-reflectance-for-determining-root-27e1ajof64.pdf

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

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