Quantitative Analysis of Agricultural Soils Using near Infrared Reflectance Spectroscopy and a Fibre-Optic Probe

Abstract

The objective of this work was to investigate the usefulness of near infrared (NIR) reflectance spectroscopy in conjunction with a fibre-optic probe for determining various constituents (total N, organic C, active N, biomass and mineralisable N and pH) in agricultural soils. A NIRSystems model 6500 spectrometer equipped with a fibre-optic reflectance probe was used to scan soil samples (n = 180) obtained from experimental plots at two locations with three replicate plots under plow and no till practices at each location with three rates of NH 4 NO 3 for each plot (2 × 3 × 2 × 3 = 36). For each of these, samples were taken from five depths for a total of 2 × 3 × 2 × 3 × 5 or 180 samples. Optimal calibrations were achieved using first derivative spectra, and only data from 1100 to 2300 nm, with every 20 data points averaged from 1900 to 2300 nm. Compared to results achieved using a spinning cup, the use of the probe resulted in more concentration outliers (up to 6% with probe and none with the spinning cup), the reason for which is unknown. Otherwise, the final calibration results were quite similar to those achieved using a spinning cup to obtain spectra, with calibration (outliers removed) R 2 and RMSD/Mean of 0.96, 6.6; 0.95, 6.5; 0.88, 15.3; and 0.80, 19.3; for organic C, total N, active N and biomass N, respectively. The R 2 and RMSD for pH were 0.80 and 4.5 pH units, respectively. In summary, the work presented here demonstrated that NIR spectroscopy, based on data obtained using a fibre-optic probe, can be successfully used to determine compositional parameters of agricultural soils (particularly organic C and total N). However, there appeared to be a greater problem with outliers when using a fibre-optic probe than was true when obtaining spectra using a spinning cup.

Determination of carbon and nitrogen contents in Alfisols, Oxisols and Ultisols from Africa and Brazil using NIRS analysis: Effects of sample grinding and set heterogeneity

“…15%, respectively). For a fairly homogeneous set of ground fine-textured samples (silty loam Ultisols), Reeves and McCarty (2001) also found similar calibration R 2 and CV for Corg and Nt (0.97 and 6%, respectively). For a more heterogeneous set of ground sandy samples originating from different regions of Norway (59°N to 67°N), data from Fystro (2002) showed that validation CV was similar for Corg and Nt but that R 2 was higher for Corg.…”

“…However, many authors tested several preprocessing methods and selected the most suitable for each property (Reeves and McCarty, 2001;McCarty et al, 2002;Russell, 2003;Sørensen and Dalsgaard, 2005). Moreover, some other authors have reported that normalisation did not improve the calibration (Shepherd and Walsh, 2002).…”

Transferability of a Visible and Near-Infrared Model for Soil Organic Matter Estimation in Riparian Landscapes

“…It has proven to be a rapid and efficient technique for estimating a variety of soil properties, including the SOM content [3,13,14]. The prediction accuracies of the VNIR models for SOM content have, however, varied from less than satisfactory [15] to satisfactory [16], depending on the land use [7], the source of the VNIR data, the calibration methods, scanning environments, soil chromophores (e.g., iron oxide) and even the spectroscopic instruments [10,11,13,17]. The mechanisms for the VNIR estimation of SOM content are its broad absorptions in the visible region, due to chromophores and the darkness of humic acid, and the absorptions in the NIR region from the overtones of O-H, C-H and N-H, or their combination [13,18].…”

A rapid spectral-reflectance-based fingerprinting approach for documenting suspended sediment sources during storm runoff events

“…Recent research has suggested that visible and nearinfrared (VIS/NIR, 0.4-2.5 μm) diffuse reflectance spectroscopy (DRS) can provide a relatively inexpensive means of characterising the physical, chemical and biological properties of soils (Ben-Dor and Banin 1994; Reeves III et al 1999;Reeves III and McCarty 2001;Shepherd and Walsh 2002;Islam et al 2003;Dunn et al 2002). DRS is an indirect analytical method that is based on the development of empirical models in which the concentration of a constituent is predicted from multivariate spectral data (Awiti et al 2008).…”