Prediction of soil organic and inorganic carbon concentrations in Tunisian samples by mid-infrared reflectance spectroscopy using a French national library

Author(s):  
Tiphaine Chevallier ◽  
Cécile Gomez ◽  
Patricia Moulin ◽  
Imane Bouferra ◽  
Kaouther Hmaidi ◽  
...  

<p>Mid-Infrared Reflectance Spectroscopy (MIRS, 4000–400 cm<sup>-1</sup>) is being considered to provide accurate estimations of soil properties, including soil organic carbon (SOC) and soil inorganic carbon (SIC) contents. This has mainly been demonstrated when datasets used to build, validate and test the prediction model originate from the same area A, with similar geopedological conditions. The objective of this study was to analyze how MIRS performed when used to predict SOC and SIC contents, from a calibration database collected over a region A, to predict over a region B, where A and B have no common area and different soil and climate conditions. This study used a French MIRS soil dataset including 2178 soil samples to calibrate SIC and SOC prediction models with partial least squares regression (PLSR), and a Tunisian MIRS soil dataset including 96 soil samples to test them. Our results showed that using the French MIRS soil database i) SOC and SIC of French samples were successfully predicted, ii) SIC of Tunisian samples was also predicted successfully, iii) local calibration significantly improved SOC prediction of Tunisian samples and iv) prediction models seemed more robust for SIC than for SOC. So in future, MIRS might replace, or at least be considered as, a conventional physico-chemical analysis technique, especially when as exhaustive as possible calibration database will become available.</p>

2021 ◽  
Author(s):  
Cécile Gomez ◽  
Tiphaine Chevallier ◽  
Patricia Moulin ◽  
Bernard G. Barthès

<p><span>Mid-Infrared reflectance spectroscopy (MIRS, 4000 – 400 cm<sup>-1</sup>) is being considered to provide accurate estimations of soil inorganic carbon (SIC) contents. Usually, the prediction performances by MIRS are analyzed using figures of merit based on entire test datasets characterized by large SIC ranges, without paying attention to performances at sub-range scales. This work aims to <em>1)</em> evaluate the performances of MIR regression models for SIC prediction, for a large range of SIC test data (0-100 g/kg) and for several regular sub-ranges of SIC values (0-5, 5-10, 10-15 g/kg, etc.) and <em>2)</em> adapt the prediction model depending on sub-ranges of test samples, using the absorbance peak at 2510 cm<sup>-1</sup> for separating SIC-poor and SIC-rich test samples. This study used a Tunisian MIRS topsoil dataset including 96 soil samples, mostly rich in SIC, to calibrate and validate SIC prediction models; and a French MIRS topsoil dataset including 2178 soil samples, mostly poor in SIC, to test them. Two following regression models were used: a partial least squares regression (PLSR) using the entire spectra and a simple linear regression (SLR) using the height of the carbonate absorbance peak at 2150 cm<sup>-1</sup>.</span></p><p><span>First, our results showed that PLSR provided <em>1) </em>better performances than SLR on the Validation Tunisian dataset (R<sup>2</sup><sub>test</sub> of 0.99 vs. 0.86, respectively), but <em>2) </em>lower performances than SLR on the Test French dataset (R<sup>2</sup><sub>test</sub> of 0.70 vs. 0.91, respectively). Secondly, our results showed that on the Test French dataset, predicted SIC values were more accurate for SIC-poor samples (< 15 g/kg) with SLR (RMSE<sub>test</sub> from 1.5 to 7.1 g/kg, depending on the sub-range) than with PLSR prediction model (RMSE<sub>test </sub>from 7.3 to 14.8 g/kg, depending on the sub-range). Conversely, predicted SIC values were more accurate for carbonated samples (> 15 g/kg) with PLSR (RMSE<sub>test</sub> from 4.4 to 10.1 g/kg, depending on the sub-range) than with SLR prediction model (RMSE<sub>test</sub> from 6.8 to 14 g/kg, depending on the sub-range). Finally, our results showed that the absorbance peak at 2150 cm<sup>-1</sup> could be used before prediction to separate SIC-poor and SIC-rich test samples (452 and 1726 samples, respectevely). The SLR and PLSR regression methods applied to these SIC-poor and SIC-rich test samples, respectively, provided better prediction performances (<em>R²</em><sub><em>test </em></sub>of 0.95 and <em>RMSE</em><sub><em>test</em></sub> of 3.7 g/kg<sup></sup>). </span></p><p><span>Finally, this study demonstrated that the use of the spectral absorbance peak at 2150 cm<sup>-1</sup> provided useful information on Test samples and helped the selection of the optimal prediction model depending on SIC level, when using calibration and test sample sets with very different SIC distributions.</span></p>


2009 ◽  
Vol 89 (5) ◽  
pp. 579-587 ◽  
Author(s):  
C Nduwamungu ◽  
N Ziadi ◽  
L -É Parent ◽  
G F Tremblay

Near-infrared reflectance spectroscopy (NIRS) is a cost-effective and environmentally friendly technique of soil analysis that is particularly advantageous in intensive soil sampling and soil nutrient management as well. This study evaluated the potential of NIRS for predicting P, K, Ca, Mg, Cu, Zn, Mn, Fe, and Al extracted by Mehlich 3. We used 150 air-dried samples collected from a 15-ha site dominated by Orthic Humic Gleysol and Gleyed Dystric Brunisol soils. Calibration equations were developed using modified partial least squares regression. The accuracy of NIRS prediction was evaluated using the coefficient of determination (R2), the ratio of performance deviation (RPD), and the ratio of error range (RER). Reliable calibrations were found for Ca, Cu, and Mg (R2 ≥ 0.7, RPD ≥ 1.75, and RER ≥ 8). Less-reliable calibrations were found for Al, Fe, K, Mn, P, and Zn (R2 < 0.7, RPD < 1.75, and RER < 8). In the validation with independent samples, acceptable regression coefficients (i.e., 0.8 ≤ slope ≤ 1.2) were only found for Ca, Mg, and Mn. We presumed that the pH of the Mehlich 3 extractant (2.5 ± 0.1) may affect the solubility of most of these nutrients, regardless the soil texture and, consequently, the potential of NIRS to predict them. The more a nutrient was correlated to clay content, the more it was likely predictable by NIRS. The prediction models obtained for Al, Ca, Cu, Fe, K, Mg, and Mn could still be used for screening purposes in cases where high accuracy is not required. These NIRS prediction models should be validated across larger geographic areas of geological homogeneity. Key words: Soil analysis, Mehlich 3, near-infrared reflectance spectroscopy, calibration


2020 ◽  
Vol 193 ◽  
pp. 105078 ◽  
Author(s):  
Andreas Morlok ◽  
Benjamin Schiller ◽  
Iris Weber ◽  
Mohit Melwani Daswani ◽  
Aleksandra N. Stojic ◽  
...  

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 303-303
Author(s):  
Jordan N Moody ◽  
Reid Redden ◽  
Faron A Pfeiffer ◽  
Ronald Pope ◽  
John W Walker

Abstract Lab scoured yield (LSY) is a major indicator of wool quality. LSY is used for the valuation of wool in commercial settings and can be used by growers as selection criteria for breeding stock. Current laboratory methods for LSY are costly and labor intensive. Evaluation of fleece core samples using Near-Infrared Reflectance Spectroscopy (NIR) may present an efficient, cost-effective alternative to predict LSY. Lamb and yearling fleece core samples from flocks originating from Texas were scanned on a FOSS 6500 spectrometer. Constituent data were obtained from the Bill Sims Wool and Mohair Laboratory using ASTM methodology. LSY ranged from 48–68%. Spectral data were pretreated with a 14 nm moving average and Savitsky-Golay 2nd derivative. Eight outlier spectra were removed. Samples were parsed from the center of the distribution to minimize the Dunn effect creating calibration (n = 108) and test (n = 41) sets. Calibrations were executed using a partial least squares regression on spectra from 1100 to 2492 nm. Test set calibration statistics for LSY were: r2=0.64, RMSE=3.39, and slope=0.91. Independent validation statistics for LSY using spectra for different years were: r2=0.33, RMSE=3.69, and slope=0.29. RMSE for independent validation and lab methods on side samples are similar. Between flock independent validations were less promising. Accuracy of laboratory methods for estimating yield is 2 percentage units. NIRS calibrations can be improved by developing calibration sets with a uniform distribution, which can be difficult within flocks because of the small number of fleeces in the tails of the distribution. These data demonstrate that when calibration and test sets are developed such that test samples are drawn from the calibration population, NIR is a reliable predictor of LSY. However, when test samples are drawn from populations dissimilar to the calibration set, reliability of NIR predictions are greatly reduced.


1994 ◽  
Vol 2 (3) ◽  
pp. 153-162 ◽  
Author(s):  
James B. Reeves

The objective of this work was to explore the relative merits of near and mid-infrared diffuse reflectance spectroscopy in determining the composition of sodium chlorite treated forages and by-products. Sixteen feed-stuffs (174 total samples treated at one of 11 levels of sodium chlorite, 0 to 0.394 g per gram of feedstuff) were examined in the near and mid-infrared spectral regions using diffuse reflectance on a Fourier transform spectrometer, and in the near infrared region using a grating monochromator. Samples were scanned as is and as 5% sample in KBr on the Fourier spectrometer and as is on the grating monochromator. Samples were analysed chemically and spectroscopically for neutral and acid detergent fibre, in vitro digestibility, permanganate lignin, crude protein and lignin nitrobenzene oxidation products. Results showed that diffuse mid-infrared reflectance spectroscopy can perform as well as, and sometimes better than, diffuse near infrared reflectance spectroscopy in determining the composition of chlorite-treated forages and by-products. In addition, Fourier near infrared spectroscopy did not perform as well as either near infrared using a grating monochromator or the Fourier mid-infrared spectrometer. Finally, diluting samples with KBr was often beneficial for mid-infrared based determinations.


Sign in / Sign up

Export Citation Format

Share Document