Soil total carbon analysis in Hawaiian soils with visible, near-infrared and mid-infrared diffuse reflectance spectroscopy

Geoderma ◽  
2012 ◽  
Vol 189-190 ◽  
pp. 312-320 ◽  
Author(s):  
Meryl L. McDowell ◽  
Gregory L. Bruland ◽  
Jonathan L. Deenik ◽  
Sabine Grunwald ◽  
Nichola M. Knox
Keyword(s):  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Total Carbon ◽  
Mid Infrared ◽  
Carbon Analysis

Near-infrared, mid-infrared or combined diffuse reflectance spectroscopy for assessing soil fertility in rice fields in sub-Saharan Africa

Geoderma ◽  
2019 ◽  
Vol 354 ◽  
pp. 113840 ◽  
Author(s):  
Jean-Martial Johnson ◽  
Elke Vandamme ◽  
Kalimuthu Senthilkumar ◽  
Andrew Sila ◽  
Keith D. Shepherd ◽  
...  
Keyword(s):  
Soil Fertility ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Rice Fields ◽  
Sub Saharan Africa ◽  
Mid Infrared ◽  
Sub Saharan

Mid-Infrared and Near-Infrared Diffuse Reflectance Spectroscopy for Soil Carbon Measurement

2002 ◽  
Vol 66 (2) ◽  
pp. 640-646 ◽  
Author(s):  
G. W. McCarty ◽  
J. B. Reeves ◽  
V. B. Reeves ◽  
R. F. Follett ◽  
J. M. Kimble
Keyword(s):  
Soil Carbon ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Mid Infrared

Determination of Protein in Ground Wheat Samples by Mid-Infrared Diffuse Reflectance Spectroscopy

1997 ◽  
Vol 51 (8) ◽  
pp. 1200-1204 ◽  
Author(s):  
James B. Reeves ◽  
Stephen R. Delwiche
Keyword(s):  
Protein Content ◽  
Infrared Spectra ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Mid Infrared ◽  
Near Infrared Spectra ◽  
Hard Red Winter Wheat ◽  
Ground Wheat

The objective of this study was to determine whether mid-infrared diffuse reflectance spectroscopy could be used in the same manner as near-infrared diffuse reflectance spectroscopy to quantitatively determine the protein content of ground wheat samples. One hundred and thirty hard red winter wheat samples were assayed for protein by combustion and scanned in the near- and mid-infrared. Samples (UDY ground) were scanned neat in the near-infrared from 1100 nm (9091 cm−1) to 2498 nm (4003 cm−1) on a scanning monochromator and in the mid-infrared from 4000 cm−1 (2500 nm) to 400 cm−1 (25,000 nm) on a Fourier transform spectrometer at 4-and 16-cm−1 resolutions. Protein content varied from a low of 8.98% to a high of 18.70% (average of 12.86% with a standard deviation of 1.66%). Calibrations developed with the use of partial least-squares gave an R2 and bias-corrected standard error of performance of 0.999 and 0.054 for the near-infrared and 0.997 and 0.085 for the mid-infrared (4 cm−1 resolution). Calibration results based on mid-infrared spectra, while not as good as those for near-infrared spectra, were nevertheless quite good. These results demonstrate that it is possible to develop satisfactory calibrations for protein in ground wheat with the use of mid-infrared spectra without the need for sample dilution with KBr.

scholarly journals Effects of Subsetting by Carbon Content, Soil Order, and Spectral Classification on Prediction of Soil Total Carbon with Diffuse Reflectance Spectroscopy

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Meryl L. McDowell ◽  
Gregory L. Bruland ◽  
Jonathan L. Deenik ◽  
Sabine Grunwald
Keyword(s):  
Diffuse Reflectance ◽  
Near Infrared ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Total Carbon ◽  
Coefficient Of Determination ◽  
Spectral Classification ◽  
Sample Set

Subsetting of samples is a promising avenue of research for the continued improvement of prediction models for soil properties with diffuse reflectance spectroscopy. This study examined the effects of subsetting by soil total carbon (Ct) content, soil order, and spectral classification withk-means cluster analysis on visible/near-infrared and mid-infrared partial least squares models forCtprediction. Our sample set was composed of various Hawaiian soils from primarily agricultural lands withCtcontents from <1% to 56%. Slight improvements in the coefficient of determination (R2) and other standard model quality parameters were observed in the models for the subset of the high activity clay soil orders compared to the models of the full sample set. The other subset models explored did not exhibit improvement across all parameters. Models created from subsets consisting of only lowCtsamples (e.g.,Ct< 10%) showed improvement in the root mean squared error (RMSE) and percent error of prediction for lowCtsoil samples. These results provide a basis for future study of practical subsetting strategies for soilCtprediction.

Mid-Infrared and Near-Infrared Diffuse Reflectance Spectroscopy for Soil Carbon Measurement

2002 ◽  
Vol 66 (2) ◽  
pp. 640 ◽  
Author(s):  
G. W. McCarty ◽  
J. B. Reeves ◽  
V. B. Reeves ◽  
R. F. Follett ◽  
J. M. Kimble
Keyword(s):  
Soil Carbon ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Mid Infrared

scholarly journals Near-infrared (NIR) diffuse reflectance spectroscopy for the prediction of carbon and nitrogen in an Oxisol

2014 ◽  
Vol 32 (1) ◽  
pp. 86-94 ◽  
Author(s):  
Jesús H. Camacho-Tamayo ◽  
Yolanda Rubiano S. ◽  
María del Pilar Hurtado S.
Keyword(s):  
Precision Agriculture ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Total Carbon ◽  
Least Squares Regression ◽  
Carbon And Nitrogen ◽  
Digital Maps

The characterization of soil properties through laboratory analysis is an essential part of the diagnosis of the potential use of lands and their fertility. Conventional chemical analyzes are expensive and time consuming, hampering the adoption of crop management technologies, such as precision agriculture. The aim of the present paper was to evaluate the potential of near-infrared (NIR) diffuse reflectance spectroscopy for the prediction of the carbon and nitrogen of Typic Hapludox. In the A and B horizons, 1,240 samples were collected in order to determine the total carbon (TC) and nitrogen (TN) contents, obtain the NIR spectral curve, and build models using partial least squares regression. The use of diffuse reflectance spectroscopy and statistical techniques allowed for the quantification of the TC with adequate models of prediction based on a small number of samples, an residual prediction deviation RPD greater than 2.0, an R2 greater than 0.80 and a low root mean square error RMSE. For TN, models with a good level of prediction were not obtained. The results based on the NIR models were able to be integrated directly into the geostatistical evaluations, obtaining similar digital maps from the observed and predicted TC. The use of pedometric techniques showed promising results for these soils and constitutes a basis for the development of this area of research on soil science in Colombia.

Sign in / Sign up

Export Citation Format

Share Document