scholarly journals Laboratory Spectroscopy Assessments of Rainfed Paddy Soil Samples on Visible and Near-Infrared Spectroscopy Reflectance for Estimating Soil Organic Carbon

2016 ◽  
Vol 9 ◽  
pp. ASWR.S40173 ◽  
Author(s):  
Sakda Homhuan ◽  
Wanwisa Pansak ◽  
Siam Lawawirojwong ◽  
Chada Narongrit

Visible and near-infrared spectroscopy is a rapid, less expensive, and nondestructive alternative to conventional methods of soil analysis. This study aimed to investigate appropriate soil sample preparations and particle sizes for estimating soil organic carbon (SOC) through the use of laboratory spectroscopy. Rainfed paddy soils were sampled from 240 sampling sites to record their spectral reflectance and to measure their SOC contents in the laboratory. Partial least squares regression was applied to select the best model to estimate SOC using soil spectra. The results showed that the highest accuracy of SOC estimation was gained from soil samples prepared by 2 mm sieving. A short-wave infrared region was the most appropriate spectral wavelength for SOC estimation of rainfed paddy soil. Although the model showed potential in SOC prediction, the accuracy of partial least squares regression prediction in each spectral region varied between sampling times. Therefore, these models and methods should be further tested in soils sampled from different seasons and other regions to prove consistent validity. However, these results are useful for wavelength selection and soil sample preparation in future laboratory spectroscopy.

2019 ◽  
Vol 59 (6) ◽  
pp. 1190 ◽  
Author(s):  
A. Bahri ◽  
S. Nawar ◽  
H. Selmi ◽  
M. Amraoui ◽  
H. Rouissi ◽  
...  

Rapid measurement optical techniques have the advantage over traditional methods of being faster and non-destructive. In this work visible and near-infrared spectroscopy (vis-NIRS) was used to investigate differences between measured values of key milk properties (e.g. fat, protein and lactose) in 30 samples of ewes milk according to three feed systems; faba beans, field peas and control diet. A mobile fibre-optic vis-NIR spectrophotometer (350–2500 nm) was used to collect reflectance spectra from milk samples. Principal component analysis was used to explore differences between milk samples according to the feed supplied, and a partial least-squares regression and random forest regression were adopted to develop calibration models for the prediction of milk properties. Results of the principal component analysis showed clear separation between the three groups of milk samples according to the diet of the ewes throughout the lactation period. Milk fat, protein and lactose were predicted with good accuracy by means of partial least-squares regression (R2 = 0.70–0.83 and ratio of prediction deviation, which is the ratio of standard deviation to root mean square error of prediction = 1.85–2.44). However, the best prediction results were obtained with random forest regression models (R2 = 0.86–0.90; ratio of prediction deviation = 2.73–3.26). The adoption of the vis-NIRS coupled with multivariate modelling tools can be recommended for exploring to differences between milk samples according to different feed systems, and to predict key milk properties, based particularly on the random forest regression modelling technique.


2018 ◽  
Vol 11 (7) ◽  
pp. e201700365 ◽  
Author(s):  
Raphael Henn ◽  
Christian G. Kirchler ◽  
Zora L. Schirmeister ◽  
Andreas Roth ◽  
Werner Mäntele ◽  
...  

2002 ◽  
Vol 82 (4) ◽  
pp. 413-422 ◽  
Author(s):  
P D Martin ◽  
D F Malley ◽  
G. Manning ◽  
L. Fuller

This study explored the use of near-infrared spectroscopy (NIRS) for the rapid analysis of organic C (Corg) and organic N (Norg) in the A horizon of soil within a single field. Soil was sampled throughout a field in Manitoba, Canada to capture soil variability associated with topography. The soil samples were oven-dried and treated with acid to remove carbonates, after which C and N were determined by dry combustion. In this study, portions of the dried soil samples not treated with acid were scanned with a near-infrared scanning spectrophotometer between 1100 and 2500 nm. Correlating the spectral and the chemical analytical data using multiple linear regression or principal component analysis/partial least squares regression gave useful correlations for Corg. Over the range of 0–40 mg g-1 Corg, NIR-predicted values explained 75–78% of the variance in the chemical results. Results were improved to 80% for calibrations developed for the 0–20 mg g-1 organic C range. Useful results were not obtained for Norg although the literature shows that total N in soil is predictable using NIRS. It is likely that the acid treatment altered the composition of the samples in an inconsistent manner such that the chemically analyzed samples and those scanned by NIRS were different from each other in Norg concentration or composition. Extrapolation of these Corg results to the landscape scale implies that NIRS has potential to be a suitable method for mapping C for the purposes of monitoring C sequestration. Key words: Near-infrared spectroscopy, soil, carbon, nitrogen, topography, soil monitoring


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