scholarly journals Use of near-infrared spectroscopy to assess phosphorus fractions of different plant availability in forest soils

2015 ◽  
Vol 12 (11) ◽  
pp. 3415-3428 ◽  
Author(s):  
J. Niederberger ◽  
B. Todt ◽  
A. Boča ◽  
R. Nitschke ◽  
M. Kohler ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Forest Soils ◽  
Soil Type ◽  
Near Infrared ◽  
Soil Samples ◽  
P Fractions ◽  
Inorganic P ◽  
Plant Availability ◽  
Inorganic P Fractions

Abstract. The analysis of soil phosphorus (P) in fractions of different plant availability is a common approach to characterize the P status of forest soils. However, quantification of organic and inorganic P fractions in different extracts is labor intensive and therefore rarely applied for large sample numbers. Therefore, we examined whether different P fractions can be predicted using near-infrared spectroscopy (NIRS). We used the Hedley sequential extraction method (modified by Tiessen and Moir, 2008) with increasingly strong extractants to determine P in fractions of different plant availability and measured near-infrared (NIR) spectra for soil samples from sites of the German forest soil inventory and from a nature reserve in southeastern China. The R2 of NIRS calibrations to predict P in individual Hedley fractions ranged between 0.08 and 0.85. When these fractions were combined into labile, moderately labile and stable P pools, R2 of calibration models was between 0.38 and 0.88 (all significant). Model prediction quality was higher for organic than for inorganic P fractions and increased with the homogeneity of soil properties in soil sample sets. Useable models were obtained for samples originating from one soil type in subtropical China, whereas prediction models for sample sets from a range of soil types in Germany were only moderately useable or not useable. Our results indicate that prediction of Hedley P fractions with NIRS can be a promising approach to replace conventional analysis, if models are developed for sets of soil samples with similar physical and chemical properties, e.g., from the same soil type or study site.

scholarly journals Use of near-infrared spectroscopy to assess phosphorus fractions of different plant availability in forest soils

2015 ◽  
Vol 12 (1) ◽  
pp. 555-592
Author(s):  
B. Todt ◽  
J. Niederberger ◽  
A. Boča ◽  
R. Nitschke ◽  
M. Kohler ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Forest Soils ◽  
Near Infrared ◽  
Eastern China ◽  
Soil Samples ◽  
P Fractions ◽  
Inorganic P ◽  
Plant Availability ◽  
Inorganic P Fractions

Abstract. The fractionation of soil P into fractions of different plant availability is a common approach to characterize the P status of forest soils. However, quantification of organic and inorganic P fractions in different extracts is labour-intensive and therefore rarely applied for large sample numbers. Therefore, we examined whether different P fractions can be predicted using near-infrared spectroscopy (NIRS). We used the Hedley method with increasingly strong extractants to determine P in fractions of different plant availability and measured NIR spectra for soil samples from sites of the German forest soil inventory and from a nature reserve in south-eastern China. The R2 of NIRS calibrations to predict P in individual Hedley fractions ranged between 0.08 and 0.85. When these were pooled into labile, moderately labile and stable fractions, R2 of calibration models was between 0.38 and 0.88. Model prediction quality was higher for organic than for inorganic P fractions and increased with the homogeneity of soil sample sets. Useful models were obtained for samples originating from one soil type in subtropical China, whereas prediction models for sample sets from a range of soil types in Germany were only moderately useful or not useful. Our results indicate that prediction of Hedley P fractions with NIRS is a promising approach to replace conventional analysis, if models are developed for sets of soil samples with similar physical and chemical properties.

scholarly journals Estimation of selected properties of forest soils using near-infrared spectroscopy (NIR)

2016 ◽  
Vol 67 (1) ◽  
pp. 32-36 ◽  
Author(s):  
Mateusz Kania ◽  
Piotr Gruba
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Forest Soils ◽  
Near Infrared ◽  
C:N Ratio ◽  
Soil Samples ◽  
Initial Assessment ◽  
Calibration Model ◽  
Organic Carbon Content ◽  
Spectra Analysis

Abstract The study was focused on the application of near-infrared spectroscopy (NIR) as a tool for evaluation of selected properties of forest soils. We analysed 144 soil samples from the topsoil of nine plots located in southern Poland. Six plots were established under pine stands, and three plots under oak stands. The NIR measurements were performed using Antharis II FT scanner. On the basis of the spectrum files obtained from scanning of 96 samples and the measurement results obtained for selected properties of the soil samples, we developed a calibration model. The model was validated using 48 independent samples. We attempted to estimate the following properties of forest soils: pH, C:N ratio, the organic carbon content (Ct), total nitrogen (Nt), clay content (Clay), base cation content (BC), cation exchange capacity (CEC) and total acidity (TA). We conclude that estimation of soil properties using NIR method can be applied as additional (to laboratory analysis) or initial assessment of soil quality. Our results also suggest that forest species composition may affect the mathematical model applied to NIR spectra analysis, however, this hypothesis needs some of further investigations.

scholarly journals Characterization of soil organic matter by near infrared spectroscopy – determination of glomalin in different soils

2016 ◽  
Author(s):  
Jiří Zbíral ◽  
David Čižmár ◽  
Stanislav Malý ◽  
Elena Obdržálková
Keyword(s):  
Organic Matter ◽  
Infrared Spectroscopy ◽  
Soil Organic Matter ◽  
Near Infrared Spectroscopy ◽  
Forest Soils ◽  
Near Infrared ◽  
Arable Land ◽  
Soil Samples ◽  
Calibration Model ◽  
Calibration Models

Abstract. Determining and characterizing soil organic matter (SOM) cheaply and reliably can help to support decisions concerning sustainable land management and climate policy. Glomalin, a glycoprotein produced by arbuscular mycorrhizal fungi, was recommended as a promising indicator of SOM quality. But extracting glomalin from and determining glomalin in soils using classical chemical methods is too complicated and time consuming and therefore limits the use of this parameter in large scale surveys. Near infrared spectroscopy (NIRS) is a very rapid, non-destructive analytical technique that can be used to determine many constituents of soil organic matter. Representative sets of 84 different soil samples from arable land and grasslands and 75 forest soils were used to develop reliable NIRS calibration models for glomalin. One calibration model was developed for samples with a low content of glomalin (arable land and grasslands), the second for soils with a high content of glomalin (forest soils), and the third calibration model for all combined soil samples. Calibrations were validated and optimized by leave-one-sample-out-cross-validation (LOSOCV) and by the external validation using eight soil samples (arable land and grassland), and six soil samples (forest soils) not included in the calibration models. Two different calibration models were recommended. One model for arable and grassland soils and the second for forest soils. No statistically significant differences were found between the reference and the NIRS method for both calibration models. The parameters of the NIRS calibration model (RMSECV = 0,70 and R = 0,90 for soils from arable land and grasslands and RMSECV = 3,8 and R = 0,94 for forest soils) proved that glomalin can be determined directly in air-dried soils by NIRS with adequate trueness and precision.

scholarly journals Determination of glomalin in agriculture and forest soils by near-infrared spectroscopy

2017 ◽  
Vol 63 (No. 5) ◽  
pp. 226-230 ◽  
Author(s):  
Zbíral Jiří ◽  
Čižmár David ◽  
Malý Stanislav ◽  
Obdržálková Elena
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Forest Soils ◽  
Near Infrared ◽  
Arable Land ◽  
Calibration Model ◽  
Sustainable Land Management ◽  
Chemical Methods ◽  
Calibration Models

Determining and characterizing soil organic matter (SOM) cheaply and reliably can help to support decisions concerning sustainable land management and climate policy. Glomalin was recommended as one of possible indicators of SOM quality. Extracting glomalin from and determining it in soils using classical chemical methods is too complicated and therefore near-infrared spectroscopy (NIRS) was studied as a method of choice for the determination of glomalin. Representative sets of 84 different soil samples from arable land and grasslands and 75 forest soils were used to develop NIRS calibration models. The parameters of the NIRS calibration model (R = 0.90 for soils from arable land and grasslands and R = 0.94 for forest soils) proved that glomalin can be determined in air-dried soils by NIRS with adequate trueness and precision simultaneously with determination of nitrogen and oxidizable carbon.

Determination of soil organic carbon and nitrogen at the field level using near-infrared spectroscopy

2002 ◽  
Vol 82 (4) ◽  
pp. 413-422 ◽  
Author(s):  
P D Martin ◽  
D F Malley ◽  
G. Manning ◽  
L. Fuller
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Analytical Data ◽  
Principal Component ◽  
Soil Samples ◽  
Organic N ◽  
Least Squares Regression ◽  
Total N ◽  
Organic C

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

scholarly journals Prediction of soil organic matter and clay contents by near-infrared spectroscopy - NIRS

2020 ◽  
Vol 50 (1) ◽  
Author(s):  
Bruno Pedro Lazzaretti ◽  
Leandro Souza da Silva ◽  
Gerson Laerson Drescher ◽  
André Carnieletto Dotto ◽  
Darines Britzke ◽  
...  
Keyword(s):  
Organic Matter ◽  
Infrared Spectroscopy ◽  
Soil Organic Matter ◽  
Near Infrared Spectroscopy ◽  
Calibration Curve ◽  
Near Infrared ◽  
Soil Analysis ◽  
Clay Content ◽  
Soil Samples ◽  
Support Vector

ABSTRACT: Among the soil constituents, special attention is given to soil organic matter (SOM) and clay contents, since, among other aspects, they are key factors to nutrient retention and soil aggregates formation, which directly affect the crop production potential. The methods commonly used for the quantification of these constituents have some disadvantages, such as the use of chemical reactants and waste generation. An alternative to these methods is the near-infrared spectroscopy (NIRS) technique. The aim of this research is to evaluate models for SOM and clay quantification in soil samples using spectral data by NIRS. A set (n = 400) of soil samples previously analyzed by traditional methods were used to generate a NIRS calibration curve. The clay content was determined by the hydrometer method while SOM content was determined by sulfochromic solution. For calibration, we used the original spectra (absorbance) and spectral pretreatment (Savitzky-Golay smoothing derivative) in the following models: multiple linear regression (MLR), partial last squares regression (PLSR), support vector machine (SVM) and Gaussian process regression (GPR). The curve validation was performed with the SVM model (best performance in the calibration based on R² and RMSE) in two ways: with 40 random samples from the calibration set and another set with 200 new unknown samples. The soil clay content affects the predictive ability of the calibration curve to estimate SOM content by NIRS. Validation curves showed poorer performance (lower R² and higher RMSE) when generated from unknown samples, where the model tends to overestimate the lower levels and to underestimate the higher levels of clay and SOM. Despite the potential of NIRS technique to predict these attributes, further calibration studies are still needed to use this technique in soil analysis laboratories.

Wet or dry? The challenges of NIR to analyse soil samples

NIR news ◽  
2017 ◽  
Vol 28 (4) ◽  
pp. 3-5 ◽  
Author(s):  
JJ Roberts ◽  
D Cozzolino
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Soil Samples ◽  
High Moisture ◽  
Mid Infrared ◽  
Infrared Instruments ◽  
New Algorithms ◽  
The Way

The increasing use of hand-held or portable infrared instruments (near infrared and mid infrared (MIR)), the development of new algorithms and the increasing use of chemometrics have changed the way that infrared spectroscopy is used to measure different properties in soils, allowing the measurement of samples in the field (on-the go). However, key important aspects on the use of near infrared spectroscopy to analyse soil samples containing high moisture levels still not well understood. A brief summary of the main issues related with applications of NIR spectroscopy to measure soil samples in the field are discussed in this article.

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
Keyword(s):  
Infrared Spectroscopy ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Near Infrared Spectroscopy ◽  
Paddy Soil ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Soil Samples ◽  
Least Squares Regression ◽  
Soc Estimation

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.

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