Determination of organic carbon and nitrogen in particulate organic matter and particle size fractions of Brookston clay loam soil using infrared spectroscopy
ABSTRACT The objective of this study was to determine whether models developed from infrared spectroscopy could be used to estimate organic carbon (C) content, total nitrogen (N) content and the C:N ratio in the particulate organic matter (POM) and particle size fraction samples of Brookston clay loam. The POM model was developed with 165 samples, and the particle size fraction models were developed using 221 samples. Soil organic C and total N contents in the POM and particle size fractions (sand, 200053 mu m; silt, 532 mu m; clay, <2 mu m) were determined by using dry combustion techniques. The bulk soil samples were scanned from 4000 to 400 cm-1 for mid-infrared (MIR) spectra and from 8000 to 4000 cm-1 for near-infrared (NIR) spectra. Partial least squares regression (PLSR) analysis and the leave-one-out' cross-validation procedure were used for the model calibration and validation. Organic C and N content and C:N ratio in the POM were well predicted with both MIR- and NIR-PLSR models ( = 0.840.92; = 0.780.87). The predictions of organic C content in soil particle size fractions were also very good for the model calibration ( = 0.840.94 for MIR and = 0.860.92 for NIR) and model validation ( = 0.790.94 for MIR and = 0.840.91 for NIR). The prediction of MIR- and NIR-PLSR models for the N content and the C:N ratio in the sand and clay fractions was also satisfactory ( = 0.730.88; = 0.670.85). However, the predictions for the N content and C:N ratio in the silt fraction were poor ( = 0.230.55; = 0.200.40). The results indicate that both MIR and NIR methods can be used as alternative methods for estimating organic C and total N in the POM and particle size fractions of soil samples. However, the NIR model is better for estimating organic C and N in POM and sand fractions than the MIR model, whereas the MIR model is superior to the NIR model for estimating organic C in silt and clay fractions and N in clay fractions.
SourceAvailable from: Raphael A Viscarra Rossel[Show abstract] [Hide abstract]
ABSTRACT: The capture and storage of soil organic carbon (OC) should improve the soil's quality and function and help to offset the emissions of greenhouse gases. However, to measure, model or monitor changes in OC caused by changes in land use, land management or climate, we need cheaper and more practical methods to measure it and its composition. Conventional methods are complex and prohibitively expensive. Spectroscopy in the visible and near infrared (vis–NIR) is a practical and affordable alternative. We used samples from Australia's Soil Carbon Research Program (SCaRP) to create a vis–NIR database with accompanying data on soil OC and its composition, expressed as the particulate, humic and resistant organic carbon fractions, POC, HOC and ROC, respectively. Using this database, we derived vis–NIR transfer functions with a decision-tree algorithm to predict the total soil OC and carbon fractions, which we modelled in units that describe their concentrations and stocks (or densities). Predictions of both carbon concentrations and stocks were reliable and unbiased with imprecision being the main contributor to the models' errors. We could predict the stocks because of the correlation between OC and bulk density. Generally, the uncertainty in the estimates of the carbon concentrations was smaller than, but not significantly different to, that of the stocks. Approximately half of the discriminating wavelengths were in the visible region, and those in the near infrared could be attributed to functional groups that occur in each of the different fractions. Visible–NIR spectroscopy with decision-tree modelling can fairly accurately, and with small to moderate uncertainty, predict soil OC, POC, HOC and ROC. The consistency between the decision tree's use of wavelengths that characterize absorptions due to the chemistry of soil OC and the different fractions provides confidence that the approach is feasible. Measurement in the vis–NIR range needs little sample preparation and so is rapid, practical and cheap. A further advantage is that the technique can be used directly in the field.European Journal of Soil Science 03/2015; DOI:10.1111/ejss.12237 · 2.39 Impact Factor
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ABSTRACT: The distribution of extracellular enzyme activities in particle-size fractions of sediments was investigated in a subtropical mangrove ecosystem. Five enzymes involved in carbon (C), nitrogen (N), and phosphorus (P) cycling were analyzed in the sand, silt, and clay of sediments. Among these fractions, the highest activities of phenol oxidase (PHO), β-D glucosidase (GLU), and N-acetyl-glucosiminidase (NAG) were found in sand, and greater than bulk sediments of both intertidal zone (IZ) and mangrove forest (MG). This result implied that sand fractions might protect selective enzymes through the adsorption without affecting their activities. Additionally, the enzyme-based resource allocation in various particle-size fractions demonstrated that nutirents availability varied with different particle-size fractions and only sand fraction of MG with highest total C showed high N and P availability among fractions. Besides, the analysis between elemental contents and enzymes activities in particle-size fractions suggested that enzymes could monitor the changes of nutrients availability and be good indicators of ecosystem responses to environmental changes. Thus, these results provided a means to assess the availability of different nutrients (C, N, and P) during decomposition of sediment organic matter (SOM), and thus helping to better manage the subtropical mangrove ecosystems to sequester C into SOM.
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ABSTRACT: Abstract Total and human ingestion bioaccessible polycyclic aromatic hydrocarbon fractions of individual polycyclic aromatic hydrocarbons were determined (representative of a combination of the saliva, gastric and upper intestine compartments) on 26 soil samples from 3 gasworks sites and from a domestic garden. A Random Forest model using the Infra-red spectra of the soils and the PAH properties successfully predicted the bioaccessibility of PAHs in the soils. The Near Infra-red and Mid Infra-red diffuse reflection spectra of the soils were subjected to a mixture resolution algorithm. Comparison with spectra of known minerals tentatively identified carbonate, silica, clay and iron oxide components in the Mid Infra-red spectra. Multiple linear regression analysis suggested that three Mid Infra-red components were associated with the organic carbon. Principal Component Analysis of polycyclic aromatic hydrocarbon properties identified three components associated with the hydrophobicity, the aliphatic nature and the vapour phase partition coefficient of the polycyclic aromatic hydrocarbons.12/2014; 3:35-45. DOI:10.1016/j.eti.2014.11.001