Determination of organic carbon and nitrogen in particulate organic matter and particle size fractions of Brookston clay loam soil using infrared spectroscopy
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.
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Available from: Nilusha Henakaarchchia
- "functional groups) is ambitious and important work. This has been done previously, both indirectly as in now (via high predictive power models) and directly by infrared of isolated aggregates (Calderón et al., 2011;Verchot et al., 2011;Jindalunag et al., 2013) and, the prediction of soil carbon and nitrogen fractions is established byYang et al. (2012). Also, much of the recent work focusing on assessing soil carbon using infrared spectroscopy has focused on measuring soil carbon relating to the conceptual pools describing soil carbon (Table 4) and the analysis of the spectra has is usually completed using a partial least squares approach. "
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ABSTRACT: There is a persistent general concern with carbon sequestration and modeling of soil carbon change affecting global issues, such as climate change and food security. To address these concerns requires the measurement of carbon everywhere and routinely, but the rate limiting step is the need to physically fraction the soil carbon to establish; where it is stored in soil, to model the formation of soil aggregates that physically protect soil carbon, and in-turn to populate soil carbon models. To remove the need for this fractionation pretreatment, commonly done by wet-sieving, this study scopes the notion of the efficacy of using near- (NIR) and mid- (MIR) infrared derived spectra taken of bulk soil samples to predict carbon in the separated aggregate fractions contained within. Forty five surface soil samples were collected from three bioregions of New South Wales providing for a range of soil types and associated soil carbon. The carbon content was measured of the bulk soil samples and their aggregate fractions of < 63 μm, 63–250 μm, and > 250 μm subsequently separated by wet-sieving. The bulk soil samples were scanned in the spectral ranges 800–2500 nm (NIR region) and 2500–25,000 nm (MIR region). The Cubist regression tree model was used to predict the carbon content in the aggregate fractions scanned from the bulk soil samples. The cross-validation results reveal that the MIR demonstrated the strongest correlation between measured and predicted carbon of the aggregate fractions demonstrated by high R2 (0.63–0.85) and ration of performance to inter-quintile distance (RPIQ, 0.53–0.93). The wavelengths selected in the Cubist model coincide with wavelengths identified as characterizing adsorption due to chemistry of soil carbon in some recently published works in this area of research.
Available from: Raphael A Viscarra Rossel
- "Several large databases that include measurements of soil OC content exist (Viscarra Rossel & Webster, 2012; Stevens et al., 2013), but few include measurements of the soil carbon fractions. Most published databases with measurements of the carbon fractions have been developed with mid-IR spectra (Reeves et al., 2006; Janik et al., 2007; Zimmermann et al., 2007; Bornemann et al., 2008; Yang et al., 2012; Baldock et al., 2013b), which are reported to produce good predictions. Coefficients of determination (R 2 ) values commonly reported are between 0.70 and 0.95. "
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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.
- "Soil structure is consisted of mineral particles (sand, silt and clay) and organic compounds forming aggregates of different sizes and stability (Stemmer et al., 1998). Recent studies demonstrated that turnover time of soil organic matter varied with different particle-size fractions, and turnover rates generally decreased as sand < silt < clay (Bol et al., 2009; Yang et al., 2012). Due to the linkage between turnover rates of organic matter and the particle-size fractions, these fractions were proposed to play a key role in C sequestration (Six et al., 2002). "
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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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