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, 2000-53 micro m; silt, 53-2 micro m; clay, <2 micro 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 ( R cal2=0.84-0.92; R val2=0.78-0.87). The predictions of organic C content in soil particle size fractions were also very good for the model calibration ( R cal2=0.84-0.94 for MIR and R cal2=0.86-0.92 for NIR) and model validation ( R val2=0.79-0.94 for MIR and R cal2=0.84-0.91 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 ( R cal2=0.73-0.88; R val2=0.67-0.85). However, the predictions for the N content and C:N ratio in the silt fraction were poor ( R cal2=0.23-0.55; R cal2=0.20-0.40). 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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ABSTRACT: Assessment and monitoring of soil organic matter (SOM) quality are important for determining and developing management practices that will enhance and maintain the productivity of agricultural soils. This requires routine analysis of multiple soil parameters, which can be time-consuming and expensive. Research has suggested that visible near infrared reflectance spectroscopy (VNIRS) may be used as a rapid and cost-efficient tool for SOM quality assessment. In this study, VNIRS (400–2498 nm)was used for the first time to simultaneously predict microbial biomass nitrogen (MBN),water-extractable organic N (WEON), light fraction organic matter N (LFOMN), particulate organic matter N (POMN), soil total N (TN), soil organic carbon (SOC) and soil C/N ratio as soil SOM quality indicators in Chernozemic soils of western Canada. The soil samples (n = 200) were collected at the 0–15 cm depth from a crop rotation experiment conducted at 6 sites in 2010 and 2011. After removal of outliers (five samples) identified by principal components analysis (PCA), 75% of the sample set was randomly selected for calibration (n=146) and the remainder used for validation (n=49).Modified partial least squares regression with cross-validation was used to develop prediction models. The reliability of the models was assessed using the coefficient of determination in validation (R2V) and the ratio of standard deviation of the reference data in the validation set to the standard error of prediction (RPDV). The VNIRS predictions were considered reliable for LFOMN, POMN, TN, and SOC (R2V N 0.80, RPDV N 2.4), aswell as forMBN (R2V= 0.74, RPDV= 1.93),but less reliable for WEON (R2V = 0.67, RPDV = 1.70) and soil C/N ratio (R2V = 0.54, RPDV = 1.45). This study showed that VNIRS has the potential as a non-destructive and cost-efficient tool for rapid determination of SOM quality indicators.Geoderma 01/2014; 232-234:449-458. · 2.35 Impact Factor
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ABSTRACT: This review addresses the applicability of visible (Vis), near-infrared (NIR), and mid-infrared (MIR) reflectance spectroscopy for the prediction of soil properties. We address (1) the properties that can be predicted and the accuracy of the predictions, (2) the most suitable spectral regions for specific soil properties, (3) the number of predictions reported for each property, and (4) in-field versus laboratory spectral techniques.We found the following properties to be successfully predicted: soil water content, texture, soil carbon (C), cation exchange capacity, calcium and magnesium (exchangeable), total nitrogen (N), pH, concentration of metals/metalloids, microbial size, and activity. Generally, MIR produced better predictions than Vis-NIR, but Vis-NIR outperformed MIR for a number of properties (e.g., biological). An advantage of Vis-NIR is instrument portability although a new range of MIR portable devices is becoming available. In-field predictions for clay, water, total organic C, extractable phosphorus, total C and N appear similar to laboratory methods, but there are issues regarding, for example, sample heterogeneity, moisture content, and surface roughness.The nature of the variable being predicted, the quality and consistency of the reference laboratory methods, and the adequate representation of unknowns by the calibration set must be considered when predicting soil properties using reflectance spectroscopy.Applied Spectroscopy Reviews 02/2014; 49(2):139-186. · 2.92 Impact Factor