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1.
Mid‐infrared spectroscopy (MIRS) is assumed to be superior to near‐infrared spectroscopy (NIRS) for the prediction of soil constituents, but its usefulness is still not sufficiently explored. The objective of this study was to evaluate the ability of MIRS to predict the chemical and biological properties of organic matter in soils and litter. Reflectance spectra of the mid‐infrared region including part of the near‐infrared region (7000–400 cm–1) were recorded for 56 soil and litter samples from agricultural and forest sites. Spectra were used to predict general and biological characteristics of the samples as well as the C composition which was measured by 13C CPMAS‐NMR spectroscopy. A partial least‐square method and cross‐validation were used to develop equations for the different constituents over selected spectra ranges after several mathematical treatments of the spectra. Mid‐infrared spectroscopy predicted well the C : N ratio: the modeling efficiency EF was 0.95, the regression coefficient (a) of a linear regression (measured against predicted values) was 1.0, and the correlation coefficient (r) was 0.98. Satisfactorily (EF ≥ 0.70, 0.8 ≤ a ≤ 1.2, r ≥ 0.80) assessed were the contents of C, N, and lignin, the production of dissolved organic carbon, and the contents of carbonyl C, aromatic C, O‐alkyl C, and alkyl C. However, the N mineralization rate, the microbial biomass and the alkyl–to–aromatic C ratio were predicted less satisfactorily (EF < 0.70). Limiting the sample set to mineral soils did generally not result in improved predictions. The good and satisfactory predictions reported above indicate a marked usefulness of MIRS in the assessment of chemical characteristics of soils and litter, but the accuracies of the MIRS predictions in the diffuse‐reflectance mode were generally not superior to those of NIRS.  相似文献   

2.
The presence of relatively inert organic materials such as char has to be considered in calibrations of soil C models or when calculating C‐turnover times in soils. Rapid and cheap spectroscopic techniques such as near‐infrared (NIRS) or mid‐infrared spectroscopy (MIRS) may be useful for the determination of the contents of char‐derived C in soils. To test the suitability of both spectroscopic techniques for this purpose, artificial mixtures of C‐free soil, char (lignite, anthracite, charcoal, or a mixture of the three coals) and forest‐floor Oa material were produced. The total C content of these mixtures (432 samples) ranged from 0.5% to 6% with a proportion of char‐derived C amounting to 0%, 20%, 40%, 50%, 60%, or 80%. All samples were scanned in the visible and near‐IR region (400–2500 nm). Cross‐validation equations for total C and N, C and N derived from char (Cchar, Nchar) and Oa material were developed using the whole spectrum (first and second derivative) and a modified partial least‐square regression method. Thirty‐six samples were additionally scanned in the middle‐IR and parts of the near‐IR region (7000–400 cm–1 which is 1430–25,000 nm) in the diffuse‐reflectance mode. All properties investigated were successfully predicted by NIRS as reflected by RSC values (ratio of standard deviation of the laboratory results to standard error of cross‐validation) > 4.3 and modeling efficiencies (EF) ≥ 0.98. Near‐infrared spectroscopy was also able to differentiate between the different coals. This was probably due to structural differences as suggested by wavelength assignment. Mid‐IR spectroscopy in the diffuse‐reflectance mode was also capable to successfully predict the parameters investigated. The EF values were > 0.9 for all constituents. Our results indicated that both spectroscopic techniques applied, NIRS and MIRS, are able to predict C and N derived from different sources in soil, if closed populations are considered.  相似文献   

3.
Plant‐litter chemical quality is an important driver of many ecosystem processes, however, what actually constitutes high‐ or low‐quality litter (chemical potential for fast and slow decomposition, respectively) is often interpreted by the indices available. Here, near‐infrared spectroscopy (NIRS) was used to explore leaf‐litter chemical quality and the controls on decomposition in the tropical rainforest region of north Queensland Australia. Leaf‐litter samples from litterfall collections and litterbag studies were used. NIRS was used to calibrate the chemical compositions of the material (N, P, C, Mg, Ca, acid detergent fiber, acid detergent lignin, α‐cellulose, and total phenolics) from a smaller sample set covering the spectral range in the full set of samples. Calibrations were compared for both separate (local) and combined models, for litterbags, and litterfall. Coefficients of determination (r2) in the local models ranged from 0.88 (litterbag Mg) to 0.99 (litterfall N), with residual prediction deviation ratios > 3 for all constituents except Mg (≈ 2.5). Mass loss in the litterbags was strongly related to the NIR spectra, with model r2's of 0.75 (in situ leaves) and 0.76 (common control leaf). In situ decomposability was determined from modeling the initial NIR spectra prior to decomposition with litterbag exponential‐decay rates (model r2 of 0.81, n = 85 initial samples). A best subset model including litter‐quality, climate, and soil variables predicted decay better than the NIR decomposability model (r2 = 0.87). For litter quality alone the NIR model predicted decay rate better than all of the best predictive litter–chemical quality indices. The decomposability model was used to predict in situ decomposability in the litterfall samples. The chemical variables explaining NIR decomposability for litterfall were initial P, C, and phenolics (linear model r2 = 0.80, n = 2471). NIRS is a holistic technique that is just as, if not more accurate, than litter–chemical quality indices, when predicting decomposition and decomposability, shown here in a regional field study.  相似文献   

4.
Abstract

The objective of this study was to compare mid‐infrared (MIR) an near‐infrared (NIR) spectroscopy (MIRS and NIRS, respectively) not only to measure soil carbon content, but also to measure key soil organic C (SOC) fractions and the δ13C in a highly diverse set of soils while also assessing the feasibility of establishing regional diffuse reflectance calibrations for these fractions. Two hundred and thirty‐seven soil samples were collected from 14 sites in 10 western states (CO, IA, MN, MO, MT, ND, NE, NM, OK, TX). Two subsets of these were examined for a variety of C measures by conventional assays and NIRS and MIRS. Biomass C and N, soil inorganic C (SIC), SOC, total C, identifiable plant material (IPM) (20× magnifying glass), the ratio of SOC to the silt+clay content, and total N were available for 185 samples. Mineral‐associated C fraction, δ13C of the mineral associated C, δ13C of SOC, percentage C in the mineral‐associated C fraction, particulate organic matter, and percentage C in the particulate organic matter were available for 114 samples. NIR spectra (64 co‐added scans) from 400 to 2498 nm (10‐nm resolution with data collected every 2 nm) were obtained using a rotating sample cup and an NIRSystems model 6500 scanning monochromator. MIR diffuse reflectance spectra from 4000 to 400 cm?1 (2500 to 25,000 nm) were obtained on non‐KBr diluted samples using a custom‐made sample transport and a Digilab FTS‐60 Fourier transform spectrometer (4‐cm?1 resolution with 64 co‐added scans). Partial least squares regression was used with a one‐out cross validation to develop calibrations for the various analytes using NIR and MIR spectra. Results demonstrated that accurate calibrations for a wide variety of soil C measures, including measures of δ13C, are feasible using MIR spectra. Similar efforts using NIR spectra indicated that although NIR spectrometers may be capable of scanning larger amounts of samples, the results are generally not as good as achieved using MIR spectra.  相似文献   

5.
Several algorithms exist for the calibration procedures of near‐infrared spectra in soil‐scientific studies, but the potential of a genetic algorithm (GA) for spectral feature selection and interpretation has not yet been sufficiently explored. Objectives were (1) to test the usefulness of near‐infrared spectroscopy (NIRS) for a prediction of C and N from char and forest‐floor Oa material in soils using either a partial least squares (PLS) method or a GA‐PLS approach and (2) to discuss the mechanisms of GA feature selection for the examined constituents. Calibration and validation were carried out for measured reflectance spectra in the visible and near‐IR region (400–2500 nm) on an existing set of 432 artificial mixtures of C‐free soil, char (lignite, anthracite, charcoal, or a mixture of the three coals), and forest‐floor Oa material. For all constituents (total C and N, C and N from all coals and from the Oa material, C derived from mixed coal, charcoal, lignite, and anthracite), the GA‐PLS approach was superior over the full‐spectrum PLS method. The RPD values (ratio of standard deviation of the laboratory results to standard error of prediction) ranged from 2.4 to 5.1 in the validation and indicated a better category of prediction for three constituents: “approximate quantitative” instead of a “distinction between high and low” for C derived from mixed coal and “good” instead of “approximate quantitative” for C and N derived from all coals. Overall, this study indicates that the approach using GA may have a greater potential than the PLS method in NIRS.  相似文献   

6.
Abstract

The use of ultraviolet (UV), visible (VIS), near infrared reflectance (NIR), and midinfrared (MIR) spectroscopy techniques have been found to be successful in determining the concentration of several chemical properties in soils. The aim of this study was to evaluate the effect of two reference methods, namely Bray and Resins, on the VIS and NIR calibrations to predict phosphorus in soil samples. Two hundred (n=200) soil samples were taken in different years from different locations across Uruguay with different physical and chemical characteristics due to different soil types and management. Soil samples were analyzed by two reference methods (Bray and Resins) and scanned using an NIR spectrophotometer (NIRSystems 6500). Partial least square (PLS) calibration models between reference data and NIR data were developed using cross‐validation. The coefficient of determination in calibration (R2) and the root mean square of the cross validation (RMSECV) were 0.58 (RMSECV: 3.78 mg kg?1) and 0.61 (RMSECV: 2.01 mg kg?1) for phosphorus (P) analyzed by Bray and Resins methods, respectively, using the VIS and NIR regions. The R2 and RMSECV for P using the NIR region were 0.50 (RMSECV: 3.78 mg kg?1) and 0.58 (RMSECV: 2.01 mg kg?1). This study suggested that differences in accuracy and prediction depend on the method of reference used to develop an NIR calibration for the measurement of P in soil.  相似文献   

7.
The chemical composition of organic layers of forest soils shows a high spatial variability and fast methods may be required for its study at a landscape level. The objective was to assess the applicability of near infrared spectroscopy (NIRS) to measure several chemical and biological properties of organic layers in spruce, beech, and mixed spruce‐beech stands. Spectra in the VIS‐NIR region (400—2500 nm) were recorded for 406 samples representing Oi, Oe, and Oa layers of forest soils from Solling (Germany), 195 of them were used for calibration and 211 for validation. The calibration equations for each constituent were developed using the whole spectrum (0th to 3rd derivative). Humus samples were analyzed for contents of C and N and contents of P, S, Na, K, Ca, Mg, Mn, Fe, and Al after pressure digestion in HNO3. Additionally, basal respiration and microbial C (Cmic) were measured. NIRS predicted well the contents of C, N, P, S, Ca, Na, K, Fe, and Al and C/N and C/P ratios: the regression coefficients (a) of a linear regression (measured against predicted values) ranged from 0.9 to 1.1, and the correlation coefficients (r) were greater or equal 0.9. Cmic (a = 0.87, r = 0.83) was predicted satisfactorily, whereas the prediction of the basal respiration (a = 0.74, r = 0.87) was less satisfactory. Due to liming of some of the plots NIRS failed to predict contents of Mg (a = 1.27, r = 0.68). For all chemical and biological characteristics the best prediction performances were achieved using the whole sample population. Splitting the samples into smaller groups according to a dominant tree species or an organic layer did not improve the predictions.<?show $6#>  相似文献   

8.
Fourty‐one soil samples from the “Eternal Rye” long‐term experiment in Halle, Germany, were used to test the usefulness of near‐infrared spectroscopy (NIRS) to differentiate between C derived from C3 and C4 plants by using the isotopic signature (δ13C) and to predict the pools considered in the Rothamsted Carbon (RothC) model, i.e., decomposable plant material, resistant plant material, microbial biomass, humified organic matter, and inert organic matter. All samples were scanned in the visible‐light and near‐infrared region (400–2500 nm). Cross‐validation equations were developed using the whole spectrum (first to third derivative) and a modified partial least‐square regression method. δ13C values and all pools of the RothC model were successfully predicted by NIRS as reflected by RSC values (ratio between standard deviation of the laboratory results and standard error of cross‐validation) ranging from 3.2 to 3.4. Correlations analysis indicated that organic C can be excluded as basis for the successful predictions by NIRS in most cases, i.e., 11 out of 16.  相似文献   

9.
Abstract

Near‐infrared reflectance spectroscopy (NIRS) has potential to provide rapid estimates of phosphorus (P) and nitrogen (N) concentrations in broiler litter to assist managers in establishing application rates of litter to grazing lands that fall within productive and environmentally safe levels. An experiment was conducted to determine accuracy of NIRS estimates of moisture, P, N, and acid detergent fiber (ADF) concentrations in broiler litter. Broiler litter samples were collected from various farms to develop sample sets that were either with or without bedding material, and each sample set was subdivided into processed (i.e., dried and ground) and unprocessed samples to develop local equations for each constituent. Equations were developed by using all samples from each set and using samples following random removal of 20% of total for equation validation. Moisture was determined to be accurately measured by using NIRS based on a high R2 (≥0.96), low SEC (<10 g kg?1), and high sx/SECV (>5.0). ADF also had a high R2 (0.96), but the Sx/SEC (3.00) value was too low for the equation to be considered truly accurate. Estimations of P and N by calibrations that included all samples had a moderate to high R2 values, but estimations for the validation set were relatively low in R2 (≤0.78) and Sx/SEC (≤2.00). Concentrations of P and N were not estimated by NIRS with a high degree of accuracy, but other methodologies could enhance the usefulness of this technology to rapidly provide these nutrient measures.  相似文献   

10.
The prediction accuracy of visible and near‐infrared (Vis‐NIR) spectroscopy for soil chemical and biological parameters has been variable and the reasons for this are not completely understood. Objectives were (1) to explore the predictability of a series of chemical and biological properties for three different soil populations and—based on these heterogeneous data sets—(2) to analyze possible predictive mechanisms statistically. A number of 422 samples from three arable soils in Germany (a sandy Haplic Cambisol and two silty Haplic Luvisols) of different long‐term experiments were sampled, their chemical and biological properties determined and their reflectance spectra in the Vis‐NIR region recorded after shock‐freezing followed by freeze‐drying. Cross‐validation was carried out for the entire population as well as for each population from the respective sites. For the entire population, excellent prediction accuracies were found for the contents of soil organic C (SOC) and total P. The contents of total N and microbial biomass C and pH were predicted with good accuracy. However, prediction accuracy for the other properties was less: content of total S was predicted approximately quantitatively, whereas Vis‐NIR spectroscopy could only differentiate between high and low values for the contents of microbial N, ergosterol, and the ratio of ergosterol to microbial biomass C. Contents of microbial biomass P and S, basal respiration, and qCO2 could not be predicted. Prediction accuracies were greatest for the entire population and the Luvisol at Garte, followed by the Luvisol at Hohes Feld, whereas the accuracy for the sandy Cambisol was poor. The poor accuracy for the sandy Cambisol may have been due to only smaller correlations between the measured properties and the SOC content compared to the Luvisols or due to a general poor prediction performance for sandy soils. Another reason for the poor accuracy may have been the smaller range of contents in the sandy soil. Overall, the data indicated that the accuracy of predictions of soil properties depends largely on the population investigated. For the entire population, the usefulness of Vis‐NIR for the number of chemical and biological soil properties was evident by markedly greater correlation coefficients (measured against Vis‐NIR predicted) compared to the Pearson correlation coefficients of the measured properties against the SOC content. However, the cross‐validation results are valid only for the closed population used in this study.  相似文献   

11.
This study tests the potential of near infrared reflectance spectroscopy (NIRS) for predicting soil fertility and management history from topsoil (0–10 cm deep) spectra. Soil fertility was assessed by measuring the growth of a test plant, and soil management history was determined through inquiries with farmers. Moreover, NIRS predictive value was compared with that of a group of topsoil parameters: total carbon and nitrogen, nitrate, potential respiration and denitrification, and microbial biomass. Modelling used partial and modified partial least square regressions to ensure comparisons between predictions by NIRS versus by soil parameters. Soil fertility and management history were well predicted by NIRS (Q2 = 0.78 and R2 = 0.89 both; Q2 and R2 are cross-validation and calibration coefficients of determination, respectively), as were the soil parameters (Q2 = 0.79–0.92 and R2 = 0.86–0.98). Soil fertility and management history were more accurately predicted by NIRS than by the set of soil parameters.  相似文献   

12.
Advances in laboratory instrumentation and chemometrics provide alternatives to traditional methods of conducting soil chemical analysis. One of these is infrared diffuse reflectance spectroscopy in the near-infrared spectral range (NIRS). Herein we report the results of a multinational study to develop useful calibrations associating NIRS spectra with laboratory-measured results for total soil carbon (C), total soil nitrogen (N), δ13C, and δ15N from a single soil site in Mexico subjected to zero- and conventional-tillage regimens with and without crop residues and crop rotations of maize and wheat across 16 years. Modified partial least squares regression (MPLS) was used to obtain useful NIR predictions for total soil C and N, with ratio performance deviation (RPD) values of 6.8 and 2.6, respectively. Corresponding multiple correlation coefficients (RSQs) for C and N were 0.98 and 0.85, with standard errors of prediction (SEPs) of ±0.45 g C kg–1 and ±0.09g Nkg–1, respectively. The generation of δ15N and δ13C models produced different NIR recordings in soils with and without crop residues. Application of discriminant partial least squares (DPLS) statistics to the NIR spectral data allowed us to discriminate soils with and without residues. The prediction confidence for stable isotopes was 90% (internal validation) and 94% (external validation). Modified partial least squares regression was used to estimate δ15N and δ13C. Ratio performance deviation, RSQ, and SEP values obtained for δ13C and δ15N were 2.44 and 3.57, 0.83 and 0.81, ±0.5‰ (parts per thousand) and ±0.45‰ in soils with residues and 2.5 and 3.8, 0.93 and 0.92, and ±0.2‰ and ±0.23‰ in soils without residues, respectively. Overall, results obtained with NIRS were comparable to those obtained using conventional analytical methods, a finding that has wide relevance to agricultural soils and environmental studies in tropical locations. However, further testing is necessary to confirm that the calibration models are neither site nor instrument specific.  相似文献   

13.
Abstract

The feasibility of using near‐infrared reflectance spectroscopy (NIRS) was investigated for the analysis of pH, electrical conductivity (EC), phosphorus (P), sulfur (S), calcium (Ca), magnesium (Mg), sodium (Na), potassium (K), iron (Fe), and manganese (Mn) in 28 Canadian soil samples from three boreholes down to 10 m in depth. Field moist soil samples were scanned for pH and EC, and air‐dry samples were scanned for the analysis of the elements. Calibrations were developed between the near‐infrared spectral data and results obtained by conventional analyses. The NIR‐predicted values were highly correlated to the measured values obtained by the conventional methods (r2>0.9) for P, Ca, Mg, K, Fe, and Mn, and almost as highly correlated (r2>0.8) for S and Na Results for pH were somewhat less successful (r2>0.6), and appeared to be useful only for screening purposes, whereas EC was not successfully predicted by NIRS in this study. It appeared that NIRS could be a useful method for the rapid, non‐destructive, simultaneous analysis of elemental concentrations in dry soils, useful in routine analysis.  相似文献   

14.
The objective of this work was to investigate the usefulness of near infrared reflectance spectroscopy (NIRS) in determining some C and N fractions of soils: labile compounds, microbial biomass, compounds derived from added 13C- and 15N-labelled straw. Soil samples were obtained from a previous experiment where soils were labelled by addition of 13C- and 15N-labelled wheat straw and incubated in coniferous forests in northern Sweden (64-60°N) and south France (43°N). The incubation lasted three years with 7-9 samplings at regular time steps and four replicates at each sampling (204 samples). Samples were scanned using a near infrared reflectance spectrophotometer (NIRSystem 6500). Calibrations were obtained by using a modified partial least squares regression technique with reference data on total C and N, 13C, 15N, control extract-C, -N, -13C and -15N, fumigated extract-C, -N, -13C and -15N, biomass-C, -N, -13C and -15N contents. Mathematical treatments of the absorbance data were first or second derivative with a gap from 4 to 10 nm. The standard error of calibration (SEC)-to-standard deviation of the reference measurements ratio was ≤0.2 for 10 models, namely total C and N, 13C, 15N, control extract-C, fumigated extract-C and -N, biomass-C and -N and biomass-15N models and therefore considered as very good. With an R2=0.955, the fumigated extract-15N model is also good. The standard error of performance calculated on the independent set of data and SEC were within 20% of each other for all the best equations except for the biomass-15N model. The ability of NIRS to detect 13C and 15N in total C and N and in the extracts is noteworthy, not because of its predictive function that is not really of interest in this case, but because it indicates that the spectra kept the signature of the properties of the organic matter derived from the straw even after two- or three-year decomposition. The incorporation of the 13C in the biomass was less well predicted than that of the 15N. This could indicate that the biomass derived from the straw was characterised by a particular protein or amino acid composition compared to the total biomass that includes a large proportion of dormant micro-organisms. The predictive ability of NIRS for microbial biomass-C and -N is particularly interesting because the conventional analyses are time consuming. In addition, NIRS allows detecting analytical errors.  相似文献   

15.
近红外光谱法在土壤有机质研究中的应用   总被引：4，自引：2，他引：2  
近红外光谱技术(Near Infrared Reflectance Spectroscopy,NIRS)具有快速、低成本、无损等优点。目前利用NIRS获取土壤信息已成为国内外学者研究的重点,但是在我国利用NIRS对土壤成分进行定量分析才刚刚起步。本文简要介绍了近红外光谱分析的基本原理、模型的建立及评价,详细论述了该技术在预测土壤有机质及其组分方面的应用,并对NIRS在我国土壤有机质定量研究方面的应用前景进行了展望。  相似文献   

16.
Prediction of carbon (C) and nitrogen (N) mineralization patterns of plant litter is desirable for both agronomic and environmental reasons. Near infrared reflectance (NIR) spectroscopy has recently been introduced in decomposition studies to characterize biochemical composition. The purpose of the current study was to use empirical techniques to predict C and N mineralization patterns of a wide range of plant materials incubated under controlled temperature and moisture conditions. We hypothesized that the richness of information in the NIR spectra would considerably improve predictions compared to traditional stepwise chemical digestion (SCD) or C/N ratios. Initially, we fitted a number of empirical functions to the observed C and N mineralization patterns. The best functions fitted with R2=0.990 and 0.949 to C and N, respectively. The fractions of C and N mineralized at different points in time were then either predicted directly with regression functions or indirectly by prediction of the parameters of the empirical functions fitted to incubation data. In both cases, partial least squares (PLS) regressions were used and predictions were validated by cross-validations. We found that the NIR spectra (best R2=0.925) were able to predict C mineralization patterns marginally better than the SCD fractions (best R2=0.911), but considerably better than the C/N ratios (best R2=0.851). In contrast, N mineralization was better predicted by SCD fractions (best R2=0.533) than the C/N ratio (best R2=0.497), which was better than NIR predictions (best R2=0.446). Although the predictions with the NIR spectra were only slightly better for C and worse for N mineralization compared to SCD fractions, NIR spectroscopy still holds advantages, as it is a much less laborious and cheaper analytical method. Furthermore, exploration of the applications of NIR spectroscopy in decomposition studies has only just begun, and offers new ways to gain insights into the decomposition process.  相似文献   

17.
Forest soils have large contents of carbon (C) and total nitrogen (TN), which have significant spatial variability laterally across landscapes and vertically with depth due to decomposition, erosion and leaching. Therefore, the ratio of C to TN contents (C:N), a crucial indicator of soil quality and health, is also different depending on soil horizon. These attributes can cost-effectively and rapidly be estimated using visible–near infrared–shortwave infrared (VNIR–SWIR) spectroscopy. Nevertheless, the effect of different soil layers, particularly over large scales of highly heterogeneous forest soils, on the performance of the technique has rarely been attempted. This study evaluated the potential of VNIR–SWIR spectroscopy in quantification and variability analysis of C:N in soils from different organic and mineral layers of forested sites of the Czech Republic. At each site, we collected samples from the litter (L), fragmented (F) and humus (H) organic layers, and from the A1 (depth of 2–10 cm) and A2 (depth of 10–40 cm) mineral layers providing a total of 2505 samples. Support vector machine regression (SVMR) was used to train the prediction models of the selected attributes at each individual soil layer and the merged layer (profile). We further produced the spatial distribution maps of C:N as the target attribute at each soil layer. Results showed that the prediction accuracy based on the profile spectral data was adequate for all attributes. Moreover, F was the most accurately predicted layer, regardless of the soil attribute. C:N models and maps in the organic layers performed well although in mineral layers, models were poor and maps were reliable only in areas with low and moderate C:N. On the other hand, the study indicated that reflectance spectra could efficiently predict and map organic layers of the forested sites. Although, in mineral layers, high values of C:N (≥ 50) were not detectable in the map created based on the reflectance spectra. In general, the study suggests that VNIR–SWIR spectroscopy has the feasibility of modelling and mapping C:N in soil organic horizons based on national spectral data in the forests of the Czech Republic.  相似文献   

18.
The present study aims to evaluate the potential of near-infrared reflectance (NIR) spectroscopy to determine the carbon and nitrogen content in soils and also to assess the effectiveness of NIR spectroscopy to predict carbon and nitrogen content in freshly collected soil samples. Soil samples (n = 179) were collected from different locations in India. Soil carbon and nitrogen contents were successfully predicted (R2 = 0.90 for carbon and R2 = 0.85 for nitrogen) by NIR spectroscopy. The root mean square error (RMSE) and ratio performance deviation (RPD) for the validation of predicted equations for carbon and nitrogen were 0.83 and 2.83 and 0.01 and 6.98, respectively. The efficacy of NIR spectroscopy on the prediction of carbon and nitrogen content in Indian soils is highly reliable. Water content in soil samples could affect the NIR absorbance spectra and in turn affect the quantification of carbon and nitrogen.  相似文献   

19.
Due to high nitrogen deposition in central Europe, the C : N ratio of litter and the forest floor has narrowed in the past. This may cause changes in the chemical composition of the soil organic matter. Here we investigate the composition of organic matter in Oh and A horizons of 15 Norway spruce soils with a wide range of C : N ratios. Samples are analyzed with solid‐state 13C nuclear magnetic resonance (NMR) spectroscopy, along with chemolytic analyses of lignin, polysaccharides, and amino acid‐N. The data are investigated for functional relationships between C, N contents and C : N ratios by structural analysis. With increasing N content, the concentration of lignin decreases in the Oh horizons, but increases in the A horizons. A negative effect of N on lignin degradation is observed in the mineral soil, but not in the humus layer. In the A horizons non‐phenolic aromatic C compounds accumulate, especially at low N values. At high N levels, N is preferentially incorporated into the amino acid fraction and only to a smaller extent into the non‐hydrolyzable N fraction. High total N concentrations are associated with a higher relative contribution of organic matter of microbial origin.  相似文献   

20.
In the clay‐illuvial horizons (Bt) of Luvisols, surfaces of biopores and aggregates can be enriched in clay and organic matter (OM), relative to the bulk of the soil matrix. The OM composition of these coatings determines their bio‐physico‐chemical properties and is relevant for transport and transformation processes but is largely unknown at the molecular scale. The objective of this study was to improve the interpretation of spectra from Fourier transform infrared spectroscopy in diffuse reflectance mode (DRIFT) by using thermograms and released ion intensities obtained with pyrolysis‐field ionization mass spectrometry (Py‐FIMS) for a more detailed analysis of the mm‐scale spatial distribution of OM components at intact structural surfaces. Samples were separated from earthworm burrow walls, crack coatings, uncoated cracks, root channels, and pinhole fillings of the Bt‐horizons of Luvisols. The information from Py‐FI mass spectra enabled the assignment of OM functional groups also from spectral regions of overlapping DRIFT signal intensities to specific OM compound classes. In particular, bands from C=O and C=C bonds in the infrared range of wave numbers between 1,641 and 1,605 cm?1 were related to heterocyclic N‐compounds, benzonitrile, and naphthalene. The OM at earthworm burrow walls was composed of chemically labile aliphatic C‐rich and rather stable lignin and alkylaromatic compounds whereas the OM of thick crack coatings and pinholes was dominated by heterocyclic N and nitriles and high‐molecular compounds, likely originating from combustion residues. In combination with Py‐FIMS, DRIFT applications to intact samples seem promising for generating a more detailed mm‐scale spatial distribution of OM‐related sorption and wettability properties of crack and biopore surfaces that may serve as preferential flow paths in structured soils.  相似文献   

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