Influence of Soil Particle Size on the Measurement of Sodium by Near-Infrared Reflectance Spectroscopy

This study evaluates the effect of soil particle size (SPS) on the measurement of exchangeable sodium (Na) (EXC-Na) by near-infrared reflectance (NIR) spectroscopy. Three hundred thirty-two (n = 332) top soil samples (0–10 cm) were taken from different locations across Uruguay, analyzed by EXC-Na using emission spectrometry, and scanned in reflectance using a NIR spectrophotometer (1100–2500 nm). Partial least squares (PLS) and principal component regression (PCR) models between reference chemical data and NIR data were developed using cross validation (leaving one out). The coefficient of determination in calibration (R²) and the root mean square of the standard error of cross validation (RMSECV) for EXC-Na concentration were 0.44 (RMSECV: 0.12 mg kg^–1) for soil with small particle size (SPS-0.053) and 0.77 (RMSECV: 0.09 mg kg^–1) for soils with particle sizes greater than 0.212 mm (SPS-0.212), using the NIR region after second derivative as mathematical transformation. The R² and RMSECV for EXC-Na concentration using PCR were 0.54 (RMSECV: 0.07 mg kg^–1) and 0.80 (RMSECV: 0.03 mg kg^–1) for SPS-0.053 and SPS-0.212 samples, respectively.     

Near-Infrared-Spectroscopy for Determination of Carbon and Nitrogen in Indian Soils

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.     

Near-Infrared Spectroscopy Coupled with Chemometrics Tools: A Rapid and Non-Destructive Alternative on Soil Evaluation

Near-infrared (NIR) spectroscopy is a rapid, non-destructive and accurate technique for analyzing a wide variety of samples, thus, the growing interest of using this technique in soil science. The objective of this study was to evaluate the potential of NIR spectroscopy to predict organic carbon (OC), total nitrogen (TN), available phosphorus (P) and available potassium (K) in the soil. NIR spectra from 20 cm³ of soil samples were acquired on the range of 750 to 2500 nm in diffuse reflectance mode, resolution of 16 cm^?1 and 64 scans. Eight models of calibration/validation were constructed. Calibration and validation models showed that the predictive potential of NIR varied with the specific soil property (OC, TN, P and K) under evaluation and according to the methodology employed in the model construction (cross-validation or test set). Good prediction models were obtained for OC and TN content based on the statistical parameters. Test set methodology was able to predict soil OC, TN, P, and K better than cross-validation methodology.     

Near‐infrared spectroscopy for rapid estimation of microbial properties in reclaimed mine soils

Several chemical and microbial properties of mine soils need to be measured for comprehensive assessment of the reclamation success. The objective of this study was to evaluate the ability of NIR spectroscopy to predict organic C (C_org), total N (N_t), and several microbial properties of mine soils reclaimed for forestry. Soils samples (n = 154) were collected at two reclaimed areas in central and S Poland, and their spectra in the NIR region (including the visible range, 400–2500 nm) were recorded. A half of the samples was used to develop calibration equations, and another half was used for validation. The modified partial least squares regression was applied to build calibration equations using the whole spectrum (0 to 2nd derivative). The best predictions were obtained for C_org and N_t (ratio of standard deviation to standard error of prediction in the validation stage [RPD] = 3.4 and 4.1; the regressions coefficients [a] of linear regression [measured against predicted values] = 0.94 and 0.96; correlation coefficients [r] = 0.96 and 0.97, respectively). Very well predictive models applicable for quantitative measurements were obtained also for microbial biomass, basal respiration, and the activities of dehydrogenase and acid phosphatase (RPD = 2.3–2.5, a = 0.90–0.99, r = 0.90–0.92). Prediction of urease activity was slightly worse (RPD = 2.1, a = 0.88, r = 0.87) but sufficient for rough estimation. The obtained results indicated the ability of NIR spectroscopy to predict complex soil microbial properties. Therefore, application of this analytical method may improve the assessment of recovery of microbial functions in reclaimed post‐mining barrens.     

Near‐infrared spectroscopy can predict the composition of organic matter in soil and litter

The usefulness and limitations of near‐infrared reflectance spectroscopy (NIRS) for the assessment of several soil characteristics are still not sufficiently explored. The objective of this study was to evaluate the ability of visible and near‐infrared reflectance (VIS‐NIR) spectroscopy to predict the composition of organic matter in soils and litter. Reflectance spectra of the VIS‐NIR region (400–2500 nm) 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 modified partial least‐square method and cross‐validation were used to develop equations for the different constituents over the whole spectrum (1st to 3rd derivation). Near‐infrared spectroscopy predicted well the C : N ratios, the percentages of O‐alkyl C and alkyl C, the ratio of alkyl C to O‐alkyl C, and the sum of phenolic oxidation products: the ratios of standard deviation of the laboratory results to standard error of cross‐validation (RSC) were greater than 2, 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 than 0.9. Satisfactorily (0.8 ≤ a ≤ 1.2, r ≥ 0.8, and 1.4 ≤ RSC ≤ 2.0) assessed were the contents of C, N, and production of DOC, the percentages of carbonyl C and aromatic C and the ratio of alkyl C to aromatic C. However, the N‐mineralization rate and the microbial biomass were predicted unsatisfactorily (RSC < 1.4). The good and satisfactory predictions reported above indicate a marked usefulness of NIRS in the assessment of biological and chemical characteristics of soils and litter.     

Soil reflectance as a generic tool for assessing infiltration rate induced by structural crust for heterogeneous soils

Raindrop energy disintegrates soil aggregates and rearranges soil particles to form a structural crust on the upper soil layer. The structural crust affects the physical properties of the soil, which can be observed by significant colour changes on the soil surface. Spectral differences observed in the structural crust are caused by rearrangement of the soil surface texture, mainly an increase in the clay fraction. Previous studies conducted on crusted soils using reflectance spectroscopy were limited to a certain soil type or area and seemed to be strongly dependent on the small range of soil types. In the current study, the influence of raindrop energy on the NIR‐SWIR spectral reflectance (1200–2400 nm) of heterogeneous soils was evaluated and used in combination with partial least squares (PLS) regression to construct a model that correlates the infiltration rate (IR) with its reflectance. Four soils from Israel and three soils from the USA were studied to provide a single data set. A relatively small root mean square error of cross‐validation (RMSECV) of 15.2% was found. A ratio of prediction to deviation (RPD) value of 1.98 indicates a promising generic model. Additionally, PLS models were run on different combinations of soil types (RPD values ranging between 2.4 and 3.2). For all models, whether all soils were run in one cross‐validation data set, or run for different combinations of soils, the best assessment of IR was achieved when using reduced wavelength range (selected wavelengths based on Martens’ significance test selection). These results allowed us to conclude that a generic approach aimed at assessing the structural crust for a variety of soils is feasible. A generic model using the suggested spectral approach has the potential to provide NIR‐SWIR spectral soil IR predictions with either a local or global data base of soils worldwide and may contribute to improved protection of crusted soils from erosion or water loss by runoff.     

Soil carbon mapping using on-the-go near infrared spectroscopy, topography and aerial photographs

Efficient tools for accurate soil carbon (SC) mapping are imperative for large scale assessment of total SC stocks and their changes in time as well as for site-specific tailoring of agricultural management practices. On-the-go near infrared (NIR) reflectance spectroscopy has been used recently in aid to the conventional, laborious and expensive soil analyses, since NIR measurements are taken in-situ quickly and non-destructively. However, NIR spectrum data need to be effectively calibrated with conventionally measured SC. Our objectives are to compare calibration approaches, including pre-processing transformations (Savitzky-Golay derivatives, standard normal variate and mean centering) and multivariate statistical methods (principal component regression, partial least squares, partial least squares leaving one-outlier-out) for using NIR spectra data as SC predictor, to evaluate NIR reliability in predicting SC under low carbon contents typical for Midwest Alfisols; and finally to compare predictions of SC by using three sources of auxiliary information (NIR spectral data, visible-NIR reflectance obtained from aerial photographs and topographical features). No improvements in calibration accuracy were observed when using pre-processing transformations. Partial least squares (RMSE = 1.90) tended to perform better than principal component regression (RMSE = 1.96); especially when spectral-NIR outliers are dropped (RMSE = 1.68). Our results suggested that visible-NIR data from aerial photographs used along with topographical attributes outperformed on-the-go spectral NIR data. Topographical data improved prediction in the models with aerial photograph visible-NIR data; however no improvement was noticed when used with spectral-NIR data. Though, NIR spectral data is frequently used as a proxy for SC prediction, we notice that this auxiliary information is not well suited under all scenarios. Particularly, when SC levels are low and the range of SC data is narrow, as in this study, NIR was only moderately successful in predicting SC.     

农田土壤铅有效态分析法(AB-DTPA)的优化与验证

中国土壤类型丰富多样，不同区域土壤重金属赋存形态各异，难以实现对不同区域不同类型土壤重金属有效性的比较与评价。为筛选建立适宜区域农田土壤重金属的有效性评价的方法，该研究选择滇东地区5种性质差异较大的典型农地土壤（黄棕壤、黄壤、红壤、石灰土和紫色土）运用正交试验法将AB-DTPA法的提取条件（固液比、提取剂pH值和振荡时间）进行正交组合，建立土壤-作物Pb迁移模型与总量法、CaCl₂-DTPA法和AB-DTPA法进行拟合优度对比，并以白菜和菠菜为指示作物开展盆栽试验，探讨不同分析方法与作物Pb累积能力的相关性，进而综合评价不同方法的适宜性。结果表明，1）固液比（g/mL）1:3、提取剂pH值7.6以及振荡时间120 min对Pb有效态的提取量有较大的影响；2）与其他方法相比，优化AB-DTPA法（固液比1:3、pH值7.6和振荡时间120 min）能更好地预测作物对Pb的吸收能力；3）盆栽试验表明，优化AB-DTPA法提取的土壤 Pb有效态含量与白菜和菠菜吸收Pb含量具有更好的相关性（R²=0.898, R²=0.752），Pb的加标回收率范围为99.5%～113.0%，准确度高。4）将土壤pH值加入预测模型表明，土壤pH值对土壤Pb与在作物可食部分中相关性影响很小，表明优化AB-DTPA法很稳定。因此，优化AB-DTPA法适用于滇东地区农田土壤Pb有效态的提取，该法具有可行性、广普性和准确性。研究结果为区域农田土壤修复技术效果及污染风险评价提供基础依据。     

(Methyl)Mercury,Arsenic, and Lead Contamination of the World’s Largest Wastewater Irrigation System: the Mezquital Valley (Hidalgo State—Mexico)

We investigate the potential of near-infrared (NIR) spectroscopy to predict some heavy metals content (Zn, Cu, Pb, Cr and Ni) in several soil types in Stara Zagora Region, South Bulgaria, as affected by the size of calibration set using partial least squares (PLS) regression models. A total of 124 soil samples from the 0–20 and 20–40 cm layers were collected from fields with different cropping systems. Total Zn, Cu, Pb, Cr and Ni concentrations were determined by Atomic Absorption Spectrometry. Spectra of air dried soil samples were obtained using an FT-NIR Spectrometer (spectral range 700–2,500 nm). PLS calibration models were developed with full-cross-validation using calibration sets of 90 %, 80 %, 70 % and 60 % of the 124 samples. These models were validated with the same prediction set of 12 samples. The validation of the NIR models showed Cu to be best predicted with NIR spectroscopy. Less accurate prediction was observed for Zn, Pb and Ni, which was classified as possible to distinguish between high and low concentrations and as approximate quantitative. The worst model performance in cross-validation and prediction was for Cr. Results also showed that values of root mean square error in cross-validation (RMSE_cv) increased with decreasing number of samples in calibration sets, which was particularly clear for Cu, Pb, Ni and Cr content. A similar tendency was observed in the prediction sets, where RMSEP values increased with a decrease in the number of samples, particularly for Pb, Ni and Cr content. This tendency was not clear for Zn, while even an increase in RMSEP for Cu with the sample size was observed. It can be concluded that NIR spectroscopy can be used to measure heavy metals in a sample set with different soil type, when sufficient number of soil samples (depending on variability) is used in the calibration set.     

Prediction of volumetric soil organic carbon from field‐moist intact soil cores

This study investigated the potential for visible–near‐infrared (vis–NIR) spectroscopy to predict locally volumetric soil organic carbon (SOC) from spectra recorded from field‐moist soil cores. One hundred cores were collected from a 71‐ha arable field. The vis–NIR spectra were collected every centimetre along the side of the cores to a depth of 0.3 m. Cores were then divided into 0.1‐m increments for laboratory analysis. Reference SOC measurements were used to calibrate three partial least‐squares regression (PLSR) models for bulk density (ρ_b), gravimetric SOC (SOC_g) and volumetric SOC (SOC_v). Accurate predictions were obtained from averages of spectra from those 0.1‐m increments for SOC_g (ratio of performance to inter‐quartile (RPIQ) = 5.15; root mean square error (RMSE) = 0.38%) and SOC_v (RPIQ = 5.25; RMSE = 4.33 kg m^?3). The PLSR model for ρ_b performed least well, but still produced accurate results (RPIQ = 3.76; RMSE = 0.11 Mg m^?3). Predictions for ρ_b and SOC_g were combined to compare indirect and direct predictions of SOC_v. No statistical difference in accuracy between these approaches was detected, suggesting that the direct prediction of SOC_v is possible. The PLSR models calibrated on the 10‐cm depth intervals were also applied to the spectra originally recorded on a 1‐cm depth increment. While a bigger bias was observed for 1‐cm than for 10‐cm predictions (1.13 and 0.19 kg m^?3, respectively), the two populations of estimates were not distinguishable statistically. The study showed the potential for using vis–NIR spectroscopy on field‐moist soil cores to predict SOC at high depth resolutions (1 cm) with locally derived calibrations.     

Evaluation of Chemical Indices of Soil Pb Availability in Calcareous Soils

The objective of this study was to compare six soil tests (1 M KNO₃, 1 M NH₄OAC, 0.005 M DTPA, 0.1 M EDTA, 1 M HNO₃ and 0.025 M Ca DTPA B₄O₇) as extractants for soil Pb and as predictors of plant available Pb for wheat (Triticum aestivum L.) in the greenhouse. The soils recieved 0, 200 and 400 mg Pb kg^?1 as Pb(NO₃)₂ and are referred to as Pb₀, Pb₁ and Pb₂ treatments respectively. Of the six soil extractants, 1 M HNO₃ was the most effective extractant for Pb from Pb₀ treatment whereas 0.1 M EDTA and 0.025 M Ca DTPA-B₄O₇ were the best and equally effective in their ability to extract Pb from Pb₁ and Pb₂ treatments. Regression analysis was used to develop two variable models for predicting Pb uptake by wheat as a function of extractable Pb and selected soil properties. The 0.025 M Ca DTPA-B₄O₇ extractant was the best in predicting uptake by wheat in Pb₀ (r = 0.791^*** significant at p = 0.001) and Pb₁ (r = 0.726^***) and Pb₂ (r = 0.942^***) treatments.     

An Alternate Method for Fourier Transform Infrared (FTIR) Spectroscopic Determination of Soil Nitrate Using Derivative Analysis and Sample Treatments

This study aimed at examining effective sample treatments and spectral processing for an alternate method of soil nitrate determination using the attenuated total reflectance (ATR) of Fourier transform infrared (FTIR) spectroscopy. Prior to FTIR measurements, soil samples were prepared as paste to enhance adhesion between the ATR crystal and sample. The similar nitrate peak heights of soil pastes and their supernatants indicated that the nitrate in the liquid portion of the soil paste mainly responded to the FTIR signal. Using a 0.01-M CaSO₄ solution for the soil paste, which has no interference bands in the characteristic spectra of the analyte, increased the concentration of the nitrates to be measured. Second-order derivatives were used in the prediction model to minimize the interference effects and enhance the performance. The second-order derivative spectra contained a unique nitrate peak in a range of 1,400–1,200 cm^?1 without interference of carbonate. A partial least square regression model using second-order derivative spectra performed well (R ²?=?0.995, root mean square error (RMSE)?=?23.5, ratio of prediction to deviation (RPD)?=?13.8) on laboratory samples. Prediction results were also good for a test set of agricultural field soils with a CaCO₃ concentration of 6% to 8% (R ²?=?0.97, RMSE?=?18.6, RPD?=?3.5). Application of the prediction model based on soil paste samples to nitrate stock solution resulted in an increased RMSE (62.3); however, validation measures were still satisfactory (R ²?=?0.99, RPD?=?3.0).     

NIR Spectroscopy: An Alternative for Soil Analysis

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.     

Soil Organic Matter Quality and Zinc and Lead Sorption as Affected by a Sewage Sludge Or a Sewage Sludge Compost Application

Sewage sludge (SS) or sewage sludge compost (SSC) were applied to soil under controlled conditions, at rates of 0 or 200 Mg ha^?1, to investigate changes in dissolved organic matter (DOM), humic acids (HA), and Pb and Zn sorption in the soil. Infrared spectroscopy, visible spectrophotometry, and sorption isotherms (mono-metal and competitive sorption systems) methods were used to assess the changer. The E₄/E₆ ratio (λ at 465 / λ at 665 nm) and the infrared spectra of DOM and HA showed aromatic behaviour in compost-soil (SSC-S); in contrast sewage sludge-soil (SS-S) showed an aliphatic behaviour. Application of either SS or SSC increased the Pb and Zn sorption capacity of soil. The Pb and Zn sorption increased in soil and soil mixtures with a competitive metal system. The metal affinity sequence for soil, SS-S, and SSC-S was compared with the predicted affinity sequences obtained from metal properties. Poor correspondence was observed between the metal affinity sequence and the metal affinity sequence predicted by ionic potential, indicating that metals bonding to soils were not predominantly electrostatic. An affinity sequence based on Pearson's theory agreed with the metal affinity sequences for soils. A statistical analysis showed that the bands assigned to esters (1080 cm^?1) of DOM, phenolic OH (1420 cm^?1), amide I (1650 cm^?1), carboxyl and carbonyl C=O stretches of different nature, C=O stretch of aromatic esters, aliphatic cetone, aldehyde (1720 cm^?1), ethers and esters (1230 cm^?1), aliphatic alcohols (1125 cm^?1), and lignin (1380 cm^?1) of HA were correlated with Zn constants of Langmuir adsorption isotherm (P < 0.05).     

基于局部加权回归的土壤全氮含量可见-近红外光谱反演

全氮是土壤肥力的重要指标,对作物产量具有决定性作用,采用土壤可见-近红外(Vis-NIR)光谱预测技术及时获取土壤全氮含量信息具有重要意义。采用来自5省的450个土壤样本来验证局部加权回归方法(LWR)结合Vis-NIR光谱技术预测大面积土壤全氮含量的适用性。结果表明,LWR模型的预测效果优于偏最小二乘回归(PLSR)、人工神经网络(ANN)和支持向量机(SVM),选取主成分数为5,相似样本为40时,模型验证的决定系数(RP2)为0.83,均方根误差(RMSEP)为0.25 g kg-1,测定值标准偏差与标准预测误差的比值(RPD)达到2.41。LWR从建模集中选取与验证样本相似的土样作为局部建模样本,降低了差别大的样本对模型的干扰,从而提高了模型的预测能力。因此,LWR建模方法通过大范围、大样本土壤光谱数据进行大尺度区域的全氮等土壤属性预测时能够发挥更好的作用。     

Empirical predictions of plant material C and N mineralization patterns from near infrared spectroscopy, stepwise chemical digestion and C/N ratios

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.     

Comparison of Near Infrared Reflectance Spectroscopy with Combustion and Chemical Methods for Soil Carbon Measurements in Agricultural Soils

As interest in soil organic carbon (SOC) dynamics increases, so do needs for rapid, accurate, and inexpensive methods for quantifying SOC. Objectives were to i) evaluate near infrared reflectance (NIR) spectroscopy potential to determine SOC and soil organic matter (SOM) in soils from across Tennessee, USA; and ii) evaluate potential upper limits of SOC from forest, pasture, no-tillage, and conventional tilled sites. Samples were analyzed via dry-combustion (SOC), Walkley–Black chemical SOM, and NIR. In addition, the sample particle size was classified to give five surface roughness levels to determine effects of particle size on NIR. Partial least squares regression was used to develop a model for predicting SOC as measured by NIR by comparing against SOM and SOC. Both NIR and SOM correlated well (R² > 0.9) with SOC (combustion). NIR is therefore considered a sufficiently accurate method for quantifying SOC in soils of Tennessee, with pasture and forested systems having the greatest accumulations.Abbreviations SOC, soil organic carbon; NIR, Near Infrared Reflectance Spectroscopy; MTREC, Middle Tennessee Research and Education Center; RECM, Research and Education Center at Milan; PREC, Plateau Research and Education Center; PLS, Partial least squares.     

贵州铅锌冶炼区农田土壤镉铅有效性评价与预测模型研究

农田土壤重金属的不同活性库分布和土壤-溶液分配模型能够提供重金属的生物有效性和浸出能力等信息,因而在风险评价和修复实践中非常重要。本研究采集毕节铅锌冶炼区30个历史污染农田土壤,同时在贵州省范围内采集5种类型背景土壤制成不同浓度Pb/Cd单一污染土壤;经3个月老化,分别测定由0.43 mol/L HNO_3、0.1 mol/L HCl和0.005 mol/L DTPA提取态表征的重金属反应活性库以及由0.01 mol/L CaCl_2提取态表征的直接有效库;分析铅锌冶炼区农田土壤Cd、Pb不同有效库的分布特征,建立土壤-溶液分配模型,并讨论土壤理化性质的影响。结果表明:历史污染土壤中Cd和Pb的直接有效库占全量比例分别比人工污染土壤低4倍和223倍,然而历史污染土壤Cd和Pb的反应活性库(0.43 mol/L HNO_3提取态)占全量比例要高于相应人工污染土壤中的比例。拓展Freundlich形式吸附方程能够准确描述各提取态表征的Cd和Pb活性库与土壤全量Cd和Pb的关系,尤其0.43 mol/L HNO_3提取方法能够克服土壤理化性质对土壤Cd和Pb提取的影响而与总量建立极显著的相关关系。pH依附性Freundlich吸附方程准确描述了Cd和Pb的总反应活性库分别与土壤溶液Cd和Pb的关系,对于Pb而言,还要考虑土壤有机质和有效磷的影响。本研究可为矿区农田土壤重金属污染评价、修复以及农田有效态标准的推导提供参考。     

Availability to Lettuce of Arsenic and Lead from Trace Element Fertilizers in Soil

Some waste-derived trace element fertilizers may contain elevated amounts of arsenic (As) and/or lead (Pb), and the impact of their use on As and Pb accumulation in soil and uptake by plants should be investigated. This greenhouse study examined how increasing rates of an iron (IR) fertilizer, containing 4806 mg kg^?1 of As, and a zinc fertilizer (G-Zn), containing 18080 mg kg^?1 of Pb, and liming affected As and Pb availability and uptake by lettuce (Lactuca sativa L.) in a silt loam soil. Additions of As as As₂O₃ and Pb as PbCl₂ to the soil were included for comparison. Soil total and NaHCO₃-extractable As increased with increasing inputs of As regardless of As source. This was true also for soil total and DTPA-extractable Pb. Sufficient oxidation of arsenopyrite (FeAsS) in the IR could have occurred, resulting in 36.4% of total As in IR being extractable by NaHCO₃. Only extremely small fractions of the added As from IR (5×10^?3%) and of the added Pb from G-Zn (7×10^?3%) were accumulated in the plant. Source had an effect on As, but not Pb, accumulation by lettuce. The lower accumulation of As by the plant from IR than from As₂O₃ could be attributed to a high molar ratio of Fe to As (419) in the readily labile As fraction to render As in IR less available than As in As₂O₃. The molar ratio of Fe to Pb in the readily labile fraction of Pb in G-Zn was zero, which limited the influence of Fe on the accumulation of Pb from G-Zn despite its extremely high total Fe (18.3%) content. The transfer coefficients of the added As (0.013) and Pb (0.014) over all sources and lime rates were very low. Since nearly all of As and Pb added from the fertilizers remained in the soil, effects of long-term use of the products on As and Pb accumulation in the soil and uptake by the plant remain to be studied.     

Measurement of Phosphorus in Soils by Near Infrared Reflectance Spectroscopy: Effect of Reference Method on Calibration

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.