An error budget for digital soil mapping of cation exchange capacity using proximally sensed electromagnetic induction and remotely sensed γ‐ray spectrometer data

The cation exchange capacity (CEC) of soil is widely used for agricultural assessment as a measure of fertility and an indicator of structural stability; however, its measurement is time‐consuming. Although geostatistical methods have been used, a large number of samples must be collected. Using pedometric methods and incorporating easy‐to‐measure ancillary data into models have improved the efficiency of spatial prediction of soil CEC. However, mapping uncertainty has not been evaluated. In this study, we use an error budget procedure to quantify the relative contributions that model, input and covariate error make to prediction error of a digital map of CEC using gamma‐ray (γ‐ray) spectrometry and apparent electrical conductivity (EC_a) data. The error budget uses empirical best linear unbiased prediction (E‐BLUP) and conditional simulation to produce numerous realizations of the data and their underlying errors. Linear mixed models (LMMs) estimated by residual maximum likelihood (REML) are used to create the prediction models. The combined error of model [5.07 cmol(+)/kg] and input error [12.88 cmol(+)/kg] is ~12.93 cmol(+)/kg, which is twice as large as the standard deviation of CEC [6.8 cmol(+)/kg]. The individual covariate errors caused by the γ‐ray [9.64 cmol(+)/kg] and EM error [8.55 cmol(+)/kg] were large. Preprocessing techniques to improve the quality of the γ‐ray data could be considered, whereas the EM error could be reduced by the use of a smaller sampling interval in particular near the edges of the study area and at pedoderm boundaries.     

Model Development to Predict CEC Using the Intelligence Data Mining Approaches

ABSTRACT

Using easily measurable soil properties and pedotransfer functions (PTFs) is a time-saving, non-destructive and cost-saving way in the prediction of the cation exchange capacity (CEC). The purpose of this study was to compare and evaluate the regression tree (RT), multiple linear regression (MLR) and Mamdani fuzzy inference system (MFIS) in estimating CEC. For this work, 100 soil samples from unsaturated soil hydraulic database (UNSODA) data-set were used. %Organic matter (OM), bulk density (BD), the geometric mean particle diameter (dg) and fractal dimension of particle size (D) were applied as the input predictive variables. First, the type of relationship between easily measurable soil properties and CEC was investigated and, then used for the development of PTFs and fuzzy membership functions. The results showed that MLR method was developed only based on %OM (r = 0.68, p < .01) and D (r = 0.68, p < .01). While in the RT method, all of the predictive variables were appeared in the tree-like based on their correlation coefficient with CEC. The D and %OM also were considered as input variables in developing fuzzy membership functions. Results also revealed that RT method had a higher performance than MLR and MFIS in the estimation of CEC with the highest coefficient of determination (R² = 0.77), smallest root-mean-square error (RMSE = 5.14 meq/100_gsoil), normalized root-mean-square error (NRMSE = 0.25 meq/100_gsoil) and mean error (ME = ?1.80 meq/100_gsoil). In addition, the MFIS had a higher efficiency than the MLR in the CEC estimation.     

Soil properties prediction of western Mediterranean islands with similar climatic environments by means of mid‐infrared diffuse reflectance spectroscopy

This study investigated the suitability of mid‐infrared diffuse reflectance Fourier transform (MIR‐DRIFT) spectroscopy, with partial least squares (PLS) regression, for the determination of variations in soil properties typical of Italian Mediterranean off‐shore environments. Pianosa, Elba and Sardinia are typical of islands from this environment, but developed on different geological substrates. Principal components analysis (PCA) showed that spectra could be grouped according to the soil composition of the islands. PLS full cross‐validation of soil property predictions was assessed by the coefficient of determination (R²), the root mean square error of cross‐validation and prediction (RMSECV and RMSEP), the standard error (SECV for cross‐validation and SEP for prediction), and the residual predictive deviation (RPD). Although full cross‐validation appeared to be the most accurate (R² = 0.95 for organic carbon (OC), 0.96 for inorganic carbon (IC), 0.87 for CEC, 0.72 for pH and 0.74 for clay; RPD = 4.4, 6.0, 2.7, 1.9 and 2.0, respectively), the prediction errors were considered to be optimistic and so alternative calibrations considered to be more similar to ‘true’ predictions were tested. Predictions using individual calibrations from each island were the least efficient, while predictions using calibration selection based on a Euclidian distance ranking method, using as few as 10 samples selected from each island, were almost as accurate as full cross‐validation for OC and IC (R² = 0.93 for OC and 0.96 for IC; RPD = 3.9 and 4.7, respectively). Prediction accuracy for CEC, pH and clay was less accurate than expected, especially for clay (R² = 0.73 for CEC, 0.50 for pH and 0.41 for clay; RPD = 1.8, 1.5 and 1.4, respectively). This study confirmed that the DRIFT PLS method was suitable for characterizing important properties for soils typical of islands in a Mediterranean environment and capable of discriminating between the variations in soil properties from different parent materials.     

Using support vector machines to predict cation exchange capacity of different soil horizons in Qingdao City,China

Agricultural, environmental and ecological modeling requires soil cation exchange capacity (CEC) that is difficult to measure. Pedotransfer functions (PTFs) are thus routinely applied to predict CEC from easily measured physicochemical properties (e.g., texture, soil organic matter, pH). This study developed the support vector machines (SVM)‐based PTFs to predict soil CEC based on 208 soil samples collected from A and B horizons in Qingdao City, Shandong Province, China. The database was randomly split into calibration and validation datasets in proportions of 3:1 using the bootstrap method. The optimal SVM parameters were searched by applying the genetic algorithm (GA). The performance of SVM models was compared to those of multiple stepwise regression (MSR) and artificial neural network (ANN) models. Results show that the accuracy of CEC predicted by SVM improves considerably over those predicted by MSR and ANN. The performance of SVM for B horizon (R² = 0.85) is slightly better than that for A horizon (R² = 0.81). The SVM is a powerful approach in the simulation of nonlinear relationship between CEC and physicochemical properties of widely distributed samples from different soil horizons. Sensitivity analysis was also conducted to explore the influence of each input parameter on the CEC predictions by SVM. The clay content is the most sensitive parameter, followed by soil organic matter and pH, while sand content has the weakest influence. This suggests that clay is the most important predictor for predicting CEC of both soil horizons.     

Statistical and geostatistical techniques for geospatial modeling of soil cation exchange capacity

Soil cation exchange capacity (CEC) is a main criterion of soil quality and pollutant sequestration capacity. This research was carried out to evaluate cokriging prediction map of soil CEC spatial variability with the principal components derived from soil physical and chemical properties. Two hundred and forty-seven soil samples were collected that 75% of them were used for training soil CEC and 25% for testing of prediction. The first principal component (PC1) was highly correlated with soil CEC (r = 0.81, P < 0.01), whiles there was no significant correlation between CEC and PC2 (r = -0.19). Then, the PC1 was used as an auxiliary variable for the prediction of soil CEC in cokriging method. The determination coefficient (R²) of cross-validation for the test dataset was 0.47 for kriging and 0.71 for cokriging. Therefore, according to the results, principal components that have the highest positive and significant correlation with the dependent variable have the most potential for cokriging prediction.     

Simplified methylene blue method for rapid determination of cation exchange capacity of mineral soils

Abstract

Rapid determination of cation exchange capacity (CEC) of soils can be useful for soil testing to improve efficiency of fertilizer use. The methylene blue (MB) method of Wang et al. (1989) has been simplified for rapid determination of CEC of mineral soils in field. For the temperate and tropical soils used, the relationships between the CEC by the ammonium acetate (NH₄OAc) method and the simplified MB method were linear (r² = 0.97) with a slope ranging from 0.84 to 1.02. These results suggest that the simplified MB method has the potential for a rapid determination of the CEC of mineral soils.     

Prediction of Soil Cation Exchange Capacity Using Different Soil Parameters by Intelligent Models

ABSTRACT

The traditional methods for the measurement of soil cation exchange capacity (CEC) are time-consuming and laborious. It is also difficult to maintain stability for long-term experiments and projects. Therefore, it is necessary to develop an indirect approach such as pedotransfer functions (PTFs) to estimate this property from more easily available soil data. The aim of this study was to compare multiple linear and nonlinear regression, classification and regression trees (C&RT), artificial neural network (ANN) model included multiple layer perceptron (MLP) and k-nearest neighbors (k-NN) to develop PTFs for predicting soil CEC. Soil samples, 929, were used into two subsets for training and testing of the models. Sensitivity and statistical analyzes were conducted to determine the most and the least influential variables affecting soil CEC. The prediction capability of models was assessed by statistical indicators included the normalized root-mean-square error (NRMSE) and the coefficient of determination (R²). Results of the present investigation showed that the k-NN and ANN models had the ability to estimate soil CEC by computing easily measurable variables with a guarantee of authenticity, reliability, and reproducibility. Therefore, the results of this study provide a superior basis for predicting soil CEC and could be applied to other parts of the world with similar challenges.     

Assessing the plant minimal exchangeable potassium of a soil

The plant minimal exchangeable K (E_Pl,min) defines the lower accessible limit of the most available pool of soil K to plants. It is also an index of long‐term K reserve in soils. However, its estimation by the classical method of exhaustion cropping is laborious. This study aimed at comparing E_Pl,min values obtained by the exhaustion cropping method with E_Pl,min values estimated by an alternative approach based on the cationic exchange capacity (CEC) of the infinitely high selective sites for K (i.e., always saturated with K) in the K‐Ca exchange (E_K‐Ca,min). A set of 45 soil samples, corresponding to the various fertilization K treatments of 15 long‐term K fertilization trials, was used in this study. The selected soil samples presented a wide range of texture, CEC, and exchangeable K. The plant minimal exchangeable K was found more or less independent of the K treatment, whereas E_K‐Ca,min increased when the soil exchangeable K content increased. The plant minimal exchangeable K was systematically lower than E_K‐Ca,min, showing that E_K‐Ca,min is at least partially available to the plant. Hence, E_K‐Ca,min is not a surrogate of E_Pl,min. Conversely, the plant minimal exchangeable K was strongly, positively correlated to soil CEC (measured at soil pH; r² = 0.90^***). This soil property can consequently be used as a proxy of E_Pl,min.     

Digital mapping of cation exchange capacity using genetic programming and soil depth functions in Baneh region,Iran

This study evaluates the performances of a combination of genetic programming and soil depth functions to map the three-dimensional distribution of cation exchange capacity (CEC) in a semiarid region located in Baneh region, Iran. Using the conditioned Latin hypercube sampling method, the locations of 188 soil profiles were selected, which were then sampled and analyzed. In general, results showed that equal-area quadratic splines had the highest R², 89%, in fitting the vertical CEC distribution compared to power and logarithmic functions with R² of 81% and 84%, respectively. Our findings indicated some auxiliary variables had more influence on the prediction of CEC. Normalized difference vegetation index (NDVI) had the highest correlation with CEC in the upper two layers. However, the most important auxiliary data for prediction of CEC in 30–60 cm and 60–100 cm were topographic wetness index and profile curvature, respectively. Validation of the predictive models at each depth interval resulted in R² values ranging from 66% (0–15 cm) to 19% (60–100 cm). Overall, results indicated the topsoil can be reasonably well predicted; however, the subsoil prediction needs to be improved. We can recommend the use of the developed methodology in mapping CEC in other parts in Iran.     

The effects of electrolyte concentrations on calcium-sodium exchange equilibria in two soil samples of india

The effects of total electrolyte concentrations of the equilibrium solutions (t.e.c.) on Ca²⁺-Na⁺ exchange equilibria in two soil samples (high and low in organic matter, clay content and CEC) were studied. Homoionic (Na⁺-saturated) soil samples were equilibrated with solutions having a large range in sodium adsorption ratio (SAR) at 25, 50, 75 and 100 meq. 1^-1 t.e.c. The exchange equilibria data were analysed, using a thermodynamic approach and the selectivity coefficients of Gapon (1933), Vanselow (1932) and Krishnamoorthy et al. (1948) (K_G, K_V and K_KDO). At a given proportion of Ca²⁺: Na⁺ in the equilibrium solution, the development of the exchangeable sodium percentage (ESP) in both soil samples increased with the increase in t.e.c. At a given SAR, the effect of t.e.c. on the development of ESP was less on a soil sample with high organic matter (O.M.), clay content and cation exchange capacity (CEC) than on a soil sample with low O.M., clay content and CEC. The values of exchange selectivity coefficients decreased with the increase in t.e.c, and did not remain constant throughout the exchange isotherm for any of the t.e.c. tried.     

Selection of a Suitable Soft Computing Model for Estimation of Soil Cation Exchange Capacity

ABSTRACT

Measuring of soil cation exchange capacity (CEC) is difficult and time-consuming. Therefore, it is essential to develop an indirect approach such as pedotransfer functions (PTFs) to predict this property from more readily available soil data. The aim of this study was to compare multiple linear and nonlinear regression, adaptive neurofuzzy inference system, and an artificial neural network (ANN) model to develop PTFs for predicting soil CEC. One hundred and seventy-one soil samples were used into two subsets for training and testing of the models. The model's prediction capability was evaluated by statistical indicators that include RMSE, R², MBE, and RI. Results showed that the ANN model had the most reliable prediction when compared with other models. This study provides a strong basis for predicting soil CEC and identifying the most determinant properties influencing soil CEC in the north regions of Iran. Analytical framework results could be applied to other parts of the world with similar challenges.

Abbreviations: ANFIS: Adaptive Neuro-Fuzzy Inference System; ANN: Artificial Neural Network; CEC: Cation Exchange Capacity; CV: Coefficient of Variation; FFBP: Feed-Forward Back-Propagation; FIS: Fuzzy Inference System; MBE: Mean Bias Error; MF: Membership Function; MLR: Multiple Linear Regressions; MNLR: Multiple Non-Linear Regressions; MLP: Multi-layer Perceptron; OC: Organic Carbon; PTFs: Pedotransfer Functions; R²: Determination Coefficient; RI: Relative Improvement; RMSE: Root Mean Square Error; SD: Standard Deviation     

Soil property changes following conversion of acacia woodland into grazing and farmlands in the Rift Valley area of Ethiopia

Continued conversion of woodlands into grazing and farmland is seriously undermining the natural ecosystem of the dry and fragile Rift Valley areas of Ethiopia. This study investigated the effects of land‐use change on soil organic carbon (SOC), total nitrogen (N), pH, exchangeable bases, cation exchange capacity (CEC) and base saturation (per cent) in three adjacent land‐use types: controlled grazing, open‐grazing and farmland. A total of 81 soil samples were collected and analysed. Contents of SOC and total N decreased drastically in open‐grazing and farmland (p < 0·001), and were significantly higher in the top 0·2 m than in the subsurface soil layer. Compared with the controlled grazing, reductions in the contents of SOC and total N in the top 1 m soil layer were 22–30 and 19 per cent, respectively, due possibly to the decrease in plant biomass input into the soil and the fast decomposition of organic materials. Long‐term cultivation had significantly increased the concentration of exchangeable K. Exchangeable Na was high in the lower layers, while Mg was higher in the top surface soil. CEC also varied with soil depth (p = 0·016); it was higher in the topsoil than in the subsurface soil, which may be, among others, due to the differences in soil organic matter distribution with depth. Although these semi‐arid soils are known to have low organic carbon and CEC levels, the values from the current study area are critically low, and may indicate the further impoverishment of the soils under high agricultural and grazing pressures. Copyright © 2010 John Wiley & Sons, Ltd.     

Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese vis–NIR spectral library

We need to determine the best use of soil vis–NIR spectral libraries that are being developed at regional, national and global scales to predict soil properties from new spectral readings. To reduce the complexity of a calibration dataset derived from the Chinese vis–NIR soil spectral library (CSSL), we tested a local regression method that combined geographical sub‐setting with a local partial least squares regression (local‐PLSR) that uses a limited number of similar vis–NIR spectra (k‐nearest neighbours). The central idea of the local regression, and of other local statistical approaches, is to derive a local prediction model by identifying samples in the calibration dataset that are similar, in spectral variable space, to the samples used for prediction. Here, to derive our local regressions we used Euclidean distance in spectral space between the calibration dataset and prediction samples, and we also used soil geographical zoning to account for similarities in soil‐forming conditions. We tested this approach with the CSSL, which comprised 2732 soil samples collected from 20 provinces in the People's Republic of China to predict soil organic matter (SOM). Results showed that the prediction accuracy of our spatially constrained local‐PLSR method (R² = 0.74, RPIQ = 2.6) was better than that from local‐PLSR (R² = 0.69, RPIQ = 2.3) and PLSR alone (R² = 0.50, RPIQ = 1.5). The coupling of a local‐PLSR regression with soil geographical zoning can improve the accuracy of local SOM predictions using large, complex soil spectral libraries. The approach might be embedded into vis–NIR sensors for laboratory analysis or field estimation.     

Using Thermogravimetry as a Simple Tool for Nutrient Assessment in Fire Affected Soils

Nutrient availability can be a limiting factor in the recovery of ecosystems after wildfire. Its evaluation is therefore critical for selecting appropriate restoration strategies in the post‐fire period. This study explores, for the first time, the use of thermogravimetry (TG) as a rapid proxy for nutrient availability and soil recovery. Soil samples from five burned and unburned sites in Andisols of Tenerife (Spain) were selected to examine the medium‐term impact of fire. Key soil chemical parameters [pH, electric conductivity, cation exchange capacity (CEC), main cation and anions in the soil solution, total organic carbon (TOC), total nitrogen (TN) and available phosphorus] were determined and thermogravimetry performed. Burned soils showed significantly higher pH, Ca²⁺ and Mg²⁺ and a lower CEC, TOC and TN than the unburned counterparts, and a site‐dependent response for soluble SO₄²⁻ and available phosphorus was observed in the medium term. Time elapsed since fire could have masked additional fire impacts. Thermogravimetry data allowed reasonable prediction of most soil properties and parameters, with r ² ranging from 0·4 to 0·9. The results demonstrate that soluble nutrient content is directly related to the amount of ash in the soil. The decrease of labile carboxyl‐C was associated with an increase of pH and decrease of CEC, whereas the increase of recalcitrant and refractory pools was associated with the amount of TOC and TN. The results suggest that this novel application of an established method can provide, following an initial calibration step, rapid and inexpensive proxies for key parameters necessary for assessing fire‐induced ecosystem degradation and designing suitable restoration strategies in the post‐fire period. Copyright © 2017 John Wiley & Sons, Ltd.     

Interpretation of infrared spectra for OM characterization of soil structural surfaces of Bt‐horizons

The surfaces of macropores or aggregates can act as hot spots for biogeochemical processes and solute transport during preferential flow. For the characterization of organic matter (OM) at macropore surfaces non‐destructive methods have been applied such as diffuse reflectance infrared Fourier transform spectroscopy (DRIFT). However, effects of organic components on DRIFT signal intensities are often difficult to distinguish from those of mineral components. Here, DRIFT spectra from intact earthworm burrow walls and coated cracks were re‐evaluated to improve the interpretation of C–H and C=O bands. We compared DRIFT and transmission Fourier transform infrared (FTIR) spectra of entire samples that were from the same pedogenetic soil horizon (Bt) but different in mineral composition and texture (i.e., glacial till vs. loess). Spectra of incinerated samples were subtracted from the original spectra. Transmission FTIR and DRIFT spectra were almost identical for entire soil samples. However, the DRIFT spectra were affected by the bulk mode bands (i.e., wavenumbers 2000 to 1700 cm^?1). These bands affected spectral resolution and reproducibility. The ratios between C–H and C=O band intensities as indicator for OM quality obtained with DRIFT were smaller than those obtained from transmission FTIR. The results demonstrated that DRIFT and transmission FTIR data required separate interpretations. DRIFT spectroscopy as a non‐destructive method for analyzing OM composition at intact surfaces in structured soils could be calibrated with information obtained with the more detailed transmission FTIR and complementary methods. Spectral subtraction procedure was found useful to reduce effects of mineral absorption bands. The improved DRIFT data may be related to other soil properties (e.g., cation exchange capacity) of hot spots in structured soils.     

Development of ensemble pedotransfer functions for cation exchange capacity of soils of Qingdao in China

Soil cation exchange capacity (CEC), which is considered to be an indicator of buffering capacity, is an important soil attribute that influences soil fertility but is costly, time‐consuming and labour‐intensive to measure. Pedotransfer functions (PTFs) have routinely been used to predict soil CEC from easily measured soil properties, such as soil pH, texture and organic matter content. However, uncertainty in which one to select can be substantial as different PTFs do not necessarily produce the same result. In this study, a total of 100 soil samples were collected from surface horizons (0–20 cm) in different regions of Qingdao City, China. Three ensemble PTFs (ePTFs), including simple ensemble mean (SEM), individually bias‐removed ensemble mean (IBREM) and collective bias‐removed ensemble mean (CBREM), were developed to reduce the uncertainty in CEC prediction based on 12 published regression‐based PTFs. In addition, a local PTF (LPTF) for CEC was also developed using multiple stepwise regression and basic soil properties. The performances of the three ePTFs were compared with those of the published PTFs and LPTF. Results show that the differences between the performances of the published PTFs were substantial. When the systematic bias of each published PTF was removed separately, the prediction capability of the PTFs was increased. The performance of LPTF was significantly better than that of SEM, but slightly worse than IBREM. It is noted that CBREM had higher accuracy than all of the other methods. Overall, CBREM is a promising approach for estimating soil CEC in the study area.     

Scope to map soil management units at the district level from remotely sensed γ‐ray spectrometry and proximal sensed EM induction data

The traditional method of soil mapping involves classifying soil into pre‐existing classes using morphological observations and then air‐photograph interpretation to extrapolate the information. To accelerate the process, less costly ancillary data can be used to assist mapping. However, digital soil mapping (DSM) is still affected by the classifications used to identify soil types. One reason is because the morphological characteristics are not mutually exclusive, which causes misclassification. In this study, we used a DSM approach, where ancillary data were surrogate for morphological data, with soil types identified by numerical clustering of remotely and proximally sensed data collected across a farming district near Gunnedah, Australia. Remotely sensed data were obtained from an air‐borne gamma‐ray (γ‐ray) spectrometer survey, including potassium (K), thorium (Th), uranium (U) and total counts. Proximally sensed data were measured using EM38 (i.e. EM38h and EM38v). Using fuzzy k‐means and a linear mixed model with measured physical (e.g. clay) and chemical (e.g. CEC) properties from the topsoil (0–0.30 m) and subsoil (0.9–1.2 m), we found that k = 5 was also optimal given that mean‐squared prediction error (i.e. ) was minimised. The approach highlighted subtle differences in physical and chemical properties in productive areas. The DSM was unsuccessful in identifying small units; however, inclusion of elevation data might overcome this limitation. This research has implications for providing fast, accurate and meaningful DSM at a district scale, where traditional methods are too expensive.     

Soil texture affects the conversion factor of electrical conductivity from 1:5 soil-water to saturated paste extracts

Electrical conductivity(EC) of soil-water extracts is commonly used to assess soil salinity. However, its conversion to the EC of saturated soil paste extracts(ECe), the standard measure of soil salinity, is currently required for practical applications. Although many regression models can be used to obtain ECe from the EC of soil-water extracts, the application of a site-specific model to different sites is not straightforward due to confounding soil factors such as soil texture. This study was...     

Tolerance of forage legumes to lime‐stabilized sludge

Abstract

Lime‐stabilized sludge (LSS) from dairy processing waste‐water treatment plants is a desirable product for land application. The material contains lime, which neutralizes soil acidity, and P, which is useful as a plant nutrient. The fineness of the lime and the solubility of P make LSS especially desirable in establishing forage legumes. This greenhouse study had two objectives: to determine a reasonable quantity of LSS for establishing forage legumes such as alfalfa (Medicago sativa L.) and red clover (Trifolium pratense L.) and to prevent adverse effects on seedlings. Sludge was applied at 0, 2.5, 5.0, 7.5 g kg^‐1 to an acid, low P soil in pots, and alfalfa and red clover seeds were sown. All treatments received 123 μg g^‐1 potassium as KCl. A completely randomized design with four replications was used. Each species was handled as a separate study. Dry matter production was measured at one‐tenth bloom stage. Plant samples were analyzed for P, K, Ca, and Mg content. Soil samples taken at the end of the study were analyzed for pH, organic matter, Bray P, K, Ca, Mg, exchangeable Al, EC, and CEC. The higher quantities of LSS (7.5 g kg^‐1 for alfalfa and 5.0 g kg^‐1 for red clover) had negative effects on seedling germination and establishment. Lime‐stabilized sludge resulted in an increase in total nutrient uptake of Ca, Mg, K, and P up to 5.0 and 2.5 g kg^‐1 in alfalfa and red clover, respectively. In both species significant dry matter yield increases were obtained with LSS up to 5.0 g kg^‐1; however, 7.5 g kg^‐1 caused a reduction in dry matter yield. Based on these results, applications of LSS at 5.0 for alfalfa and 2.5 g kg^‐1 for red clover had positive effects in seedling establishment, nutrient uptake, and dry matter production. Lime‐stabilized sludge application resulted in significant increases in soil pH, available P, Ca, Mg, EC, and CEC; decreases were seen in neutralizable acidity and exchangeable Al levels in soil. This study indicates that LSS is appropriate for the acidic, low P soils of Southern Missouri for alfalfa and red clover establishment and production, if applied in appropriate quantities.     

The use of visible and near‐infrared spectroscopy for the analysis of soil water repellency

This study investigated the potential of visible/near‐infrared reflectance spectroscopy (Vis‐NIRS) to predict soil water repellency (SWR). The top 40 mm of soils (n = 288) across 48 sites under pastoral land‐use in the North Island of New Zealand, which represented 10 soil orders and covered five classes of drought proneness, were analysed by standard laboratory methods and Vis‐NIRS. Soil WR was measured by using the molarity of ethanol droplet (MED) and the water drop penetration time (WDPT) tests. Soil organic carbon content (%C) was also measured to examine a possible relationship with SWR. A partial least squares regression (PLSR) model was developed by using Vis‐NIRS spectral data and the reference laboratory data. In addition, we explored the power of discrimination based on WDPT classes using partial least squares discriminant analysis (PLS‐DA). The PLSR of the processed spectra produced moderately accurate prediction for MED (R²_val = 0.61, RPD_val = 1.60, RMSE_val = 0.59) and good prediction for %C (R²_val = 0.82, RPD_val = 2.30, RMSE_val = 2.72). When the data from the 10 soil orders were considered separately and based on soil order rather than being grouped, the prediction of MED was further improved except for the Allophanic, Brown, Organic and Ultic soil orders. The PLS‐DA was successful in classifying 60% of soil samples into the correct WDPT classes. Our results indicate clearly that Vis‐NIRS has the potential to predict SWR. Further improvement in the prediction accuracy of SWR is envisaged by increasing the understanding of the relationship between Vis‐NIRS and the SWR of all New Zealand soil orders as a function of their physical properties and chemical constituents such as hydrophobic compounds.