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Field-moist soil cores were scanned in duplicate, in 5cm increments, with clear plastic film separating the moist soil from the PXRF scanning window, to prevent contamination of scans from smeared soil obscuring the scanning window. A Delta Premium portable x-ray fluorescence spectrometer (PXRF; Olympus Innov-X, Woburn, MA, USA) was used in this study. This device employs a Ta/Au x-ray tube operated at ~15–40 KeV, for x-ray generation. Scanning consisted of 30 seconds per beam, with the light element analysis program (LEAP) engaged in a software configuration termed ‘soil mode’ (Innov-X Systems, 2010). This proprietary program corrects for soil chemistry variability, as part of the soil matrix, for

optimized elemental quantification. The Delta PXRF was calibrated using a stainless steel ‘316’ alloy clip (containing 16.130% Cr, 1.780% Mn, 68.760% Fe, 10.420% Ni, 0.200% Cu, and 2.100% Mo) tightly fitted over the 2 cm aperture. Two National Institute of Standards and Technology (NIST) standard reference soils (NIST 2702 and 2781) were used to validate the

accuracy of the PXRF prior to sample scanning, with recalibration and validation additionally being conducted after every 20 scans.

After initial scanning of field-moist core samples, cores were divided into ten segments, at 5cm depth increments, and subjected to gravimetric moisture content analysis, per traditional laboratory methods (Black, 1965). Samples were then dried at 105°C for 24 hours, and ground to pass through a 2mm sieve, before being subjected to further analyses. A second set of PXRF scans were collected in duplicate from oven-dry samples, to provide additional elemental datasets for prediction modeling.

3.2.2 Laboratory determination of soil carbon contents

3.2.2.1 Identification and removal of inorganic carbon. To test for the presence of

inorganic carbon, soil samples were subject to HCl effervescence testing. Sampled depths from 5 of 30 total cores, collected from alluvial Site 2 (see Figure 2.1), tested positive for carbonates. The 39 samples from these five cores were treated by HCl applications to facilitate carbonate destruction, per traditional analysis methods (Nelson and Sommers, 1996).

3.2.2.2 Instrumental analysis for total carbon determination. Samples were analyzed for total carbon content via the Dumas high-temperature combustion method, using an Elementar Vario El Cube CN Analyzer (Hanau, Germany) (Soil Survey Staff, 1993; Pansu et al, 2001). As removal of inorganic carbon ensures that all remaining carbon found is in organic form, total carbon concentrations provided the laboratory-measured organic carbon used in this study.

3.2.3 pH determination

Soil reaction (pH) measurements were made using the saturated paste method, with a four-hour equilibration time following additions of deionized water (Sparks e al., 1996). Sample

pH readings were taken using a Control Company Treaceable bench/portable pH meter (Friendswood, TX, USA).

3.3 Statistical techniques

3.3.1 Characterization of multivariate data

Multivariate analysis of variance (MANOVA) techniques were used to evaluate significant elemental differences between the parent material types. An autoregressive

covariance structural analysis was employed in the MANOVA analyses to determine significant elemental differences between alluvial and loessal PXRF datasets. Additional differences in elemental soil constituents were evaluated by application of the MANOVA technique, to compare PXRF readings at various depth intervals, sampling sites, and between individual soil cores within alluvial and loess elemental datasets. All statistical procedures were performed using SAS version 9.3 (Cary, North Carolina).

3.3.2 SOC prediction modeling

3.3.2.1 Data preparation. Elemental data from 300 total soil samples were collected from the upper 5-50cm of 30 soil cores taken from alluvial and loess soils in Louisiana. Data resulting from PXRF scanning and traditional laboratory methods were analyzed for normal distribution, using the Shapiro-Wilk test of normality, with log transformations applied to correct elemental datasets failing to exhibit normal distribution of the means. The modeling dataset was comprised of the fourteen elements (consistently demonstrating PXRF elemental concentrations within the instrument’s limits of detection), in addition to variables representing depth and soil reaction (pH level). Separate datasets were created using stable-element treatments, which involved

normalization of elemental data to Zr and Ti concentrations observed for each sample. This produced ‘Zr-stable’ and ‘Ti-stable’ datasets containing each of the 150 samples collected from

each parent material. These three treatments, termed ‘Raw’, Zr-stable, and Ti-stable datasets (for the purposes of this study) were used in constructing separate SOC prediction models for alluvial and loess soil datasets.

Calculations for means and standard deviations of elemental data were conducted on whole datasets. Modeling datasets were divided into generation and validation sub-datasets, with 20% of the total parent material dataset randomly selected and removed from the generation dataset, to be used for validation of prediction models. This produces datasets comprised of 120 samples for model generation and 30 samples for validation, for each parent material soil type.

3.3.2.2 Multiple linear regression analysis. A stepwise, multiple linear regression (MLR) analysis was applied to each generation dataset to determine the statistical modeling parameters producing the optimal predictive performance for each model’s generation. The MLR technique selects predictor variables that are independently correlated to the independent variable (i.e. SOC content). The model selectively includes the predictor variable contributing the greatest

correlated relationship to laboratory-measured SOC contents, by ‘kicking-out’ variables that demonstrate multiple-collinearity; collinear variables are unable to provide an independent predictor for SOC modeling when used in combination. Therefore, only inclusion of the variable demonstrating the strongest correlation with the dependent variable is used in the MLR model, with less significant collinear variables being excluded. In addition to the 14 elemental variables (K, Ca, Ti, Cr, Mn, Fe, Co, Zn, Rb, Sr, Ba, Pb, Cu, and Zr) included in the multiple regression procedures, variables for pH, and depth (in cm) were also included in all prediction modeling datasets.

All prediction models were alternately subjected to log-normalization of: 1) no variables, 2) the independent variable (laboratory-measured SOC percentage), 3) dependent variables

(PXRF elemental variables, in addition to depth and pH measurements), and 4) both independent and dependent variables. Prediction models were then assessed for statistical validity by testing prediction value residuals for normality, with models achieving a Shapiro-Wilk value (Pr < W) greater than 0.05 being considered statistically valid.

Prediction models were applied to both oven-dry validation sub-datasets, as well as validation datasets resulting from field-moist core scanning. Field-moist datasets (N=150 for loess soils, N=111 for alluvial soils, excluding data from samples containing carbonates) were also used for the construction of additional MLR prediction models, with separate models generated from Raw, Ti-stable, and Zr-stable datasets. Sub-datasets were assembled using the same observations for generation and validations datasets that were used in the oven-dry prediction modeling, with samples exhibiting a presence of carbonates removed from inclusion in field-moist model generation and validation datasets (generation datasets: N= 120 and 88; and validation datasets: N= 30 and 23, for loess and alluvial wet datasets, respectively).

3.3.2.3 Principal components analysis. The same datasets used for multiple linear regression SOC prediction modeling (described in the previous section; comprised of ‘Raw’, Ti- stable, and Zr-stable datasets for alluvial and loess parent material types) were used for

prediction model construction using the principal components analysis (PCA) technique for data characterization. Using this method, principal components (or factors) are determined that are assumed to represent a process or feature which describes a specific source of variation observed in the dataset. Varimax rotation was used to maximize the variance in the loadings (or weights) of each factor, to provide for the generation of a more robust prediction model (Davis, 2002). This was accomplished by use of the varimax option in the factor procedure, available in SAS version 9.3 (Cary, North Carolina, USA). Components derived from the covariance matrix

having eigenvalues greater than one were considered to be significant for inclusion in prediction models. This method for component selection, termed the Kaiser/Guttman criterion, is

commonly used in the analysis of geochemical data, oftentimes providing superior results over other methods (Reimann et al., 2002).

To determine the relationship between selected components and SOC content, factors were subject to multiple linear regression analysis. This allowed for coefficients to be assigned to factors, in to calculate appropriate prediction modeling parameters. Prediction models were then assessed for statistical validity by testing resultant model residual values for normality, with models achieving a Shapiro-Wilk value (Pr < W) > 0.05 being considered statistically valid. Validations of model performances on oven-dry and field-moist datasets were conducted using the same observations used in the multiple linear regression (MLR) approach, described in section 3.3.2.1.

3.3.3 Elemental differences between wet and dry samples and modeling effects

Parent material oven-dry and field-moist datasets were examined to determine whether increasing moisture contents for individual soil samples were significantly correlated to differences in elemental concentrations detected between PXRF datasets. Elements were separately analyzed to calculate the extent to which field-moist data was correlated to oven-dry PXRF elemental concentrations for each sample. To test whether moisture exhibited a significant effect on prediction model accuracy, the absolute value of residuals (resulting from differences between model-predicted and laboratory-measured SOC values) were compared to sample moisture contents, to determine whether increasing moisture elicits larger discrepancies between predicted and actual SOC values.