C UARTA FASE : S EXISMO Y MISOGINIA EN LA ESTRUCTURA DEL CONTENIDO

Model validations were generally reasonable to good, with results showing that the available covariates (and sampling design) facilitate effective spatial predictions of the required soil properties using DSM techniques. Training and validation model metrics are shown for continuous target variables in Table 4-2, and discrete or categorical target property metrics are provided in Table 4-3. Validations are shown is square parentheses. In general, the coarse fragments (CCC = 0.93), clay % (CCC = 0.93), drainage (CCC = 0.86), and exchangeable calcium (CCC = 0.84) and magnesium (CCC = 0.82) were the better spatial predictions, in terms of the calculated training and validation metrics. Soil depth (CCC = 0.29) and sodicity depth (CCC = 0.42) did not model well in the Meander area, due to a lack of soil samples where soil depth limit (‘C’ horizon or impeding rock) was reached, and very few sodic soils. pH in both areas modelled well (CCC = 0.79 and 0.82), but validated relatively poorly (CCC = 0.35

150 and 0.45) due to the unexplained variability introduced by land management inputs. EC was generally poor in Meander (CCC = 0.29), but modelled better in the Tunbridge area using RF-RK (CCC = 0.53), mainly due to the higher levels of salinity in this area (Kidd, 2003).

Soil depth (CCC = 0.69) and depth to sodic layer (CCC = 0.62) modelled better in the Tunbridge area due to a higher proportion of sodic soils than in Meander, and the shallow soils prevalent in the rocky hills throughout the area. Coarse fragments greater than 0.2 m (CF > 0.2 m) initially modelled poorly for both areas, with very few sites recording this stone size, but was drastically improved using a desktop training approach, where rocky areas were identified by the surveyors using local knowledge, and satellite imagery to provide supplementary training data. Tunbridge Exchangeable magnesium (Ex Mg) modelled very poorly (CCC = 0.15), which implies that available covariates fail to explain the spatial variability of this cation concentration. Validation RMSE values (as a measure of average modelling error magnitude) were generally within realistic ranges, i.e. within acceptable margins of error relative to the soil attribute of interest.

Soil Property Training [Validation] (0-0.15m) Meander R2 CCC RMSE pHw 0.75[0.15] 0.79[0.35] 0.17[0.23] EC (dS/m) 0.19[0.16] 0.29[0.26] 0.03[0.03] CF>0.2m (%) 0.86[0.48] 0.93[0.59] 0.49[0.68] Clay (%) 0.74[0.40] 0.80[0.58] 3.61[4.75] Drainage Index 0.79[0.39] 0.86[0.57] 0.27[0.46] Ex Ca (meq/100g) 0.78[0.52] 0.84[0.67] 2.20[3.70] Ex Mg (meq/100g) 0.76[0.36] 0.82[0.57] 1.21[2.30] Soil Depth (cm) n/a*[0.13] n/a*[0.29] n/a*[9.21] Sodicity Depth (cm) n/a*[0.30] n/a*[0.42] n/a*[19.6]

151 Tunbridge R2 CCC RMSE pHw 0.80[0.20] 0.82[0.45] 0.15[0.23] EC (dS/m) 0.84[0.36] 0.53[0.58] 0.04[0.05] CF>0.2m (%) 0.90[0.87] 0.92[0.91] 0.55[0.61] Clay % 0.85[0.50] 0.90[0.63] 3.12[4.75] Drainage Index 0.43[0.34] 0.57[0.53] 0.48[0.50] Ex Ca (meq/100g) 0.42[0.33] 0.59[0.57] 3.11[3.92] Ex Mg (meq/100g) 0.07[0.04] 0.15[0.14] 1.93[1.70] Soil Depth (cm) 0.58[0.56] 0.69[0.69] 16.9[20.8] Sodicity Depth (cm) 0.64[0.19] 0.62[0.33] 6.94[8.11]

*No training metrics, as all data was incorporated into the model, using a cross-validation

Table 4-2. Continuous Soil Property Modelling Metrics (Meander)

Soil Property

Kappa Coefficient with Linear Weighting Training [Validation] Meander Tunbridge CF > 0.06m n/a*[0.18] 0.84[0.34] CF 0.002-0.2m n/a*[0.22] 0.97[0.39] Soil Drainage (Class) 0.72[0.37] 0.48[0.47] Duplex Clay** < 0.4m (y/n) n/a*[0.45] n/a*[0.34]

*No training metrics, as all data was incorporated into the model, using cross-validation.

**Sharp change in clay% between the ‘A’ and ‘B’ horizons.

Table 4-3. Modelling Metrics - Categorical Variables

A lack of recorded presence of surface coarse fragments (0 to 0.15 m) in the Meander area meant that there was insufficient training data with non-zero values to effectively form a DT model; consequently, the training and validation datasets were combined, and coarse fragment abundance classes (National Committee on Soil and Terrain, 2009) modelled out as ordinal categorical variables, using a ten-fold cross validation as described in Kidd et al. (2014). Validation kappa values (linear

152 weighted for ordinal data (Cohen, 1968)) were generally poor to fair in the Meander area (0.18), mainly due to a lack of training data and the subjective class estimates. Modelling was superior in the Tunbridge area where there were more surface coarse fragments recorded in the surface and upper soil horizons, achieving very good kappa coefficients for training (kappa = 0.84), with reasonable validation (kappa =

0.34) for

CF > 0.06 m. Training and validation kappa coefficients were 0.97 and 0.39 respectively for CF between 0.002 and 0.2 m. Similarly, insufficient non-zero training data was recorded in Meander for the presence of duplex clay within 0.4 m of the surface. A cross-validation was again used, providing a moderate validation rate for the Meander area (kappa = 0.45), with a fair validation for Tunbridge (kappa = 0.34), as per the qualitative classes described by Fleiss et al. (2004). Figure 4-1 shows the training and validation observed versus predicted graphs for clay % in the Meander area, and the relationship to the 1:1 (dotted) line of concordance (expected value). It can be seen from these graphs that clay % was slightly under predicted.

Figure 4-1. Clay %, Meander. Observed vs Predicted (Training Left), Observed vs Predicted (Validation, Right)

The DSM approaches piloted in the ‘Wealth from Water’ project areas were shown to produce acceptable predictions of soil properties, with adequate relationship

153 evident between the target variables of interest, and the available spatial covariates. The mapping also demonstrated that both quantitative property measurements, and qualitative (expert-based) descriptions and estimates could be integrated into the assessments. The methodology was reliant on numerous soil property measurements and chemical analyses, and would therefore be unviable without the use of MIR predictions of soil properties (due to the associated costs of conventional ‘wet chemistry’ analyses) (Pirie et al., 2005).

154 Figure 4-2 shows spatial clay content (0 to 0.15 m) for the Tunbridge area, demonstrating the fine-textured alluvial soils to the north, and the coarser-textured Triassic Sandstone soils to the south. Heaviest clays occur in drainage depressions and recent floodplains (Kidd, 2003) on the black cracking clays (Vertosols (Isbell, 2002), Vertisols (IUSS Working Group WRB, 2007)).

The RT approach was favoured for most continuous property predictions, as it generally achieved the most realistic model validations, whereas RF modelling tended to over-fit the data (a larger disparity between training and validation metrics). However, EC modelling in Tunbridge produced the best training and validation metrics when using the RF-RK approach. It was also found that target variable transformations, or using principal-components to de-correlate covariates didn’t substantially improve RT modelling. Similarly, validation of RT models was not overly improved by kriging the residuals, and the variogram of the residuals mainly show a nugget effect, with poor autocorrelation. Another perceived advantage of the RT (Cubist) approach is that model outputs produce rulesets that describe how the covariate values are partitioned and the regression models are applied to each partition, which makes model interpretation more pragmatic and facilitates spatial- interrogation that can demonstrate how the modelling was applied to different parts of the landscape.