Otros estudios sobre puteros

The SLGA forms a substantial component of the Oceania node’s contribution to the ‘Global Soil Map’ (GlobalSoilMap) project. GlobalSoilMap (GSM) is a world-wide collaboration to produce fine-resolution grids of functional soil properties, for standard depths with associated uncertainties of predictions for the entire globe, to better inform global issues such as food and water security, climate change and

31 environmental condition decline (Hartemink et al., 2010). This global alliance has further contributed to the rapid developments in DSM, where predictions are being applied and tested to country-based operational needs, and is driving increased participation, quality and volume of DSM research.

For example, Akpa et al. (2014) demonstrated an ‘operational’ DSM approach to a National scaled prediction of particle size distribution data to GSM specifications in Nigeria, using Random Forest predictions. Random Forests are similar to RT, but ‘more random’ (Cutler et al., 2007; Grimm et al., 2008; Stum et al., 2010; Wiesmeier et al., 2011; Viscarra Rossel et al., 2014), where trees are generated independently using bootstrap samples, and variable separations are used to split each tree-node (Stum et al., 2010). Akpa et al. produced encouraging results, showing realistic spatial interpretations of soil texture extent, for example, finer textured loams and clay loams in Western areas, and reasonable validation (on a 33 % ‘holdback sample’), for example, 15.6 % RMSE (root-mean squared error) for surface clay %. The authors also found that including the sample ‘depth’ value as a covariate improved overall model performance, and concluded that the results were potentially applicable to agricultural planning and environmental modelling on a National scale.

Adhikari et al. (2012) produced a soil class map of Denmark using DSM, combining 17

scorpan covariates, 1170 legacy soil profile data with a DT modelling approach. The authors note the developing maturation of DSM into adequately providing an operational framework for soil assessment from local to global scales, describing the application of DSM to predict fine resolution (30 m) mapping of soil classes for the national coverage across Denmark as an example. They developed relationships between the soil groups from the legacy data and the covariates using ‘See5’ software (Quinlan, 2014), a DT approach, which applies data-mining to develop relationships between the target class and covariate values into a series of trees, recursively partitioning until no further subset variation can be determined (Adhikari et al., 2012; McBratney et al., 2003). GSM products, namely clay content for 2 depths (topsoil and subsoil) were used as a covariate to effectively differentiate

32 between possible soil groups, and was found to improve the overall categorical predictions of the groups. The prediction accuracy for all groups was 66 %, which was deemed sufficient for various environmental management requirements in Denmark, for example, soil erosion and land use policy. The maps could also be used for refining the mapping of many soil properties, potentially improving the contributions to GSM, and demonstrated the value of such classification approaches to producing GSM-specified property predictions for countries where little point- source soil legacy data is available.

Vaysse and Lagacherie (2015) also identify the operational evolution that DSM is following, majorly influenced and guided by the agreed and clearly defined specifications of GSM, and an increase in “practical” examples requiring data-specific goals. As an example, Vaysse and Lagacherie describe a 27,236 km2 French DSM endeavour which aimed to spatially predict GSM-specified soil properties as a “proof of concept”, testing four popular methodologies including scorpan regression- kriging, spatial-disaggregation, and area-weighted means. The authors affirm the need for region-specific approaches of DSM due to the variations in soil-forming factors across the globe, availability of covariates and spatial density and quality of existing soil profile data. Therefore, a diversity of available DSM methodologies will require testing to determine the optimal approach and best available covariates before being applied to new areas, especially in an operational context, (as emphasised by the research presented in this thesis for the specific Tasmanian geomorphological conditions). Vaysse and Lagacherie used a suite of terrain, parent material, climate and land-use related covariates, in conjunction with available soil database sites, which were fitted to standard GSM-specified depths using mass- preserving depth splines (Malone et al., 2009), removing extreme outliers, and transformation (for example, logarithmic and square-root) for non-normal datasets. The methodologies for soil property predictions included;

• An area-weighted mean (AWM), where the spatial representation of a soil property was determined by the proportion of the minor soils mapped as a soil mapping-unit for a 1:250,000-scaled legacy map.

33 • A testing of the potential for spatial disaggregation of individual soil types

within soil mapping units; and

• Scorpan modelling with and without regression kriging using Random Forests (RF).

Vaysse and Lagacherie (2015) obtained variable predictions across the different soil properties and methodologies, achieving better results using the scorpan prediction methods than those using the legacy soil mapping (AWM and disaggregation). There was little model improvement when using regression-kriging, with optimum predictive validations achieved when using RF alone as the modelling method. With R-squared performances as high as 0.75 for subsoil pH, as well as reasonable validations for other soil properties such as organic carbon, particle sizes and coarse fragments, the authors showed the potential for applying the RF scorpan modelling to other areas of France for GSM-specified contributions, but acknowledged that some soil property predictions such as soil depth (to bedrock) will need further research, and the modelling was limited by the quality of the available soil data inputs.

Again following GSM specifications, Hong et al. (2013) predicted spatial soil available water capacity for Korea using existing soil map, applying pedotransfer functions (PTFs (McBratney et al., 2002)) to this data to produce predictions for bulk density, field capacity and wilting point (Cassel and Nielsen, 1986), where the available water capacity (AWC) was determined as the difference between the field capacity (FC) and the wilting point (WP), and adjusted by bulk density (BD). For each South Korean soil series, the modal AWC was predicted by applying a series of PTFs to appropriate input data, such as particle size analyses, organic matter content, and exchangeable cations. A depth-spline function was applied to determine the values at each of the standard soil GSM depths (Malone et al., 2011). Hong et al. obtained spatial validations (using an independent validation dataset) ranging from 0.36 in the topsoil, to 0.47 at depth (R-squared), which is comparable with other studies using a

scorpan DSM approach. The authors showed that by applying PTFs to existing data, gaps in this data could effectively be filled which allowed successful spatial

34 predictions of the desired soil property, (in this case, BD, FC and WP), which will provide a means of evaluation or biophysical simulations for important agricultural considerations such as potential evapotranspiration.