Data-driven analysis of soil quality indicators using limited data #
9.4.2. Morphological properties as parameters for estimation of hydraulic conductivity In Figure 9-3a, the threshold silt and sand values of 31.47% and 37.6% indicate distinction
in textural classes, i.e. fine, medium and coarse. The model tree reproduces the theoretical approach of having different parameters for estimating Ks according to soil texture (Lilly et al., 2008). On the other hand, when soil morphological properties were added as predictor variables, a simple tree was built (Figure 9-3b). Despite the tree’s simplicity, the model performance was higher when morphological parameters were included (CC of 0.75 and an RMSE of 0.67 cm h-1). This shows the potential of quantifying soil structure to explain hydraulic properties. Lin et al. (1999) demonstrated that pedality and porosity are crucial in characterizing hydraulic behaviour in the macropore flow region, and are better alternatives than the classical approach of using particle-size distribution, BD and SOC.
The model tree displayed in Figure 9-3b indicates that the soil structure index and the earthworm number are important parameters in the estimation of Ks for the soils studied. The soil structure index corresponds to the visual evaluation of the clods and aggregate size distribution by using the VSA method of Shepherd (2009). A higher proportion of coarse fragments represents poor soil structural condition. Overall coarse fragments are clods with high rupture resistance and low porosity, which limit the conductivity of the water. Earthworm number is a biological parameter that can be
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154 related to biopores between and within aggregates (earthworm burrows). The higher the number of earthworms the better the soil physical condition is expected in terms of soil porosity and hence water movement (Shepherd, 2009). Regarding the selection of the soil structure index as a predictor variable of Ks, authors such as Guber et al. (2003) have demonstrated that aggregate size distribution parameters can be useful in estimating parameters of soil water retention when using regression trees.
The models could adequately reproduce the effect of both the interaction between soil chemical and physical characteristics, and the arrangement of the soil fragments and the biological activity of the soil macro fauna on the Ks thresholds. Three out of the six variables selected by the models (SOC, soil structure index and earthworm number) (Table 9-2), are parameters highly affected by soil management and land use, which is physically meaningful in the estimation of Ks in agricultural soils.
These results are valuable in that they enable to identify morphological variables that are useful for prediction. The models presented here are encouraging because prediction of changes in soil structure and hydraulic conductivity, due to management and soil type, could be achieved with the collection of only a few variables.
Because a single rigorous means for quantifying soil structure does not really exist, Lin et al. (1999) proposed the use of soil profile description data as a major source of soil structural information for predicting hydraulic properties using PTFs. However, in those cases where the soil profile description data is not available or the study scale is more detailed, the data obtained by visual examination and evaluation methods (e.g. VSA) are capable in providing morphological information of the soil quality.
Andrews et al. (2004) mention that the analyses of integrated data in some cases can give more information than observed data alone. The information obtained from VSA, which summarised in a single score the whole evaluation of different indicators (i.e.
texture, soil structure, soil colour, potential rooting depth, earthworms, among others);
contributes to a more comprehensive evaluation of soil quality. Finally, the use of decision tree techniques that involve VSA data could be considered in further researches as a useful tool for the integration of soil management practices, soil physical properties and soil and plant processes.
The statistical technique applied in this Chapter is perhaps simpler than other frameworks presented by authors such as Karlen and Stott (1994), Andrews et al. (2004), and Armenise et al. (2013) for selecting important indicators of soil quality. However, it has the advantage of including categorical and numerical variables for evaluating soil quality.
9.5. Conclusions
Results demonstrate that the combination of soil physical and chemical properties with morphological evaluation of the soil quality using classification trees may provide reliable frameworks for soil quality evaluation under different environments. Classification trees
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155 could overcome the difficulties in using classified and numerical data together. This makes the selection of SQIs more flexible and allows integrated assessment of the soil quality status across different soil types, regions and management systems. Despite the limited database used in this study, physical reliable explanation was found in the models constructed. Predictions of Ks were improved when using morphological parameters such as soil structure index and number of earthworms, as explanatory variables. Decision trees are encouraging in the selection not only of well-developed SQIs, but also of the most influential morphological properties to be used in the prediction of key soil properties such as Ks. These statistical techniques appear to be helpful in future research directions for the evaluation of soil quality in relation to agricultural productivity. Visual soil assessment could be considered as dependable morphological data not only for predicting other soil properties, but also for developing soil quality frameworks (agricultural interest) more capable of representing structural dynamic to contribute to soil conservation and sustainable agriculture approaches.
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# This Chapter is based on:
Pulido Moncada, M., Ball, B.C., Gabriels, D., Lobo, D., Cornelis, W.M., Evaluation of Soil Physical Quality Index S for Some "Tropical" and "Temperate" Medium Textured Soils. Soil Sci. Soc.
Am. J., doi:10.2136/sssaj2014.06.0259.
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