Soil function assessment for Switzerland

Soil provides functions that support life and are essential to human society and the environment. For instance, soil regulates water and nutrient cycles, sequesters carbon, prevents contaminants leaching into groundwater, buffers acidic inputs, contributes to biodiversity by providing a habitat for organisms, and supports biomass production. The ecosystem service (ES) concept is widely used to evaluate the values of natural resources and allows them to be included in decision-making processes. Up to now, however, little account has been taken of soil in any ES assessments. ES mapping studies that included soil in their foci were reviewed in this thesis. About 60% of these studies used at least one soil property as an indicator of soil-related ESs and more than two soil functions were considered in a minority of studies. Better integration of soil into ESs requires tools that are effective and readily applicable. In this thesis, a set of operational soil function assessment (SFA) methods is proposed. This set of SFA methods is intended to act as a starting point to allow the ways soil systems underpin a wide range of ESs to be quantified. The work described in this thesis was part of two projects in Swiss National Research Programme (NRP) 68 \Sustainable use of soil as a resource", namely PMSoil and OPSOL. The PMSoil project was focused on digital soil mapping (DSM) approaches and provided spatial information on soil properties, and the aim of the OPSOL project was to develop a land-use decision model for use in spatial planning processes. 10 SFA methods were selected from various national and international methods. The methods were adapted and used in a study of an agricultural area on the Swiss Plateau. Soil property maps for four soil depths were available for the study area. These were produced using digital soil mapping techniques and each had a raster resolution of 20 m. Pedotransfer functions were used to derive secondary soil properties from the results of previous studies performed by this and other research groups. The resulting maps for the 10 soil functions revealed distinctive spatial patterns for most of the regulation, habitat, and production functions, clearly indicating the multiple roles in which soils support ESs. These soil functions are linked to the inherent properties of the soils, the terrain, and climate conditions. Assessment of how reasonable the soil function maps were was undertaken by comparing soil function fullfillment with the soil type, land use, and hydromorphic features of the soil for more than 7000 soil profiles. It was concluded that a quite comprehensive set of soil functions that can be used to assess the multi-functionality of soils can be determined using a relatively small number of basic properties of soil to at least 1 m deep. The soil function maps indicated spatial variability in soil function fullfillment and were easy for stakeholders to understand because they were presented using a simple ordinal assessment scale. These maps could improve awareness of the multi-functionality of soil and allow visualization iv of the ways soil systems underpin the supply of ESs. SFA methods for production function are already well established, but methods for assessing habitat and regulation functions need to be developed further. Four different approaches to aggregating soil functions to give a total assessment value (a soil index) were tested. The soil index maps had quite different spatial patterns, indicating that merging soil functions can average out spatial variations in certain functions. It was concluded that soil function maps could be aggregated to provide single soil index maps. Stakeholders, though, should take into consideration the importance of each soil function. Uncertainties in soil function maps are required to allow informed and transparent decisions to be made about the sustainable use of soil resources. In general, uncertainties in soil properties propagated using the SFA methods led to substantial uncertainties in the mapped soil functions. Two types of uncertainty map were proposed, each of which is easy for stakeholders to understand. The cumulative distribution functions for the soil function fullfillment scores indicated that the SFA methods responded differently to the propagated soil property uncertainties. Different methods may not be comparable in terms of uncertainty propagation even if the methods are comparable in terms of complexity and assessment scale. This thesis contains an operational framework for assessing soil functions to facilitate the incorporation of SFA into decision-making, thereby highlighting the multiple functions of soils, which will enable the sustainable use of soil resources to be promoted during spatial planning

Evaluation of digital soil mapping approaches with large sets of environmental covariates

The spatial assessment of soil functions requires maps of basic soil properties. Unfortunately, these are either missing for many regions or are not available at the desired spatial resolution or down to the required soil depth. The field-based generation of large soil datasets and conventional soil maps remains costly. Meanwhile, legacy soil data and comprehensive sets of spatial environmental data are available for many regions. Digital soil mapping (DSM) approaches relating soil data (responses) to environmental data (covariates) face the challenge of building statistical models from large sets of covariates originating, for example, from airborne imaging spectroscopy or multi-scale terrain analysis. We evaluated six approaches for DSM in three study regions in Switzerland (Berne, Greifensee, ZH forest) by mapping the effective soil depth available to plants (SD), pH, soil organic matter (SOM), effective cation exchange capacity (ECEC), clay, silt, gravel content and fine fraction bulk density for four soil depths (totalling 48 responses). Models were built from 300–500 environmental covariates by selecting linear models through (1) grouped lasso and (2) an ad hoc stepwise procedure for robust external-drift kriging (georob). For (3) geoadditive models we selected penalized smoothing spline terms by component-wise gradient boosting (geoGAM). We further used two tree-based methods: (4) boosted regression trees (BRTs) and (5) random forest (RF). Lastly, we computed (6) weighted model averages (MAs) from the predictions obtained from methods 1–5. Lasso, georob and geoGAM successfully selected strongly reduced sets of covariates (subsets of 3–6 % of all covariates). Differences in predictive performance, tested on independent validation data, were mostly small and did not reveal a single best method for 48 responses. Nevertheless, RF was often the best among methods 1–5 (28 of 48 responses), but was outcompeted by MA for 14 of these 28 responses. RF tended to over-fit the data. The performance of BRT was slightly worse than RF. GeoGAM performed poorly on some responses and was the best only for 7 of 48 responses. The prediction accuracy of lasso was intermediate. All models generally had small bias. Only the computationally very efficient lasso had slightly larger bias because it tended to under-fit the data. Summarizing, although differences were small, the frequencies of the best and worst performance clearly favoured RF if a single method is applied and MA if multiple prediction models can be developed

Soil quality indicators – From soil functions to ecosystem services

ISSN:1470-160

Soil function assessment: review of methods for quantifying the contributions of soils to ecosystem services

ISSN:0264-8377ISSN:1873-575

Mapping of soil properties at high resolution in Switzerland using boosted geoadditive models

High-resolution maps of soil properties are a prerequisite for assessing soil threats and soil functions and for fostering the sustainable use of soil resources. For many regions in the world, accurate maps of soil properties are missing, but often sparsely sampled (legacy) soil data are available. Soil property data (response) can then be related by digital soil mapping (DSM) to spatially exhaustive environmental data that describe soil-forming factors (covariates) to create spatially continuous maps. With airborne and space-borne remote sensing and multi-scale terrain analysis, large sets of covariates have become common. Building parsimonious models amenable to pedological interpretation is then a challenging task. We propose a new boosted geoadditive modelling framework (geoGAM) for DSM. The geoGAM models smooth non-linear relations between responses and single covariates and combines these model terms additively. Residual spatial autocorrelation is captured by a smooth function of spatial coordinates, and non-stationary effects are included through interactions between covariates and smooth spatial functions. The core of fully automated model building for geoGAM is component-wise gradient boosting. We illustrate the application of the geoGAM framework by using soil data from the Canton of Zurich, Switzerland. We modelled effective cation exchange capacity (ECEC) in forest topsoils as a continuous response. For agricultural land we predicted the presence of waterlogged horizons in given soil depths as binary and drainage classes as ordinal responses. For the latter we used proportional odds geoGAM, taking the ordering of the response properly into account. Fitted geoGAM contained only a few covariates (7 to 17) selected from large sets (333 covariates for forests, 498 for agricultural land). Model sparsity allowed for covariate interpretation through partial effects plots. Prediction intervals were computed by model-based bootstrapping for ECEC. The predictive performance of the fitted geoGAM, tested with independent validation data and specific skill scores for continuous, binary and ordinal responses, compared well with other studies that modelled similar soil properties. Skill score (SS) values of 0.23 to 0.53 (with SS = 1 for perfect predictions and SS = 0 for zero explained variance) were achieved depending on the response and type of score. GeoGAM combines efficient model building from large sets of covariates with effects that are easy to interpret and therefore likely raises the acceptance of DSM products by end-users.ISSN:2199-3971ISSN:2199-398

Mapping of soil properties at high resolution in Switzerland using boosted geoadditive models

High-resolution maps of soil properties are a prerequisite for assessing soil threats and soil functions and for fostering the sustainable use of soil resources. For many regions in the world, accurate maps of soil properties are missing, but often sparsely sampled (legacy) soil data are available. Soil property data (response) can then be related by digital soil mapping (DSM) to spatially exhaustive environmental data that describe soil-forming factors (covariates) to create spatially continuous maps. With airborne and space-borne remote sensing and multi-scale terrain analysis, large sets of covariates have become common. Building parsimonious models amenable to pedological interpretation is then a challenging task. We propose a new boosted geoadditive modelling framework (geoGAM) for DSM. The geoGAM models smooth non-linear relations between responses and single covariates and combines these model terms additively. Residual spatial autocorrelation is captured by a smooth function of spatial coordinates, and non-stationary effects are included through interactions between covariates and smooth spatial functions. The core of fully automated model building for geoGAM is component-wise gradient boosting. We illustrate the application of the geoGAM framework by using soil data from the Canton of Zurich, Switzerland. We modelled effective cation exchange capacity (ECEC) in forest topsoils as a continuous response. For agricultural land we predicted the presence of waterlogged horizons in given soil depths as binary and drainage classes as ordinal responses. For the latter we used proportional odds geoGAM, taking the ordering of the response properly into account. Fitted geoGAM contained only a few covariates (7 to 17) selected from large sets (333 covariates for forests, 498 for agricultural land). Model sparsity allowed for covariate interpretation through partial effects plots. Prediction intervals were computed by model-based bootstrapping for ECEC. The predictive performance of the fitted geoGAM, tested with independent validation data and specific skill scores for continuous, binary and ordinal responses, compared well with other studies that modelled similar soil properties. Skill score (SS) values of 0.23 to 0.53 (with SS = 1 for perfect predictions and SS = 0 for zero explained variance) were achieved depending on the response and type of score. GeoGAM combines efficient model building from large sets of covariates with effects that are easy to interpret and therefore likely raises the acceptance of DSM products by end-users.ISSN:2199-3971ISSN:2199-398

Uncertainty indication in soil function maps – transparent and easy-to-use information to support sustainable use of soil resources

Spatial information on soil function fulfillment (SFF) is increasingly being used to inform decision-making in spatial planning programs to support sustainable use of soil resources. Soil function maps visualize soils abilities to fulfill their functions, e.g., regulating water and nutrient flows, providing habitats, and supporting biomass production based on soil properties. Such information must be reliable for informed and transparent decision-making in spatial planning programs. In this study, we add to the transparency of soil function maps by (1) indicating uncertainties arising from the prediction of soil properties generated by digital soil mapping (DSM) that are used for soil function assessment (SFA) and (2) showing the response of different SFA methods to the propagation of uncertainties through the assessment. For a study area of 170 km2 in the Swiss Plateau, we map 10 static soil sub-functions for agricultural soils for a spatial resolution of 20 × 20 m together with their uncertainties. Mapping the 10 soil sub-functions using simple ordinal assessment scales reveals pronounced spatial patterns with a high variability of SFF scores across the region, linked to the inherent properties of the soils and terrain attributes and climate conditions. Uncertainties in soil properties propagated through SFA methods generally lead to substantial uncertainty in the mapped soil sub-functions. We propose two types of uncertainty maps that can be readily understood by stakeholders. Cumulative distribution functions of SFF scores indicate that SFA methods respond differently to the propagated uncertainty of soil properties. Even where methods are comparable on the level of complexity and assessment scale, their comparability in view of uncertainty propagation might be different. We conclude that comparable uncertainty indications in soil function maps are relevant to enable informed and transparent decisions on the sustainable use of soil resources

Assessment of soil multi-functionality to support the sustainable use of soil resources on the Swiss Plateau

Spatial information on soils and their abilities to fulfil their functions is key to sustainable soil resource use. Maps indicating how soils fulfil their functions, e.g., regulating nutrient and water flows, providing appropriate habitats, and supporting biomass production, have allowed soil information to be embedded in spatial planning programmes. We adapted 10 static soil function assessment (SFA) methods and applied them to agricultural soils in a study area on the Swiss Plateau. Soil function maps were created by applying the SFA methods to maps of eight basic soil properties generated previously using digital soil mapping techniques. The soil function maps were compared with results obtained by applying the SFA methods to data for >7000 soil profiles to determine how reasonable the maps were. Soil in the study area had distinctive spatial patterns for most of the regulation, habitat, and production functions, clearly indicating the multiple roles played by soil in supporting ecosystem services. The fulfilment of individual soil functions is linked to the inherent soil properties, the terrain, and climatic conditions. The soil function maps agreed well with the SFA results for the profiles in terms of land use, soil type, and drainage class. Four aggregation rules were tested to give total assessment values (soil indices). Aggregating the 10 soil functions into an overall soil functionality value gave quite diverse spatial patterns, indicating that merging might average out the spatial characteristics of certain soil functions. We conclude that a quite comprehensive set of soil functions can be assessed using spatial information for eight basic soil properties to a soil depth of at least 1 m and approved pedotransfer functions for secondary soil properties. SFA methods for the production function are well established, but methods for assessing habitat and regulation functions need to be developed further. This is also true for forest soils, for which SFA methods are yet to be established