What makes soil landscape robust? Landscape sensitivity towards land use changes in a Swiss southern Alpine valley.

Landscape sensitivity is a concept referring to the likelihood that changes in land use may affect in an irreversible way physical and chemical soil properties of the concerned landscape. The objective of this study is to quantitatively assess the sensitivity of the southern Alpine soil landscape regarding land use change-induced perturbations. Alpine soil landscapes can be considered as particularly sensitive to land use changes because their effects tend to be enhanced by frequent extreme climatic and topographic conditions as well as intense geomorphologic activity. In detail, the following soil key properties for soil vulnerability were analysed: (i) soil texture, (ii) bulk density, (iii) soil organic carbon (SOC), (iv) saturated hydraulic conductivity (Ksat), (v) aggregate stability and (vi) soil water repellency (SWR). The study area is characterized by a steep, east-west oriented valley, strongly anthropized in the last centuries followed by a progressive abandonment. This area is particularly suitable due to constant lithological conditions, extreme topographic and climatic conditions as well as historic land use changes. The analysis of land use change effects on soil properties were performed through a linear mixed model approach due to the nested structure of the data. Our results show a generally high stability of the assessed soils in terms of aggregate stability and noteworthy thick soils. The former is remarkable, since aggregate stability, which is commonly used for detecting land use-induced changes in soil erosion susceptibility, was always comparably high irrespective of land use. The stability of the soils is mainly related to a high amount of soil organic matter favouring the formation of stable soil aggregates, decreasing soil erodibility and hence, reducing soil loss by erosion. However, the most sensitive soil property to land use change was SWR that is partly influenced by the amount of soil organic carbon and probably by the quality and composition of SOM.

New pedotransfer approaches to predict soil bulk density using WoSIS soil data and environmental covariates in Mediterranean agro-ecosystems.

For the estimation of the soil organic carbon stocks, bulk density (BD) is a fundamental parameter but measured data are usually not available especially when dealing with legacy soil data. It is possible to estimate BD by applying pedotransfer function (PTF). We applied different estimation methods with the aim to define a suitable PTF for BD of arable land for the Mediterranean Basin, which has peculiar climate features that may influence the soil carbon sequestration. To improve the existing BD estimation methods, we used a set of public climatic and topographic data along with the soil texture and organic carbon data. The present work consisted of the following steps: i) development of three PTFs models separately for top (0-0.4 m) and subsoil (0.4-1.2 m), ii) a 10-fold cross-validation, iii) model transferability using an external dataset derived from published data. The development of the new PTFs was based on the training dataset consisting of World Soil Information Service (WoSIS) soil profile data, climatic data from WorldClim at 1 km spatial resolution and Shuttle Radar Topography Mission (SRTM) digital elevation model at 30 m spatial resolution. The three PTFs models were developed using: Multiple Linear Regression stepwise (MLR-S), Multiple Linear Regression backward stepwise (MLR-BS), and Artificial Neural Network (ANN). The predictions of the newly developed PTFs were compared with the BD calculated using the PTF proposed by Manrique and Jones (MJ) and the modelled BD derived from the global SoilGrids dataset. For the topsoil training dataset (N = 129), MLR-S, MLR-BS and ANN had a R2 0.35, 0.58 and 0.86, respectively. For the model transferability, the three PTFs applied to the external topsoil dataset (N = 59), achieved R2 values of 0.06, 0.03 and 0.41. For the subsoil training dataset (N = 180), MLR-S, MLR-BS and ANN the R2 values were 0.36, 0.46 and 0.83, respectively. When applied to the external subsoil dataset (N = 29), the R2 values were 0.05, 0.06 and 0.41. The cross-validation for both top and subsoil dataset, resulted in an intermediate performance compared to calibration and validation with the external dataset. The new ANN PTF outperformed MLR-S, MLR-BS, MJ and SoilGrids approaches for estimating BD. Further improvements may be achieved by additionally considering the time of sampling, agricultural soil management and cultivation practices in predictive models.