3D-Digital soil property mapping by geoadditive models

ISSN:1029-7006ISSN:1607-796

Life beyond MSE and R2 — improving validation of predictive models with observations

ISSN:1029-7006ISSN:1607-796

Generalized cross-covariances and their estimation

Generalized cross-covariances describe the linear relationships between spatial variables observed at different locations. They are invariant under translation of the locations for any intrinsic processes, they determine the cokriging predictors without additional assumptions and they are unique up to linear functions. If the model is stationary, that is if the variograms are bounded, they correspond to the stationary cross-covariances. Under some symmetry condition they are equal to minus the usual cross-variogram. We present a method to estimate these generalized cross-covariances from data observed at arbitrary sampling locations. In particular we do not require that all variables are observed at the same points. For fitting a linear coregionalization model we combine this new method with a standard algorithm which ensures positive definite coregionalization matrices. We study the behavior of the method both by computing variances exactly and by simulating from various models.ISSN:1874-8961ISSN:0882-8121ISSN:1874-8953ISSN:1573-886

A Fractal Approach to Model Soil Structure and to Calculate Thermal Conductivity of Soils

Heat transport in soils depends on the spatial arrangement of solids, ice, air and water. In this study, we present a modified fractal approach to model the pore structure of soils and to describe its influence on the thermal conductivity. Three different fractal generators were sequentially applied to characterize a wide range of particle- and pore-size distributions. The given porosity and particle-size distribution of a clay, clay loam, silt loam and loamy sand were successfully modeled. The thermal conductivity of the fractal soil model was calculated using a network of resistors. We applied a renormalization approach to include the effects of smaller scale structures. The predictions were compared with the empirical Johansen' model (Johansen, 1975), that postulates a simple linear relationship between ice content and thermal conductivity. For high ice-saturated conditions, the calculated thermal conductivity agrees well with the empirical model. To describe partial ice saturation, we assumed that some pores were coated by ice films enclosing the air-filled center. In addition, we introduced a reduced heat exchange coefficient of the particles for unsaturated conditions. The ice-saturated and -unsaturated thermal conductivity calculated with this approach was very similar to that estimated by the empirical model. The variation of the thermal conductivities for different spatial arrangements of pores and particles in the prefractals were determined. Extreme values deviate more than 50% from the mean values.ISSN:0169-3913ISSN:1573-163

A hierarchy of environmental covariates control the global biogeography of soil bacterial richness

Soil bacterial communities are central to ecosystem functioning and services, yet spatial variations in their composition and diversity across biomes and climatic regions remain largely unknown. We employ multivariate general additive modeling of recent global soil bacterial datasets to elucidate dependencies of bacterial richness on key soil and climatic attributes. Although results support the well-known association between bacterial richness and soil pH, a hierarchy of novel covariates offers surprising new insights. Defining climatic soil water content explains both, the extent and connectivity of aqueous micro-habitats for bacterial diversity and soil pH, thus providing a better causal attribution. Results show that globally rare and abundant soil bacterial phylotypes exhibit different levels of dependency on environmental attributes. Surprisingly, the strong sensitivity of rare bacteria to certain environmental conditions improves their predictability relative to more abundant phylotypes that are often indifferent to variations in environmental drivers.ISSN:2045-232