The goal of this study was to discuss current methods to create soils adapted for various green infrastructure (GI) designs. Investigating these new soils for several design categories of GI will provide technical information for management and design agencies. Moreover, these studies can serve as pioneer experiments to prevent recurring errors and, thus, provide improved plant growth practices. Results show that these constructed soils have a high potential to provide multiple soil functions.

We do not understand the short- and long-term temperature response of soil denitrifiers, which produce and consume N₂O. Boreal forest soils from a long-term climate gradient were incubated in short-term warming experiments. We found stronger N₂O consumption at depth, inconsistent microbial gene abundance and function, and consistent higher N₂O emissions from warmer-climate soils at warmer temperatures. Consideration of our results in models will contribute to improved climate projections.

In this paper we introduce the use of game theory to interpret a digital soil mapping (DSM) model to understand the contribution of environmental factors to the prediction of soil organic carbon (SOC) in Chile. The analysis corroborated that the SOC model is capturing sensible relationships between SOC and climatic and topographical factors. We were able to represent them spatially (map) addressing the limitations of the current interpretation of models in DSM.

Most soil management activities are implemented at farm scale, yet digital soil maps are commonly available at regional/national scales. This study proposes Bayesian area-to-point kriging to downscale regional-/national-scale soil property maps to farm scale. A regional soil carbon map with a resolution of 100 m (block support) was disaggregated to 10 m (point support) information for a farm in northern NSW, Australia. Results are presented with the uncertainty of the downscaling process.

We developed a model to simulate long-term development of soils and landscapes under varying rainfall and land-use conditions to quantify the temporal variation of soil patterns. In natural landscapes, rainfall amount was the dominant factor influencing soil variation, while for agricultural landscapes, landscape position became the dominant factor due to tillage erosion. Our model shows potential for simulating past and future developments of soils in various landscapes and climates.