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A 3–4D soil model represents a logical step forward from one‐dimensional soil columns (1D), two‐dimensional soil maps (2D), and three‐dimensional soil volumes (3D) toward dynamic soil models (4D), with time as the fourth dimension. The challenge is to develop modeling tools that account for the states of soil properties, including the spatial structure of solids and pores, as well as their dynamics, including soil mass and solute transfers in landscapes. Our envisioned 3–4D soil model approach aims at improving the capability to predict fundamental soil functions (e.g., plant growth, storage, matter fluxes) that provide ecosystem services in the socioeconomic context. This study provides a structured overview on current soil models, challenges, open questions, and urgent research needs for developing a 3–4D soil model. A 3–4D soil model should provide an inventory of spatially distributed and temporally variable soil properties. As basis for this, we propose a mass balance model for the solid phase, which needs to be supplemented by a model describing its structure. This should eventually provide adequate 3D parameter sets for the numerical modeling of soil functions (e.g., flow and transport). The target resolution is decameters in the horizontal plane and centimeters to decimeters in the vertical direction to represent characteristic soil properties and soil horizons. The actual state of soils and their properties can be estimated from spatial data that represent the soil forming factors, with the use of machine learning tools. Improved modeling of the dynamics of soil bulk density, biological processes, and the pore structure are required to relate the solid mass balance to matter fluxes. A 3–4D soil model can be built from several types of modeling approaches. We distinguish between (1) process models that simulate mass balances, fluxes and soil structure dynamics, (2) statistical pedometric models using machine learning and geostatistics to estimate the soil inventory within landscapes, and (3) pedotransfer functions to link observable attributes to specific model parameters required to simulate soil functions including water and matter fluxes. This should provide the prerequisites to predict the spatial distribution of soil functions and their changes in response to external forcing. This endeavor can draw upon many already established models and techniques, yet combining them into a newly created 3–4D soil model is a truly an ambitious, but promising task. The core of such a model is the bookkeeping of the solid mass together with soil structure, while accounting for biogeochemical and mechanical processes. The presented concepts are ambitious in context for research avenues toward the improvement of soil modeling by conjoining methods from a wide range of disciplines, including geological, geophysical, pedological, and remote sensing and visualization applications. The paper reviews and outlines research tools and needs for the 3‐D, spatially continuous representation of relevant soil properties and the modeling to represent the dynamics of soil properties and soil functions. [ABSTRACT FROM AUTHOR]

Agroecosystem models need to reliably simulate all biophysical processes that control crop growth, particularly the soil water fluxes and nutrient dynamics. As a result of the erosion history, truncated and colluvial soil profiles coexist in arable fields. The erosion‐affected field‐scale soil spatial heterogeneity may limit agroecosystem model predictions. The objective was to identify the variation in the importance of soil properties and soil profile modifications in agroecosystem models for both agronomic and environmental performance. Four lysimeters with different soil types were used that cover the range of soil variability in an erosion‐affected hummocky agricultural landscape. Twelve models were calibrated on crop phenological stages, and model performance was tested against observed grain yield, aboveground biomass, leaf area index, actual evapotranspiration, drainage, and soil water content. Despite considering identical input data, the predictive capability among models was highly diverse. Neither a single crop model nor the multi‐model mean was able to capture the observed differences between the four soil profiles in agronomic and environmental variables. The model's sensitivity to soil‐related parameters was apparently limited and dependent on model structure and parameterization. Information on phenology alone seemed insufficient to calibrate crop models. The results demonstrated model‐specific differences in the impact of soil variability and suggested that soil matters in predictive agroecosystem models. Soil processes need to receive greater attention in field‐scale agroecosystem modeling; high‐precision weighable lysimeters can provide valuable data for improving the description of soil–vegetation–atmosphere process in the tested models. [ABSTRACT FROM AUTHOR]

We studied trace elements in soils, plants, water and stream sediments in the Gisuma–Kibilira catchment of the Gatumba area of western Rwanda which has a long tradition of artisanal to small-scale tin–tantalum mining from rare-metal pegmatites. The geochemical fingerprint of soil, plant, water (springs and surface water in dry and rainy seasons) and stream sediment samples reveals elevated concentrations of Li, Rb, Cr, and Cs, but low As and U abundances at or below the global average. Trace element contents of soils and most plant materials are below internationally accepted guideline values. All water samples analyzed meet the World Health Organization (WHO) drinking water guidelines, and the stream sediments are below critical values of Dutch environmental standards. These data provide a baseline for environmental impact studies for rare-metal mining projects in the Central Africa Region. [ABSTRACT FROM AUTHOR]

In the soil–plant–atmosphere continuum, fluxes of water, energy, and carbon determine the water and carbon balance of peat bogs. We used eddy covariance (EC) measurements to study surface atmosphere exchange and its drivers above an ombrotrophic peat bog (Odersprungmoor) in the Harz Mountains, Germany, with nonideal measurement conditions during the growing season in 2013. For montane peatlands, only very few EC flux measurements exist due to site constraints, for example, surface slope, limited fetch, and frequent dew formation on open path sensors. The measured data were carefully filtered resulting in valid and representative fluxes for the bog. The evapotranspiration (ET) was further characterized by determining the adjusted crop coefficient (Kc*) for July and August and comparing it with Kc* values from 7 years of the FLUXNET site Mer Bleue bog, Ontario, Canada. While soil moisture was taken into consideration, the adjustment was nevertheless necessary as plant health and nutrient supply were not evaluated as required by FAO guidelines. Actual ET at OM was well described by the Kc* model (Kc* = 0.85, R2 = 0.85). The primary control on ET was available energy and atmospheric conditions and, marginally, the soil moisture conditions. This Kc* value is comparable to the calculated Kc* values for MB, which ranged between 0.82 and 0.86 (R2 between 0.84 and 0.97). Since these Kc* ranges are narrow for the different sites and years, we hypothesize that these values are good estimates for the true crop coefficients of Sphagnum‐dominated peat bogs. [ABSTRACT FROM AUTHOR]

Waterlogging affects millions of hectares traditionally used for food production every year. Despite this, existing literature and process-based frameworks enabling simulation of waterlogging are sparse. Here, we reveal a lack of field experiments that have enumerated effects of waterlogging on plant growth. We call for more research on waterlogging, particularly in controlled field conditions with quantified soil properties and continuous monitoring of soil moisture. We opine that future experiments should explicitly focus on the impact of waterlogging on phenology, root development, and water and nutrient uptake, including interactions with atmospheric CO 2 concentration, temperature and other biotic/abiotic stresses. Such experimental data could then be used to develop waterlogging algorithms for crop models. Greater understanding of how waterlogging impacts on plant physiology will be conducive to more robust projections of how climate change will impact on global food security. • Existing process-based frameworks enabling simulation of waterlogging are sparse. • We advocate that waterlogging experiments in field conditions are needed. • More accurate projections of crop production under climate change need robust models. [ABSTRACT FROM AUTHOR]