Knowledge of how soil carbon stocks and flows change in response to agronomic management decisions is a critical step in devising management strategies that best promote food security while mitigating greenhouse gas emissions. Here, we present 40 years of data demonstrating that increasing productivity both leads to greater carbon stocks and accelerates the decomposition of soil organic matter, thus providing more nutrients back to the crop.

We evaluated mineral control of organic carbon dynamics by relating the content and age of carbon stored in soils of varied mineralogical composition found in the landscapes of Kruger National Park, South Africa. Carbon associated with smectite clay minerals, which have stronger surface–organic matter interactions, averaged about a thousand years old, while most soil carbon was only decades to centuries old and was associated with iron and aluminum oxide minerals.

This research investigates how fires of different intensities affect soil organic matter properties. This study identifies critical temperature thresholds of significant soil organic matter changes. Findings from this study will contribute towards estimating the amount and rate of changes in soil carbon, nitrogen, and other essential soil properties that can be expected from fires of different intensities under anticipated climate change scenarios.

We discuss pathways towards better soil protection in the 21st century. The efficacy of soil conservation technology is not a fundamental barrier for a more sustainable soil management. However, soil conservation is generally not directly beneficial to the farmer. We believe that the solution of this conundrum is a rapid, smart intensification of agriculture in the Global South. This will reduce the financial burden and will, at the same time, allow more effective conservation.

This paper shows that three out of four frequently used methods to calculate soil organic carbon stocks lead to systematic overestimation of those stocks. Stones, which can be assumed to be free of carbon, have to be corrected for in both bulk density and layer thickness. We used data of the German Agricultural Soil Inventory to illustrate the potential bias and suggest a unified and unbiased calculation method for stocks of soil organic carbon, which is the largest terrestrial carbon pool.

Here we demonstrate the use of a probabilistic approach for quantifying soil physical properties and variability using time and environmental input. We applied this approach to a synthesis of soil chronosequences, i.e., soils that change with time. The model effectively predicted clay content across the soil chronosequences and for soils in complex terrain using soil depth as a proxy for hill slope. This model represents the first attempt to model soils from a probabilistic viewpoint.

Model-based analyses of the effect of soil erosion on carbon (C) dynamics are associated with large uncertainties partly resulting from oversimplifications of erosion processes. This study evaluates the need for process-oriented modelling to analyse erosion-induced C fluxes in different catchments. The results underline the importance of a detailed representation of tillage and water erosion processes. For water erosion, grain-size-specific transport is essential to simulate lateral C fluxes.

Prior research found nitrate losses in mid-Atlantic streams following drought but no mechanistic explanation. We aim to understand how legacy sediments influence soil–stream nitrate transfer. We found that surface legacy sediments do not retain excess nitrate inputs well; once exposed, previously buried soils experience the largest drought-induced nitrate losses; and, restoration that reconnects stream and floodplain via legacy sediment removal may initially cause high losses of nitrate.

We examined the spatial variability in SOC in a 10 000 ha landscape in SW China. The spatial variability in SOC was largest at the plot scale (1 ha) and the associations between SOC and land use, soil properties, vegetation, and topographical attributes varied across plot to landscape scales. Our results show that sampling designs must consider the controlling factors at the scale of interest in order to elucidate their effects on SOC against the variability within and between plots.

Roots deeper in the soil are made up of more carbon and less nitrogen compared to roots at shallower depths, which may help explain deep-carbon origin. A comparison of prairie and maize rooting systems showed that in moving from prairie to maize, a large, structural-tissue-dominated root carbon pool with slow turnover concentrated at shallow depths was replaced by a small, nonstructural-tissue-dominated root carbon pool with fast turnover evenly distributed in the soil profile.

Alexander von Humboldt was an inspiring scientist in the early 1800s, traveling widely, making many measurements, and linking different scientific disciplines while keeping an eye open to the needs of society. This is particularly relevant today in our information society, and researchers in developing countries are advised to follow the von Humboldt example when planning their future research.

Injection of biogas digestates (BDs) is suspected to increase losses of N₂O and thus to counterbalance prevented NH₃ emissions. We determined N₂O and N₂ losses after mixing high concentrations of BD into two soils by an incubation under an artificial helium–oxygen atmosphere. Emissions did not increase with the application rate of BD, probably due to an inhibitory effect of the high NH₄⁺ content in BD on nitrification. However, cumulated gaseous N losses may effectively offset NH₃ reductions.

Several studies have shown that the cation adsorption sites in soils are heterogeneously distributed in space. In many soil system models this knowledge is not included yet. In our study we proposed a new method to map the 3-D distribution of cation adsorption sites in undisturbed soils. The method is based on three-dimensional X-ray scanning with a contrast agent and image analysis. We are convinced that this approach will strongly aid the development of more realistic soil system models.

Digital soil mapping (DSM) relates soil property data to environmental data that describe soil-forming factors. With imagery sampled from satellites or terrain analysed at multiple scales, large sets of possible input to DSM are available. We propose a new statistical framework (geoGAM) that selects parsimonious models for DSM and illustrate the application of geoGAM to two study regions. Straightforward interpretation of the modelled effects likely improves end-user acceptance of DSM products.

The application of lipids in soils as molecular proxies, also often referred to as biomarkers, has dramatically increased in the last decades. Applications range from inferring changes in past vegetation composition to unraveling the turnover of soil organic matter. However, the application of soil lipids as molecular proxies comes with several constraining factors. Here we provide a critical review of the current state of knowledge on the applicability of molecular proxies in soil science.

An advantage of geostatistics for mapping soil properties is that, given a statistical model of the variable of interest, we can make a rational decision about how densely to sample so that the map is sufficiently precise. However, uncertainty about the statistical model affects this process. In this paper we show how Bayesian methods can be used to support decision making on sampling with an uncertain model, ensuring that the probability of meeting certain levels of precision is high enough.