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.