Soils store large carbon and are important for global warming. We do not know what factors are important for soil carbon storage in the alpine Andes or how they work. We studied how rainfall affects soil carbon storage related to soil structure. We found soil structure is not important, but soil carbon storage and stability controlled by rainfall is dependent on rocks under the soils. The results indicate that we should pay attention to the rocks when we study soil carbon storage in the Andes.

Soil quality depends on the functioning of soil microbiota. Only a few standardized methods are available to assess this as well as adverse effects of human activities. So we need to identify promising additional methods that target soil microbial function. Discussed are (i) molecular methods using qPCR for new endpoints, e.g. in N and P cycling and greenhouse gas emissions, (ii) techniques for fungal enzyme activities, and (iii) field methods on carbon turnover such as the litter bag test.

The application of machine learning (ML) has shown an accelerated adoption in soil sciences. It is a difficult task to manually review all papers on the application of ML. This paper aims to provide a review of the application of ML aided by topic modelling in order to find patterns in a large collection of publications. The objective is to gain insight into the applications and to discuss research gaps. We found 12 main topics and that ML methods usually perform better than traditional ones.

Amazon soils hold as much carbon (C) as is contained in the vegetation. In this work we sampled soils across 8 different Amazonian countries to try to understand which soil properties control current Amazonian soil C concentrations. We confirm previous knowledge that highly developed soils hold C through clay content interactions but also show a previously unreported mechanism of soil C stabilization in the younger Amazonian soil types which hold C through aluminium organic matter interactions.

Data sharing and collaboration are critical to solving large-scale problems. The prevailing soil data-sharing model is of a centralized nature and, consequently, results in the participants ceding control and governance over their data to the lead party. Here we explore the use of a distributed ledger (blockchain) to solve the aforementioned issues. We also describe the potential use case of developing a global soil spectral library between multiple, international institutions.

The use of non-invasive geophysical imaging of root system processes is of increasing interest to study soil–plant interactions. The experiment focused on the behaviour of grapevine plants during a controlled infiltration experiment. The combination of the mise-à-la-masse (MALM) method, a variation of the classical electrical tomography map (ERT), for which the current is transmitted directly into the stem, holds the promise of being able to image root distribution.

Global warming leads to increased mineralisation of soil organic matter, inducing a positive climate–carbon cycle feedback loop. Loss of organic matter can be associated with loss of soil structure. Here we use a strong geothermal gradient to investigate soil warming effects on soil organic matter and structural parameters in subarctic forest and grassland soils. Strong depletion of organic matter caused a collapse of aggregates, highlighting the potential impact of warming on soil function.

Soils contain one of the largest and most dynamic pools of carbon on Earth, yet scientists still struggle to understand the reactivity and fate of soil organic matter upon disturbance. In this study, we found that with increasing thermal stability, the turnover time of organic matter increased from decades to centuries with a concurrent shift in chemical composition. In this proof-of-concept study, we found that ramped thermal analyses can provide new insights for understanding soil carbon.

This study aimed at comparison of various experimental strategies for incubating soil samples to determine the N₂ flux. Such experiments require addition of isotope tracer, i.e. nitrogen fertilizer enriched in heavy nitrogen isotopes (¹⁵N). Here we compared the impact of soil homogenization and mixing with the tracer and tracer injection to the intact soil cores. The results are well comparable: both techniques would provide similar conclusions on the magnitude of N₂ flux.

Four commercial phytases were evaluated as candidates to be used as biological fertilizer to release inorganic phosphorus (P) from phytates and other soil P organic forms. All phytases were able to release inorganic P throughout the pH and temperature ranges for optimum crop production and had a low affinity for the solid phase, with some differences between them. These results indicate that the use of phytases to complement P fertilization may be a feasible tool to enhance soil P availability.

The objective of this study was to develop a soil grouping system based on spectral, climate, and terrain variables with the aim of developing a quantitative way to classify soils. To derive the new system, we applied the above-mentioned variables using cluster analysis and defined eight groups or "soil environment groupings" (SEGs). The SEG system facilitated the identification of groups with similar characteristics using not only soil but also environmental variables for their distinction.

The long-term evolution of soil organic carbon in an olive orchard (planted in 1856) was evaluated and compared to an adjacent undisturbed natural area. Total soil organic carbon in the top 40 cm of the soil in the orchard was reduced to 25 % of that in the undisturbed area. The deposition downslope in the orchard of sediment coming from the eroded upslope area did not increase the accumulation of organic carbon in soil, but it quadrupled available phosphorus and improved overall soil quality.

Our study aimed to disentangle the contribution of several drivers to explaining the proportion of soil carbon that can be released to CO₂ through microbial respiration. We found that boreal-forest soil chemistry is an important driver of the amount of carbon that microbes can process. Our results emphasize the need to include the effects of soil chemistry into models of carbon cycling to better anticipate the role played by boreal-forest soils in carbon-cycle–climate feedbacks.

Pedotransfer functions (PTFs) for soil water retention were developed for two tropical soil landscapes using machine learning. The models corresponding to these PTFs had to be adjusted by tuning their parameters. The standard tuning approach was compared to mathematical optimization. The latter resulted in much better model performance. The PTFs derived are of particular importance for soil process and hydrological models.

In this study, we demonstrate how new equations describing base cation release from mineral weathering can reproduce patterns in observations from stream and soil water. This is a major step towards modeling base cation cycling on the catchment scale, which would be valuable for defining the highest sustainable rates of forest harvest and levels of acidifying deposition.