We conducted a service soil laboratory comparison study and tested the individual effect of common sieving, grinding, drying, and quantification methods on total, inorganic, and organic soil carbon (C) measurements. We found that inter-lab variability is large and each soil processing step impacts C measurement accuracy and/or precision. Standardizing soil processing methods is needed to ensure C measurements are accurate and precise, especially for C credit allocation and model calibration.

We investigated relationships between mineral nitrogen (N) fertilisation rates and additional manure amendment with different crop types through an analysis of soil environmental characteristics and microbiomes, soil N₂O and N₂ emissions as well as biomass production. The results show that wheat grew well at a fertilisation rate of 80 kg N ha⁻¹, and newly introduced sorghum showed good potential for cultivation in temperate climates.

We examined historical sources for the Ecological Survey of Zambia, 1932–1943. This found how normal erosion gave rise to soil variation in the upper Zambezi valley, which was expressed in vegetation patterns which African farmers interpreted to select sites for cultivation and traditional production systems. The survey challenged colonial assumptions about traditional practices. We identify lessons for modern-day approaches to traditional agricultural knowledge in Africa.

Soil produces 98.8 % of the calories consumed by humans, but the contribution that the anthropogenic use of soil makes to global warming is not clear. We show that soil has contributed 15 % of the total global warming caused by well-mixed greenhouse gases. Thus, our finding that soil is a substantial contributor to global anthropogenic greenhouse gas emissions represents a "wicked problem" – how do we continue to increase food production from soil whilst also decreasing emissions?

Soil compaction resulting from inappropriate agricultural practices affects soil ecological functions, decreasing the water-use efficiency of plants. Recent developments contributed to innovative sensing approaches aimed at safeguarding soil health. Here, we explored how the most used geophysical methods detect soil compaction. Results, validated with traditional characterization methods, show the pros and cons of non-invasive techniques and their ability to characterize compacted areas.

This study characterized pore structure and soil organic carbon (SOC) fractions of aggregates during the seasonal freeze–thaw process. Freezing was associated with SOC accumulation, while the early stage of thawing was characterized by SOC loss. In the freezing period, pore structure could enhance SOC accumulation by promoting formation of > 80 μm pores. In the thawing period, pores of < 15 μm might inhibit SOC loss. These results present new perspectives on soil microstructure–SOC interactions.

We studied greenhouse (GHG) gas fluxes at an agricultural peat soil site with willow, forage and set-aside. The mean annual water table rose from 80 to 30 cm in 2019–2022. Raising the water table slowed down annual CO₂ emissions. CH₄ fluxes changed from uptake to emissions, and nitrous oxide emissions decreased towards the end of the experiment. The total greenhouse gas balance was relatively high, highlighting the challenge in mitigating emissions from cultivated peatlands.

The aim of this work was to evaluate the correlation between soil organic carbon (SOC) and various soil properties. Nine plots across an altitudinal range from 10 to 320 m were investigated in the deglaciated region of James Ross Island (Antarctica). Our results indicate that the primary factor influencing the SOC content is likely not altitude or coarse-fraction content; rather, other hard-to-quantify factors, such as the presence of liquid water during the summer period, impact SOC content.

This paper compares the soil organic carbon fractions obtained from a new thermal fractionation scheme and a well-known physical fractionation scheme on an unprecedented dataset of French topsoil samples. For each fraction, we use a machine learning model to determine its environmental drivers (pedology, climate, and land cover). Our results suggest that these two fractionation schemes provide different fractions, which means they provide complementary information.