Patterns and thresholds for soil pH across Europe in relation to soil health and degradation
Bibliographic record
Abstract
Soil pH indicates the level of acidity or alkalinity in the soil environment, influencing various biogeochemical and physical processes. Additionally, soil pH levels are crucial in determining the bioavailability of elements such as iron, aluminium, and heavy metals which can be harmful. As such, pH is an important soil health and degradation indicator. Although there is a well-established understanding of soil pH at localized levels, the spatial and temporal variations, as well as significant thresholds at national and continental scales, are not sufficiently documented. Here we analyse the European topsoil pH data (LUCAS) in combination with other soil properties from the LUCAS survey, to identify thresholds and spatial patterns of soil pH across Europe in relation to soil health and degradation. At the European scale we found: 1) the water balance, calculated as mean annual precipitation minus potential evapotranspiration (MAP-PET), provides essential context to interpret soil pH; 2) the shift from organic carbon-rich soils to those dominated by inorganic carbon is observed at a pH of about 7.2, however, soil moisture levels may be more critical than pH for the accumulation of soil organic carbon; 3) we identified three distinct clusters within the multivariate regression tree: acidophiles (below pH 5.2), neutrophiles (pH 5.2–6.9) and alkaliphiles (above pH 6.9), while optimum microbial diversity occurred between pH 6 and 7. Earthworm abundance, as reported by the sWorm database, is more nuanced and dependent on land use; 4) risk of degradation by heavy metals cannot be captured by a single pH threshold. Finally, we identify soil pH thresholds that can aid policymakers in identifying regions that may require protection or intervention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".