The Positive Effects of Soil Organic Carbon on European Cereal Yields Level Off at 1.4%
Bibliographic record
Abstract
ABSTRACT Introduction Increasing soil organic carbon (SOC) in croplands is a natural climate mitigation effort that can also enhance crop yields. However, there is a lack of comprehensive field studies examining the impact of SOC on crop yields across wide climatic, soil, and farming gradients. Furthermore, it is largely unknown how water retention, soil microbial diversity, and nutrient availability modulate the SOC‐crop yield relationship. Materials and Methods We conducted an observational study across 127 cereal fields along a 3000 km north‐south gradient in Europe, measured topsoil (0–20 cm) organic C content, and collected data on climate, soil properties, crop yield and farming practices. Additionally, we explored the relationship between crop yield, particulate organic carbon (POC) and mineral‐associated organic carbon (MAOC) contents at three soil depths (0–20, 20–40 and 40–60 cm) in a subset of sites. Results Relative yield increases levelled off at 1.4% SOC, indicating an optimal SOC content for cereals along a European gradient. The quadratic relationship between SOC and cereal yield was conspicuous even after controlling for large differences in climate, soil and farming practices across countries. The relationship varied significantly across soil depths and C fractions. MAOC dominated the SOC pool, and was significantly related to relative yield up to an optimal level that varied with soil depth. Soil microbial diversity and nutrient availability emerged as main drivers of the SOC‐yield relationship, while water retention did not exhibit a notable influence. Conclusions Our study demonstrates that SOC is as a key determinant of cereal yield along a European gradient, and identifying this threshold can inform soil management strategies for improved carbon capture based on initial SOC levels. Nevertheless, the complex SOC‐yield relationship highlights the necessity for tailored soil management strategies that consider specific site conditions to optimize C storage and crop yield.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".