Feasibility of the 4 per 1000 aspirational target for soil carbon: A case study for France
Bibliographic record
Abstract
Abstract Increasing soil organic carbon (SOC) stocks is a promising way to mitigate the increase in atmospheric CO 2 concentration. Based on a simple ratio between CO 2 anthropogenic emissions and SOC stocks worldwide, it has been suggested that a 0.4% (4 per 1000) yearly increase in SOC stocks could compensate for current anthropogenic CO 2 emissions. Here, we used a reverse RothC modelling approach to estimate the amount of C inputs to soils required to sustain current SOC stocks and to increase them by 4‰ per year over a period of 30 years. We assessed the feasibility of this aspirational target first by comparing the required C input with net primary productivity (NPP) flowing to the soil, and second by considering the SOC saturation concept. Calculations were performed for mainland France, at a 1 km grid cell resolution. Results showed that a 30%–40% increase in C inputs to soil would be needed to obtain a 4‰ increase per year over a 30‐year period. 88.4% of cropland areas were considered unsaturated in terms of mineral‐associated SOC, but characterized by a below target C balance, that is, less NPP available than required to reach the 4‰ aspirational target. Conversely, 90.4% of unimproved grasslands were characterized by an above target C balance, that is, enough NPP to reach the 4‰ objective, but 59.1% were also saturated. The situation of improved grasslands and forests was more evenly distributed among the four categories (saturated vs. unsaturated and above vs below target C balance). Future data from soil monitoring networks should enable to validate these results. Overall, our results suggest that, for mainland France, priorities should be (1) to increase NPP returns in cropland soils that are unsaturated and have a below target carbon balance and (2) to preserve SOC stocks in other land uses.
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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".