A model-based assessment of the soil C storage potential at the national scale: A case study from France
Bibliographic record
Abstract
The recent controversy about the 4 per 1000 initiative has emphasized the need for a quantitative assessment of the C storage potential of agricultural soils. Moreover a clear distinction is required between the biophysically and the economically achievable potentials. Here we used a modelling approach at a fine spatial-scale resolution (< 8 km2) to quantify the additional C storage in agricultural soils of mainland France following the implementation, when feasible, of a range of soil C storing practices (i.e. cover crops, reduced tillage, new C inputs, grazing instead of mowing,…). The additional cost for farmers was also calculated, thus yielding the cost per Mg of additional C stored in soils. Results showed that the average additional C storage calculated over 30 years ranged between +0.028 and + 0.466 Mg C ha-1 yr-1 (i.e. between +0.5 and +7.2‰) for the different tested practices, with a very high spatial variability over France for each practice related to initial C stocks and pedo-climatic conditions. The storing practices where then ranked according to the cost of the additional C stored in soils (expressed in euro per Mg of C) and an optimal cost-efficient strategy was proposed at the national level.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".