Photosynthetic limits on carbon sequestration in croplands
Bibliographic record
Abstract
How much C can be stored in agricultural soils worldwide to mitigate rising carbon dioxide (CO2) concentrations, and at what cost? This question, because of its critical relevance to climate policy, has been a focus of soil science for decades. The amount of additional soil organic C (SOC) that could be stored has been estimated in various ways, most of which have taken the soil as the starting point: projecting how much of the SOC previously lost can be restored, for example, or calculating the cumulative effect of multiple soil management strategies. Here, we take a different approach, recognizing that photosynthesis, the source of C input to soil, represents the most fundamental constraint to C sequestration. We follow a simple “Fermi approach” to derive a rough but robust estimate by reducing our problem to a series of approximate relations that can be parameterized using data from the literature. We distinguish two forms of soil C: ‘ephemeral C’, denoting recently-applied plant-derived C that is quickly decayed to CO2, and ‘lingering C,’ which remains in the soil long enough to serve as a lasting repository for C derived from atmospheric CO2. First, we estimate global net C inputs into lingering SOC in croplands from net primary production, biomass removal by humans and short-term decomposition. Next, we estimate net additional C storage in cropland soils globally from the estimated C inputs, accounting also for decomposition of lingering SOC already present. Our results suggest a maximum C input rate into the lingering SOC pool of 0.44 Pg C yr−1, and a maximum net sequestration rate of 0.14 Pg C yr−1 – significantly less than most previous estimates, even allowing for acknowledged uncertainties. More importantly, we argue for a re-orientation in emphasis from soil processes towards a wider ecosystem perspective, starting with photosynthesis.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".