Photosynthetic limits on carbon sequestration in croplands
Why this work is in the frame
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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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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 it