The Consequences of Soil Organic Carbon for Crop Yield, Farm Productivity and Profit
Bibliographic record
Abstract
ABSTRACT Crop choices affect soil organic carbon (SOC) stocks, allowing farmers to manipulate the amount of carbon sequestered in the soil over time. This paper examines the private and public benefits of crop rotations that sequester additional carbon across the province of Saskatchewan, Canada using a novel field‐level dataset from the Saskatchewan Crop Insurance Corporation matched with data on weather, soil and simulated SOC. I begin by estimating the on‐farm shadow value of SOC using a dynamic panel regression model and find that it is positive with diminishing marginal returns; the yield (and thus profit) response to SOC is larger in soil zones with less SOC and smaller in soil zones with more SOC. I then use dynamic simulations to estimate the SOC and yield response to different crop rotations over time. The adoption of the Canola‐Spring Wheat‐Peas‐Spring Wheat crop rotation leads to a 27.5%, 8.2% and 4.4% increase in long‐term average profit compared to current levels in the brown, dark brown and black & grey soil zones attributable to increased SOC over 32 years of average weather conditions. Finally, I compute the external social benefit from the Canola‐Spring Wheat‐Peas‐Spring Wheat rotation relative to Spring Wheat‐Fallow‐Spring Wheat‐Fallow on all insurable hectares in Saskatchewan from 2023 to 2055, and find benefits amounting to 108 billion CAD when employing a social cost of carbon of 185 USD/Mg of CO 2 . These results show that selecting crop rotations that have greater SOC sequestration potential not only improves on‐farm profitability over time, but can generate substantial environmental benefits.
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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".