Carbon Storage in Soils of the North American Great Plains: Effect of Cropping Frequency
Bibliographic record
Abstract
Summer fallow (fallow) is still widely used on the North American Great Plains to replenish soil moisture between crops. Our objective was to examine how fallowing affects soil organic carbon (SOC) in various agronomic and climate settings by reviewing long‐term studies in the midwestern USA (five sites) and the Canadian prairies (17 sites). In most soils, SOC increased with cropping frequency though not usually in a linear fashion. In the Canadian studies, SOC response to tillage and cropping frequency varied with climate—in semiarid conditions, SOC gains under no‐till were about 250 kg ha−1 yr−1 greater than for tilled systems regardless of cropping frequency; in subhumid environments, the advantage was about 50 kg ha−1 yr−1 for rotations with fallow but 250 kg ha−1 yr−1 with continuous cropping. Specific crops also influenced SOC: Replacing wheat (Triticum aestivum L.) with lentil (Lens culinaris Medikus) had little effect; replacing wheat with lower‐yielding flax (Linum usitatismum L.) reduced SOC gains; and replacing wheat with erosion‐preventing fall rye (Secale cereale L.) increased SOC gains. In unfertilized systems, cropping frequency did not affect SOC gains, but in fertilized systems, SOC gains often increased with cropping frequency. In a Colorado study (three sites each with three slope positions), SOC gains increased with cropping frequency, but the response tended to be highest at the lowest potential evaporation site (where residue C inputs were greatest) and least in the toeslope positions (despite their high residue C inputs). The Century and the Campbell et al. SOC models satisfactorily simulated the relative responses of SOC although they underestimated gains by about one‐third.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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 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".