Adaptive multi-paddock grazing increases soil carbon stocks and decreases the carbon footprint of beef production in Ontario, Canada
Bibliographic record
Abstract
Adaptive multi-paddock (AMP) grazing has been promoted to increase soil organic carbon (SOC) stocks over continuously grazed (CG) pastures; however, the evidence for this is still limited, both in terms of the number of studies conducted and the climates and biomes they cover. The objectives of this study were to determine the effect of grazing management on pasture SOC stocks and to incorporate this SOC sequestration into a life cycle assessment on the greenhouse gas (GHG) intensity (or carbon footprint) of Ontario beef production. Soil cores collected from AMP and CG pastures and annual row crop fields in southern Ontario showed that pastures managed with AMP grazing had significantly higher SOC stocks than CG pastures. Both pastures had SOC stocks higher than annual cropland, resulting in a sequestration rate of 0.957 Mg C ha −1 yr −1 for AMP and 0.507 Mg C ha −1 yr −1 for CG. Without consideration of SOC sequestration, the GHG intensity of beef production is 13.10 kg CO 2 eq kg LW −1 , while including SOC sequestration decreased this intensity by 42% for CG pastures and by 65% for AMP. These results illustrate the importance of including pasture SOC sequestration into beef carbon footprint assessments and should encourage the adoption of AMP grazing in temperate regions to achieve environmental goals. • Adaptive multi-paddock (AMP) grazing increases soil organic carbon (SOC) stocks. • Pastures, regardless of management, had greater SOC stocks than annual cropland. • SOC sequestration under AMP grazing decreases carbon footprint of beef by 65%.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".