Management of Canadian prairie region grazed grasslands: Soil C sequestration, livestock productivity and profitability
Bibliographic record
Abstract
The GrassGro model (a computer simulation of management-induced changes in range and pasture forage and livestock productivity) was combined with spreadsheet analyses to estimate the influence of improved grazing practices on soil organic carbon (SOC), and farm profitability, across native rangelands and tame pastures of the southern Canadian Prairies. Improved practices included complementary grazing (CG) and reduced stocking density (RSD) on rangeland; and N fertilization (FERT), seeded grass/legumes grazed continuously (GLGC) or rotationally (GLGR), and RSD on tame pastures. The analysis was stratified into three ecoregions on the basis of similarities in climate and soil type. Averaged over 30 yr and ecoregions, SOC rates of gain through improved management were 5 (RSD) to 26 (CG) kg C ha-1yr-1 for rangelands, and 86 (RSD), 75 (GLGC), 62 (GLGR) and 222 (FERT) kg C ha-1yr-1 for tame pastures. Gains with FERT were considered largely negated by associated energy (C) costs, N2O emissions, and shifts in grassland species. The CG system alone improved net returns to the producer. The estimated potential combined SOC gain on prairie grazinglands (11.5 Mha) was 1.63 MMT CO2 yr-1 (or 0.465 MMT C yr-1), slightly less than the 1.70 MMT CO2 yr-1 currently emitted from agricultural soils in Canada. Key words: Soil carbon, GrassGro model, grazing management, rangeland, tame pasture
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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 | 0.001 |
| 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 | 0.001 | 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".