Effect of Increasing Species Diversity and Grazing Management on Pasture Productivity, Animal Performance, and Soil Carbon Sequestration of Re-Established Pasture in Canadian Prairie
Bibliographic record
Abstract
The objective of the study was to determine the effect of type of pasture mix and grazing management on pasture productivity, animal response and soil organic carbon (SOC) level. Pasture was established in 2001 on 16 paddocks of 2.1 ha that had been primarily in wheat and summer fallow. Treatments consisted of a completely randomized experimental design with two replicates: two pasture mixes (7-species (7-mix) and 12-species (12-mix)) and two grazing systems (continuous grazing (CG) and deferred-rotational grazing (DRG)). Pasture was stocked with commercial yearling Angus steers (Bos Taurus, 354 ± 13 kg) between 2005 and 2014. All pastures were grazed to an average utilization rate of 50% (40% to 60%). Average peak and pre-grazing pasture dry matter (DM) yield and animal response were independent of pasture seed mixture but varied with grazing management and production year. Average peak DM yield was 26.4% higher (p = 0.0003) for pasture under DRG relative to CG (1301 kg ha−1). However, total digestible nutrient for pasture under DRG was 4% lower (p < 0.0001) as compared to CG (60.2%). Average daily weight gain was 18% higher (p = 0.017) for CG than DRG (0.81 kg d−1), likely related to higher pasture quality under CG. Soil carbon sequestration was affected by seed mixture × grazing system interaction (p ≤ 0.004). Over the fourteen years of production, pasture with 7-mix under CG had the lowest (p < 0.01) average SOC stock at 15 cm (24.5 Mg ha−1) and 30 cm depth (42.3 Mg ha−1). Overall, the results from our study implied that increasing species diversity for pasture managed under CG may increase SOC gain while improving animal productivity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".