Sod-Seeding Perennial Legumes into Beef Cattle Pastures in Central Alberta
Bibliographic record
Abstract
With low inputs from fertilisation and seeding new plants, and ongoing removal of nutrients via outputs of forage biomass, northern temperate pastures in Alberta can suffer from low productivity and poor nutrient availability. Increasing the amount of legumes, plants that can have symbiotic relationships with diazotrophic bacteria, can increase the soil nitrogen without direct fertilisation. Sod-seeding legumes is a pasture rejuvenation method that can introduce legumes into an existing stand with previously low legume abundance, with the goal of increasing forage productivity from nitrogen fixation and improving forage nutritive quality. Chapter 2 explores the efficacy of sod-seeding alfalfa, cicer milkvetch, and sainfoin, and the impact of sod-seeding on plant and soil cover. While alfalfa emergence increased with sod-seeding at one site immediately following seeding (2023), no other sites showed an increase in legume emergence with sod-seeding in either year of observance (2023 or 2024). Plant cover was inconsistently impacted by sod-seeding, with some sites showing increased grass or legume cover from sod-seeding, and others a decrease. Aside from an initial increase in bare soil and loss of plant litter, there were no long-term soil or litter changes by the second growing season after seeding. Chapter 3 examined how pasture biomass and nutritive value change with sod-seeding. Shortly after sod-seeding in 2023, two of three sites showed no change in biomass, while total forage biomass decreased at the third site. By 2024, two sites had increased biomass production, mainly due to increased grass, rather than legume, biomass. There was no consistent change in forage value with sod-seeding across either year. This study showed that while sod-seeding may have had little to no positive effect in the first two years, it also had little to no negative effect on soil pasture health indicators and biomass production.
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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 | 0.001 |
| 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 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".