Determining the Optimal Sowing Density for a Mixture of Native Plants Used to Revegetate Degraded Ecosystems
Bibliographic record
Abstract
Abstract No standardized, objective methodology exists for optimizing seeding rates when establishing herbaceous plant cover for pastures, hay fields, ecological restoration, or other revegetation activities. Seeding densities, fertilizer use, season of seeding, and the interaction of these treatments were tested using native plants on degraded sites in northern British Columbia, Canada. A mixture of 20% Achillea millefolium , 20% Carex aenea , 20% Elymus glaucus , 20% Festuca occidentalis , 16% Geum macrophyllum , and 4% Lupinus polyphyllus seed was applied at 0, 375, 750, 1,500, 3,000, and 6,000 pure live seed (PLS) per m 2 in 2.5 × 2.5–m rototilled test plots, established in the fall and spring, with and without fertilizer. There was no significant difference in plant cover of sown species between fall seeding and spring seeding, and few treatment interactions were identified in the first 2 years after sowing. There was no significant difference in cover between seed densities of 3,000 and 6,000 PLS/m 2 in the first year, nor among 1,500, 3,000, and 6,000 PLS/m 2 treatments in the second year. Seed densities as low as 375 PLS/m 2 produced year 2 plant cover equivalent to that observed at 3,000 PLS/m 2 in year 1. Plots sown to seed densities less than or equal to 750 PLS/m 2 generally exhibited an increase (infilling) in plant density from year 1 to year 2, whereas plots sown to seed densities greater than or equal to 1,500 PLS/m 2 generally exhibited a decrease (density‐dependent mortality) in plant density. These results imply a most efficient sowing density between 750 and 1,500 PLS/m 2 (corresponding to 190–301 established plants . m −2 after two growing seasons). It is suggested that net changes in plant populations observed over a range of sowing densities are a robust and objective means of determining optimal sowing densities for the establishment of herbaceous perennials.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".