On-farm Assessments of Pasture Rejuvenation Methods on Soil Quality Indicators in Northern Alberta (Canada)
Bibliographic record
Abstract
The study was aimed at providing livestock producers with options on practical methods to improve soil quality of pastures for improved forage production and livestock carrying capacity. The study was carried out on-farm from 2015 to 2017 at two sites in northern Alberta, Canada. The methods of rejuvenation (treatments) evaluated were: sub-soiling, break & re-seed (pasture renewal), a combination of manure application plus subsoiling, pasture rest, inorganic fertilizer application, high stock density grazing and bale grazing. A check/control treatment was included for comparison. For break & re-seed, the forage mixture seeded (16.8 kg ha-1) consisted of 60% grasses and 40% legumes. Overall, bale grazing improved soil organic matter (SOM) by up to 3.80% over other methods including check. In terms of soil compaction penetration resistance, water infiltration rate, water content and nutrients particularly N, P and K, bale grazing system as a method of rejuvenating old pastures significantly showed higher values than check at both sites. Without having to break and re-seed old pastures, the first option that livestock producers would have success with in improving soil quality for better pasture productivity would be bale grazing. The next two methods or rejuvenation strategies with great potential for improving soil conditions for pastures would be a combination of manure application plus subsoil in fall and high stock density grazing.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".