Grazing and fertilizer, compost or manure application effects on a meadow bromegrass pasture on a thick black chernozem I. Productivity and sustainability
Bibliographic record
Abstract
Short duration, intensive grazing management with high stocking rates may result in sufficient turn-over of nitrogen (N) to compensate for production-limiting soil-N deficiencies for grass pasture. In central Alberta a 0.5 ha block was seeded to “Fleet” meadow bromegrass ( Bromus riparius Rehmann) in August 2002. Within this block, six fenced (9 m × 30 m) treatments were established in three replicates. They were (1) ungrazed—clip removal, (2) grazed—alone, (3) grazed—fertilizer, (4) grazed—fertilizer-compost, (5) grazed—hog manure, and (6) grazed—alfalfa ( Medicago sativa L.) grass. Measurements were conducted over a 4-year period between 2003 and 2006 and grazing occurred at identical times as vegetative mass permitted. Biomass was harvested before and after grazing for calculation of dry matter (DM) yield and biomass consumed. Sub-samples were used for determination of N concentration and in vitro digestibility. Mean herbage N-yield for grazed treatments was 131% of ungrazed and greatest for grazed-fertilizer and grazed-fertilizer plus compost. Grazed paddocks with no added N produced similar DM yield to those with added N. Estimated nitrogen fixation contributed an annual average of 82 kg ha−1 to herbage-N yield from the alfalfa-grass paddocks. Barley ( Hordeum vulgare L.) silage grown after termination of the grazed pastures produced 72% more herbage DM from grazed paddocks than ungrazed, but no significant ( P < 0.05) differences occurred among amendments.
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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 | 0.000 |
| 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 | 0.000 |
| 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".