Yield, quality and cost effectiveness of using fertilizer and/or alfalfa to improve meadow bromegrass pastures
Bibliographic record
Abstract
A 4-yr study was conducted to determine the effects of forage type and fertilization on yield and quality of dryland pastures on the Canadian prairies. Pastures contained either meadow bromegrass (Bromus biebersteinii Roem & Schult.) (G) or alfalfa (Medicago sativa L.)-meadow bromegrass (A) and were either unfertilized (U) or fertilized (F) in order to increase the availability of essential plant nutrients to recommended levels. Average pasture yields (1995–1998) of AF, AU, GF and GU treatments were 3.88, 3.12, 3.95 and 1.94 ± 0.19 t DM ha-1 and average carrying capacities were 200.4, 163.9, 208.7 and 127.6 ± 3.3 cow-days ha-1, respectively. Alfalfa content declined (P < 0.05) over the 4 yr from 75.4 and 84.1% in 1995 to 32.5 and 40.3% in 1998 for AF and AU pastures, respectively. Simple incorporation of alfalfa into grass pastures (AU) improved carrying capacity by 28% and met the nutritional requirements of lactating beef cows at no additional cost. Fertilization of meadow bromegrass pastures (GF) improved the carrying capacity by 64% and met the nutrient requirements of lactating beef cows. Incorporating alfalfa with fertilization (AF) improved carrying capacity of pasture by 57% and met the nutrient requirements of lactating beef cows. Both the AF and GF treatments entailed significant financial risk as they were only cost-effective strategies when precipitation was not limiting. The AU treatment did not entail financial risk and was always a cost-effective treatment. Key words: Alfalfa, meadow bromegrass, productivity, forage quality, 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 | 0.000 | 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) | 0.000 | 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".