Effect of rotation frequency and stocking rate on herbage quality and animal performance of cow-calf pairs raised on permanent pasture in Quebec
Bibliographic record
Abstract
In Quebec, 62% of agricultural land is devoted to forage production and 20% of this is pasture. Pasture management provides the opportunity for farmers to maintain and improve the productivity of agricultural land, and to engage in sustainable ruminant production. An experiment was conducted on 42 hectares of pasture land to study the impact of management intensive grazing (MIG) on cow-calf productivity. The pasture area was divided into 18 paddocks and the experiment was conducted as a randomized complete block design with two blocks. The treatments were arranged as a 3 x 3 factorial of stocking rate and rotational frequency. The stocking rates (SR) were 0.5, 0.7, and 0.9 hectares per cow (HSR, MSR and LSR respectively); the rotation frequencies (RF) were two days, six days and continuous grazing (2d, 6d and C). Sixty-one purebred Angus cow-calf pairs were randomly assigned to each of the nine treatments, and the animals were grazed during two consecutive grazing seasons (1997 and 1998). Hay harvested early in the season was used for pasture supplementation late in the season. Increasing RF had no effect (P > 0.05) on forage mass available. Increasing SR from 0.9 to 0.5 cow-calf pairs ha -1 resulted in a linear reduction (P < 0.01) in individual cow gain, but increasing the SR caused a linear increase in cow gains ha-1. Calf gain ha-1 increased linearly (P < 0.01) in response to SR, but was unaffected (P > 0.05) by RF. A system of 6d rotation and high SR generated the greatest net revenue. The study showed little benefit of MIG on animal performance, but substantial benefits on efficiency of land use and economic performance.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".