Validating and using the GrassGro decision support tool for a mixed grass/alfalfa pasture in western Canada
Bibliographic record
Abstract
This paper presents predictions of pasture composition and liveweight gain of steers using the GrassGro simulation model. Predictions are compared with field data measured during a 4-yr experiment at Brandon, Manitoba, in which steers grazed alfalfa (Medicago sativa)/grass (Bromus biebersteinii and Psathyrostachys juncea) pastures at 1.1 and 2.2 steers ha -1 in continuous or rotational grazing systems. The predictions of average daily gain, mean forage mass and botanical composition were found to accurately reflect the field data. Predictions of digestibility and protein were less accurate and reasons for this are discussed. Steers from the field trial were not considered finished for slaughter directly off pasture. GrassGro was used to examine the effects of feeding a barley supplement to the steers while at pasture. The results indicated that all steers could have been finished at pasture. Simulation indicated that supplementation at pasture makes the stocking rate of 2.2 steers ha -1 more attractive because twice the number of steers could be finished with little additional requirement for barley supplement. Further simulations provided information on the effects of climate variations during a 30-yr period (1967–1996) on steer production in both continuous and rotational grazing systems using a range of stocking rates from 1.1 to 5.5 steers ha -1 . Key words: GrassGro, decision support, steers, grass, alfalfa, pasture
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".