Forage production, economic performance indicators and beef cattle nutritional suitability of multispecies annual crop mixtures in northwestern Alberta, Canada
Bibliographic record
Abstract
A 2-year field study compared annual crop mixtures and monoculture cereal crops (controls) for forage yield and quality value for beef cattle production. Each of the mixtures consisted of 2 to 9 crop species. The cropping treatments investigated significantly influenced (P < 0.05) forage dry matter (DM) yield, quality and economic performance parameters. Forage DM yield was up to 9.25 t/ha for the mixtures compared to 7.72 t/ha for the control crops. Forage yield advantage from mixtures was up to 50% over controls. Forage crude protein (CP) was >13.0% for most mixtures, while CP for controls was ≤12.0%. All mixtures and controls mostly exceeded the suggested required levels of K, Mg, Na, S, Fe and Zn for beef cattle. The four top ranked mixtures in terms of marginal returns and benefit/cost ratio were mixtures #4, 8, 10 and 12 in that order. Study results demonstrated that growing a minimum of 3 annual crops, rather than 1 or 2 crops, increased forage production and offered a forage-based diet that, which in most cases, was able to adequately meet the nutritional requirements of beef cattle. The mixture with the highest forage yield consisted of crops from 3 different species categories: Poaceae, Leguminosae and Brassicaceae.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".