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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".