289 Evaluating cover crop cocktails for forage production in the Peace region of Alberta
Bibliographic record
Abstract
Interest in the potential for a multispecies cover crop (CC) blend (cocktails) for silage, swath grazing, greenfeed, and pastures has been growing amongst beef cattle producers in the Peace Country region of Alberta, Canada. The idea of CC cocktails is new in the region, indicating that the concept of a CC cocktail mix is an area where local research for local producers is needed. The objective of this study was to test several cocktails to identify those with superior forage production (yield and nutritional value) for beef cattle production. Twelve cocktails (treatments) consisting of 2 to 9 cover crops and an oat (Avena sativa) monocrop as a check were arranged in a randomized complete block design in 3 replications in 2016 cropping season. The CC species used were from cereal grains/grasses, legumes, and brassicas. From forage DM yield and nutritional value obtained in this study for all cocktails and the check (CDC Haymaker oat), all cocktails generally had higher DM yield as well as better nutritional value than the check. Six of the 12 cocktails produced more DM yields (109–137%) than the check. Overall, cocktail number 1 (peas, oats, hairy vetch, tillage radish, purple top turnips, and crimson clover) produced the most forage DM yield (10 t/ha) than other cocktails and the check. The forage CP, TDN, and NEm significantly varied (P < 0.05) from 8.87 to 23.7%, from 60.1 to 70.1, and from 1.46 to 1.75 Mcal/kg, respectively, with the check recording the least values in each range. All cocktails were able to meet the protein requirements of mature beef cattle, and in most cases, cocktails were well within the CP requirements for growing and finishing beef calves. The cocktails in most cases had enough %TDN for mature beef cattle, whereas the check was able to meet the %CP and %TDN requirements of only a gestating beef cow. The benefits of cocktails were obvious, with higher forage macro- and trace minerals than the check. Four of the 12 cocktails seemed to have greater potential for forage production in the Peace Country region of Alberta. Field notes taken show that the following crops in the cocktails did not germinate and that where they did germinate, their growth was not impressive: Persian clover (Trifolium resupinatum L.), BMR hybrid sorghum (Sorghum spp.), berseem clover (Trifolium alexandrinum), and teff grass (Eragrostis tef).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".