Evaluation of Low Heat Unit Corn Hybrids Compared to Barley for Forage Yield and Quality on the Canadian Prairies
Bibliographic record
Abstract
Corn (Zea mays L.) production is expanding in the prairie region of western Canada. The objectives of this study were to compare three new low heat unit corn hybrids to barley (Hordeum vulgare L.) for forage yield, nutrient profile, and total nutrient production. The study was conducted at 4 sites (Evansburg and Fairview, Alberta; Melfort and Scott, Saskatchewan) with different soil characteristics (Gray Luvisolic, Grey Wooded, Dark Brown, and Black soil zones) over three consecutive years (2012-2014). At each site, annually, 16 plots (2.4×7.2 m) were randomly assigned to one of four forage crops (corn:Monsanto DKC26-25, Hyland 2D093, Pioneer P7443R; barley: cv. AC Ranger) in a replicated (n = 4) trial. Number of cobs per plant was not different (p = 0.23) between corn hybrids averaging 1.22 ± 0.32/plant (mean ± sd). Forage yield among the corn hybrids was negligible (p > 0.05), but the corn hybrids exhibited 40% higher yield (p < 0.05; avg. 11.3 ± 3.6 t/ha on DM basis) compared to barley (avg. 6.7 ± 1.7 t/ha). Corn hybrids were lower (p < 0.05) in CP content [7.6 ± 1.4% versus (vs.) 12.4 ± 0.1%] than barley. No difference was observed between the 4 forage crops in TDN content (68.2 ± 2.8% DM).Study results suggestthat new cool-season corn hybrids can produce high quality forage to meet the nutrient requirements of grazing beef cows in mid- and late-stage pregnancy.New corn hybrids may be suitable alternatives for winter grazing strategies since forage harvest costs would be eliminated.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".