Forage yield, nutritive value, and ensilability of sweet pearl millet and sweet sorghum in five Canadian ecozones
Bibliographic record
Abstract
Sweet pearl millet [Pennisetum glaucum (L.) R. Br.] and sweet sorghum [Sorghum bicolor (L.) Moench], previously tested for ethanol production, were evaluated as high sugar crops for animal feeds to possibly replace silage corn (Zea mays L.). We compared the forage yield, nutritive value, and ensilability of one hybrid of sweet pearl millet and two of sweet sorghum to a locally adapted silage corn hybrid in five Canadian ecozones. Forage yields of sweet pearl millet and sorghum were similar to that of silage corn in the Boreal Shield, Mixedwood Plain, and Atlantic Maritime ecozones, greater in the Prairies, and lower in the Pacific Maritime ecozone. Across sites, forage dry matter concentration was less for sweet pearl millet (289 g kg−1) and sweet sorghum (245 g kg−1) than for silage corn (331 g kg−1). Sweet pearl millet had a lower total digestible nutrient (TDN) concentration (452 g kg−1 DM) and aNDF digestibility (NDFd) than sweet sorghum and silage corn along with greater neutral detergent fibre (aNDF) and water-soluble carbohydrate (WSC) concentrations than silage corn. Sweet sorghum had greater aNDF and WSC, lower starch, and similar TDN (534 g kg−1 DM) concentrations, but greater NDFd compared with silage corn. Sweet pearl millet and sorghum fermented as well as silage corn, reaching low pH values and acceptable concentrations of lactic and volatile fatty acids. Sweet sorghum is therefore a viable alternative to silage corn in Canada except in the Pacific Maritime ecozone, but early-maturing hybrids with acceptable DM concentration at harvest are required.
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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 | 0.001 | 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) | 0.001 | 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".