Sweet Pearl Millet Yields and Nutritive Value as Influenced by Fertilization and Harvest Dates
Bibliographic record
Abstract
Sweet pearl millet [ Pennisetum glaucum (L.) R.Br.] can be used for ethanol production from the extracted juice with residues used as livestock feed, but optimal fertilization and harvest dates are unknown for this “sugary stem” hybrid developed from forage pearl millet. We evaluated the effects of five equally spaced N fertilization rates (0–200 kg ha −1 ), K fertilization rates of 0 and 66 kg ha −1 , and four harvest dates (approximately every 15 d from early or mid‐August in 2007 and 2008) on water soluble carbohydrate (WSC) concentration and yield, dry matter (DM) yield, and nutritive value of sweet pearl millet, at two sites with 2300 to 2500 and 2900 to 3100 crop heat units (CHU) in Québec, Canada. Delaying harvest dates increased DM (22–99%) and WSC yields (98–173%), and WSC concentration but decreased N concentration by 36% and neutral detergent fiber digestibility (dNDF) by 19%. Increasing N fertilization increased DM and WSC yields, and N concentration, but had no effects on WSC concentration, and decreased dNDF moderately. Potassium fertilization had limited effects on DM and WSC yields, WSC concentration, and on most attributes of nutritive value. Fertilization with 78 to 90 kg N ha −1 and an accumulation of 2100 to 2200 CHU corresponding to harvest dates in September are required to maximize WSC yield (1.86–2.83 Mg ha −1 ) of sweet pearl millet in eastern Canada. Maximizing WSC yield with the goal of producing ethanol, however, would result in reduced nutritive value.
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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 | 0.000 | 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) | 0.001 | 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".