Yield and Composition of Sweet Pearl Millet as Affected by Row Spacing and Seeding Rate
Bibliographic record
Abstract
Sweet pearl millet [ Pennisetum glaucum (L.) R. BR.] seems promising for ethanol production, but optimal cropping practices are unknown for the cool and humid conditions of eastern Canada. We evaluated the effects of two row spacings (18 and 36 cm) and four seeding rates (5, 10, 15, and 20 kg ha −1 ) on biomass dry matter (DM) yield, water soluble carbohydrate (WSC) concentration and yield, and nutritive value of sweet pearl millet at two sites in Québec, Canada. Increasing seeding rates decreased biomass DM and WSC yields at Sainte‐Anne‐de‐Bellevue but had a limited effect at Saint‐Augustin‐de‐Desmaures. Hence, a seeding rate of 5 kg ha −1 resulted in maximum biomass DM (12.4–19.1 Mg ha −1 ) and WSC (1.56–2.64 Mg ha −1 ) yields. Row spacing did not affect biomass DM and WSC yields at Saint‐Augustin‐de‐Desmaures but biomass DM yield was greater at a row spacing of 18 cm at Sainte‐Anne‐de‐Bellevue. The WSC concentrations in leaves and stems were inconsistently affected by seeding rate but were not affected by row spacing. The WSC concentrations were greater in stems (140.7–162.4 g kg −1 DM) than in leaves (35.7–48.1 g kg −1 DM). Increasing the seeding rate generally decreased N concentrations of leaves and stems and digestibility of neutral detergent fiber (NDF) but increased concentrations of acid detergent fiber and NDF. Row spacing did not affect any nutritive value attributes. Seeding at 5 kg ha −1 with a row spacing of 18 cm maximize biomass and WSC yield with minimal effect on forage nutritive value of sweet pearl millet.
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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 | 0.000 | 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) | 0.000 | 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".