Does increasing plant population density alter sugar yield in high stalk-sugar maize hybrids?
Bibliographic record
Abstract
Escalating demands for food and green energy have renewed interest in the dual-purpose use of maize (Zea mays L.) for a biofuel and high-energy forage crop. Recently, maize hybrids with high stalk-sugar (sugarcorn) have been developed. It is important to determine how agronomic practices, for example altering plant population density (PPD), affect stalk-sugar yields of these newly developed hybrids and to advance knowledge required for producing sugarcorn as a dual-purpose bioenergy–high energy silage crop in short-season regions unable to grow sugarcane. A field experiment was conducted for 3 years to assess the effect of PPD on stalk-sugar accumulation, dry matter production, silage and sucrose yields of sugarcorn compared with two commercial silage hybrids. Targeted PPD ranged from 75 000 to 150 000 plants ha–1 in increments of 25 000 plants ha–1. We found that increasing PPD from 75 000 to 125 000 plants ha–1 increased stalk sugar concentrations by up to 25% in some of the sugarcorn hybrids, with minimum change in the conventional check hybrids. The sugarcorn hybrid CO348 × C103 had the highest stalk sugar concentration (128 g kg–1) and sucrose yield of up to 3.8 Mg ha–1 at the targeted PPD of 125 000 (or actual 118 000 ± 7000) plants ha–1. By contrast, the check silage hybrids produced at most 2.0 Mg ha–1 of sucrose yield with much lower stalk sugar concentrations (53–65 g kg–1). Sugarcorn hybrids had generally lower grain yield with greater plant barrenness (the failure of a plant to produce a normal ear) and severer head smut infestation than the conventional silage hybrids. Our results suggest that as a silage crop, the current recommended PPD of 75 000–85 000 plants ha–1 for commercial silage maize production in the region is likely suitable for sugarcorn, and a higher PPD is required if sugarcorn hybrids are designated as a biofuel crop or for dual-purpose use.
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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".