Performance and Profitability of Released Climate-Smart Maize Varieties Under Increased Plant Densities
Bibliographic record
Abstract
The study was conducted to; validate the genotypic performance of the popular climate-smart released maize varieties under increased plant densities in the three sub-ecologies (Mid-altitude, semi-arid, and tropical rain forest zones), which are important maize crop production belts and testing sites for new hybrids, and estimate the profitability of growing the varieties under optimum plant density in the different sub-ecologies. Seven popular varieties; MM3, Longe 6H, Longe 9H, Longe 10H, Bazooka, WE 3106, and FH5160 were tested for two seasons at four different spacing levels: 0.75 × 0.6 M (44,444 plants ha-1), 0.75 × 0.30 M (44,444 plants ha-1), 0.75 × 0.25 M (53,333 plants ha-1), and 0.5 × 0.3 M (66,667 plants ha-1). The varieties varied significantly for grain yield (GY) and ear aspect (EA) traits across the spacings and environments. Using the closest spacing (0.50 × 0.30 M) resulted in higher GY productivity and net profit in the testing sites. At optimal spacing, FH5160 was the most stable genotype for cultivation across the three sub-ecologies. All varieties exhibited superior genotypic performance for GY (7-12 t ha-1) and EA traits in the mid-altitude zone. Bazooka and Longe 10H were the best varieties for the tropical rainforest zone. While MM3, WE3106 and Bazooka were more suitable for semi-arid zone. Growing all the studied maize varieties in the mid-altitude sub-ecological zone is profitable, especially at optimal spacing.
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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".