Effect of Male Planting Date and Female Plant Population on Hybrid Maize Yield and Evaluation of Use of Hybrid-Maize Simulation Model for Grain Yield Estimation in Hybrid Maize Seed Production
Bibliographic record
Abstract
The study was carried out to determine the effect of male planting date (MPD) and female plant population (FPP) on the grain yield (GY) performance of a three-way hybrid and to evaluate Hybrid-Maize simulation model for grain yield estimation in hybrid seed maize production. Fifteen treatment combinations of five MPD as a deviation from the female planting date and three FPP replicated three times were used. The Hybrid-Maize simulation model programme was used to forecast the possible GY outcomes for the fifteen treatments of the experiment using estimated parameters and weather data for the 2006/7 season. The field experiment produced significant (P < 0.005) main effects but non-significant interaction effects for GY, yield components and antheis-silking interval (ASI). Female seed yield was affected by time of male pollen shed relative to female silking: ASI, with highest yields associated with close synchrony (ASI= +/-3 days). ASI had a significant effect on the number of kernels per ear (KPE), with the greatest KPE (318) associated with an ASI of +/-3 days. FPP effects on yield are typical for maize, showing a curvilinear response from low to high density. The optimum population density for GY was 5.4 plants m-2. Simulation output from the Hybrid-Maize simulation model showed an overestimation of GY compare to the observed yield. Furthermore, the model was unable to predict yields for the low FPP of 2.7 plants m-2. We found that Hybrid-Maize simulation model has limited potential for simulating hybrid maize seed production, as it does not accommodate limitations that may occur during the growing season: difference in male and female planting dates, pollen density and dispersion. Hence, the fixed parameters for the Hybrid-Maize simulation model can only be used in maize commercial production.
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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 | 0.001 | 0.001 |
| 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".