Planting Dates and Harvesting Stages Influence on Maize Yield under Rain-Fed Conditions
Bibliographic record
Abstract
Understanding the challenges associated with variation in weather conditions and stages of maturity in maize are essential for farmers to achieve continuous production under climate changes. This research evaluated the interactive effect of planting date and stages of maturity at harvest on maize yield (Zea mays L.). Field trials were conducted during the 2014/15 and 2015/16 seasons at university of KwaZulu-Natal research farm Pietermaritzburg South Africa. Planting dates comprised of early (November), mid (December) and late planting dates (January). While, harvesting occurred at milk stage, dent stage and physiological maturity. A split plotdesign with four replications was used. The main plot and sub-plot consisted of planting dates and harvesting stages respectively. Response of maize to planting dates and harvesting stages was determined by variables of plant physiological growth and yield parameters. Significant differences in growth and physiological parameters were more obvious in 2015/16 season which was a drier season than 2014/15. Early and mid-planting had positive effect on parameters measured at both seasons. However, mid planting date favoured maize growth and yield more in drier season. The interaction of planting dates and harvesting stages significantly influenced grain yield, thousand seed weight, ear length and diameter. Thousand seed weight, ear length and diameter obtained at dent stage under mid planting outperformed its counterpart from physiological maturity under which late planting. With the increase in climate variability there is high risk that maize planted lately would have lower yield irrespective of its planting dates and might not attain physiological maturity. However, maize harvested at dent stage under early and mid-planting dates gave substantially high yield.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".