Evaluation of Drought Tolerant Maize Varieties under Drought and Rain-Fed Conditions: A Rainforest Location
Bibliographic record
Abstract
<p>Newly developed drought tolerant maize (<em>Zea mays</em> L.) varieties have been hypothesized to be of different genetic make-ups, hence this study was conducted to evaluate drought-tolerant maize varieties under drought and rain-fed conditions, identify the highest yielding varieties and determine the relationship between agronomic traits and varieties’ yield under these conditions in a rainforest location. Nine drought tolerant varieties of maize and a local check were evaluated during the early part of the early cropping season (March) (Drought) as well as the actual early cropping season (May-June) (Rain-fed) of 2015 at the Teaching and Research Farm (T&amp;RF) of Obafemi Awolowo University, Ile-Ife (7°28'N, 4°33'E and 244 m above sea level). There were significant differences among the two environments for all the traits measured. The performance of the drought tolerant maize varieties is better under the rain-fed condition. The yield performance of all varieties are not significantly different from each other (1.36 tons/ha to 2.75 tons/ha) under drought except EVDT-W 2000 STR C<sub>0</sub> (1.10 tons/ha). All the varieties evaluated under rain-fed condition produce higher grain yield (2-44 tons/ha-3.69 tons/ha) which are not significantly different each other. All the yield components except ear height and length had a strong positive relationship with grain yield under drought while only ear number and weight had relationship with yield under rain-fed condition. In conclusion, among the drought tolerant maize varieties, EVDT-W 2000 STR C<sub>0</sub> need further improvements to drought. Furthermore, ear weight can be used to select parents for the development of drought tolerant maize varieties in this agro-ecology.</p>
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".