Performance of tropical early-maturing maize cultivars in multiple stress environments
Bibliographic record
Abstract
Maize (Zea mays L.) production in west Africa (WA) is constrained by drought, Striga hermonthica infestation and low soil nitrogen (N). Maize varieties resistant to Striga, drought, and low N are ideal for WA, but genotype × environment interaction on these traits are usually significant due to differential responses of cultivars to growing conditions. Three studies were conducted from 2007 to 2009 at five locations in Nigeria to evaluate the performance of selected early-maturing cultivars under drought stress versus well-watered, Striga-infested versus Striga-free, and in low- versus high-N environments. Drought stress reduced grain yield by 44%, Striga infestation by 65%, and low N by 40%. GGE biplot analysis showed that the genotypes TZE-W DT STR C4, Tillering Early DT, TZE-W DT STR QPM C0 and TZE-Y DT STR C4 performed relatively well in all study environments. TZE-W DT STR C4 and TZE Comp3 C1F2 were outstanding under drought, TZE-W DT STR C4, EVDT-W 99 STR QPM C0 and TZE-W DT STR QPMC0 under Striga infestation and Tillering Early DT, EVDT 97 STRC1, TZE-W DT STR C4, and TZE Comp3 C3 under N deficiency. Maize productivity in WA can be significantly improved by promoting cultivation of genotypes that combine high resistance/tolerance to Striga and drought with improved N-use efficiency.
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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) | 0.000 | 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 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".