Assessing the representativeness and repeatability of testing sites for drought-tolerant maize in West Africa
Bibliographic record
Abstract
Badu-Apraku, B., Akinwale, R. O., Obeng-antwi, K., Haruna, A., Kanton, R., Usman, I., Ado, S. G., Coulibaly, N., Yallou, G. C. and Oyekunle, M. 2013. Assessing the representativeness and repeatability of testing sites for drought-tolerant maize in West Africa. Can. J. Plant Sci. 93: 699–714. The selection of suitable breeding and testing sites is crucial to the success of a maize (Zea mays L.) improvement programme. Twelve early-maturing maize cultivars were evaluated for 3 yr at 16 locations in West Africa to determine the representativeness, discriminating ability, and repeatability of the testing sites and to identify core testing sites. Genotype main effect plus genotype by environment interaction (GGE) biplot analysis revealed that Zaria (Nigeria), Nyankpala (Ghana), and Ejura (Ghana) displayed the highest discriminating ability. Two mega-environments were identified. Bagou, Nyankpala, Bagauda, Ikenne, and Mokwa constituted the first mega-environment (ME1); Ejura, Ina and Sotuba represented the second (ME2). The ME1 would be more useful for evaluating early maize genotypes for tolerance to drought than ME2 because locations in ME1 were more strongly correlated to Ikenne (managed drought stress site). Among the test locations, Bagou and Mokwa were found to be closely related to Ikenne in their ranking of the cultivars for drought tolerance; Zaria was the exact opposite, indicating that this was the least suitable location for evaluating genotypes for drought tolerance. Nyankpala and Ikenne were identified as the core testing sites for ME1 and Ejura for ME2. TZE Comp 3 C2F2 was identified as the highest yielding cultivar for ME1 and Syn DTE STR-Y for ME2, indicating that they could be used as check cultivars. Ikenne, Nyankpala, and Ejura had moderately high repeatability. They were closer to the average environment axis of each mega-environment and will be useful for culling unstable genotypes during multi-locational testing. Other sites were less representative and not repeatable and will not be useful for evaluating early maize cultivars for drought tolerance.
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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 | 0.000 | 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 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".