Growth and Yield Performance of Improved Cowpea (Vigna Unguiculata L.) Varieties in Ghana
Bibliographic record
Abstract
Cowpea is well adapted to environmental conditions that affect crop production such as drought, high temperatures and other biotic stresses compared with other crops. Notwithstanding, growth and development of many cowpea cultivars are affected by drought and high temperatures, especially during floral development. This is because cowpea cultivars tend to have narrow range of adaptation as cultivars developed for one zone usually are not very productive in other zones. A study on the growth and yield performance of seven cowpea varieties was conducted during the 2012 major and minor rainy seasons at the CSIR-Crops Research Institute, Kwadaso-Kumasi, Ghana to compare the performance of the seasonal variation on each variety. These improved varieties Nhyira, Tona, Asetenapa, Asomdwe, Hewale, Videza and IT 89KD374-57 were evaluated using a randomized complete block design and replicated three times. The results showed that varieties Hewale, Videza and Nhyira gave higher seed yields, whereas IT 89KD374-57 and Asetenapa had lower seed yields. Nhyira and Hewale gave comparatively better seed yields under both conditions. Hewale was the highest seed-yielding genotype under both major and minor raining season. Cowpea production could be a profitable agribusiness for cowpea growers in Ghana considering the higher returns in terms of grain yield obtained in both seasons.
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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) | 0.001 | 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".