Growth and Yield Performance of Improved Cowpea (Vigna Unguiculata L.) Varieties in Ghana
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 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 it