Disadoption of Improved Agronomic practices in Cowpea and Maize at Ejura-Sekyeredumase and Atebubu-Amantin Districts in Ghana
Bibliographic record
Abstract
The improved cowpea and maize production methods developed in Ghana with the help of farmers are fundamental to increasing cowpea and maize productivity. Improved agronomic practices in cowpea and maize production believed to increase yield are row planting, the use of cover crops and the use of poultry manure. However, the practices are gradually losing their importance in cowpea and maize production. The paper therefore explores the extent to which various factors affect the disadoption of improved agronomic practices and reasons behind disadoption. Multistage sampling techniques were used to select hundred farmers from two cowpea and maize producing areas. Results revealed adoption of cover crops, row planting, poultry manure dropped from 13% to 6%, 99% to 53% and 77% to 10%, respectively. Financial constraint, difficulty in use, time and labour intensity were reasons for disadoption. Empirical results revealed that number of years in education, gender of farm household head, household size, access to extension and hired labour influenced disadoption of improved agronomic practices. Access to production inputs and continuous supply of information are important for farmers’ continuous use of improved agricultural technologies.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".