Determinants of Nutritious Drought Tolerant Maize Adoption and Mineral Fertilizer Application Under Smallholder Farm Conditions in Ghana
Bibliographic record
Abstract
The study sought to examine adoption of nutritious drought tolerant (DT) maize using social science research methods and participatory demonstration trials. The social science study used mixed method approach which combined both qualitative and quantitative methods. A split-plot demonstration trial of three improved maize varieties and a local variety, and two levels of Nitrogen fertilizer were established. The two fertilizer levels were low N [LN] (30 kg N ha-1) and high N [HN] (90 kg N ha-1). The results of the social science study showed 85% of men consider early maturity, grain quality and storability in adopting DT maize. Results also revealed that beside earliness and higher yield, taste and easy to harvest influenced women farmer’s choice for DT maize varieties. Climatic endurance, increased yield and grain quality showed a positive and statistically significant relationship with adoption of DT maize. On average, HN fertilization increased DT maize grain yields by 41% compared to the LN fertilization. The improved DT maize varieties had yield advantages ranging from 25 to 43% over the local variety. From this, DT maize appears to have a potential for its use with N-fertilizer in the fight against food insecurity with improved adoption and utilization in Ghana.
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 | 0.002 |
| 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) | 0.002 | 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".