Tackling post-COVID-19 pandemic food crises through the adoption of improved maize seeds and technologies by smallholder farmers: The case of Ejura Sekyeredumase in Ghana
Bibliographic record
Abstract
Amidst the COVID-19 pandemic, the need to accelerate food production efforts to achieve the UN SDG two, i. e., zero hunger target by 2030, is gaining momentum across the global food security discourse. One way to accelerate food production is to adopt improved seeds and technologies that may close existing yield gaps and support food security efforts in regions such as Sub-Saharan Africa. This paper uses mixed methods, including key informant interviews, structured household questionnaire surveys and focus group discussions, to examine the factors influencing the adoption of improved seeds and complementing technologies in Ghana. In particular, we draw insight from theories of failed market-induced behavior, innovation diffusion and induced-innovation theories to explore farmers' perceptions and adoption of different specific improved maize varieties and technologies for agricultural productivity. Our findings suggest that the level of awareness of improved seeds, particularly hybrid seeds and technologies, and the adoption rate of these technologies are low among Ghana's rural farmers. The findings reveal that socio-demographic and economic factors such as gender, age, cost of seeds, the promise of more yields, market access, social networks' influence, seed availability and accessibility are essential determinants of adopting improved planting technologies among smallholder farmers. This paper argues that location and context-specific-targeted extension services delivery to enhance the widespread adoption of improved seeds and technologies across scales can build farmers' capacity to increase agricultural productivity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".