Agro-Diversity in the Forest-Savannah Transition Zone of Ghana: A Strategy for Food Security against Climatic and Socio-Economic Stressors
Bibliographic record
Abstract
Food security is a major issue affecting about 239 million people in Sub-Saharan Africa. Therefore, local ecosystems-based adaptive strategies for reducing the impact of climate change and other stressors on food production systems are very relevant in the national food security agenda. This study assessed how farmers in communities of the environs of the Kogyae Strict Nature Reserves in the forest-savanna transition zone of Ghana exploit a range of options for food production that spread and reduce risks and ensure sustainability of the local environment. Through a cross-sectional survey involving focus group discussions, institutional data search and on-site observations, the study investigated different ways to which the natural diversity of the environment has been used by farmers to enhance farm productivity and farmer income. The study observed that climate variability, land expropriation for protected area establishment, inappropriate use of farm technology and low pricing for farm produce pose as major threats to sustainable agriculture in the area. These constraints have compelled farmers to adopt a range of agro-diversity practices for increased farm productivity and income. They include introduction of new crop varieties, adoption of innovative farm management practices, diversified farm fields and sequential cropping systems. The study noted that the diversified systems have boosted farmer productivity and incomes and contributed to their socio-cultural needs. The study recommends greater crop intensification through the use of appropriate technologies and improved access to markets to consolidate farmer gains and livelihoods as well as ensuring food and nutritional security.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".