Challenges in adoption and wide use of agroforestry technologies in Africa and pathways for improvement: A systematic review
Bibliographic record
Abstract
In recent years, agroforestry technologies have emerged as promising alternative measures for addressing major environmental crises. However, their use in Africa remains below anticipated levels. Therefore, this systematic review aims to investigate the underlying reasons for the low adoption and limited use of such technologies in Africa. Employing the Preferred Reporting Items for Systematic reviews and Meta-analyses protocol (PRISMA), we conducted a comprehensive search for relevant scientific papers in databases such as Google Scholar, Scopus and Web of Science. A total of 351 articles were initially identified. Following the predefined inclusion and exclusion criteria, 36 articles were selected from which data were manually extracted for inclusion in this review. Descriptive statistics were employed to assess the farmers’ perceptions of agroforestry technologies and the constraints they face when adopting them. Several constraints were identified, and the top five constraints were pests, problems of land access, lack of knowledge and skills, lack of capital and lack of seeds. To maximise the adoption of agroforestry technologies in Africa, it is imperative to introduce the technologies by considering the local context, the specific needs of farmers and the existing socio-economic dynamics. Such initiatives must include robust training and education programmes, accessible financing solutions, appropriate land tenure reforms and effective support mechanisms for access to seed and pest management. These factors could considerably improve the adoption and effectiveness of agroforestry technologies in Africa, thereby contributing to more sustainable and resilient agricultural practices.
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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 | 0.027 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".