A review of agricultural extension roles in climate change adaptation and mitigation among farmers in Nigeria
Bibliographic record
Abstract
This review aims to provide an overview of agricultural extension roles in climate change adaptation and mitigation. It deals with farmers’ access to extension services, roles of extension services, climate change-related training needs of extension personnel, the influence of extension contact on adopting climate change adaptation strategies, and constraints experienced by agricultural extension from existing empirical studies. Preferred Reporting Items for Systematic Reviews and Meta-Analyses was used to retrieve and analyse 78 studies. The majority (89.6%) of the studies have shown that farmers do not have access to climate change-related extension services. Extension services focused more on the transfer of information (98.2%), provision of technical advice (48.3%), and support of indigenous adaptation and mitigation strategies (32.6%). The main training needs of extension personnel were skills in the utilization of information and communication technologies (76.5%) and assessment and utilization of climate change-related farming technologies (45.7%). Extension contacts had a positive influence on climate change adaptation (95.7%), while the extension faced the challenges of poor funding (86.8%), inadequate manpower (76.4%), and lack of capacity (67.7%). The study concluded that agricultural extension plays an active role in climate change adaptation and mitigation and recommended more funding and capacity development should be provided for extension personnel. More studies are needed to identify the extent of the positive outcome of extension contact on climate change management among farmers.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".