Addressing Information Disorder on Climate-Smart Agriculture: Rethinking Advisory and Extension Services for Sustainable Rural Development in Nigeria
Bibliographic record
Abstract
The usefulness of Climate Smart Agriculture is becoming very important in making the agri-food sector stronger and more sustainable in the midst of climate change. Although Nigerian farmers are adopting CSA practices, the adoption is hindered by information disorder, which encompasses misinformation, disinformation, and malinformation. This can alter the access to information available to farmers in the region, weaken extension officers’ advisory efforts, and lead to a reduction in trust in the adoption of innovations in farming. This study, therefore, explores how information disorder affects CSA communication in Oyo State, Nigeria, and also looks into different strategies for transforming agricultural extension efforts with a focus on extant Extension 4.0. This study utilized a mixed method of research by combining survey data and interview responses from smallholder farmers, extension officers, and agricultural communication experts. SPSS and NVivo were used to analyze data and identify patterns as well as comments from respondents. Findings show that social media, farmers' groups, and unverified digital content were the major sources of information. It was revealed that these sources contributed to doubt of farmers towards CSA messages and increased the credibility gap between farmers and extension officers. Moreover, the study emphasized major challenges such as low digital literacy, insufficient regulatory oversight critical challenges such as low digital literacy, weak regulatory oversight, and limited participatory mechanisms in the current extension framework. Lessons were also drawn from Canadian advisory models with comparative ideas on cross-sectoral collaboration and media literacy integration. The study concluded that combating information disorder requires a reimagination of local communication systems. This entails the incorporation of digital knowledge and advisory strategies that are community-driven and evidence-based.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".