Agricultural Communication for Addressing Climate Change Challenges: Understanding Farmers' Responses to Misinformation
Bibliographic record
Abstract
The successful adaptation of agricultural practices to address climate change challenges and ensure food security depends on access to accurate and reliable information. However, the quality of available information could hinder this process. This study investigates how Nigerian farmers navigate contradictory or confusing information related to climate change and food security. An online survey and semi-structured interviews were used to explore the actions farmers take when faced with such information and the underlying motivations driving their choices. Descriptive analysis, Multinomial Logistic Regression, and Binary Logit Model in SPSS were used to analyze the survey, while thematic coding in Nvivo was used for the interviews. The results reveal that farmers are more likely to encounter inaccurate information within their peer networks, highlighting the challenges associated with informal knowledge transmission. Farmers exhibit a range of responses upon receipt of such information. Notably, 40.16% of farmers disseminate the information further through face-to-face interactions, group discussions, or social media platforms. However, only half of these farmers verified the accuracy of the information before sharing it. Other observed responses include withholding the information or simply taking no action, suggesting varying levels of engagement and trust with the received knowledge. These responses deepen our understanding of knowledge exchange within agricultural communities, especially where farmers unintentionally spread misinformation. Global implications also exist for developing effective communication strategies to drive sustainable agri-food systems.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".