Smallholder Farmers’ Responses to Scientific Early Warning on Weather in the Okavango Delta, Botswana
Bibliographic record
Abstract
Although formal channels of communication exist for conveying early warning scientific weather messages, it is widely believed that small-scale farmers continue to utilize traditional practices in obtaining weather information. This study identifies and assesses the factors which influence the uptake of scientific early warning weather information by small farmers in the Okavango Delta, Botswana. A descriptive-analytical design was used to study 90 farmers in Kareng and Bodibeng communities situated within the delta basin. A multi-stage sampling procedure was used to select the sample from an existing household listing. A semi-structured interview and focus group discussion (FGD) were used to elicit information from the respondents. Findings show that most farmers (68.9%) moderately utilize scientific weather information, while 16.7% had a low uptake of the messages. Nonetheless, 14.4% of farmers had a high uptake of weather information. There was significant positive correlation, at p≤0.01 confidence level, between uptake of early warning scientific weather information and educational level, age, traditionalism and fatalism. The uptake of scientific weather information or messages had a strong association with information sources such as Kgotla meetings, TV, print media, agricultural extension agents and the radio. The uptake of modern scientific weather information needs to be promoted through these modes of communication, coupled with well-resourced extension services, and in ways that may not be perceived to denigrate indigenous knowledge. Sectoral departments should collaborate in addressing existing challenges for appropriate climate response action.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".