Farmers’ Perception of Indigenous Seasonal Forecast Indicators in North Central Burkina Faso
Bibliographic record
Abstract
West African Sahel is one of the most exposed areas to the adverse effects of climate variability in the world. All agricultural production systems are affected. However, farmers use indigenous knowledge that enable them to make short, medium, and long-term seasonal predictions in order to adapt their agricultural calendar to these climatic risks. In the North Central region of Burkina Faso, this knowledge is not well documented. Therefore, this study aimed to identify the indigenous indicators of seasonal forecasts and analyze factors affecting their reliability. Surveys were carried out in focus group discussions with 204 farmers in 10 localities across the region. Results showed that farmers use meteorological (cold, heat, wind, clouds, rainfall distribution), biological (food plants phenology, migratory bird behaviour, occurrence of insects), astronomical (sun, moon, stars), religious or magical indicators to predict the coming rainy season. The intensity and duration of the cold period, heat intensity and the formation of dark cloud (April-May) are signs of an early start of the rainy season (or a wet season). Likewise, the abundant leafing, flowering and fruiting of Vitellaria paradoxa, Lannea microcarpa, Lannea acida, Adansonia digitata and Tamarindus indica (April-May) predict a wet rainy season, while abundant fruiting of Sclerocarya birrea indicates a drought. The arrival period (May-June) of migratory birds heralds a start of the season. Nowadays, climate change, the degradation of plant resources and increasing human pressure are affecting the reliability of these indigenous seasonal forecast indicators in the North Central region of Burkina Faso.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".