Comparative performance of scientific and indigenous knowledge on seasonal climate forecasts: A case study of Lupane, semi- arid Zimbabwe
Bibliographic record
Abstract
Seasonal climate forecasting (SCF) is weather prediction over a period ranging from 3-6 months period. Forecasting can be done using scientific forecasts (SF) or indigenous knowledge forecasts (IKF) systems. Forecast results can be very fruitful to smallholder farmers in semi-arid areas where rainfall is highly variable. Effective use of SCF has faced challenges including: rainfall variability, access to forecast information, interpretation of forecast results and generation gap. There is limited research on comparative performance of the two forecasts. The research seeks to evaluate comparative performance of the two forecasting methods in predicting outcome of the following rainfall season. The study was carried out in Daluka and Menyezwa wards of Lupane district, south-western Zimbabwe, which receives annual average rainfall of 450-650 mm. Focus group discussions and personal interviews were used in 2008/09 and 2009/10 seasons to capture farmers’ experiences and knowledge on SCFs and their application. The predicted outcome of the IKF and SF were compared with actual rainfall recorded from the predicted period. Results indicated high dependence on the use of the IKFs by Lupane farmers in predicting the outcome of the following season’s rainfall. Both the IKF and SF predicted inadequate rainfall in the two consecutive seasons and the results concurred with recorded rainfall in Daluka ward in the two seasons and in Menyezwa ward in 2009/10 season only. Results demonstrate that in the absence of SF, farmers may use IKFs. It is imperative that the two forecasts complement each other to increase farmer adaptation to climate variability.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".