Data-Driven Weather Forecasting in South African Farming: Impacts on Crop Yields
Bibliographic record
Abstract
Data-driven weather forecasting applications have become integral in modern agriculture to improve crop yields by providing accurate and timely predictions of climatic conditions. A mixed-methods approach involving surveys, interviews with farmers, and statistical analysis was employed. Data from meteorological stations and agricultural records were analysed using regression models to quantify effects. An empirical model revealed a significant positive correlation ($R^2 = 0.75$, $p < 0.01$) between the use of weather forecasting applications and increased crop yield variability, indicating substantial benefits in precision agriculture. The findings suggest that sophisticated data-driven tools can enhance agricultural productivity by optimising planting strategies based on climate predictions, although further research is needed to validate these results across different regions. Farmers should be encouraged to adopt advanced weather forecasting technologies and policymakers should support the development of such applications in rural areas. South Africa, Agricultural Output Variance, Weather Forecasting Applications, Regression Analysis
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".