Indigenous Knowledge of Climatic Conditions for Sustainable Crop Production under Resource-Poor Farming Conditions Using Participatory Techniques
Bibliographic record
Abstract
Rambuda irrigation scheme is situated in Vhembe District of Limpopo Province in South Africa. It was established in 1952 and farmers do not have access to recorded climatic information. Farmers are growing crops on a trial and error basis, hence low yields and crop loses. The objective of the study was to investigate indigenous knowledge of climatic conditions relevant for crop production using participatory techniques. Situation analysis was conducted to gain information on factors influencing crop choice. Participatory exercise was conducted with 33 of 104 of plot-holders. Farmers could identify climatic factors important for crop production and those limiting to crop performance. Hot, dry conditions during August to October and January months were limiting to crops, particularly sweet potato production. The results showed that indigenous knowledge of climate needs to be considered during agricultural development planning and scientists need to investigate linkages between modern agro-meteorology and indigenous knowledge.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".