The Use of Climate Forecasts Information by Farmers in Limpopo Province, South Africa
Bibliographic record
Abstract
The primary aim of the paper was to examine the role of climate forecasts information as a potential tool to reduce impact of climate change in the Vhembe District in Limpopo province. A representative sample of 90 farmers participated in the study. The study involved the Vhembe District with Tshakhuma, Rabali and Tshiombo as study sites. Vhembe district is located in a semi-arid area that is frequently troubled by dry spells, often escalating into severe drought. Such situations have compelled regular assistance from the Limpopo Province in the form of drought relief. Such reactive measures are, however, costly and alternative risk-reduction measures should be sought. One alternative measure to enhance adaptive capacity and possibly improve local decision-making is seasonal climate forecasts. A tool is needed to measure how farmers have responded to past events, what techniques and methods are being used, including forecasts, and a range of other coping and adaptive mechanisms. Many recent efforts to reduce vulnerability to such events include improved adaptation and risk reduction. Farmers in the Vhembe district in Limpopo Province, South Africa (including Tshakhuma, Rabali and Tshiombo study sites) have developed their own local climate forecast strategies and adaptation practices during times of climate risks. For many years the importance of local knowledge in small-scale farmer practices was overlooked. These farmers have developed their own indicators for monitoring climate conditions such as observing the behaviour of birds, behaviour of insects and the characteristics of plants. This paper shows how local knowledge forecasting and external knowledge forecasting can be used as a way to reduce impact of climate change. The following findings were found: (1) Local knowledge forecasting is useful to local farmers, (2) Young farmers, however, prefer to use or rely on external forecasting information, (3) Some farmers within Tshakhuma, Rabali and Tshiombo prefer to integrate both local knowledge and other types of forecasting, (4) Some farmers within the three areas use crop diversification as security measures during uncertain periods.
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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 | 0.001 | 0.000 |
| 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 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".