Climate Change Adaptation Strategies used by Limpopo Province Farmers in South Africa
Bibliographic record
Abstract
The aim of the paper was to identify the adaptation strategies used by Limpopo province farmers against climate variability and change. A representative sample of 300 farmers aged 16-65+ years (46 percent males and 54 percent females) participated in the study. The study involved Sekhukhune and Capricorn districts, with 56 percent farmers in Capricorn and 44 percent in Sekhukhune district. The following 11 local municipalities were visited: Elias Motsoaledi, Makhuduthamaga, Fetakgomo, Ephraim Mogale, Tubatse, Lepelle Nkumpi, Blouberg, Aganang, Polokwane, and Molemole. Focus group discussions, questionnaire and observations were used to identify climate variability and change adaptation strategies in Limpopo province.The paper presented adaptation strategies of selected Limpopo province farmers. Some of their adaptation strategies included: (a) Use of DACOM system for monitoring, (b) Use of Indigenous Knowledge practices (c) Use of wind directions and clouds to determine rainfall expectation , (d) Use of nets to monitor temperature level etc. Other important adaptation strategies being used by farmers were also discussed in this paper including different adaptation measures against colds, heat, frost, abnormal wind, hail, lack of extension support, nematodes, insecticides, worms, temperature and rainfall. The adaptation strategies identified in this paper are potentially valuable to the agricultural sector considering the threats that climate change poses across climate sensitive sectors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".