Influence of Farmers’ Socio-economic Characteristics in Selection of Climate Change Adaptive Strategies
Bibliographic record
Abstract
Climate change is a global phenomenon that has been of great concern and its tackle is of outmost importance for food security and poverty alleviation among other things. This paper assessed the decisions informing selection of climate change adaptive strategies in Limpopo Province based on farmers’ socio-economic characteristics. Data was collected from all five districts of Limpopo province namely; Sekhukhune, Waterberg, Mopani, Capricorn and Vhembe. A questionnaire administered in a face to face interviews was used to collect data on various climate change adaptive strategies used by emerging farmers in Limpopo province of South Africa. The most identified strategies identified were crop diversification, substitution of crops, calendar redefinition, changing crop rotation patterns and fertilizer application. Results from the multinomial logistic regression model employed indicated that farmers’ socio-economic characteristics such as gender, age group, formal education, farming experience and household size significantly influenced farmers’ selection of climate change adaptive strategies. As a conclusion, farmers’ socio-economic characteristics are the major influencing factors determining selection of specific adaptive strategies. The study recommended that to enhance adaptive strategy uptake there should be a careful consideration of socio-economic characteristics that suits a need of specific farmers as farmers’ adaptation to climate change varies individually.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".