Climate Change Perception and Farmers’ Adoption of Sustainable Land Management for Robust Adaptation in Cameroon
Bibliographic record
Abstract
The certainty of a changing climate is asserted in the perception of observable changes in rainfall and temperature reported by more than 52% of farm managers in Cameroon’s dry North region and almost 70% in the humid West region. Responding to these observable changes, soil and crop management techniques are adopted to ease climatic stress and insure farms from income shocks and associated vulnerabilities. Farmers were surveyed on their participation in sustainable land management (SLM) programs, and a probit model reveals that the probability of adopting recommended SLM techniques is influenced by land tenure, education, gender, experience and non-farm income. Noting that producers’ adoption of recommended SLM measures is the initial step for medium to long-term adaptation of the productive capacity of their farmland, the Switching Regression Model shows that property rights, access to market, access to extension and adaptation due to farmers’ perception of a changing climate significantly contribute to income security. While this is informative for policy measures required to promote technology adoption, however, participating and employing SLM is a plausible insurance to both current climate variability and long-term climate change.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".