Adaptation strategies by smallholder farmers to climate change and variability: The case of the savannah zone of Ghana
Bibliographic record
Abstract
In semi-arid regions, the biggest threat to agriculture is climate change . This is because agricultural activities in these regions rely heavily on rainfall thus making the communities there particularly vulnerable. Sustainable adaptation techniques are therefore one way to survive in these circumstances. The Multinomial Logit Model (MNL) is thus utilised in ascertaining the dynamics of the adaptation techniques that are being applied by farmers in the Savannah zone of Ghana. The farmers acknowledged the existence of climate change and listed some detrimental effects on their means of existence. While many of the farmers were making an effort to adjust to the circumstances, some were not using any adaptation strategies despite the alleged climate changes they had observed. Among the most effective adaptation techniques found were planting of drought-resistant varieties, adjusting planting schedule and timing of different crops. The choice of an adaptation technique is known to be influenced by several factors. A few of those acknowledged were years of farming experience, farm size and educational attainment. It was discovered that educational attainment was the major factor influencing adaptability. The more educated a person is, the more likely they will use an adaptation strategy. The primary cause of adaptation restrictions was determined to be financial constraints, which were closely followed by restricted access to climatic information. It was found that most of the techniques employed by the farmers are reactionary. However, because of the complexity of climate change, effective adaptation requires a combination of both proactive and reactive techniques.
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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 | 0.001 | 0.000 |
| 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".