Climate Smart Agriculture Practices in Semi-arid Northern Ghana: Implications for Sustainable Livelihoods
Bibliographic record
Abstract
Achieving food security and livelihood development among vulnerable households in the semi-arid regions is challenged by water scarcity and climate change. To alleviate the challenges of water scarcity and climate change impacts, farmers are adopting different climate smart agriculture (CSA) practices. However, there is limited knowledge on the contribution of CSA practices to livelihoods of farmers in semi-arid northern Ghana. Therefore, this paper explored CSA practices adopted by smallholder farmers and assessed the contribution of dry season farming to livelihoods in rural semi-arid Ghana. Using data from 100 households, farm income, household food security and subjective wellbeing (SWB) were compared between dry season farmers and non-dry season ones. The findings showed that socio-economic factors hindered the utilisation of practices with high start-up cost such as rain water harvesting. Farmers also adopted other practices based on the benefits, ease of use and geographical context. Comparing dry season farmers and non-dry season ones, the results showed that dry season farming had great potentials of improving income, food security (66%) and wellbeing (P>0.01) of rural households in semi-arid Ghana. However, adopting CSA practices only without instituting programmes to address other socio-economic challenges faced by smallholder farmers will yield minimal impacts. Complementing CSA initiatives with poverty alleviation programmes will effectively contribute to improved livelihoods in resource poor communities.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".