Adoption of Climate Smart Agricultural Technologies among Smallholder Farmers in Semi-Arid Ghana
Bibliographic record
Abstract
The evidence of climate change and variability in semi-arid Ghana is glaring and the adverse impact is being felt mostly by smallholder farmers because of their over dependence on agriculture for livelihood and subsistence. As a solution to building the resilience of the smallholder farmers, the Climate Smart Agriculture (CSA) concept was introduced a decade ago by the Food and Agriculture Organization, guided by three key principles of adaptation to climate change, greenhouse gas emissions reduction and promotion of food security. The paper sought to assess the level of awareness of climate smart agriculture practices and the respective rate of adoption of these practices. Moreso, this paper established how Normalised Difference Water Index (NDWI) and Land Surface Temperature (LST) affects the adoption rate of CSA practices or technologies. The study employed the explanatory sequential mixed research methods. A semi-structured questionnaire was used to collect data from 300 smallholder farmers and 16 focus group discussions were conducted, with a total of 180 persons taking part in the focus group discussions. Key informant interviews were also conducted for 11 relevant stakeholders from governmental and non-governmental institutions. Findings from this study reveal that CSA practices such as intercropping, manure management and mulching had a 100% adoption rate, and the least adopted practice was irrigation followed by dry season gardening. The NDWI and LST analysis concluded that Nandom is the most viable among the two municipals to support irrigation projects since it has more capacity to retain surface water during the dry and wet seasons.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".