(Re)assessing Climate-Smart Agriculture practices for sustainable food systems outcomes in sub-Saharan Africa: The case of Bono East Region, Ghana
Bibliographic record
Abstract
This research paper assesses the reality of Climate-Smart Agriculture (CSA) practices’ potential to promote the outcomes of sustainable food systems (SFS) within Ghana’s smallholding agriculture context. The study demonstrates that rural farmers generally perceive CSA’s contribution to ‘food and nutrition security’ and ‘economic performance’ as more important than CSA’s contribution to ‘social equity’ and ‘environmental stewardship’. From a narrow perspective, the study demonstrates that farmers perceive CSA’s potential to ‘prevent pest and disease outbreaks’ and ‘increase human capital information’ as the most important contribution of CSA to SFS outcomes. In contrast, CSA’s potential to promote environmental stewardship is perceived as the least important among Ghana’s rural farmers. This enormity of displacement of smallholders’ perceptions at large is motivated by demographic, socioeconomic and ecological factors. Moreso, the CSA for SFS outcomes narratives is driven by farmers’ self-apprise, social networks and other local information dissemination agents. Furthermore, research findings suggest farmers’ awareness of CSA practices and interventions is deficient owing to unmet training and information needs for approximately 82% of the CSA practices and interventions. This situation elucidates the dichotomy of CSA practices’ narratives as tools for attaining food, nutrition security and economic performance to the detriment of critical issues such as increasing awareness and building farmers’ capacity to engage with CSA practices while also managing socio-ecological trade-offs that emerge over time due to engagement with CSA. Critical (re)orientation is needed across the scale to drive CSA practices and interventions that confine climate adaptation and food production practices within safe planetary boundaries without undermining social, economic, food and nutrition security needs.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".