Why "formal" climate adaptation strategies fail in sub‐Saharan Africa: Ignoring adapters' agency in the case of smallholding agriculture farming practices in Bono East Region of Ghana
Bibliographic record
Abstract
Abstract This paper reviewed a body of literature on climate adaptation options in sub‐Saharan Africa's (SSA) smallholding agriculture and complemented it with a case study involving experts interviews, focus group discussions, large‐scale household surveys, and farmer practices observation while drawing insight from the concept of "everyday adaptation and interrupted agency" and agency theory to assess farmer perceived limitations with climate‐smart agriculture (CSA) and climate‐wise food systems (CWFS) practices for climate adaptation in the SSA. The study noted that the narrow focus on CSA and/or CWFS as a silver bullet for climate change adaptation suitable for smallholding agriculture ignores food producers' agency to undermine sustainable and inclusive adaptation solutions. Moreover, smallholder farmers' everyday climate adaptation practices could be grouped into three categories; on‐farm adaptation, off‐farm adaptation, and Indigenous agroecological adaptation options. The on‐farm adaptation options are usually agriculture intensification and extensification. The off‐farm adaptation options include livelihood diversification activities, petty trading, seasonal labor jobs, and migration. The Indigenous agroecological adaptation strategy uses observing nature and weather elements to predict the onset of the rainy season. The study noted that smallholders' adaptation options, which is an expression of their agency, are motivated by smallholders' desire to be resilient to changing climate, increase productivity and income, and social network influence but not necessarily because the strategy is being promoted by the government or Non‐Governmental Organizations (NGOs). Therefore, we propose a sustainable food agency (SFA)—a multifaceted blended constellation of climate adaptation and mitigation strategies, as the best approach to addressing the climate crises in the SSA. The SFA allows individuals or groups to decide what climate change adaptation options best work for them to adapt to changing climate and produce and distribute their food without undermining the economic, social, and environmental bases that generate food security and nutrition for present and future generations.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".