Building resilience in Africa’s smallholder farming systems: contributions from agricultural development interventions—a scoping review
Bibliographic record
Abstract
In this paper we use a scoping review to examine how the concept of resilience is framed and empirically applied with respect to agricultural development interventions in smallholder farming systems in Africa. We reviewed a total of 50 studies and found that most focused on two major strategies for building resilience. The first approach prioritized matching solutions, like Climate Smart Agriculture (CSA), to the biophysical attributes of problems, such as the stresses and shocks associated with climate change. The second approach focused on advancing social equity goals to improve resiliency, while also integrating climate-related adaptation measures. Among such measures were co-created innovations that sought to affect social change on issues related to human agency, power relations, and equity considerations in resource access and use. The different conceptions and responses to climate and non-climate related risks and vulnerability in the reviewed literature also revealed growing tensions. There are especially strong critiques concerning resilience building interventions that prioritize technical solutions adapted to the bio-physical aspects of climate change. We argue for more constructive dialogue around what each of the two approaches might offer to contribute to improving resilience on a range of adverse social-ecological changes in Africa’s smallholder farming systems. Specifically, we emphasize the importance of valuing the complementarity contributions that both technocratic-focused and social equity-centered approaches offer as none of the different approaches on their own is up to the task.
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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 | 0.017 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".