The Political Economy of Agricultural Commercialisation: Insights from Crop Value Chain Studies in sub-Saharan Africa
Bibliographic record
Abstract
Agricultural commercialisation is seen as one of the most important avenues for fundamental structural transformation and development in sub-Saharan Africa, and is assumed to help enhance a wide array of household welfare indicators among rural households whose livelihoods directly derive from agriculture. Over recent years, sub-Saharan African countries have experimented with different models of agricultural commercialisation but, while there have been some success stories, the performance track record of agricultural commercialisation has generally been dismal. While there is a growing literature on drivers and obstacles for commercialisation at regional and national levels, less is known about how these factors play out in particular value chains, where there is still a need to better understand what drives or hinders the success of commercialisation. A set of APRA studies were carried out to address this gap, exploring the dynamics of crop value chains as a way of understanding the drivers, obstacles and pathways to agricultural commercialisation. A total of 11 case studies were carried out over 2020–21 in six countries, namely Ethiopia (rice), Ghana (oil palm and cocoa), Malawi (groundnuts), Nigeria (maize, cocoa and rice), Tanzania (rice and sunflower) and Zimbabwe (tobacco and maize). This briefing paper summarises some of the key findings from these studies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".