Confronting genetic gains with markets: Retrospective lessons from New Rice for Africa (NERICA) in Uganda
Bibliographic record
Abstract
Breeders have two non-exclusive strategic investment options for increasing smallholder farmers’ and consumers’ livelihoods through genetic improvement of crop varieties: (i) enhancing productivity; and (ii) enhancing value and market access. New Rice for Africa (NERICA) varieties with superior agronomic characteristics were bred and introduced in various African countries in the early 2000s. Two decades later, drought tolerant NERICA4 is among the popular upland rice varieties grown across Africa. We analyze market evidence for NERICA4 from Uganda in 2011, i.e. well before it massively reached urban markets, where it is currently commingled with standard rice. We then compare the breeding priorities that would have ensued from the 2011 market evidence with the reality a decade later. Non-hypothetical auction experiments with consumers were conducted in urban Uganda in 2011 to predict potential market share and value of non-fragrant NERICA4 and fragrant NERICA1, relative to two market standards, i.e. non-fragrant Kaiso, and Supa, the most popular fragrant rice variety in the region. Average consumer bids positioned the two NERICAs between both market standards. Whereas NERICA1 easily outcompeted NERICA4 and Kaiso in the non-fragrant rice category, it failed to compete with Supa in the fragrant category. The 2011 market evidence would have suggested breeders prioritize investment in breeding programs for fragrant NERICAs to help smallholders gain access to high-value markets and expand consumers’ choice with cheaper fragrant rice alternatives. However, the popularity of NERICA4 relative to NERICA1 in farmers’ fields seems to suggest that agronomic genetic gains may have outweighed market traits such as fragrance.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".