Assessment of Seed Maize Systems and Potential Demand for Climate-Smart Hybrid Maize Seed in Africa
Bibliographic record
Abstract
<p>Africa lacks a steady supply of good quality seed due in part to lack of timely development and conveyance of seed technologies to farmers. This study evaluated the performance of national seed systems of five countries in eastern and southern Africa where a consortium of public-private organizations are implementing the Water Efficient Maize for Africa (WEMA) project. WEMA aimed at developing and deploying drought-tolerant and insect-pest-protected maize hybrids (climate-smart hybrids) to smallholder farmers royalty-free. The objectives of the study were to assess the main components of the seed maize systems, their institutional frameworks, and estimate the potential demand for WEMA climate-smart hybrid maize seed to guide in upscaling the seed technology to other African countries. Primary data was gathered from key experts in the seed maize value-chain of each country. Secondary data came from national statistics bureaux to estimate the potential demand for WEMA seed maize. Results showed that farmers in all five countries except South Africa overwhelming rely on the informal seed system. The formal seed system is over-regulated creating unnecessary delays in the release and commercialization of improved seed varieties. It also increases the cost of developing new seed technology. Except South Africa, the demand for seed maize in the other four WEMA countries will grow by 4.1% annually from 31,614 to 35,863 metric tons of certified seed over 2014-2020 period. The study advances several recommendations to improve the efficiency of the seed maize systems, which when implemented, will undoubtedly improve food security in Africa.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".