Modelling Seed System Networks in Mali to Improve Farmers Seed Supply
Bibliographic record
Abstract
Food security remains a constant source of concern in Mali, where over 75% of the population derives their living from agriculture. Despite the huge theoretical need, the production and use of improved seeds remain very limited. Concurrently, genetic erosion and the disappearance of certain local varieties have been observed. The dual need to preserve agricultural biodiversity as a factor of resilience for production systems while disseminating improved varieties as a means to increase production raises questions regarding what type or types of organization could best respond to these agricultural challenges. The farmer seed network in Mali is based on the self-production of pearl millet and sorghum seeds and operates through non-commercial, community-based exchanges. The formal seed sector distributes certified seeds through cooperatives, with cost-effectiveness the main priority. The joining of these two seed networks could allow agro-biodiversity to be considered in such a way that genetic diversity can be maintained. The various pearl millet and sorghum seed exchange networks often are considered to be in opposition, with the formal network pitted against the informal one. By highlighting points where the two systems could come together, the proposed model allows a new perspective on seed flows and agro-biodiversity management. The global seed network that could emerge from this would, on one hand, remedy the failure of the state seed system inspired by a Western model in which seed production and distribution is disconnected from agricultural production, and, on the other, compensate for shortcomings in the traditional farmer seed system to increase productivity and sustainably manage farmer pearl millet and sorghum varieties while continuously introducing new genetic resources.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".