Perspectives on sustainability of smallholder seed enterprises: a case of African indigenous vegetables in Tanzania
Bibliographic record
Abstract
Perspectives on sustainability of smallholder seed enterprises: a case of African indigenous vegetables in Tanzania SummaryBetween 2013 and 2016, CABI's Good Seed Initiative (GSI) worked with partners in Tanzania to strengthen the seed system for Africa Indigenous Vegetables (AIVs) through promotion of farmer seed enterprises using two models -contract farming (Arusha) and Quality Declared Seed (QDS) (Dodoma).This study, conducted in 2019 aimed to assess the sustainability of farmer seed enterprises and project strategies that were important in satisfying the continued functioning of farmer seed production, 3 years after GSI project closure.Data were collected through focus group discussions (FGDs) (73 men, 69 women), and individual interviews with seed sector stakeholders.Results show that farmer seed enterprises under both models continued to thrive, creating avenues for income diversification and contributing substantially to household incomes (>50%).Quality Declared Seed was a viable strategy for providing quality seed to farmers, in central Tanzania (Dodoma), which lacks a strong formal seed sector.However, QDS production was challenged by lack of access to foundation seed, extension services, inspections and seed testing services, which are key for quality seed production.Contract farmers in Arusha continued to engage in contractual arrangements with seed companies for bulking AIV seeds, including globally important vegetables such as tomatoes and onions, building on farmers' experience in producing AIV seed.Two interviewed seed companies reported an increase in contracted seed quantities in Arusha and Manyara regions from 14 MT (2016) to 41 MT (2019), and an increase in the number of contracted farmers from 112 to 250 in the same period.However, contract farming was dominated by men, due to land ownership and decision-making dynamics, and challenges still existed emanating principally from how they were negotiated.Linking seed producers to the market through innovation platforms and contracts, stimulation of demand for AIVs through nutritional awareness, and promotion of the value of using quality seed for increased productivity were important in ensuring the continued functioning of farmer seed enterprises.The commercial viability of seed production also provided incentives for continued seed production.Development efforts supporting farmer seed enterprises should consider facilitating women to equally participate and take advantage of the benefits of contract farming to the same extent as their male counterparts.Government mandated agencies should support the functioning of the QDS system to facilitate the continued supply of quality seed in areas less served by the formal seed sector, and for crops not well integrated into the formal system. Key highlights• Farmer seed enterprises under QDS and contract farming continued to thrive in the two study locations, providing financial benefits to seed producers and the community at large.• The quantity of AIV seed produced and number of farmers producing seed more than doubled under either system.• Market linkages, nutritional awareness campaigns, and promotion of the value of using quality seed for increased productivity were important in supporting the continued functioning of farmer seed enterprises.• Seed policy regulations, especially quality assurance should be implemented to ensure production and distribution of quality seed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".