Farmer decision making for hybrid maize seed purchases: Effects of brand loyalty, price discounts and product information
Bibliographic record
Abstract
CONTEXT: Each year public and private sector maize breeding programs in Kenya deliver high-yielding hybrids that are resistant to drought, pests, and diseases. Yet, most Kenyan maize farmers purchase older, well-known hybrids. While the 'varietal turnover' problem is well known, few solutions have emerged. OBJECTIVE: The potential for seed companies and retailers to influence farmers' product selection towards new products remains an open question. In-store marketing that induces farmers to experiment with new products may be a scalable and cost-effective way to advance seed systems development. METHODS: Our controlled field experiment with 600 farmers in Kenya comprised a mock agrodealer store stocked with locally available hybrids, where half the farmers who participated faced an out-of-stock situation for their preferred product. The influence of price promotions and product performance information on farmers' seed choice were assessed. RESULTS AND CONCLUSIONS: When a participant's preferred product was available, performance information and discounts had no effect on decisions. However, when the preferred product was unavailable, the treatments had limited effects on product selection. Prior experience and brand loyalty stood out as the strongest predictors of seed product selection. SIGNIFICANCE: Our work explored the potential for two interventions-information and price discounts-to influence farmers' product selection. While these interventions showed limited influence on selection, the study design provides a clear starting point for future related experiments. More public and private investments are required to generate timely, comparable, and reliable information on seed performance. The strong effect of brand loyalty favors larger-sized seed companies with sizable marketing budgets.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".