Unravelling Farmer Preferences for Contract Design Attributes in Ghana’s Agricultural Sector: A Discrete Choice Experiment Approach
Bibliographic record
Abstract
Contract farming is important for integrating smallholder farmers into agricultural value chains in developing countries. While previous research has explored the impacts of contract farming on welfare and productivity, there remains a gap in understanding how specific contract design attributes influence farmers’ willingness to participate. Existing studies often overlook the heterogeneity of farmer preferences and the interaction between socioeconomic/demographic/institutional factors. This study investigates Ghanaian farmers' preferences for agricultural contract design, examining how socioeconomic, demographic, and institutional willingness to participate in contract farming. A discrete choice experiment was conducted with 279 Ghanaian farmers across four regions. The DCE used a conditional logit model in analyzing farmers' preferences for contract type, pricing mechanism, yield commitments, input support, and partner reliability, and a logit model to identify participation determinants. It offers insights into the dynamics of contract farming by examining farmers' preferences for contract design attributes using a discrete choice experiment. The findings reveal that farmers favor written contracts, quality-based pricing, full yield commitments, limited input support, and openness to new partnerships – underscoring the importance of formalization, autonomy, and market incentives. Importantly, the study highlights that farmers are not a homogeneous group; preferences vary significantly by gender, household size, income, access to extension, and market distance. This study highlights the need for differentiated, farmer-centric contract models that reflect the diverse socioeconomic realities of smallholders. The findings extend contract farming theory by revealing key preference patterns and interaction effects, offering actionable insights for designing flexible, inclusive contracts that improve participation, retention, and long-term sustainability.
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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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".