win–win contract farming in dual-channel agribusiness supply chains under yield, quality, and price uncertainty
Bibliographic record
Abstract
Despite the benefits of contract farming, power imbalances between farmers and agribusiness firms often result in unfair agreements. This study aims to design a fair (win–win) contract farming model for a dual-channel (fresh and processed) blueberry supply chain , incorporating an incentive mechanism to ensure that all parties benefit. We examine the benefits of incentive-based contracts over penalty-based contracts by analytically investigating three farming situations: (1) no contract, (2) penalty-based contract farming, and (3) incentive-based contract farming. We establish analytical conditions for collaborative incentive-based contract terms that benefit both parties and validate these conditions numerically using data from a representative blueberry farm. Our findings indicate that the incentive-based contract farming leads to mutually beneficial outcomes and higher supply chain profits compared to the penalty-based contract. We conduct numerical and comprehensive sensitivity analyses to assess the impact of contract farming on the profits of the farmer, agribusiness firm, and the overall supply chain. Our study has significant theoretical and practical implications, emphasizing the importance of balanced and mutually beneficial contract farming arrangements that account for yield, price, and quality uncertainties within a dual-channel supply chain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".