Market dynamics between retail channels and short food supply chains: A case of organic fruits
Bibliographic record
Abstract
Organic farming and short food supply chains have gained considerable attention as the key contributors of sustainability to agricultural practices. Despite the significant existing literature on these two research realms, the competition between short and retail channels for organic products has not yet been examined. In this study, we consider an organic food supply chain consisting of two farmers, where the traditional farmer sells his organic products through a retail channel, and the local farmer sells his organic products through a short channel. Using real-world data, we first empirically estimate the demand function of organics in the retail and short channels while analyzing the characteristics of these functions. Having estimated the demand functions, we then study the supply chain problem analytically based on a game-theoretic leader-follower Stackelberg model. We find the optimal closed-form solutions for quantities and prices by maximizing profit functions. The results indicate that consumers are willing to pay a premium for organic products in the short channel. Additionally, the empirical findings reveal short and retail channels engaging in price competition, higher transition from the retailer network to the short channel, and greater producer market power in the latter. The sensitivity analysis indicates that an increase in the cultivation area leads to an increase in the quantity of production in both channels and wholesale prices but a decrease in consumer prices. However, an increase in linear cost factor leads to a reduction in the quantity of production in the retail channel, along with an increase in the consumer prices and quantity of production in the short channel. Our findings also reveal valuable implications for retailers and farmers supplying their products in short food supply chains. The results indicate that retailers must monitor the prices in the short channel and keep price stability relative to this channel. Finally, we offer managerial guidance to farmers within the short channel to enhance the channel's performance.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".