Technology, Policy, and Market Adaptation Mechanisms for Sustainable Fresh Produce Industry: The Case of Tomato Production in Florida, USA
Bibliographic record
Abstract
Tomato (Solanum lycopersicum L.) is an important vegetable crop in Florida, a state located in the south-eastern region of the United States. The state is the second largest producer of tomatoes in the country and contributes to almost 90% of the domestic winter tomato supplies. However, tomato farmers in Florida have come under increasing pressure due to climate changes, foreign imports, and rising production costs. The purpose of this paper is to analyze whether Florida tomato growers will continue to sustain their production given the seasonal and geographic production advantage, yet against various internal and external threats emerging throughout the fresh produce supply chain. We developed our study on a multi-disciplinary conceptual model of network (supply chain) relationship and primary and secondary data gathered from various stakeholders and the literature. We found that Florida farmers have done remarkably well by adapting to warming temperatures and changing consumer expectations about environmental sustainability and responsible labor practices. However, foreign competition, labor shortage, the rising costs of inputs, extreme weather events (hurricanes), and pests and diseases due to humid climate continue to affect the sustainability of the Florida tomato production. Our paper suggests various farm-, market-, and institution-level adaptation mechanisms for preventing the regional production advantage of the Florida tomato industry from eroding. Newer immigration laws are necessary for easing the labor situation. In order to have a level playing field with respect to the use of protected agriculture technology such as in Mexico and Canada, U.S. farmers in general and Florida farmers in particular need government support. Florida farmers need to diversify their fresh produce market strategies, finding new product streams. There is also a need for reforming the product certification landscape, which some growers find cumbersome and cost prohibitive. Growers may gain from being better able to convey to consumers the information regarding their effort put into environmental sustainability, workers welfare, and safe food.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".