Contribution of Active Controlled Atmosphere (CA) Technology to the Value-Chain of Perishable Fruits and to Rural Development: Case of Atemoya in Taiwan
Bibliographic record
Abstract
Atemoya is an important tropical fruit export for Taiwan, mainly produced in Taitung, a rural area of South-Eastern Taiwan. However, it was sold to virtually a single market—China—and when that market suddenly announced an import ban on the fruit in 2021, the rural farmers and the local economy were adversely affected. They had to quickly explore new overseas markets. Unfortunately, its short postharvest life makes it infeasible for long-distance transport. This study measured the impacts of the ban on the local economy using an input-output (IO) analysis. It also tested the technical feasibility of using a controlled atmosphere (CA) preservation technology, which was necessary for long-distance exports. The benefits of this strategy for the rural economy were also assessed using IO techniques. Results reveal that the atemoya value chain accounted for 2.12% of the production value, 2.75% of the value-added, and 3.62% of the employment in Taitung. Furthermore, the CA technology successfully doubled its postharvest life; thereby allowing exports to countries as far as Canada, and easing the impacts of the earlier ban. This development, together with facilitating domestic sales, boosted the local economy’s output value by NTD 491 million and its value-added by NTD 237 million. In addition, it can also increase rural employment by 2235 people. Using a smart agriculture technology in this case protected a perishable fruit industry that has a thin domestic market, from the risk of relying only on a single export destination. Consequently, this has supported the sustainability of rural communities and helped them to remain resilient.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".