Technical Change and Scale Effects in Relation to Profit Efficiency—Case of Family-Based Eggplant Production in Japan
Bibliographic record
Abstract
Little is known about how data-driven greenhouse horticulture impacts profit efficiency. Furthermore, the interaction between technical change (e.g., adoption of ECDs) and scale effects (e.g., farmland size) remains underexplored, particularly during periods of high fuel costs. Thus, by investigating the adoption of environmental control devices and farmland size, this study aims to determine whether technical changes (TC) and scale effects (SEs) contribute positively to profit effciency. Therefore, we hypothesize that both factors, TC and SEs, synergistically enhance profit efficiency. For our study, we observed both technical change affecting profit efficiency between 2017 and 2019. However, the technical change effect diminished in recent years, especially since 2020, due to increased input costs. Further, scale effects were rather limited as we observed an inverted U-shaped relationship between profit efficiency and farm size, the optimal farm size being equal and more than 41 and less than 46 are. The input costs negatively impacting the profit efficiency, namely repair and labor hiring costs, should be the foremost urgent issues to be resolved for TC and SEs to take place in the family-based facilitated greenhouse eggplant production in Japan.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".