Economic Benefits of Using Sterile Insect Technique and Mating Disruption to Control Codling Moth
Bibliographic record
Abstract
In the Okanagan/Similkameen region, the tree fruit industry value chain forms a significant part of the Agricultural Products Cluster. In 2011, 8677 acres of apples comprised 38% of the total horticulture land base. The remaining acreage was planted primarily to sweet cherries (3500 acres) and wine grapes (8100 acres). However, the tree fruit industry value chain is undergoing significant transformational change as the apple acreage has declined from 13 430 acres in 2001, a loss of 4753 acres. This change in the primary production base has had profound processing and marketing implications for the apple sector, and for the government programs such as the Okanagan Kootenay Sterile Insect Release Program (OKSIR) that provide services to the sector. A social benefit-cost analysis (B/C) was used to evaluate the OKSIR. This analysis measures both the benefits, in the form of cost savings and Sterile Insect Release services that accrue to the commercial apple and pear producers, and the benefits that non-agricultural residents receive, the consumer surplus. The study compares the Benefit/Cost ratios for both the existing Sterile Insect Technique (SIT) to control Codling moth and the use of a mating disruption (MD) technique. The net benefit for MD ($281.47/acre) is higher than for SIT ($258.65/acre). This is also reflected in the higher NPV for MD. The break-even point for SIT is 6238 acres and 1264 acres for MD. The lower acreage requirement for MD reflects the lower costs associated with MD, and supports the idea that MD is a viable method for controlling Codling moth in areas with relatively small acreages.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".