Nut bush pesticide limits: urgent need for a comprehensive strategy to address current and emerging insect pests and insecticide options in the Australian macadamia industry
Bibliographic record
Abstract
In Australia, macadamia orchards are attacked by four main insect pest groups. Management and control of three of these key pests currently relies on broad-spectrum insecticides whose long-term future is questionable. Of the 23 insecticides registered for use in macadamia in Australia, 19 face issues affecting their availability and 12 are presently not approved in the EU, the USA or Canada. These international markets may refuse produce that does not adhere to their own insecticide use standards, hence Australian produce may be excluded from market access. Many of the potential replacement integrated pest management methods of pest control are generally considered less effective by the industry and have not been adopted. There are 17 insect pest groups identified by the industry, any of which have potential to become major problems if broad-spectrum insecticide options become unavailable. Thirteen pest groups need urgent attention as they are at risk of losing current effective control methods, and no replacement solutions have yet been developed. The lag period for research and development to identify new chemical and biological control solutions means there is now an urgent need for the macadamia industry to craft a strategy for sustainable pest management for each pest. Critically, this industry strategy needs to address the vulnerabilities identified in this paper, identify potential solutions for any cases of market failure and consider funding mechanisms to address these gaps. On economic and sustainability grounds, potential biological control options should be explored, especially in cases where insecticide control options are vulnerable. © 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".