Complexities in the Implementation and Maintenance of Integrated Pest Management in Potato
Bibliographic record
Abstract
Integrated pest management (IPM) is an educated and systematic effort to use multiple control techniques to reduce pest damage to economically acceptable levels while minimizing negative environmental impacts. Although its benefits are widely acknowledged, IPM is not universally practiced by farmers. Potato farming, which produces one of the most important staple crops in the world, provides a good illustration of the issues surrounding IPM adoption. Potatoes are attacked by a complex of insect pests that can inflict catastrophic crop losses. Potato production has gone through the processes of consolidation and intensification, which are linked to increased pest problems, particularly selection for insecticide-resistant pest populations. While use of insecticides remains the most common method of pest control in potatoes, other techniques, including crop rotation and natural enemies, are also available. In addition, there are effective monitoring techniques for many potato pests. However, reliable economic thresholds are often lacking. Potato ecosystems are complex and diverse; therefore, the knowledge necessary for developing ecologically based pest management is not easily obtained or transferable. Furthermore, potato systems change with the arrival of new pest species and the evolution of existing pests. Modern technological advances, such as remote sensing and molecular biotechnology, are likely to improve potato IPM. However, these tools are not going to solve all problems. IPM is not just about integrating different techniques; it is also about integrating the efforts and concerns of all stakeholders. The collaboration of farmers and scientists in agricultural research is needed to foster the development of IPM systems that are appropriate for grower implementation and thus more likely to be adopted. Additional emphasis also needs to be placed on the fact that not only does IPM decrease degradation of the environment, but it also improves the economic well-being of its practitioners.
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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 | 0.048 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".