Integrated Crop Pest Management Practices: A Classification Based on a Rapid Review of International and Canadian Literature
Bibliographic record
Abstract
Abstract Crops are vulnerable to weeds, fungi, insects, nematodes, rodents and diseases. To address these threats rapidly, farmers tend to adopt a curative approach based on the use of synthetic pesticides (fungicides, herbicides, insecticides) rather than a preventive approach without pesticides. This reliance on pesticides poses risks to human health, the environment, and wildlife, including pollinators such as bees and bats. Reducing pesticide use has thus become an important societal and political objective worldwide. In Canada, the Government of Quebec’s Sustainable Agriculture Plan (PAD) 2020–2030 promotes, as its first objective, the adoption of alternative practices aimed at reducing pesticide sales by 500,000 kilograms by 2030 and decreasing health and environmental risks by 40%. This article takes stock of alternative practices of synthetic pesticides use in the literature in Canada and internationally from the perspective of Integrated Crop Pest Management (ICPM) stages and the Ministry of Agriculture Fisheries and Food of Quebec (MAFFQ) typology 1 , to see which ones could possibly be used in Quebec. To do this, we used the “Rapid Review” method of the literature based on the exploitation of 71 scientific references. The findings indicate that countries with agroeconomic conditions comparable to those of Canada are adopting alternative and good agricultural practices, as well as physical, mechanical, biological, and biotechnical control methods; however, pesticide use often persists alongside these approaches. While practices belonging to the prevention and intervention stages through physical, mechanical, biological, and chemical control appear to be highly adopted by producers, practices belonging to the pest knowledge, monitoring, evaluation, and feedback stages appear to be poorly adopted by producers. The authors recommend improving access to information on crop pests and ICPM practices, along with enhancing farmers’ awareness of the economic, health, and environmental risks associated with pesticide use. Future research should focus on classifying and analyzing ICPM practices by stage to support the development of public policy recommendations tailored to each stage, particularly regarding incentives and barriers to adoption, as well as their impacts on producers.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.141 | 0.145 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".