SOCIAL DETERMINANTS OF ADOPTION OF INTEGRATED PEST MANAGEMENT (IPM) BY QUEBEC GRAIN FARMERS
Bibliographic record
Abstract
The purpose of this paper is to determine the socioeconomic factors that influence the behavior of adoption of Integrated Pest Management (IPM) by Quebec grain farmers. Using an econometric model of discrete choice, ordered logit model, the results show that majority of Quebec grain producers are practicing IPM. Seven explanatory variables, such as amount of IPM information received, lack of weed control knowledge, level of environmental concern, perception that IPM is an organic production, need for monetary incentives to adopt, numbers of years as a producer, education level appear to be the determinants of the producers' decision process. Nevertheless, there was a gap between those who believe they are practicing IPM and those who actually do. IPM is quite misunderstood; producers often equated it with organic production practices. Increased information campaigns are needed to teach appropriate IPM pest identification practices. In fact, producer organizations appear to be an ideal structure for increasing IPM information dissemination because of the level of trust shared among producers. Most producers worried that IPM practice might reduce yields; therefore, 75% believe that financial assistance is needed before they would more widely adopt IPM. Level of agricultural training plays a significant role in IPM adoption. The foundations of IPM practices should be taught as early as possible in existing agricultural education programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".