Farmers’ Knowledge, Perception and Practices in Apple Pest Management and Climate Change in the Fes-Meknes Region, Morocco
Bibliographic record
Abstract
Apple production in the Fes-Meknes region of Morocco is highly affected by pests and adverse weather conditions. A survey of apple farmers’ knowledge, perceptions and practices (KPP) in pest management and climate change was conducted in spring 2018 in two major apple-producing provinces of the region. Each farmer reported three insect pests and two diseases on average affecting their orchards. Pest management was performed by a combination of cultural and chemical methods. All farmers used dormant chemical sprays. About 60% of the respondents adopted pest surveillance based on visual inspection and 41.9% chose their pesticides on the basis of the information received from pesticide sellers. An average of 20 treatments per year was applied in each orchard. Regression analysis showed that neither the age of apple trees nor the number of pesticide applications influenced yield. Adverse weather conditions affected all apple plantations and the most frequent problems perceived were frosts, hailstorms, hot winds and water shortage. Of the orchards reported, 51.3% were protected with anti-hail nets. In order to reduce the rate of pesticide applications, better information on integrated pest management is required. Introduction of organic farming is necessary as 40.5% of the farmers agreed to convert to this practice in the future provided that market facilitation is established.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".