Farmer Knowledge of Climate Change Impacts and Adaptation Strategies in the Management of Vegetable Insect Pests in Zimbabwe
Bibliographic record
Abstract
Farmer knowledge of insect pests’ risks in a changing climate is important in managing insect pests’ incidence. A total of 250 vegetable farmers from 5 wards in Zimbabwe were sampled using a semi-structured questionnaire to assess their knowledge on climate change risk, its impact on vegetable insects pests and management strategies to reduce the increased incidence of insect pests. Focus group discussions, key informant interviews and field observations were also used. Droughts and elevated temperatures were perceived to have the greatest impact on vegetable insect pests resulting in their increased incidence. Aphids, cutworms and whiteflies were identified among the major pests that have increased. The majority (53%) of the farmers cited high vegetable losses from insect pests attack. All the respondents (100%) revealed the use of chemical insecticides during production of vegetables. A higher proportion (60%) perceived effective control, 34% perceived reduced efficacy and 6% were not sure of effectiveness of chemical insecticides. Management strategies to cope with the increasing insect pests and diseases on vegetable production also included planting insect resistant cultivars, certified seeds, increased frequency of application of synthetic insecticides, insecticide mixtures, use of more hazardous chemical insecticides and increasing the rates of application resulting in insecticide overuse. There is need for government to facilitate development and adoption of Integrated Insect Pest Management (IIPM) and raise awareness on avoiding overdependence on chemical insecticides. Modelling tools that support adaptation planning needs to be developed to forecast climate change risk and the resultant incidence of insect pests.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".