Farmers’ Perceptions of Rice Production Constraints and Stem Borers Management Practices in Tanzania
Bibliographic record
Abstract
Rice farmers in Tanzania continue to experience losses due to stem borers. However, the information on farmers’ knowledge and perceptions of rice stem borers is limited and farmers’ efforts on managing this insect have been ineffective. The aim of this study was to investigate constraints affecting rice production and farmers’ approaches of stem borer management in irrigated low land rice ecosystems in Tanzania. Research method: A focus group discussion with farmers using a semi-structured questionnaire was used for data collection. The information collected included: farmers’ socio-economic profiles, farm characteristics, knowledge and perceptions of stem borers and their management practices.Farmers reported insect pests such as stem borers, white flies, leaf rollers and grasshoppers as major constraints of rice production. Other constraints reported were such as diseases, damage by birds, drought, weeds and lack of access to credit for purchasing inputs. Most of the farmers apply chemical insecticides against rice insect pests particularly stem-borers where number of health hazards have been reported. Very few farmers use cultural methods including crop residue disposal and split application of nitrogenous fertilizers for reduction of stem borer damages. The study revealed that, most farmers have limited knowledge on the recommended stem borer management practices and suggests that environmentally friendly pest management methods to be designed and implemented to minimize losses associated with rice stem borers.
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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 | 0.001 |
| 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 | 0.001 |
| 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".