Smallholder Farmers’ Knowledge, Perception and Management of Rice Blast Disease in Upland Rice Production in Tanzania
Bibliographic record
Abstract
The objective of this paper was to investigate farmers’ knowledge and management of rice blast disease in Tanzania. Farmers’ household survey was conducted in five districts namely Mvomero, Morogoro rural, Ulanga, Korogwe and Muheza in April and May 2017. Data were collected through face-to-face interviews using semi-structured questionnaire and observations made through transect walks across selected villages. Farmers observed symptoms of rice blast disease for the first time in the past 3 to 10 years, with higher severity of blast disease in April to May each year. About 46.3% of the respondents were not aware of the cause and spread of rice blast disease. About 39.9% of the respondents associated rice blast disease with drought, high rainfall and temperature (8.7%) and soil fertility problems (5.1%). About 18.7% of the farmers reported burning of crop residues, 17.0% use of ash, 4.0% use of nitrogen fertilizer and 6.3% application of fungicide for management of rice blast disease. The majority (54.0%) of farmers did not apply any management method. Most farmers planted local upland rice varieties, with only 7.7% using improved varieties. About 69.6% of the respondents shared information on disease management among themselves. Lack of knowledge, ability to afford and unavailability of effective blast disease control methods were reported to affect the management of the disease. Strengthening the capacity of farmers to identify the disease and proper management practices will sustainably solve the problem of rice blast disease in upland rice production.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".