Integrating ethnophytopathological knowledge and field surveys to improve tomato disease management in Tanzania
Bibliographic record
Abstract
The prevalence of tomato diseases and local smallholder farmer knowledge was assessed in five villages in the Morogoro Region of Tanzania during the rainy and dry production seasons. The most commonly occurring foliar diseases in both seasons were early blight (88 of 100 fields), bacterial spot/speck (49), viral diseases (42) and Septoria leaf spot (34). Bacterial spot/speck, tomato yellow leaf curl virus, other viral diseases, and late blight were present in significantly higher numbers of fields during the rainy season than the dry season, while significantly more plants per field were affected by early blight in the rainy season than the dry season. A root health assay was conducted to assess root knot nematode damage and root rot severity, and root knot nematodes were found in 44 of 50 fields surveyed. Farmers used local names for plant diseases, which tended to be associated with the symptomatology of the disease, concepts borrowed from other aspects of life, perceived causal agent or weather conditions. Identification of local names improved communication between farmers and researchers and elucidated how farmers perceived key diseases in the region. Extension materials were developed to improve farmers’ identification and management of key tomato diseases in the region. Farmers can better allocate limited resources to manage key diseases through an improved understanding of prevalent diseases and local plant disease knowledge. This study serves as an example of how plant pathologists can develop a baseline understanding of key regional plant disease constraints through the integration of field surveys and ethnophytopathological studies.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".