Understanding farmer knowledge, practices and decision-making in pest and disease management: the case of Irish potato (Solanum tuberosum) cultivation in Mbeya, Tanzania
Bibliographic record
Abstract
Abstract Irish potato ( Solanum tuberosum ) is an important food and cash crop for smallholder farmers in Tanzania, particularly in the Southern Highlands. Despite these, yields remain low due to persistent challenges from pests and diseases, threatening both productivity and household incomes. This study examined knowledge, perceptions and practices of smallholder farmers in managing pests and diseases of Irish potato in Mbeya, Tanzania. A total of 225 farmers from five wards were surveyed using structured interviews and focus group discussions. Most respondents (83%) identified pests and 73% identified diseases as major constraints. Aphids (83%), whiteflies (71%) and potato tuber moth (39%) were the most commonly reported pests, while early blight (91%), late blight (45%) and Fusarium wilt (29%) were the most cited diseases. Despite the widespread use of chemical pesticides (92%) and fungicides (72%), access to these inputs was constrained by high costs, limited availability and insufficient knowledge, with only 24% of farmers reporting effective use. Non-chemical methods, such as crop rotation, intercropping and botanical extracts, were rarely practiced, reflecting low awareness and limited extension support. Most farmers relied on experience rather than consulting agricultural officers, and over 90% used traditional seed varieties. Yields varied significantly among wards, with averages ranging from 1.1 to 22.4 t/ha. While farmers demonstrated awareness of pests and diseases, management practices were heavily dependent on synthetic chemicals, with minimal integration of sustainable strategies. These findings highlight the need to promote integrated pest and disease management approaches tailored to local conditions for improving productivity of Irish potato.
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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 | 0.000 | 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".