Evidence Note Tomato leafminer (Tuta absoluta): Impacts and coping strategies for Africa
Bibliographic record
Abstract
The recently introduced tomato leafminer, Tuta absoluta, has become the most important pest constraint to tomato production in Africa.Spreading at an average 800 km/year, it is now present in 41 African countries.The socio-economic impacts of Tuta absoluta were assessed through a household survey in Kenya and Zambia in 2018, covering 400 respondents in Kenya and 426 in Zambia.We found that 97.9% of farmers in Zambia, and 99% in Kenya reported Tuta absoluta as the main problem on tomato.A majority of farmers in Zambia (57%) had lost a large proportion of their crop to Tuta absoluta, compared to 41% in Kenya.Mean seasonal production loss based on farmers' own estimates was 114,000 tonnes for Kenya and 10,700 tonnes for Zambia, equivalent to US$ 59.3, and US$ 8.7 million in economic losses respectively.Pesticides were the predominant control method for Tuta absoluta, used by 96.5% of farmers in Kenya and 97.6% of farmers in Zambia, with 6.4% using highly toxic products.However, only 27.2% and 17.2% of farmers in Kenya and Zambia, respectively, indicated the pesticide treatments were very successful.In Kenya, 73.1% of farmers applied 1-5 sprays/season, and in Zambia 29.2% applied 1-5 sprays, and 33.9% applied 6-10.The average amount spent on pesticides per household against Tuta absoluta was US$ 47.2 in Kenya, about US$ 33.7/ha, while in Zambia, this cost was US$ 42.1 per household, and US$ 9.4/ha.The average cost for a pesticide application against Tuta absoluta in Kenya was US$ 12.3, and US$ 4.2 in Zambia.The implications of these findings for sustainable management of this pest are discussed.
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 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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".