Malaria mosquito control in rice paddy farms using biolarvicide mixed with fertilizer in Tanzania: semi-field experiments
Bibliographic record
Abstract
The wide distribution of malaria mosquito breeding sites within tropical environments limits the mosquito larval source management efforts to control malaria. Rice farming contributes substantially in supporting malaria mosquito productivity in tropical countries. To overcome this challenge, this study was carried out to determine the effect of applying a mixture of biolarvicide and fertilizer on mosquito larvae density in rice farms under semi-field conditions in Tanzania. A semi-field experiment was designed to determine the timing of application of a biolarvicide, Bacillus thuringiensis israelensis (Bti) and fertilizer (di-ammonium phosphate-DAP or urea) and assess their effect on mosquito larvae density and rice grain outputs. The experiment had five blocks (4 treatment arms and one control arm) and each had four replicates. Treatment arms had different intervals of days between treatments for mixtures of fertilizer and biolarvicides. The dosages used were 10 g of Bti/16 M2 and 160 g of DAP/Urea/16 m2. In overall, the intervention blocks (with biolarvicide) had lowest mean mosquito larvae abundance compared to control block (F = 22.42, P < 0.001). Similarly, the control arm maintained highest density of Anopheles gambiae sensu lato larvae compared to interventions blocks (F = 21.6, P < 0.001). The best determined timing for application of Bti was in 7 and in 10 days (F = 3.753, P < 0.001). There was neither significant different in mean rice grain harvest per ten panicle (F = 1.453, P = 0.27) nor mean difference in rice grain harvest (F = 1.479, P = 0.26) per intervention arms. The findings of this study have shown that application of a mixture of Bti and fertilizer have impact on both mosquito larvae density and maintaining yield rice harvest. Thus, application of a combination of biolarvicide and fertilizer can be an alternative approach in malaria mosquito intervention among rice farming communities of rural Tanzania.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| 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 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".