A New Biopesticide for the Control of Fruit Flies in Organic Mango Production: An Ex-Ante Assessment of Returns to Research Using Economic Surplus Model
Bibliographic record
Abstract
Nowadays, several technical and financial partners are reluctant to support agricultural research because they don’t perceive its impact. So, to gain the support of local authorities and decision-makers, research scientists must bring evidence of its financial viability. Fruit flies are a major constraint to increasing mango productivity in Africa. However, there are other challenges as well. Research scientists have investigated several methods to control fruit flies. This study aims to evaluate the potential economic impact of developing a new biopesticide to control mango fruit flies in Burkina Faso. This concept’s main idea is that the adoption of this new technology would result in higher yields and cheaper production costs. The economic surplus model is the methodology applied in this assessment. This concept’s main idea is that implementing better technology lowers production costs while increasing yield. According to the mango research findings, the net present value is calculated to be 76,740,608 US$, either 46,428,067,840 FCFA, while the social gain is estimated to be 76,836,954 US$, either 46,486,357,170 FCFA. This investment yielded an estimated internal rate of return of 190.54%, which is significantly higher than the interest rates that banks charge. Mango production would benefit from the research, notwithstanding the scarce resources. These findings imply that funding research on the new biopesticide would be a fascinating and financially feasible substitute for governmental bodies. If research on bodies could benefit from more funding or financial independence, the benefits of developing new biopesticides would be amplified.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".