Insect pollinators can unlock an annual monetary value of more than US $100 million from crop production in Rwanda
Bibliographic record
Abstract
Insect pollinators provide a natural ecosystem service to more than 80% of known flowering plants, many of which are part of our diet. However, their importance in Africa and an agriculture-dependent country like Rwanda has yet to receive attention. This encumbers policy formulation and investments in insect pollinators as a strategic agronomic input. Meanwhile, Rwanda cultivates crops that can benefit significantly from insect pollinators for superior agronomic outputs. To uncover this, we characterized the dependence of the crop production subsector on insect pollinators. Using the bioeconomic approach, we assessed the total economic value and the value due to insect pollinators of crops cultivated in Rwanda. We also evaluated the crop's production value per ton and whether production would meet consumption demands in the complete absence of insect pollinators. Using 71 representative crops currently grown in Rwanda, we found a direct dependency of 62% on insect pollinators. Of 32 representative crops used for economic valuation in two years (2014 and 2020), their total monetary value is estimated at $2.551 billion to $2.788 billion. Direct insect pollinator-dependent crops accounted for 20% (2014) to 18% (2020) of this value, with the share attributed to insect pollinators above $100 million. The sector's vulnerability to insect pollinators decreased from 7.3% in 2014 to 4.3% in 2020. The mean production value per ton of the direct insect pollinator-dependent crops was found to be higher in 2014 before declining in 2020. Using 21 representative crops from 2014 to 2020, we found that many direct insect pollinator-dependent crops will struggle to meet consumption demands in the complete absence of all suitable insect pollinators. Finally, we propose interventions and future research that could be undertaken. These insights are a critical first step to propel the government to act on insect pollination to support its food security agenda.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".