How to develop a biofungicide based on a bacterial strain: the main steps for turning your discover into a plant protection product
Bibliographic record
Abstract
Less than 0.1 % of the potentially bioactive microbial biocontrol agents reaches the market (estimation based on the number of active strains reported in scientific journals, ‘grey literature’ and theses available on web and the number of registered commercial products). In the last decade research efforts on microbial biocontrol increased dramatically in EU, US and Canada, but also in India, China, Africa, Central and South America. However the EU market returns a very gloomy picture with very few commercial products available for the growers, all based on ‘old’ active ingredients (some of these strains have been identified 30 years ago or even more). The ‘new entries’ are mostly new strains of the same well-known species. To explain such situation we commonly refer to the intrinsic limiting factors in their use (i.e. microbial pesticides are expected to be less effective and more inconsistent than chemicals, they need specific environmental conditions for the application, high technical skills by growers and frequent crop monitoring, etc.) or in the economics (i.e. they are more expensive than chemicals, registration costs are too high for the companies, the market is too narrow to justify investments, etc.). However most of the product development fails for other reasons: as mistakes in the selection of the right strain (both in term of technological properties and level of efficacy) or the target disease and crop (type of disease, market size, etc.), in the IP protection and in the choice of the industrial partner to scale-up the production. This ‘how to’ presentation will define some of the most important steps in the development of a bacterial biofungicide starting from the very beginning and highlights some of the most commons mistakes that prevents these products reaching the market.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.013 |
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".