Biological control using invertebrates and microorganisms: plenty of new opportunities
Bibliographic record
Abstract
In augmentative biological control (ABC), invertebrate and microbial organisms are seasonally released in large numbers to reduce pests. Today it is applied on more than 30 million ha worldwide. Europe is the largest commercial market for invertebrate biological control agents, while North America has the largest sales of microbials. A strong growth in use of ABC, particularly of microbial agents, is taking place in Latin America, followed by Asia. The current popularity of ABC is due to (1) its inherent positive characteristics (healthier for farm workers and persons living in farming communities, no harvesting interval or waiting period after release of agents, sustainable as there is no development of resistance against arthropod natural enemies, no phytotoxic damage to plants, better yields and a healthier product, reduced pesticide residues [well below the legal Maximum Residue Levels (MRLs)], (2) professionalism of the biological control industry (inexpensive large scale mass production, proper quality control, efficient packaging, distribution and release methods, and availability of many (>440 species) control agents for numerous pests), (3) a number of recent successes showing how biological control can save agricultural production when pesticides fail or are not available, (4) several non-governmental organizations (NGOs), consumers, and retailers demanding pesticide residues far below the legal MRLs, and (5) policy developments in several regions of the world aimed at reduction and replacement of synthetic pesticides by more sustainable methods of pest management. We are convinced, however, that ABC can be applied on a much larger area than it is today. We plead in the short term for a pragmatic form of agriculture that is adaptable, non-dogmatic and combines the sustainability gain from all types of agriculture and pest management methods. We then propose to move to “conscious agriculture”, which involves participation of all stakeholders in the production and consumer chain, and respects the environment and resource availability for future generations. Were “conscious agriculture” to be considered a serious alternative to conventional farming, ABC would face an even brighter future.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".