Biological Control of Lepidopteran Pests in Rice: A Multi-Nation Case Study From Asia
Bibliographic record
Abstract
Abstract We provide a case study where Integrated Pest Management (IPM) for rice production systems has been introduced to the Greater Mekong Subregion (GMS). Funded by the European Union (EU), this IPM initiative brought together local and international partners to develop an environmentally friendly and economically sustainable rice pest management strategy for southwestern China, Laos, and Myanmar. A key component of the strategy was to establish 12 Trichogramma spp. rearing facilities (TRFs) that mainly targeted rice stem borers. Four TRFs were established in each of the participating countries. The most promising strains of Trichogramma chilonis (Ishii) and T. japonicus (Ashmead) were selected for production in the TRFs based on extensive field surveys as well as laboratory and field release studies. The project also considered the potential for Trichogramma spp. of each strain to withstand the high temperatures expected under a changing climate. Implementation of the IPM strategy resulted in higher rice yields (2–10%), an increase in natural enemy abundance (e.g., twice as many spiders), and a reduction in insecticide applications (1.5 fewer applications). During a capacity-building program, IPM practices with strong cultural and biological control-based components were promoted among ca 50 IPM trainers and ca 6,400 rice farmers. This case study indicates the potential successes of advanced biological control-based IPM systems. We believe that these systems merit wider consideration, including adaptations for other regions and crops.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".