Factors Influencing the Efficacy of Biological Control Agents Used to Manage Insect Pests in Indoor Cannabis (Cannabis sativa) Cultivation
Bibliographic record
Abstract
Current biological control strategies in cannabis (Cannabis sativa) cultivation have resulted in poor efficacy for managing certain insect pests. The cannabis industry has grown at a rapid pace, surpassing our ability to develop knowledge on the production systems for this crop. Currently, the research focus is on optimizing agronomic and environmental factors to maximize the yield and quality of cannabis. However, cannabis growers are increasingly challenged by severe insect pest pressure, with few effective options. Decades of research have optimized biological control strategies in other crops. The implementation of effective biological control strategies in cannabis is hindered by a variety of morphological, biochemical, and agronomic factors unique to this crop. Here, we review the rather limited literature relevant to insect pest management in indoor cannabis production. Further, we have identified three factors that we believe are primarily responsible for the ineffectiveness of biological control in cannabis: Plant morphology including trichome density and floral resources, effects of plant biochemistry on prey suitability, and finally the effects of supplemental lighting including photoperiod, intensity, and spectrum. We highlight the importance of prioritizing the evaluation of these factors to improve our understanding of the tritrophic interactions governing the success of biological control in cannabis cultivation. As intensive research efforts are underway to optimize agronomic practices for cannabis, it is also important to consider their relevance to biological control.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".