A Meta-Analysis to Determine the State of Biological Control of Aphanomyces Root Rot
Bibliographic record
Abstract
The increasing incidence and prevalence of the pathogen Aphanomyces euteiches in various pulse-growing regions worldwide necessitates the development of effective management strategies, including biological control agents. Numerous labs have undertaken research examining biological control methods to evaluate aphanomyces root rot suppression in multistep processes that include isolation of inhibitory organisms, lab assays, growth chamber assays, and field trials. Given the emergence of various biocontrol agents and the need to mitigate aphanomyces yield losses, we have undertaken a meta-analysis approach to analyze the effectiveness of biocontrol agents in relation to application method, biocontrol agent richness, biocontrol agent type, the type of study, and reporting system-oriented moderator variables. An effect size, calculated as a natural log response ratio, resulted in a summary weighted mean of −0.411, suggesting the overall effectiveness of biocontrol agents (p < .001). Aphanomyces root rot suppression using biological treatments showed significant heterogeneity for all moderator variables, confirming that the studies do not share a common effect size and the use of a random effect model was appropriate. Across studies, meta-analyses revealed that soil amendments, biocontrol agent application as a seed coating and suspension, bacterial and fungal biocontrol agents, mixed applications, growth chamber and field studies, and qualitative and quantitative reporting systems were all associated with significantly positive outcomes for aphanomyces root rot suppression. Our findings suggest that there is potential promise for biological control of aphanomyces root rot, and more field trials need to be conducted to demonstrate the efficacy level observed under growth chamber conditions. Moreover, we identified a lack of detailed understanding of the mechanism(s) of biological control of aphanomyces root rot as a research priority.
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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 | 0.033 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.065 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".