Mitigating apple replant disease with biocontrol soil treatments
Bibliographic record
Abstract
Apple replant disease (ARD) can increase apple tree ( Malus domestica Borkh) mortality, delay production, and reduce yield, resulting in losses of up to $60 K/ha over an orchard’s lifespan. Common fumigation treatments can harm human and environmental health, have variable effectiveness, and disrupt beneficial soil microbial activity and processes. An experiment was conducted at the Simcoe Research Station in Norfolk County, Ontario to assess the effectiveness of commercially available plant growth-promoting (PGP) microbial biocontrols to treat ARD. Five treatments were replicated in-field five times as a randomized block design. Treatments included: untreated control, fumigation control (chloropicrin (FC)), PGP fungi (PGP-F), PGP rhizobacteria (PGP-R), and a combination of PGP-F and PGP-R (PGP-M). Trees growth and health and rhizosphere microbial diversity was assessed at three points over 2 years. PGP-R produced the greatest mean root mass followed by the chemical fumigation, which was 32% and 10% more root mass than the untreated control, respectively. Chemical fumigation resulted in the greatest above-ground biomass tree growth followed by the PGP-R, accumulating 30% and 6% more biomass than the untreated control. However, PGP-F accumulated less biomass than the untreated control. FC and PGP-R both resulted in strong growth but impacted the microbial community differently. PGP-R increased rhizosphere bacterial diversity and decreased fungal diversity while FC did the opposite. PGP-R changes to bacterial communities persisted while FC soils resembled the untreated control most closely after two years. These results indicate PGP-R biocontrol treatments are viable alternatives to fumigation for apple growers facing ARD.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".