Etiology and epidemiology of Pythium root rot in hydroponic crops: current knowledge and perspectives
Bibliographic record
Abstract
The etiology and epidemiology of Pythium root rot in hydroponically-grown crops are reviewed with emphasis on knowledge and concepts considered important for managing the disease in commercial greenhouses. Pythium root rot continually threatens the productivity of numerous kinds of crops in hydroponic systems around the world including cucumber, tomato, sweet pepper, spinach, lettuce, nasturtium, arugula, rose, and chrysanthemum. Principal causal agents include Pythium aphanidermatum, Pythium dissotocum, members of Pythium group F, and Pythium ultimum var. ultimum. Perspectives are given of sources of initial inoculum of Pythium spp. in hydroponic systems, of infection and colonization of roots by the pathogens, symptom development and inoculum production in host roots, and inoculum dispersal in nutrient solutions. Recent findings that a specific elicitor produced by P. aphanidermatum may trigger necrosis (browning) of the roots and the transition from biotrophic to necrotrophic infection are considered. Effects on root rot epidemics of host factors (disease susceptibility, phenological growth stage, root exudates and phenolic substances), the root environment (rooting media, concentrations of dissolved oxygen and phenolic substances in the nutrient solution, microbial communities and temperature) and human interferences (cropping practices and control measures) are reviewed. Recent findings on predisposition of roots to Pythium attack by environmental stress factors are highlighted. The commonly minor impact on epidemics of measures to disinfest nutrient solution as it recirculates outside the crop is contrasted with the impact of treatments that suppress Pythium in the roots and root zone of the crop. New discoveries that infection of roots by P. aphanidermatum markedly slows the increase in leaf area and whole-plant carbon gain without significant effect on the efficiency of photosynthesis per unit area of leaf are noted. The platform of knowledge and understanding of the etiology and epidemiology of root rot, and its effects on the physiology of the whole plant, are discussed in relation to new research directions and development of better practices to manage the disease in hydroponic crops. Focus is on methods and technologies for tracking Pythium and root rot, and on developing, integrating, and optimizing treatments to suppress the pathogen in the root zone and progress of root rot.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".