Identifying and Managing Root Rot of Pulses on the Northern Great Plains
Bibliographic record
Abstract
Pulse crops (annual grain legumes such as field pea, lentil, dry bean, and chickpea) have become an important component of the cropping system in the northern Great Plains of North America over the last three decades. In many areas, the intensity of damping-off, seedling blight, root rot, and premature ripening of pulse crops is increasing, resulting in reduction in stand establishment and yield. This review provides a brief description of the important pathogens that make up the root rot complex and summarizes root rot management on pulses in the region. Initially, several specific Fusarium spp., a range of Pythium spp., and Rhizoctonia solani were identified as important components of the root rot disease complex. Molecular approaches have recently been used to identify the importance of Aphanomyces euteiches on pulses, and to demonstrate that year-to-year changes in precipitation and temperature have an important effect on pathogen prevalence. Progress has been made on management of root rot, but more IPM tools are required to provide effective disease management. Seed-treatment fungicides can reduce damping-off and seedling blight for many of the pathogens in this disease complex, but complex cocktails of active ingredients are required to protect seedlings from the pathogen complex present in most commercial fields. Partial resistance against many of the pathogens in the complex has been identified, but is not yet available in commercial cultivars. Cultural practices, especially diversified cropping rotations and early, shallow seeding, have been shown to have an important role in root rot management. Biocontrol agents may also have potential over the long term. Improved methods being developed to identify and quantify the pathogen inoculum in individual fields may help producers avoid high-risk fields and select IPM packages that enhance yield stability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".