Managing Plant Disease Risk in Diversified Cropping Systems
Bibliographic record
Abstract
Diversification of cereal cropping systems with alternative crops, such as oilseed, pulse, and forage crops, furnishes producers with a range of agronomic and economic options. Crop diversification also improves management of plant diseases through manipulation of host factors such as crop and cultivar selection; interruption of disease cycles through crop rotation, fungicide application, and removal of weeds and volunteer crop plants; and modification of the microenvironment within the crop canopy using tillage practices and stand density. Management practices, such as seed treatment, date and rate of seeding, balanced fertility, control of weeds, field scouting, harvest management, and record keeping, can also be utilized to manage plant diseases. This review evaluates the risks to diversified crop production systems associated with the major plant diseases in the northern Great Plains and the influence of host, pathogen, and environmental factors on disease control. Principles to help producers reduce and manage the risk from plant diseases are presented, and discussion includes strategies for countering fusarium head blight (Fusarium spp.), commonly called scab; and leaf spot diseases in cereals; sclerotinia stem rot [Sclerotinia sclerotiorum (Lib.) De Bary] in oilseed and pulse crops; and ascochyta blight (Ascochyta lentis Vassil.; teleomorph: Didymella lentis Kaiser, Wang & Rogers) and anthracnose blight [Colletotrichum truncatum (Schwein.) Andrus & W.D. Moore] in pulse crops. Producers should not rely exclusively on a single management practice but rather integrate a combination of practices to develop a consistent long‐term strategy for disease management that is suited to their production system and location.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".