Production strategies management to minimize root rot disease in field pea grown on infected southwestern Saskatchewan soils
Bibliographic record
Abstract
Multilocational field studies were conducted near North Battleford, Wilkie and Cando in Saskatchewan (SK), Canada during the 2015 (year 1) and 2016 (year 2) summer crop growing seasons to evaluate Aphanomyces euteiches Drechs and Fusarium root rot response to different seed place fertility, preemergence herbicide combination, and foliar fertility. Seven seed place fertility treatments (F1 to F7) were Nitrogen and phosphorus inoculant, liquid Optimize inoculant (LCO), LCO /5.5-25-0-0 (N-P-K-S), LCO /15-25-0-0, LCO /15-25-0-15, LCO/liquid orthophosphate (2-8-1) at 33.7 L/ha/15-0-0-15 and LCO/15-40-40-15 respectively. The four preemergence herbicide (P RE1 to P RE4) were: Glyphosate/Saflufenacil; Glyphosate/Sulfentrazone/Carfentrazone; Glyphosate/Sulfentrazone/Pyroxasulfone and Glyphosate/Sulfentrazone/Trifluralin. Pea was treated with five postemergence (POST 1-5) herbicide with/without adjuvants/foliar fertility at 4-5 node stage as Imazamox/bentazon (Viper) plus 28% v/v Urea Ammonium Nitrate (UAN) – POST 1; Viper plus 2X 28% UAN – POST 2; Viper plus 1X 28% UAN plus foliar fertility – POST 3; Viper plus 2X 28% UAN plus foliar fertility – POST 4 and Quizalofop-p-ethyl at 494.23 mL/ha – POST 5. Growth response, root rot disease severity and yield (kg/ha) were analyzed using SAS statistical software. Significant means were at F < 0.05 were separated by the Fisher’s Protected LSD test at the 5% level. Root rot was significantly lower in P RE4 compared to other treatments. Postemergence herbicide treatments and seed-place fertility recorded significant interaction on root rot incidence and yield. There was over 30% yield improvement between 2016 and 2015 trials due to soil moisture. The trial minimized pea root rot from infested soils and increased yield.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".