Candidate Tools for Integrated Weed Management in Soybean at the Northern Frontier of Production
Bibliographic record
Abstract
Abstract The development of early-maturing soybean [ Glycine max (L.) Merr.] varieties has led to an increase in soybean production in canola ( Brassica napus L.)-dominant crop rotations in western Canada. Herbicide-resistant (HR) volunteer B. napus can be difficult to manage in HR soybean using herbicides alone. In 2013 and 2014, four field experiments were conducted in Manitoba, Canada, to evaluate soybean row spacing, seeding density, nitrogen supply, and interrow tillage as candidate nonchemical weed management tools for an integrated program to manage volunteer B. napus in soybean. Among treatments and sites, volunteer B. napus produced about 830 seeds plant −1 and resulted in large seedbank inputs (averaging about 20,300 seeds m −2 ). Volunteer B. napus seedling recruitment differed among sites, and resulted in two distinct classes of sites based on average seedling densities of 39 and 89 plants m −2 . Weed management tools were more effective at the sites with higher volunteer B. napus densities. At these sites, soybean yield was greater when using an increased soybean-seeding density (44% greater yield using a seeding density of 682,500 vs. 455,000 seeds ha −1 ) or interrow tillage (36% greater yield with vs. without using interrow tillage). Soybean row spacing (19 vs. 38 vs. 76 cm) did not affect soybean yield, unless the reduction in row spacing was combined with an increased seeding density (65% greater yield with narrow-row soybean seeded at 682,500 vs. wide-row soybean seeded at 455,000 seeds ha −1 ). At the sites with higher volunteer B. napus densities, seed production of canola volunteer B. napus was greater when nitrogen fertilizer was applied to simulate an environment with greater nitrogen supply (77% greater number of volunteer B. napus seeds produced with vs. without broadcast application of 23 kg N ha −1 urea). In northern climates, seeding soybean at increased densities using narrow-row spacing in fields with limited soil inorganic nitrogen and using interrow tillage in wide-row production systems are effective strategies that could augment chemical weed management in an integrated program for management of volunteer B. napus , and perhaps also other competitive early-season weeds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".