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 napusL.)-dominant crop rotations in western Canada. Herbicide-resistant (HR) volunteerB. napuscan 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 volunteerB. napusin soybean. Among treatments and sites, volunteerB. napusproduced about 830 seeds plant−1and resulted in large seedbank inputs (averaging about 20,300 seeds m−2). VolunteerB. napusseedling 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 volunteerB. napusdensities. 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 volunteerB. napusdensities, seed production of canola volunteerB. napuswas greater when nitrogen fertilizer was applied to simulate an environment with greater nitrogen supply (77% greater number of volunteerB. napusseeds produced with vs. without broadcast application of 23 kg N ha−1urea). 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 volunteerB. napus, and perhaps also other competitive early-season weeds.
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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 | 0.001 | 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".