Combining agronomic practices and herbicides improves weed management in wheat–canola rotations within zero-tillage production systems
Bibliographic record
Abstract
Development of more comprehensive and cost-effective integrated weed management systems is required to facilitate greater integrated weed management adoption by farmers. A field experiment was conducted at two locations to determine the combined effects of seed date (April or May), seed rate (recommended or 150% of recommended), fertilizer timing (applied in fall or spring), and in-crop herbicide dose (50% or 100% of recommended) on weed growth and crop yield. This factorial set of treatments was applied in four consecutive years within a spring wheat–spring canola–spring wheat–spring canola rotation in a zero-till production system. Both wheat and canola phases of the rotation were grown each year. Weed biomass was often lower with May than with April seeding because more weeds were controlled with preplant glyphosate. However, despite fewer weeds being present with May seeding, wheat yield was only greater in 1 of 7 site-years, and canola yield was never greater with May compared with April seeding. Higher crop seed rates had a consistently positive effect on reducing weed growth and the weed seedbank. Crop yield was sometimes greater, and never lower, with higher seed rates. Fertilizer timing did not have a large effect on crop yield, but applying N in the spring compared with fall was less favorable for weeds as indicated by lower weed biomass and a 20% decrease in the weed seedbank. In-crop herbicides applied at 50% compared with 100% doses often resulted in similar weed biomass and crop yield, especially when higher crop seed rates were used. Indeed, the weed seedbank at the conclusion of the 4-yr experiment was not greater with the 50% compared with 100% herbicide dose at one of two locations. This study demonstrates the combined merits of early seeding (April), higher crop seed rates, and spring-applied fertilizer in conjunction with timely but limited herbicide use to manage weeds and maintain high crop yields in rotations containing wheat and canola.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".