Soybean yield loss from delayed postemergence herbicide application based on weed height, days after emergence, accumulated crop heat units, and soybean growth stage
Bibliographic record
Abstract
Abstract Limited information exists on the critical time of weed removal (CTWR) with the currently used soybean cultivars in Ontario. A study consisting of eight field experiments was conducted from 2017 to 2019 in Ontario, Canada, to determine the impact of delayed postemergence (POST) herbicide application on soybean yield based on average weed height at application, days after crop emergence (DAE), accumulated crop heat units (CHU) from the date of planting, and soybean growth stage. The regression model estimated the weed size at herbicide application that led to 1%, 2.5%, and 5% yield loss in soybean was 9, 14, and 20 cm under low weed density (averaging 73 to 134 plants m−2) and 3, 4, and 6 cm under high weed density (143 to 153 plants m−2) conditions, respectively. The estimated DAE at herbicide application time that led to 2.5%, 5%, 10%, and 25% yield loss in soybean was 24, 30, 37, and 53 DAE under low weed density and 8, 10, 14, and 23 DAE under high weed density, respectively. The predicted crop stage at herbicide application that resulted in 2.5%, 5%, 10%, and 25% yield loss in soybean was V4, V5, R2, and R5 under low weed density and VE, VC, V1, and V4 under high weed density, respectively. This study concludes that soybean yield loss is influenced by the weed density (low vs/high) and the time of the first POST herbicide application. When the first POST herbicide application was delayed until soybean was at the V2 stage the monetary loss was Can$20.46 and Can$221.20 ha−1 in low and high weed-density environments, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 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".