Effect of hybrid varieties, application timing, and herbicide rate on field corn tolerance to tolpyralate plus atrazine
Bibliographic record
Abstract
Abstract A wide margin of crop safety is a desirable trait of POST herbicides, and investigation of crop tolerance is a key step in evaluation of new herbicides. Six field experiments were conducted in Ontario, Canada, from 2017 to 2018 to examine the influence of corn (Zea mays L.) hybrid (DKC42-60RIB, DKC43-47RIB, P0094AM, and P9840AM), application rate (1X and 2X), and application timing (PRE, V1, V3, and V5) on the tolerance of field corn to tolpyralate, a new 4-hydroxyphenyl pyruvate dioxygenase inhibitor, co-applied with atrazine. Two corn hybrids (DKC42-60RIB and DKC43-47RIB) exhibited slightly greater visible injury from tolpyralate + atrazine, applied POST, than P0094AM and P9840AM at 1 to 2 wk after application (WAA); hybrids responded similarly with respect to height, grain moisture, and yield. Applications of tolpyralate + atrazine at a 2X rate (80 + 2,000 g ai ha−1) induced greater injury (≤31.6%) than the field rate (40 + 1,000 g ha−1) (≤11.6%); the 2X rate applied at V1 or V3 decreased corn height and slightly increased grain moisture at harvest. On average, field rates resulted in marginally higher grain yields than 2X rates. Based on mixed-model multiple stepwise regression analysis, the air temperature at application, time of day, temperature range in the 24 h before application, and precipitation following application were useful predictor variables in estimating crop injury with tolpyralate + atrazine; however, additional environmental variables also affected crop injury. These results demonstrate the margin of corn tolerance with tolpyralate + atrazine, which provides a basis for optimization of application timing, rate, and corn hybrid selection to mitigate the risk of crop injury with this herbicide tank mixture.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".