Corn Plant Density Effects on Grain Yield and Palmer Amaranth Response to Dicamba
Bibliographic record
Abstract
Palmer amaranth (Amaranthus palmeri S. Wats.) is primarily found in fallow and cultivated fields throughout Kansas (KS). Previously, we reported that the effective dose of dicamba for 50% control (ED50) of a 10-30 cm tall A. palmeri accession from KS (KSP) during the fallow-year of a wheat-corn-fallow rotation (WCF) ranged between 40.4 to 283.5 g ae ha-1 Here, we investigated corn (Zea mays L.) plant density effects on grain yield and the KSP response to dicamba in the corn-year of the WCF rotation. The experiments used a randomized complete block design with four replicates and a split-plot arrangement of treatments. Main plots consisted of corn planted at five densities (49,400; 61,700; 74,100; 86,400 and 98,800 plants ha-1) and sub-plots consisted of six doses of dicamba [(70, 140, 210, 280, 420, and 560 g ae ha-1) applied near V6], a weedy-check, and a weed-free check. In general, acceptable grain yield and KSP control with dicamba doses < 560 g ae ha-1 were only achieved when corn was planted at a density ≥ 74,100 plants ha-1. Based on the ED50 estimates, the KSP required 4.4-8.2% less dicamba for height and biomass reduction as corn plant density increased from 49,400 to 98,800 plants ha-1. Conversely, the amount of dicamba required for 50% reduction of KSP density increased 45.1% from 144.4 to 209.6 g ae ha-1 as corn plant density increased from 49,400 to 98,800 plants ha-1, respectively. Taken together, this study shows that integration of high-density corn planting with dicamba is a cost-effective tool for controlling A. palmeri. However, resistance mitigation and a season-long control of this species cannot be achieved without the integration of other more diversified and robust strategies that include the use of preemergence fb postemergence herbicide programs that overlap residuals with cultural, biological, and mechanical weed control tactics.
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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 | 0.000 | 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".