Performance of grain sorghum hybrids resistant to acetolactate synthase and acetyl coenzyme‐A carboxylase inhibitor herbicides
Bibliographic record
Abstract
Abstract Acreage under grain sorghum [Sorghum bicolor (L.) Moench] in the United States has sharply declined over the past several decades. Among the major causes are the lack of better postemergence weed control options. Farmers opt for crops and tools that allow better management of weeds, and sorghum is not one of them. The discovery of sources of resistance to acetolactate synthase (ALS) and acetyl‐coenzyme A carboxylase (ACCase) inhibitor herbicides in feral relatives of sorghum opened a new horizon for development of a resistance‐based weed control option for the crop. The objective of this study is to demonstrate the agronomic potential of sorghum hybrids resistant to ALS and ACCase inhibitor herbicides and shed light on concerns that deployment of resistance traits may cause yield drags. A total of 186 hybrids involving homozygous ALS resistant, homozygous ACCase resistant, heterozygous ALS and ACCase resistance, and conventional hybrids plus commercial checks were grown in three sets consisting of 68, 62 and 56 entries for Set I, Set II, and Set III, respectively. The experiments were conducted during the 2014 and 2015 season in three replications at Kansas State University Agronomy Research Farm near Manhattan, KS. Data were collected on plant height, maturity, yield, and yield components, as well as grain nutritional traits. The analysis of the data revealed that the resistance technology has no negative effect on agronomic adaptability, yield potential, and nutritional traits of grain sorghum.
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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".