Crop yield losses due to kochia (Bassia scoparia) interference
Bibliographic record
Abstract
Kochia [Bassia scoparia (L.) A.J. Scott] is a problematic summer-annual tumbleweed that infests cropped and noncropped areas in the Great Plains of North America. Efficient seed dispersal, prolific seed production, and abiotic stress tolerance facilitate invasiveness of kochia, while both resource-limiting and non-resource-limiting interference aid in rapid colonization of disturbed areas. Resistance to up to four herbicide sites-of-action allow kochia to escape herbicidal control in several field crops and contribute to crop yield losses. Near-complete crop failure (>90% yield loss) has been reported due to kochia interference in corn (Zea mays L.), sorghum [Sorghum bicolor (L.) Moench ssp. bicolor], sugar beet (Beta vulgaris L.), and sunflower (Helianthus annuus L.). Mean reported yield losses due to kochia interference were greatest in grain corn (68%), followed by sorghum (62%), soybean [Glycine max (L.) Merr.] (52%), sugar beet (46%), silage corn (40%), sunflower (23%), spring wheat (Triticum aestivum L.) (20%), spring canola (Brassica napus L.) (13%), field pea (Pisum sativum L.) (13%), and spring oat (Avena sativa L.) (7%). However, crop yield losses due to kochia interference depend on several factors, including kochia density, relative emergence timing, duration of interference, the environment, and potentially also fitness penalties caused by pleiotropic effects of herbicide resistance traits. This review provides a synthesis of the impact of kochia on farm operations, crop yield losses due to kochia interference, factors affecting kochia interference, and interference mechanisms. Together, this synthesis highlights the critical need for research identifying integrated strategies for kochia management, and their subsequent adoption by the farming community.
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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".