Potential Yield Loss in Dry Bean Crops Due to Weeds in the United States and Canada
Bibliographic record
Abstract
Abstract Earlier reports have summarized crop yield losses throughout various North American regions if weeds were left uncontrolled. Offered here is a report from the current WSSA Weed Loss Committee on potential yield losses due to weeds based on data collected from various regions of the United States and Canada. Dry bean yield loss estimates were made by comparing dry bean yield in the weedy control with plots that had >95% weed control from research studies conducted in dry bean growing regions of the United States and Canada over a 10-year period (2007 to 2016). Results from these field studies showed that dry bean growers in Idaho, Michigan, Montana, Nebraska, North Dakota, South Dakota, Wyoming, Ontario, and Manitoba would potentially lose an average of 50%, 31%, 36%, 59%, 94%, 31%, 71%, 56%, and 71% of their dry bean yield, respectively. This equates to a monetary loss of US $36, 40, 6, 56, 421, 2, 18, 44, and 44 million, respectively, if the best agronomic practices are used without any weed management tactics. Based on 2016 census data, at an average yield loss of 71.4% for North America due to uncontrolled weeds, dry bean production in the United States and Canada would be reduced by 941,000,000 and 184,000,000 kg, valued at approximately US $622 and US $100 million, respectively. This study documents the dramatic yield and monetary losses in dry beans due to weed interference and the importance of continued funding for weed management research to minimize dry bean yield losses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".