Perspectives on Potential Soybean Yield Losses from Weeds in North America
Why this work is in the frame
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Bibliographic record
Abstract
Weeds are one of the most significant, and controllable, threats to crop production in North America. Monetary losses because of reduced soybean yield and decreased quality because of weed interference, as well as costs of controlling weeds, have a significant economic impact on net returns to producers. Previous Weed Science Society of America (WSSA) Weed Loss Committee reports, as chaired by Chandler (1984) and Bridges (1992), provided snapshots of the comparative crop yield losses because of weeds across geographic regions and crops within these regions after the implementation of weed control tactics. This manuscript is a second report from the current WSSA Weed Loss Committee on crop yield losses because of weeds, specifically in soybean. Yield loss estimates were determined from comparative observations of soybean yields between the weedy control and plots with greater than 95% weed control in studies conducted from 2007 to 2013. Researchers from each US state and Canadian province provided at least three and up to ten individual comparisons for each year, which were then averaged within a year, and then averaged over the seven years. These percent yield loss values were used to determine total soybean yield loss in t ha −1 and bu acre −1 based on average soybean yields for each state or province as well as current commodity prices for a given year as summarized by USDA-NASS (2014) and Statistics Canada (2015). Averaged across 2007 to 2013, weed interference in soybean caused a 52.1% yield loss. Based on 2012 census data in the US and Canada soybean was grown on 30,798,512 and 1,679,203 hectares with production of 80 million and 5 million tonnes, respectively. Using an average soybean price across 2007 to 2013 of US $389.81 t −1 ($10.61 bu −1 ), farm gate value would be reduced by US $16.2 billion in the US and $1.0 billion in Canada annually if no weed management tactics were employed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it