Exploiting the resource-ratio (R*) hypothesis for weed management in legume crops: An example of volunteer Brassica napus in soybean
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Bibliographic record
Abstract
Poor competitive ability and sensitivity to many herbicides create challenges for weed management in legume production. The resource-ratio (R*) hypothesis may provide insight into how to manipulate the competitive balance between nitrogen (N)-fixing legume crops and non-leguminous weed species. A field study was conducted to test whether the level of soil mineral N affected yield loss of an annual legume crop, soybean [ Glycine max (L.) Merr.], in the presence of four different populations of an interfering non-leguminous weed, volunteer canola ( Brassica napus L.), compared with a weed-free control. The experiment consisted of banding five rates of urea fertilizer (0–180 kg N ha -1 ) prior to seeding soybean and volunteer canola, and was repeated in three environments in Manitoba, Canada. Soybean yield remained unaffected by N rate in the absence of volunteer canola. Interference from the volunteer canola populations caused a linear decline in soybean yield by 2.6 kg ha -1 for every 1 kg ha -1 increase in soil mineral N. In the presence of volunteer canola, soybean yield decreased by 17% from the lowest to the highest soil mineral N. In the lowest-N conditions (30 kg residual-N ha -1 ), soybean yield was greatest (3,350 kg ha -1 ) and volunteer canola seed production and aboveground biomass were lowest (decline in canola seed production by 19%, 50%, and 74% of the maximum seed production in the 2015i, 2015ii, and 2016 environments, respectively). Therefore, growing legume crops like soybean on fields with lower soil mineral N may reduce interference from unmanaged non-leguminous weeds. As N fertilization intensifies interference of many weed species, tailoring weed management in legume crops around their capacity for N-fixation could provide the crop with a competitive advantage, thereby minimizing the impact of weed interference on legume crop yield.
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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 | 0.001 | 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