Exploiting the resource-ratio (R*) hypothesis for weed management in legume crops: An example of volunteer Brassica napus in soybean
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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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".