Influence of nitrogen rate on the efficacy of herbicides with different modes of action
Bibliographic record
Abstract
Sønderskov M, Swanton CJ & Kudsk P (2012). Influence of nitrogen rate on the efficacy of herbicides with different modes of action. Weed Research 52, 169–177. Summary Outdoor pot experiments and field experiments were conducted to examine the influence of nitrogen rate on herbicide efficacy. Growth of unsprayed Tripleurospermum inodorum increased with increasing nitrogen rate in pot experiments; increasing nitrogen also increased the susceptibility to tribenuron‐methyl. An increased susceptibility at high nitrogen rate was also observed for Anagallis arvensis, but in contrast to T. inodorum, growth of unsprayed A. arvensis was unaffected by nitrogen rate. Growth of unsprayed Chenopodium album was also promoted by nitrogen supply. However, no influence of nitrogen rate on herbicide efficacy was evident for C. album with tribenuron‐methyl or ioxynil+bromoxynil in pot experiments. Field experiments with tribenuron‐methyl were conducted on natural populations of C. album in spring barley grown at different nitrogen rates showed similar results. In conclusion, nitrogen rate affected herbicide efficacy for some but not all combinations of weed species and herbicide. Decreased herbicide efficacy was only observed at very low nitrogen rates. The results suggest that the effect of nitrogen rates on herbicide efficacy will be marginal in intensive farming systems with high nitrogen supply, but in low intensity systems, weed patchiness might increase.
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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) | 0.001 | 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".