Nitrogen Fertilizer Rate Effects on Weed Competitiveness is Species Dependent
Bibliographic record
Abstract
Information on nitrogen fertilizer effects on crop–weed competitive interactions might aid in developing improved weed management programs. A controlled environment study was conducted to examine the effect of three N rates on the competitive ability of four weed species grown with wheat. The four weed species were chosen to represent species that varied in their growth responsiveness to nitrogen (N): Persian darnel (low), Russian thistle (low), redroot pigweed (high), and wild oat (high). Wheat and each weed species were grown in a replacement series design at N rates of 60, 120, and 240 mg N kg−1soil. The competitive ability of the low N-responsive species, Persian darnel and Russian thistle, was not influenced by N rate, supporting our hypothesis that N rate would have little effect on the competitiveness of species responding minimally to N. Conversely, the competitiveness of the high N-responsive species redroot pigweed progressively improved as N rate increased. However, wild oat competitiveness was unaffected by N fertilizer rate. There is some evidence from this study to suggest that fertilizer management strategies that favor crops over weeds deserve greater attention when weed infestations consist of species known to be highly responsive to higher soil N levels. Information gained in this study will be used to advise farmers of the importance of strategic fertilizer management in terms of both weed management and crop yield.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".