Nitrogen fertilizer timing and application method affect weed growth and competition with spring wheat
Bibliographic record
Abstract
Managing crop fertilization may be an important component of integrated weed management systems that protect crop yield and reduce weed populations over time. A field study was conducted to determine the effects of various timings and application methods of nitrogen (N) fertilizer on weed growth and spring wheat yield. Nitrogen fertilizer was applied the previous fall (October) or at planting (May) at a dose of 50 kg ha−1. Nitrogen application treatments consisted of granular ammonium nitrate applied broadcast on the soil surface, banded 10 cm deep between every crop row, banded 10 cm deep between every second crop row, or point-injected liquid ammonium nitrate placed between every second crop row at 20-cm intervals and 10 cm deep. Treatments were applied in 4 consecutive yr to determine annual and cumulative effects over years. Density and biomass of wild oat, green foxtail, wild mustard, and common lambsquarters were sometimes lower with spring- than with fall-applied N. Spring wheat yield was never lower and was higher in 50% of the cases, when N was spring rather than fall applied. Nitrogen application method generally had larger and more consistent effects than application timing on weed growth and wheat yield. Shoot N concentration and biomass of weeds were often lower with subsurface banded or point-injected N than with surface broadcast N, and concurrent increases in spring wheat yield usually occurred with these N placement treatments. Depending on the weed species, the weed seedbank at the conclusion of the 4-yr study was reduced by 25 to 63% with point-injected compared with broadcast N. Information gained in this study will contribute to the development of more integrated and cost-effective weed management programs in wheat.
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 | 0.000 | 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) | 0.001 | 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".