Phosphorus Fertilizer Application Method Affects Weed Growth and Competition with Wheat
Bibliographic record
Abstract
Strategic fertilizer management is an important component of integrated weed management systems. A field study was conducted to determine the effect of various application methods of phosphorus (P) fertilizer on weed growth and wheat yield. Weed species were chosen to represent species that varied in their growth responsiveness to P: redroot pigweed (medium), wild mustard (medium), wild oat (medium), green foxtail (high), redstem filaree (high), and round-leaved mallow (high). P fertilizer application methods were seed placed at a 5-cm depth, midrow banded at a 10-cm depth, surface broadcast immediately before seeding, and surface broadcast immediately after seeding of wheat. An unfertilized control was included. P treatments were applied to the same plot in four consecutive years to determine annual and cumulative effects over years. Shoot P concentration and biomass of weeds were often lower with seed-placed or subsurface-banded P fertilizer compared with either surface-broadcast application method. This result occurred more frequently with the highly P-responsive weeds and was more evident in the latter study years. P application method had little effect on weed-free wheat yield but often had a large effect on weed-infested wheat yield. Seed-placed or midrow-banded P compared with surface-broadcast P fertilizer often resulted in higher yields when wheat was in the presence of competitive weeds. Seedbank determinations at the conclusion of the study indicated that the seed density of five of six weed species was reduced with seed-placed or subsurface-banded P compared with surface-broadcast P. Information gained in this study will aid development of more effective weed management systems 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".