Nitrogen Fertilizer, Manure, and Compost Effects on Weed Growth and Competition with Spring Wheat
Bibliographic record
Abstract
Crop fertilization is an important component of integrated weed management systems. A field experiment was conducted to determine the effect of various application timing–tillage intensities and N sources on weed growth and spring wheat (Triticum aestivum L.) yield. Timing–tillage treatments consisted of applying the various N sources in either the previous fall or in spring each under zero‐till or tilled conditions. Nitrogen sources consisted of granular ammonium nitrate fertilizer applied either surface broadcast or subsurface banded 10 cm deep between every second wheat row, fresh cattle (Bos taurus) manure, and composted cattle manure. An unfertilized control also was included. Treatments were applied in four consecutive years to determine annual and cumulative effects. Subsurface‐banded N compared with broadcast N fertilizer often reduced N uptake by weeds, decreased weed biomass, and increased wheat yield. Weed N uptake and growth with fresh and composted manure tended to be intermediary between banded and broadcast N fertilizer in the initial year but was similar to or greater than that with broadcast N fertilizer in subsequent years. The gradual N release from manure and compost over years appeared to benefit weeds more than spring wheat. The ranking of the weed seedbank at the conclusion of the 4‐yr experiment was composted manure = fresh manure ≥ broadcast N fertilizer > banded N fertilizer. Information gained in this study will be utilized to develop more efficient fertilization strategies as components of integrated weed management programs in spring wheat production systems.
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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 | 0.000 | 0.000 |
| 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".