Differential response of weed and crop species to potassium and sulphur fertilizers
Bibliographic record
Abstract
Fertilization may affect the relative competitive ability of weeds and crops if the growth response to fertilizer differs among species. Greenhouse studies were conducted to evaluate the relative biomass yield response of 19 weed species and the crops canola (Brassica napus L.) and spring wheat (Triticum aestivum L.) to potassium (K) and sulphur (S) fertilization. Seven weed species showed a significant increase in biomass production at 6 wk with K fertilization, but biomass production of canola and wheat did not increase with K addition. Sulphur fertilizer increased the biomass production of canola, flixweed (Descurainia sophia L.) and wild mustard (Sinapis arvensis L.), three of the four Cruciferae species evaluated, as well as that of hairy nightshade (Solanum sarrachoides Sendtner), round-leaved mallow (Malva pusilla Sm.) and stork’s-bill [Erodium cirutarium (L.) L’Her. Ex. Ait.]. Sulphur fertilizer did not increase the biomass production of spring wheat or of any grass weed species. The data from the greenhouse study support the concept that fertilization may shift the relative competition between a crop and the weed population if the weeds are more responsive than the crop to the type of fertilizer applied. However, this concept should be tested under field conditions. Key words: Integrated weed management, sulphur, potassium
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 | 0.000 |
| 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) | 0.002 | 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".