Agronomic and economic responses to integrated weed management systems and fungicide in a wheat-canola-barley-pea rotation
Bibliographic record
Abstract
Changes in tillage intensity and herbicide use can influence the incidence of weeds, insects and diseases, crop yields and economic returns. We examined the effects of six integrated weed management systems (with varying combinations of tillage methods, seeding rate, seeding date, time when weed control was applied, and annual fungicide applications on pest incidence, grain yield and quality, and economic returns for a spring wheat (Triticum aestivum L.)-canola (Brassica napus L.)-barley (Hordeum vulgare L.)-pea (Pisum sativum L.) rotation in the Dark Brown soils of the Moist Mixed Grassland Ecoregion of Saskatchewan. Herbicide use intensity was reduced without a significant increase in weed biomass in five of the six systems in most crops and years. The complete elimination of herbicides in one system resulted in significant crop yield losses. Certain insects were more prevalent in the cropping systems with early planting dates. Zero tillage systems produced higher yields, and yields generally declined as tillage intensity increased. For all crops, the high herbicide-zero tillage system produced the highest yields, whereas the lowest yields were obtained in the no herbicide-high tillage system. Management method had minimal impact on seed quality. Application of fungicide generally increased yields of barley, wheat and pea, but the increases were not sufficient to recover fungicide cost. High herbicide-zero tillage, medium herbicide-zero tillage, and low herbicide-zero tillage systems produced the highest net return and no herbicide-high tillage system the lowest net return, under all grain price scenarios. Key words: Agronomic, economic, tillage, herbicide, fungicide, weed management systems, weed, insect, disease
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) | 0.000 | 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".