Competitive ability of wheat in conventional and organic management systems: A review of the literature
Bibliographic record
Abstract
Wheat (Triticum aestivum L.) is the world’s most widely grown crop, cultivated in over 115 nations. Organic agriculture, a production system based on reducing external inputs in order to promote ecosystem health, can be defined as a system that prohibits the use of synthetic fertilizers, chemical pesticides and genetically modified organisms. Organic agriculture is increasing in popularity, with a 60% increase in the global acreage of organically managed land from the year 2000 to 2004. Constraints that may be associated with organic grain production include reduced yields due to soil nutrient deficiencies and competition from weeds. Global wheat breeding efforts over the past 50 yr have concentrated on improving yield and quality parameters; in Canada, disease resistance and grain quality have been major foci. Wheat varieties selected before the advent of chemical fertilizers and pesticides may perform differently in organic, low-input management systems than in conventional, high-input systems. Height, early-season growth, tillering capacity, and leaf area are plant traits that may confer competitive ability in wheat grown in organic systems. Wheat root characteristics may also affect competitive ability, especially in low-input systems, and more research in this area is needed. The identification of a competitive crop ideotype may assist wheat breeders inthe development of competitive wheat varieties. Wheat varieties with superior performance in low-input systems, and/or increased competitive ability against weeds, could assist organic producers in overcoming some of the constraints associated with organic wheat production. Key words: Triticum aestivum L., wheat breeding, low-input agriculture, plant height, early-season growth, tillering capacity, leaf area index
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".