Understanding the Long-Term Weed Community Dynamics in Organic and Conventional Crop Rotations Using the Principal Response Curve Method
Bibliographic record
Abstract
Abstract Weeds have acquired evolutionary adaptations to the diverse crop and weed management strategies used in cropping systems. Therefore, changes in crop production practices such as conventional to organic systems, tillage-based to no-till systems, and diversity in crop rotations can result in differences in weed community composition that have management implications. A study was carried out to understand the weed community dynamics in a long-term alternative cropping systems study at Scott, SK, Canada. Long-term (18-yr) weed community composition data in wheat (Triticum aestivumL.) in ORG (organic), RED (reduced-input, no-till), and HIGH (high-input, conventional tillage) systems with three levels of crop rotation diversity, LOW (low diversity), DAG (diversified annual grains), and DAP (diversified annuals and perennials), were used to study the effect of different cropping systems and the effect of environment (random temporal effects) on residual weed community composition using the principal response curve (PRC) technique. The interaction between cropping systems and year-to-year random environmental changes was found to be the predominant factor causing fluctuations in weed community composition. Furthermore, the single most predominant factor influencing the weed composition was year-to-year random changes. Organic systems clearly differed from the two conventional systems in most years and had more diverse weed communities compared with the two conventional systems. The two conventional systems exhibited similar weed composition in most years. In this study, the use of the PRC method allowed capture of the real temporal dynamics reflected in the cropping systems by time interaction. This study further concludes that moving from a tillage-based, high-input conventional system to a no-till, reduced-input system did not cause significant changes in the weed community composition throughout the time period, but diversity in organic systems was high, probably due to increased occurrence of some difficult to control species.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".