Integrated weed management for the suppression of rhizomatous perennial weeds in organic agriculture
Bibliographic record
Abstract
Convolvulus arvensis (L.) (field bindweed) and Cirsium arvense (L.) Scop. (Canada, creeping, or Californian thistle) are the most problematic weeds for organic grain farmers in semi-arid Montana and the wider Northern Great Plains (NGP) due to their rhizomatous, perennial root systems. Historically, intensive tillage was used to disrupt and control the root systems. An integrated approach, which combines biological, cultural, and mechanical controls, has been recommended to manage difficult perennial weeds while reducing reliance on a singular management strategy like intensive soil cultivation. Two associated studies were conducted to assess C. arvensis and C. arvense responses to different combinations of tillage, grazing, and crop sequences. In both studies, treatments were arranged on a spectrum of crop competition and tillage intensity, which were inversely related. For example, a two-year sequence of perennial alfalfa (Medicago sativa L.) constituted maximum crop competition and minimal tillage intensity, whereas wheat (Triticum spp.) followed by two consecutive years of tilled fallow represented minimal crop competition and maximum tillage intensity. Treatments within this gradient had crop sequences with varied mixtures of annual and biennial monocot and dicot crops and minimal to moderate tillage regimes, sometimes including livestock grazing with sheep (Ovie aries).Tilled fallow and multi-year alfalfa treatments prevented or decreased both C. arvensis and C. arvense populations over three-year periods across the two studies. For C. arvensis, a treatment using a biennial sweet clover crop and livestock grazing for crop and weed termination increased C. arvensis density. Crop sequences with multiple years of annual monocot or dicot crops increased C. arvense density and biomass over time. There were no differences in C. arvense populations due to tillage method (e.g., standard or reduced). Both studies demonstrated that intensive soil cultivation or intensive crop competition in the form of perennial alfalfa were most successful in suppressing perennial weeds. Highly diverse annual or biennial crop rotations, under either standard or reduced tillage methods, did not prevent population perennial C. arvensis or C. arvense from increasing. Incorporating a perennial forage or temporary pasture phase into annual organic grain rotations of the NGP is recommended to reduce perennial weed pressure.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".