Combining a field experiment and literature to model the regrowth probability of perennial storage organs fragmented by tillage: Case study of Cirsium arvense (L.) Scop
Bibliographic record
Abstract
Management of perennial weeds has become increasingly difficult with the reduction of herbicide use. Creeping perennials accumulate reserves in specialized belowground organs from which they regenerate new plants after a disturbance. Through tool selection, tillage operations could be optimized to reduce perennial-weed reserves and limit regeneration. In the present study, the effect of five tools on the fragmentation of the creeping roots of Cirsium arvense (L.) Scop. (Canada thistle), a major perennial weed in arable crops, were analysed. A field trial was set up to measure the lengths of the root fragments left after tillage. Five tools were tested: mouldboard ploughing, rotary harrow, disc harrow, rigid-tine cultivator and goose-foot cultivator. Fragment-length distribution varied according to the tool: rotary harrow left the smallest (3.7 cm on average) and least variable fragment lengths, mouldboard ploughing the longest (12.7 cm) and most variable ones. The other tools produced intermediate-sized fragments (8–10 cm). Based on these results and literature, a model was proposed to predict perennial-weed regeneration probability from storage-organ fragments after one tillage run. The effects of six factors, which were agronomic (tillage tool), environmental (soil conditions and temperature) and biological (storage-organ fragment diameter, maximal belowground-shoot length and pre-tillage storage-organ distribution), were tested through a sensitivity analysis. According to the model, the probability of fragment regeneration success is lower for the rotary harrow than for the mouldboard plough. The most important drivers of fragment regeneration success were the biological traits: fragment diameter and maximal belowground-shoot length per unit fragment biomass. The present model should be complemented to predict the effect of tillage on perennial-weed regrowth and help improving non-chemical weed-management strategies. To achieve this, further research is needed on plant regrowth potential from storage organs and their architecture in the soil. • Alternatives to herbicides are needed to control the perennial weed Cirisum arvense. • The lengths of root fragments were measured after tillage in field experiments. • A model predicting regeneration probability of perennials from fragments was built. • The choice of the tool affects fragmentation rates and plant regeneration probabilities. • Weed biological traits were the most important drivers of regeneration probability.
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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 | 0.001 | 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 | 0.001 |
| 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 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".