Mowing strategies for controlling <i>Cirsium arvense</i> in a permanent pasture in New Zealand compared using a matrix model
Bibliographic record
Abstract
Defoliation has frequently been proposed as a means of controlling Cirsium arvense (L.) Scop. (Californian thistle, Canada thistle, creeping thistle, perennial thistle), an economically damaging pastoral weed in temperate regions of the world, but its optimization has remained obscure. We developed a matrix model for the population dynamics of C. arvense in sheep-grazed pasture in New Zealand that accounts for the effects of aerial shoot defoliation on a population's photosynthetic opportunity and consequential overwintered root biomass, enabling mowing regimes varying in the seasonal timing and frequency of defoliation to be compared. The model showed that the long-term population dynamics of the weed is influenced by both the timing and frequency of mowing; a single-yearly mowing, regardless of time of year, resulted in stasis or population growth, while in contrast, 14 of 21 possible twice-yearly monthly mowing regimes, mainly those with mowing in late spring, summer, and early autumn, resulted in population decline. Population decline was greatest (with population density halving each year) with twice-yearly mowing either in late spring and late summer, early summer and late summer, or early summer and early autumn. Our results indicate that mowing can be effective in reducing populations of C. arvense in pasture in the long term if conducted twice each year when the initial mowing is conducted in mid spring followed by a subsequent mowing from mid summer to early autumn. These mowing regimes reduce the photosynthetic opportunity of the C. arvense population and hence its ability to form the overwintering creeping roots upon which population growth depends.
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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 | 0.000 | 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 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".