Seasonal grazing of Canada goose (Branta canadensis) on high country farmland, Canterbury, New Zealand
Bibliographic record
Abstract
There is ongoing debate between landowners and recreational hunters about the significance of grazing by Canada goose (Branta canadensis) on New Zealand's high country farmland. The South Island Canada Goose Management Plan (1995), which includes in its aims the alleviation of such impacts, was developed in the absence of any quantitative measures of goose grazing intensity. This study aimed to quantify the impacts of Canada geese on one high country farm, through an exclosure study at Lake Grasmere, inland Canterbury. Fieldwork was conducted from July 1999 to June 2000, in conjunction with monthly observations of Canada geese on 69 ha of paddocks adjacent to Lake Grasmere. Canada goose numbers on the study site varied throughout the year, ranging from fewer than 10 geese in October and November 1999 to peak of over 400 in March 2000. These geese significantly reduced pasture production (p<0.001) on paddocks adjacent to the lake, with the differences in monthly dry-matter production between goose-grazed and ungrazed pastures ranging from less than 100 kg/ha in winter to 900 kg/ha in late summer and early autumn. The impact on pasture production was positively correlated with the number of geese each month (p<0.05). Observations of the behaviour of geese on the paddocks indicated that neither season nor time of day had any pronounced effect on their foraging intensity. Consequently, grazing pressure on pasture is determined primarily by the number of geese on the paddocks. Goose numbers and impacts were highest in late summer and early autumn. Goose damage at this time is of particular concern for high country farmers who are typically trying to maintain autumn-saved pasture to assist in over-wintering their stock. At present the North Canterbury Fish and Game Council culls this goose population annually. These results may in future assist managers to better assess the costs versus benefits of any proposed changes to goose management in the high country.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| 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".