Pasture pest impact and control options used on dairy farms in south-eastern Australia
Bibliographic record
Abstract
To better understand the impact of pasture pests and their control measures used on dairy farms in south eastern Australia, a survey of 76 farmers was conducted between November 2016 and July 2017. The survey covered true insects as well as other pests such as mites, slugs, snails and millipedes. Seventy percent of the survey respondents were from the state of Victoria, 16% from Tasmania, with the remainder from New South Wales and South Australia. There were few significant regional differences in farmer responses regarding impacts and the use of control measures, so these are not presented. Overall, 42% of dairy farmers surveyed reported they had resown a pasture at least once in the last five years as a response to insect damage. Beetles, including cockchafers and wireworms, were identified as the most common pest causing damage leading to pasture resowing. During the last 5 years, approximately half of the survey respondents did not use insecticide sprays or seed treatments, while approximately one quarter of respondents used these products every year. Insecticides were most commonly used in autumn months to protect establishing pastures, and the main pests targeted at this time were mites, lucerne flea, beetles, caterpillars and grasshoppers. Endophyte-infected pasture cultivars were used by approximately half of the respondents in at least 1 of the last 5 years, and were used every year by 15% of respondents. These results, combined with field survey and expert elicitation workshop data, will contribute to an estimate of the impact of pasture pests on dairy farm businesses.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".