Crop stubbles are as important for sheep production as annual pastures in the Victorian Mallee
Bibliographic record
Abstract
The impact of different management strategies on production and profit can be evaluated with knowledge of how sheep production responds to changes in the available feed base and sheep or pasture management. This study aimed to quantify on-farm pasture and sheep production in mixed sheep and cropping systems in the Victorian Mallee of south-eastern Australia (325 ± 50 mm annual rainfall) as a prelude to computer simulation modelling. During 2001 (average rainfall) and 2002 (extreme drought) pasture production, the feed base and sheep production were monitored in 15 paddocks on 5 properties located across the region. Crop stubbles were the major source of feed for 6 months of the year, enabling ewes to maintain liveweight. There was more variation in pasture parameters between paddocks at the 1 location than between locations. The botanical composition, plant density, soil fertility and management were key variables associated with between-paddock variation in pasture production. Variation in pasture production between years was larger than within-year differences. In contrast, stocking rates were not much lower in the drought year of 2002 than in 2001. This study suggests there is potential for management to improve pasture production, and demonstrates the importance of feed sources other than annual pasture for sheep production in environments where the annual pasture growing season is short.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".