Grazing winter and spring wheat crops improves the profitability of prime lamb production in mixed farming systems of Western Australia
Bibliographic record
Abstract
Grazing immature cereal crops, particularly different varieties of wheat, has become widely adopted in the high rainfall areas of southern Australia. Recently, there has been growing interest in applying this technology in drier parts of the mixed farming zones of Western Australia. A modelling study was conducted to examine farm business returns with or without the grazing of immature wheat (winter and spring varieties) in different locations of Western Australia (Merredin, Wickepin and Kojonup), representing the low to high rainfall (319–528 mm) cropping regions, respectively. A combination of APSIM (crop simulation model) and GrassGro (pasture and livestock simulation model), were used to evaluate the changes in farm gross margins with the grazing of cereal crops at three locations of Western Australia. The results of the study showed that grazing the two wheat varieties (dual-purpose winter and spring) at the high rainfall location increased the profitability of the livestock enterprise by 2.5 times more than grazing crops at both low rainfall locations (P < 0.05). Across all years and sites, the average supplementary feeding costs were reduced by the inclusion of grazed winter (12%) and spring (2%) wheat crops in the lamb production system. The comparative reduction in the cost of supplementary feeding varied between locations and by crop variety within locations, due to both the frequency and average duration of the grazing of wheat crops in these regions, and the farm-stocking rate that was chosen. Both wheat varieties were grazed frequently at the lowest rainfall site (68% and 30% of years for winter and spring wheat varieties respectively), whereas grazing spring wheat was less frequent at the higher rainfall location and averaged 16% of years due to a greater difference in the relative availability of wheat crops versus pasture for grazing among regions. The grazing model assumed that there were abundant productive mixed ryegrass and subterranean clover pasture in the farming system. Overall, this study suggests that both winter and spring wheat crops are likely to supply green feed during the winter feed shortage (April–July) and reduce supplementary feed requirements for a short period of time in some seasons. The value of grazing crops is likely to be higher on farms with poorer soils and less productive pastures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".