Comparative water use by Dorycnium hirsutum-, lucerne-, and annual-based pastures in the Western Australian wheatbelt
Bibliographic record
Abstract
Dryland salinity in southern Australia has been caused by inadequate water use by annual crops and pastures. The purpose of this study was to compare the water use of annual pastures and Medicago sativa L. (lucerne) with Dorycnium hirsutum (L.) Ser., a potential new perennial forage species. The soil water dynamics under bare ground, annual legume-, lucerne-, and D. hirsutum-based pastures were compared at 2 sites in the low- (Merredin) and medium- (New Norcia) rainfall wheatbelt of Western Australia between September 2002 and February 2005. Soil under D. hirsutum was drier than under annual pastures by 8–23 mm in Year 1, 43–57 mm in Year 2, and 81 mm in Year 3. Lucerne used little additional water (<19 mm, n.s.) compared with D. hirsutum and profile soil water content was similar under both species throughout the experiment. At Merredin, annual pastures used water to a depth of 1.0 m, whereas under both D. hirsutum and lucerne in the first 3 years after establishment the successive maximum depth of water use was 1.0, 1.8, and 2.2 m. At New Norcia, additional soil water was extracted by lucerne and D. hirsutum at depths <1.0 m and no difference between treatments was detected below 1.0 m. Biomass of D. hirsutum pasture harvested in autumn contained minimal annual components and was 15–50% of that produced by lucerne- or annual legume-based pastures. D. hirsutum and lucerne plant density declined each summer (25–80%), but D. hirsutum density was lower than lucerne due to poorer establishment. Nonetheless, the comparable water use of lucerne and D. hirsutum suggests that D. hirsutum could make reductions in recharge similar to those of lucerne in the Western Australian wheatbelt.
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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 | 0.000 |
| 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 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".