Production and persistence of subtropical grasses in environments with Mediterranean climates
Bibliographic record
Abstract
The agricultural region of south-west Western Australia (WA) has a Mediterranean climate, characterised by a winter-dominant rainfall pattern. Perennial subtropical grasses are increasingly being grown to increase productivity and reduce erosion on infertile sandy soils in the northern agricultural region (NAR) of WA, an area with mild winters and dry, hot summers. However, little information exists on the persistence of different species or their expected seasonal production and feed quality. On the south coast of WA, an area with dry, warm summers and a maritime influence, kikuyu (Pennisetum clandestinum) has been widely sown, but there is little information on the potential of other subtropical grasses. To address these issues, five trials were established across the agricultural area of south-west WA to measure the seasonal production, feed quality and persistence of the main, commercially available subtropical grasses over 3–4 years. This study demonstrates that subtropical grasses have a long-term role in the NAR in areas with mild winters and/or where the rainfall is >400 mm. The best performing subtropical grasses across a range of sites were panic grass (Megathyrsus maximus) and Rhodes grass (Chloris gayana). These species can be expected to have a water-use efficiency of ~10 kg ha–1 mm–1 over a 12-month period, provided there is a good perennial grass density. On the south coast, panic grass, Rhodes grass and setaria (Setaria sphacelata) persisted well and produced significantly more biomass than kikuyu. These grasses could complement kikuyu by increasing out-of-season production. At Kojonup, a more inland site, most of the subtropical grasses died over winter from a combination of occasional frosts and cold, wet soils. However, kikuyu re-grew from rhizomes in spring and maintained >90% ground cover 4 years after sowing. The results from these experiments are likely to be applicable to other regions across the globe with Mediterranean climates and similar soil types.
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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 | 0.001 | 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 | 0.000 | 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".