Adaptations for growing wheat in the drying climate of Western Australia
Bibliographic record
Abstract
This study investigated the effects of predicted changes in rainfall distribution in marginal (=325 mm annual rainfall) parts of the south-west Australian wheatbelt and options for management and adaptation of the wheat crop. Field experiments with rain-out shelters and irrigation were conducted in 2008 and 2009 to investigate the interactions of rainfall distribution, row spacing, genotype and timing of nitrogen application on growth, water use and grain yield of spring wheat. Water storage before seeding showed potential to maintain or increase yields despite lower in-season rainfall. Widening row spacing reduced biomass and slowed water use but did not increase grain yield, because of increased soil evaporation and water left in the soil at crop maturity. The Agricultural Production Systems Simulator (APSIM) wheat model was used to investigate the effects of recent and projected climate change on yield in relation to row spacing, phenology and nitrogen. Two climate-change scenarios were applied to historical climatic data to create two plausible future climates (‘optimistic’ and ‘pessimistic’) for the year 2030. None of the strategies tested increased wheat yield under the predicted climate scenarios. Simulated yields at wider row spacings were consistently lower due to insufficient biomass, increased soil evaporation and the inability of the crop to use all of the available water before maturity. Simulated yields of short-season genotypes were always greater than yields of longer season genotypes. Nitrogen regimes had little effect in this study. This study points to several genotypic traits that could improve the performance of wheat grown at wider row spacings. These include early vigour to reduce soil evaporation and increase competition with weeds, greater tillering/biomass to reduce limitation by sink size, and a vigorous root system with appropriate lateral spread and growth to depth to access available soil water.
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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 | 0.000 | 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".