Do spring cover crops rob water and so reduce wheat yields in the northern grain zone of eastern Australia?
Bibliographic record
Abstract
During the 14-month-long fallow that arises when moving from summer to winter crops, stubble breakdown can denude the soil surface and leave it vulnerable to erosion. Cover crops of millet have been proposed as a solution, but this then raises the question, how often is there sufficient water in the system to grow a cover crop without reducing the soil water reserves to the point of prejudicing the following wheat crop? An on-farm research approach was used to compare the traditional long fallow (TF) with a millet fallow (MF) in a total of 31 commercial paddocks over 3 years. Each treatment was simulated using the simulation-modelling framework (APSIM) to investigate the outcomes over a longer timeframe and to determine how often a millet fallow could be successfully included within the farming system. The on-farm trials showed that early-sown millet cover crops removed before December had no effect on wheat yield, but this was not true of millet cover crops that were allowed to grow through to maturity. Long-term simulations estimated that a spring cover crop of millet would adversely affect wheat yields in only 2% of years if planted early and removed after 50% cover had been achieved.
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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".