Managing production constraints to the reliability of chickpea (Cicer arietinum L.) within marginal areas of the northern grains region of Australia
Bibliographic record
Abstract
The poor reliability of chickpea yield produced in the marginal (<600 mm rainfall) areas of the northern cropping zone is a constraint to the wide adoption of the crop. Chickpea is a valuable rotation crop and is currently the only viable winter grain legume suitable to this region. This paper uses results from in-crop monitoring and crop simulation, to identify practical management strategies to improve the reliability of chickpea crops in this region. APSIM-Chickpea successfully simulated the commercial yields of chickpea crops monitored during the study. Soil water at sowing and sowing date were identified as key determinants of yield. A ‘rule of thumb’ was derived, which showed that crops sown with a starting plant-available water of ~100 mm at sowing had an 80% probability of producing a better than break-even yield for the majority of the region and this was independent of the soil’s plant-available water capacity or crop sowing date. The probability of accumulating 100 mm of stored water in this western region is 90% following harvest of a May–sown wheat crop. Increased plant population improved crop yields in 60% of years, but this only translated to improved returns in ~50% of those years. The use of these simple management approaches will improve the reliability of chickpea production and ensure that these marginal areas have the option of a viable winter grain legume in their rotations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".