Simulating lupin development, growth, and yield in a Mediterranean environment
Bibliographic record
Abstract
Simulation of narrow-leafed lupin (Lupinus angustifolius L.) production would be a useful tool for assessing agronomic and management options for the crop. This paper reports on the development and testing of a model of lupin development and growth, designed for use in the cropping systems simulator, APSIM (Agricultural Production Systems Simulator). Parameters describing leaf area expansion, phenology, radiation interception, biomass accumulation and partitioning, water use, and nitrogen accumulation were obtained from the literature or derived from field experiments. The model was developed and tested using data from experiments including different locations, cultivars, sowing dates, soil types, and water supplies. Flowering dates ranged from 71 to 109 days after sowing and were predicted by the model with a root mean square deviation (RMSD) of 4–5 days. Observed grain yields ranged from 0.5 to 2.7 t/ha and were simulated by the model with a RMSD of 0.5 t/ha. Simulation of a waterlogging effect on photosynthesis improved the model performance for leaf area index (LAI), biomass, and yield. The effect of variable rainfall in Western Australia and sowing date on yield was analysed using the model and historical weather data. Yield reductions were found with delay in sowing, particularly in water-limited environments. The model can be used for assessing some agronomic and management options and quantifying potential yields for specific locations, soil types, and sowing dates in Western Australia.
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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".