Simulating lupin development, growth, and yield in a Mediterranean environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it