Development and evaluation of a new Canadian spring wheat sub-model for DNDC
Bibliographic record
Abstract
Kröbel, R., Smith, W. N., Grant, B. B., Desjardins, R. L., Campbell, C. A., Tremblay, N., Li, C. S., Zentner, R. P. and McConkey, B. G. 2011. Development and evaluation of a new Canadian spring wheat sub-model for DNDC. Can. J. Soil Sci. 91: 503-520. In this paper, the ability of the DNDC model (version 93) to predict biomass production, grain yield and plant nitrogen content was assessed using data from experiments at Swift Current, Saskatchewan, and St-Blaise, Quebec, Canada. While predicting wheat grain yields reasonably well, the model overestimated the growth of above-ground plant biomass and nitrogen uptake during the first half of the growing season. A new spring wheat sub-model (DNDC-CSW) was introduced with a modified plant biomass growth curve, dynamic plant C/N ratios and modified plant biomass fractioning curves. DNDC-CSW performed considerably better in simulating plant biomass [modeling efficiency (EF): 0.75, average relative error (ARE): 6.0%] and plant nitrogen content (EF: 0.61, ARE: -2.7%) at Swift Current and St-Blaise (EF of 0.75 and ARE of 2.3%), compared with DNDC 93 (biomass SC: EF 0.49, ARE 17.1%, SB: EF 0.02 ARE 33.4%). In comparison with DNDC 93, DNDC-CSW better captured inter-annual variations in crop growth for a range of wheat rotations, increasing the EF from 0.32 to 0.52 for grain and from 0.35 to 0.39 for straw yields. DNDC-CSW also performed considerably better than DNDC 93 in estimating soil carbon changes at Swift Current. Hence, DNDC-CSW has the potential to improve the performance of DNDC 93 in simulating wheat biomass, plant nitrogen, yield and soil carbon at various Canadian sites.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".