Simulating Potato Growth and Nitrogen Uptake in Eastern Canada with the STICS Model
Bibliographic record
Abstract
The ability of process‐based soil–crop models to simulate potato (Solanum tuberosum L.) yield and N uptake for a range of N fertilization under the conditions of eastern Canada has never been tested. Our objectives were (i) to calibrate and evaluate the performance of the STICS model for the Shepody and Russet Burbank cultivars with cultivar‐specific critical N concentration dilution curves, and (ii) to quantify the gain in model performance with cultivar‐specific N concentration curves rather than a generic curve. Data sets including measurements of leaf area index (LAI), total and tuber biomass, and total and tuber N uptake for several N rates (0–280 kg N ha−1) collected at Charlottetown, PE; Fredericton, NB; and Québec, QC, Canada were used. Calibration was done with one data set from Charlottetown for Shepody and one data set from Québec for Russet Burbank, while all the other data sets were used to evaluate model performance. Following calibration, the STICS model generally performed well, with a normalized root mean square error (NRMSE) < 30% and a normalized mean error (NME) ranging from –8% to 23%, for LAI and biomass. Model performance was slightly worse for total and tuber N uptake, although cultivar‐specific N concentration curves for Shepody and Russet Burbank improved model performance compared with a generic curve with a lower NRMSE (18–50% vs. 21–63%) and NME (–9 to 23% vs. –14 to 23%). Cultivar‐specific critical N concentration curves should therefore be used for assessing the N budget of potato cropping systems. Core Ideas The STICS soil–crop model was calibrated and evaluated for two potato cultivars. Calibrated and evaluated variables were LAI, total and tuber biomass, and total and tuber N uptake. Cultivar‐specific critical N concentration curves are recommended for N budget assessment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".