Simulating maize yield at county scale in southern Ontario using the decision support system for agrotechnology transfer model
Bibliographic record
Abstract
The objectives of this study were to evaluate the ability of the decision support system for agrotechnology transfer (DSSAT) CERES-Maize model to simulate the response to applied nitrogen and soil water storage for maize (Zea mays L.) yields in Woodslee, Ontario. A second objective was to evaluate the CERES-Maize module for maize yield in five southern Ontario counties. The calibrated CERES-Maize module was used in 117 maize yield simulations involving combinations of 45 regional soil datasets and 35 weather datasets covering the five counties. The model evaluation showed a good agreement between the simulated and measured grain yields (i.e., index of agreement, d ≥ 0.96; modeling efficiency, EF ≥ 0.83; normalized root-mean-square error, nRMSE ≤ 15%). The model showed a large deviation using the default soil parameters from 0 to 0.4 m. A sensitivity analysis was made for three soil water parameters, and the calibrated soil parameters showed moderate to good agreements for total soil water storage in the 0–1.1 m soil profile. The model resulted in moderate to good agreement between the simulated and the measured above-ground biomass across growing seasons. There were significant yield differences across the soil types. Drought periods in August 2010 resulted in lower yields in 2010 compared with 2011 and 2012. The simulated average maize yields at each county matched well with the measured data for 2010–2012 except for lower estimated yields in Lambton county in 2010. We concluded that DSSAT CERES-Maize can adequately simulate regional maize yields using the CERES-Maize module calibrated to regional soil and daily weather databases.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".