Application of DSSAT Model to Simulate Corn Yield under Long-Term Tillage and Residue Practices
Why this work is in the frame
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Bibliographic record
Abstract
<abstract> Long-term (1991-2013) field experiments were conducted to study the effects of different tillage and residue management practices on grain corn production in eastern Canada. Three different tillage practices, namely conventional tillage (CT), reduced tillage (RT), and no tillage (NT), along with two residue management practices, namely with residue (R) and without residue (NR), were considered. Field measurements of grain yield, biomass, soil organic carbon (SOC), soil moisture, and mineral nitrogen were used to evaluate the performance of the DSSAT model and to understand the impacts of long-term tillage and residue management practices. The observed corn yield, biomass, LAI, and nitrate-nitrogen (NO<sub>3</sub>-N) were not found to be significantly different under the various tillage and residue practices. The observed soil moisture showed a significant difference in the upper 10 cm soil layer. The SOC pool at 0-20 cm depth was reduced by 9.5% for CT and increased by 17.3% and 7.6% for RT and NT treatments, respectively. The DSSAT model was able to simulate corn and biomass yield, LAI, soil moisture, and SOC with RMSE ranging from 2% to 31%, indicating reasonable model performance. The model did not provide accurate results for NO<sub>3</sub>-N and soil moisture simulations, with RMSE ranging from 34% to 78%. Further improvements in the model are needed to better simulate soil moisture and N dynamics under different tillage and residue practices.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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