RZWQM2 Performance in Simulating Maize Yield and Evapotranspiration under Rainfed and Irrigated Conditions
Bibliographic record
Abstract
<b><sc>Abstract.</sc></b> Precise simulation of actual crop evapotranspiration (ETa) is essential for precisely monitoring drought and scheduling in-season irrigation events thereby optimizing crop yields and enabling sustainable agricultural planning. The objective of this study is to compare the RZWQM2 model performance in simulating ETa and crop yields under both rainfed and irrigated conditions across varying climates which have been rarely studied. We comprehensively calibrated and evaluated the RZWQM2 model using field experiment data, including crop growth stages, soil water content, leaf area index, crop yield, and ETa, from five sites: four in USA (Ames, IA, Bushland, TX, Greeley, CO, and Mead, NE) and one in eastern Canada (Ottawa, ON).Among these, Ames, Mead, and Ottawa represented rainfed systems, while Bushland and Greeley were fully irrigated. Simulated soil water content showed satisfactory agreement with measured data, with RRMSE values below 30% and PBIAS within ±15% in most cases. The RRMSE for daily ETa simulation during the calibration and validation periods was 41.7% and 45.2% (Ames), 28.0% and 29.3% (Mead), 37.2% and 38.9% (Ottawa), 34.3% and 22.3% (Bushland), and 26.1% and 22.5% (Greeley), respectively. The corresponding PBIAS values were –2.5% and –0.7% (Ames), –9.5% and –3.7% (Mead), 3.2% and –6.0% (Ottawa), –4.0% and –0.7% (Bushland), and –5.7% and –1.4% (Greeley). These results suggest acceptable model performance for daily ETa at irrigated sites, while performance was unsatisfactory at rainfed sites except for Mead, based on RRMSE. Nevertheless, PBIAS values remained within acceptable limits across all sites. However, the model performance for weekly cumulative ETa was acceptable at rainfed sites. Simulated crop yields exhibited strong agreement with observed values, with RRMSE below 30% and PBIAS within ±15% at all sites. Overall, the RZWQM2 model demonstrated reliable performance in simulating crop yields and deep layer soil water content across both rainfed and irrigated systems. However, its ability to simulate daily ETa was satisfactory in irrigated conditions than rainfed.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".