Direct assimilation of measured soil water content in Root Zone Water Quality Model calibration for deficit‐irrigated maize
Bibliographic record
Abstract
Abstract Correct soil water simulation is critical for water balance and plant growth in agricultural systems. Crop production simulation errors have often been attributed to a lack of accuracy in soil water content (SWC) estimates. However, only a few studies have quantified the effects of SWC estimate errors on crop production and evapotranspiration (ET), especially under different irrigation treatments. The objective of this study was to investigate the impacts of direct assimilation of measured SWC during model calibration for deficit irrigated maize (Zea mays L.) on simulated ET, leaf area index (LAI), biomass, and yield. The CERES‐Maize model within the Root Zone Water Quality Model (RZWQM) was calibrated using the automatic parameter estimation (PEST) software. Simulation results showed that, using PEST‐optimized crop parameters, RZWQM was able to adequately predict crop yield (relative root mean squared error, rRMSE, of 4.8%) and biomass (rRMSE of 7.1%) in response to irrigation levels, in spite of the bias in SWC and ET simulation. However, with the same crop parameters but replacing simulated SWC with measured data, simulations of crop yield and biomass became worse, with higher rRMSE values (14.5% for yield and 21.5% for biomass). This unexpected model performance with SWC assimilation was mainly associated with the water addition and removal from the soil, which was improved only by recalibration of both soil and crop parameters. This study suggested compensating effects between soil and crop parameters during model calibration. Caution should be applied when using measured SWC as model inputs, especially under water stress conditions.
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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) | 0.000 | 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 | 0.001 | 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 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".