Calibration and Validation of CERES-Wheat (Triticum Aestivum) Model for Simulating Fertilizer Application Rates in Management Zones
Bibliographic record
Abstract
Precision agriculture requires precise urea fertilizer application rates for site-specific applications to maximize crop yield across the management zones (MZs). A two years (2010-11 to 2011-12) field experimental study was conducted at Postgraduate Agricultural Research Station, University of Agriculture, Faisalabad, Pakistan to simulate urea fertilizer application rates for four MZs using CERES-Wheat (Triticum Aestivum) model. The model was calibrated using grain yield data of urea fertilizer application rate of 247 kg-urea/ha during growing season of 2010-11 in MZ 1. It was validated against two years independent yield data sets for all treatments ranging from no urea application to 247 kg-urea/ha application in each MZ. The model simulations were found to be acceptable for calibration as well as validation period, as the model evaluation indicators showed root mean square error of 314 kg/ha having its range from 77 to 566 kg/ha, model efficiency of 66% ranging from 24 to 98%, mean percent difference of -4.83%, ranging from -9.93 to 3.70%, against all observed grain yield data in four MZs. Scenario simulations revealed that urea fertilizer application rates of 221, 210 , 208 and 197 kg-urea/ha simulated maximum wheat grain yield of 3679, 3582, 3689, 3690 kg/ha, in MZs of 1, 2, 3 and 4, respectively. These simulated urea fertilizer application might be used to maximize wheat grain yield for each MZs within the field. Furthermore, field verification should be required by applying the simulated urea fertilizer application rates in each MZ.
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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 | 0.001 | 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 | 0.001 |
| 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".