Evaluation of the Simple Algorithm for Yield Estimate Model in Winter Wheat Simulation under Different Irrigation Scenarios
Bibliographic record
Abstract
Winter wheat ( Triticum aestivum L. ) is one of the major grain crops grown in Guanzhong Plain, China. Rapid and accurate crop growth and yield estimation are important for agricultural monitoring and policy decision‐making. Crop model simulation is an effective way to provide objective growth and yield forecast. In this study, we investigated the applicability of the Simple Algorithm for Yield Estimate (SAFY) model for estimating winter wheat dry shoot biomass ( M S,d ) and grain yield, using two growing seasons field data from different irrigation scenarios. Results showed that the leaf area index (LAI) could be reasonably well simulated, with a minimum root mean square estimate (RMSE) of 0.11. The water stress intensity of each irrigation scenario was well accounted for through stress factor of effective radiation use efficiency. Good accuracy were achieved for M S,d simulation in both calibration (RMSE = 0.054–0.183 kg m −2 ) and validation (RMSE = 0.146 kg m −2 ) datasets, but showed a general overestimation during later growth stages. The grain yield was well estimated with a relative error of 1.9% to 16.7% in 2013 to 2014 and 0.1% to 16.7% in 2014 to 2015. The comparison between estimated and measured yield of all irrigation scenarios were robust in both seasons, with the minimum RMSE and MRE of 35.0 g m −2 and 4.7%, respectively. This work demonstrates the potential of a simple crop model for estimating biomass and yield only by calibrate the LAI without further in situ data, and prefigures assimilation application with remote sensing data in future. Core Ideas The SAFY model was evaluated in different irrigation scenarios. The variation of LAI was highly sensitive to leaf partition parameters. SAFY effectively simulated wheat biomass and yield under various irrigation scenarios. A general overestimation on biomass was noted during later growth stages.
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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 | 0.000 |
| 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".