Are open-source data efficient to calibrate an agricultural system model to simulate crop evapotranspiration and yield in a data-scarce region?
Bibliographic record
Abstract
Abstract. Process-based agricultural systems models are considered widely to mimic the bio-physical and soil-water dynamic process of soil-plant systems under numerous agricultural management practices and climate conditions. To set up the agricultural systems models, extensive soil, weather, crop, and management practice data are needed. However, these data are not available at the desired spot of interest. In this case, open-source data, including regional soil database, local common crop management practices, remote sensing actual crop evapotranspiration (ETa), and county scale crop yield, can be used to calibrate agricultural systems models and address regional issues. The objective of the study was whether open-source data have the potential to calibrate the crop model. Therefore, a study was carried out to assess the potential of using open-source data to model corn (ETa) and corn yield from the year 2002 to the year 2018 at an experimental site near Ottawa, Canada. The RZWQM2 model was separately calibrated by remote sensing ETa and county scale crop yield. The calibrated model showed an acceptable performance to simulate corn yield and seasonal ETa. Model performance was later validated with in-situ measured crop yield and Eddy Covariance (EC) ETa data. In general, model performance was satisfactory, except for daily ETa in the drought periods. Overall, this study suggests that open-source data can be used to calibrate the model in the region. This approach can be applied to simulate crop yield and ETa in different climatic conditions using numerous other crop models for strategic crop planning, however, it needs further testing if this strategy will be used in other regions.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".