LOGO Framework: Calibration and Evaluation Datasets for Rice Crop Models

Pseudo-observation datasets generated across six sites representing rice production environments in Asia. The data supports the calibration and evaluation of the Local Optimization Global Optimization (LOGO) framework for crop models. The dataset was authored by Shinwoo Hyun and last updated in May 2026.

Use Cases

• Calibrating phenology-related parameters in crop models based on pseudo-observation data.
• Evaluating the impact of initial parameter values on yield-related parameter estimates.
• Testing hybrid optimization frameworks like LOGO to improve calibration robustness.
• Comparing calibration outcomes across diverse agricultural sites.

Strengths

• Data covers six distinct sites representing rice production environments in Asia.
• Includes multiple file formats (TXT, XLSX, CSV, etc.) for flexibility.
• Released under a permissive CC-BY-4.0 license.

Limitations

• Row count and specific column definitions are unknown, limiting suitability assessment.
• Data is based on pseudo-observations, not direct field measurements.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare, author Shinwoo Hyun

Collection Method

Pseudo-observation datasets generated for model calibration experiments.

Freshness

Last updated 2026-05-07 22:21:47

Geography

Asia (six rice production sites)