Global Sensitivity Analysis of the Advanced ORYZA-N Model with Different Rice Types and Irrigation Regimes.

Area of Science:

• Agricultural Science
• Agronomy
• Crop Modeling

Background:

• Accurate crop modeling is essential for optimizing agricultural practices and ensuring food security.
• The ORYZA-N model is a widely used tool for simulating rice growth and yield.
• Understanding parameter sensitivity is vital for improving model reliability and application.

Purpose of the Study:

• To conduct a sensitivity analysis of 23 parameters in the ORYZA-N rice model.
• To evaluate parameter sensitivity across different rice types and irrigation regimes.
• To compare the impact of water-saving irrigation on parameter sensitivity.

Main Methods:

• Employed the Extended FAST (eFAST) method for sensitivity analysis.
• Investigated parameter sensitivity for single-season and double-season rice under traditional flood irrigation (TFI) and shallow-wet irrigation (SWI).
• Analyzed sensitivity for six crop growth outputs across four developmental stages and final yields.

Main Results:

• Parameters RGRLMX, FRPAR, and FLV0.5 were identified as highly influential across all model outputs and developmental stages.
• Water stress induced by SWI had a greater impact on the parameter sensitivity of single-season rice compared to double-season rice.
• Parameter sensitivity varied significantly across different developmental stages and irrigation scenarios.

Conclusions:

• RGRLMX, FRPAR, and FLV0.5 are critical parameters for the ORYZA-N model that require careful calibration.
• Water-saving irrigation strategies, like SWI, can alter the sensitivity of rice models, particularly for single-season rice.
• The findings provide valuable insights for refining rice crop models and informing irrigation management decisions.