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Evaluation of experiments for estimation of dynamical crop model parameters
Ilya Ioslovich1, Per-Olof Gutman
1Faculty of Civil and Environmental Engineering, Technion-Israel Institute of Technology, Haifa 32000, Israel. agrilya@tx.technion.ac.il
Bulletin of Mathematical Biology
|June 20, 2007
Summary
Optimal experimental design is challenging when limited parameters can be estimated. This study introduces the dominant parameter selection (DPS) procedure to identify key parameters for informative experiments, ensuring cost-effectiveness.
Area of Science:
- Experimental Design
- Mathematical Modeling
- Agricultural Science
Background:
- Optimal experimental design aims to maximize information gained within cost constraints.
- Non-informative experiments lead to difficulties in parameter estimation.
- Limited resources often restrict the number of parameters that can be reliably estimated.
Purpose of the Study:
- To introduce and illustrate the dominant parameter selection (DPS) procedure for optimal experimental design.
- To determine the maximal number and list of estimable parameters under specific constraints.
- To address challenges in parameter estimation due to non-informative experimental data.
Main Methods:
- Application of the dominant parameter selection (DPS) procedure.
- Utilizing a modified E-criterion based on the Fisher information matrix.
- Constraining the conditional number of the Fisher information matrix to an upper bound.
Main Results:
- The DPS methodology successfully identified key parameters for informative experiments.
- The study determined the maximum number of parameters that could be estimated.
- The methodology was validated using data from five planned experiments for the NICOLET lettuce growth model.
Conclusions:
- The dominant parameter selection (DPS) procedure offers a practical solution for optimal experimental design with limited resources.
- This method enhances the informativeness of experiments by focusing on dominant parameters.
- The approach ensures efficient parameter estimation within cost and information constraints.
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