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Modeling controlled nutrient release from a population of polymer coated fertilizers: statistically based model for
Avi Shaviv1, Smadar Raban, Elina Zaidel
1Faculty of Civil and Environmental Engineering, Technion-IIT, Haifa, Israel. agshaviv@tx.technion.ac.il
Environmental Science & Technology
|June 6, 2003
Summary
A new statistical model accurately predicts nutrient release from polymer-coated controlled release fertilizer (CRF) granules. This tool helps optimize fertilizer design for better crop yields and environmental protection.
Area of Science:
- Agricultural Science
- Materials Science
- Chemical Engineering
Background:
- Controlled release fertilizers (CRFs) are crucial for efficient nutrient delivery.
- Polymer-coated CRFs offer enhanced agronomic and environmental benefits.
- Variability in granule size and coating thickness complicates release prediction.
Purpose of the Study:
- To develop a statistically based model for predicting nutrient release from polymer-coated CRFs.
- To account for the complex, nonlinear release mechanisms from individual granules.
- To integrate individual granule behavior into population-level release predictions.
Main Methods:
- Constructed a mathematical-mechanistic model for single CRF granule release.
- Incorporated statistical distributions of granule radii and coating thickness.
- Verified the model using experimental data and release curves of polymer-coated CRFs.
- Performed sensitivity analysis on key parameters like permeability and granule variations.
Main Results:
- Water permeability controls the lag period; solute permeability governs linear release rate and duration.
- Increased mean granule radii or coating thickness extend the lag and linear release periods.
- Granule size and coating thickness variations impact release significantly only at high standard deviations or altered water permeability.
Conclusions:
- The developed model effectively predicts release from polymer-coated CRFs.
- The model serves as a valuable tool for designing and enhancing CRF effectiveness.
- Optimized CRF design can improve agronomic efficiency and reduce environmental impact.