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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Portfolio optimization for seed selection in diverse weather scenarios
Oskar Marko1, Sanja Brdar1, Marko Panić1
1BioSense Institute, University of Novi Sad, Novi Sad, Serbia.
Plos One
|September 2, 2017
Summary
This study developed a data-driven method to select optimal soybean varieties for the American Midwest, enhancing yield and stability. The winning approach for the Syngenta Crop Challenge 2017 identified top-performing varieties for regional seed distribution.
Area of Science:
- Agricultural Science
- Data Analytics
- Computational Biology
Background:
- Soybean production in the American Midwest faces challenges from variable weather and soil conditions.
- Optimizing soybean variety selection is crucial for maximizing yield and stability.
Purpose of the Study:
- To develop and apply a data analytics method for selecting optimal soybean varieties for the American Midwest.
- To identify soybean varieties that offer superior yield amount and stability across diverse subregions and weather scenarios.
Main Methods:
- Extracted data on 174 soybean varieties, including weather, soil, and yield parameters.
- Predicted variety yields across 6,490 subregions under various historical weather scenarios.
- Employed portfolio optimization techniques using predicted yields and inter-variety covariance.
Main Results:
- Identified specific soybean variety selections for each subregion, balancing yield amount and stability.
- Aggregated results to select up to five top soybean varieties for wider distribution.
- The developed method was the winning solution for the Syngenta Crop Challenge 2017.
Conclusions:
- Data analytics and portfolio optimization provide an effective framework for selecting optimal soybean varieties.
- This approach enhances decision-making for agricultural practices in the American Midwest.
- The study successfully identified high-performing soybean varieties for improved regional crop production.
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