Please use this identifier to cite or link to this item: https://open.uns.ac.rs/handle/123456789/2982
Title: Portfolio optimization for seed selection in diverse weather scenarios
Authors: Marko, Oskar 
Brdar, Sanja 
Panić, Marko 
Šašić, Isidora
Despotović, Danica
Knežević, Milivoje
Crnojević, Vladimir 
Issue Date: Sep-2017
Journal: PLoS ONE
Abstract: The aim of this work was to develop a method for selection of optimal soybean varieties for the American Midwest using data analytics. We extracted the knowledge about 174 varieties from the dataset, which contained information about weather, soil, yield and regional statistical parameters. Next, we predicted the yield of each variety in each of 6,490 observed subregions of the Midwest. Furthermore, yield was predicted for all the possible weather scenarios approximated by 15 historical weather instances contained in the dataset. Using predicted yields and covariance between varieties through different weather scenarios, we performed portfolio optimisation. In this way, for each subregion, we obtained a selection of varieties, that proved superior to others in terms of the amount and stability of yield. According to the rules of Syngenta Crop Challenge, for which this research was conducted, we aggregated the results across all subregions and selected up to five soybean varieties that should be distributed across the network of seed retailers. The work presented in this paper was the winning solution for Syngenta Crop Challenge 2017.
URI: https://open.uns.ac.rs/handle/123456789/2982
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0184198
Appears in Collections:IBS Publikacije/Publications

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