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Portfolio optimization for seed selection in diverse weather scenarios
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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5580993/ https://www.ncbi.nlm.nih.gov/pubmed/28863173 http://dx.doi.org/10.1371/journal.pone.0184198 |
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author | Marko, Oskar Brdar, Sanja Panić, Marko Šašić, Isidora Despotović, Danica Knežević, Milivoje Crnojević, Vladimir |
author_facet | Marko, Oskar Brdar, Sanja Panić, Marko Šašić, Isidora Despotović, Danica Knežević, Milivoje Crnojević, Vladimir |
author_sort | Marko, Oskar |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5580993 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-55809932017-09-15 Portfolio optimization for seed selection in diverse weather scenarios Marko, Oskar Brdar, Sanja Panić, Marko Šašić, Isidora Despotović, Danica Knežević, Milivoje Crnojević, Vladimir PLoS One Research Article 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. Public Library of Science 2017-09-01 /pmc/articles/PMC5580993/ /pubmed/28863173 http://dx.doi.org/10.1371/journal.pone.0184198 Text en © 2017 Marko et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Marko, Oskar Brdar, Sanja Panić, Marko Šašić, Isidora Despotović, Danica Knežević, Milivoje Crnojević, Vladimir Portfolio optimization for seed selection in diverse weather scenarios |
title | Portfolio optimization for seed selection in diverse weather scenarios |
title_full | Portfolio optimization for seed selection in diverse weather scenarios |
title_fullStr | Portfolio optimization for seed selection in diverse weather scenarios |
title_full_unstemmed | Portfolio optimization for seed selection in diverse weather scenarios |
title_short | Portfolio optimization for seed selection in diverse weather scenarios |
title_sort | portfolio optimization for seed selection in diverse weather scenarios |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5580993/ https://www.ncbi.nlm.nih.gov/pubmed/28863173 http://dx.doi.org/10.1371/journal.pone.0184198 |
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