Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Marko, Oskar, Brdar, Sanja, Panić, Marko, Šašić, Isidora, Despotović, Danica, Knežević, Milivoje, Crnojević, Vladimir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
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
_version_ 1783260975920578560
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
work_keys_str_mv AT markooskar portfoliooptimizationforseedselectionindiverseweatherscenarios
AT brdarsanja portfoliooptimizationforseedselectionindiverseweatherscenarios
AT panicmarko portfoliooptimizationforseedselectionindiverseweatherscenarios
AT sasicisidora portfoliooptimizationforseedselectionindiverseweatherscenarios
AT despotovicdanica portfoliooptimizationforseedselectionindiverseweatherscenarios
AT knezevicmilivoje portfoliooptimizationforseedselectionindiverseweatherscenarios
AT crnojevicvladimir portfoliooptimizationforseedselectionindiverseweatherscenarios