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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2017-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0184198&type=printable |
| _version_ | 1826578937584549888 |
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| author | Oskar Marko Sanja Brdar Marko Panić Isidora Šašić Danica Despotović Milivoje Knežević Vladimir Crnojević |
| author_facet | Oskar Marko Sanja Brdar Marko Panić Isidora Šašić Danica Despotović Milivoje Knežević Vladimir Crnojević |
| author_sort | Oskar Marko |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-19T10:14:00Z |
| format | Article |
| id | doaj.art-45ef965c28ed4600993ce6fad0bfcdf1 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2025-03-14T14:10:19Z |
| publishDate | 2017-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-45ef965c28ed4600993ce6fad0bfcdf12025-02-27T05:37:05ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01129e018419810.1371/journal.pone.0184198Portfolio optimization for seed selection in diverse weather scenarios.Oskar MarkoSanja BrdarMarko PanićIsidora ŠašićDanica DespotovićMilivoje KneževićVladimir CrnojevićThe 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.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0184198&type=printable |
| spellingShingle | Oskar Marko Sanja Brdar Marko Panić Isidora Šašić Danica Despotović Milivoje Knežević Vladimir Crnojević Portfolio optimization for seed selection in diverse weather scenarios. PLoS ONE |
| 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 |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0184198&type=printable |
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