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Portfolio optimization for seed selection in diverse weather scenarios

Author

Listed:
  • Oskar Marko
  • Sanja Brdar
  • Marko Panić
  • Isidora Šašić
  • Danica Despotović
  • Milivoje Knežević
  • Vladimir Crnojević

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.

Suggested Citation

  • Oskar Marko & Sanja Brdar & Marko Panić & Isidora Šašić & Danica Despotović & Milivoje Knežević & Vladimir Crnojević, 2017. "Portfolio optimization for seed selection in diverse weather scenarios," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-27, September.
    • Handle: RePEc:plo:pone00:0184198
    DOI: 10.1371/journal.pone.0184198
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    1. Deepak K. Ray & Navin Ramankutty & Nathaniel D. Mueller & Paul C. West & Jonathan A. Foley, 2012. "Recent patterns of crop yield growth and stagnation," Nature Communications, Nature, vol. 3(1), pages 1-7, January.
    2. Deepak K. Ray & James S. Gerber & Graham K. MacDonald & Paul C. West, 2015. "Climate variation explains a third of global crop yield variability," Nature Communications, Nature, vol. 6(1), pages 1-9, May.
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    1. Yihang Zhu & Yinglei Zhao & Jingjin Zhang & Na Geng & Danfeng Huang, 2019. "Spring onion seed demand forecasting using a hybrid Holt-Winters and support vector machine model," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-18, July.

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