Portfolio optimization for seed selection in diverse weather scenarios

PLoS One. 2017 Sep 1;12(9):e0184198. doi: 10.1371/journal.pone.0184198. eCollection 2017.

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.

MeSH terms

  • Agriculture / methods
  • Climate Change
  • Crops, Agricultural*
  • Glycine max / genetics*
  • Midwestern United States
  • Models, Statistical
  • Regression Analysis
  • Seeds / genetics
  • Uncertainty
  • Weather*

Grants and funding

This work was supported by Ministry of Education, Science and Technological Development of Republic of Serbia (www.vtg.mod.gov.rs), through project 44006 - Development in ICT using mathematical methods with application in medicine, telecommunications, power engineering, protection of national heritage and education (O.M., S.B., M.P. and V.C. were funded), and by Provincial Secretariat for Higher Education and Scientific Research of Vojvodina (www.vojvodina.gov.rs/en/provincial-secretariat-higher-education-and-scientific-research), through projects: Sensor technologies for integrated monitoring of agricultural production and Remote sensing in agricultural, water and forest monitoring applications (O.M., S.B., M.P. and V.C. were funded). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.