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Syngenta Crop Challenge applies analytics to solve global hunger

Syngenta Crop Challenge (l-r): Joseph Byrum of Syngenta, winning team members Marko Panić and Oskar Marko, committee chair Robin Lougee and Analytics Society President Stefan Karisch.

The 2017 Syngenta Crop Challenge in Analytics asked participants to use data analytics to predict which soybean seed variety – or assortment of varieties – is most likely to be chosen by farmers within a specific growing region. Since seed variety selection is one of the most important decisions farmers make each season and no two seasons are alike, data-driven models are increasingly being deployed in making seed decisions. By helping farmers increase the potential of their crop production, competitors are contributing to a solution for the growing issue of global hunger.

This year’s winning entry, “Portfolio Optimization for Seed Selection in Diverse Weather Scenarios,” was presented by Oskar Marko, Sanja Brdar, Marko Panić, Isidora Šašić, Milivoje Knežević, Danica Despotović and Vladimir Crnojević from the BioSense Institute, University of Novi Sad, Serbia.

More than 600 teams registered to participate in the 2017 Challenge. The submissions where reviewed by an expert committee representing the diverse nature of the Challenge: William Beavis, Iowa State; Joseph Byrum, Syngenta; Arnie Greenland, CAP, University of Maryland; Robin Lougee (chair), IBM Research; Claudia Perlich, Dstillery; Alexander Platt, Temple University; Stein Wallace, Norwegian School of Economics; and Jim Williams, CAP, FICO.

The Committee selected five teams for the last round of the competition. The finalists presented their work at the 2017 INFORMS Business Analytics Conference in Las Vegas on April 3. The presentations were evaluated based on the rigor and validity of their approach, the quality of the proposed solution, and their ability to clearly articulate their solution and its methodology.

The winning team from Serbia was awarded $5,000.

The team of Zhongshun Shi, Yu Zhao, Xi Zhang and Leyuan Shi from Peking University, China, finished second and received $2,500 for their work titled, “A Decision-Making Approach for Soy Seed Variety Selection via Hedging Against Weather Risk.”

Third place and $1,000 went to Durai Sundaramoorthi, Lingxiu Dong, Iva Rashkova and Piruthiviraj Sivaraj of Washington University in St Louis for their entry, “A Hierarchical-Ensemble of Machineries to Optimize the Choice of Soybean Varieties.”

Additional finalists included “Seed Stocking Via Multi-Task Learning,” Yunhe Feng and Wenjun Zhou of the University Tennessee-Knoxville, and “Soybean Portfolio Selection with LASSO Model Averaging and Integer Linear Programming,” by Benjamin Harlander and Taylor Thiel of the University of Illinois.

This year’s first-place team from the BioSense Institute finished fourth in last year’s inaugural Crop Challenge in Analytics. Using material developed for the 2016 competition, the team helped their university receive agriculture research funding from Horizon 2020, the largest European Union research and innovation program aimed at excellent in science, industrial leadership and tackling societal challenges.

The Syngenta Crop Challenge in Analytics is presented by the INFORMS Analytics Society in collaboration with Syngenta, which conceived of the competition and donated the prize money from its 2015 Franz Edelman Award win to sponsor the Challenge for four consecutive years.

Details regarding the 2018 Crop Challenge will be announced in May 2017 with submissions due in January 2018. For more information about the Syngenta Crop Challenge in Analytics or to register for the 2018 Challenge, visit www.ideaconnection.com/syngenta-crop-challenge.

Robin Lougee, chair, 2017 Syngenta Crop Challenge in Analytics

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