Providing adequate food for the world’s population is humanity’s toughest challenge. An environmentally sustainable solution is to improve crop output through plant breeding. However, with current breeding methodologies the rate of increase in crop production is not sufficient to meet today’s food needs. Syngenta is changing that by applying operations research (O.R.) methods to make better breeding decisions that reduce the time and cost required to develop crops with higher productivity. This data-based transformation is making a quantifiable contribution to Syngenta’s commitment to meeting the world’s growing food needs in an economically and environmentally sustainable way. Syngenta won the 2015 Franz Edelman Award for their work, "Advanced Analytics for Agricultural Product Development"
Competing for the Edelman award places you and your company among the upper echelons of those within the O.R. and Analytics fields today.
Four O.R. tools (described below) have been developed by Syngenta R&D since 2010, allowing progress toward meeting the following five objectives:
Each of the four O.R. tools is supporting one or more of the three pipeline phases: variety design, variety development, and variety evaluation.
The trait introgression (TI) tool and the breeding project lead (BPL) tool use discrete-event simulation platforms to make processes within the pipeline more efficient. The project lead is using the TI tool to design a strategy for transferring favorable traits from one soybean variety to another and the BPL tool to design a strategy for developing varieties with higher productivity. With each tool, the project lead compares alternative TI and breeding strategies until a strategy is identified with a high POS and uses minimal amounts of cost and time.
Yield trial design (YTD) Optimizer and data quality cart (DQC) tools are supporting decisions that impact the quality of variety selection and advancement toward commercialization. The project lead uses the DQC tool’s residual analysis and correction capabilities to identify and correct for systematic error in field trial data resulting in dramatic improvements in data quality and variety selection decisions. The YTD Optimizer uses probabilistic modeling, discrete-event simulation, optimization, and sensitivity analysis to enable the project lead to effectively allocate field trial resources such that variety selection accuracy improves
and costs decrease.
The new analytical tools dramatically improve project lead training, decision making, and planning, resulting in a cost avoidance for soybean R&D of more than $287 million from 2012--2016 and substantially improve the probability of successfully delivering a portfolio value exceeding $1.5 billion. Syngenta recognizes the positive impact these tools have on soybean R&D and is initiating a multi-year effort to customize and launch similar tools across all major crops.
Syngenta crop R&D program operations are transformed and their products are more competitive as a result of using advanced analytics and O.R. methods.