Machine learning and optimization models are digitally connecting our R&D pipeline to maximize value for farmers and helping Bayer redesign the testing network.
The 2018 discovery science and modeling efforts shifted from a regional corn product placement model, a key component of the current Seed Advisor from FieldView product, to a field-level seed placement tool. This shift enabled modeling to better account for field variability in environmental characteristics (e.g., soils) and farmer operations (e.g., crop rotation and tillage). The models were built on Bayer R&D pipeline data, which includes small research plots managed by the Breeding Organization and large strips managed by the Technology Development Organization. The objective of this blog is to highlight the different machine learning and optimization models driving seed selection decisions for the 2019 farmer alpha program and collaborations with Bayer Crop Science enterprise. Seed recommendation for farmers is driven by an integration of multiple models.
The regional-level placement model has been validated in the field with select farmers in Iowa, Illinois, and Minnesota in 2017 and 2018. Split planter trials were used as a demonstration of model predictiveness. These trials are configured as a Seed Advisor selected product planted side by side against a farmer selected product. Trials were successful nearly 80% of the time with an average >6 bushel per acre of lift using the Seed Advisor selected product.
Several new models will enable Seed Advisor to take product recommendations down to the field level. This is highly valuable for farmers as it takes into account many key features that are of importance to them. These include features like pH, organic matter, and cation exchange capacity (CEC), elevation, drainage, tillage, and crop rotation. In total, there are >150 different features that the field level model uses to create a product recommendation.
Under the hood, there are currently four models that capture key decisions for recommending products:
When conditions change, field guides ensure that farmers can select high performing products even if they cannot plant the top product from field assignment - for example, if that top product is unavailable.
The alpha program is a pre-commercial program that takes models that have been built (using research data) and validated (against FieldViewTM data) and tests them on farm with farmers in the FieldView Innovators program. Each farmer’s operation is different, and recommendations are built using knowledge about the specific farmer’s operation. The alpha program is a season long partnership that feeds insights back into the Climate Corporation science team to refine models.
The alpha program started in 2017 with one farmer in central Illinois (~20K acres). It expanded in 2018 to Illinois, Iowa, and Minnesota for a total of ~100K acres. This year, the program expanded geographically to include Illinois, Iowa, Minnesota, Wisconsin, Indiana, and Missouri for a total of ~100K acres.
We worked across Bayer Crop Science and initiated internal use of hybrid selection model insights in Breeding and Tech Development & Agronomy for 2019 testing shifts. This work started by identifying top predictive features from field level seed placement model and a deep understanding of the data coverage by feature class in the past R&D testing. We then leveraged FieldView data insights to metricize coverage by feature class in testing networks for opportunity on site selection shifts. With the excellent collaboration, Breeding has identified additional new locations to better capture conditions in FieldView farmer fields.
|Bayer research pipeline network aligning with field seed selection model predictors in 2019|