Developing big-data models of soybean SDS with Agmatix
A coalition of extension pathologists from five midwest universities (Iowa State University, Michigan State University, Purdue University, University of Illinois, and University of Wisconsin-Madison) and Ontario Ministry of Agriculture are participating in a study of SDS outbreaks to determine how to predict where they are most likely to occur and the severity of their occurrence. Their findings may allow soybean farmers to make optimal decisions regarding the time for planting, use of cultivars, population density, and other factors to prevent this disease.
This study was based on data derived from six studies of a total of 90 SDS field trials carried out over 5 years in six locations in the United States (5) and Canada (1). SDS field observations were augmented with relevant management data and weekly weather information, and served as input to a ML model – XGBoost, an ensemble of decision trees. The challenge of standardizing the terminology, measurements, etc., of such a large amount of data was met using the Agmatix protocol GUARDS (Global Universal Agronomic Data Standard), an agronomy data standardization tool in which trial-specific definitions and data measurements are standardized and anomalies that could skew analytical results are identified. Thus, they were able to transform trial-related data into a harmonized and Interoperable dataset.