Leveraging feature engineering to boost placement predictability for seed product selection and recommendation by field

A computer-implemented method comprising:

• receiving, by a server computer system, a plurality of agricultural data records, which include a yield property(ies) of products grown in a given field and continuous data indicative of multiple raw field features specific to the given field;
• identifying ones of the agricultural data records, for which the products grown in the given field include multiple products concurrently grown in the given field;
• identifying, in the identified ones of the agricultural records, a subset including multiple ones of the multiple raw field features of the products, the multiple ones of the multiple raw field features including at least one soil feature and/or at least one topography feature;
• for each raw field feature included in the subset:
• transforming, by the server computer system, the continuous data for the raw field feature into one of multiple distinct feature classes based on a value of the continuous data of the raw field feature relative to distinct numeric classification criteria of the feature classes;
• generating, by the server computer system executing a best linear unbiased prediction model, based on the distinct feature classes but not the continuous data for the subset of the multiple ones of the multiple raw field features, genomic-by-environmental relationships between one or more of the products grown in the field;
• generating, by the server computer system executing a statistical mixed effects model, based at least in part on the genomic-by-environmental relationships between the one or more of the products grown in the field, predicted yield performance for a set of products associated with one or more target environments thereby capturing variability in yield for the set of products under different environmental conditions for the one or more target environments;
• generating, by the server computer system executing a recommendation model, product recommendations for the one or more target environments based on the predicted yield performance for the set of products;
• and
• providing one or more instructions configured to cause display, on a display device communicatively coupled to the server computer system, of the product recommendations.