Video Highlights Four Primary Variety Selection Methods
Whether you are new to Medius.Re or a seasoned veteran, we strive to make the platform as friendly and intuitive to the user as possible. As with most software, adaptations are not uncommon as a user needs change, networks improve, and technologies evolve. Utilizing actual wheat data generated in the Official Variety Trials program at North Carolina State University, the video below serves as a useful resource for those who may be familiar with the system already as well as those who may be relying on it for the first time.
With this feature, a user may jump directly to a specific variety that may have been recommended to him or her. Upon selection of a specific variety, the user will be greeted with an overview of the respective variety, including a summary, trial location(s), images, and a document library.
When a user is interested in identifying a set of varieties that meet specific criteria--a particular yield threshold, for example--he or she can select which characteristics are of interest and the value that the variety must meet for it to be included in the results. This feature is particularly useful when a grower is searching for a subset of varieties that exhibit a suite of desired characteristics.
This option can take the user directly to the entire set of data by year(s) and trial location(s). This is most useful to someone who is interested in seeing how all varieties in a trial performed in a specific location. An example of when this feature might be selected would be when a trial location most closely reflects the growing conditions and/or soil types of a grower’s farm. The true power of this option is demonstrated when specific characteristic filters are applied. A good way to think of this is as if you were using the “Query Variety” tool for a pre-determined trial location and year.
The final feature that the tutorial illustrates is called “Analytics.” With Analytics, a user can view the relative performance of each variety across years and locations according to specific characteristics in a completely customizable fashion. This feature is particularly useful when trying to quickly identify the above-average or top-performing varieties within a specific combination of years and locations.