Farming Smarter – Success Stories from the Field

As new technology continues to flood agriculture, we hear about ‘smart farming’ constantly. However, many are struggling to see where these products accomplish this, not in theory, but in their operation. With an increasingly competitive landscape, it is critical to be able to implement smart farming practices, but first, we need products that work. Below are real-life examples three growers shared where AgMRI closed the gap between science fiction and reality in the congested ag-tech space.


Proactive Weed Management

AgMRI’s proprietary analytics create weed maps to visualize and quantify weed pressure in fields as well as rank the fields by these metrics. Using the map view on bare soil imagery, Grower A identified which groups of fields were experiencing the most weed pressure and used this to prioritize burn down applications, reducing turnaround time between scouting and application, improving the efficiencies of the sprayers, and reducing ineffective herbicide applications. As the season progressed, he used the maps to identify areas of regrowth and resistant weeds, which enabled him to take action faster on these issues. Lastly,he implemented a spot spray strategy on his last round of herbicide application by understanding where the weeds were in the fields and which fields had weeds without extensive in-crop scouting.


Tillage Practices with AgMRI

Something difficult to quantify is the effect of tillage practices on crop health and emergence. AgMRI generates stand estimates and crop health imagery in the early season over an entire operation to quantify the impact of different tillage practices on a macro level. This information drove Grower B to completely overhaul how they managed bean residue from 2018 to 2019. In the past, he strip-tilled, but when he saw emergence issues in diagonal patterns in corn planted on beans, he found stunted plants due to residue accumulation. These stunted areas would never have been spotted from the road or without specifically looking for them. This year, by changing how he works these farms, he reduced emergence issues due to residue accumulation.



Applicator Performance with AgMRI

Nitrogen management is a hotly debated topic and an area where data has failed to meet expectations. An aspect we don’t hear talked about nearly as frequently as timing or quantification is the method of application. Grower C found many of the AgMRI Yield Risk alerts in the mid-season were related to nitrogen deficiencies, but what was interesting was that they were almost always man-made patterns. Upon investigation, he came away with two changes.


First, where an airflow was used to apply encapsulated urea, there were gaps at the start of each pass that faded in severity. It took the equipment time to reach a full rate of the product, which drove management adjustments for future applications.


Secondly, AgMRI helped him spot single rows that were yellow from end row to end row in a repeated pattern, where his bar left a gap row at the end. After scouting, he realized this was causing a 15 bushel an acre yield impact across every field, and decided to change equipment to allow a knife pass on each row in the field.


AgMRI. Helping Farmers Prosper.



by Conner Schmidt, Account Manager

Growing up on a family farm that produced livestock, corn, and soy, Conner learned from a young age the hard work farmers put into their livelihoods. He works with customers in the field to help them make the most of their AgMRI analytics. 

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