Track On-Farm Trials with AGMRI

In 2023, farmers will test new products to determine which could have the most impact on their farming operation versus their current products and practices.  These trials will happen this growing season, just as they have in past seasons, across a multitude of acres. The trial focus can be improved yields with new hybrids or varieties, comparing varied application methods of crop protection products, such as fungicides, or perhaps even variable rates of crop nutrients.  And, with the growing interest in the use of biological products – of various attributes – on-farm trials continue to provide insights for new product evaluations.

Tracking the performance of new versus current products or operational practices is often a challenge as the season progresses. In other words, typical seasonal activities and unexpected situations can distract us from monitoring and evaluation of these trials. With AGMRI images and analytics, farmers can track the potential impact of these new products and practices throughout the growing season at regular intervals.

The AGMRI platform enables farmers to compare their trials, side x side, in real time at key growth stages from planting through harvest. As in the example below of the hybrid comparison trial, a farmer or their trusted agronomic advisor can monitor any differences between the two hybrids regarding emergence, and any difference in response to environmental impacts, such as crop stress.


On-Farm Trial Example:

Corn Test Plot-Middle strip down corn


If your on-farm trials are an important part of your evaluation process and planning, consider AGMRI and its capabilities to support your decision making in future seasons.


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Yield Forecast

Understand where your corn yield is based on the current state of the crop. As the season unfolds, see how it is having an impact on your final yield.

Yield Loss

Powered by years of Nitrogen research at the University of Missouri, our corn Yield Loss analytic, powered by NVision Ag, gives insight into potential yield loss due to Nitrogen deficiency. Optional analytic for nitrogen management.

Variable Dry Down

variable dry down

Understand which fields and which areas of the field are drying down to help plan your harvest logistics.

Underperforming Area

low crop health

Not all areas of your fields perform the same and low NDVI doesn’t necessarily mean there is anything you can do to fix it this year. Underperforming Area alerts you to the fields and areas of the fields that are performing below their historical potential. This will allow you to quickly find those fields and areas and make adjustments to get them back on target and protect yield.

Nutrient Deficiency

nutrient deficiency

As the crop grows, it can tell us more of what is wrong with it. This analytic finds the fields and areas of the fields where there is a nutrient deficiency so that issues can be addressed before grain fill.

Disease Stress

disease risk

In conjunction with the Thermal Stress, Disease Stress alert takes into account weather information to more precisely indicate the type of stress impacting the crop.

Thermal Stress

thermal risk

Using our thermal imagery, AGMRI can detect elevated heat patterns of the crop that indicate crop stress.

Crop Health

Crop Health

Get a complete view of your farms and fields and identify where yield potential is ranked highest to the lowest.

Weed Map & Weed Escape

weed escape

Know what fields and areas of the fields have weeds. With machine integration or based on planting date, be alerted to what fields have weeds that may be impacting yield.

Historical Field Performance

AGMRI creates 5 performance zones in each field based on the historical average of those zones. This data is used to compare the current season to help understand where you are underperforming from the zone potential.

Low Emergence

low emergence

Notification of what fields and areas of the field have poor emergence.

Stand Assessment

strand assessment

AGMRI detects the established rows and uses computer vision and machine learning to determine the best segment of row and compares the rest of the field to that segment to give you a relative map. If machine data is integrated, a stand population map is returned.