Background
During a mid-season meeting, an agronomy team and crop specialist reviewed AGMRI imagery across several fields. In one particular field, the imagery revealed a pattern of uneven emergence that caught the team’s attention. The variation appeared in distinct waves, prompting a closer look.
Upon further discussion, the crop specialist noted that the angle of the emergence pattern matched the tillage direction used earlier in the season. The area had also experienced a challenging spring with excessive rainfall, which likely compounded the issue by creating variable soil conditions at planting. Scouting pins placed earlier in the season supported this theory, as several aligned with low-lying areas and poor emergence zones tied to weather stress.
This case presented an opportunity to connect AGMRI maps and related insights with field-level validation to better understand how tillage and weather interactions were affecting stand establishment.
Challenge
The concern was that if the same tillage practices were used across the grower’s entire operation, similar problems could exist in other fields — potentially leading to inconsistent emergence, uneven stands, and reduced yield potential.
While the crop specialist had been somewhat skeptical about the value analytics, this field provided a clear example that could demonstrate how AGMRI helps visualize, verify, and diagnose field variability in real time.
The challenge was twofold: to confirm that the AGMRI maps was accurately detecting the issue, and to identify whether the root cause was tillage-related, weather-related, or a combination of both.
Solution
The agronomy team and crop specialist decided to ground-truth the maps by field scouting the affected fields. After reviewing the NDVI and VEG layers on AGMRI, they observed the same wave-like emergence pattern during the field walk.
The visual alignment between the imagery patterns and tillage direction confirmed that the unevenness seen from above matched what was happening on the ground. The field inspection also showed that wet conditions and compaction from spring rainfall likely amplified the variability.
The crop specialist planned to review the findings with the grower to discuss potential adjustments to tillage methods—such as changing the tillage angle, reducing pass intensity, or incorporating residue management practices—to improve emergence consistency in future seasons.
Results
This collaboration demonstrated how AGMRI’s analytics and field validation can work together to diagnose agronomic issues quickly and accurately. By connecting insights from field scouting, both the agronomist and grower gained a clear understanding of the interactions among tillage patterns, soil conditions, and weather on crop emergence.
The crop specialist expressed greater confidence in using AGMRI for real-time field diagnostics and plans to continue using it to monitor tillage impacts across other fields. Moving forward, the grower will explore refinements to spring tillage practices to minimize soil variability and promote more uniform stands.
