1. Field-Scale Sensor Evaluation Ken Sudduth, Newell Kitchen, Scott Drummond USDA-ARS Columbia MO

2. Objectives Investigate row-to-row variability in field-scale reflectance sensor data Document differences between data collected with Holland Scientific Crop Circle (amber) and NTech GreenSeeker (green)

3. N Application System 6-row system with sensors mounted over rows 2 and 5 System tested on 7 producer sites in 2004

4. Control Hardware Application Control System Green GreenSeeker 1 Green GreenSeeker 2 Crop Circle 3 Crop Circle 4 Laptop Computer GPS Stored Data: All sensor data GPS data Processed data Valve commands 1x, 2x, and 4x Solenoid Valves

5. Analysis of Response Plot Sensor Data Each field site included two strips of N-rate response plots Reflectance data were collected at the time of sidedress N application Mean reflectance ratio and NDVI were calculated for each of the four sensors for each 50-foot plot

6. Response Plot Reflectance Ratio Data N application at the Diederich (D) field was done near dusk, with only diffuse lighting. Work at all other field sites was completed before 6 pm.

7. Response Plot Reflectance Ratio Data

9. Row-to-row differences are apparent Is there an ambient light effect? Within a row, relative differences in sensor output are generally consistent between sensor types Scaling differences are apparent between sensor types Amber reflectance vs. green reflectance? Amber reflectance vs. green reflectance? Normalize data - divide by mean of each sensor reading within each field Normalize data - divide by mean of each sensor reading within each field

10. Normalized Reflectance Ratio Data Row 2 Row 5

11. Normalized Reflectance Ratio Data

13. Within-site, by-sensor normalization removed much of the sensor-type variability in many (but not all) cases In practice, a similar normalization is accomplished using reference strip data Well-fertilized as opposed to unfertilized Well-fertilized as opposed to unfertilized How well does it work? How well does it work?

14. Comparing Sources of Variation Sensor Variation Row-to-row Variation Row 2Row 5 GreenSeekerCrop Circle SE = 0.13 SE = 0.11 SE = 0.13SE = 0.10

15. Comparing Sources of Variation Considerable variability in ratio (or NDVI) readings between sensor types Mean normalization removed much of the variation The remaining variation was of similar magnitude as the variation between corn rows 90 inches apart How many sensors are needed to “adequately” describe variability? More in MO where we can’t seem to get uniform corn stands? More in MO where we can’t seem to get uniform corn stands?

16. Does Between-Sensor Variability Affect N Rate?

17. In this case, there was not much effect when looking at large-scale patterns of N rate changes

18. Does Between-Sensor Variability Affect N Rate? Strong relationship between rates from the two sensors, but somewhat offset from 1:1 line

19. Does Between-Sensor Variability Affect N Rate? In some fields, GreenSeeker N rate range was considerably reduced compared to Crop Circle Diederich field was a worst-case example, perhaps because of a different relationship between the two sensor outputs in low light

20. Summary Sensor “types” are different So are individual crop rows, at a similar magnitude Application rates with the different sensors are similar in some field conditions, but not in others Are sensors interchangeable within algorithms, or do we need to consider them as a “package”?