1. Landscape Position Zones and Reference Strips
Larry Hendrickson

2. Landscape Position Zones (LSP)
Extracting landscape position (LSP) from elevation data
RTK elevation data is becoming widely available
Elevation derivatives work well for post-mortem analysis
Elevation itself isn’t usually a good means for classifying site conditions
Developed a method to extract LSP from elevation data by comparing elevation of each pixel with its neighbors 4 3 2 1
1400 ft
1300 ft
1200 ft
| NUE Hendrickson 5 August, 2009

3. W Nebraska Center Pivot field
| NUE Hendrickson 5 August, 2009

4. LSP Zone Polygons over Corn Yield
Toeslope
Shoulder
| NUE Hendrickson 5 August, 2009

5. Yield variability related to LSP—Across W NE fields
Toeslope
Shoulder
| NUE Hendrickson 5 August, 2009

6. Yield variability related to LSP—Across years
Toeslope
Shoulder
| NUE Hendrickson 5 August, 2009

7. Iowa field just after planting
| NUE Hendrickson 5 August, 2009

8. Iowa field just after planting5 4 3 2 1
| NUE Hendrickson 5 August, 2009

9. Iowa Corn Yield Toeslope Shoulder
May-June Rainfall
6.1
7.8
7.6
9.4
13.2
14.9
Toeslope
Shoulder
Normal May-June rainfall is 9.5 inches
Data from Jaynes and Kaspar
| NUE Hendrickson 5 August, 2009

10. Iowa Soybean Yield Toeslope Shoulder
May-June Rainfall
7.3
1.6
16.8
12.8
14.8
Toeslope
Shoulder
Normal May-June rainfall is 9.5 inches
Data from Jaynes and Kaspar
| NUE Hendrickson 5 August, 2009

11. Landscape Position Zones
LSP is an intuitive characteristic
Water runs downhill
Drier on shoulders, wetter on toeslopes
Often better relationship to yield than soil conductivity
In regions where topography was the primary soil forming factor
| NUE Hendrickson 5 August, 2009

12. Role of Reference Strip in N Rate Decisions
Studies conducted by Sawyer in 2005 and 2006
Focus on reference strip observations from these studies
Patterns observed with aerial imagery
Compare N stress outcomes with and without reference strips
| NUE Hendrickson 5 August, 2009

13. Approach 3 reference strips in each field with 240 or 270 #N/acre
Aerial images were taken between V10 and V14
Calculated GNDVI from aerial images
SPAD readings were taken within 3 days of images
| NUE Hendrickson 5 August, 2009

14. Field 1 240 N GNDVI GNDVI with upper 10% in blue
| NUE Hendrickson 5 August, 2009

15. Field 2
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16. Field 3
| NUE Hendrickson 5 August, 2009

17. Imagery patterns
Considerable variability in N stress within and between reference strips
High GNDVI values were found within all fertilized (60-180N) strips, but not within zero N strips
High GNDVI values were also found in other parts of all fields examined
High GNDVI values were often impacted more by soil variability than by reference strips
| NUE Hendrickson 5 August, 2009

18. | NUE 2009 Hendrickson 5 August, 2009

19. Data Analysis Extracted underlying GNDVI from each SPAD location
Related relative SPAD, relative GNDVI, and GNDVI relative to “best” area to yields underlying these points
Relationships across 8 studies in 2006
| NUE Hendrickson 5 August, 2009

20. R2 = 0.44
| NUE Hendrickson 5 August, 2009

21. R2 = 0.53
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22. R2 = 0.52
| NUE Hendrickson 5 August, 2009

23. Summary
Imagery (and presumably sensors) were more closely related to yields than SPAD values across fields
GNDVI relative to “best” areas was equivalent to using a reference strip
Reference strips were unnecessary for N decisions made after V10 in fields that had received significant rates of fertilizer earlier in the season (in Iowa)
Likely can’t extrapolate these results to other regions or earlier N applications
| NUE Hendrickson 5 August, 2009

24. Landscape position on N requirement
Shoulder Toeslope
Yield Potential
N Mineralization
N Loss Potential
Patterns of N stress observed will depend upon which factor dominates in a particular region or year
| NUE Hendrickson 5 August, 2009

25. Estimating N Requirement
Sensor Observes Differential:
Residual N
Mineralization
Losses of soil and fertilizer N
Crop stand and growth patterns
All are impacted by LSP or SC zones
Early N
Soil N PL
Sensing
| NUE Hendrickson 5 August, 2009

26. Estimating N Requirement
Sensor Observes Differential:
Residual N
Mineralization
Losses of soil and fertilizer N
Crop stand and growth patterns
Estimate Differential:
Losses of soil and fertilizer N
Mineralization
Crop stand and growth patterns
Early N
Both are often impacted by LSP or SC zones ?
Soil N PL
Sensing
| NUE Hendrickson 5 August, 2009

27. Linking Soil Zones to Sensors
Soil zones are likely quite stable over time
Opportunity to re-evaluate existing sensor data obtained in large field studies by acquiring soil zones
Incorporate soil zones in future sensor evaluations
Soil zones may be useful for:
Early season applications (in fields that behave consistently across years)
Modifying algorithms to reflect differential N requirements
Directing initial pass with sensors
| NUE Hendrickson 5 August, 2009

28. Use of zones to help select “best” areas
Imagery has advantage over sensors in that it provides data for entire field
Selection of “best” area during initial pass may offer sufficient assessment
Seems logical that “best” area in field will be in extreme soil zones (wettest/driest, darkest/lightest soils)
First pass in field should be through areas with most extreme soil conditions
| NUE Hendrickson 5 August, 2009

29. New 2510H Applicator Extends Sidedress Window
Clearance allows application to 30” corn
High Speed—10 mph
NH3 or UAN
| NUE Hendrickson 5 August, 2009

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