Landscape Position Zones and Reference Strips

1. Landscape Position Zones and Reference Strips Larry Hendrickson

2. 4 1400 ft 3 2 1300 ft 1 1200 ft 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 | NUE 2009 Hendrickson 5 August, 2009

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

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

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

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

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

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

9. Iowa Corn Yield 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 2009 Hendrickson 5 August, 2009

10. Iowa Soybean Yield 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 2009 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 2009 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 2009 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 2009 Hendrickson 5 August, 2009

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

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

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

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

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

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