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Field-Scale N Application Using Crop Reflectance Sensors

Field-Scale N Application Using Crop Reflectance Sensors. Ken Sudduth and Newell Kitchen USDA-ARS. Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO. Questions addressed in this presentation.

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Field-Scale N Application Using Crop Reflectance Sensors

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  1. Field-Scale N Application Using Crop Reflectance Sensors Ken Sudduth and Newell Kitchen USDA-ARS Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

  2. Questionsaddressed in this presentation • Why the reflectance sensor approach? • How to implement it? • What are some results from Missouri research? • What are additional considerations? Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

  3. Why the reflectance sensor approach? • Timing • Temporal variability • Spatial variability • Automation Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

  4. V7-V10 Application can be synchronized to time of maximum crop need 30% Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO Adapted from Schepers et al., NE, U.S.A.

  5. Temporal variability in climate – crop – soil interaction Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

  6. Oran00 Rep1 Block6 16 12 Yield (Mg ha-1) 8 Nopt 4 0 0 100 200 300 300 100 200 N rate (kg ha-1) Oran00 Rep3 Block26 16 12 8 Nopt 4 Yield (Mg ha-1) 0 0 N rate (kg ha-1) Spatial variability in optimum N rate 32% of fields had within-field variation in EONR ≥ 100 lbs N/acre. Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

  7. Passive (sunlight) crop sensors Chlorophyll meter Active light source crop sensors Remote sensing Automating plant-based N sensing

  8. Implementing N sensing with active crop canopy reflectance sensors • Sensors • Real-time sensing and control system • Algorithm • Application hardware Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

  9. Active reflectance sensors By using an internal light source, these sensors eliminate problems with sun angle and cloud variations • GreenSeeker by NTechIndustries (now Trimble) • Crop Circle by Holland Scientific (now marketed by Ag Leader)

  10. GreenSeeker

  11. Crop CircleACS-210 Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

  12. Sensor outputs • Raw reflectance data – visible and NIR • Ratio data – Visible/NIR • Vegetation index data, e.g. NDVI: NDVI = (NIR – visible)/(NIR + visible)

  13. Non-N-limiting reference area • Reflectance from a non-N-limiting reference strip or area is used to standardize the reflectance from the application area • Requires N application to part of the field prior to sidedress

  14. Real-time sensing and control Prior to Application Collect Reference Data Create whole-field reference map Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

  15. Real-time sensing and control Prior to Application Collect Reference Data Create whole-field reference map

  16. Real-time sensing and control Prior to Application Sensor 1 Sensor 2 Sensor 3 Sensor 4 Collect Reference Data Select and/or Combine Sensor Outputs Create whole-field reference map Spatial or time-base filtering Get Reference Value at Current Point Get Current Position by GPS

  17. Real-time sensing and control Prior to Application Sensor 1 Sensor 2 Sensor 3 Sensor 4 Collect Reference Data Select and/or Combine Sensor Outputs Create whole-field reference map Spatial or time-base filtering Get Reference Value at Current Point N Recommendation Algorithm Smoothing, Deadband, Hysteresis Valve Control Output So what about that algorithm? Get Current Position by GPS Application System

  18. Algorithms, algorithms, and more algorithms……. • Research groups around the country have developed algorithms : • Missouri • Oklahoma • Nebraska • Virginia • etc…. • There is ongoing work to test these algorithms under a variety of conditions • Can we get to a common algorithm? Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

  19. Missouri algorithm developed from previous plot research • Notes: • Maximum N rate should not exceed 220 lbs N/acre. • For V6-V7 corn, the value of ratioreference should not exceed 0.37 for Crop Circle and 0.30 for GreeenSeeker. Set this as a ceiling. • For V8-V10 corn, the value of ratioreference should not exceed 0.25 for Crop Circle and 0.18 for GreeenSeeker. Set this as a ceiling.

  20. Missouri algorithm graphically Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

  21. Sensors+System+ Algorithm Integrated systems are available =Confusion? Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

  22. Dry N Application Hardware Fluid Anhydrous Ammonia

  23. Fluid However… Not all application hardware can accurately provide the ~ 4:1 range in rates needed Dry N Application Hardware Anhydrous Ammonia

  24. Commercial options are available

  25. Fields and situations most suited for sensor-based variable rate N application • Fields with extreme variability in soil type • Fields experiencing a wet spring or early summer (loss of applied N) and where additional N fertilizer is needed • Fields that have received recent manure applications • Fields receiving uneven N fertilization because of application equipment failure • Fields coming out of pasture, hay, or CRP management • Fields of corn-after-corn, particularly when the field has previously been cropped in a different rotation • Fields following a droughty growing season Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

  26. Risks, concerns, and considerations • Technical aptitude/ability • Suitability of N application hardware • Narrow window for application without high-clearance equipment • Balance between meeting early-season N need and crop stress detection • Suitability of a single reference for a large, variable field • Algorithm? • How many, and which type of sensor? Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

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