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FIELD-SCALE N APPLICATION USING CROP REFLECTANCE SENSORS Ken Sudduth and Newell Kitchen USDA-ARS Translating Missouri USDA-ARS Research and Technology.

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Presentation on theme: "FIELD-SCALE N APPLICATION USING CROP REFLECTANCE SENSORS Ken Sudduth and Newell Kitchen USDA-ARS Translating Missouri USDA-ARS Research and Technology."— Presentation transcript:

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, October 2012, Columbia, MO

2 Questions addressed 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, 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, October 2012, Columbia, MO

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

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, October 2012, Columbia, MO

6 Oran00 Rep1 Block N rate (kg ha -1 ) Yield (Mg ha -1 ) N opt Oran00 Rep3 Block N rate (kg ha -1 ) Yield (Mg ha -1 ) N opt 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, October 2012, Columbia, MO

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

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, 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 NTech Industries (now Trimble)  Crop Circle by Holland Scientific (now marketed by Ag Leader)

10 GreenSeeker

11 Crop Circle ACS-210 Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 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 Collect Reference Data Create whole-field reference map Prior to Application Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, October 2012, Columbia, MO

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

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

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

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, October 2012, Columbia, MO

19 Missouri algorithm developed from previous plot research Equations for calculating N rates (lbs N/acre) from active canopy sensors Corn Growth Stage Sensor TypeV6-V7 (1 to 1.5-ft tall corn)V8-V10 (2 to 4-ft tall corn) Crop Circle(330 x ratio target / ratio reference ) - 270(250 x ratio target / ratio reference ) GreenSeeker(220 x ratio target / ratio reference ) - 170(170 x ratio target / ratio reference )  Notes:  Maximum N rate should not exceed 220 lbs N/acre.  For V6-V7 corn, the value of ratio reference 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 ratio reference 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, 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, October 2012, Columbia, MO

22 Anhydrous Ammonia Fluid Dry N Application Hardware

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

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, 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, October 2012, Columbia, MO

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