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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 into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO
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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, 10-11 October 2012, Columbia, MO
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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
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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, 10-11 October 2012, Columbia, MO
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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
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Oran00 Rep1 Block6 0 4 8 12 16 0100200300 N rate (kg ha -1 ) Yield (Mg ha -1 ) N opt Oran00 Rep3 Block26 0 4 8 12 16 0 100200 300 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, 10-11 October 2012, Columbia, MO
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Automating plant-based N sensing Passive (sunlight) crop sensors Active light source crop sensors Remote sensing Chlorophyll meter
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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
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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)
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GreenSeeker
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Crop Circle ACS-210 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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Sensor outputs Raw reflectance data – visible and NIR Ratio data – Visible/NIR Vegetation index data, e.g. NDVI: NDVI = (NIR – visible)/(NIR + visible)
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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
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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, 10-11 October 2012, Columbia, MO
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Real-time sensing and control Collect Reference Data Create whole-field reference map Prior to Application
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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
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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?
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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
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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 ) - 200 GreenSeeker(220 x ratio target / ratio reference ) - 170(170 x ratio target / ratio reference ) - 120 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.
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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
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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
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Anhydrous Ammonia Fluid Dry N Application Hardware
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Anhydrous Ammonia Dry N Application Hardware Fluid However… Not all application hardware can accurately provide the ~ 4:1 range in rates needed
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Commercial options are available
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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
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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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