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Missouri algorithm for N in corn. Peter Scharf, Newell Kitchen, and John Lory University of Missouri and USDA-ARS. Missouri Algorithm. Based on direct empirical relationship between measured reflectance and measured optimal N rate Site characteristics

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missouri algorithm for n in corn

Missouri algorithm for N in corn

Peter Scharf, Newell Kitchen, and John Lory

University of Missouri and USDA-ARS

missouri algorithm
Missouri Algorithm
  • Based on direct empirical relationship between measured reflectance and measured optimal N rate
    • Site characteristics
  • Very compatible with current sensor group approach
    • We will likely use the algorithms that will be developed from group activities
missouri algorithm3
Missouri Algorithm
  • Original calibration: Cropscan passive at V6
    • Green, Red edge, Blue-green best
    • Green/Infrared best combination
  • Optimal N rate = 330 * (G/NIR)target/(G/NIR)high N – 270
  • Works with either 0 or 100 N applied preplant
  • Tentatively applied with Crop Circle active sensor
    • Subsequent research agrees fairly well
greenseeker
Greenseeker
  • Values swing more widely than Crop Circle over the same range of corn N status
  • Need equation with smaller slope
growth stages
Growth stages
  • Original calibration was for V6
    • Also use for V7
  • Chlorophyll meter, sensor research show that slope decreases as season progresses
    • Decreased slope to 3/4 for V8 to V10
on farm demos using missouri algorithms
On-farm demos using Missouri algorithms
  • 7 in 2004
  • 12 in 2005
  • 19 in 2006
  • 28 in 2007
slide12
Kansas producer 2006: 4000 acres of corn fertilized in six days using high-clearance spinner, sensors, & Missouri algorithm
on farm demonstrations
On-farm demonstrations
  • 32 on-farm demonstrations 2004-2006 with producer rate & sensor variable-rate side-by-side and replicated
  • Average N savings = 31 lb N/acre
  • Average yield loss = 1.7 bu/acre
  • Yield & N economics
    • $2 to $10/ac benefit depending on prices used
    • Doesn’t count technology & management costs
on farm demonstrations14
On-farm demonstrations
  • Complication: sensor values change during the day
  • Probably mainly due to changes in:
    • Canopy architecture
    • Internal leaf properties
    • External leaf properties
why diurnal changes in sensor values
Why diurnal changes in sensor values?
  • Leaf wetness is the only reason we’re sure of
  • Wet leaves are darker
  • Need to re-measure high-N reference when leaf wetness changes
  • Reference strips perpendicular to rows can make this feasible
reference strips
Reference strips
  • Perpendicular to rows?
    • Tried in on-farm demo in 2007
    • Real-time update of high-N reference value
    • Worked great
  • Apply with 4-wheeler + spinner?
  • Aerial?
diurnal changes other impacts
Diurnal changes: other impacts
  • We may consider changing to an algorithm based on NDVI
    • Especially Greenseeker
  • Less sensitive to diurnal changes in sensor values
slide21

Thanks!!

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