OSU Corn Algorithm

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OSU Corn Algorithm
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Can Yield Potential (similar to yield goals) be
Predicted MID-SEASON?Is it better than a
preplant N decision?
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NDVI at F5

INSEY
Days from planting to sensing, GDDgt0
Winter Wheat
Units biomass, kg/ha/day, where GDDgt0
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Predicting Yield Potential in Corn
NDVI, V8 to V10

INSEY
Days from planting to sensing
CORN
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Long-Term Winter Wheat Grain Yields, Lahoma, OK
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Response to Fertilizer N, Long-Term Winter Wheat
Experiment, Lahoma, OK
After the FACT N Rate required for MAX Yields
Ranged from 0 to 140 lbs N/ac
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Can RI be Predicted in Wheat?.... YES
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Can RI Be Predicted in Corn?... YES
MullenAgronomy Journal 95347-351 (2003)Winter
Wheat
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Improved Prediction of Yield Potential
SuperPete to the Rescue
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All GDD Class Yield Prediction Equations for Corn
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RI-NFOAYPNYP0 RI
YP0
YPN
YPN
YPMAX
RI1.5
RI2.0
Grain yield
INSEY (NDVI/days from planting to sensing)
Nf (YP0RI) YP0))/Ef

• The mechanics of how N rates are computed are
  really very simple
• Yield potential is predicted without N
• The yield achievable with added N is 1 times the
  RI
• Grain N uptake for 2 minus 1 Predicted
  Additional N Need
• Fertilizer Rate 3/ efficiency factor (usually
  0.5 to 0.7)

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INSEY works, but needs to be more robust

• Problems
• Extremely early season prediction of yield can be
  overestimated
• (Feekes 4, wheat)
• (V6, corn)
• Inability to reliably predict yield potential at
  early stages of growth should be accompanied by
  more risk averse prediction models (small slope)

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Combined

• RI (NDVI-N Rich Strip/NDVI-Farmer Practice)
• CoefA (0.323123Gdd2 - 77.8 Gdd 5406)
• CoefB -0.0003469Gdd2 0.08159Gdd - 2.73372
• YP0 (CoefA exp(CoefB NDVI-FP))
• If ((NDVI-N Rich Strip/NDVI-FP)lt 1.72)
• RI (NDVI-N Rich Strip/NDVI-FP)1.69 - 0.7
• If (RIlt1) RI1
• YPN YP0RI
• NRate ((YPN-YP0)0.0239/0.6)
• Determine based on N in the grain

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Variable Rate Technology Treat Temporal and
Spatial Variability Returns are higher but
require larger investment
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Just remember boys, you can always trust
SuperPete!
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GLOBAL WARMING
ATMOSPHERE
15-40 kg/ha
N2O NO N2
INDUSTRIAL FIXATION
LIGHTNING, RAINFALL
PLANT AND ANIMAL RESIDUES
N2 FIXATION
SYMBIOTIC
NON-SYMBIOTIC
MESQUITE RHIZOBIUM ALFALFA SOYBEAN
BLUE-GREEN ALGAE AZOTOBACTER CLOSTRIDIUM
MATERIALS WITH N CONTENT lt 1.5 (WHEAT STRAW)
MATERIALS WITH N CONTENT gt 1.5 (COW MANURE)
FERTILIZATION
10-80 kg/ha
PLANT LOSS
AMINO ACIDS
MICROBIAL DECOMPOSITION
0-50 kg/ha
NH3
AMMONIA VOLATILIZATION
IMMOBILIZATION
AMINIZATION
HETEROTROPHIC
ORGANIC MATTER
R-NH2 ENERGY CO2
BACTERIA (pHgt6.0) FUNGI (pHlt6.0)
pHgt7.0
R-NH2 H2O
FIXED ON EXCHANGE SITES
AMMONIFICATION
NH2OH
IMMOBILIZATION
R-OH ENERGY 2NH3
N2O2-
Pseudomonas, Bacillus, Thiobacillus
Denitrificans, and T. thioparus
2NH4 2OH-
MINERALIZATION NITRIFICATION
O2
NO2-
Nitrosomonas
DENITRIFICATION
NO3- POOL
NITRIFICATION
2NO2- H2O 4H
OXIDATION STATES
O2
Nitrobacter
DENITRIFICATION LEACHING
LEACHING VOLATILIZATION NITRIFICATION
NH3 AMMONIA -3 NH4 AMMONIUM -3 N2 DIATOMIC
N 0 N2O NITROUS OXIDE 1 NO NITRIC OXIDE 2 NO2-
NITRITE 3 NO3- NITRATE 5
ADDITIONS
Joanne LaRuffa Wade Thomason Shannon
Taylor Heather Lees Department of Plant and Soil
Sciences Oklahoma State University
TEMP 50F
LEACHING
LEACHING
LOSSES
OXIDATION REACTIONS
LEACHING
REDUCTION REACTIONS
pH 7.0
0-40 kg/ha