1. OSU Corn Algorithm

2. Can Yield Potential (similar to “yield goals”) be Predicted MID-SEASON
Can Yield Potential (similar to “yield goals”) be Predicted MID-SEASON? Is it better than a preplant N decision?

3. = Winter Wheat NDVI at F5 INSEY
Days from planting to sensing, GDD>0
Winter Wheat
Units: biomass, kg/ha/day, where GDD>0

4. Predicting Yield Potential in Corn
NDVI, V8 to V10 =
INSEY
Days from planting to sensing
CORN

5. Long-Term Winter Wheat Grain Yields, Lahoma, OK

6. 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

7. Can RI be Predicted in Wheat?.... YES

8. Can RI Be Predicted in Corn?... YES
Mullen Agronomy Journal 95: (2003) Winter Wheat

9. Improved Prediction of Yield Potential SuperPete to the Rescue

10. All GDD Class Yield Prediction Equations for Corn

16. The mechanics of how N rates are computed are really very simple
RI-NFOA YPN=YP0 * RI
YPN
YPN
YP0
YPMAX
RI=1.5
Grain yield
RI=2.0
INSEY (NDVI/days from planting to sensing)
Nf = (YP0*RI) – 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)

17. 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)

18. Combined RI = (NDVI-N Rich Strip/NDVI-Farmer Practice)
CoefA = ( *Gdd * Gdd )
CoefB = *Gdd *Gdd
YP0 = (CoefA * exp(CoefB * NDVI-FP))
If ((NDVI-N Rich Strip/NDVI-FP)< 1.72)
RI = (NDVI-N Rich Strip/NDVI-FP)*
If (RI<1) RI=1
YPN = YP0*RI;
NRate = ((YPN-YP0)*0.0239/0.6)
Determine based on %N in the grain

19. Variable Rate Technology Treat Temporal and Spatial Variability Returns are higher but require larger investment

20. Just remember boys, you can always trust SuperPete!

21. OXIDATION STATES ATMOSPHERE N2O NO N2 INDUSTRIAL FIXATION LIGHTNING,
GLOBAL WARMING
15-40 kg/ha
N2O NO N2
INDUSTRIAL
FIXATION
LIGHTNING,
RAINFALL
N2 FIXATION
PLANT AND ANIMAL
RESIDUES
HABER BOSCH
3H2 + N NH3
(1200°C, 500 atm)
SYMBIOTIC
NON-SYMBIOTIC
MESQUITE
RHIZOBIUM
ALFALFA
SOYBEAN
BLUE-GREEN ALGAE
AZOTOBACTER
CLOSTRIDIUM
MATERIALS WITH N
CONTENT > 1.5%
(COW MANURE)
MATERIALS WITH N
CONTENT < 1.5%
(WHEAT STRAW)
10-80 kg/ha
FERTILIZATION
PLANT
LOSS
AMINO
ACIDS
MICROBIAL
DECOMPOSITION
0-50 kg/ha
NH3
IMMOBILIZATION
AMMONIA
VOLATILIZATION
AMINIZATION
ORGANIC
MATTER
HETEROTROPHIC
R-NH2 + ENERGY + CO2
BACTERIA (pH>6.0)
FUNGI (pH<6.0)
R-NH2 + H2O
pH>7.0
NH2OH
AMMONIFICATION
FIXED ON
EXCHANGE
SITES
IMMOBILIZATION
R-OH + ENERGY + 2NH3
MICROBIAL/PLANT
SINK
Pseudomonas, Bacillus,
Thiobacillus Denitrificans,
and T. thioparus
N2O2-
2NH4+ + 2OH-
MINERALIZATION
+ NITRIFICATION
+O2
NO2-
Nitrosomonas
DENITRIFICATION
NO3-
POOL
NITRIFICATION
OXIDATION STATES
2NO2- + H2O + 4H+
DENITRIFICATION
LEACHING
LEACHING
VOLATILIZATION
NITRIFICATION
Nitrobacter
+ O2
NH3 AMMONIA -3
NH4+ AMMONIUM -3
N2 DIATOMIC N 0
N2O NITROUS OXIDE 1
NO NITRIC OXIDE 2
NO2- NITRITE 3
NO3- NITRATE 5
Joanne LaRuffa
Wade Thomason
Shannon Taylor
Heather Lees
Department of Plant and Soil Sciences
Oklahoma State University
ADDITIONS
TEMP 50°F
LEACHING
LEACHING
LOSSES
LEACHING
OXIDATION REACTIONS
pH 7.0
REDUCTION REACTIONS
0-40 kg/ha