1. Predicting Yield Potential, 2007

2. Can Yield Potential (similar to “yield goals”) be Predicted MID-SEASON
Can Yield Potential (similar to “yield goals”) be Predicted MID-SEASON? 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. 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)

11. 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)

12. NDVI and days from planting to sensing where GDD>0 interact with one another Model includes > 2800 observations (1996 to present)

13. Response Mean
Root MSE
R-Square
Coefficient of Variation
Type I Sum
Regression DF of Squares R-Square F Value Pr > F
Linear <.0001
Quadratic <.0001
Crossproduct <.0001
Total Model <.0001
Sum of
Residual DF Squares Mean Square
Total Error
Standard from Coded
Parameter DF Estimate Error t Value Pr > |t| Data
Intercept <
days
ndvi <
days*days
ndvi*days <
ndvi*ndvi <

14. 47-66 days, GDD>0

15. 67-95 days, GDD>0

16. days, GDD>0

17. days, GDD>0

18. days, GDD>0

20. 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)

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

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