1. Corn Algorithm Comparisons, NUE Workshop
CIMMYT University of Nebraska University of Missouri University of Minnesota Kansas State University LSU Iowa State University Ohio State University Virginia Tech

2. Variables Employed √ √ 168 √250 √220 √200 td √ 170, 220 Variable 56 50
Predicted yield √
SI or RI
RI adjustment
N rate based on projected yield increase
GDD’s, days
N rate function of expected NUE
Max N Rate
√ 168
√250
√220
√200 td
Max Yield
Farmer specified
300
Lim. Pre N, lowest N rate
Farmer Practice 56
½ of Total N Rate 50
Pivotal N rate
√ 170, 220
CV of plant stand
Soil moisture at sensing

3. 586 farmer’s fields 2007-2008 (3000-4000, N Rich or Ramps)
Danny Peeper, Wheeler Brothers (>500, 2006; 800, 2007)

4. Yield Potential Prediction, Corn, Ohio

5. Yield Potential Prediction, Winter Wheat, Oklahoma

6. Predicting N Responsiveness

7. Lahoma, OK, Winter Wheat
Exp. 502, N rate = (N uptake 100 lb/ac - N uptake 40 lb/ac)/0.5
Optimum N Rate (assuming 40 lbs N/ac preplant) Average Yield Avg. 60 N/ac bu/ac +/ Avg. Loss = $27.5/acre (N at $0.70/lb)

9. Mead, NE,

11. Data compiled by Dr. Robert Mullen, The Ohio State University

12. Data compiled by Dr. Robert Mullen, The Ohio State University

13. In field double-triple 12%
Home Run 4%
In field double-triple 12%
Pop fly-out 25%
In-field single 25% In-field out 15%
Pop-up out 10% In-field grounder 9%

14. SBNRC (YP0*RI = YPN) 100 Pre (100 lbs N/ac applied preplant)

15. Check yield * RI Difference in N uptake (optimum N rate observed – check yield N uptake)/0.5

16. Adjusting NUE
CORN WHEAT

17. Conclusions Soil moisture at sensing Soil temperature
Climatological probability
Combining components
Predicting mid-season NUE