1. Missouri algorithm: Design & objectives Peter Scharf University of Missouri Peter Scharf Newell Kitchen, Ken Sudduth, Glenn Davis, John Lory, Vicky Hubbard, Kent Shannon, Harlan Palm

2. On the way here, I saw a lot of money laying on the ground!!

3. Missouri Algorithm: Objectives 1.Don’t leave money laying on the ground –Supply enough N to the crop to support full yield –Don’t apply N that the crop doesn’t need 2.Don’t let N escape from fields to water

4. Crop N need is variable Twenty on-farm N rate experiments in Missouri, corn after soybean, no manure Most profitable N rates were 109, 114, 175, 0, 90, 190, 244, 63, 119, 300, 0, 146, 146, 180, 52, 175, 112, 149, 136, 114 lb N/acre

5. Crop N need is variable: Missouri lb/ac

6. Crop N need is variable: Minnesota

7. Overapplication = leftover N in soil N underapplied N overapplied Wasted $ Environmental risk

8. Mouth of Mississippi River Huge algal bloom

9. Spatially intensive diagnosis is needed How?

10. Diagnosing where to put more N Predictor % of variability in N need explained Yield2 to 20 Soil nitrate17 to 25 Soil N quick tests0 to 18 Soil conductivity8 Corn color53 to 77

11. Sensor advantages over other color measurements Immediate—no waiting, minimal interference from weather Works earlier than remote sensing Accuracy probably better than remote sensing Manages variability better than chlorophyll meter

12. Missouri algorithm design: Just an empirical relationship John Lory and I: initial calibration with Cropscan Newell Kitchen et al: more recent field- scale calibration of Greenseeker and Crop Circle Multi-state (country) data from this group

13. Missouri Algorithm: Objectives, Set 2 1.Deal with spatial variability in N need 2.Support producer, retailers, consultants in planned sidedress operations from V6 to V16 3.Support producer, retailers, consultants in rescue N applications when previously applied N has been lost

14. Supporting producers in planned sidedress operations using sensors 26 demo fields in 2007 ( ) 61 demo fields 2004-2007 Nearly 30 demo fields 2008, including first cotton field

15. Color sensors can be used for sidedressing anhydrous… sensors Computer in cab reads sensors, calculates N rate, directs controller Controller runs ball valve to change fertilizer rate

16. …or sidedressing solution

17. …or with a high-clearance spinner

18. …with a big sprayer

19. …or a big injector

20. On-farm sensor demos 2004-2007 N rate system Average yield Average N rate Producer rate 157 Sensor- controlled $ to sensor

21. On-farm sensor demos 2004-2007 N rate system Average yield Average N rate Producer rate 157 Sensor- controlled 156 $ to sensor

22. On-farm sensor demos 2004-2007 N rate system Average yield Average N rate Producer rate 157 Sensor- controlled 156 $ to sensor-$3

23. On-farm sensor demos 2004-2007 N rate system Average yield Average N rate Producer rate 157145 Sensor- controlled 156 $ to sensor-$3

24. On-farm sensor demos 2004-2007 N rate system Average yield Average N rate Producer rate 157145 Sensor- controlled 156123 $ to sensor-$2

25. On-farm sensor demos 2004-2007 N rate system Average yield Average N rate Producer rate 157145 Sensor- controlled 156123 $ to sensor-$2+$15 Overall: +$13/ac to sensors

27. Sensor Benefits: Make sure enough N is appliedMake sure enough N is applied Avoid unneeded N application Avoid unneeded N application

30. N application to head- high corn N rate map June 20, 2007

31. 129 bu/ac 149 bu/ac High-N reference area 115 175

32. Sensor Benefits: Make sure enough N is appliedMake sure enough N is applied Avoid unneeded N application Avoid unneeded N application

35. August 1 Aerial Photo after the June 13 UAN Application

36. 215.4212.1204.2212.4215.5204.9206.6 214.1208.0208.5206.6 211.6205.4 Variable Fixed Avg Bu/A 208.6 210.2

37. 2008: Our first cotton demo