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B. Tubana, R. Teal, K. Freeman, B. Arnall, B. Chung, O. Walsh, K. Lawles, C. Mack and W. Raun

Adjusting Mid-Season Nitrogen Fertilizer Using a Sensor-Based Optimization Algorithm to Increase Use Efficiency in Corn. B. Tubana, R. Teal, K. Freeman, B. Arnall, B. Chung, O. Walsh, K. Lawles, C. Mack and W. Raun Annual ASA Meeting, Indianapolis 9:30 am, Nov. 15, 2006. Presentation Outline.

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B. Tubana, R. Teal, K. Freeman, B. Arnall, B. Chung, O. Walsh, K. Lawles, C. Mack and W. Raun

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  1. Adjusting Mid-Season Nitrogen Fertilizer Using a Sensor-Based Optimization Algorithm to Increase Use Efficiency in Corn B. Tubana, R. Teal, K. Freeman, B. Arnall, B. Chung, O. Walsh, K. Lawles, C. Mack and W. Raun Annual ASA Meeting, Indianapolis 9:30 am, Nov. 15, 2006

  2. Presentation Outline • Technology Developed by OSU • Background of the Study • Components of the Algorithm • Methodology • Results • Conclusion

  3. Need to Improve NUE • Cereal grain NUE averages only 33% worldwide • Rise in the price of fuel and N fertilizer • Increase environmental risk

  4. Applications

  5. Success of the Technology A 15 % increase in wheat NUE was achieved compared with conventional methods (OSU 2002, Agronomy Journal 94:815).

  6. Yield Potential Equation (Teal et al., 2006)

  7. Algorithm Components Nitrogen Fertilization Optimization Algorithm (NFOA) • YP0 Estimates of corn grain yield potential using NDVI and cumulative GDD • RI N Responsiveness estimated using NDVI in the N Rich Strip and NDVI in the farmer practice or check • CV Coefficient of variation determined from NDVI sensor readings collected in each plot

  8. Components of Algorithm • YPN = (YP0*RI) • N Rate =

  9. Capability of Algorithm • YP0 does not rely on historical data but rather is a simple predictive model. This approach uses seasonally dependent data capable of predicting differing yield potentials and adjusting N rates accordingly. • YP0 changes every year as does RI.

  10. YP0 and RI are independent of one another (on-farm trials 2002-2005)

  11. CV= 23 CV= 10 RICV-NFOA Spatial variability can be masked by larger plants Do these areas have the same yield potential? NDVI= 0.60 NDVI= 0.60

  12. YP0 YP0 CV CV RI = 2.0 RI = 2.0 Grain Yield RI = 1.5 INSEY INSEY RICV-NFOA RI-NFOA RI-NFOA and RICV-NFOA YPN YPN YPmax

  13. Description of NFOA • RI-NFOA – consists of YP0 and RI YPN = YP0*RI • RICV-NFOA – consists of YP0, RI and CV

  14. Objectives • To evaluate different nitrogen fertilization optimization algorithms for prescribing mid-season fertilizer N. • To determine the optimum resolution for treating spatial variability in corn.

  15. Methodology • Established in 2004 at 3 sites (1-irrigated, 2-rainfed system) in Oklahoma. • Employed RCB Design with 3 replications

  16. Treatment Structure

  17. Results Common Flat Rate versus Algorithms at Efaw site from 2004-2006 With Preplant Nitrogen

  18. Results Common Flat Rates versus Algorithms at Efaw site from 2004-2006 Without Preplant Nitrogen

  19. Results RICV- versus RI-NFOA at Efaw from 2004-2006. With Preplant N

  20. Results On-average * CFR : Common Flat Rate

  21. Summary • NUE was generally higher when mid-season N rates were generated by NFOA compared with flat farmer rates. • Increased NUE was attributed to the lower N rates applied.

  22. Summary • Use of RI NFOA resulted in a higher increase in NUE than RICV NFOA. • There was limited benefit of treating spatial variability at the high resolution (0.34 m2, RICV algorithm). • NFOA approaches didn’t project high N rates that did not affect increased yields.

  23. Conclusions • Functional N rate algorithm developed for corn can increase NUE. • Applications - Sensor Based N Rate Calculator - Variable Rate Technology (0.4m2)

  24. THANK YOU! www.nue.okstate.edu

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