1 / 21

Missouri algorithm for N in corn

Missouri algorithm for N in corn. Peter Scharf, Newell Kitchen, and John Lory University of Missouri and USDA-ARS. Missouri Algorithm. Based on direct empirical relationship between measured reflectance and measured optimal N rate Site characteristics

talib
Télécharger la présentation

Missouri algorithm for N in corn

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Missouri algorithm for N in corn Peter Scharf, Newell Kitchen, and John Lory University of Missouri and USDA-ARS

  2. Missouri Algorithm • Based on direct empirical relationship between measured reflectance and measured optimal N rate • Site characteristics • Very compatible with current sensor group approach • We will likely use the algorithms that will be developed from group activities

  3. Missouri Algorithm • Original calibration: Cropscan passive at V6 • Green, Red edge, Blue-green best • Green/Infrared best combination • Optimal N rate = 330 * (G/NIR)target/(G/NIR)high N – 270 • Works with either 0 or 100 N applied preplant • Tentatively applied with Crop Circle active sensor • Subsequent research agrees fairly well

  4. Relationship between optimal N rate and sensor measurements Y = 330(X) – 270

  5. Greenseeker • Values swing more widely than Crop Circle over the same range of corn N status • Need equation with smaller slope

  6. Growth stages • Original calibration was for V6 • Also use for V7 • Chlorophyll meter, sensor research show that slope decreases as season progresses • Decreased slope to 3/4 for V8 to V10

  7. Current Missouri Algorithms

  8. On-farm demos using Missouri algorithms • 7 in 2004 • 12 in 2005 • 19 in 2006 • 28 in 2007

  9. 21 with USDA Spra-Coupe

  10. 35 with producer-owned applicators

  11. 10 with retailer-owned applicators

  12. Kansas producer 2006: 4000 acres of corn fertilized in six days using high-clearance spinner, sensors, & Missouri algorithm

  13. On-farm demonstrations • 32 on-farm demonstrations 2004-2006 with producer rate & sensor variable-rate side-by-side and replicated • Average N savings = 31 lb N/acre • Average yield loss = 1.7 bu/acre • Yield & N economics • $2 to $10/ac benefit depending on prices used • Doesn’t count technology & management costs

  14. On-farm demonstrations • Complication: sensor values change during the day • Probably mainly due to changes in: • Canopy architecture • Internal leaf properties • External leaf properties

  15. Dew Rain Leaf wetness effect on sensor values

  16. Why diurnal changes in sensor values? • Leaf wetness is the only reason we’re sure of • Wet leaves are darker • Need to re-measure high-N reference when leaf wetness changes • Reference strips perpendicular to rows can make this feasible

  17. Reference strips • Perpendicular to rows? • Tried in on-farm demo in 2007 • Real-time update of high-N reference value • Worked great • Apply with 4-wheeler + spinner? • Aerial?

  18. Diurnal changes: other impacts • We may consider changing to an algorithm based on NDVI • Especially Greenseeker • Less sensitive to diurnal changes in sensor values

  19. Diurnal sensitivity of N recs: Greenseeker/cotton example NDVI-based VIS/NIR-based

  20. Diurnal sensitivity of N recs: Crop Circle/cotton example NDVI-based VIS/NIR-based

  21. Thanks!! Questions? Comments? Discussion?

More Related