1 / 25

Challenges to sensor-based N-Management for Cotton

E.M. Barnes 1 , T. Sharp 2 , J. Wilkerson 3 , Randy Taylor 2 , Stacy Worley 3. Challenges to sensor-based N-Management for Cotton. 1 Cotton Incorporated, Cary NC 2 Oklahoma State University, Okmulgee & Stillwater 3 University of Tennessee, Knoxville. Acknowledgements.

cherokee-guerrero
Download Presentation

Challenges to sensor-based N-Management for Cotton

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. E.M. Barnes1, T. Sharp2, J. Wilkerson3, Randy Taylor2, Stacy Worley3 Challenges to sensor-based N-Management for Cotton 1Cotton Incorporated, Cary NC 2Oklahoma State University, Okmulgee & Stillwater 3University of Tennessee, Knoxville

  2. Acknowledgements • Tom Clarke, Glenn Fitzgerald, P. Pinter • USDA, ARS, Arid Land Research Center • Maricopa, AZ • Pete Waller, University of Arizona • Paul Colaizzi, USDA, ARS – Lubbock, TX • Julio Haberland – Chile • Mike Kostrzewski - Arizona

  3. Outline • Cotton 101 • Why cotton interest in sensors is high • The challenges of Cotton • One proposed solution

  4. Cotton 101

  5. The Cotton Belt Data from USDA, NASS

  6. Cotton & Nitrogen • Perennial plant managed as an annual • Indeterminate flowering pattern • ~50 lbs-N per lint bale (1 bale = 480 lbs) • Over-application of N: • Energy partition to vegetative vs. reproductive development • Large plants prevent efficient harvest • Growth regulators applied to control vegetative development

  7. Why interest in sensors now? • Cost of N • Producers receiving In-Time images • And now Deere imagery through Jimmy Sanders • On-farm tests done in Alabama to use GreenSeekerTM to apply growth regulator (PIX) • Cotton researchers joining in

  8. 4.5 gpa, 24 fl. oz Prep, 1.5 dry oz Dropp 1 Lowest Biomass 5.0 gpa 2 6.0 gpa 3 7.0 gpa 4 8.0 gpa 5 8.0 gpa 6 8.0 gpa, 42.67 fl. oz Prep , 2.67 dry oz Dropp 7 Highest Biomass Variable Rate Defoliation Location: Arkansas Delta Crop: Cotton Field Size: 339.5 Acres Imagery Acquired: September 7, 2004 VR Defoliation Applied: September 14, 2004 Notes: This prescription was applied using a hydraulic aerial VR system. The consultant was able to achieve a one-time defoliation on this field, for $15.94/A in chemical.

  9. 1 Lowest Biomass 2 3 4 5 6 7 Highest Biomass 0 lbs./A 0 lbs./A 100 lbs./A 100 lbs./A 100 lbs./A 0 lbs./A 0 lbs./A Variable Rate Nitrogen Top-Dressing Location: Arkansas Delta Crop: Cotton Field Size: 156.53 A’s Imagery Acquired: July 5, 2004 VR Fertilizer Applied: July 13, 2004 Notes: This prescription was applied using a variable rate equipped high clearance spreader. Unity [16% nitrogen (N)] was applied midseason, to supplement areas in the field which had become N deficient. Classes 1 and 2 were beyond salvaging with the additional N, while classes 6 and 7 required no additional N.

  10. Challenges

  11. Wind blows & Index Changes + Heliotropic; + New Growth

  12. Sample data set • 1999 Growing season • AGIIS sensor (calibration panel every minute) • Water and Nitrogen treatments

  13. 1999 CCCI (relative to WN) Last N Application Green Boll Open Boll Squares

  14. Yield = -m*NDVI + C ?

  15. Possible solution?

  16. Combining Data • Use NDVI / Greenseeker as a “biomass” sensor • Historic yield maps.

  17. Concept

  18. Application

  19. Theoretical Example

  20. Combined

  21. Conclusions • Cotton can be tricky to manage • Efforts to apply sensors for N management are increasing rapidly • Hope to learn from work here most efficient methods to develop cotton N management strategies

  22. AgIIS (Agricultural Irrigation Imaging System) Bands (nm):Green (555), Red (670), Edge (720), NIR (790)IRT

  23. October 1, 1999 AgIIS Field during 1999 Cotton Season

  24. CCCI A C CCCI = (C-B)/(A-B) B

  25. 1999 RVI (relative to WN) Last N Application Green Boll Open Boll Squares

More Related