UTILIZATION OF CROP SENSORS TO DETECT COTTON GROWTH AND N NUTRITION

1. UTILIZATION OF CROP SENSORS TO DETECT COTTON GROWTH AND N NUTRITION Tyson B. Raper, Jac J. Varco, Ken J. Hubbard, and Brennan C. Booker Plant and Soil Science Department Mississippi State University

2. INTRODUCTION • N in cotton production • Recent increase in fertilizer costs • Deficiency limits yield and lowers quality • Excess N causes • rank growth • boll rot • difficulty in harvesting • increased need for growth regulators, insecticides, and defoliants • Variable Rate N • Increase Nitrogen Use Efficiency (NUE) • Decrease environmental pollution

3. INTRODUCTION • Ground-Based Sensors • Provide real-time cotton biomass and greenness • Fertilize response to crop reflectance • Need a more thorough understanding of relationship between canopy reflectance, cotton growth, and N nutrition.

4. OBJECTIVE • Examine the effectiveness of a ground-based sensor to predict cotton • Plant growth • Leaf N

5. METHODS • Location • Plant Science Research Farm, Mississippi State, MS • Randomized complete block design • 4 Treatments x 4 Replications • 12 rows • 125’ long • 3 10’ alleys • 38” row spacing • 4 sub-locations Courtesy: Web Soil Survey 2009

6. METHODS (CONT.) • Treatment • 0, 40, 80, and 120 lb N/acre in a split-application • Planting (50%) • Early square (50%) • Cultural • No-till on beds • DPL BG/RR 445 • No growth regulator applied • Low pest thresholds established and maintained

7. METHODS (CONT.) • Data Collection • Reflectance • YARA N Sensor (YARA International ASA, Oslo, Norway)

8. METHODS (CONT.) • YARA N Sensor • Tractor mounted spectrometer • Wavelength Channels: 5, user selectable* • Wavelength range: 450-900 nm • Optical inputs: 4 reflectance, 1 irradiance • Acquisition interval: 1 second • Area scanned: 50-100 m²/s • Positioning Data: Trimble Pro XR • Speed: 3.5 mph • Bandwidth= ±5 nm Source: YARA (Hydro Agri), tec5Hellma

9. METHODS (CONT.)

10. METHODS (CONT.) • Data Collection • Reflectance (cont.) • YARA N Sensor • Set 76” above soil • Sense entire field • Views rows 2, 3, 4, 9, 10, 11 • Data Processing • Sub-plot locations • Center of 15’ buffer • 4 points selected

11. METHODS (CONT.) • Data Collection • Sub-Location Plant Data • Plant Height • 5 measured per sub-location • Leaf Sample • 5 recently matured per sub-location • % Leaf N • Whole Plant Sample • Prior to defoliation • Yield, total N uptake

12. Physiological Stages Pre-Square Early Square 2nd Week of Square 3rd Week of Square Early Flower 2nd Week of Flowering Peak flower METHODS (CONT.) Sensing / Sampling Stages

13. RESULTS

14. RESULTS

15. RESULTS

16. GNDVI vs PLANT HEIGHT

17. GNDVI vs LEAF N

18. GNDVI vs LEAF N

19. GNDVI vs LEAF N

20. GNDVI vs LEAF N

21. RED EDGE INFLECTION • First Derivative of Reflectance Signature

22. RED EDGE INFLECTION • REIP calculated on a per plot basis • Gaussian 4 Parameter Peak Equation • Utilize l • 700 • 710 • 720 • 740

23. REIP vs LEAF N

24. REIP vs LEAF N

25. REIP and NDVI

26. CONCLUSIONS • GNDVI relationships with leaf N and plant height improve through to peak flower. • Consistency across growing seasons supports the utility of crop reflectance. • GNDVI and NDVI have the potential to be effective measurements of plant growth in cotton. • REIP has the potential to be an effective measurement of N status in cotton. • These results support previous REIP publications (Buscaglia et al., 2002; Fridgen et al., 2004).

27. Questions?