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

INTRODUCTION Recent increase in fertilizer costs 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

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.

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

Courtesy: Web Soil Survey 2009 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

METHODS (CONT.) Treatment Cultural 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

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

Source: YARA (Hydro Agri), tec5Hellma 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

METHODS (CONT.)

METHODS (CONT.) Data Collection Reflectance (cont.) Data Processing 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

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

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

RESULTS

RESULTS

RESULTS

GNDVI vs PLANT HEIGHT

GNDVI vs LEAF N

GNDVI vs LEAF N

GNDVI vs LEAF N

GNDVI vs LEAF N

RED EDGE INFLECTION First Derivative of Reflectance Signature

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

REIP vs LEAF N

REIP vs LEAF N

REIP and NDVI

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).

Questions?