Real-time Sensor Systems for Fertility Management Earl Vories Agricultural Engineer USDA-ARS Delta Center Portageville, MO Earl.Vories@ars.usda.gov Mention of trade names or commercial products is solely for purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture.
Acknowledgments Information provided by: • Christine Morgan, Alex Thomasson, Ruixiu Sui; Texas A&M Univ. • John Wilkerson, Philip Allen; Univ. of Tenn. • Newell Kitchen, Ken Sudduth; USDA-ARS (Mo.) • Peter Scharf; Univ. of Mo. • Randy Taylor; Ok. St. Univ. • Leo Espinoza; Univ. of Ark.
Why not use uniform application rates for nutrients?
Underapplication = lost yield Overapplication = leftover N in soil N underapplied N overapplied Wasted $ Environmental risk (Gulf hypoxia)
Crop N need is variable: from year to year Minnesota corn: the places that needed the most and least N were not the same in the two years G. Malzer data from Doerge (2002) Crop Mgmt. doi. 10.1094/cm-2002- 0905-01-RS
So we need to look at Variable Rate Application (VRA) Production inputs are applied on an optimum basis for the local conditions. VRA requires Knowledge of economic optimum rates at chosen management scale Ability to apply desired rate at desired scale
Imagery has shown promise as basis for VRA, but many believe that in-field sensing is the future of nutrient management • The primary benefit of sensor-based measurements is improved accuracy. • Sensors can increase sampling intensity by orders of magnitude compared to traditional methods. As a result, a significant decrease in overall error can be realized.
Sensor-Based Nutrient Management Monitor (measure) nutrient status in the field Apply supplemental nutrients at variable rates to meet crop needs
It Makes Sense • Soil Sensing • Plant Sensing
Soil Fertility/Chemistry Sensors Sense levels of nutrients important for plant growth to control fertilizer additions Macro-nutrients (Nitrogen, Potassium, Phosphorus), pH (commercially available), trace nutrients Sense compounds toxic to plants and/or bad for the environment High-throughput, on-the-go sensing is preferred to efficiently obtain data needed to map variations
Lint Yield r 2 = 0.14 r 2 = 0.59 2006 2007 r 2 = 0.26 r 2 = 0.71 2000 2000 Dryland Dryland Lint Yield, kg ha -1 1600 1600 Irrigated Irrigated 1200 1200 800 800 400 400 0 0 0 40 80 120 0 40 80 120 Soil Electrical Conductivity, mS m -1
Remote Sensing System for Plant Nitrogen Determination 60 Control Reflectance of Cotton Leaf (%) 50 N deficiency 40 30 20 10 0 250 500 750 1000 1250 1500 1750 2000 Wavelength (nm) Spectral reflectance of cotton plant canopy relates to N status of the plants 11/19/2008 13 Free Template from www.brainybetty.com Free Template from www.brainybetty.com
Missouri Reflectance Study Six N rate experiments 3 in 2006, 3 in 2007 Loamy sand, silt loam, clay each year Three commercial sensors (GreenSeeker, Crop Circle, and Cropscan) Three stages (early square, mid square, and first bloom) Revised protocol for 2008
Sensor vs. optimal N rate None of the sensors could predict optimal N rate at first square All of the sensors could predict optimal N rate at mid-square and first flower Optimal N rate would have increased profit by $43/acre relative to typical producer rate of 100 lb N/acre Required comparison to high-N area (may present problem for cotton)
Ground-Based Remote Sensing System for Plant Nitrogen Determination (Real-time management) ● Measure spectral reflectance of plant canopy and plant height ● Diagnose plant N status ● Apply what the plant needs “On -the- go” 11/19/2008 16 Free Template from www.brainybetty.com Free Template from www.brainybetty.com
Ground-Based Remote Sensing Active Reflectance Sensors • CropCircle 2 bands (amber @590nm; NIR @880nm) • GreenSeeker 2 bands (red @660nm; NIR @770nm) • Experimental Unit 4 bands (blue, green, red, NIR) 11/19/2008 17 Free Template from www.brainybetty.com Free Template from www.brainybetty.com
Ground-Based Remote Sensing System for Plant Nitrogen Determination Multi-Spectral Optical Sensor • Active optical sensor • Modulated LED light source • Measure reflectance at four wavebands Four Wavebands Blue band Green band Red band NIR band 11/19/2008 18 Free Template from www.brainybetty.com Free Template from www.brainybetty.com
Cropscan passive sensor uses ambient light (solar) Multiple sensors (wavelengths) pointing up to measure incoming radiation Same sensors pointing down to measure reflected radiation
YARA-N-Sensor (Hydro-N) Initial system was passive, but an active light system has been developed that provides multiple spectral indices. Images From: http://fert.yara.co.uk/en/crop_fertilization/advice_tools_and_services/n_sensor/index.html
• Reflectance above the row appeared sufficient for corn. • Do we need another piece of information for cotton? • Plant height (may be useful for PGR management)? • Between-the-row reflectance?
Ground-Based Remote Sensing System for Plant Nitrogen Determination Ultrasonic sensor for measuring plant height Polaroid Ultrasonic Sensor Frequency: 50 KHz; Beam angle: 12 0 ; Temp: -30 – 70 0 C Univ. of Tenn. has also built ultrasonic sensor 11/19/2008 22 Free Template from www.brainybetty.com Free Template from www.brainybetty.com
Ground-Based Remote Sensing System for Plant Nitrogen Determination Sensor transmits ultrasonic pulses Ultrasonic Sensor toward plant canopy, then waits for the echo to return from the canopy. Distance from the sensor to the canopy (D 2 ) can be determined based on the speed of sound and D 2 the time taken for the ultrasonic pulse to travel the distance from the sensor to the canopy and back to D 1 the sensor. Plant Height = D 1 -D 2 D 1 : Known D 2 : Measured D 2 = ½ Time*Sound speed 11/19/2008 23 Free Template from www.brainybetty.com Free Template from www.brainybetty.com
NDVI differed by plant population 0.85 * * 0.75 * 0.65 * * NDVI 0.55 * 0.45 * 0.35 Supplemental N applied 0.25 6/11/08 6/21/08 7/1/08 7/11/08 7/21/08 7/31/08 8/10/08 Date 16400 28700 50225 Plant height differed by plant population 40 * 35 * 30 * Plant Height (in) 25 * 20 ** 15 * 10 5 Supplemental N applied NDVI sensor 0 6/11/2008 6/21/2008 7/1/2008 7/11/2008 7/21/2008 7/31/2008 8/10/2008 Date Height sensor 16400 28700 50225
Strong relationship between NDVI and plant height (46 days after planting) 0.90 0.80 y = 0.0203x + 0.198 R 2 = 0.8154 0.70 0.60 0.50 NDVI 0.40 0.30 0.20 0.10 0.00 0 5 10 15 20 25 30 Plant Height (in)
Another Approach for Cotton Measuring NDVI directly over the row with four sensors and between the rows with three sensors Collected data from research plots and farmers’ fields on multiple dates
Estimating Canopy Closure 0.80 80% 0.70 70% 0.60 60% 0.50 50% NDVI 0.40 40% 0.30 30% 0.20 20% Over Row Row Middle 0.10 10% Ratio 0.00 0% 40 60 80 100 120 Days After Planting
Sensor Data – July 25 0.80 0.70 0.60 y = -7E-06x 2 + 0.0015x + 0.5822 0.50 R 2 = 0.4105 NDVI 0.40 0.30 0.20 y = -8E-06x 2 + 0.0018x + 0.1791 R 2 = 0.649 0.10 0.00 0 50 100 150 200 250 Applied N, lbs/ac Over Row Between Row Poly. (Over Row) Poly. (Between Row)
Great deal of on-going work aimed at developing real-time nutrient-management system (especially for nitrogen). Cotton Incorporated encouraging communication among research teams.
On-farm field-scale nitrogen/sensor demo conducted in Missouri in 2008. USDA-NRCS Conservation Innovation Grant will allow additional on-farm demonstrations.
An effective, reliable, real-time sensor systems for cotton nitrogen management should be available soon . Systems for other nutrients will follow.
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