Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Presenters: Hoon Cheong, Will Heart, Lisa Watkins, & Harry Yoo Faculty Advisor: Dr. George Donohue Sponsor: West Rhode Riverkeeper Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Bo?om Line: Up Front The purpose of this project is to use the Upper Chesapeake Bay in Maryland as a case study in order to design a system for monitoring nitrogen and phosphorus levels on farms while incenCvizing farmers to increase their uHlizaHon of Best Management PracHces in an effort to reduce polluHon from agricultural runoff -  Recommend using a manned aircraW with a mulHspectral imagery system -  Test a proof of concept with this monitoring system in conjuncHon with a category-­‐based incenHve system framework -  If the proof of concept is successful, move to a UAV pla[orm for sustained monitoring operaHons IncenHves Offered to Farmers Farmer Implement Best Management PracHces 2 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Riverkeeper Context & Stakeholders 3 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Water PolluCon: Excess nutrients and algal blooms -  AquaHc ecosystem is damaged -  Poor water quality deters recreaHonal use -  Hurts small businesses relying on the waters The excess nutrients cause algal blooms Freshwater with excess nitrogen and phosphorus layers over saltwater x
x
x
Fish and other organisms are stressed and die Algae dies and decomposes DecomposiHon deprives saltwater of oxygen creaHng a dead zone 4 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Where excess nitrogen and phosphorus come from Point Source PolluCon Nonpoint Source PolluCon Discernible, confined, and discrete Spread over a wide area from uncontrolled sources Examples: -­‐  Pipes, ditches, channels -­‐  Containers, floaHng vessels -­‐  Concentrated animal feeding operaHons Examples: -­‐  Agricultural Runoff -­‐  Industrial Runoff -­‐  Urban Runoff DefiniHon Source: water.epa.gov 5 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Agriculture is the biggest contributor of excess nutrients in the Maryland region of the Chesapeake Bay based on 2013 data 18,000,000 16.8 mil 16,000,000 14,000,000 12,000,000 10,000,000 lbs 8,000,000 6,000,000 4,000,000 Agriculture Agriculture-­‐
Regulated Forest Non-­‐Tidal Water DeposiCon Onsite Regulated Stormwater Urban Wastewater Source Data: stat.chesapeakebay.net 6 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Phosphorus Nitrogen Phosphorus Nitrogen Phosphorus Nitrogen Phosphorus Nitrogen Phosphorus Nitrogen Phosphorus Nitrogen Phosphorus Nitrogen Phosphorus Nitrogen Nitrogen 0 Phosphorus 1.5 mil 2,000,000 Wastewater-­‐
CSO Agricultural Best Management PracCces (BMPs) are various systems and methods that currently exist to reduce excess nutrients in agricultural runoff Livestock waste management reduces runoff from manure Nutrient management gives crops no more ferClizer than needed Buffers absorb nutrients before they enter water ConservaCon Cllage retains nutrients Nutrients absorbed by cover crops Without cover crops, nutrients are exported Though BMPs are available opCons for farmers, they may or may not be uClized 7 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay All elements have a unique spectral signature Carbon Nitrogen Neon Sunlight is absorbed and reflected by objects on the ground Magnesium Data is measured in intensity vs. wavelength Spectrometers capture reflectance MulCspectral and Hyperspectral Imaging are two types of spectral imaging used today 8 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2014 Design of an Agricultural Runoff Monitoring and Reward System for the WRR Watershed Stakeholders InteracCons Primary Stakeholder Secondary Stakeholder 9 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Gap Analysis: Nutrient levels in the Upper Chesapeake Bay from agriculture and the EPA’s Total Maximum Daily Load (TMDL) goals Actual Nitrogen Load VS Final Target Nitrogen Load 25,000,000 Hurricane Irene Hurricane Sandy 20,000,000 15,000,000 7,050,703 lbs TMDL Established 10,000,000 Final TMDL Goal Actual Nitrogen Load Final Target Nitrogen Load 5,000,000 0 2009 Baseline 2010* 2011 Progress 2012 Progress 2013 Progress Actual Phosphorus Load VS Final Target Phosphorus Load 2,000,000 Hurricane Irene Goal by 2025: -­‐  9,770,000 lbs Nitrogen -­‐  550,000 lbs Phosphorus Hurricane Sandy 1,500,000 1,000,000 956,596 lbs TMDL Established Actual Phosphorus Load Final Target Phosphorus Load 500,000 Final TMDL Goal 0 2009 Baseline 2010* 2011 Progress 2012 Progress Source Data: stat.chesapeakebay.net; Chesapeake Bay Watershed ImplementaHon Plan 10 2013 Progress * No data captured for 2010 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Problem & Need 11 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Problem Statement The EPA’s Clean Water Act currently addresses polluHon issues for point sources such as pipes, channels, and concentrated animal feeding operaHons. However… Addressing nonpoint sources such as agricultural runoff is sHll in an early stage. 12 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Need Statement In order to reduce pollutants from entering the Maryland Watershed, systems must be introduced to posiCvely encourage farmers to adopt Best Management PracHces (BMPs) for reducing nutrient pollutants in agricultural runoff. A monitoring system is needed to measure the progress of these efforts. “If you can’t measure it, you can’t improve it” -­‐ Old Management Adage 13 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Requirements & CONOP 14 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Concept of OperaCons FuncConal Requirements The reward system shall posiHvely encourage farmers to introduce Beper Management FR.1 PracHces (BMPs) The monitoring system shall detect nitrogen and/or phosphorus levels FR.2 FR.3 The monitoring system shall be non-­‐intrusive to farmers FR.4 The monitoring system shall be remote Monitoring System Farmer BMP Farmer receives incenHve opHons based on N and P concentraHon on farm IncenCve System Green <IncenHves OpHons> Yellow <IncenHves OpHons> Red <IncenHves OpHons> Riverkeeper Riverkeeper assesses N and P levels on farm Farmer implements BMPs to reduce N and P runoff 15 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2014 Design of an Agricultural Runoff Monitoring and Reward System for the WRR Watershed Design AlternaCves 16 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Monitoring System AlternaCves Satellite To monitor farmland, a vehicle pla[orm will be coupled with a spectral imagery subsystem Ex: DEIMOS-­‐1 $0.11/km2 Note: Dollar values indicate approximate total system acquisiHon cost Manned Aerial Vehicle (MAV) Ex: Cessna 152 MulC: $38,505 Hyper: $67,419 Unmanned Aerial Vehicle (UAV) RotorcraW Ex: X8-­‐M MulC: $23,895 Hyper: $54,509 Fixed-­‐Wing Ex: Aero-­‐M MulC: $23,895 Hyper: $54,509 Spectral Imagery Subsystem MulCspectral Ground Vehicle Ex: ADC Lite Ex: XUV-­‐550 MulC: $17,295 Hyper: $46,909 Hyperspectral Ex: Pika II 17 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay IncenCve System AlternaCves Nitrogen & Phosphorus Report Overview of nutrient concentraHon on farms Awareness Report ConsolidaHon of federal and state programs beneficial to farmers TMDL Goal Membership Card Discount program with local area businesses Farmer’s Insurance LegislaCve Change Append Farmer’s Insurance to cover risks of implemenHng new BMPs Assistance Program LegislaCve Change Create assistance program for VRA and Manure Digesters 18 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay IncenCve System AlternaCves Nitrogen & Phosphorus Report Overview of nutrient concentraHon on farms Awareness Report ConsolidaHon of federal and state programs beneficial to farmers TMDL Goal Membership Card Discount program with local area businesses Farmers Insurance LegislaCve Change Append Farmers Insurance to cover risks of implemenHng new BMPs Assistance Program LegislaCve Change Create assistance program for VRA and Manure Digesters Current Federal Crop Insurance CorporaHon (FCIC) insures farmers when: •
•
Proposed Extend policy to provide coverage for BMP implementaHon. Actual yields << Avg expected yields
Losses incur from unpreventable variables such as pest, fires, and weather factors
Market price fluctuates •
19 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay IncenCve System AlternaCves Nitrogen & Phosphorus Report Overview of nutrient concentraHon on farms Variable Rate ApplicaCon (VRA) §  Sensors detect nutrient levels §  PrescripHon map is generated based on soil analysis §  Control system applies variable rate inputs at precise locaHons with respect to site-­‐specific deficiencies Cost of Equipment; $40,000 -­‐ $50,000 Awareness Report ConsolidaHon of federal and state programs beneficial to farmers TMDL Goal Membership Card Discount program with local area businesses Farmers Insurance LegislaCve Change Append Farmers Insurance to cover risks of implemenHng new BMPs Assistance Program LegislaCve Change Create assistance program for VRA and Manure Digesters Manure Digester Systems Digester separates manure into solid and liquid components §  Solid components can be used as animal bedding §  Liquid components converted into natural gas equivalent Cost of InstallaHon: ~$250,000 (Only applicable on farms with 500+ cows) 20 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Method of Analysis 21 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay SimulaCon Algorithm Beer’s Law states that there is a linear relaHonship between absorbance and concentraHon in spectrophotometry. Thus as the concentraHon increases, the absorbance is addiCve. 1.25"
-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐ 0.50 -­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐ 2 ppm 0.5"
-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐ Absorbance*
0.75 -­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐ 3 ppm 0.75"
0.25 -­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐ 1 ppm -­‐-­‐-­‐-­‐-­‐-­‐-­‐ 0.25"
0"
0"
1"
2"
3"
Concentra-on*
22 4 ppm -­‐-­‐-­‐-­‐-­‐-­‐-­‐ 1.00 1"
4"
5"
1.
2.
3.
4.
5.
6.
7.
8.
9.
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13.
14.
15.
16.
17.
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for each element Set confidence interval for element Reset overall low to 0 Reset overall high to 0 for x iteraHons Reset lowest within iteraHon to Integer.MAX_VALUE for each spectral band Add normal error to band Determine concentraHon at band if concentraHon is lowest in this iteraHon Reset lowest end for if concentraHon is higher than overall high Reset high if concentraHon is lower than overall low Reset low end for end for Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Spectral Imagery System SimulaCon Model Inputs Spectrometer Spectral ResoluHon Spectral Range Device Error Chemical Elements Spectral Signatures ConcentraHons Signal Noise in SimulaCon Outputs Determined ConcentraHon Average DetecHon Error Spectral Signatures Data Source: Na7onal Ins7tute of Standards and Technology (NIST) Typical Concentra;ons in Plants Data Source: University of Wisconsin, Department of Soil Science 23 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Spectral Imagery System SimulaCon Model Intensity (unitless) Element: Nitrogen ConcentraHon: 1 ppm Wavelength (nanometers) 24 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Spectral Imagery System SimulaCon Model Intensity (unitless) Element: Nitrogen ConcentraCon: 2 ppm Wavelength (nanometers) 25 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Spectral Imagery System SimulaCon Intensity (unitless) Model Element: Nitrogen ConcentraHon: 2 ppm Spectrometer: MulC Wavelength (nanometers) 26 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Spectral Imagery System SimulaCon Intensity (unitless) Model Element: Nitrogen ConcentraHon: 2 ppm Spectrometer: MulH Error: 1% Wavelength (nanometers) 27 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Spectral Imagery System SimulaCon Intensity (unitless) Model Element: Combined ConcentraCon: ?? Spectrometer: MulH Wavelength (nanometers) 28 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Spectral Imagery System SimulaCon Intensity (unitless) Model Element: Combined ConcentraHon: ?? Spectrometer: MulH Possibly 3 ppm of Nitrogen based on this wavelength (N ConcentraHon = 3 ppm) Wavelength (nanometers) 29 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Spectral Imagery System SimulaCon Intensity (unitless) Model Element: Combined ConcentraHon: ?? Spectrometer: MulH Only 2 ppm of Nitrogen possible here, Thus previous wavelength had noise from other elements (N ConcentraHon = 3ppm 2 ppm) Wavelength (nanometers) 30 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Spectral Imagery System SimulaCon Intensity (unitless) Model Element: Combined ConcentraHon: ?? Spectrometer: MulH No signature for Nitrogen at this wavelength (N ConcentraHon = 2 ppm) Wavelength (nanometers) 31 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Spectral Imagery System SimulaCon Intensity (unitless) Model Element: Combined ConcentraHon: ?? Spectrometer: MulH 2 ppm of Nitrogen possible here (N ConcentraHon = 2 ppm) Wavelength (nanometers) 32 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Spectral Imagery System SimulaCon Intensity (unitless) Model Element: Combined ConcentraCon: 2 ppm Spectrometer: MulH ConCnuing this process for the enCre wavelength range, we find our Nitrogen concentraCon to be 2 parts per million Wavelength (nanometers) 33 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Monitoring System Physical Model Vehicle Pla[orm Spectral Imagery Subsystem Area Coverage Rate
ACR
Ground Sample Distance
GSD
Signal-­‐to-­‐Noise Ratio
SNR
Number of Spectral Bands
K
Height Above Ground hg
Focal Length
ck
Aperture Size
d
Formulae Sources: Spectral Imaging for Remote Sensing (MIT); Trends for Digital Aerial Mapping Cameras (ISPRS) 34 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Area Coverage Rate (ACR) Assuming the same imagery system is used for each vehicle pla[orm, At maximum height, ACRMAV(max) = 13.97 km2/min Mul7spectral object distance limit = 0.91 km (3000 k)
ACR = 10.8089 (hg x v) FAA Code of Federal Regula;ons on UAV (Sec.91.57): No flight higher than 0.12 km (400 k)
ACRUAV(rotorcraW) = 0.62 km2/min ACRUAV(fixed wing) = 1.01 km2/min At minimum height, ACRMAV(min) = 2.30 km2/min FAA Code of Federal Regula;ons on MAV (Sec.91.119): no flight under 0.15 km (500 k)
ACRGround = 0.01 km2/min 35 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2014 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Design of Experiments 36 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Design of Experiments Spectral Imagery Subsystem Device Error SimulaHon (%) IteraHons MulHspectral Hyperspectral MulHspectral Hyperspectral MulHspectral Hyperspectral MulHspectral Hyperspectral 1.0 1.0 3.0 3.0 6.0 6.0 9.0 9.0 100,000 100,000 100,000 100,000 100,000 100,000 100,000 100,000 Spectral Range (nm) Spectral Average N Average P ResoluHon DetecHon Error DetecHon Error (nm) (%) (%) 520 -­‐ 920 400 -­‐ 970 520 -­‐ 920 400 -­‐ 970 520 -­‐ 920 400 -­‐ 970 520 -­‐ 920 400 -­‐ 970 10.0 3.3 10.0 3.3 10.0 3.3 10.0 3.3 -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ Combined Average Error (%) -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ Difference in Average Errors (%) -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ (SimulaHon outputs will show detecHon accuracy for nitrogen and phosphorus amongst noise generated by 12 other chemical elements) Vehicle Planorm Ground UAV (rotorcraW) UAV (fixed-­‐wing) MAV (min height) MAV (max height) Satellite 37 ACR (km2/min) -­‐-­‐-­‐ 0.01 0.62 1.01 2.30 13.97 ∞ ACR/Dollar (km2/$) -­‐-­‐-­‐ 3 137 223 314 1907 0.0003 Ground ResoluHon Object Distance (W) Inch/pixel -­‐-­‐-­‐ 6 < 1.9 1.9 400 1.9 700 3,000 3.4
2,180,000 14.4
6 866.1 Total Weighted Score -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Score Card: IncenCve AlternaCves IncenHve Scalability ImplementaHon Maintainability Community Total Weighted Score -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ Nitrogen & Phosphorous Report 2 4 3 2 TMDL Membership Goal Card 1 2 2 5 Awareness Report 3 5 4 3 Farmers Insurance LegislaHon Change 5 1 5 4 -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ -­‐-­‐-­‐ Assistance LegislaHon Change 5 1 5 4 -­‐-­‐-­‐ (Scores for incenHve alternaHves determined by Delphi Method with project team) Likert Scale: 1-­‐5 (1=worst, 5=best) Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2014 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Results 39 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Spectral Imagery SimulaCon Results Spectral Imagery Device Error SimulaCon Subsystem (%) IteraCons MulHspectral Hyperspectral MulHspectral Hyperspectral MulHspectral Hyperspectral MulHspectral Hyperspectral 1.0 1.0 3.0 3.0 6.0 6.0 9.0 9.0 Average N & P DetecCon Error (%) 45 100,000 100,000 100,000 100,000 100,000 100,000 100,000 100,000 Spectral Range (nm) Spectral Average N Average P Combined Difference in Average ResoluCon DetecCon Error DetecCon Error Average Error Errors (%) (nm) (%) (%) (%) 520 -­‐ 920 400 -­‐ 970 520 -­‐ 920 400 -­‐ 970 520 -­‐ 920 400 -­‐ 970 520 -­‐ 920 400 -­‐ 970 10.0 3.3 10.0 3.3 10.0 3.3 10.0 3.3 4.59 4.29 13.79 13.18 27.599 26.847 41.45 40.512 4.54 4.21 13.93 12.81 28.175 25.713 42.425 38.636 4.56 4.25 13.86 13.00 27.89 26.28 41.94 39.57 0.31 0.86 1.61 2.36 Device Error VS Average N & P DetecCon Error 40 35 30 25 MulHspectral 20 Hyperspectral 15 10 5 0 1.0 3.0 6.0 Device Error (%) 40 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay 9.0 Value Hierarchy 41 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Monitoring System Ranking & Cost-­‐Benefit Analysis Monitoring System AlternaHves with MulHspectral Imagery Subsystem Monitoring System AlternaCves MAVmax UAV fixed UAV rotor Ground ResoluHon ground ACR MAV min Requires Specialized Operator Satellite 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Value 0.80 UAV(fixed-­‐wing) UAV(rotorcrak) 0.75 Ground Value 0.70 MAV(max) 0.65 MAV(min) 0.60 0.55 0.50 0.45 Satellite 0.40 0 42 5,000 10,000 15,000 20,000 25,000 Cost ($) 30,000 35,000 40,000 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay 45,000 SensiCvity Analysis ACR SensiHvity Analysis Satellite 1 0.8 Value MAV(max) 0.6 0.4 MAV(min) 0.2 0 0 10 20 ACR: 24% 30 40 50 60 70 80 90 100 UAV(fixed-­‐wing) UAV(rotorcraW) Ground Weight MAV(max) Ground ResoluCon SensiHvity Analysis 1 Value 0.8 UAV(fixed-­‐wing) UAV(rotorcraW) Ground 0.6 0.4 MAV(min) 0.2 Satellite 0 0 10 20 30 40 Ground ResoluCon: 49% 43 50 60 70 80 90 Weight Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay 100 IncenCve System Ranking Normalized T-­‐scores for Equivalent IncenHve AlternaHves Nitrogen & Phosphorous Report TMDL Membership Goal Card Awareness Report Farmers Insurance LegislaHon Change Assistance LegislaHon Change Rank (Tied) 44 Scalability 0.02 1.50 2.75 0.25 2.25 2.25 ImplementaHon Sustainability 0.07 1.72 0.74 2.95 1.97 1.97 0.05 1.37 3.09 0.34 2.06 2.06 Community 0.02 3.14 2.75 1.18 0.78 0.78 IncenHve AlternaHve 1 TMDL Goal Membership Card 2 Farmer’s Insurance LegislaHon Change 2 Assistance Program LegislaHon Change 3 Nitrogen & Phosphorus Report 4 Awareness Report Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Total Weighted Score 0.29 0.33 0.27 0.32 0.32 Conclusion & RecommendaCon 45 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Conclusion: Monitoring System UAV’s have the best balance of cost and value, however they are currently illegal for commercial use. The FAA is currently moving in the direcHon of legalizing UAV’s for agricultural applicaHons. Notice of Proposed Rulemaking (NPRM):
Operation and Certification of Small Unmanned Aircraft Systems
Agency: Federal Aviation Administration (FAA)
Department of Transportation (DOT)
Date: February 23, 2015
Aero-­‐M (UAV) AircraW weight with bapery: 6.8 lbs. Payload capacity: 1.1 lbs. ADC Lite (MulCspectral Imagery Subsystem) Weight: 7 oz. (0.44 lbs.) System Weight: 7.24 lbs. Highlights:
-  UAV must weight less than 55 lbs.
-  Max altitude of 500 ft. above ground level
-  Operators required to pass a written test and be vetted by the Transportation
Security Administration (TSA)
-  Written test recurrent every 2 years
“In the Regulatory Evalua;on, the FAA explores four poten;al markets [in this NPRM]: (1) Aerial Photography, (2) Precision Agriculture, (3) Search and Rescue/Law Enforcement, (4) Bridge Inspec;on” NPRM Source: hpps://www.faa.gov/regulaHons_policies/rulemaking/recently_published/ 46 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay RecommendaCon Ø  Acquire a manned aircrak with a mulCspectral imagery subsystem for monitoring operaHons and test a proof of concept with the incenHve system Ø  If proof of concept is successful, move to a UAV-­‐planorm for sustained monitoring operaHons Ø  Use the following incenHve system framework to encourage BMP-­‐uHlizaHon from farmers: Category NDVI IncenHve OpHons TMDL Membership Goal Card Farmers Insurance LegislaHon Change Green 0.8 – 1.0 Assistance LegislaHon Change Nitrogen & Phosphorous Report Awareness Report Farmers Insurance LegislaHon Change Yellow 0.4 – 0.7 Assistance LegislaHon Change Nitrogen & Phosphorous Report Awareness Report Red 47 0.0 – 0.3 Farmers Insurance LegislaHon Change Assistance LegislaHon Change Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Backup Slides 49 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay ACR DerivaCon for Vehicle Planorm Comparison 50 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Average farm size is 166 acres with 68.8% of all MD farms being cropland (2012 Census Data) Woodland 17.2% 6,000 5,260 Pastureland 7.9% 5,000 Other Uses 6.1% Cropland 68.8% 4,000 3,000 1,988 2,000 1,000 282 0 1 to 99 acres 100 to 999 acres 1,000 + acres Note: Histogram shows harvested cropland which accounts for the majority of MD farms Source Data: Maryland Department of Agriculture Census Data 51 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Detailed Chart: Monitoring System AlternaHves Vehicle Planorm Satellite (Ex: DEIMOS-­‐1) Unmanned Aerial Manned Aerial (Ex: Cessna 152) Fixed-­‐Wing (Ex: Aero-­‐M) Rotorcrak (Ex: X8-­‐M) Vehicle Vehicle Speed RunCme N/A N/A Max Speed 205 min 126 mph Min Speed 40 min 22 mph TBD Sensor Model MulHspectral Eastman Kodak sensor KLI linear CCD Data Link Spectrum Spectral SpaCal Focal Aperture Data Approximate Range Range ResoluCon ResoluCon Length Size Interpreter System Cost N/A G,R,NIR NIR: 0.77 -­‐ 0.90 μm Red: 0.63 – 0.69 μm Green: 0.52 – 0.60 μm 22 m 155.9mm F 53 N/A $0.11 / km^2 MulHspectral ADC Air 0.62 mi 520nm-­‐920n
m 10 nm 3.2 Megapixels 8.5mm TBD Hyperspectral AisaEAGLE 0.62 mi 400 -­‐ 970nm 3.3 nm 1 Megapixel 23 mm F 2.4 MulHspectral ADC Lite 0.62 mi 520nm-­‐920n
m 10 nm 3.2 8.5 mm Megapixels TBD Hyperspectral AisaEAGLE 0.62 mi 400 -­‐ 970nm 3.3 nm 1 Megapixel 23 mm F 2.4 MulHspectral ADC Lite 0.62 mi 520nm-­‐920n
m 10 nm 3.2 8.5 mm Megapixels TBD Hyperspectral AisaEAGLE 0.62 mi 400 -­‐ 970nm 3.3 nm 1 Megapixel 23 mm F 2.4 MulHspectral ADC 0.62 mi 520nm -­‐920nm 10 nm 3.2 8.0 mm Megapixels TBD John Deere Mobile Farm Manager $17,295 Hyperspectral AisaEAGLE 0.62 mi 400 -­‐ 970nm 3.3 nm 1 Megapixel F 2.4 John Deere Mobile Farm Manager $46,909 14 min Ground Speed 120 min (Ex: Used John Deere Max 28 mph XUV 550) 52 Imagery System 23mm Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Pix4Dmapper Pro 3DR ediHon $38,505 $67,419 Pix4Dmapper Pro 3DR ediHon Pix4Dmapper Pro 3DR ediHon $23,895 $54,509 $23,895 $54,509 BMP UClizaCon Trends by Maryland Basin (2000-­‐2014) 350000 300000 250000 Ch o p
tank 200000 150000 h
estern S
W
r
e
p
p
U
ore 100000 50000 Patapsco Back River Lower Western Shore (Includes West & Rhode Rivers) 0 2000 2001 2002 Choptank 2003 2004 Lower Western Shore 2005 2006 2007 Patapsco Back River 2008 2009 2010 Upper Eastern Shore 2011 2012 Upper Western Shore Source Data: Maryland Department of Agriculture 53 2013 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay 2014 Stakeholder Tensions Nutrient runoff from agriculture affects the health of the Bay and limits its usability for residents and harms aquaCc life Maryland Farmers Maryland Farmers & Watermen Farmers and watermen have li?le incenCve to change their ways and are resistant to new laws and regulaCons MDA The EPA was established to encourage environmental protecCon which oken conflicts with the MDA whose main concern is to promote the economic well-­‐being of farmers Maryland Residents & Watermen MDA & EPA EPA MDA = Maryland Department of Agriculture EPA = Environmental ProtecHon Agency 54 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2014 Design of an Agricultural Runoff Monitoring and Reward System for the WRR Watershed IncenHve AlternaHve: Nitrogen & Phosphorus Report 1.  Overview of nitrogen & phosphorous levels on farmland 2.  Overview of different categories & benefits 3.  Current nitrogen & phosphorous levels on farmland 4.  Benefits associated with current nitrogen & phosphorous levels 5.  Nitrogen & phosphorous reducHon informaHon •  Cropland BMPs & VRA Technology Awareness •  Livestock BMPs & Animal Waste Management Awareness 55 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2014 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay IncenHve AlternaHve: TMDL Goal Membership Card Problem: ConservaHon organizaHons seek posiHve community relaHons with farmers. Overview: TMDL Goal Membership Card •  Geared towards building supporHve relaHons with local area businesses & farmers •  Encourages further Nitrogen & Phosphorous reducHon •  Rewards complying farmers Source: Prices obtained from www.duracard.com (lowest pricing esHmate) Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2014 Design of an Agricultural Runoff Monitoring and Reward System for the WRR Watershed IncenHve AlternaHve: Awareness Report Problem: Farmers may not be aware of current state & federal programs. Overview: Awareness Report. •  State & federal programs exist in order to encourage farmers to uHlize BMPs. •  Programs may change or update without farmers being aware of the benefits. RecommendaCon: Awareness Report. •  Recommend that a report be formed that can provide farmers with a knowledge of current state and federal programs available. Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2014 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay IncenHve AlternaHve: Farmers Insurance LegislaCve Change Problem: Farmers opt out of BMPs due to profit loss and/or costs. Overview: Farmers Insurance. •  Farmers are backed by crop insurance that typically covers poor deviaHons in yield amount compared to average yields (usually from natural disasters). •  We can expand this insurance to cover profit loss due to VRA/
Digester technology to ensure and encourage farmers to uHlize new BMP technology without risk. LegislaCon RecommendaCon: Farmers Insurance that extends coverage to include VRA/Digester Technology. •  Recommend that Farmers Insurance include loss of profit for VRA Technology (which uses less ferHlizer) and manure reducHon plans (such as digester operaHng losses). Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2014 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay IncenHve AlternaHve: Assistance Program LegislaCve Change Variable Rate ApplicaHon Technology (VRA) Overview: •  The sensor detects nutrient levels. •  PrescripHon map is generated based on the soil analyses. •  Control system applies variable rate inputs at precise locaHons with respect to site-­‐specific deficiencies Variable Rate ApplicaCon Sensor CapabiliCes: •  Manures •  FerHlizers •  PesHcides Benefits: •
•
•
Precise nutrient applicaHon PotenHal for lower cost & beper growth Environmental: Reduce over applicaHon Cost of Equipment: •
$40,000 – $55,000 Source: www.nrcs.usda.gov 59 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2014 Design of an Agricultural Runoff Monitoring and Reward System for the WRR Watershed IncenHve AlternaHve: Assistance Program LegislaCve Change Manure Digester Systems Manure Digesters: An animal waste management system that converts livestock manure into biogas, methane, that can be purified into a natural gas equivalent. Overview: •  Digester separates the manure into solid and liquid components •  Solid – can be used as animal bedding •  Liquid – converted into biogas, methane, fuel Benefits: •  Converts manure into renewable energy producHon source Fig.1. Complete Mix Digester •  Reduces greenhouse gases pods.dasnr.okstate.edu •  Improves water quality by reducing pathogen runoff from entering ground water •  Significantly reduces odor and decreases livestock yard fly populaHon Disadvantage: •  Cost of installaHon ~ $250,000 and up depending on the selected digester. •  Only applicable for farms with 500+ cows
Source: www.epa.gov Note: Cornell University notes several case studies with average payback period of 5 years. Source: hpp://www.manuremanagement.cornell.edu/Pages/General_Docs/Reports/Turning_Manure_to_Gold.pdf 60 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2014 Design of an Agricultural Runoff Monitoring and Reward System for the WRR Watershed Case Study: West/Rhode Rivers and the Upper Chesapeake Bay Upper Bay West/Rhode Rivers 61 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay Yes, the fixed-­‐wing and rotorcrak UAVs really are the same price 62 Department of Systems Engineering and OperaHons Research – Senior Design -­‐ 2015 Design of an Agricultural Runoff Monitoring and Reward System for the Upper Chesapeake Bay