Embed Size (px)
Citation preview
Irrigation Efficiency: Integrated Data Reporting for Decision Support Solutions
David TerryASERTTI Executive Director
August 19, 2013
Energy Applications and Cloud Computing Webinar Series
ASERTTI• ASERTTI's mission is to increase the effectiveness of energy research
efforts in contributing to economic growth, environmental quality, and energy security.
• ASERTTI promotes applied research and technology commercialization in energy efficiency and renewable energy through state, federal, and private collaboration on emerging technologies. ASERTTI works to:
– Foster cooperative relationships among its members
– Advocate for policies that support clean energy research, development, demonstration, and deployment (RDD&D)
www.asertti.org
ASERTTI Overview ASERTTI Members Upcoming Activities
ASERTTI MembersASERTTI’s membership includes state energy agencies, university energy centers, national laboratories, non-profit organizations, utilities, and other public interest technology organizations.
www.asertti.org
ASERTTI Overview ASERTTI Members Upcoming Activities
Upcoming Activities• ASERTTI Webinar Series: Energy Applications and Cloud Computing
– Smart Manufacturing: Cloud Data and Computation Services for Performance Management Modeling (SMLC and EPRI)September 16, 2013
• ASERTTI Fall Meeting: October 2-4, 2013 – Raleigh, NCIntegrating Smart Grid Technologies for Buildings, Industry, and Vehicles
www.asertti.org
ASERTTI Overview ASERTTI Members Upcoming Activities
5
Energy and Water Savings from Optimal Irrigation Management and Precision ApplicationLori Rhodig, Northwest Energy Efficiency Alliance (NEEA)Dr. Charles Hillyer, Oregon State University (OSU)
6
NEEA’s Role
Fill the energy efficiency pipeline
Accelerate market adoption
Leverage the power of the region
7
Impact of Ag Irrigation in the Region
Electrical Energy Use (aMW)
Residential 7,424
Commercial 6,129
Industrial 3,744
Other Ag 105
Dairy Milk 55
Irrigation 848
Dir Serv Ind 764
Transportation 71
~ 5% or $335M
Based on 2007 usage – data from NW Power Conservation Council’s Sixth Power Plan
8
Initiative Goal, Objectives and Deliverables
Created by NW growers, utilities and NEEA in partnership with key global suppliers
KEY DELIVERABLES
THE GOALEconomic
enhancement through 20% Agricultural
Irrigation energy efficiency by 2020
20% by 2020
DELIVERABLES Improve yield uniformity Improve energy intensity Water goes further More profit per acre Decrease energy consumption
OBJECTIVES
Water and energy savings
Irrigation technology + practices
Industry-wide data standards
9
Condition of crop
Feel of soil
Personal calendar schedule
Scheduled by water delivery or-ganization
Soil moisture sensing device
Reports on daily crop-water evapo- transpiration (ET)
Commercial or government scheduling service
When neighbors begin to irrigate
Computer simulation models
Plant moisture sensing device
Other
1988 1994 1998 2003 2008
2008 – 1.4%
2008 – 78%
(Farm And Ranch Irrigation Survey, USDA)
Methods Used in Deciding When to Irrigate
1010
Today’s Standalone Tools Don’t Integrate
LOCALIZED HARDWARE
Weather stations Moisture
sensors Pumping plants Smart meters Flow valves
EXTERNAL DATA
SOURCES Soil maps Weather
networks
ONLINE ADVISORY SYSTEMS
Crop type ET Schedules Weather SIS
IRRIGATIONSCHEDULING
TOOLS VRI SIS
11
In-Field Equipment: Weather & Moisture
1. Total solar radiation (pyranometer): 2. Soil temperature (thermistor): 3. Air temperature/relative humidity: 4. Wind Vane (Wind Direction)5. Anemometer (Wind Speed)6. Tipping-bucket rain gauge7. 12v Solar Panel+battery8. Telemetry uplink9. GPS Pivot Location10. Soil Moisture monitoring: 3x Decagon HS10 11. Soil Moisture Aquacheck probe 12. Irrometer Tensiometer13. TDR (Time Domain Reflectrometer) 14. Panametrics Flow meters 15. Smart Meters (at pump)
Measurement
14
Weather
Automated Field Moisture Monitors13
15
9
1
3 6
4 & 5
10 11 12
1212
Integrated Decision Support SolutionIN
PU
TS STATIC DATA
Soil mapsYield maps
ONLINE ADVISORY SYSTEM (ex. AgriMet)
Crop type, ET, weather integration, irrigation scheduling, etc.
RISK MANAGEMENT
ON-FARM INFORMATION
Weather
Pumping + distribution system
Telemetry
Moisture sensors
OU
TP
UT
S
DATA OUTPUTReports, trends, analysis, etc.
DYNAMIC
FIXED
OPTIMAL IRRIGATION
MANAGEMENT
Uniform Fields
VRI Fields
Iterative Feedback
Loop
DECISION SUPPORT
1313
Product: Technology Levels
Level 0
Level 1
Level 2
Level 3
On-farm weather station with in-field correction
Soil moisture monitoring Flow monitoring Energy use monitoring
Optimal Irrigation Scheduling Soil mapping to calibrate deficit
strategies Yield mapping to verify crop
response VSI ( or VRI-Speed)
Variable Rate Irrigation (or called VRI Site-Specific or Zone)
Conventional practice Remote weather station
14
Optimal Irrigation
0 10 20 30 40 50 60 70 800
200
400
600
800
1,000
1,200
0.0
2.0
4.0
6.0
8.0
10.0
12.0Winter Wheat Production Function
from English and Raja (1996)
Production Costs ($/ha) Gross Income @ 147 $/ton Yield (kg/ha)Applied Water (cm)
Co
sts
An
d I
nco
mes
($/
ha)
(T
ho
usa
nd
s)
Yie
ld (
kg/h
a)Maximum Income @ 51.2 cm
Maximum Yield @ 60.9 cm
15
Preliminary Demonstration Results
Integration Data and model development
Initial performance characterization
Demonstrated water savings (informal)
16
Lessons Learned
17
2013 Demonstration Sites
Key: = ’12 VRI site = ‘13 VRI site = ‘13 VSI site X
1818
Integrated Decision Support SolutionIN
PU
TS STATIC DATA
Soil mapsYield maps
ONLINE ADVISORY SYSTEM (ex. AgriMet)
Crop type, ET, weather integration, irrigation scheduling, etc.
RISK MANAGEMENT
ON-FARM INFORMATION
Weather
Pumping + distribution system
Telemetry
Moisture sensors
OU
TP
UT
S
DATA OUTPUTReports, trends, analysis, etc.
DYNAMIC
FIXED
OPTIMAL IRRIGATION
MANAGEMENT
Uniform Fields
VRI Fields
Iterative Feedback
Loop
DECISION SUPPORT
19
CHARTERProvide a common set of
data standards and formats to convert
weather, soil moisture and other relevant data from
OEM hardware and software programs to be used by irrigation data
analysis and prescription programs.
Precision Ag Irrigation Leadership (PAIL)
Jointly sponsored NW Energy Efficiency Alliance AgGateway
Driven by business needs Voice of the Grower
Manufacturers
20+ partner companies
20
For More InformationTelephone or email: Lori Rhodig, [email protected], 503-688-5431 Dr. Charles Hillyer, [email protected], 541-207-2387
Website information:www.neea.org/irrigation
(Video on right side)
21
Yield Model Calibration
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.40
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
f(x) = 1.92103456982011 x
1 - (Ya / Ym)
1 –
(E
Ta
/ E
Tm
)
22
Irrigation Management Online
Web Application for: Conventional irrigation scheduling Managing limited water supply Optimal irrigation Irrigation optimization
The User is the most important part of optimization algorithm
23
Key Modeling Challenges
Farm level optimization
depends on all fields
Optimization implies some level of deficit
irrigation
Irrigation depends on all farm operations
24
Sites Summary (2013)
Field No.Area (Ac)
CropTechnology
LevelPumping Lift
(ft)Location
M13 124 Canola Level 1 477
OR
M21 121.3 Canola+FCS Level 3 428M22 132.1 Canola Level 2 515M10 125.5 Field Corn Level 1 379M54 122.6 Field Corn Level 3 365M56 123.3 Field Corn Level 2 360TR240 74.1 Sw Corn Sd/SunFlwr Level 3 528
WATR251 78.9 Sw Corn Sd/SunFlwr Level 2 489TR253 119 Sw Corn Sd/SunFlwr Level 1 509TD5 125 Field Corn Level 1 239
IDTD7 125 Field Corn Level 3 258TD11 125 Field Corn Level 2 234B114 125 Field Corn Level 3 400
WAB116 125 Field Corn Level 2 386B211 97 Field Corn Level 1 445
