Transcript Irrigation Efficiency: Integrated Data Reporting for Decision Support Solutions Energy Applications and Cloud Computing Webinar Series David Terry ASERTTI Executive Director August 19, 2013

Irrigation Efficiency: Integrated Data
Reporting for Decision Support Solutions
Energy Applications and Cloud Computing
Webinar Series
David Terry
ASERTTI Executive Director
August 19, 2013
ASERTTI Overview
ASERTTI Members
Upcoming Activities
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 Members
ASERTTI’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, NC
Integrating Smart Grid Technologies for Buildings, Industry, and Vehicles
www.asertti.org
Energy and Water Savings from Optimal Irrigation
Management and Precision Application
Lori Rhodig, Northwest Energy Efficiency Alliance (NEEA)
Dr. Charles Hillyer, Oregon State University (OSU)
5
NEEA’s Role
 Fill the energy efficiency pipeline
 Accelerate market adoption
 Leverage the power of the region
6
Impact of Ag Irrigation in the Region
~ 5% or
$335M
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
7
Based on 2007 usage – data from NW Power Conservation Council’s Sixth Power Plan
Initiative Goal, Objectives and Deliverables
OBJECTIVES
THE GOAL
Economic
enhancement
through 20%
Agricultural
Irrigation energy
efficiency by 2020
20% by 2020
Water and
energy
savings





Irrigation
technology
+ practices
Industrywide data
standards
DELIVERABLES
Improve yield uniformity
Improve energy intensity
Water goes further
More profit per acre
Decrease energy consumption
Created by NW growers, utilities and NEEA in partnership with key global suppliers
8
Methods Used in Deciding When to Irrigate
Condition of crop
Feel of soil
2008 – 78%
Personal calendar schedule
Scheduled by water delivery
organization
Soil moisture sensing device
Reports on daily crop-water
evapo- transpiration (ET)
Commercial or government
scheduling service
When neighbors begin to irrigate
2008 – 1.4%
Computer simulation models
Plant moisture sensing device
(Farm And Ranch
Irrigation Survey, USDA)
Other
1988
9
1994
1998
2003
2008
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
IRRIGATION
SCHEDULING
TOOLS
 VRI
 SIS
10





ONLINE
ADVISORY
SYSTEMS
Crop type
ET
Schedules
Weather
SIS
In-Field Equipment: Weather & Moisture
Measurement
Weather
1
6
3
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
Total solar radiation (pyranometer):
Soil temperature (thermistor):
Air temperature/relative humidity:
Wind Vane (Wind Direction)
Anemometer (Wind Speed)
Tipping-bucket rain gauge
14
12v Solar Panel+battery
Telemetry uplink
GPS Pivot Location
Soil Moisture monitoring: 3x Decagon HS10
Soil Moisture Aquacheck probe
9
Irrometer Tensiometer
TDR (Time Domain Reflectrometer)
Panametrics Flow meters
Smart Meters (at pump)
15
Automated Field Moisture Monitors
10
11
12
13
4&5
11
Integrated Decision Support Solution
Iterative
Feedback
Loop
ON-FARM INFORMATION
Weather
Telemetry
Moisture sensors
DECISION
SUPPORT
STATIC DATA
Soil maps
Yield
maps
Uniform
Fields
VRI
Fields
ONLINE ADVISORY
SYSTEM (ex. AgriMet)
Crop type, ET, weather
integration, irrigation
scheduling, etc.
RISK MANAGEMENT
OPTIMAL
IRRIGATION
MANAGEMENT
FIXED
12
DATA OUTPUT
Reports, trends,
analysis, etc.
OUTPUTS
INPUTS
Pumping +
distribution system
DYNAMIC
Product: Technology Levels
 On-farm weather station
with in-field correction
 Soil moisture monitoring
 Flow monitoring
 Energy use monitoring
 Variable Rate Irrigation
(or called VRI Site-Specific
or Zone)
Level 3
Level 2
Level 1
 Optimal Irrigation Scheduling
 Soil mapping to calibrate deficit
strategies
 Conventional practice
13
 Remote weather station
 Yield mapping to verify crop
response
 VSI ( or VRI-Speed)
Optimal Irrigation
Winter Wheat Production Function
1,200
12.0
from English and Raja (1996)
10.0
Maximum Yield
@ 60.9 cm
800
8.0
Maximum Income
@ 51.2 cm
600
6.0
400
4.0
200
2.0
0
0.0
0
10
20
Production Costs ($/ha)
30
40
50
Applied Water (cm)
Gross Income @ 147 $/ton
14
60
70
Yield (kg/ha)
80
Yield (kg/ha)
Costs And Incomes ($/ha) (Thousands)
1,000
Preliminary Demonstration Results
Integration
Data and model
development
Initial performance
characterization
Demonstrated water
savings (informal)
15
Lessons Learned
16
2013 Demonstration Sites
Key:
 = ’12 VRI site
 = ‘13 VRI site
 = ‘13 VSI site
17
X
Integrated Decision Support Solution
Iterative
Feedback
Loop
ON-FARM INFORMATION
Weather
Telemetry
Moisture sensors
DECISION
SUPPORT
STATIC DATA
Soil maps
Yield
maps
Uniform
Fields
VRI
Fields
ONLINE ADVISORY
SYSTEM (ex. AgriMet)
Crop type, ET, weather
integration, irrigation
scheduling, etc.
RISK MANAGEMENT
OPTIMAL
IRRIGATION
MANAGEMENT
FIXED
18
DATA OUTPUT
Reports, trends,
analysis, etc.
OUTPUTS
INPUTS
Pumping +
distribution system
DYNAMIC
Precision Ag Irrigation Leadership (PAIL)
CHARTER
Jointly sponsored
 NW Energy Efficiency Alliance
 AgGateway
Driven by business needs
 Voice of the Grower
 Manufacturers
20+ partner companies
19
Provide 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.
For More Information
Telephone 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)
20
Yield Model Calibration
0.8
0.7
y = 1.921x
1 – (ETa / ETm)
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.05
0.1
0.15
0.2
1 - (Ya / Ym)
21
0.25
0.3
0.35
0.4
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
22
Key Modeling Challenges
Irrigation
depends on all
farm operations
Optimization
implies some
level of deficit
irrigation
23
Farm level
optimization
depends on all
fields
Sites Summary (2013)
Field No.
Area
(Ac)
Crop
M13
M21
M22
M10
M54
M56
TR240
TR251
TR253
TD5
TD7
TD11
B114
B116
B211
124
121.3
132.1
125.5
122.6
123.3
74.1
78.9
119
125
125
125
125
125
97
Canola
Canola+FCS
Canola
Field Corn
Field Corn
Field Corn
Sw Corn Sd/SunFlwr
Sw Corn Sd/SunFlwr
Sw Corn Sd/SunFlwr
Field Corn
Field Corn
Field Corn
Field Corn
Field Corn
Field Corn
24
Technology Pumping Lift
Location
(ft)
Level
Level 1
Level 3
Level 2
Level 1
Level 3
Level 2
Level 3
Level 2
Level 1
Level 1
Level 3
Level 2
Level 3
Level 2
Level 1
477
428
515
379
365
360
528
489
509
239
258
234
400
386
445
OR
WA
ID
WA