Allen Brandt, UW Stevens Point GIS Center & College of Natural Resources Aquaculture is one of the fastest growing food production systems in the world.The aquaculture industry in Wisconsin, comprised of approximately 2,000 registered fish farms, contributes over $7 million to the state’s economy annually. There are many species of game, food, and bait fish cultivated throughout the state in a variety of production systems. In recent years, the growth of the aquaculture industry in Wisconsin has slowed and the number of registered fish farms is beginning to decrease.The reason for the decrease in fish farms is not known, but it could be from the tough economic times, high costs of production, limited markets, environmental restrictions, or the site location of the fish farms.The objective of the study is to determine if the site location characteristics play a part in the success of the fish farm operation.The utilization of Geographic Information Systems (GIS) to create an evaluative model examining the environmental and socio-economic characteristics of current registered fish farms and those that have closed.The characteristics considered are land cover, soil types, elevation, water quality and source, and proximity to infrastructure and potential markets.The evaluative model will then be used to create a predictive model using a multi-criteria evaluation procedure using a GIS.The predictive model will be able to determine suitable locations for sustainable system-specific and species-specific aquaculture facilities.

1. By: Allen Brandt and Jon Galloy
University of Wisconsin-Stevens Point
WLIA Conference 2012

2. Outline
 Background
 Aquaculture
 GIS and Aquaculture
 Objectives
 Data Collection
 Methods
 Results
 Current and Anticipated
 Future Work

3. Background
 Definition of aquaculture
 Fastest growing form of food production (NOAA)
 Wisconsin 2000+ registered fish farms
 3 types of registered farms
 Aquaculture production systems
 WI aquaculture contribute $7 mil. to economy
 Presently there is a slow decline
 Exact reason for decline is not known

4. Production Systems
 Three main production systems used
 Ponds
 Flow-Through
 Recirculating Aquaculture System (RAS)
 Systems usually get water supply from wells and
springs but can be obtained from other sources
 Each system varies in the intensity that is needed for
managing and maintaining the system

5. Pond Production System
 Most common production
system used
 Can vary in surface area size
from ¼ acre to over 5 acres
 Normally have a varying
depth from 2.5 feet to 7-8
feet
 Use a large amount of land
 Least intensive
management needed

6. Flow-Through/Raceway System
 Continual flow of water
through the system
 Rectangular in shape with a
length:width:depth ratio of
30:3:1
 Often used in cultivation of
salmonids (salmon and
trout)
 Is more intensive than the
pond system

7. Recirculating Aquaculture
System (RAS)
 The water is recycled (filter)
and put back into the system
 Requires more system
components than ponds and
raceways
 Usually located indoor and in
smaller spaces
 Used for aquaponics
 Most intensive system to
manage

8. Fish Species
 Cold-water species
 Salmon and trout
 < 60F
 Cool-water species
 Walleye, perch, and northern pike
 60—75F
 Warm-water species
 Tilapia, sunfish, and bass
 >75F

9. GIS’s Importance
 GIS and Aquaculture
 One study estimated surface area and locations for
catfish and crawfish in Louisiana
 Looked at soil and slope of sites
 In Arizona, a study looked at what characteristics are
found at the aquaculture sites
 Site Suitability Modeling Process by National Oceanic
and Atmospheric Administration (NOAA)
 Used in comparing locations for mariculture based on
economic and environmental characteristics.

10. Objectives
 Determine if site location characteristics of
aquaculture operations are significant for determining
success or failure of commercial operations in
Wisconsin
 Locate areas in watershed sites in the state that have
suitable characteristics for successful fish farms

11. Data Collection
 County GIS data is being collected from the County
offices
 Data obtained includes: parcels, zoning, land
use, hydrology, and orthophotos
 Fish farm location data was obtained from the
Department of Agriculture Trade and Consumer
Protection(DATCP)
 Other needed data has been gather from the US
Geological Survey, WI Dept. of Natural
Resource, SSURGO, and WI Dept. of Transportation

12. Methods
 Create an evaluative model from fish farms site
location characteristics
 The model will determine important characteristics
 Environmental
 Land cover/use, hydrology, parcel ownership…etc.
 Socio-economic
 Proximity of infrastructure and markets
 Result will identify and classify best to worst site
characteristics of fish farms

13. Methods
 Create a predictive model for the state using the
characteristics from the evaluative model
 ArcGIS multi-criteria evaluation to make predictive
model
 Using weighted overlays for data layers
 Predictive model will be able to indicate locations by
production system or species by thermal range
 Weighting data layers that are important for each
category

14. Methods
 Compare predictive model to actual aquaculture farms
 Using parametric and spatial correlation statistics
 To ascertain initial validity
 Model accuracy used to determine predictive power

15. Example Scoring Scheme, Adams County
Ksat Permeability Score %Slope Score Soil Score pH Score
Texture (Soil)
0.0-.01 Very Low 5 0-1% 1 Sand 1 <=5 0
Clay
Loam
0.01-.1 Low 4 2-4% 2 Sandy 1 6-9 1
Clay
0.1-1 Mod. Low 3 5-7% 1 Clay 1 >=10 0
1-10 Mod. High 2 >=8% 0 Clay 2
Loam
10-100 High 1 Silty Clay 2
Loam
100-705 Very High 0 Silty Clay 3
All 0
Others*
*Includes: Sand, Loamy Sand, Sandy Loam, Loam, Silt Loam, Silt

16. Soil Scores,
Adams County,
WI
 Ksat(permeability)
 Soil texture
 pH
 Clay content

17. Slope
Scores, Adams
County, WI
 Idealized slopes for siting
between 2-4%
 Important consideration
in pond construction

18. Combined
Scores, Adams
County, WI
 Ksat(permeability)
 Soil texture
 pH
 Clay content
 Slopes

19. Fish Farm
Sites, Adams
County, WI
 Scores for fish farms
ranged from 1-4 based on
the combined soils and
slopes
 Reflects importance of
water source

20. Predicting Watersheds from Distance
 Predicted watersheds suitable for fish farms based on
distance markets
 Markets broken down into 3 population classes
 Market Class A population ≥10,000 and < 50,000
 Market Class B ≥ 50,000 and < 100,000
 Market Class C ≥ 100,000
 Separate watersheds based on fish farms status
 Open = registered in 2011
 Closed = not registered in 2011
 Calculated distance to five closest markets in each class
from the mean center of watersheds

21. Predicting Watersheds from Distance
 Depicts location of #
0
watersheds with
open and closed
#
0
fish farms and #
0 #
0 # #
0 0#
0 #
0#
0
#
0
#
0 ##
0
location of market #
0
##
00 0
## #
00 0
## #
#
0
#0
00 0
classes #
0 Market Class A
>10,000 & < 50,000 #
0
#
0
#
0
#
0 # 0
#
0
#
0 Market Class B
>50,000 & < 100,000 #
0 # ##
0 00
# ##
0 00
#
0 Market Class C
#0# # # ###
0 0 0 0 ##0
# 0#0
#
0
##
00
0##
00
>100,000 # # # 000#
0 0 0 ##0 #0
0
0#
Watersheds with #
0 #
0
# #
0 0 #
0
open fish farms #
0
Watersheds with
closed fish farms
#
0

22. Predicting Watersheds from Distance
 Located watersheds without prior fish farms with a
closer distance than watersheds with closed fish farms
 The average distance to Market Class A was 22.7 mi. for
open watersheds and 44.4 mi. for closed watersheds
 For Markets B and C the difference between average
distance was small with 94.9 mi (B) and 156.3 mi (C)
for open watersheds and 100 mi (B) and 159.2 mi (C)
for closed watersheds
 The model was able to predict 46 watersheds a suitable
distance from markets

23. Predicting Watersheds from Distance
 Location of
suitable
watersheds
 Located in
southeast portion
of the state

24. Anticipated Results
 Predictive Model will be system-specific and species-
specific
 It will evaluate potential fish farm sites throughout WI
based on watersheds
 The model will hopefully aid in success of new
aquaculture facilities in the state
 Predict what system/species should be sited based on
several characteristics
 Anticipate pond and flow-through systems to have
higher predictive ability than RAS

25. Future Work
 Complete the evaluative model
 Complete the predictive model using the evaluative
model
 Conduct a random sample of farm visits to gain more
information on factors of success

26. After Completion
 Predictive Model will be available to public
 Through Wisconsin Aquaculture Association, WI
DNR, WI DATCP, UWSP- NADF, Midwest Tribal
Aquaculture Network, UW Extension

27. Acknowledgments
Project Advisors
Dr. Chris Hartleb
Dr. Keith Rice
Doug Miskowiak
GIS Data
WI Dept. of Agriculture, Trade & Consumer Protection,
Wisconsin County LIO’s, Surveyors, and Land Records
personal
Funding for this project was provided by the
NOAA/Sea Grant programs, project #
R/SFA5.

28. Questions?