1. By: Allen Brandt and Jon GalloyUniversity of Wisconsin-Stevens PointWLIA 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 andsprings but can be obtained from other sources Each system varies in the intensity that is needed formanaging and maintaining the system 5. Pond Production System Most common productionsystem used Can vary in surface area sizefrom acre to over 5 acres Normally have a varyingdepth from 2.5 feet to 7-8feet Use a large amount of land Least intensivemanagement needed 6. Flow-Through/Raceway System Continual flow of waterthrough the system Rectangular in shape with alength:width:depth ratio of30:3:1 Often used in cultivation ofsalmonids (salmon andtrout) Is more intensive than thepond system 7. Recirculating AquacultureSystem (RAS) The water is recycled (filter)and put back into the system Requires more systemcomponents than ponds andraceways Usually located indoor and insmaller spaces Used for aquaponics Most intensive system tomanage 8. Fish Species Cold-water species Salmon and trout < 60F Cool-water species Walleye, perch, and northern pike 6075F Warm-water species Tilapia, sunfish, and bass >75F 9. GISs 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 arefound at the aquaculture sites Site Suitability Modeling Process by National Oceanicand Atmospheric Administration (NOAA) Used in comparing locations for mariculture based on economic and environmental characteristics. 10. Objectives Determine if site location characteristics ofaquaculture operations are significant for determiningsuccess or failure of commercial operations inWisconsin Locate areas in watershed sites in the state that havesuitable characteristics for successful fish farms 11. Data Collection County GIS data is being collected from the County offices Data obtained includes: parcels, zoning, landuse, hydrology, and orthophotos Fish farm location data was obtained from theDepartment of Agriculture Trade and ConsumerProtection(DATCP) Other needed data has been gather from the USGeological Survey, WI Dept. of NaturalResource, SSURGO, and WI Dept. of Transportation 12. Methods Create an evaluative model from fish farms sitelocation characteristics The model will determine important characteristics Environmental Land cover/use, hydrology, parcel ownershipetc. Socio-economic Proximity of infrastructure and markets Result will identify and classify best to worst sitecharacteristics of fish farms 13. Methods Create a predictive model for the state using thecharacteristics from the evaluative model ArcGIS multi-criteria evaluation to make predictivemodel 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 eachcategory 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 CountyKsatPermeability Score %Slope Score Soil ScorepHScoreTexture(Soil)0.0-.01 Very Low 5 0-1% 1 Sand 1 =10 01-10Mod. High2 >=8% 0 Clay 2Loam10-100High 1Silty Clay 2Loam100-705 Very High0Silty Clay 3All0Others**Includes: Sand, Loamy Sand, Sandy Loam, Loam, Silt Loam, Silt 16. Soil Scores, Adams County, WI Ksat(permeability) Soil texture pH Clay content 17. SlopeScores, AdamsCounty, WI Idealized slopes for sitingbetween 2-4% Important considerationin 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 farmsranged from 1-4 based onthe combined soils andslopes Reflects importance ofwater source 20. Predicting Watersheds from Distance Predicted watersheds suitable for fish farms based ondistance 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 classfrom the mean center of watersheds 21. Predicting Watersheds from Distance Depicts location of #0watersheds withopen and closed#0fish 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##000## 00>100,000 # # # 000# 0 0 0 ##0#00 0#Watersheds with #0 #0 # # 0 0#0open fish farms #0Watersheds withclosed fish farms#0 22. Predicting Watersheds from Distance Located watersheds without prior fish farms with acloser distance than watersheds with closed fish farms The average distance to Market Class A was 22.7 mi. foropen watersheds and 44.4 mi. for closed watersheds For Markets B and C the difference between averagedistance 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 suitabledistance from markets 23. Predicting Watersheds from Distance Location ofsuitablewatersheds 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 WIbased on watersheds The model will hopefully aid in success of new aquaculture facilities in the state Predict what system/species should be sited based onseveral 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 evaluativemodel Conduct a random sample of farm visits to gain moreinformation 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. AcknowledgmentsProject Advisors Dr. Chris Hartleb Dr. Keith Rice Doug MiskowiakGIS Data WI Dept. of Agriculture, Trade & Consumer Protection, Wisconsin County LIOs, Surveyors, and Land Records personalFunding for this project was provided by theNOAA/Sea Grant programs, project #R/SFA5. 28. Questions?