CAFOs of Missouri CAFOs of Missouri A GIS Mapping A GIS Mapping Project Project Elizabeth Schiller Elizabeth Schiller
Jan 22, 2016
CAFOs of MissouriCAFOs of MissouriA GIS Mapping ProjectA GIS Mapping Project
Elizabeth SchillerElizabeth Schiller
What We’ll CoverWhat We’ll Cover
Brief Industry HistoryBrief Industry History Swine Industry Swine Industry
HistoryHistory Production ProcessProduction Process Economic ArgumentsEconomic Arguments Data UsedData Used Methods & ToolsMethods & Tools ResultsResults RecommendationsRecommendations
Purpose of ProjectPurpose of Project
The purpose of this project is to map the The purpose of this project is to map the location of Concentrated Animal Feed location of Concentrated Animal Feed Operations (CAFOs) in the State of Operations (CAFOs) in the State of Missouri. In addition, I attempted to Missouri. In addition, I attempted to correlate water quality violation data using correlate water quality violation data using linear regression techniques with the size linear regression techniques with the size of the facility, type of livestock, and of the facility, type of livestock, and manure management method.manure management method.
History of AgricultureHistory of Agriculture Organized agriculture began over 10,000 years agoOrganized agriculture began over 10,000 years ago The most important economic activity in the US from the early The most important economic activity in the US from the early
1600’s to late 1800’s1600’s to late 1800’s By end of WW1, agriculture had settled in regional patterns which By end of WW1, agriculture had settled in regional patterns which
allowed producers to maximize production of a particular crop for allowed producers to maximize production of a particular crop for nearby urban markets. nearby urban markets.
It was a highly productive, but not profitable industry.It was a highly productive, but not profitable industry. The percentage of the population employed in farming drastically The percentage of the population employed in farming drastically
decreased in the 20th century:decreased in the 20th century: 1930 - 25% of the population lived on farms1930 - 25% of the population lived on farms Late 1980’s - only 2.5% lived on farmsLate 1980’s - only 2.5% lived on farms
Production levels actually increased during this time due to Production levels actually increased during this time due to development of mechanized and animal-driven tools which reduced development of mechanized and animal-driven tools which reduced the need for human input to produce at the same or higher levelsthe need for human input to produce at the same or higher levels
History of Swine ProductionHistory of Swine Production Pigs first domesticated in China in 4900 B.C. and in Europe in 1500 Pigs first domesticated in China in 4900 B.C. and in Europe in 1500
B.C.B.C. Brought to America by Hernando de Soto in 1539Brought to America by Hernando de Soto in 1539 By mid-1800s pigs were being commercially slaughteredBy mid-1800s pigs were being commercially slaughtered After the Civil War, the invention of the refrigerated rail car allowed After the Civil War, the invention of the refrigerated rail car allowed
pork processing to become more centralized to points of production pork processing to become more centralized to points of production instead of consumption centersinstead of consumption centers
Hog farmers became concentrated in the “Corn Belt” - Iowa, Illinois, Hog farmers became concentrated in the “Corn Belt” - Iowa, Illinois, Minnesota, Nebraska, Indiana, and MissouriMinnesota, Nebraska, Indiana, and Missouri
Farmers found it more profitable to use corn to feed hogs for market Farmers found it more profitable to use corn to feed hogs for market instead of selling directlyinstead of selling directly
25% return on investment for converting grain to pork vs. 10% return 25% return on investment for converting grain to pork vs. 10% return for grainsfor grains
Industrialization of AgricultureIndustrialization of Agriculture During the 50s & 60s technological advances in genetics, feed, and housing During the 50s & 60s technological advances in genetics, feed, and housing
practices allowed farmers to be more efficient in converting feed to pork.practices allowed farmers to be more efficient in converting feed to pork. This led to the formation of CAFOs - confined animal feed operationsThis led to the formation of CAFOs - confined animal feed operations
large scale animal feed operation consisting of a single farm with numerous sows large scale animal feed operation consisting of a single farm with numerous sows and hogs housed in climate-controlled buildings. Also called factory farms or and hogs housed in climate-controlled buildings. Also called factory farms or corporate farms.corporate farms.
North Carolina passed laws to encourage CAFO creation - tax breaks, no North Carolina passed laws to encourage CAFO creation - tax breaks, no regulation, liability exemptions from environmental damage, etc. From 1989 regulation, liability exemptions from environmental damage, etc. From 1989 to 1999 hog production increased 500% in NCto 1999 hog production increased 500% in NC
Missouri created similar legislation in 1993 and other Midwestern states Missouri created similar legislation in 1993 and other Midwestern states soon followedsoon followed
In the past ten years, much consolidation has occurred in hog ownership In the past ten years, much consolidation has occurred in hog ownership and slaughterhouse operations. Four companies handle 60% of the marketand slaughterhouse operations. Four companies handle 60% of the market
Latest trend is contract growing - farmer signs a contract with a large Latest trend is contract growing - farmer signs a contract with a large corporation to raise hogs from wean to finish in exchange for compensationcorporation to raise hogs from wean to finish in exchange for compensation
Economic ArgumentsEconomic Arguments
Major school: Bigger is betterMajor school: Bigger is better Capitalizes on economies of scaleCapitalizes on economies of scale
Emerging thoughts: Bigger is NOT betterEmerging thoughts: Bigger is NOT better Proponents of large scale facilities ignore Proponents of large scale facilities ignore
measurable externalitiesmeasurable externalities• Waste ManagementWaste Management• Mis-calculation of capital investment costsMis-calculation of capital investment costs
Small farms are able to meet or exceed Small farms are able to meet or exceed efficiencies of large operationsefficiencies of large operations
How are pigs produced for market?How are pigs produced for market?Small FarmSmall Farm
Farm use acreage to produce cereal
grains such as corn, barley, &
wheat
Grains produced are used to:
1. Feed livestock2. Bedding
Sows convert feed to marketable
hogs
Produce Manure – used to fertilize acreage
Create cash inflows for
capital investment
How are pigs produced for market?How are pigs produced for market?Small FarmSmall Farm
How are pigs produced for market?How are pigs produced for market?Large Farm (CAFO)Large Farm (CAFO)
Cereal grains and fabricated feed
purchased from out of area suppliers
Grains produced are used to:
1. Feed livestock
Sows convert feed to marketable
hogs
Produce Manure – stored in
waste lagoon
Create cash inflows for
capital investment
How are pigs produced for market?How are pigs produced for market?Large Farm (CAFO)Large Farm (CAFO)
How are pigs produced for market?How are pigs produced for market?Large Farm (CAFO)Large Farm (CAFO)
Data UsedData Used Data was acquired from several agencies including:Data was acquired from several agencies including:
Missouri Department of Natural Resources (MDNR)Missouri Department of Natural Resources (MDNR) United States Department of Agriculture (USDA)United States Department of Agriculture (USDA) Environmental Protection Agency (EPA)Environmental Protection Agency (EPA) University of Missouri Columbia OSEDA project office.University of Missouri Columbia OSEDA project office. Missouri Spatial Data Information ServiceMissouri Spatial Data Information Service USGS.govUSGS.gov
Data gathered includes:Data gathered includes: Complete GIS locator data of all permitted CAFOs in Missouri Complete GIS locator data of all permitted CAFOs in Missouri
including latitude, longitude, owner, production, and outfall including latitude, longitude, owner, production, and outfall information.information.
Relatively complete water quality violation data including size & Relatively complete water quality violation data including size & type of spill, environmental impact reports, and monetary type of spill, environmental impact reports, and monetary settlements.settlements.
Methods & ToolsMethods & Tools
GIS:GIS: ArcCatalog to organize & import data from ArcCatalog to organize & import data from
other formatsother formats ArcToolbox to import dataArcToolbox to import data ArcMap to create map & layersArcMap to create map & layers
StatisticsStatistics SPSSSPSS Excel Data Analysis ToolsExcel Data Analysis Tools
CAFOs by ClassCAFOs by Class
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Holt
Franklin
Vernon
Miller
Shannon Wayne
Adair
Boone
Carroll
Oregon
Benton
Wright
Taney Ripley
Knox
Douglas
Phelps
Johnson
Clark
Laclede
Ralls
Jasper
Dade
Nodaway
Callaway
Osage
Stoddard
Clay
Chariton
Greene
Perry
Barton
Lincoln
Audrain
Lewis
Monroe
St. Clair
Stone
Reynolds
Dallas
Cole
Harrison
Camden
Scott
Sullivan
Newton
Crawford
Carter
Cedar
Morgan
Maries
Cooper
Pulaski
Jackson
Platte
Shelby
Dunklin
Gentry
Jefferson
Daviess
BollingerWebster
Lafayette
PutnamMercer
Marion
Washington
Christian
Atchison
Lawrence
Howard
Clinton
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Warren
Grundy
Madison
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DeKalbAndrew
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Livingston
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Legend
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CAFOs by Major Animal Waste TypeCAFOs by Major Animal Waste Type
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Iron
Howell
Cass
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Pettis
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Butler
Holt
Franklin
Vernon
Miller
Shannon Wayne
Adair
Boone
Carroll
Oregon
Benton
Wright
Taney Ripley
Knox
Douglas
Phelps
Johnson
Clark
Laclede
Ralls
Jasper
Dade
Nodaway
Callaway
Osage
Stoddard
Clay
Chariton
Greene
Perry
Barton
Lincoln
Audrain
Lewis
Monroe
St. Clair
Stone
Reynolds
Dallas
Cole
Harrison
Camden
Scott
Sullivan
Newton
Crawford
Carter
Cedar
Morgan
Maries
Cooper
Pulaski
Jackson
Platte
Shelby
Dunklin
Gentry
Jefferson
Daviess
BollingerWebster
Lafayette
PutnamMercer
Marion
Washington
Christian
Atchison
Lawrence
Howard
Clinton
St. Louis
Warren
Grundy
Madison
New Madrid
DeKalbAndrew
Pemiscot
Hickory
McDonald
St. Charles
Livingston
Randolph
Caldwell
Scotland
Gasconade
Moniteau
Worth
Montgomery
Buchanan
Mississippi
St. Francois
Cape Girardeau
Schuyler
Ste. Genevieve
St. Louis city
Legend
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Texas
Dent
Pike
Bates
Barry
Polk
Linn
Ray
Iron
Howell
Cass
Ozark
Saline
Henry
Pettis
Macon
Butler
Holt
Franklin
Vernon
Miller
Shannon Wayne
Adair
Boone
Carroll
Oregon
Benton
Wright
Taney Ripley
Knox
Douglas
Phelps
Johnson
Clark
Laclede
Ralls
Jasper
Dade
Nodaway
Callaway
Osage
Stoddard
Clay
Chariton
Greene
Perry
Barton
Lincoln
Audrain
Lewis
Monroe
St. Clair
Stone
Reynolds
Dallas
Cole
Harrison
Camden
Scott
Sullivan
Newton
Crawford
Carter
Cedar
Morgan
Maries
Cooper
Pulaski
Jackson
Platte
Shelby
Dunklin
Gentry
Jefferson
Daviess
BollingerWebster
Lafayette
PutnamMercer
Marion
Washington
Christian
Atchison
Lawrence
Howard
Clinton
St. Louis
Warren
Grundy
Madison
New Madrid
DeKalbAndrew
Pemiscot
Hickory
McDonald
St. Charles
Livingston
Randolph
Caldwell
Scotland
Gasconade
Moniteau
Worth
Montgomery
Buchanan
Mississippi
St. Francois
Cape Girardeau
Schuyler
Ste. Genevieve
St. Louis city
Legend
#* Beef
! Bird
" Dairy
# Layers
# Poultry
$ Swine
# Turkey
303dstreams
CAFOs with Marijuana Production Arrests 2000CAFOs with Marijuana Production Arrests 2000
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Texas
Dent
Pike
Bates
Barry
Polk
Linn
Ray
Iron
Howell
Cass
Ozark
Saline
Henry
Pettis
Macon
Butler
Holt
Franklin
Vernon
Miller
Shannon Wayne
Adair
Boone
Carroll
Oregon
Benton
Wright
Taney Ripley
Knox
Douglas
Phelps
Johnson
Clark
Laclede
Ralls
Jasper
Dade
Nodaway
Callaway
Osage
Stoddard
Clay
Chariton
Greene
Perry
Barton
Lincoln
Audrain
Lewis
Monroe
St. Clair
Stone
Reynolds
Dallas
Cole
Harrison
Camden
Scott
Sullivan
Newton
Crawford
Carter
Cedar
Morgan
Maries
Cooper
Pulaski
Jackson
Platte
Shelby
Dunklin
Gentry
Jefferson
Daviess
BollingerWebster
Lafayette
PutnamMercer
Marion
Washington
Christian
Atchison
Lawrence
Howard
Clinton
St. Louis
Warren
Grundy
Madison
New Madrid
DeKalbAndrew
Pemiscot
Hickory
McDonald
St. Charles
Livingston
Randolph
Caldwell
Scotland
Gasconade
Moniteau
Worth
Montgomery
Buchanan
Mississippi
St. Francois
Cape Girardeau
Schuyler
Ste. Genevieve
St. Louis city
Legend
X Class IA
X Class IB
X Class IC
X Class II
X Class NP
Volume
0
1 - 104
105 - 169
170 - 240
241 - 336
337 - 6431
CAFOs, Commercial Dog Breeding & MarijuanaCAFOs, Commercial Dog Breeding & Marijuana
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Texas
Dent
Pike
Bates
Barry
Polk
Linn
Ray
Iron
Howell
Cass
Ozark
Saline
Henry
Pettis
Macon
Butler
Holt
Franklin
Vernon
Miller
Shannon Wayne
Adair
Boone
Carroll
Oregon
Benton
Wright
Taney Ripley
Knox
Douglas
Phelps
Johnson
Clark
Laclede
Ralls
Jasper
Dade
Nodaway
Callaway
Osage
Stoddard
Clay
Chariton
Greene
Perry
Barton
Lincoln
Audrain
Lewis
Monroe
St. Clair
Stone
Reynolds
Dallas
Cole
Harrison
Camden
Scott
Sullivan
Newton
Crawford
Carter
Cedar
Morgan
Maries
Cooper
Pulaski
Jackson
Platte
Shelby
Dunklin
Gentry
Jefferson
Daviess
BollingerWebster
Lafayette
PutnamMercer
Marion
Washington
Christian
Atchison
Lawrence
Howard
Clinton
St. Louis
Warren
Grundy
Madison
New Madrid
DeKalbAndrew
Pemiscot
Hickory
McDonald
St. Charles
Livingston
Randolph
Caldwell
Scotland
Gasconade
Moniteau
Worth
Montgomery
Buchanan
Mississippi
St. Francois
Cape Girardeau
Schuyler
Ste. Genevieve
St. Louis city
Legend
X Class IA
X Class IB
X Class IC
X Class II
X Class NP
Number_Breeder_Dogs_Sold_two
0 - 439
440 - 1216
1217 - 2220
2221 - 4323
4324 - 6758
Statistical InterpretationStatistical Interpretation
Ran regression to establish relationship Ran regression to establish relationship between:between:
Y = monetary penalty amountY = monetary penalty amount
XX11 = Type of animal (hogs, poultry, etc) = Type of animal (hogs, poultry, etc)
XX22 = Number of gallons spilled = Number of gallons spilled
XX33 = Size of facility = Size of facility
Results ComparisonResults ComparisonSUM M ARY OUTPUT
Regression StatisticsM ultiple R 0.898119948R Square 0.806619441Adjusted R Square 0.799951146Standard Error 20434.22122Observations 91
ANOVAdf SS M S F Significance F
Regression 3 1.51527E+11 50509149168 120.9633682 6.14996E-31Residual 87 36327493536 417557397Total 90 1.87855E+11
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%Intercept -9478.332811 11416.02818 -0.830265366 0.408661343 -32168.9414 13212.27578 -32168.9414 13212.27578ANIM AL_N 171.1893091 2481.790222 0.068978154 0.945165303 -4761.641062 5104.01968 -4761.641062 5104.01968GAL_SPIL 0.256285877 0.014538193 17.6284546 1.85622E-30 0.227389623 0.285182131 0.227389623 0.285182131CLASS_NO 4905.217674 1562.24024 3.139861303 0.002309103 1800.093787 8010.341561 1800.093787 8010.341561
RecommendationsRecommendations
There were no problems obtaining data about There were no problems obtaining data about CAFO locations. It required a phone call and an CAFO locations. It required a phone call and an e-mailed request.e-mailed request.
It was far more difficult to obtain violation data It was far more difficult to obtain violation data due to the retirement of an employee who had due to the retirement of an employee who had previously tracked this data. After visiting previously tracked this data. After visiting MDNR in person, the data was given to me MDNR in person, the data was given to me electronically in a short time.electronically in a short time.
ConclusionsConclusions
Spatial Analysis shows relationships Spatial Analysis shows relationships between CAFOs and:between CAFOs and: State 303d watersState 303d waters Dog BreedersDog Breeders Marijuana ProductionMarijuana Production
Relationships need further analysis to Relationships need further analysis to clarifyclarify
Need better dataNeed better data
Questions?Questions?