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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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

O

Legend

X Class IA

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303dstreams

CAFOs by Major Animal Waste TypeCAFOs by Major Animal Waste Type

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Dent

Pike

Bates

Barry

Polk

Linn

Ray

Iron

Howell

Cass

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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

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303dstreams

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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?