FARM OPERATIONS, FARM OPERATORS, AND COMMODITY PAYMENTS IN
2007: A STATISTICAL AND GEOSPATIAL APPROACH
A THESIS
SUBMITTED TO THE GRADUATE SCHOOL
IN PARTIAL FULLFILMENT OF THE REQUIREMENTS
FOR THE DEGREE
MASTER OF SCIENCE
NATURAL RESOURCE AND ENVIRONMENTAL MANAGEMENT
BY
DAVA R. MCCANN
DR. AMY GREGG
DR. JOSHUA GRUVER
BALL STATE UNIVERSITY
MUNCIE, INDIANA
DECEMBER 2012
2
FARM OPERATIONS, FARM OPERATORS, AND COMMODITY PAYMENTS IN
2007: A STATISTICAL AND GEOSPATIAL APPROACH
A THESIS
SUBMITTED TO THE GRADUATE SCHOOL
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE
MASTER OF SCIENCE
NATURAL RESOURCES AND ENVIRONMENTAL MANAGEMENT
BY
DAVA R. MCCANN
Committee Approval:
Committee Chairperson
Date
Committee Member
Date
Committee Member
Date
Departmental Approval:
Departmental Chairperson
Date
Dean of Graduate School
Date
BALL STATE UNIVERSITY
MUNIE, INDIANA
(DECEMBER 2012)
3
Abstract
The Farm Bill is a large omnibus bill that covers many titles, including commodity
programs, and accounted for $23.9 billion in government spending in 2006. The purposes
of this study are to determine if commodity variables are the only variables that are closely
correlated to government commodity payments, and if government payments are
distributed equitably by Farm Resource Region, based on the inequitable distribution of
payments cited by other researchers. Data included economics, operator characteristics,
farm typologies, tenure, and geographic variables. Kendall’s correlations and location
quotients examined the relationship between these variables and government payments.
Choropleth maps were created to visually examine the relationships. This study found that
corn, soybean, wheat, and cropland variables were strongly correlated to government
payment variables, supporting the hypothesis. However, other variables
were also strongly correlated to government payment variables, and payments varied
widely by Farm Resource Region. The hypotheses were rejected.
4
Acknowledgements
It is a great pleasure to give respect to the people who made this thesis possible. I
owe sincere gratitude to Dr. Joshua Gruver, Dr. Amy Gregg, and Dr. James Eflin, without
whom this thesis would never have come to fruition. I would like to express my
thankfulness to my family and friends for their unending support over these past years. I
would also like the thank Dr. Paul Chandler, who had nothing to do with this thesis but so
much to do with growth throughout my formal education at Ball State University.
5
Table of Contents
Abstract .............................................................................................................................. iii
Acknowledgements............................................................................................................ iv
List of Tables .................................................................................................................... vii
List of Figures ..................................................................................................................... x
Chapter I: Introduction........................................................................................................ 1
Statement of the Problem ................................................................................................ 1
Purpose of the Study ....................................................................................................... 2
Methods........................................................................................................................... 4
Chapter II: Literature Review ............................................................................................. 6
History of the Farm Bill .................................................................................................. 6
Farm Security and Rural Investment Act........................................................................ 9
Broader Implications of the Farm Bill .......................................................................... 11
Previously Cited Problems Derived from the Farm Bill............................................... 16
Farm Resource Regions ................................................................................................ 19
Chapter III: Methods......................................................................................................... 23
Data ............................................................................................................................... 23
6
Analysis......................................................................................................................... 26
Chapter IV: Results........................................................................................................... 32
Salient Variables ........................................................................................................... 33
National ......................................................................................................................... 34
County Level Analysis.................................................................................................. 35
Government Payment Variables ............................................................................... 36
Economic Variables .................................................................................................. 42
Occupation and Tenure Variables............................................................................. 54
Typology Variables................................................................................................... 67
Commodity Variables ............................................................................................... 81
Farm Resource Regions .............................................................................................. 102
Chapter V: Discussion and Conclusion .......................................................................... 115
Relation to Previous Studies ....................................................................................... 119
Broader Implications of the Farm Bill ........................................................................ 122
Future Research .......................................................................................................... 128
References ....................................................................................................................... 130
Appendix A ..................................................................................................................... 134
Appendix B ..................................................................................................................... 140
Appendix C ..................................................................................................................... 158
7
List of Tables
Table 1. The means, mean location quotients, and ordinal ranks of farms with commodity
payments by Farm Resource Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37
Table 2. The means, mean location quotients, and ordinal ranks of commodity payments
by Farm Resource Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Table 3. The mean, mean location quotients, and ordinal ranks of income dependence by
Farm Resource Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41
Table 4. The mean, mean location quotients, and ordinal ranks of market value by Farm
Resource Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Table 5. The mean, mean location quotients, and ordinal ranking of gross income by
Farm Resource Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .45
Table 6. The means, mean location quotients, and ordinal ranks of production expenses by
Farm Resource Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
Table 7. The means, mean location quotients, and ordinal ranks of net income by Farm
Resource Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
Table 8. The means, mean location quotients, and ordinal ranks of farming occupation
by Farm Resource Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
8
Table 9. The means, mean location quotients, and ordinal ranks of part owner farms by
Farm Resource Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .56
Table 10. The means, mean location quotients, and ordinal ranks of part owner farm
acres by Farm Resource Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
Table 11. The means, mean location quotients, and ordinal ranks of tenant farms by
Farm Resource Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .61
Table 12. The means, mean location quotients, and ordinal ranks of tenant farm acres by
Farm Resource Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .64
Table 13. The means, mean location quotients, and ordinal ranks of small family higher
sales farms by Farm Resource Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .67
Table 14. The means, mean location quotients, and ordinal ranks of large family farms
by Farm Resource Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
Table 15. The means, mean location quotients, and ordinal ranks of large family farm
acres by Farm Resource Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
Table 16. The means, mean location quotients, and ordinal ranks of very large family
farms by Farm Resource Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
Table 17. The means, mean location quotients, and ordinal ranks of very large family
farm acres by Farm Resource Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
Table 18. The means, mean location quotients, and ordinal ranks of corn farms by Farm
Resource Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
Table 19. The means, mean location quotients, and ordinal ranks of corn farm acres by
Farm Resource Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .85
9
Table 20. The means, mean location quotients, and ordinal ranks of soybean farms by
Farm Resource Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .86
Table 21. The means, mean location quotients, and ordinal ranks of soybean farm acres by
Farm Resource Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
Table 22. The means, mean location quotients, and ordinal ranks of wheat farms by Farm
Resource Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
Table 23. The means, mean location quotients, and ordinal ranks of wheat farm acres by
Farm Resource Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .93
Table 24. The means, mean location quotients, and ordinal ranks of total cropland farms
by Farm Resource Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
Table 25. The means, mean location quotients, and ordinal ranks of total cropland farm
acres by Farm Resource Region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
Table 26. Kendall’s tau correlations of location quotients in the Heartland. . . . . . . . . .102
Table 27. Kendall’s tau correlations of location quotients in the Northern Crescent. . . 103
Table 28. Kendall’s tau correlations of location quotients in the Northern Great Plains. . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
Table 29. Kendall’s tau correlations of location quotients in the Prairie Gateway. . . . .105
Table 30. Kendall’s tau correlations of location quotients in the Eastern Uplands. . . . .107
Table 31. Kendall’s tau correlations of location quotients in the Southern Seaboard. . .108
Table 32. Kendall’s tau correlations of location quotients in the Fruitful Rim. . . . . . . .109
Table 33. Kendall’s tau correlations of location quotients in the Basin and Range. . . . 110
Table 34. Kendall’s tau correlations of location quotients in the Mississippi Portal. . . 111
1
List of Figures
Figure 1. The Farm Resource Regions as created by the Economic Research Service. . 19
Figure 2. The location quotient of farms receiving commodity payments was highest on
average in the Heartland, Northern Great Plains, and Prairie Gateway region. . . . . . . . .36
Figure 3. The location quotients of commodity payment dollars were highest in the
Heartland, Southern Seaboard, and Mississippi Portal. . . . . . . . . . . . . . . . . . . . . . . . . . . 38
Figure 4. The location quotients of income dependence was highest in the Mississippi
Portal, Heartland, and Prairie Gateway. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
Figure 5. The mean location quotient of market value of agricultural products sold was
highest in the Fruitful Rim, Northern Crescent, and Southern Seaboard. . . . . . . . . . . . . 44
Figure 6. The location quotients of gross income were highest in the Northern Crescent,
followed by the Fruitful Rim and Southern Seaboard. . . . . . . . . . . . . . . . . . . . . . . . . . . .46
Figure 7. The location quotients of production expenses were above normal in the
Fruitful Rim, Northern Crescent, Southern Seaboard, and Heartland. . . . . . . . . . . . . . . .49
Figure 8. The location quotients of net income of operation were highest in the Fruitful
Rim, Northern Crescent, and Heartland. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .51
Figure 9. The location quotients of farming occupation were above normal in the
Northern Great Plains. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
Figure 10. The location quotients of part owner farms were above normal in the Northern
Great Plains and below normal in the Fruitful Rim. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .57
11
Figure 11. The location quotients of part owner farm acres were lowest in the Fruitful
Rim. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .60
Figure 12. The location quotients of tenant farms were highest in the Mississippi Portal
and lowest in the Eastern Uplands. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .62
Figure 13. The location quotients of tenant farm acres were above normal only in the
Mississippi Portal. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .65
Figure 14. The location quotients of small family farming occupation higher sales farms
were above normal in the Northern Great Plains and Heartland. . . . . . . . . . . . . . . . . . . .68
Figure 15. The location quotients of large family farms were highest in the Northern
Great Plains and Heartland, and lowest in the Eastern Uplands. . . . . . . . . . . . . . . . . . . . 71
Figure 16. The location quotients of large family farm acres were highest in the Northern
Great Plains and Heartland. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
Figure 17. The location quotients of very large family farms were above normal in the
Northern Great Plains, Heartland, and Mississippi Portal. . . . . . . . . . . . . . . . . . . . . . . . .76
Figure 18. The location quotients of very large family farm acres were above normal in the
Mississippi Portal, Heartland, and Northern Great Plains. . . . . . . . . . . . . . . . . . . . . .78
Figure 19. The location quotients of corn farms were highest in the Heartland. . . . . . . .82
Figure 20. The location quotients of corn acres were highest in the Heartland and the
Northern Crescent. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
Figure 21. The location quotients of soybean farms per county were highest in the
Heartland followed distantly by the Mississippi Portal. . . . . . . . . . . . . . . . . . . . . . . . . . .87
Figure 22. The location quotients of soybean acres were highest in the Heartland and
Mississippi Portal. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .89
12
Figure 23. The location quotients of wheat farms were highest in the Northern Great
Plains and Prairie Gateway. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
Figure 24. The location quotients of wheat acres were highest in the Northern Great
Plains and Prairie Gateway. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
Figure 25. The location quotients of total cropland farms were normal for all regions. .96
Figure 26. The location quotients of total cropland acres were highest in the Heartland,
Northern Crescent, and Mississippi Portal. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .99
Chapter I: Introduction
The United States Department of Agriculture’s (USDA) 2002 Farm Security and
Rural Investment Act, better known as the Farm Bill, is an omnibus bill that contains
legislation controlling forestry, agricultural trade, energy and fuels development,
conservation, agricultural commodity production, and nutrition and food distribution
programs such as food stamps. The Farm Bill is of interest to people because it directly
affects the food system of the United States, the food security of millions of Americans,
and federal spending of taxpayer dollars as income to people and agribusinesses enrolled
in conservation and commodity programs. The purpose of this study is to determine if
commodity payments are distributed inequitably, like previous researchers suggest, based
on farm operator and operation variables and Farm Resource Regions.
Statement of the Problem
When Farm Bill legislation began in1933, the government attempted to control the
supply of commodities by providing price support to farmers who participated in
voluntary production reduction programs. It was an effort to provide financial security to
the 25 percent of the U.S. population residing on farms by reducing commodity
surpluses. In the 1970s, the Farm Bill was revolutionized into direct payments to people
2
who produced certain commodities, thereby reinvigorating the surpluses of these certain
commodities. By the 2002 Farm Bill, the social goal of saving the family farm had
transformed to the economic goal of mitigating financial risks for farm businesses,
despite numerous objections to the legislation (Flinchbaugh and Knutson, 2004).
This shift in goals resulted in: (1), the absorption of small farms into large
agricultural businesses (Goodwin, 2000); (2), the limited number of farms reached by
government payments (Westcott & Young, 2000); (3), increased dependence on
government payments for income (Barnard, Nehring, & Collender, 2001); (4), increased
farmland values based on government payments (Barnard et. al, 2001; Burfisher &
Hopkins, 2003); and (5), payments being received by non-family or very large family
farms that do not need income support, thereby leaving out small family and limited
resource farms (Hoppe & MacDonald, 2001).
In 2007, farm support subsidies provided for by the 2002 Farm Bill accounted for
$11.2 billion in government spending with over $8 billion paid in commodity subsidies
(Chite, 2008). The Farm Bill is due to be rewritten in 2012. According to the
Congressional Budget Office, the Farm Bill will be up for budget cuts. Exploring the
complexity of factors related to the commodity payments of the Farm Bill will provide
insight in the efficacy of the legislation.
Purpose of the Study
The purpose of this research is to explore the previously cited inequitable
distribution of Farm Bill program payments. In order to do this, this research seeks to
determine the farm and land, economic, operator characteristic, geographic, commodity,
and farm organization and typology variables that are related to government commodity
3
payments and to examine these relationships by Farm Resource Regions. In other words,
is commodity production the only variable that influences participation in programs and
distribution of program payments? Commodity payments are calculated by the USDA
using set target prices or payment rates for certain commodities as well as the acreage
and the yield of the commodity produced. Therefore, the only variables that directly
affect commodity payments are variables based on the commodities covered under the
program. However, previous studies show that certain types of farm operators, such as
renters (Monke, 2004), and farm typologies, such as nonfamily or very large family
(Hoppe & MacDonald, 2001), are underrepresented in terms of payments received. This
study analyzes if commodity production is the only variable correlated to government
payment, or if there are other variables also correlated to payments. By combining
geospatial and statistical analysis of farm data, including operator characteristics and
economic variables, this study provides a unique contribution to the literature. The
hypotheses that only commodity variables are strongly correlated to commodity payment
variables and the payments are equitably distributed based on Farm Resource Region are
based on the previous research that suggests that distribution of program payments is
inequitable.
The Farm Bill directly affects the food system and food security of Americans.
Not only does it directly determine the price of food, but also indirectly impacts the types
of food (whole versus processed) available to and consumed by Americans by artificially
deflating the price of meat and processed foods (Pollan, 2007). The Farm Bill is indirectly
linked to both obesity and heart disease in Americans (“Fresh Fruit”, 2012). However, it
also affects the food system and food security of other developing nations
4
(Pollan, 2007). It distorts trade, depresses world prices of agricultural products (Mayrand
et al., 2003), and cotton subsidies have been ruled illegal by the World Trade
Organization (Pollan, 2004). The Farm Bill also directly affects soil land and water
resources by promoting agricultural intensification (Plantinga, 1996), and encourages the
addition of chemicals that cause pollution (Lingard, 2002).
Methods
This study used data collected by the National Agricultural Statistics Service
(NASS) in the 2007 Census of Agriculture. The Census is conducted once every five
years. Kendall’s rank correlations identified trends on the national level between
payment variables and other operator and farm characteristic variables. Descriptive
statistics identified operator and farm characteristics that vary by Farm Resource Region
(FRR), regions created by the Economic Research Service (ERS) to group geographic
areas with similar farm productions, performance, and economics. Location quotients
determined counties and Farm Resource Regions where operator and farm characteristics,
and payment variables were concentrated or underrepresented. Kendall’s correlations
were also calculated to determine trends among the location quotients of variables on the
national level and by Farm Resource Region. County level choropleth maps were created
to provide a spatial representation of these variables at the county level. To limit this
study to the most salient variables, only those variables with strong national correlations
were included in this paper.
Chapter II: Literature Review
The Farm Security and Rural Investment Act of 2002, better known as the Farm
Bill, contains legislation to provide government payments to farmers to increase farm
financial security, to mitigate for financial risks inherent in agricultural production, and to
promote conservation. These price and income support programs, started in the 1930s to
relieve disparity between farm and non-farm incomes (Gardner, 1992), continue today
despite the fact that this disparity is no longer as great of a concern (Goodwin, 2000;
Mishra et. al, 2002). Numerous objections have been voiced against the Farm Bill,
including the inequitable distribution of payments (Hoppe & MacDonald, 2001). The
purpose of this study is to examine the disparity in government payments suggested by
previous researchers based on numerous variables, including Farm Resource Regions.
History of the Farm Bill
Farm Bill programs, as most people think of them, have their origins in the New
Deal legislation of the Great Depression. The 1933 Agricultural Adjustment Act (AAA,
1933), set in motion by Secretary of Agriculture Henry A. Wallace, was an attempt to
increase the value of agricultural products. The government enacted price supports to
guarantee that commodity prices did not fall below a set level for producers
6
who voluntarily participated in production reduction programs (Cain & Lovejoy, 2004).
When commodity prices fell below the target, the government purchased these
commodities from participating farmers and stored them. This led to large government
stocks of commodities significant enough to start many domestic and international food
distribution programs (Flinchbaugh & Knutson, 2004).
Over the years the price support guarantee was offered to farmers who participated
in conservation programs instead of production reduction programs (Cain & Lovejoy,
2004). In 1935, the Soil Conservation Act (SCA, 1935) established the Soil Conservation
Service (SCS). This made money available to farmers who established soil conservation
practices from payments through the SCS. The 1936 Soil Conservation and Domestic
Allotment Act (SCDAA, 1936) attempted to protect land resources from soil erosion by
paying farmers to replace soil depleting crops with cover crops. These soil depleting
crops (i.e., corn, cotton, wheat) were also in surplus. This was an attempt to control
surplus, commodity prices, and soil conservation. However, this did not stop the surplus.
Farmers would enroll their worst land in conservation programs and use the payments to
buy machinery and fertilizer to increase yields on their land still in production (Cain &
Lovejoy, 2004). The 1956 Agricultural Act (AA, 1956) created the Soil Bank and took 29
million acres out of production to decrease surpluses and protect soil from erosion even
further than previous Farm Bills. This bill paid farmers for conservation improvements
on land that they could not harvest or graze for three years.
During the 1970s, under Secretary Earl L. Butz, the price support system that
began in the New Deal era was revolutionized into an income support system. These
programs created target prices and loan rates for agricultural commodities that paid
7
farmers when commodity prices fell below these set targets, much the same way
programs pay farmers today. The most revolutionary part of this shift was that money
was transferred from the government directly to the farmer neither for commodities that
the government was purchasing nor for conservation efforts. The inception of these
programs affected planting decisions as farmers were more likely to plant commodities
that would yield the highest government payment (Flinchbaugh & Knutson, 2004).
This shift in the way money was transferred to farmers was accompanied by a shift
in conservation practices as well. To accommodate the increased world demand on food,
the Secretary of Agriculture told American farmers to plant from fence row to fence row,
thus damaging the conservation efforts made so far by the Farm Bill. Farmers met the
needs by adding their few remaining acres of idled cropland, woodland, and grassland into
production. In fact, a 1977 Congressional survey found that 26 percent of farmers who
had participated in the Great Plains Conservation Program, set up in the 1940s, took their
new conservation grasslands out of the program for wheat production (Cain & Lovejoy,
2004).
The 1985 Farm Bill (FSA, 1985) was the first one to have a separate title for
conservation and attempted to again make the conservation advancements that were
interrupted in the 1970s. It introduced Sodbuster, Swampbuster, Conservation Reserve
Program (CRP), and Conservation Compliance to the legislation. Payments were no
longer made to people based on a voluntary conservation program. In fact, violation of
conservation practices set forth in the Farm Bill now meant heavy penalties. These
included not only loss of price-support program income but also the loss of crop
insurance benefit payments, and commodity loans (Cain & Lovejoy, 2004).
8
The 1996 Federal Agriculture Improvement and Reform Act (FAIRA, 1996) was
an attempt to make the Farm Bill more market oriented (Goodwin, 2000). Planting
decisions had been linked to income support programs since the 1970s. The new bill
attempted to allow farmers to plant what they wanted based on market prices instead of
price and income support programs (Flinchbaugh & Knutson, 2004). This new bill also
cut the grain reserve program, thereby inducing farmers to plant as much as they could,
creating a surplus as well as a drop in grain prices. In 1998 the government enacted adhoc emergency payments to support farm income (Imhoff, 2012c).
Under the 2002 Farm Bill, these ad-hoc programs were made permanent. This
lead to the adoption of policy that no longer attempted to control supply and price, but
rather a policy that encourages low prices and market instability and then supports
income to the farmer in the form of subsidies (Imhoff, 2012c). Also, the goal of
decoupling payments, removing the ties between payments and price or production, was
pushed even further, though still was not successful. Payments were made directly to
farmers based on set payment rates for a commodity, even if that commodity was not
currently produced (Monke, 2004) This, in effect, decoupled payments from production.
However, there were still programs, (e.g., Counter Cyclical Income Support Program,
Marketing Assistance Loans, and Loan Deficiency Payments) which were still tied to
commodity prices. Since payments were made when commodity prices fell below set
prices, technically, the 2002 Farm Bill still has not fully decoupled payments
(Flinchbaugh & Knutson, 2004).
9
Farm Security and Rural Investment Act
The 2002 Farm Bill includes ten titles covering Commodity Programs,
Conservation, Trade, Nutrition Programs, Credit, Rural Development, Agricultural
Research, Forestry, and Energy (FSRIA, 2002). This research is concerned with the first
title. Title I Commodity Programs include: Fixed Direct Payments (DP), Counter
Cyclical Income Support Program (CCP), Marketing Assistance Loans (MALs), Loan
Deficiency Payments (LDP), Sugar, and Dairy.
Direct Payments (DPs) replaced Production Flexibility Contracts in the 2002
Farm Bill. Eligible landowners receive annual DPs that are equal to government payment
rates per specific crop multiplied by eligible crop acres. Producers sign annual
agreements to receive payments. DPs are limited to $40,000 per year per crop. Producers
who earn more than $2.5 million per year for three years cannot qualify for DPs, unless
more than 75% of their income comes from agriculture. Enrolled land must remain in
agricultural uses and must meet conservation and wetland provisions to be eligible to
receive DPs. A farmer may receive payments for a commodity without producing that
commodity as long as they have a production history of that commodity. For example,
they may receive payments for corn but grow soybeans on those acres instead. However,
farmers may not plant fruits and vegetables on the land that is enrolled in DPs. DPs apply
only to wheat, corn, grain sorghum, barley, oats, upland cotton, rice, soybeans, oilseeds,
and peanuts (FSRIA, 2002; Monke, 2004).
In contrast, the Counter Cyclical Income Support Program (CCP) is a new
program for the 2002 Farm Bill created to replace most ad-hoc market loss assistance
payments that were used between 1998 and 2001. CCP payments vary by crop and
10
depend both on historic and current production rates by crop. CCPs are available for a
crop when the effective commodity price is lower than the target price set by the federal
government. Payment limits on CCPs are $65,000 per year per crop, and the same limits
apply to farmers earning more than $2.5 million. CCPs apply to the same commodities as
DPs (FSRIA, 2002; Monke, 2004).
Marketing Assistance Loans (MALs) are administered by the Farm Service
Agency through the Commodity Credit Corporation (CCC). By pledging the crop as
collateral the producer receives a loan from the government at crop specific loan rates, set
in federal legislation. The producer may receive a full or partial loan on their crop for the
next year’s production. MALs cover the same commodities as DPs and CCPs, but also
cover mohair, wool, honey, small chickpeas, lentils, and dry peas. There are three ways
to repay MALs: at the loan rate plus interest costs, by forfeiting the pledged crop to CCC
at loan maturity, or at the alternative loan repayment rate. Alternative loan repayment
rates are used when market prices for the commodity drop below the loan rate thus
creating a profit for the producer (FSRIA, 2002; Monke, 2004).
Farmers may choose to receive direct loan deficiency payments when market
prices are lower than commodity loan rates. The LDP is equal to the profit a producer
would have earned using the alternative loan repayment rate with MALs. When an LDP
is paid on a portion of the crop, that portion cannot be used for subsequent loans. LDPs
apply to the same commodities as MALs and payment is limited to $75,000 per year per
crop. The same limits apply to farmers earning more than $2.5 million (FSRIA, 2002;
Monke, 2004).
11
Broader Implications of the Farm Bill
The Farm Bill, by paying producers to produce certain commodities, affects the
food system and food security of Americans. The Farm Bill directly affects the health of
the environment through soil erosion, water pollution, habitat degradation, and
greenhouse gas emissions (Lingard, 2001). This American legislation also impacts the
economies and environments of less developed countries (Mayrand et al., 2003), even
being ruled illegal by the World Trade Organization (Pollan, 2007). Closer to many
American’s wallets and waistlines are the impacts of the Farm Bill food prices and
unhealthy diets. The Farm Bill artificially depresses the prices of processed foods and
meats (Pollan, 2007), and indirectly contributes to heart disease, obesity, and diabetes
(“Fresh Fruit”, 2012).
The environmental impacts of agricultural subsidies are hard to quantify due to the
highly tangled nature of the equation. However, agricultural subsidies have been tied to
soil erosion, decreased water quality, increased water pollution, increased greenhouse gas
emissions, decreased biodiversity, habitat degradation, and introduction of non-native
plants and diseases (Lingard, 2001; Mayrand et al., 2003; Plantinga, 1996). Agricultural
subsidies encourage intensive agricultural practices that increase the above noted
environmental problems (Lingard, 2001), as well as give an incentive to increase
acreages and yields to get higher payments (Mayrand et al., 2003). Not to mention that
agriculture contributes eight percent of U.S. greenhouse gas emissions (Imhoff, 2012b).
Highly erodible land is marginal farmland for commodity production. In the U.S.,
about one-third of total agricultural land is susceptible to erosion at rates that jeopardize
its future use in agricultural production (Mayrand et al., 2003). When agricultural prices
12
(or price supports as agricultural subsidies) are higher, more land is kept in production.
Marginal land would normally be taken out of production when prices are low, since the
production costs of keeping this lower quality land in production would exceed the
income received from the land. However, when prices are high this land is kept in
production because the income exceeds the production costs. This marginal land is
highly erodible and increases sediment in bodies of water (Plantinga, 1996).
In addition to the increased sedimentation from highly erodible agricultural land
the runoff from fertilizers and pesticides is also increased on this land (Plantinga, 1996).
Agricultural chemicals are responsible for roughly 80 percent of the added nitrogen and
phosphates in surface water (Mayrand et al., 2003). In addition, 80 percent of samples of
water from rivers in agricultural areas, as well as fish from these rivers, have pesticides in
them (Mayrand et al., 2003). The nitrogen balance in the U.S. has increased 24 percent
from 1985-1987 to 1995-1997 (Mayrand et al., 2003). Chemical runoff from agriculture is
a major contributor to the 7,000 square mile dead zone in the Gulf of Mexico off the
coasts of Louisiana and Texas (Borders & Burnett, 2006; Imhoff, 2012a).
The environmental impacts of the Farm Bill also extend to wildlife species. The
increased intensification of agriculture based on subsidies also decreases agrobiodiversity. This is because subsidies encourage large monocultures of very limited
crops (Imhoff, 2012a; Mayrand et al, 2003). These monocultures reduce the natural
biodiversity, result in habitat fragmentation, and introduce non-native pests (Mayrand et
al., 2003). In 1995, agriculture was estimated to affect roughly 58 percent of threatened
and endangered species in the United States, due to agrochemical exposure and habitat
degradation (Mayrand et al., 2003). One of the largest sources of habitat degradation is
13
the conversion of wetlands to agricultural land. Between 1986 and 1997, roughly onehalf (300,000 acres) of wetlands lost in the United States were converted to agricultural
use (Borders & Burnett, 2006).
Another problem associated with the Farm Bill is the harm that agricultural
subsidies create on the economies of other countries. Agricultural subsidies artificially
deflate the prices of commodities on the world market. This occurs because subsidies pay
for the inputs (i.e., production expenses such as agro-chemicals) of commodities as well
as supporting the price of commodities in developed nations (Mayrand et al., 2003). Also,
due to the incentives subsidies offer to increase yield and acreage of, certain commodities
are overproduced, surpluses are created (Pollan, 2007), and world production and trade of
these commodities are distorted (Mayrand et al., 2003).
When these surpluses of low priced commodities hit the world market, farmers in
less developed countries cannot compete with the prices (Mayrand et al., 2003). When
farmers in less developed countries cannot compete with the prices of the commodities
being imported from countries with subsidies, they are forced off their land. Many people
migrate off-farm to cities or to other countries. Since the creation of the North American
Free Trade Agreement, Mexican immigration into the U.S. has been fueled by American
corn flowing into Mexico (Pollan, 2007). This results in lower food security and higher
levels of poverty in developing nations (Mayrand et al., 2003).
One example of agricultural subsidies as large obstacles to agricultural growth in
developing countries is cotton. World cotton prices, in 2006, were down by one-half
since the mid-1990s. This price was lower than even during the Great Depression,
adjusting for inflation. However, cotton production in the U.S. from 1998 to 2001 grew
14
by 42 percent in the midst of a global price fall. Cotton subsidies provided by the Farm
Bill meant that U.S. farmers were getting up to 73 percent more than the world market
price for cotton. These cotton subsidies cost $302 million in lost production and export
revenue in sub-Saharan Africa in 2001 and 2002 alone, even though the cost to produce
cotton there is one-third of what it is in the United States (Borders & Burnett, 2006). In
fact the World Trade Organization has ruled U.S. cotton subsidies illegal (Pollan, 2007).
The commodities covered under the commodity title of the Farm Bill are limited in
their scope. One common theme among the covered variables is that they rarely reach the
dinner table of American consumers as whole foods. Crops that may reach the consumer
in whole forms include roasted peanuts or rice. Some grains, including corn and soybean,
end up in the fuel tanks of vehicles as biodiesel or ethanol (ERS, 2102c). In
2011, about 40 percent of U.S. corn was used for ethanol production. Ethanol
production, while meant to bolster energy independence, requires about two-thirds of a
gallon of fossil fuels to create one gallon of ethanol (Imhoff, 2012b).
Most of the covered commodities are used for livestock feed, including corn,
barley, oats, and grain sorghum (ERS, 2012b). Subsidies on these commodities decrease
the cost of feed grain for livestock, thereby reducing meat prices. In 2010, it cost $27.33
of feed for an $81.11 return on each hundredweight gain of hogs in the U.S. (ERS,
2012a). Imhoff (2012c) went so far as to say the largest beneficiaries of subsidies are the
large livestock and processed-food manufacturers. The top four chicken and hog
producers saved a total of $20 billion between 1996 and 2006 (Imhoff, 2012c).
Carbonated beverage manufactures save roughly $100 million each year, as corn
is used for high fructose corn syrup. This high fructose corn syrup, and other foods
15
processed from commodities, is produced by companies like Archer Daniels Midland and
Cargill, large grain trading businesses who turn large profits by buying low cost
commodities, processing them into food ingredients, and then selling them off to other
businesses who process these ingredients into food (Imhoff, 2012c).
Other commodities end up in our food in such a form as to make them almost
invisible to the consumer. For example, corn is used for high fructose corn syrup, corn
starch, modified corn starch, and xantham gum (bacteria harvested from fermented corn)
(ERS, 2012b). Soybeans are used for soybean oil, partially hydrogenated soybean oil,
soy lecithin, and soy protein isolate (ERS, 2012c). All of these ingredients are found in a
box of Hostess Ding Dongs. The inclusion of ingredients produced from these subsidized
commodities keeps the cost of highly processed foods lower than the cost of whole foods,
despite the increased cost of production (Pollan, 2007).
Processed foods are more energy dense than whole foods. They contain less
water and fiber but more added fats and sugars (Pollan, 2007). Most of the added fats
and sugars some from the products made of covered commodities, such as soybean oil
and corn syrup. Two researchers found that, at the supermarket, one dollar will buy 1200
calories of chips or cookies, but only 250 calories of carrots (“Fresh Fruit”, 2012). Why?
Because processed foods and cheap meat are products of agricultural subsidies, thereby
keeping prices lower. From 1985 to 2000, the price of soft drinks, made with
commodities covered under the Farm Bill, decreased 23 percent. However, the real price
of fruits and vegetables increased 40 percent in that same amount of time (Pollan, 2007).
This is because fruits and vegetables, labeled “specialty” crops by the USDA, do not
receive subsidies or crop insurance (“Fresh Fruit”, 2012).
16
Although most American consumers don’t analyze their food choices based on the
number of calories purchased per dollar, many are on a budget. When given the choice
between four $1 boxes of pasta mix or one $4 pineapple, processed food choices are
sometimes the most economical (“Fresh Fruits”, 2012). However, they can also be the
most damaging. Corn, processed into sweeteners, increases the risk of type 2 diabetes.
These sweeteners are commonly found in soft drinks, sports drinks, and juice. In 2006,
children consumed an extra 130 calories per day from these drinks, leading to not only
obesity but also diabetes (“Fresh Fruits”, 2012). Health care costs in America due to the
obesity crisis are roughly $150 billion annually and are predicted to more than double in
the next six years (Imhoff, 2012b).
Also, in order to bring cattle up to slaughter weight faster, they are kept in feed
lots and given a diet of corn and other commodities that they are not accustomed to
eating. It takes 10 pounds of corn to produce 1 pound of beef (Pollan, 2008). Although
this helps them gain weight, it produces meat with more calories per gram than grass-fed
beef. It also has higher amounts of omega-6 fatty acids and lower amounts of omega-3
fatty acids. This combination is linked to heart disease (“Fresh Fruits”, 2012). In fact the
least healthy calories in the supermarket are the cheapest ones, as well as the ones the
Farm Bill encourages farmers to produce (Pollan, 2007).
Previously Cited Problems Derived from the Farm Bill
Previous studies conducted on the Farm Bill have cited numerous problems,
including: (1), the absorption of small family farms into large agricultural businesses
(Goodwin, 2000); (2), the limited number of farms reached by government payments
(Westcott & Young, 2000); (3), payments being received by non-family or very large
17
family farms that do not need income support thereby leaving out small family and
limited resource farms (Hoppe & MacDonald, 2001); (4), the increased dependence on
government payments for income (Barnard, Nehring, & Collender, 2001); and (5), the
increased farmland values based on government payments (Barnard et. al, 2001;
Burfisher & Hopkins, 2003).
The small family farm is on the decline in the United States, leading to larger
agricultural businesses. In 1935, there were over 6.5 million American farms with an
average of 150 acres each. By 1997, there were only two million American farms with
an average size of 500 acres. While farmland acreage has remained roughly the same,
numerous small family farms have been absorbed into larger commercial farms.
Consider that in 1940 11.6 percent of farms, roughly 745,000 farms, accounted for 50
percent of all American agricultural production. By 1997, only 3.2 percent, or 64,000
farms, accounted for 50 percent of production (Goodwin, 2000).
Despite the decline in the number of farms, payments still reach a limited number
of farms. In 1997, 36 percent of farms received government payments (Westcott &
Young, 2000). The limited number of farms reached by government payments is partly
due to the limited number of covered commodities. Farm Bill programs are limited to 16
commodities with an additional 2 commodities being covered under individual programs
(FSRIA, 2002). Five major crops accounted for 93 percent of all commodity programs in
2006: 46 percent corn, 23 percent upland cotton, 10 percent wheat, 8 percent rice, and 6
percent soybeans. These five crops, while dominating payments, only accounted for 21
percent of total U.S. agricultural production (ERS, 2006).
18
Most payments are going to larger farms and agricultural businesses. In 1998, 91
percent of all U.S. farms were in one of the small family farm typologies, and these
typologies owned 68 percent of all farmland. However, 66 percent of agricultural
production came from the mere 9 percent of U.S. farms that were in the large, very large,
and nonfamily farm typologies. Even more, roughly half of all government payments
went to farms in these farm typologies (Hoppe & MacDonald, 2001). Again in 1998, 60
percent of government payments went to farms with gross sales of more than $50,000. In
1997, farms with sales greater than $250,000 received roughly 40 percent of the
government payments. Large farms, by sales class, not only have higher income but
receive higher government payments (Westcott & Young, 2000).
Despite the limited number of farms receiving payments, dependence on
payments for income is increasing. In 1980 Farm Bill program payments accounted for
less than 4 percent of the net cash farm income of American farmers. By 2000 these
government payments accounted for 40 percent of net cash farm income. Eight percent
of these payments came from conservation and miscellaneous payments while 92 percent
of government farm transfers were generated from commodity payments and disaster
relief payments (Barnard, Nehring, & Collender, 2001).
The increased dependence on Farm Bill payments applies to landlords, too.
About 60 percent of acres enrolled in Farm Bill programs are rented (Monke, 2004). The
USDA intends for payments to go to the person who assumes the risk of agricultural
production. Unless the landlord and operator have a shared rental agreement, according to
the USDA, payments should go to the operator, rather than the landlord. However, most
agricultural economists believe that a large percent of the payments pass through to
19
the landlord in the form of increased farmland rent prices and that payments raise the
price of land (Burfisher & Hopkins, 2003).
Direct government payments, meant to boost farm income, increase farmland
value and thereby increase the cost of production. This increased cost of production,
either in cash rent or in farmland purchases, comes without a corresponding increase in
productivity or farm income. In 1999, landlords owned much of the land where land
values have increased based on commodity payments, varying from 39 percent in the
Eastern Uplands to 75 percent in the Mississippi Portal (see Figure 1 for Farm Resource
Region References) (Barnard et. al, 2001).
Farm Resource Regions
In 2000, the USDA’s Economic Research Service created Farm Resource Regions
(FRR) to geographically represent areas with similar productions in U.S. farm
commodities (ERS, 2000). These regions were created by a detailed analysis and
combination of USDA Farm Production Regions, NASS Crop Reporting Regions, USDA
Land Resource Regions, and a cluster analysis of farm characteristics. These regions
were intended to identify patterns in farming to better understand differences in farm
production, performance, and economics (Eathington & Swenson, 2001).
The Heartland (Figure 1) covers 543 counties in Iowa, Illinois, Indiana, and
parts of Ohio, Kentucky, Missouri, Nebraska, South Dakota, and Minnesota. The
Heartland has the highest number of farms and cropland, and specializes in cash grain
and cattle farms (ERS, 2000). More than 37 percent of all farms that receive government
payments are in the Heartland (Eathington & Swenson, 2001).
Heartland
Northern Crescent
Northern Great Plains
Prairie Gateway
Eastern Uplands
Southern Seaboard
Fruitful Rim
Mississippi
Basin and Range
Mississippi
Basin
and Portal
Figure 1. The Farm Resource Regions as created by the Economic Research Service (ERS, 2000).
20
21
The Northern Crescent (Figure 1) covers 423 counties in Maine, New Hampshire,
Vermont, New York, Massachusetts, Rhode Island, Connecticut, New Jersey, Michigan,
Wisconsin, and parts of Minnesota, Ohio, Pennsylvania, Maryland, and Delaware. It is the
most populous region and is dominated by dairy, general crop, and cash grain farms (ERS,
2000).
The Northern Great Plains (Figure 1) covers 179 counties in North Dakota and
parts of Montana, Wyoming, Colorado, Nebraska, South Dakota, and Minnesota. Wheat,
cattle, and sheep are the dominant commodities in this region, which has the largest farms
and the lowest population (ERS, 2000). The Northern Great Plains has the highest mean
percent of farms receiving payments, the highest mean percent of rented farmland, as
well as the lowest mean percent of principal operators who reported working off-farm
(Eathington & Swenson, 2001).
The Prairie Gateway (Figure 1) covers 394 counties in Kansas and parts of
Nebraska, Colorado, New Mexico, Texas, and Oklahoma. The region specializes in
cattle, wheat, sorghum, cotton, and rice production (ERS, 2000).
The Eastern Uplands (Figure 1) is a noncontiguous region that covers 410
counties in parts of Missouri, Oklahoma, and Arkansas, as well as parts of Kentucky,
Tennessee, Alabama, Georgia, North Carolina, Virginia, Maryland, Pennsylvania, and
Ohio, and all of West Virginia. The Eastern Uplands specializes in part-time cattle,
poultry, and tobacco farms and has a high number of small farms (ERS, 2000). The
Eastern Uplands had the lowest mean market value of agricultural products sold, the
lowest mean percent of farms receiving government payments, the lowest mean amount
22
of government payments, and the highest percent of principal operators who worked offfarm (Eathington & Swenson, 2001).
The Southern Seaboard (Figure 1) is another noncontiguous region that covers
478 counties in parts of Delaware, Maryland, Virginia, North and South Carolina,
Georgia, Alabama, and Mississippi, as well as parts of Arkansas, Louisiana, and Texas.
The region is comprised of both small and large farms and specializes in part-time cattle,
poultry, and general field crops (ERS, 2000).
The Fruitful Rim (Figure 1) is comprised of four broken regions that cover 280
counties in: parts of South Carolina and Georgia as well as all of Florida; southern parts
of Texas; parts of Arizona and California; and parts of Oregon, Washington, and Idaho.
Farms here specialize in fruits and vegetables, nursery products, and cotton, and have the
largest share of very large family and nonfamily farms (ERS, 2000). The Fruitful Rim
had the highest mean market value of agricultural products sold and the highest mean
amount of government payments (Eathington & Swenson, 2001).
The Basin and Range (Figure 1) region is comprised of 197 counties covering all
of Utah and Nevada, and parts of Oregon, California, Arizona, New Mexico, Colorado,
Wyoming, Montana, Idaho, and Washington. Cattle, wheat, and sorghum are the major
commodities of this region (ERS, 2000).
The Mississippi Portal (Figure 1) covers 165 counties in parts of Tennessee,
Mississippi, Louisiana, and Arkansas along the Mississippi River. The region specializes
in cotton, rice, poultry, and hog operations (ERS, 2000). The Mississippi Portal had the
highest mean ratio of government payment to income (Eathington & Swenson, 2001).
Chapter III: Methods
The purpose of this study is to determine if commodity variables are the only
variables that are closely correlated to government commodity payments, based on the
inequitable distribution of Farm Bill payments cited by other researchers. Since
commodity production acreages and yields are included in the calculation of government
commodity payments, only commodity variables should be tied to the government
payment variables in this study. This study begins with the hypotheses that the only
variables that will be closely tied to government payment variables are commodity
variables, and that payments are distributed equitably amongst Farm Resource Regions
based on commodities.
Data
The data used in this study
were collected by the National
Agricultural Statistics Service (NASS)
in the 2007 Census of Agriculture.
The Census is conducted once every
five years according to the Census of
Farm and Land Variables (NASS, 2009)
Farms. The number of farms reporting to the
NASS, defined as a place that produced and
sold a minimum of $1000 of agricultural
products per year, or would have under
normal circumstances.
Land in farms. The acres of land in farm
production plus acres of woodland and
wasteland that are not under production,
including conservation program land and
land rented under government permit in
2007.
24
Agriculture Act of 1997. Data were published by the NASS as county level aggregates
available to the public online (http://quickstats.nass.usda.gov/) and through a
downloadable data query (http://www.agcensus.usda.gov/Publications/2007/
Online_Highlights/Desktop_Application/index.asp). Data were omitted based on a nondisclosure policy that suppresses information when it would be possible to calculate
individual responses (NASS, 2009).
Economic Variables (NASS, 2009)
Market value of agricultural products sold. The gross value of all agricultural
products sold or removed from the farm in 2007, regardless of who received payment
for the goods.
Total income from farm-related sources, gross before taxes and expenses. Any direct
income in 2007 from agri-tourism and recreation, state and local government
payments, crop and livestock insurance payments, custom-work and other
agricultural services unless they constitute a separate business, cash rent or share
payments excluding non-farm rent, patronage dividends and refunds from
cooperatives, sales of forest products excluding non-farm timber tracts and separate
businesses, and other related income such as boarding fees and state fuel tax refunds.
This income excludes federal government payments from the Farm Bill programs.
Total farm production expenses. Expenses include livestock purchased or leased,
cash rent for land and buildings, chemicals and fertilizers, contract labor, hired farm
labor, custom-work, feed purchased, gasoline and fuels, interest paid on debts,
property taxes, rent and lease for equipment and machinery, supplies and
maintenance, seeds and plants, and other expenses incurred in 2007. Landlords only
reported expenses for land that they operated while tenants reported expenses that
they and the landlords paid for land they operated.
Net cash farm income of operation. Net cash income is gross income, including
government payments, less total expenses, excluding depreciation in 2007.
The Census defines a farm as any place that produced and sold, or would have
under normal circumstances, $1,000 or more of agricultural products during the Census
year. A mailing list was composed by the NASS containing 3,194,373 records of
25
operations that met or potentially met this requirement to which Census packets were
mailed. The response rate of the Census was 85.2% (NASS, 2009). A full description of
the methods of the Census preparation and data collection can be obtained in Appendix A
of the 2007 Census of Agriculture.
The county level aggregates
provided by the Census were considered
individual datum for this study. There
are 3,079 counties with published data
in the Census (2009). Of these counties,
3,069 are included in Farm Resource
Regions excluding Hawaii and Alaska
(ERS, 2000).
When county level
aggregates were real zeros, zeros were
used in calculations. When county level
aggregates were suppressed to prevent
disclosure of individual data or when
they were omitted from the published
data for any number of reasons, they
were treated as missing values in the
preparation of the data for analysis.
FRR were used to break data into
groups for analysis.
Operator Characteristic Variables
(NASS, 2009)
Farming occupation. Principal operators
who spent 50% or more of work-time in
2007 on farm operation are classified as
farming occupation.
Other occupation. Principal operators
who spent less than 50% of work-time in
2007 on farm operation are classified as
other occupation.
Worked off farm any days. Principal
operators who worked off farm any day in
2007.
Worked off farm 200 days or more.
Principal operators who worked off farm
200 or more days in 2007.
Race. The race of the principal operator
self-reported to the NASS. Operators
could select more than one race including
American Indian or Alaskan Native,
Asian, Black or African American, MultiRace, or White.
Spanish, Hispanic, or Latino origin. The
ethnicity of the principal operator reported
to the NASS, not mutually exclusive from
race.
Gender. Gender of the principal operator
self-reported to the NASS.
26
Analysis
A variety of statistical analyses were used in this study. First, descriptive
statistics were calculated using SPSS 19 to examine all variables and to provide
information at the national level, including Alaska and Hawaii. Descriptive statistics
were also calculated for all variables for each FRR, to provide data for comparison
among each region.
National level descriptive
statistics were used to determine
normality for further analysis.
To determine normality of the
data, skewness and kurtosis
values as well as standard error
were calculated using SPSS 19.
Skewness varied greatly per
variable, ranging from -0.412 to
49.279, suggesting non-normal
distributions. Kurtosis values
Farm Organization (NASS, 2009)
Family or individual. Farms held in sole
proprietorship, excluding partnership or
corporation.
Partnership. Partnership farms, including family
partnerships, include both those registered under
state law and those that are not.
Corporation other than family held. Farms that
reported to the NASS as corporation farms
excluding family held corporations.
Corporation family held. Farms classified as
corporation farms that are held by families.
Other. Other farms are classified as cooperative,
estate or trust, institutional, or do not fall into the
other farm organization categories.
also suggested non-normal data,
ranging from 3.051 to 2,522.140.
The purpose of location quotients is to quantify the concentration of a certain
commodity or other variable in a particular region in relation to the national average.
Location quotients are used to provide a number that is comparable between counties and
regions despite the variances in acres or number of farms. Location quotients were
27
calculated using Microsoft Excel 2010. LQs were calculated by dividing a county’s
percent share of one variable (e.g. acres of corn) by its percent share of another variable
(e.g. acres of farmland) considered to be a denominator. Location quotients were
calculated for each county, and descriptive statistics were calculated for each Farm
Resource Region based on location quotients, using SPSS 19. Areas with LQs higher
than 1.25 were considered to have a high relative specialization in a certain commodity or
high relative representation of farm and operator characteristics. Areas with LQs below
0.75 were considered to have a low relative specialization or low relative representation
Government Payment Variables (NASS, 2009)
Farms with commodity payments. The number of
farms receiving federal payments excluding
Conservation Reserve Program (CRP), Wetland
Reserve Program (WRP), Farmable Wetland
Program (FWP), Conservation Reserve
Enhancement Program (CREP), Commodity Credit
Corporation (CCC) loans, and state and local
payments.
Commodity payments. The dollar amount of federal
payments received excluding payments from CRP,
WRP, FWP, CREP, CCC loans, and state and local
payments.
Commodity payment dependence. Payment
dependence is the ratio of commodity payments
received to the net cash farm income of operation.
It is a measure of dependence on commodity
payments for operation and operator income.
(Champion & Wein, 2008).
LQs ranged from a minimum
of 0 to a maximum of 1356.77
per county.
Since the data violate
the assumptions of normality,
Kendall’s tau rank correlation
coefficients were used for this
study, as they do not assume
normality. The Kendall’s tau
ranks both variables in the
correlation independently then
calculates a correlation based on the number of concordant pairs and discordant pairs
(Conover, 1980). The purpose of using the Kendall’s tau was to determine the strength
of the relationship between independent variables and government payment variables on
28
the national level, including Alaska and Hawaii. Kendall’s tau correlations were also
calculated comparing the county level location quotient of each independent variable to
government payment variables, excluding Alaska and Hawaii. This was to test the
strength of the relationship between independent variable location quotients and
government payment variable location quotients. Finally, Kendall’s tau correlations were
calculated comparing the county level location quotient of each independent variable to
government payment variables by each Farm
Resource Region, to determine which variables
Farm Tenure (NASS, 2009)
were the most salient in each region.
Full owner. Principal operators
who own all the land operated.
Using ArcGIS10, choropleth maps were
created to illustrate trends in variables across
Part owner. Principal operators
who both own and rent land
operated.
the country based on location quotients. Maps
were created using the Albers equal-area conic
Tenant. Principal operators who
rent, lease, or work on shares all
land operated.
projection on a 1:19,822,089 scale. County and
state lines were drawn using shapefiles available from ESRI. Map classes were created
using low (below 0.75), normal (0.75-1.25), and high (over 1.25) location quotients.
When a variable contained outliers based on interquartile range, these outliers were used
to create map classes. Mild outliers, outliers only one interquartile range above the 75th
or below the 25th percentile, and extreme outliers, outliers two or more interquartile
ranges above or below, were broken into two separate classes for the maps. This resulted
in some maps with only three classes, and some maps with seven classes. This method
was chosen despite this inconsistency so outliers are always identified.
29
Commodity Variables (NASS, 2009)
Barley for grain. Barley is covered under Direct Payments (DP), Counter Cyclical
Payments (CCP), Marketing Assistance Loans (MAL), and Loan Deficiency
Payments (LDP).
Beans. Beans are defined as dry edible beans, excluding lima beans. Dry edible
beans are covered under MALs and LDPs.
Corn for grain. Corn is covered under DPs, CCPs, MALs, and LDPs.
Upland cotton. Upland cotton is covered under DPs, CCPs, MALs, and LDPs.
Total cropland. Total cropland includes all cropland harvested (including noncovered commodities), failed crops or abandoned cropland, cropland that was
pastured or grazed, and idled cropland in 2007.
Milk cows. Milk cows are covered under the Dairy program.
Oats for grain. Oats are covered under DPs, CCPs, MALs, and LDPs.
Peanuts for nuts. Peanuts are covered under DPs, CCPs, MALs, and LDPs.
Rice. Rice is covered under DPs, CCPs, MALs, and LDPs.
Sorghum for grain. Sorghum is covered under DPs, CCPs, MALs, and LDPs.
Soybean for beans. Soybeans are covered under DPs, CCPs, MALs, and LDPs.
Sugarbeets. Sugarbeets are covered under the Sugar program.
Sugarcane. Sugarcane is covered under the Sugar program.
Wheat for grain. Wheat is covered under DPs, CCPs, MALs, and LDPs.
Choropleth maps were also created to illustrate the differences between the
location quotients of the independent variables and the government payment variables.
Since the variables discussed in this paper have strong positive correlations based on
location quotients, it would be expected that the location quotients of government
independent variables. These maps illustrate the counties where this is not the case.
30
Farm Typology (NASS, 2009)
Small family farm limited resource. Sole proprietorship farms with market values of
agricultural products sold less than $100,000 and total principal operator household
income of less than $20,000.
Small family farm retirement. Sole proprietorship farms with market values of
agricultural products sold less than $250,000 and a principal operator who reports
being retired
Small family farm residential/lifestyle. Sole proprietorship farms with market values
of agricultural products sold less than $250,000 and a principal operator who reports
a primary occupation other than farming.
Small family farm farming occupation lower sales. Sole proprietorship farms with
market values of agricultural products sold less than $100,000 and a principal
operator who reports farming as the primary occupation.
Small family farm farming occupation higher sales. Sole proprietorship farms with
market values of agricultural products sold between $100,000 and $249,999 and a
principal operator who reports farming as the primary occupation.
Large family farm. Sole proprietorship farms with market values of agricultural
products sold between $250,000 and $499,999.
Very large family farm. Sole proprietorship farms with market values of agricultural
products sold more than $500,000.
Nonfamily farm. Farms that do not fall into the family or individual organization,
with no constraints on the market value of agricultural products sold.
Counties with lower numbers (green) have much lower location quotients of independent
variables than government payment variables, showing a concentration of government
payment variables without a corresponding concentration of independent variables. For
the purpose of this study these counties are called negative discordant counties since they
payment variables increase relative to the increase in the location quotients of result in
negative numbers. Counties with higher numbers (red) have much higher location
quotients of independent variables than government payment variables, showing a
31
concentration of the independent variable without a corresponding concentration of
government payment variables. These counties are called positive discordant counties
since they result in positive numbers. The middle range counties (yellow) are counties
with both location quotients within 0.50 of one another. These are called concordant
counties for this paper. These counties fit the correlation closer than other counties.
Chapter IV: Results
The purposes of this study are to determine if commodity variables are the only
variables closely related to government payment variables, and if payment distribution
varies by Farm Resource Region. This study analyzed the data published in the 2007
U.S. Census of Agriculture. The only variables discussed in detail in this chapter are those
variables that are strongly correlated to government payment variables. Based on the
results, the hypothesis that only commodity variables are tied to government payment
variables is rejected. Corn, soybean, wheat, and total cropland variables are all closely
correlated to government payment variables. However, other variables were also
correlated to commodity payments: three farm typologies, two farm tenures, one operator
characteristic, and four economic variables. The hypothesis that payments are distributed
equitably by Farm Resource Region was also rejected as the payment variables varied by
region, and not always based on commodity variables.
Appendix A contains Kendall’s tau correlations of all variables nationally to
government payment variables based on raw county data and location quotients. Means
and standard deviations of all variables, both nationally and by Farm Resource Region,
33
are located in Appendix B. Mean location quotients of all variables by Farm Resource
Region are available in Appendix C.
Salient Variables
The results discussed in this paper are only those variables that have a strong
Kendall’s tau correlation to government payment variables based on raw numbers
(Appendix A). This was done to limit the number of variables discussed in this paper.
Based on these correlations, there were 10 variables that were strongly (τ ≥0.5) correlated
to the number of farms with government commodity payments: farming occupation,
small family farming occupation higher sales farms, large family farms, very large family
farms, part owner farms, tenant farms, corn farms, total cropland farms, soybean farms,
and wheat farms. There were 12 variables that were strongly correlated to the amount of
commodity payments received. Eight of these variables coincide with those correlated to
the number of farms with payments: acres of large family farms, acres of very large family
farms, acres of part owner farms, acres of tenant farms, acres of corn, acres of
total cropland, acres of soybeans, and acres of wheat. Four of these variables are
economic: market value of agricultural products sold, gross total income from farm
related sources, farm production expenses, and net cash farm income of operation.
Income dependence did not have a strong correlation to any independent variables
(Appendix A). All in all, there are 14 separate independent variables correlated to
government payment variables, some of which are correlated based on both the number
of farms and the acres of farms.
34
National
In 2007 the average U.S. county contained 716 farms classified as such by the
NASS, with an average of 297,859 acres of land in farms. The average county accounted
for $96,212,060 in market value of agricultural products sold for a gross income
(excluding Farm Bill payments) of $3,392,874 per county. Total farm production
expenses averaged $78,261,619, with an average net cash farm income (including Farm
Bill payments) per county of $24,794,277 (Appendix B).
Commodity payments, federal payments excluding conservation payments,
averaged $2,196,715 per county. This money was received by an average of 223 farms
per county, for a mean payment per farm for the average U.S. county of $9,838. The
average income dependence, the ratio of government payments to net income, was 47
percent (Appendix B). This statistic varied greatly in range from 0 in some counties to
441.6 percent in one county. In total, there were 376 counties with a mean income
dependence of over 100 percent. Seventy four of these counties are in the Southern
Seaboard, followed by the Prairie Gateway at 65 and the Mississippi Portal at 54.
The average U.S. county was comprised predominantly of full owner farms (494),
followed by part owner (176), and then tenant (46) farms. However, part owner farms
included more land in farms (166,612 acres) despite being far outnumbered by full owner
farms (107,935 acres). So the average part owner farm is much larger than the average
full owner farm. On average, almost half of the farms per county (322) had an operator
whose primary occupation was farming (Appendix B).
Small family residential farms (260) were the most populous farm typology in the
average county, followed by small family retirement farms (148), small family limited
35
resource farms (100), and small family farming occupation lower sales farms (84). Very
large family farms, small family farming occupation higher sales, nonfamily, and large
family farms were the smallest farm typologies ranging from 33-28 farms per county.
However, based on acreage, very large family farms, large family farms, and nonfamily
farms had more acres than residential farms (Appendix B).
In 2007 the average county had 547 farms reporting cropland for an average of
132,548 acres. Most farms, on average, reported producing corn (113), followed by
soybeans (91), then wheat (52). Corn accounted for 31,151 acres per county, followed by
soybeans and wheat (Appendix B).
County Level Analysis
One purpose of this research is to determine if commodity variables are the only
variables that are strongly correlated to the commodity payment variables. The analysis
at this level includes Kendall’s tau correlations of raw county data, Kendall’s tau
correlations of county location quotients, mean location quotients per Farm Resource
Region, and choropleth maps. Choropleth maps were created to illustrate trends at the
county level. The first map for each variable was classified based on location quotients
using below normal (<.75), normal, and above normal (>1.25) categories. In those
instances where outliers were present in the data, based on quartiles, classes were added
to include both mild and extreme outliers either low or high. Some maps use only the
first three classes of data. However, some maps include up to 7 classes to identify these
outliers. The second map for the independent variables was classed using negative
discordant counties (<-0.50), positive discordant counties (>0.50), and relatively equal
concordant counties. There are always three classes on these maps.
36
Government Payment Variables
In 2007, the average U.S. county had 223 farms receiving government payments
(Table 1) for a mean commodity payment received per county of $2,196,715 (Table 2).
The average payment per farm was $9,838 (Table 2). Income dependence was at 47.1
percent nationally, meaning that government payments accounted for 47.1 percent of net
income of farm operations as a county mean (Table 3). The Farm Resource Region with
the highest mean number of farms per county receiving payments was the Heartland
(Table 1), and the highest mean payment per county was in the Mississippi Portal
(Table 2). On average the highest payment per farm was in the Fruitful Rim ($21,385)
followed by the Mississippi Portal ($20,096). The lowest average payment per operation
was in the Eastern Uplands at $3,410 (Table 2). The highest income dependence was in
the Mississippi Portal at 378.4 percent, and the lowest was in the Northern Great Plains at
12.4 percent (Table 3).
Farms with payments.
Figure 2 was created using the location quotients of each county for the number of
farms receiving government commodity payments. Based on the average location quotient
for each Farm Resource Region, farms with commodity payments were above normal in
the Northern Great Plains, Heartland, and Prairie Gateway. The LQs of the Eastern
Uplands, Fruitful Rim, and Basin and Range were lower than normal (Table 1), meaning
fewer farms received commodity payments than the national average.
0.000000 - 0.750000
0.750001 - 1.250000
1.250001 - 2.925703
37
Figure 2. The location quotient of farms receiving commodity payments was highest on average in the Heartland, Northern Great
Plains, and Prairie Gateway region. However, there are large pockets of above normal LQs in the Southern Seaboard and
Mississippi Portal.
38
Table 1. The means, mean location quotients, and ordinal ranks of farms with commodity
payments by Farm Resource Region.
Farms with
Farms with
Farms with
Payments
Ordinal
Ordinal
Payments
Payments
(No.)
Rank
Rank
(%)a
LQb
Heartland
453.2
55.0
1
1.77
2
Northern
239.1
31.5
5
0.81
5
Crescent
Northern
291.9
54.1
2
1.78
1
Great Plains
Prairie
276.7
33.6
4
1.37
3
Gateway
Eastern
129.1
16.6
7
0.47
9
Uplands
Southern
97.2
18.8
6
0.73
6
Seaboard
Fruitful Rim
110.0
12.3
9
0.49
8
Basin and
82.9
15.7
8
0.52
7
Range
Mississippi
194.8
35.4
3
1.15
4
Portal
National
223.3
31.2
a
b
Farms with Commodity Payments (number) / Farms (number). LQ of Farms with
Commodity Payments (number) / Farms (number).
Commodity payments.
The average location quotient of commodity payments was above normal in the
Mississippi Portal, Heartland, and Southern Seaboard, and below normal in the Basin and
Range, Eastern Uplands, and Northern Great Plains (Table 2). There were mild and
extreme high outliers in this data (Figure 3). The outliers are concentrated in the
Mississippi Portal and Southern Seaboard with a few scattered in the Fruitful Rim. This
holds consistently with the fact that these three regions had the highest mean commodity
payments per operation (Table 2).
0.000000 - 0.750000
0.750001 - 1.250000
1.250001 - 3.299397
3.299398 - 4.790325
4.790326 - 11.999322
39
Figure 3. The location quotients of commodity payment dollars were highest in the Heartland, Southern Seaboard, and Mississippi
Portal. Outliers were predominant in the Southern Seaboard and Mississippi Portal with a few outliers in parts of the Fruitful
Rim.
40
Table 2. The means, mean location quotients, and ordinal ranks of commodity payments
by Farm Resource Region.
Commodity
Ordinal
Ordinal
Ave. per
Commodity
a
b
Payment LQ
Farm ($)
Payment ($)
Rank
Rank
3,526,080
7,780
7
1.94
2
Heartland
Northern
1,328,181
5,555
8
1.15
4
Crescent
Northern
3,403,486
11,660
4
0.68
7
Great Plains
Prairie
3,036,438
10,974
6
0.89
6
Gateway
Eastern
440,287
3,410
9
0.44
8
Uplands
Southern
1,260,263
12,966
3
1.69
3
Seaboard
Fruitful Rim
2,352,371
21,385
1
1.09
5
Basin and
929,331
11,210
5
0.30
9
Range
Mississippi
3,914,677
20,096
2
2.43
1
Portal
National
2,196,715
9,838
a
b
Commodity Payments ($) / Farms with Commodity Payments (number). LQ of
Commodity Payments ($) / Land in Farms (acres).
Income dependence.
Income dependence averaged 47 percent nationally, with the highest dependence
being in the Mississippi Portal (378.4 percent) followed by the Basin and Range
(74.2 percent) (Table 3). The mean location quotients per region were highest in the
Mississippi Portal, Prairie Gateway, and Heartland. They were below normal in the
Eastern Uplands, Northern Crescent, Basin and Range, and Fruitful Rim. The map of
county location quotients of income dependence (Figure 4) illustrates the high levels of
income dependence in the Mississippi Portal with the high concentration of mild and
extreme high outliers. However, other outliers are concentrated in the Southern Seaboard
and Prairie Gateway.
0.000000 - 0.750000
0.750001 - 1.250000
1.250001 - 2.524346
2.524347 - 3.668631
3.668632 - 14.331163
Figure 4. The location quotients of income dependence were highest in the Mississippi Portal, Heartland, and Prairie Gateway.
There are extremely high outliers present in the Southern Seaboard even though the average county LQ is right at normal.
41
42
Table 3. The mean, mean location quotients, and ordinal ranks of income dependence by
Farm Resource Region.
Income
Income
Dependence
Dependence
(%)a
Ordinal Rank
LQb
Ordinal Rank
Heartland
21.1
5
1.35
3
Northern
12.9
8
0.52
8
Crescent
Northern
12.4
9
1.16
4
Great Plains
Prairie
33.6
4
1.37
2
Gateway
Eastern
17.3
7
0.46
9
Uplands
Southern
47.7
3
0.99
5
Seaboard
Fruitful Rim
20.3
6
0.62
6
Basin and
74.2
2
0.53
7
Range
Mississippi
378.4
1
2.36
1
Portal
National
47.1
a
b
Commodity Payments ($) / Net Cash Income of Farm Operation ($). Location quotient
of Commodity Payments ($) / Net Cash Income of Farm Operation ($).
Even though the Southern Seaboard had a high concentration of outliers, it had a
normal mean location quotient for income dependence (Table 3). Therefore, these outliers
must have been offset by numerous low location quotients. This means there is a very
large variation among income dependence in the Southern Seaboard.
Economic Variables
Economic variables include the market value of agricultural products sold, gross
income of operation, production expenses, and net cash income of farm operation.
Nationally, the average county had a market value of agricultural products sold of
$96,212,060 (Table 4). Gross income averaged $3,392,874 (Table 5); production
expenses averaged $78,261,619 (Table 6), and net income of operation per county
43
averaged $24,794,277(Table 7). The Fruitful Rim had the highest mean market value per
county at $217,886,746 (Table 4) and the highest mean gross income ($6,000,168)
(Table 5), production expenses ($174,821,354) (Table 6), and net income ($54,105,846)
(Table 7). Market value appears to be so much larger than gross income because market
value includes all goods removed for sale regardless of who received payment for the
goods while gross income includes only the income of the operation. Net income is
larger than gross income because net income includes Farm Bill payments even though
gross income does not.
Market value.
Market value of agricultural products sold was strongly correlated to the
commodity payment amount (τ = .529, p = .000) for raw data. However, this same
correlation was only moderate (τ = .352, p = .000) when based on location quotients
(Appendix A). Based on the mean location quotients of market value of agricultural
products sold, the Fruitful Rim was very high (3.49) followed by the Northern Crescent
and the Southern Seaboard (Table 4). This trend holds true when looking at the county
level location quotients (Figure 5). The Basin and Range and the Northern Great Plains
were tied for the lowest mean location quotient, meaning that the agricultural products
sold based on the acres of land used to produce them was below normal. Based on the
mean market value per acre, the ordinal rank of the top three FRRs changes, but is
otherwise similar to the ordinal rank based on location quotients (Table 4).
Based on the correlation of the location quotients, the location quotient of
commodity payments would be expected to increase as the location quotient of market
44
Table 4. The mean, mean location quotients, and ordinal ranks of market value by Farm
Resource Region.
Market Value Ave. per
Ordinal
Ordinal
Market
a
b
Value LQ
Acre ($)
($)
Rank
Rank
Heartland
132,582,011
517
4
1.53
4
Northern
78,855,418
617
2
2.34
2
Crescent
Northern Great
106,170,464
118
8
0.44
8
Plains
Prairie
114,977,404
207
7
0.69
7
Gateway
Eastern
42,012,873
343
5
0.98
5
Uplands
Southern
63,848,282
643
1
2.14
3
Seaboard
Fruitful Rim
217,866,746
560
3
3.49
1
Basin and
40,669,223
75
9
0.44
8
Range
Mississippi
56,067,297
315
6
0.92
6
Portal
National
96,212,060
323
a
Market Value of Agricultural Products Sold ($) / Land in Farms (acres). b Location
quotient of Market Value of Agricultural Products Sold ($) / Land in Farms (acres).
value increases moderately. The yellow counties on Figure 5 show those counties where
the location quotients of the two variables vary by less than 0.50, and follow the
correlation as expected (concordant counties). The red counties, concentrated in the
Northern Crescent, Fruitful Rim, and the border between the Eastern Uplands and
Southern Seaboard, show those counties where the location quotients of market value were
larger than the location quotients of commodity payments (positive discordant counties).
The green counties, concentrated in the Mississippi Portal, Southern Seaboard, and
Heartland, show those counties where the location quotient of the commodity payments is
larger than the location quotient of the market value (negative discordant counties). Since
the correlation is only moderate between the location quotients of
0.000000 - 0.750000
0.750001 - 1.250000
1.250001 - 3.049271
3.049272 - 4.395529
4.395530 - 283.348118
-6.861019 - -0.510000
-0.509999 - 0.510000
0.510001 - 283.348118
45
Figure 5. The mean location quotient of market value of agricultural products sold was highest in the Fruitful Rim, Northern
Crescent, and Southern Seaboard (left). The location quotients of commodity payments were larger than the location quotients of
market value in the Mississippi Portal, Southern Seaboard, and Heartland (right).
46
market value and commodity payments, it is expected that not every county will fit the
correlation.
Gross income.
Gross income from farm-related sources, as defined in this study, includes all
sources of income related to agricultural production and services, as well as insurance
and state and local payments, but does exclude Farm Bill payments. Gross income was
strongly correlated to commodity payments (τ = .559, p = .000) based on raw data. Based
on location quotients, the correlation was weaker (τ = .358, p = .000) (Appendix A).
Based on the mean location quotients of gross income, the Northern Crescent, Fruitful
Rim, Southern Seaboard, and Heartland were all higher than expected (Table 5). This
Table 5. The mean, mean location quotients, and ordinal ranking of gross income by
Farm Resource Region.
Gross
Ave. per
Ordinal
Gross
Ordinal
Acre ($)a
Income LQb
Income ($)
Rank
Rank
4,754,547
19
2
1.79
4
Heartland
4.95
Northern
3,469,548
27
1
1
Crescent
0.67
Northern Great
5,459,966
6
8
7
Plains
0.66
Prairie
3,698,723
7
7
8
Gateway
1.03
Eastern
1,353,832
11
6
5
Uplands
Southern
2.00
1,918,280
19
2
3
Seaboard
Fruitful Rim
6,000,168
15
4
2.65
2
0.64
Basin and
2,071,345
4
9
9
Range
1.01
Mississippi
2,146,152
12
5
6
Portal
National
3,392,874
11
a
b
Total Income from Farm Related Sources Gross ($) / Land in Farms (acres). Location
quotient of Total Income from Farm Related Sources Gross ($) / Land in Farms (acres).
0.000000 - 0.750000
0.750001 - 1.250000
1.250001 - 3.118991
3.118992 - 4.404729
4.404730 - 321.746072
-6.322992 - -0.510000
-0.509999 - 0.510000
0.510001 - 321.746072
47
Figure 6. The location quotients of gross income were highest in the Northern Crescent, followed by the Fruitful Rim and
Southern Seaboard (left). The location quotients of commodity payments were much larger than the location quotients of the
gross income in the Heartland and Mississippi Portal (right).
48
trend holds true when looking at the county level location quotients (Figure 6). Gross
income location quotients were lowest on average in the Basin and Range, Prairie
Gateway, and Northern Great Plains (Table 5). The mean gross income per acre was
highest in the Northern Crescent ($27) and lowest in the Basin and Range ($4). The
ordinal rank of Farm Resource Regions based on gross income per acre and location
quotients were similar (Table 5).
Based on the correlation of the location quotients, the location quotient of
commodity payments would be moderately expected to increase as the location quotient of
gross income increases. On Figure 6, the positive discordant counties are concentrated in
the Northern Crescent, Fruitful Rim, and eastern Southern Seaboard and Eastern Uplands.
The payment variables in these regions are more strongly correlated to another variable
that is causing them to remain low despite the high gross income values. The negative
discordant counties are concentrated in the Mississippi Portal and Heartland. In these
regions the government payment variable is elevated by a more strongly correlated
variable. Since the correlation is only moderate between the location quotients of gross
income and commodity payments, it is expected that not every county will fit the
correlation.
Production expenses.
Production expenses were strongly correlated to commodity payments based on
raw data (τ = .513, p = .000) but only moderately correlated based on location quotients
(τ = .315, p = .000) (Appendix A). Those farm resource regions with above normal
location quotients of production expenses were the Fruitful Rim, Northern Crescent,
Southern Seaboard, and Heartland. Production expenses location quotients were below
49
normal in the Northern Great Plains and Basin and Range (Table 6). This trend is visible
on the county level map (Figure 7) with mild and extreme high outliers being
concentrated in the regions with above normal location quotients. However, some outliers
are scattered throughout the Prairie Gateway, Basin and Range, and Mississippi Portal.
Based on the mean production expenses per acre the Southern Seaboard ($546) and the
Northern Crescent ($499) were the highest while the Basin and Range was the lowest
($68). The ordinal ranks based on the mean production expenses per acre was similar to
the ordinal ranks based on location quotients (Table 6).
The location quotient of commodity payments would be expected to increase
moderately as the location quotient of production expenses increases. On Figure 7, the
Table 6. The means, mean location quotients, and ordinal ranks of production expenses
by Farm Resource Region.
Ave. per
Production
Ordinal
Acre ($)a Ordinal
Production
b
Expenses LQ
Expenses ($)
Rank
Rank
Heartland
99,427,878
388
4
1.43
4
Northern
63,758,953
499
2
2.48
2
Crescent
Northern
82,748,067
92
8
0.42
9
Great Plains
Prairie
100,525,096
181
7
0.74
7
Gateway
Eastern
36,701,522
299
5
1.11
5
Uplands
Southern
54,219,584
546
1
2.29
3
Seaboard
Fruitful Rim
174,821,354
450
3
2.53
1
Basin and
36,597,508
68
9
0.66
8
Range
Mississippi
48,044,291
270
6
0.98
6
Portal
National
78,261,619
263
a
Total Farm Production Expenses ($) / Land in Farms (acres). b Location quotient of
Total Farm Production Expenses ($) / Land in Farms (acres).
0.000000 - 0.750000
0.750001 - 1.250000
1.250001 - 2.992505
2.992506 - 4.260652
4.260653 - 55.971026
-6.612931 - -0.510000
-0.509999 - 0.510000
0.510001 - 55.971026
50
Figure 7. The location quotients of production expenses were above normal in the Fruitful Rim, Northern Crescent, Southern
Seaboard, and Heartland (left). The location quotients of commodity payments were greater than the location quotients of
production expenses in the Heartland and Mississippi Portal (right).
51
positive discordant counties are concentrated in areas similar to those as market value and
gross income. In these regions government payment variables are being kept low by
another variable. The negative discordant counties are again concentrated in the
Mississippi Portal and the Heartland as well as the Southern Seaboard. Here, again, the
amount of commodity payments is elevated based on another more strongly correlated
variable. Since the correlation is only moderate between the location quotients of
production expenses and commodity payments, it is again expected that not every county
will fit the correlation.
Net income.
Net cash income of farm operation is gross income of operation minus production
expenses, including Farm Bill payments. Net income was strongly correlated to
commodity payments based on raw data (τ = .555, p = .000) and moderately correlated
based on location quotients (τ = .410, p = .000) (Appendix A). In Table 7, the Fruitful
Rim, Northern Crescent, Heartland, and Southern Seaboard all had above normal net
income. Location quotients of net income were below normal in the Northern Great
Plains, Prairie Gateway, and Eastern Uplands (Table 7). This data is supported by the
county level location quotient map (Figure 8). The ordinal rank based on the mean per
acre net income by Farm Resource Region varies from the rank based on location
quotients. The Heartland had the highest mean per acre net income at $165, while the
lowest was in the Basin and Range at $17 (Table 7).
The location quotient of commodity payments would be expected to increase
moderately as the location quotient of net income increases. On Figure 8, the positive
discordant counties appear to be fewer but still in a similar pattern to other economic
0.000000 - 0.750000
0.750001 - 1.250000
1.250001 - 3.327114
3.327115 - 4.878050
4.878051 - 92.905794
-6.957226 - -0.510000
-0.509999 - 0.510000
0.510001 - 92.905794
52
Figure 8. The location quotients of net income of operation were highest in the Fruitful Rim, Northern Crescent, and Heartland.
Mild and extreme high outliers were scattered throughout the Southern Seaboard and Mississippi Portal (left). The location
quotients of commodity payments were larger than the location quotients of net income in the Mississippi Portal (right).
53
Table 7. The means, mean location quotients, and ordinal ranks of net income by Farm
Resource Region.
Net Income
Ordinal
Ordinal
Ave. per
Net Income
a
b
Acre ($)
LQ
($)
Rank
Rank
42,306,560
165
1
1.85
3
Heartland
2.10
Northern
20,445,220
160
2
2
Crescent
0.56
Northern Great
38
8
9
34,148,089
Plains
42
7
0.58
8
Prairie Gateway
23,655,736
0.73
Eastern
64
6
7
7,830,151
Uplands
1.77
Southern
13,936,245
140
3
4
Seaboard
139
4
2.51
1
Fruitful Rim
54,105,846
0.82
Basin and
8,964,660
17
9
6
Range
0.86
Mississippi
82
5
5
14,508,467
Portal
83
National
24,794,277
a
b
Net Cash Farm Income of Operation ($) / Land in Farms (acres). Location quotient of
Net Cash Farm Income of Operation ($) / Land in Farms (acres).
variable maps. Negative discordant counties are again concentrated in the Mississippi
Portal but much less in the Heartland than other economic variables. Since the
correlation is only moderate between the location quotients of production expenses and
commodity payments, it is again expected that not every county will fit the correlation.
Net income is perhaps a better indicator of trends since net income takes into account
gross income and production expenses. However, one thing that is visible is that the
Heartland and the Mississippi Portal have much larger county location quotients for
commodity payments than for the economic variables. Another thing that is visible is
that the Northern Crescent and the Pacific Coast region of the Fruitful Rim have much
larger economic variable location quotients than commodity payment location quotients.
54
Occupation and Tenure Variables
Occupation and tenure variables include the occupation of the principal operator of
a farm operation as well as part owner and tenant farms and acres. Nationally, the average
county had 45.1 percent of farms with a principal operator reporting farming as their
primary occupation (Table 8). On average, 24.6 percent of operators were part owners
(Table 9), for an average of 55.9 percent of acres operated (Table 10). Finally, an average
of 6.4 percent of principal operators were tenant farmers (Table 11) for an average of 9.2
percent of acres operated (Table 12). The Northern Great Plains had the highest
percentage of principal operators (55.8 percent) reporting farming as their
primary occupation (Table 8). The Northern Great Plains also had the highest percentage
of part owner farms at 35.7 (Table 9) and the highest percentage of acres in part owner
farms (65.6 percent) (Table 10). The highest percentage of tenant farms (8.7 percent)
was in the Northern Great Plains as well (Table 11). However, the highest percentage of
tenant farm acres was in the Mississippi Portal at 19.9 (Table 12).
Farming occupation.
Farming occupation was strongly correlated to the number of farms with
commodity payments (τ = .519, p = .000) based on raw data. This correlation was weak
based on location quotients (τ = .297, p = .000) (Appendix A). Based on the mean
location quotients of farming occupation, the Northern Great Plains was the only region
above normal. All other regions were at normal (Table 8). This trend is supported by the
county level analysis (Figure 9). The ordinal rank of Farm Resource Regions based on
mean percentage of farming occupation farms differs from the ordinal rank based on
55
location quotients. However, the Northern Great Plains comes in first again at 55.8
percent, followed distantly by the Heartland at 47.2 percent (Table 8).
Table 8. The means, mean location quotients, and ordinal ranks of farming occupation
by Farm Resource Region.
Farming
Farming
Farming
Occupation
Occupation
Ordinal
Occupation
Ordinal
(%)a
LQb
(No.)
Rank
Rank
388.3
47.2
2
1.06
2
Heartland
1.03
Northern
352.6
47.1
3
3
Crescent
Northern
301.2
55.8
1
1.31
1
Great Plains
Prairie
354.9
43.1
6
1.03
3
Gateway
Eastern
317.3
40.8
8
0.90
9
Uplands
Southern
218.4
42.3
7
0.96
7
Seaboard
416.3
46.4
4
1.01
6
Fruitful Rim
Basin and
244.8
46.3
5
1.02
5
Range
Mississippi
222.2
40.4
9
0.95
8
Portal
322.8
45.1
National
a
b
Farming Occupation (number) / Farms (number). Location quotient of Farming
Occupation (number) / Farms (number).
The correlation of farming occupation and farms with payments based on location
quotients is weak. Therefore, it is hard to expect to see patterns in Figure 9. However,
this map has very visible patterns due to the small amount of concordant counties.
Positive discordant counties, counties with a high location quotient of farming occupation
without a corresponding concentration of farms with commodity payments, are focused
in the Fruitful Rim and Basin and Range with some counties in the Eastern Uplands and
Southern Seaboard. Negative discordant counties, counties with large location quotients
of farms with government payments but not farming occupation, are concentrated in the
0.000000 - 0.322721
0.322722 - 0.595756
0.595757 - 0.750000
0.750001 - 1.250000
1.250001 - 1.425079
1.425080 - 1.688144
1.688145 - 2.219538
-1.771702 - -0.510000
-0.509999 - 0.510000
0.510001 - 2.219538
56
Figure 9. The location quotients of farming occupation were above normal in the Northern Great Plains (left). The Farm
Resource Regions with larger location quotients of farms with payments than of farming occupation are concentrated in the
Northern Great Plains, Prairie Gateway, and Heartland (right).
57
Northern Great Plains, Prairie Gateway, and Heartland. It is expected that there would
be few concordant counties, counties with small differences between location quotients,
based on the weakness of the correlation.
Part owner.
Part owner farms were strongly correlated to the number of farms with
commodity payments (τ = .593, p = .000) based on raw data. This correlation was
moderate based on location quotients (τ = .464, p = .000) (Appendix A). Based on the
mean location quotients of part owner farms, the Northern Great Plains was the only
region above normal. The Fruitful Rim was the only region below normal (Table 9).
Table 9. The means, mean location quotients, and ordinal ranks of part owner farms by
Farm Resource Region.
Part
Part
Part Owner
Ordinal
Ordinal
Owner
Owner
a
b
(No.)
Rank
Rank
(%)
LQ
Heartland
238.6
29.0
2
1.18
2
Northern
192.3
25.7
4
0.97
4
Crescent
Northern Great
192.9
35.7
1
1.54
1
Plains
Prairie Gateway
221.0
26.8
3
1.17
3
Eastern
177.2
22.8
6
0.92
7
Uplands
Southern
120.3
23.3
5
0.93
6
Seaboard
Fruitful Rim
137.4
15.3
9
0.70
9
Basin and
100.9
19.1
8
0.84
8
Range
Mississippi
123.3
22.4
7
0.96
5
Portal
National
176.1
24.6
a
b
Part Owner (number) / Farms (number). Location quotient of Part Owner (number) /
Farms (number).
0.000000 - 0.338235
0.338236 - 0.750000
0.750001 - 1.250000
1.250001 - 1.639567
1.639568 - 2.905805
2.905806 - 4.057929
-2.186670 - -0.510000
-0.509999 - 0.510000
0.510001 - 4.057929
58
Figure 10. The location quotients of part owner farms were above normal in the Northern Great Plains and below normal in the
Fruitful Rim. There are a few outliers in the Heartland and Basin and Range regions (left). Counties that do not follow the
correlation were concentrated in the Heartland, Eastern Uplands, and Southern Seaboard (right).
59
Again this is supported by the county level location quotient map (Figure 10). Based on
the mean percentage of part owner farms per Farm Resource Region, the Northern Great
Plains was highest at 35.7 and the Fruitful Rim was lowest at 15.3. The ordinal ranks of
regions based mean percentages and location quotients were similar (Table 9).
Since the correlation of part owner farms to farms with payments was moderate, it
is expected that the location quotients of farms with payments increase when the location
quotients of part owner farms increase. On Figure 10, positive discordant counties are
concentrated in the Eastern Uplands and Southern Seaboard. In these regions
independent variables other than part owner farms are keeping number of farms with
payments low. Negative discordant counties are centralized in the Heartland. The
number of farms with payments is more strongly correlated to other variables than part
owner farms in the Heartland. The Prairie Gateway and the Mississippi Portal both have
large amounts of counties on both ends of the spectrum.
Part owner farm acres were strongly correlated to the amount of commodity
payments (τ = .503, p = .000) based on raw data. This correlation was moderate based on
location quotients (τ = .300, p = .000) (Appendix A). Based on the mean location
quotients of part owner farm acres, the Fruitful Rim was the only region below normal.
All other regions were normal (Table 10). The Fruitful Rim is where most low outliers are
concentrated, with the above normal counties being centralized in the Northern Great
Plains and Heartland (Figure 11). The mean percentage of part owner farm acres per
county was highest in the Northern Great Plains (65.6 percent) and Heartland (64.7
percent), and lowest in the Fruitful Rim at 41.4 percent (Table 10). The ordinal ranks,
location quotients, mean percentages, and choropleth map are all very similar.
60
Since the correlation based on location quotients was only moderate, large
numbers of discordant counties are expected on Figure 11. Positive discordant counties
are concentrated in the Northern Great Plains, Eastern Uplands, and Prairie Gateway. In
these counties, commodity payments are being kept lower based on other variables
despite large part owner farm acres. Negative discordant counties are centralized in the
Heartland, Mississippi Portal, and Southern Seaboard. In these regions, variables other
than part owner farm acres are more strongly correlated to commodity payments and the
location quotients of commodity payments are much higher than would be expected
based on part owner farm acres.
Table 10. The means, mean location quotients, and ordinal ranks of part owner farm
acres by Farm Resource Region.
Part Owner Part Owner
Ordinal Part Owner
Ordinal
a
b
Acres (%)
Acres LQ
Acres (ac)
Rank
Rank
Heartland
165,869.1
64.7
2
1.17
2
Northern
76,091.5
59.5
3
0.96
4
Crescent
Northern
588,565.5
65.6
1
1.22
1
Great Plains
Prairie
312,936.6
56.2
4
1.05
3
Gateway
Eastern
54,370.9
44.3
8
0.77
8
Uplands
Southern
46,975.0
47.3
6
0.81
6
Seaboard
Fruitful Rim
161,041.2
41.4
9
0.73
9
Basin and
273,073.0
50.5
5
0.85
5
Range
Mississippi
79,104.2
44.5
7
0.78
7
Portal
National
166,612.0
55.9
a
Part Owner (acres) / Land in Farms (acres). b Location quotient of Part Owner (acres) /
Land in Farms (acres).
0.000000 - 0.277475
0.277476 - 0.750000
0.750001 - 1.250000
1.250001 - 1.643890
1.643891 - 1.767829
-11.147299 - -0.510000
-0.509999 - 0.510000
0.510001 - 1.489519
61
Figure 11. The location quotients of part owner farm acres were lowest in the Fruitful Rim. High outliers are concentrated in the
Northern Great Plains, Heartland, and Prairie Gateway (left). The differences between the location quotients of part owner farm
acres and commodity payments were greatest in the Northern Great Plains, Heartland, and Mississippi Portal (right).
62
Tenant farms.
Tenant farms are those farms where the operator rents all the land that they
operate from someone else. Tenant farms were strongly correlated to the number of
farms receiving commodity payments based on raw data (τ = .521, p = .000). This same
correlation was moderate based on location quotients (τ = .321, p = .000) (Appendix A).
Based on the mean location quotients of tenant farms, the Mississippi Portal, Northern
Great Plains, Prairie Gateway, and Heartland regions were all above normal. The Eastern
Uplands was the only region below normal (Table 11). The concentration of mild and
extreme high outliers in these same above normal regions (Figure 12) supports the
Table 11. The means, mean location quotients, and ordinal ranks of tenant farms by
Farm Resource Region.
Tenant
Tenant
Tenant
Owner
Ordinal
Ordinal
Owner
Owner
a
b
(No.)
Rank
Rank
(%)
LQ
Heartland
68.4
8.3
2
1.34
4
Northern
37.2
5.0
7
0.86
7
Crescent
Northern
47.0
8.7
1
1.47
2
Great Plains
Prairie
59.1
7.2
4
1.38
3
Gateway
Eastern
29.0
3.7
9
0.61
9
Uplands
Southern
25.4
4.9
8
0.85
8
Seaboard
Fruitful Rim
60.8
6.8
5
1.16
5
Basin and
30.1
5.7
6
1.06
6
Range
Mississippi
44.2
8.0
3
1.77
1
Portal
National
45.7
6.4
a
Tenant Owner (number) / Farms (number). b Location quotient of Tenant Owner
(number) / Farms (number).
0.000000 - 0.750000
0.750001 - 1.250000
1.250001 - 2.286768
2.286769 - 3.150049
3.150050 - 15.825824
-1.745353 - -0.510000
-0.509999 - 0.510000
0.510001 - 15.825824
63
Figure 12. The location quotients of tenant farms were highest in the Mississippi Portal and lowest in the Eastern upland (left).
Positive discordant counties of tenant farms to farms with payments are concentrated in the Fruitful Rim and Mississippi Portal,
and negative discordant counties are scattered in the Heartland and Northern Great Plains (right).
64
location quotient analysis. Based on the mean percentage of tenant farms in each region,
the Northern Great Plains (8.7 percent) and the Heartland (8.3 percent) were the highest
with the Eastern Uplands being the lowest (3.7 percent). The ordinal ranks based on
mean percentage were different from those based on the location quotients of tenant farms
(Table 11).
The location quotient of farms with payments would be expected to increase
moderately as the location quotient of tenant farms increases. On Figure 12, the positive
discordant counties are concentrated in the Fruitful Rim, Mississippi Portal, and
northeastern coast. In these regions government payment variables are being kept low by
another variable. The negative discordant counties are concentrated in the Heartland,
Northern Great Plains, and the western part of the Northern Crescent. Here the amount
of commodity payments is elevated based on another more strongly correlated variable.
These patterns are weaker than with some other variables, as would be expected with
only a moderate correlation between tenant farms and farms with payments based on
location quotients.
Tenant farm acres were strongly correlated to commodity payment amounts
(τ = .510, p = .000). This same correlation was weak based on location quotients
(τ = .262, p = .000) (Appendix A). The Mississippi Portal was the only region with an
above normal location quotient of tenant farm acres. Location quotients were below
normal in the Eastern Uplands, Northern Crescent, and Southern Seaboard (Table 12).
Outliers tended to be concentrated in the Mississippi Portal, supporting these results.
However, there were outliers scattered throughout the Fruitful Rim, Prairie Gateway, and
Heartland (Figure 13).
The Mississippi Portal had the largest percentage of tenant farm
65
acres at 19.9 percent followed distantly by the Fruitful Rim at 12.7 percent. The Eastern
Uplands were the lowest at 4.2 percent. The ordinal ranks based on mean percentages
and location quotients were very similar (Table 12).
Since the correlation of the location quotients of commodity payments to tenant
farms is so weak, it is hard to expect a strong pattern on Figure 13. However, some
patterns are very clear. Positive discordant counties are few but follow a pattern along
the border of the Basin and Range region and the Northern Great Plains and Prairie
Gateway. Negative discordant counties are concentrated in the Heartland, Mississippi
Portal, Northern Crescent, and southern border of the Southern Seaboard. This means
Table 12. The means, mean location quotients, and ordinal ranks of tenant farm acres by
Farm Resource Region.
Tenant
Tenant
Tenant
Owner
Ordinal
Ordinal
Owner
Owner
a
b
Acres (ac)
Rank
Rank
Acres (%)
Acres LQ
Heartland
25,118.9
9.8
4
1.05
4
Northern
6,644.1
5.2
8
0.57
8
Crescent
Northern
69,635.5
7.8
5
0.90
5
Great Plains
Prairie
55,035.6
9.9
3
1.13
2
Gateway
Eastern
5,340.9
4.4
9
0.43
9
Uplands
Southern
6,133.5
6.2
7
0.63
7
Seaboard
Fruitful Rim
47,445.9
12.2
2
1.11
3
Basin and
40,499.0
7.5
6
0.83
6
Range
Mississippi
35,385.4
19.9
1
1.96
1
Portal
27,415.8
9.2
National
a
b
Tenant Owner (acres) / Land in Farms (acres). Location quotient of Tenant Owner
(acres) / Land in Farms (acres).
0.000000 - 0.750000
0.750001 - 1.250000
1.250001 - 1.939658
1.939659 - 3.081907
3.081908 - 8.934124
-10.816690 - -0.510000
-0.509999 - 0.510000
0.510001 - 8.428727
66
Figure 13. The location quotients of tenant farm acres were above normal only in the Mississippi Portal. Outliers are scattered
throughout the Fruitful Rim, Prairie Gateway, and Heartland (left). The location quotients of farms with payments are larger than
the location quotients of tenant farm acres in the Heartland, Mississippi Portal Northern Crescent, and Southern Seaboard (right).
67
that although location quotients of tenant farm acres are low, location quotients of
commodity payments are high. Therefore, other variables are more strongly correlated to
payments in these regions than tenant farm acres, as expected with a weak correlation.
Typology Variables
Typology variables group farms by similar characteristics based on sales,
occupation, and farm organization. Typology variables, as discussed in these results,
include small family farming occupation higher sales, large family, and very large family
farms. The average U.S. county had 32.5 small family farming occupation higher sales
farms (4.5 percent) (Table 13). Large family farms averaged 28.1 farms per county
(3.9 percent) (Table 14) for a total of 45,559.9 acres (15.3 percent) (Table 15). Very large
family farms averaged 32.9 farms per county (4.6 percent) (Table 16) for an average of
76,868.7 acres per county (25.8 percent) (Table 27). The Northern Great Plains had the
highest percentage of small family farming occupation higher sales farms (11.2 percent)
(Table 13). The Northern Great Plains had the highest percentage of large family farms at
8.9 percent (Table 14) and the highest percentage of large family farm acres at 19.7
(Table 15). The Northern Great Plains also had the highest percentage of very large
family farms at 8.0 (Table 16). However, the largest percentage of very large family farm
acres was in the Mississippi Portal at 39.5 percent (Table 17).
Small family higher sales farms.
Small family farming occupation higher sales farms are sole proprietorship farms
with market values of agricultural products sold between $100,000 and $249,999 and
principal operators who report farming as their primary occupation. Small family higher
sales farms were strongly correlated to farms with payments (τ = .641, p = .000). This
68
correlation was also strong based on location quotients (τ = .540, p = .000) (Appendix A).
Based on the mean location quotients of small family farming occupation higher sales
farms, the Northern Great Plains was very high (2.82) followed by the Heartland. The
Eastern Uplands was the lowest (0.30) followed by the Southern Seaboard, Mississippi
Portal, and Fruitful Rim (Table 13). This trend holds true when looking at the county level
location quotients (Figure 14). The Eastern Uplands is visibly the lowest with all counties
being in the below or normal categories on the map. Based on the mean percentage of
small family farming occupation higher sales farms per county the
Northern Great Plains were the highest at 11.2 percent followed by the Heartland at
Table 13. The means, mean location quotients, and ordinal ranks of small family higher
sales farms by Farm Resource Region.
Small
Small
Higher
Ordinal
Ordinal
Higher
Small
b
a
Higher LQ
(No.)
Rank
Rank
(%)
Heartland
60.6
7.4
2
1.60
2
Northern
49.0
6.5
3
1.12
4
Crescent
Northern Great
60.7
11.2
1
2.82
1
Plains
Prairie
32.6
4.0
4
1.21
3
Gateway
Eastern
12.6
1.6
9
0.30
9
Uplands
Southern
9.6
1.9
8
0.51
8
Seaboard
Fruitful Rim
25.4
2.8
6
0.63
6
Basin and
18.8
3.6
5
1.00
5
Range
Mississippi
12.6
2.3
7
0.56
7
Portal
32.5
4.5
National
a
b
Small Family Farming Occupation Higher Sales (farms) / Farms (number). Location
quotient of Small Family Farming Occupation Higher Sales (farms) / Farms (number).
0.000000 - 0.750000
0.750001 - 1.250000
1.250001 - 2.743190
2.743191 - 3.976291
3.976292 - 9.417600
-2.283287 - -0.510000
-0.509999 - 0.510000
0.510001 - 8.047628
69
Figure 14. The location quotients of small family farming occupation higher sales farms were above normal in the Northern Great
Plains and Heartland (left). Positive discordant counties of small family higher sales farms to farms with payments are
centralized in the Northern Great Plains and negative discordant pairs are concentrated in the Mississippi Portal (right).
70
7.4 percent. The Eastern Uplands were the lowest at 1.6 percent (Table 13). All analyses
are very similar.
Based on the correlation of location quotients, the location quotient of farms with
commodity payments would be expected to increase as the location quotient of small
family farming occupation higher sales farms increase. Based on the strength of the
correlation the large number of concordant counties on Figure 14 is expected. The
positive discordant counties are centralized in the Northern Great Plains with some in the
Basin and Range and Northern Crescent. The Northern Great Plains has very large
location quotients of small family higher sales farms without a corresponding increase in
the location quotients of farms with payments. Despite the strong correlation other
variables are having a stronger influence. The negative discordant counties are
concentrated in the Mississippi Portal. The Heartland has both positive and negative
discordant counties, yet is ranked second highest based on both mean percent per county
and location quotient. This means that although there are more negative discordant
counties than positive, the positive discordant counties must be much larger in magnitude
than the negative counties.
Large family.
Large family farms are sole proprietorship farms with market values of agricultural
products sold between $250,000 and $499,999. Large family farms were strongly
correlated to farms with commodity payments (τ = .631, p = .000) based on raw data. This
correlation was also strong based on location quotients (τ = .551, p = .000) (Appendix A).
Based on the mean location quotients of large family farms, the Northern Great Plains was
very high (2.44) followed by the Heartland as the only other region with
71
a location quotient above normal. The Eastern Uplands was the lowest (0.31) followed by
the Southern Seaboard, and Fruitful Rim (Table 14). This trend holds true when looking at
the county level location quotients (Figure 15), with some high outlying counties scattered
in all regions but the Eastern Uplands. Based on the mean percentage of large family
farms per county the Northern Great Plains was highest at 8.9 percent followed by the
Heartland at 7.0 percent. The Eastern Uplands were lowest at 1.5 percent followed by the
Southern Seaboard at 2.1 percent (Table 14).
Based on the correlation of location quotients, the location quotient of farms with
commodity payments is expected to increase as the location quotient of large family
Table 14. The means, mean location quotients, and ordinal ranks of large family farms
by Farm Resource Region.
Large
Large
Family
Ordinal
Ordinal
Family
Large
b
a
Family LQ
(No.)
Rank
Rank
(%)
Heartland
57.8
7.0
2
1.81
2
Northern
12.5
4.3
3
0.89
4
Crescent
Northern Great
18.6
8.9
1
2.44
1
Plains
Prairie
11.9
3.3
4
1.22
3
Gateway
Eastern
7.1
1.5
9
0.31
9
Uplands
Southern
11.0
2.1
8
0.60
7
Seaboard
Fruitful Rim
24.8
2.8
5
0.65
7
Basin and
13.6
2.6
7
0.84
5
Range
Mississippi
15.0
2.7
6
0.81
6
Portal
28.1
3.9
National
a
b
Large Family (farms) / Farms (number). Location quotient of Large Family (farms) /
Farms (number).
0.000000 - 0.750000
0.750001 - 1.250000
1.250001 - 2.837501
2.837502 - 4.130665
4.130666 - 5.576563
-2.283287 - -0.510000
-0.509999 - 0.510000
0.510001 - 3.705381
72
Figure 15. The location quotients of large family farms were highest in the Northern Great Plains and Heartland, and lowest in
the Eastern Uplands (left). Discordant counties of large family farms to farms with payments are scattered with patterns that
barely fit Farm Resource Region boundaries (right).
73
farms increase. Based on the strength of the correlation, concordant pairs are expected to
outnumber discordant pairs and patterns are expected to be hard to see (Figure 15).
Positive discordant pairs are focused around the border of the Heartland and the Northern
Great Plains. The negative discordant counties are scattered but predominant in the
center of the Prairie Gateway and the eastern side of the Mississippi Portal.
Large family farm acres were strongly correlated to commodity payments
(τ = .527, p = .000) based on raw data. This correlation was only moderate based on
location quotients (τ = .319, p = .000) (Appendix A). In table 15, the Northern Great
Plains and Heartland were the only regions with above normal location quotients. The
Table 15. The means, mean location quotients, and ordinal ranks of large family farm
acres by Farm Resource Region.
Large
Large
Large Family
Ordinal
Ordinal
Family
Family
a
b
Acres(ac)
Rank
Rank
Acres (%)
Acres LQ
Heartland
49,197.9
19.2
2
1.36
2
Northern
18,493.9
14.5
3
0.82
5
Crescent
Northern
176,845.9
19.7
1
1.47
1
Great Plains
Prairie
76,761.2
13.8
4
1.03
3
Gateway
Eastern
8,447.0
6.9
9
0.37
9
Uplands
Southern
9,177.7
9.2
8
0.59
8
Seaboard
Fruitful Rim
36,785.1
9.5
7
0.60
7
Basin and
72,665.1
13.4
5
0.86
4
Range
Mississippi
19,174.9
10.8
6
0.66
6
Portal
45,559.9
15.3
National
a
b
Large Family (acres) / Land in Farms (acres). Location quotient of Large Family
(acres) / Land in Farms (acres).
0.000000 - 0.750000
0.750001 - 1.250000
1.250001 - 1.735495
1.735496 - 3.133540
3.133541 - 3.298801
-11.695110 - -0.510000
-0.509999 - 0.510000
0.510001 - 3.219483
74
Figure 16. The location quotients of large family farm acres were highest in the Northern Great Plains and Heartland. Trends are
hard to see based on the high amount of omitted counties (left). The differences between the location quotients of large family
farm acres and commodity payments were greatest in the Heartland, Mississippi portal, and Northern Great Plains (right).
75
Eastern Uplands was the lowest (0.37) followed by the Southern Seaboard, and Fruitful
Rim. This trend holds true in Figure 16 with most outliers being centralized in the
Northern Great Plains and Heartland. The highest mean percentage of large family farm
acres was in the Northern Great Plains at 19.7 percent and the Heartland at 19.2 percent.
The Eastern Uplands was the lowest at 6.9 percent (Table 15).
The location quotients of commodity payments are expected to increase as the
location quotients of large family farm acres increase, but only somewhat based on the
moderate correlation. It is hard to see patterns in Figure 16 based on the large amount of
missing data, but some trends are visible. Positive discordant counties are centralized in
the Northern Great Plains. Here the location quotients of large family farm acres are
larger than the location quotients of commodity payments because another variable is
influencing the commodity payments more strongly. This is understandable based on the
strength of the correlation. Negative discordant counties are focused in the Heartland,
Mississippi Portal, and along the southern edge of the Southern Seaboard. In these
regions the location quotients of commodity payments outweigh the location quotients of
large family farm acres, again due to the moderate strength of the correlation.
Very large family.
Very large family farms are sole proprietorship farms with market values of
agricultural products sold over $500,000. Very large family farms were strongly
correlated to farms with commodity payments (τ = .505, p = .000) based on raw data.
This correlation was only moderate based on location quotients (τ = .429, p = .000)
(Appendix A). Based on the mean location quotients of very large family farms, the
Northern Great Plains, Heartland, and Mississippi Portal were all above normal. The
76
Eastern Uplands, Basin and Range, and Northern Crescent were all below normal (Table
16). This trend holds true when looking at the county level location quotients (Figure
17). Based on the mean percentages of very large family farms by Farm Resource
Region the Northern Great Plains was highest at 8.0 percent, followed by the Heartland at
6.9 percent. The Basin and Range and Eastern Uplands were lowest, tied at
2.2 percent each (Table 16).
Table 16. The means, mean location quotients, and ordinal ranks of very large family
farms by Farm Resource Region.
Very Large
Very Large
Very Large
Ordinal Family LQb Ordinal
Family
Family(No.)
Rank
Rank
(%)a
Heartland
56.8
6.9
2
1.56
2
Northern
27.3
3.6
6
0.66
7
Crescent
Northern
43.3
8.0
1
1.77
1
Great Plains
Prairie
29.2
3.5
7
1.19
5
Gateway
Eastern
17.0
2.2
8
0.40
9
Uplands
Southern
27.1
5.3
3
1.20
4
Seaboard
Fruitful Rim
45.3
5.0
5
0.98
6
Basin and
11.8
2.2
8
0.59
8
Range
Mississippi
28.0
5.1
4
1.47
3
Portal
32.9
4.6
National
a
b
Very Large Family (number) / Farms (number). Location quotient of Very Large
Family (number) / Farms (number).
The correlation of very large family farms and farms with payments based on
location quotients is only moderate. Therefore, it is harder to expect to see patterns in
Figure 17. Discordant counties, both positive and negative are more scattered and many
0.000000 - 0.750000
0.750001 - 1.250000
1.250001 - 2.786678
2.786679 - 4.076460
4.076461 - 8.071464
-2.283287 - -0.510000
-0.509999 - 0.510000
0.510001 - 7.064769
77
Figure 17. The location quotients of very large family farms were above normal in the Northern Great Plains, Heartland, and
Mississippi Portal (left). The differences between the location quotients of very large family farms and farms with commodity
payments were scattered with trends among Farm Resource Regions hard to discern (right).
78
regions have a fair share of both categories. There is a large section of positive
discordant counties in the western part of the Southern Seaboard and Eastern Uplands.
There are also large clusters of positive discordant counties on the Pacific Coast.
Negative discordant counties tend to be in the Midwest and the eastern parts of the prairie
states. However, trends by Farm Resource Region are hard to discern.
Very large family farm acres were strongly correlated to commodity payments
(τ = .607, p = .000) based on raw data. This correlation was only moderate based on
location quotients (τ = .458, p = .000) (Appendix A). Based on the mean location
quotients of very large family farm acres in Table 17, the Heartland, Mississippi Portal
Table 17. The means, mean location quotients, and ordinal ranks of very large family
farm acres by Farm Resource Region.
Very Large
Very Large
Very Large
Family Acres
Ordinal
Ordinal
Family
Family
Rank
Rank
(ac)
Acres (%)a
Acres LQb
Heartland
90,456.4
35.3
2
1.48
1
Northern
31,678.9
24.8
4
0.80
7
Crescent
Northern
238,718.8
26.6
3
1.28
3
Great Plains
Prairie
129,495.0
23.3
6
1.02
4
Gateway
Eastern
12,431.1
10.1
9
0.31
9
Uplands
Southern
21,023.3
21.2
7
0.83
6
Seaboard
Fruitful Rim
95,439.2
24.6
5
0.96
5
Basin and
96,826.4
17.9
8
0.64
8
Range
Mississippi
70,240.7
39.5
1
1.35
2
Portal
76,868.7
25.8
National
a
b
Very Large Family (acres) / Land in Farms (acres). Location quotient of Very Large
Family (acres) / Land in Farms (acres).
0.000000 - 0.750000
0.750001 - 1.250000
1.250001 - 2.577768
2.577769 - 3.533457
-10.176452 - -0.510000
-0.509999 - 0.510000
0.510001 - 2.500010
79
Figure 18. The location quotients of very large family farm acres were above normal in the Mississippi Portal, Heartland, and
Northern Great Plains (left). The differences between the location quotients of very large family farm acres and commodity
payments were greatest in the Mississippi Portal, Heartland, and Northern Great Plains (right).
80
and Northern Great Plains were above normal. The Eastern Uplands and Basin and
Range regions were below normal. This trend holds true when looking at the county
level location quotients (Figure 18), but again trends are hard to see because of the large
number of counties with omitted data. The Mississippi Portal had the highest mean
percentage of very large family farm acres at 39.5 percent, followed by the Heartland at
35.3 percent. The Eastern Uplands had the lowest mean percentage at 10.1 percent
followed by the Basin and Range at 17.9 percent. The ordinal ranks of the Farm
Resource Regions based on the location quotients and mean percentages are different
from one another (Table 17).
Based on the correlation of the location quotients, the location quotient of
commodity payments would be moderately expected to increase as the location quotient
of very large family farms acres increases. On Figure 18, the positive discordant counties
are concentrated in the Northern Great Plains and the northern part of the Prairie
Gateway. The payment variables in these regions are more strongly correlated to another
variable that is causing them to remain low despite the high very large family farm acres.
The negative discordant counties are concentrated in the Mississippi Portal and
Heartland, and southern portion of the Southern Seaboard. In these regions the
government payment variable is elevated by a more strongly correlated variable. Since
the correlation is only moderate between the location quotients of very large family farm
acres and commodity payments, it is expected that not every county will fit the
correlation.
81
Commodity Variables
Commodity variables include the number of farms growing corn, soybean, and
wheat as well as the acres of these three commodities. Farms with cropland and total
cropland acres are also included as salient variables in this paper. Nationally, an average
of 15.8 percent of farms per county produced corn (Table 18) for a total of 10.5 percent of
acres (Table 19). The mean number of farms producing soybeans per county was at
12.7 percent (Table 20) for a total of 7.7 percent of acres (Table 21). Wheat was produced
on a mean of 7.3 percent of farms per county (Table 22), and 6.4 percent of acres per
county (Table 23). Nationally, 76.4 percent of farms per county (Table 24) had cropland
for only 44.5 percent of the acreages of land in farms (Table 25). The percentage of
farms producing corn was highest in the Heartland at 41.3 percent
(Table 18), as was the percentage (37.4 percent) of acres in corn production (Table 19).
The percentage of farms producing soybean and the percentage of acres in soybean were
highest in the Heartland at 37.8 percent (Table 20) and 27.7 percent (Table 21),
respectively. The Northern Great Plains had the highest percentage (28.7 percent) of
farms producing wheat (Table 22) and the highest percentage (11.9 percent) of acres in
wheat (Table 23). Finally, the Heartland had the highest percentage of farms with total
cropland at 86.6 percent (Table 24) and the highest percentage of acres in total farmland
at 80.9 percent (Table 25).
Corn.
Corn farms were strongly correlated to farms with payments (τ = .613, p = .000).
Corn farms were also strongly correlated based on location quotients (τ = .556, p = .000)
(Appendix A). Based on the mean location quotients of corn farms, the Heartland was
82
very high (2.61) with no other regions being above normal. The Basin and Range was the
lowest followed by the Fruitful Rim, and Eastern Uplands (Table 18). This trend holds
true when looking at the county level location quotients (Figure 19) where very few
counties are above normal outside of the Heartland and its surrounding counties. Based on
the mean percentage of farms per county producing corn, the Heartland was far above
average at 41.3 percent, with the Northern Crescent following distantly at 24.0 percent.
Corn producing farms were the lowest in the Basin and Range and Fruitful Rim at 1.0
and 1.7 percent, respectively. The ordinal ranks of Farm Resource Regions by location
quotients and mean percentage were similar (Table 18).
Table 18. The means, mean location quotients, and ordinal ranks of corn farms by Farm
Resource Region.
Ordinal
Ordinal
Corn
LQb
Corn (No.)
Corn (%)a
Rank
Rank
Heartland
339.9
41.3
1
2.61
1
Northern
179.2
24.0
2
1.07
3
Crescent
Northern Great
90.9
16.8
3
1.09
2
Plains
Prairie Gateway
67.6
8.2
4
0.74
4
Eastern
35.3
4.5
7
0.27
7
Uplands
Southern
36.7
7.1
6
0.57
5
Seaboard
Fruitful Rim
15.2
1.7
8
0.15
8
Basin and
5.4
1.0
9
0.05
9
Range
Mississippi
43.3
7.9
5
0.56
6
Portal
113.0
15.8
National
a
Corn (number) / Farms (number). b Location quotient of Corn (number) / Farms
(number).
0.000000 - 0.750000
0.750001 - 1.250000
1.250001 - 3.045091
3.045092 - 4.587977
4.587978 - 4.986792
-2.474293 - -0.510000
-0.509999 - 0.510000
0.510001 - 2.310940
83
Figure 19. The location quotients of corn farms were highest in the Heartland (left). The differences between the location
quotients of corn farms and farms with commodity payments were greatest in the Heartland, Northern Great Plains, Prairie
Gateway, and Mississippi Portal (right).
84
Based on the strength of the correlation, farms with payments location quotients
were expected to increase as corn farms location quotients increase. The strength of the
correlation is visible in Figure 19. The positive discordant counties are all clustered in and
around the Heartland, exactly where the above normal location quotients were focused on
Figure 19. In these counties, the location quotients of farms with payments are lower than
expected based on the location quotients of corn farms. The negative discordant counties
are in the Northern Great Plains, Prairie Gateway, and Mississippi Portal. Here, although
the location quotients of corn farms are low, location quotients of farms with payments
are higher, meaning that another variable is more important in these regions.
Corn acres were strongly correlated to commodity payments (τ = .576, p = .000).
This correlation was also strong based on location quotients (τ = .560, p = .000)
(Appendix A). Based on the mean location quotients of corn acres, the Heartland was
very high (3.75), followed by the Northern Crescent as the only other region above
normal. All other regions but the Mississippi Portal were below normal (Table 19).
Figure 20 supports this trend, with some above normal counties in the Mississippi Portal,
Prairie Gateway, and Southern Seaboard. Based on the mean percentage of corn acres in
each county, the Heartland was again the highest at 37.4 percent followed by the
Northern Crescent at 19.5 percent. The Basin and Range and Fruitful Rim were lowest at
0.1 and 1.1 percent, respectively. The ordinal ranks of Farm Resource Regions based on
location quotients and mean percentages were very similar (Table 19).
0.000000 - 0.750000
0.750001 - 1.250000
1.250001 - 3.819450
3.819451 - 5.743412
5.743413 - 7.513393
-11.920037 - -0.510000
-0.509999 - 0.510000
0.510001 - 4.714063
85
Figure 20. The location quotients of corn acres were highest in the Heartland and the Northern Crescent. All other regions except
the Mississippi Portal were below normal (left). The differences between the location quotients of corn acres and commodity
payments were greatest in the Heartland, Mississippi Portal, and Southern Seaboard (right).
86
Table 19. The means, mean location quotients, and ordinal ranks of corn farm acres by
Farm Resource Region.
Heartland
Northern
Crescent
Northern Grea t
Plains
Prairie Gatew ay
Eastern Uplan ds
Southern
Seaboard
Fruitful Rim
Basin and
Range
Mississippi
Portal
Corn Acres
(ac)
95,835.3
Corn Acres
(%)a
37.4
Ordinal
Rank
1
Corn
Acres LQb
3.72
Ordinal
Rank
1
24,903.1
19.5
2
1.35
2
37,582.5
4.2
6
0.62
6
31,773.2
3,208.4
5.7
2.6
5
7
0.73
0.23
4
7
6,661.3
6.7
4
0.70
5
4,110.9
1.1
8
0.14
8
563.9
0.1
9
0.01
9
17,381.1
9.8
3
0.81
3
31,151.4
10.5
National
a
b
Corn (acres) / Land in Farms (acres). Location quotient of Corn (acres) / Land in
Farms (acres).
Based on the strength of the location quotient correlation, commodity payment
location quotients were expected to increase as corn farm acres location quotients
increase. Due to the large number of omitted data, some patterns may not be visible but
some strong patterns do emerge (Figure 20). Positive discordant counties again are
centralized in the Heartland and surrounding counties. Negative discordant counties are
in the Mississippi Portal and Southern Seaboard. In these regions, despite the low
location quotients of corn farm acres the location quotients of commodity payments are
higher based on other variables.
Soybeans.
Soybean farms were strongly correlated to the number of farms receiving
commodity payments based on raw data (τ = .574, p = .000). This same correlation was
87
strong based on location quotients (τ = .547, p = .000) (Appendix A). Based on the mean
location quotients of soybean farms, the Heartland was the highest (3.01). The Basin and
Range, Fruitful Rim, and Eastern Uplands had the lowest location quotients of soybean
farms (Table 20). The concentration of high outliers in the Heartland supports the
location quotient analysis, with some outliers clustered in the surrounding regions as well
as the Southern Seaboard and Mississippi Portal (Figure 21). The Heartland had the
highest mean percentage of soybean farms per county at 37.8 percent, followed distantly
by the Northern Crescent at 14.2 percent. The Basin and Range and Fruitful Rim had the
lowest mean percentages at 0.0 and 0.3 percent, respectively (Table 20).
Table 20. The means, mean location quotients, and ordinal ranks of soybean farms by
Farm Resource Region.
Soybean
Ordinal
Ordinal
Soybean
Soybean
a
b
(%)
LQ
(No.)
Rank
Rank
310.9
37.8
1
3.01
1
Heartland
Northern
106.6
14.2
2
0.77
4
Crescent
Northern Great
71.6
13.3
3
0.92
3
Plains
6.3
5
0.63
5
Prairie Gatewa y
52.0
1.9
7
0.12
7
Eastern Upland s
14.8
Southern
30.7
6.0
6
0.61
6
Seaboard
2.5
0.3
8
0.05
8
Fruitful Rim
0.0
9
0.00
9
Basin and Rang e
0.1
Mississippi
63.7
11.6
4
1.14
2
Portal
90.7
12.7
National
a
b
Soybean (farms) / Farms (number). Location quotient of Soybean (farms) / Farms
(number).
Based on the strength of the correlation between the location quotients of soybean
farms and farms with commodity payments, the location quotients of farms with
commodity payments should increase as the location quotients of soybean farms increase.
0.000000 - 0.750000
0.750001 - 1.250000
1.250001 - 2.961782
2.961783 - 4.442673
4.442674 - 6.453298
-2.597239 - -0.510000
-0.509999 - 0.510000
0.510001 - 3.539824
88
Figure 21. The location quotients of soybean farms per county were highest in the Heartland followed distantly by the
Mississippi Portal. There were some outliers scattered in the Southern Seaboard, Northern Great Plains, and Northern Crescent
(left). Discordant counties based on soybean farms are located in the Heartland, Northern Great Plains, and Prairie Gateway
89
Positive discordant counties are concentrated in the Heartland, meaning that in this region
the location quotients of soybean farms are higher than the location quotients of farms with
commodity payments (Figure 21). More importantly, the negative discordant counties are
located in the Northern Great Plains and Prairie Gateway. This means that in these regions
the location quotients of farms with payments were higher than expected based on the
location quotients of soybean farms.
Soybean acres were strongly correlated to the amount of commodity payments
(τ = .501, p = .000) based on raw data. This correlation was also strong based on location
quotients (τ = .561, p = .000) (Appendix A). Based on the mean location quotients of
soybean acres, the Heartland was the highest (3.88) followed by the Mississippi Portal
(2.19). The Basin and Range, Fruitful Rim, Eastern Uplands, and Prairie Gateway were
all below normal (Table 21). Most high outliers were concentrated in the Heartland and
Mississippi Portal (Figure 22), with some scattered along the edges of the Southern
Seaboard, Northern Great Plains and Northern Crescent. Based on the mean percentage
of soybean acres per county, the Heartland was again the highest at 27.7 percent followed
by the Mississippi Portal at 21.2 percent. The Basin and Range and Fruitful Rim were
again the lowest at 0.0 and 0.1 percent, respectively (Table 21).
Based on the large number of omitted data in Figure 22, trends are hard to
discern. However, positive discordant counties are clustered in the Heartland and
surrounding counties. Negative discordant counties may be focused in the Northern
Crescent, Southern Seaboard, and Prairie Gateway, but it is hard to be sure based on the
large numbers of omitted data in these regions.
0.000000 - 0.750000
0.750001 - 1.250000
1.250001 - 4.847404
4.847405 - 7.271106
7.271107 - 7.980652
-11.999322 - -0.510000
-0.509999 - 0.510000
0.510001 - 5.627714
90
Figure 22. The location quotients of soybean acres were highest in the Heartland and Mississippi Portal. There were a few
outliers in the Southern Seaboard, Northern Great Plains, and Northern Crescent (left). The differences between the location
quotients of soybean acres and commodity payments were greatest in the Heartland (right).
91
Table 21. The means, mean location quotients, and ordinal ranks of soybean farm acres
by Farm Resource Region.
Soybean
Ordinal
Ordinal
Soybean
Soybean
a
b
Acres (%)
Acres LQ
(ac)
Rank
Rank
Heartland
70,912.0
27.7
1
3.88
1
Northern
15,173.0
11.9
3
1.15
3
Crescent
Northern Great
37,427.3
4.2
5
0.86
5
Plains
Prairie
12,324.2
2.2
7
0.50
6
Gateway
Eastern
3,073.9
2.5
6
0.27
7
Uplands
Southern
7,349.5
7.4
4
1.11
4
Seaboard
Fruitful Rim
417.1
0.1
8
0.06
8
Basin and
0.0
0.0
9
0.00
9
Range
Mississippi
37,754.4
21.2
2
2.19
2
Portal
22,980.2
7.7
National
a
b
Soybean (acres) / Land in Farms (acres). Location quotient of Soybean (acres) / Land
in Farms (acres).
Wheat.
Wheat farms were strongly correlated to farms with commodity payments
(τ = .554, p = .000) based on raw data. This was strongly correlated based on location
quotients as well (τ = .553, p = .000) (Appendix A). Based on the mean location
quotients of wheat farms, the Northern Great Plains and Prairie Gateway were the only
regions above normal (Table 22), supported by the county level map (Figure 23). The
Eastern Uplands, Fruitful Rim, Basin and Range, and Southern Seaboard were all below
normal (Table 22). Based on the mean percentage of wheat farms per county, the
Northern Great Plains were the highest at 28.7 percent followed distantly by the Prairie
Gateway at 14.5 percent. The Eastern Uplands and the Fruitful Rim were the lowest at
1.2 and 2.4 percent, respectively (Table 22).
92
Table 22. The means, mean location quotients, and ordinal ranks of wheat farms by Farm
Resource Region.
Wheat
Ordinal
Ordinal
Wheat
LQb
(No.)
Wheat (%)a
Rank
Rank
66.8
8.1
3
1.16
3
Heartland
Northern
53.0
7.1
4
0.71
5
Crescent
Northern Great
154.7
28.7
1
3.93
1
Plains
119.7
14.5
2
2.80
2
Prairie Gateway
1.2
9
0.13
9
Eastern Upland s
9.1
Southern
16.6
3.2
7
0.62
6
Seaboard
21.6
2.4
8
0.36
8
Fruitful Rim
4.7
6
0.61
7
Basin and Rang e
24.6
Mississippi
28.4
5.2
5
0.90
4
Portal
52.2
7.3
National
a
b
Wheat (number) / Farms (number). Location quotient of Wheat (number) / Farms
(number).
Since the correlation based on location quotients was strong the location quotients,
the location quotient of farms with payments will increase as the location quotient of
wheat farms increases. Positive discordant counties are centralized in the Northern Great
Plains and the Prairie Gateway (Figure 23). This means that wheat farms were
concentrated in these regions, but farms with payments were not. Negative discordant
counties are centralized in the Heartland. This means that farms with payments were
concentrated in this region but wheat farms were not, so another variable must have a
stronger effect on the farms with payments in this region.
0.000000 - 0.750000
0.750001 - 1.250000
1.250001 - 2.781679
2.781680 - 4.187196
4.187197 - 9.732654
-2.556991 - -0.510000
-0.509999 - 0.510000
0.510001 - 7.135415
93
Figure 23. The location quotients of wheat farms were highest in the Northern Great Plains and Prairie Gateway (left).
Discordant counties based on wheat farms were concentrated in the Northern Great Plains, Prairie Gateway, and Heartland
(right).
94
Wheat acres were strongly correlated to commodity payments (τ = .504, p = .000)
based on raw data. This correlation was weaker based on the location quotients for each
county (τ = .297, p = .000) (Appendix A). Based on the mean location quotients of wheat
acres, the Northern Great Plains and Prairie Gateway were above normal. All other
regions were below normal with the lowest location quotients in the Eastern Uplands,
Northern Crescent, and Fruitful Rim (Table 23). This trend holds true when looking at
the county level location quotients (Figure 24), but again trends are hard to see because of
the large number of counties with omitted data. The mean percentage of wheat farm
acres per county was highest in the Northern Great Plains (11.9 percent) and the Prairie
Gateway (9.1 percent). The lowest mean percentages were in the Eastern Uplands (1.0
percent) and the Heartland and Northern Crescent at 2.9 percent each (Table 23).
Table 23. The means, mean location quotients, and ordinal ranks of wheat farm acres by
Farm Resource Region.
Wheat
Ordinal
Ordinal
Wheat
Wheat
a
b
Acres (%)
Acres LQ
(ac)
Rank
Rank
7,306.5
2.9
7
0.52
6
Heartland
Northern
3,735.1
2.9
7
0.38
8
Crescent
Northern Grea t
11.9
1
2.38
1
106,356.1
Plains
9.1
2
1.70
2
Prairie Gatewa y
50,901.1
1.0
9
0.12
9
Eastern Uplan ds
1,219.3
Southern
3,282.5
3.3
5
0.62
4
Seaboard
12,117.4
3.1
6
0.40
7
Fruitful Rim
3.6
4
0.60
5
Basin and Ran ge 19,240.3
Mississippi
9,590.8
5.4
3
0.68
3
Portal
19,132.9
6.4
National
a
b
Wheat (acres) / Land in Farms (acres). Location quotient of Wheat (acres) / Land in
Farms (acres).
0.000000 - 0.750000
0.750001 - 1.250000
1.250001 - 1.839130
1.839131 - 2.752493
2.752494 - 8.180753
-11.999322 - -0.510000
-0.509999 - 0.510000
0.510001 - 7.123477
95
Figure 24. The location quotients of wheat acres were highest in the Northern Great Plains and Prairie Gateway (left).
Discordant pairs based on wheat farm acres were greatest in the Northern Great Plains, Prairie Gateway, Heartland, and
Mississippi Portal (right).
96
Based on the weakness of the correlation between location quotients of wheat farm
acres and commodity payments, trends on Figure 24 are not expected to be strong. Also,
due to the large number of omitted data trends are not expected to be highly visible. The
low number of concordant counties is expected based on the weakness of the correlation
as well. Positive discordant counties are focused in the Northern Great Plains and Prairie
Gateway, as expected based on the high location quotients of wheat farm
acres in these regions. Negative discordant counties are concentrated in the Heartland,
Northern Crescent, Mississippi Portal, and Southern Seaboard. The patterns are not as
strong in the Northern Crescent and Southern Seaboard, again based on the large number
of omitted data. These regions have high location quotients of commodity payments
despite low location quotients of wheat farm acres because another variable is more
strongly correlated to commodity payments in these regions.
Total cropland.
Finally, total cropland is all cropland harvested including non-covered
commodities and idled cropland. Total cropland farms were strongly correlated to farms
with commodity payments (τ = .543, p = .000) based on raw data. This correlation was
moderate based on location quotients (τ = .472, p = .000) (Appendix A). Based on the
mean location quotients of total cropland farms, all regions were normal, ranging between
0.81 and 1.13 (Table 24). Although Figure 25 may appear to be full of high location
quotients, these are all counties that fall within the normal location quotient range. There
are below normal outliers in the Fruitful Rim, Basin and Range, and in
0.000000 - 0.364277
0.364278 - 0.619962
0.619963 - 0.750000
0.750001 - 1.250000
1.250001 - 1.305211
-1.664737 - -0.510000
-0.509999 - 0.510000
0.510001 - 1.305211
97
Figure 25. The location quotients of total cropland farms were normal for all regions (left). The differences between the location
quotients of total cropland farms and farms with commodity payments were greatest in the Northern Great Plains, Prairie Gateway,
and Heartland (right).
98
Table 24. The means, mean location quotients, and ordinal ranks of total cropland farms
by Farm Resource Region.
Cropland
Ordinal
Ordinal
Cropland
Cropland
a
b
(%)
LQ
(No.)
Rank
Rank
Heartland
713.3
86.6
1
1.13
1
Northern
637.1
85.2
2
1.09
2
Crescent
Northern Great
453.2
84.0
3
1.08
3
Plains
Prairie
577.4
70.1
6
0.95
5
Gateway
Eastern
590.2
75.9
4
0.99
4
Uplands
Southern
356.7
69.1
7
0.91
7
Seaboard
Fruitful Rim
593.8
66.2
9
0.81
9
Basin and
352.8
66.7
8
0.85
8
Range
Mississippi
392.8
71.4
5
0.93
6
Portal
547.4
76.4
National
a
b
Total Cropland (number) / Farms (number). Location quotient of Total Cropland
(number) / Farms (number).
western Prairie Gateway and Northern Great Plains. There are very few counties that are
above normal scattered throughout the Heartland and Mississippi Portal. Based on the
mean percentage of farms with total cropland per county, the highest regions were the
Heartland (86.6 percent), Northern Crescent (85.2 percent), and the Northern Great Plains
(84.0 percent). The Fruitful Rim (66.2 percent), Basin and Range (66.7 percent), and the
Southern Seaboard (69.1 percent) had the lowest mean percentages of farms with total
cropland per county (Table 24).
The location quotients of farms with payments were expected to increase as the
location quotients of farms with total cropland increase based on the correlation. In
Figure 25, the positive discordant counties were scattered but may be centralized in the
99
Eastern Uplands, Northern Crescent, and western parts of the Fruitful Rim. Negative
discordant counties were concentrated in the Northern Great Plains, Prairie Gateway, and
Heartland. In these regions, the location quotients of farms with payments were larger
than the location quotients of farms with total cropland.
Total cropland acres were strongly correlated to commodity payments (τ = .697,
p = .000) based on raw data. This correlation was also strong based on location quotients
(τ = .596, p = .000) (Appendix A). Based on the mean location quotients of total
cropland acres, the Heartland, Northern Crescent, and Mississippi Portal are all above
normal. The Basin and Range region is the only region with a below normal location
quotient (Table 25). On the county level map (Figure 26), the Heartland, Northern
Table 25. The means, mean location quotients, and ordinal ranks of total cropland farm
acres by Farm Resource Region.
Cropland
Ordinal
Ordinal
Cropland
Cropland
Acres (%)a
Acres LQb
(ac)
Rank
Rank
Heartland
207,227.4
80.9
1
1.77
1
Northern
85,057.2
66.5
2
1.31
2
Crescent
Northern
381,285.7
42.5
4
1.10
4
Great Plains
Prairie
211,425.6
38.0
6
0.95
5
Gateway
Eastern
45,492.9
37.1
7
0.82
7
Uplands
Southern
41,485.5
41.8
5
0.95
5
Seaboard
Fruitful Rim
113,765.0
29.3
8
0.80
8
Basin and
90,990.8
16.8
9
0.49
9
Range
Mississippi
115,932.2
65.2
3
1.29
3
Portal
132,547.9
44.5
National
a
b
Total Cropland (acres) / Land in Farms (acres). Location quotient of Total Cropland
(acres) / Land in Farms (acres).
0.000000 - 0.750000
0.750001 - 1.250000
1.250001 - 2.245204
-10.197265 - -0.510000
-0.509999 - 0.510000
0.510001 - 2.245204
100
Figure 26. The location quotients of total cropland acres were highest in the Heartland, Northern Crescent, and Mississippi Portal
(left). Negative discordant counties are concentrated in the Mississippi Portal and Southern Seaboard with some in the Heartland
(right).
101
Crescent, and Mississippi Portal are above normal. However, the Southern Seaboard,
Prairie Gateway, and Fruitful Rim all have large numbers of counties with above normal
location quotients as well. Based on the mean percentages of total cropland acres per
county, the Heartland was again the highest at 80.9 percent, followed by the Northern
Crescent (66.5 percent) and the Mississippi Portal (65.2 percent). The ordinal ranks of
regions based on location quotients and mean percentages were very similar (Table 25).
Based on the strength of the correlation between location quotients, commodity
payments would be expected to increase as total cropland acres increase. Logically, this
correlation would be very strong since the amount of commodity payments received is
based on the acres of covered commodities produced. The low number of discordant
counties in Figure 26 is evidence of the strength of this correlation. Positive discordant
counties are sparse and highly scattered. Negative discordant counties are concentrated
in the Mississippi Portal, with some in the Southern Seaboard and even fewer in the
Heartland. In these counties, the location quotients of commodity payments are larger
than expected based on the location quotients of total cropland acres, meaning that other
variables are more strongly correlated to payments in these areas.
The purpose of the county level analysis was to determine if commodity variables
are the only variables that are strongly correlated to government payment variables. This
hypothesis was rejected. Although corn, soybean, wheat, and total cropland were all
strongly correlated to commodity payment variables, they were not the only variables with
strong correlations. Although the correlation of the other variables does not mean they
directly determine payment amounts, they do play a role in the participation in the
commodity payment programs.
102
Farm Resource Regions
The other purpose of this research is to determine if payments are distributed
equitably by Farm Resource Region. Given that commodity payments are determined by
the commodities produced, you would expect to see variations among Farm Resource
Regions based on the commodities they produce and the amount of commodities
produced. Following this logic, those regions with the highest government payment
variables should have the highest commodity variables, and those regions with the lowest
government payment variables should have the lowest commodity variables. However,
not all commodities are created equal. In 2006, corn farms accounted for 46 percent of
commodity payments, followed by upland cotton farms (23 percent), wheat farms (10
percent), rice farms (8 percent), and soybean farms (6 percent) (ERS, 2006). Therefore,
regions specializing in corn, for example, would be expected to have higher location
quotients of payment variables than regions specializing in barley, for example, even
though barley is a covered commodity.
Heartland.
In 2007 the Heartland had the third highest mean number of farms per county
(823.3) but the fifth highest mean land in farms (256,298.4 acres). Commodity payments
went to an average of 453.2 farms per county for a mean county payment received of
$3,526,080. Income dependence averaged 21.1 percent (Appendix B). The Heartland
had the second highest location quotient of farms with commodity payments (Table 1),
and the second highest location quotient of commodity payments (Table 2).
The Heartland had strong correlations between farms with commodity payments
and all salient variables except wheat (Table 26). Commodity payments were strongly
103
correlated to market value, net income, corn acres, and total cropland acres. The
correlation to production expenses was also very high (Table 26). Based on the
correlations of corn and soybean farms to farms with payments, it was expected that the
Heartland would have a very high location quotient of farms with payments. Especially
since corn and soybean farms accounted for 52 percent of government payments in 2006
(ERS, 2006). However, based on the fact that the only covered commodity strongly
correlated to commodity payments was corn, the high location quotient of commodity
payments is slightly unexpected.
Table 26. Kendall’s tau correlations of location quotients in the Heartland.
Farms with Payments (No.) Kendall’s τ Commodity Payments ($)
0.575** Market Value ($)
Farming Occupation (No.)
0.586** Gross Income ($)
Small Family Higher (No.)
0.670** Production Expenses ($)
Large Family (No.)
0.562** Net Income ($)
Very Large Family (No.)
0.652** Large Family (ac.)
Part Owner (No.)
0.520** Very Large Family (ac.)
Tenant (No.)
0.732** Part Owner (ac.)
Corn (No.)
0.739** Tenant (ac.)
Soybean (No.)
0.075** Corn (ac.)
Wheat (No.)
0.535** Soybean (ac.)
Total Cropland (No.)
Wheat (ac.)
Total Cropland (ac.)
*p < .05. **p < .01.
Kendall’s τ
0.520**
0.343**
0.499**
0.506**
0.318**
0.422**
0.405**
0.428**
0.626**
0.437**
-0.050
0.650**
Northern Crescent
The average county in the Northern Crescent had 748.2 farms and 127,980.2 acres
of farmland. On average, 239.1 farms received government commodity payments for
$1,328,181 per county. Income dependence averaged 12.9 percent for the Northern
Crescent (Appendix B). The Northern Crescent had the fifth highest location quotient
104
farms with payments, which was on the lower end of the normal range (Table 1). The
location quotient of commodity payments was on the higher end of the normal range and
the fourth highest region (Table 2).
Farms with commodity payments were strongly correlated to corn and soybean
farms in the Northern Crescent (Table 27). Farming occupation and tenant farms have
little to do with farms with commodity payments in the Northern Crescent. Commodity
payments were strongly correlated to corn, soybean, and total cropland acres (Table 27).
Although payments were strongly correlated to these commodity variables, the mean
location quotients of these variables were all at or slightly above normal, hence the mean
location quotients of farms with commodity payments and payments were as expected.
Table 27. Kendall’s tau correlations of location quotients in the Northern Crescent.
Farms with Payments (No.) Kendall’s τ Commodity Payments ($) Kendall’s τ
0.075*
0.338**
Farming Occupation (No.)
Market Value ($)
0.441** Gross Income ($)
0.239**
Small Family Higher (No.)
**
0.437
0.286**
Large Family (No.)
Production Expenses ($)
0.351** Net Income ($)
0.429**
Very Large Family (No.)
**
0.485
0.483**
Part Owner (No.)
Large Family (ac.)
0.484**
-0.103** Very Large Family (ac.)
Tenant (No.)
0.637** Part Owner (ac.)
0.476**
Corn (No.)
**
0.587
0.296**
Soybean (No.)
Tenant (ac.)
0.482** Corn (ac.)
0.591**
Wheat (No.)
**
0.407
0.574**
Total Cropland (No.)
Soybean (ac.)
0.423**
Wheat (ac.)
0.527**
Total Cropland (ac.)
*p < .05. **p < .01.
Northern Great Plains.
The average county in the Northern Great Plains had the third lowest number of
farms (539.7) but the highest land in farms (896,661.4 acres). On average, over half of
105
the farms received commodity payments (291.9) for a county average of $3,403,486.
Income dependence was the lowest among farm resource regions at 12.4 percent
(Appendix B). The Northern Great Plains had the highest location quotient of farms with
commodity payments (Table 1), but the third lowest location quotient of commodity
payments (Table 2). This means that although many farms receive payments, the payment
per acre is lower than would be expected because the average farm is so large.
Table 28. Kendall’s tau correlations of location quotients in the Northern Great Plains.
Farms with Payments (No.) Kendall’s τ Commodity Payments ($) Kendall’s τ
0.324** Market Value ($)
0.587**
Farming Occupation (No.)
0.272** Gross Income ($)
0.670**
Small Family Higher (No.)
**
0.421
0.551**
Large Family (No.)
Production Expenses ($)
0.335** Net Income ($)
0.720**
Very Large Family (No.)
0.026
0.414** Large Family (ac.)
Part Owner (No.)
**
0.159
0.493**
Tenant (No.)
Very Large Family (ac.)
0.308** Part Owner (ac.)
0.031**
Corn (No.)
**
0.213
0.035
Soybean (No.)
Tenant (ac.)
**
**
0.564
0.463
Wheat (No.)
Corn (ac.)
0.627**
0.415** Soybean (ac.)
Total Cropland (No.)
0.535**
Wheat (ac.)
0.805**
Total Cropland (ac.)
*p < .05. **p < .01.
Farms with payments were strongly correlated to wheat farms in the Northern
Great Plains (Table 28). Farms with payments depended little on tenant farms in the
Northern Great Plains. Commodity payments were strongly correlated to market value,
gross income, production expenses, net income, and soybean, wheat, and total cropland
acres (Table 28). Part owner and tenant acres had little correlation to commodity
payments in this region. The Northern Great Plains had the highest location quotient of
farms with commodity payments, which was unexpected based on the fact that only wheat
farms were strongly correlated to farms with commodity payments, and that wheat
106
farms only accounted for 10 percent of farms with payments in 2006 (ERS, 2006). On
the other hand, the Northern Great Plains had the third highest location quotient of
commodity payments, which was low based on the fact that soybean and wheat acres
were both strongly correlated to commodity payments. However, since wheat and
soybeans only accounted for 16 percent of payments in 2006 (ERS, 2006), this was not
totally unexpected.
Prairie Gateway.
The Prairie Gateway had the second highest mean number of farms (824.1) and
land in farms (556,704.1 acres) per county. One-third of the farms per county received
government commodity payments (276.7) for a mean county receipt of $3,036,438.
Income dependence was 33.6 percent (Appendix B).
The Prairie Gateway had the third
highest location quotient of farms with payments (Table 1). The location quotient of
commodity payments was on the lower end of the normal range as the fourth lowest
(Table 2).
Table 29. Kendall’s tau correlations of location quotients in the Prairie Gateway.
Farms with Payments (No.) Kendall’s τ Commodity Payments ($) Kendall’s τ
0.518** Market Value ($)
0.458**
Farming Occupation (No.)
**
0.668
0.476**
Small Family Higher (No.)
Gross Income ($)
0.641** Production Expenses ($)
0.386**
Large Family (No.)
**
0.537
0.555**
Very Large Family (No.)
Net Income ($)
0.544** Large Family (ac.)
0.324**
Part Owner (No.)
0.425** Very Large Family (ac.)
0.396**
Tenant (No.)
**
0.433
0.301**
Corn (No.)
Part Owner (ac.)
0.459** Tenant (ac.)
0.212**
Soybean (No.)
**
0.690
0.442**
Wheat (No.)
Corn (ac.)
0.672** Soybean (ac.)
0.361**
Total Cropland (No.)
0.450**
Wheat (ac.)
0.649**
Total Cropland (ac.)
*p < .05. **p < .01.
107
In the Prairie Gateway, farms with payments were strongly correlated to farming
occupation, part owner farms, small family higher sales farms, large and very large
family farms, wheat farms, and total cropland farms (Table 29). It was also strongly
correlated to sorghum farms (τ = .546, p = .000). Hence, having the third highest location
quotient of farms with payments not only relied on the salient variables, but also grain
sorghum in the Prairie Gateway. Commodity payments were only strongly correlated to
net income and total cropland acres (Table 29); therefore the lower location quotient of
commodity payments was expected.
Eastern Uplands.
The Eastern Uplands had a mean of 777.8 farms and 122,617.1 acres in farmland
per county. Only 129.1 farms per county received commodity payments on average for
only $440,287, the lowest of all regions. Income dependence was 17.3 percent
(Appendix B). Based on location quotients, the Eastern Uplands were at or below normal
for all variables salient enough for inclusion in the results. In fact, the Eastern Uplands
had the lowest location quotient of farms with payments (Table 1) and the second lowest
location quotient of commodity payments (Table 2).
Farms with payments were not strongly correlated to any salient variable in the
Eastern Uplands, nor any other variable tested in this research (Table 30). Farming
occupation, part owner farms, tenant farms, and very large family farms were not
correlated to farms with payments. Therefore, the low mean location quotient of farms
with payments was expected. However, commodity payments were strongly correlated
to corn and soybean acres in the Eastern Uplands (Table 30). Tenant and part owner
108
farm acres were not correlated to commodity payments. This means that the farms that
do receive payments in the Eastern Uplands receive them mostly for corn and soybeans.
Table 30. Kendall’s tau correlations of location quotients in the Eastern Uplands.
Farms with Payments (No.) Kendall’s τ Commodity Payments ($) Kendall’s τ
0.076*
0.320**
Farming Occupation (No.)
Market Value ($)
**
0.380
0.286**
Small Family Higher (No.)
Gross Income ($)
0.245** Production Expenses ($)
0.310**
Large Family (No.)
**
0.171
0.269**
Very Large Family (No.)
Net Income ($)
0.015
0.354**
Part Owner (No.)
Large Family (ac.)
0.048
0.329**
Tenant (No.)
Very Large Family (ac.)
**
0.400
0.040
Corn (No.)
Part Owner (ac.)
0.433** Tenant (ac.)
0.132**
Soybean (No.)
**
0.367
0.512**
Wheat (No.)
Corn (ac.)
0.232** Soybean (ac.)
0.584**
Total Cropland (No.)
0.495**
Wheat (ac.)
0.435**
Total Cropland (ac.)
*p < .05. **p < .01.
Southern Seaboard.
The average county in the Southern Seaboard had a mean of 516 farms and
99,326.5 acres in farmland, with both of these being the lowest of all regions. Only 97.2
of these farms received commodity payments for a county mean of $1,260,263 in
payments. Income dependence in this region averaged 47.7 percent per county
(Appendix B). The Southern Seaboard had the fourth lowest location quotient of farms
with payments (Table 1), but the third highest location quotient of commodity payments
(Table 2).
In the Southern Seaboard, farms with payments were strongly correlated to corn,
soybean, and total cropland farms (Table 31). However, farms with payments were also
strongly correlated to upland cotton farms (τ = .556, p = .000) and peanut farms (τ = .524,
p = .000). Therefore, the low location quotient of farms with payments was unexpected.
109
In the Southern Seaboard, commodity payments were not strongly correlated to any of the
salient variables (Table 31). However, it was strongly correlated to both upland cotton
acres (τ = .677, p = .000) and peanut acres (τ = .595, p = .000). The high location
quotient of commodity payments was expected based on the strong correlation to upland
cotton, since upland cotton accounted for 23 percent of payments in 2006 (ERS, 2006).
Market value, production expenses, and net income had very little to do with commodity
payments in the Southern Seaboard (Table 31).
Table 31. Kendall’s tau correlations of location quotients in the Southern Seaboard.
Farms with Payments (No.) Kendall’s τ Commodity Payments ($) Kendall’s τ
0.215** Market Value ($)
0.079*
Farming Occupation (No.)
0.448** Gross Income ($)
0.206**
Small Family Higher (No.)
**
0.055
0.417
Large Family (No.)
Production Expenses ($)
0.279** Net Income ($)
0.104**
Very Large Family (No.)
0.218** Large Family (ac.)
0.423**
Part Owner (No.)
**
0.294
0.405**
Tenant (No.)
Very Large Family (ac.)
0.579** Part Owner (ac.)
0.288**
Corn (No.)
**
0.233**
0.555
Soybean (No.)
Tenant (ac.)
0.574** Corn (ac.)
0.479**
Wheat (No.)
**
0.377
0.458**
Total Cropland (No.)
Soybean (ac.)
0.454**
Wheat (ac.)
0.499**
Total Cropland (ac.)
*p < .05. **p < .01.
Fruitful Rim.
The Fruitful Rim had the highest mean number of farms per county (897.3) but
the fourth highest land in farms (388,738.6 acres). One-eighth of the farms in the average
county received government payments (110) for $2,352,371 in payments. Income
dependence was 20.3 percent (Appendix B). The Fruitful Rim had the second lowest
110
location quotient of farms with payments (Table 1). The Fruitful Rim had the fifth
highest location quotient of commodity payments on the higher end of normal (Table 2).
In the Fruitful Rim, farms with payments were not strongly correlated to the
salient variables (Table 32). This means that the low location quotient of farms with
payments was expected. Commodity payments were also not strongly correlated to any
salient variables (Table 32). So, the fact that the Fruitful Rim had the fifth highest
location quotient of commodity payments, which was on the higher end of the normal
range, was unexpected. Although correlations to covered commodity acres were
moderate, they were not as strong as they were in other Farm Resource Regions. Plus,
the correlations to economic variables, farm typology acres, and farm tenure acres were
all weak (Table 32). So the high location quotient of commodity payments was
unexplainable by the variables covered in this research.
Table 32. Kendall’s tau correlations of location quotients in the Fruitful Rim.
Farms with Payments (No.) Kendall’s τ Commodity Payments ($) Kendall’s τ
0.121** Market Value ($)
0.169**
Farming Occupation (No.)
**
0.349
0.220**
Small Family Higher (No.)
Gross Income ($)
0.295** Production Expenses ($)
0.186**
Large Family (No.)
0.232** Net Income ($)
0.197**
Very Large Family (No.)
**
0.431
0.154**
Part Owner (No.)
Large Family (ac.)
0.162** Very Large Family (ac.)
0.296**
Tenant (No.)
**
0.347
0.152**
Corn (No.)
Part Owner (ac.)
0.206** Tenant (ac.)
0.095*
Soybean (No.)
0.490** Corn (ac.)
0.415**
Wheat (No.)
**
0.287
0.327**
Total Cropland (No.)
Soybean (ac.)
0.384**
Wheat (ac.)
0.488**
Total Cropland (ac.)
*p < .05. **p < .01.
111
Basin and Range.
The average county in the Basin and Range region had the second lowest number
of farms (528.8) yet the third highest land in farms (541,200.3 acres). The mean county
had the lowest number of farms receiving commodity payments (82.9) and the second
lowest commodity payments ($929,331). Income dependence was the second highest at
74.2 percent (Appendix B). The Basin and Range had the third lowest location quotient
of farms with payments (Table 1) and the lowest location quotient of commodity
payments (Table 2).
Table 33. Kendall’s tau correlations of location quotients in the Basin and Range.
Farms with Payments (No.) Kendall’s τ Commodity Payments ($) Kendall’s τ
0.232** Market Value ($)
0.496**
Farming Occupation (No.)
0.364** Gross Income ($)
0.430**
Small Family Higher (No.)
**
0.414
0.417**
Large Family (No.)
Production Expenses ($)
0.452**
0.425** Net Income ($)
Very Large Family (No.)
0.338** Large Family (ac.)
0.250**
Part Owner (No.)
**
0.224
0.371**
Tenant (No.)
Very Large Family (ac.)
0.181** Part Owner (ac.)
0.207**
Corn (No.)
**
0.124
0.190*
Soybean (No.)
Tenant (ac.)
0.598** Corn (ac.)
0.258**
Wheat (No.)
0.409** Soybean (ac.)
Total Cropland (No.)
0.582**
Wheat (ac.)
0.545**
Total Cropland (ac.)
*p < .05. **p < .01. - Not calculable.
Farms with commodity payments were strongly correlated to wheat farms in the
Basin and Range (Table 33). Farms with payments were also strongly correlated to
barley farms (τ = .559, p = .000). Corn and soybean farms had weak correlations to
farms with commodity payments in the Basin and Range. Commodity payments were
strongly correlated to wheat and total cropland acres (Table 33). Commodity payments
112
were also strongly correlated to barley acres (τ = .545, p = .000). Commodity payments
were moderately correlated to all economic variables in the Basin and Range. The fact
that the Basin and Range had the lowest location quotient of commodity payments was
unexpected. The Fruitful Rim did not have a strong correlation of commodity payments
to any covered commodities (Table 32), nor did the Prairie Gateway (Table 29).
Therefore, the Basin and Range was expected to have a higher location quotient of
commodity payments than these regions.
Mississippi Portal.
The average county in the Mississippi Portal ranked sixth in the number of farms
(550.3) and land in farms (177,725.3 acres). On average, 194.8 farms per county received
$3,914,677 in commodity payments, the highest mean county receipt of all regions.
Income dependence was the highest of all FRRs at 378.4 percent (Appendix B). The
Mississippi Portal had the fourth highest location quotient of farms with payments (Table
1) and the highest location quotient of commodity payments (Table 2).
Table 34. Kendall’s tau correlations of location quotients in the Mississippi Portal.
Farms with Payments (No.) Kendall’s τ Commodity Payments ($) Kendall’s τ
0.188** Market Value ($)
0.336**
Farming Occupation (No.)
**
0.455
0.258**
Small Family Higher (No.)
Gross Income ($)
0.503** Production Expenses ($)
0.351**
Large Family (No.)
**
0.309**
0.385
Very Large Family (No.)
Net Income ($)
0.118*
0.405**
Part Owner (No.)
Large Family (ac.)
0.298** Very Large Family (ac.)
0.597**
Tenant (No.)
**
0.568
0.259**
Corn (No.)
Part Owner (ac.)
0.647** Tenant (ac.)
0.506**
Soybean (No.)
**
0.580
0.449**
Wheat (No.)
Corn (ac.)
0.517**
0.497** Soybean (ac.)
Total Cropland (No.)
0.491**
Wheat (ac.)
0.581**
Total Cropland (ac.)
*p < .05. **p < .01.
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Farms with payments were strongly correlated to large family farms, corn farms,
soybean farms, and wheat farms (Table 34). Upland cotton farms (τ = .562, p = .000) and
sorghum farms (τ = .506, p = .000) were also strongly correlated to farms with payments.
Therefore, the location quotient of farms with payments was expected to be higher based
on the correlation to these covered commodities. Especially since farms specializing in
upland cotton, corn, soybean, and wheat accounted for 85 percent of payments in 2006
(ERS, 2006). Farming occupation and part owner farms had weak correlations in the
Mississippi Portal (Table 34). Commodity payments were strongly correlated to tenant
acres, very large family farm acres, soybean acres, and cropland acres (Table 34). They
were also strongly correlated to upland cotton acres (τ = .636, p = .000), rice acres
(τ = .548, p = .000), and sorghum acres (τ = .541, p = .000). The fact that the Mississippi
Portal had the highest location quotient of commodity payments is expected based on the
fact that soybean, upland cotton, and rice acres accounted for 37 percent of payments in
2006 (ERS, 2006).
Therefore, the variation in the distribution of payments by Farm Resource Region
was based somewhat on commodity production. However, it did not exactly follow the
expected pattern. For example, despite the fact that the Basin and Range had stronger
correlations to covered commodities than the Fruitful Rim and Prairie Gateway did, it had
a lower location quotient of commodity payments than these Farm Resource Regions.
Also, the Mississippi Portal had the fourth highest location quotients of farms with
payments despite the fact that there were more strong correlations between covered
commodities and farms with payments than in any other region. Also, the Fruitful Rim
had the fifth highest location quotient of commodity payments despite the fact that it did
114
not have a strong correlation to any covered commodities. The hypothesis that
government payments are distributed equitably between Farm Resource Regions based
on commodities was rejected.
Variation in the number of farms with payments and commodity payments also
varied by variables other than commodities. For example, the Northern Great Plains, the
region with the highest location quotient of farms with payments, was not strongly
correlated to any variables other than wheat. However, the Heartland (Table 26), the
Prairie Gateway (Table 29), and the Mississippi Portal (Table 34) all had strong
correlations with one or more salient variables other than commodities. These are the top
four highest location quotients of farms with payments. The Heartland and the Prairie
Gateway were both strongly correlated to farming occupation, small family higher sales
farms, large family farms, very large family farms, and part owner farms. The five regions
with the lowest location quotients of farms with payments did not have strong correlations
to any non-commodity salient variables. Therefore, this hypothesis was rejected, as well,
based on the fact that salient variables other than commodities were also correlated to
government payment distribution.
Chapter V: Discussion and Conclusion
The hypotheses for this study were that only those variables related to the
commodities covered under the Farm Bill would be correlated to government payment
variables, and that payments were made equitably by Farm Resource Region. These
hypotheses were rejected. Not only were commodity payment variables correlated to
government payment variables, but some farm tenure, farm typology, occupation, and
economic variables were as well. Not only are they also strongly correlated, but they
may be more strongly correlated than the commodity variables themselves. Large
disparity was also evident between Farm Resource Regions, although this disparity was
not always based on commodity production.
Contrary to the hypothesis, not all commodity variables were correlated to
government payments. Based on Kendall’s correlations and county level location
quotient maps, corn farms, corn acres, soybean farms, soybean acres, and wheat farms
were the strongest commodity variables tied to commodity payments. However, the
location quotients of market value, gross income, production expenses, net income,
tenant farms, small family higher sales farms, large family farms, and very large family
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farms and acres were also strongly correlated to the location quotients of government
payment variables.
The salient economic variables (market value, gross income, production expenses,
and net income) might be expected to be strongly correlated to commodity payments
because income might be dependent on government payments. However, the only
variable that directly included commodity payments was net income, and net income also
included all other payments made under the Farm Bill. So the correlation between net
income and commodity payments might be inherent. Ignoring this variable, however, the
other three economic variables did not include commodity payments and were still
strongly correlated to commodity payments.
Based on the analyses, even excluding net income, three economic variables were
still strongly correlated to commodity payments. This showed that counties with high
market values, gross incomes, and production expenses all received higher commodity
payments. Although these economic variables did not directly cause commodity
payments, i.e. market value was not included in the calculation of Direct Payments, there
was a strong correlation suggesting that farms with higher economic variables did get
higher commodity payments. Also, very large family farm acres were strongly correlated
to commodity payments, suggesting that, although this variable was not included in the
calculation of payment dollars, counties with high numbers of very large family farm acres
got higher payments.
Corn acres and soybean acres were also strongly correlated to the commodity
payments. Counties with higher acres of corn and soybeans received higher commodity
payments. The difference, however, was that corn and soybean acres were included in
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the calculation of payments. This meant that corn and soybean acres were directly
correlated to the amount of payments received, and this correlation was founded. The
shocking part was that, although corn and soybean acres were included in the calculation,
these correlations were not much stronger than the correlations of commodity payments
to market value, gross income, production expenses, and very large family farm acres.
Based on the analyses, as well, tenant farms, small family higher sales farms, large
family farms, and very large family farms were strongly correlated to farms with
payments. Although these variables were not included in the calculation of commodity
payments the correlation was still strong. This showed that counties with high numbers of
these types of farms have a higher number of farms with commodity payments. Again,
corn farms, soybean farms, and wheat farms were all strongly correlated to the farms with
payments, as expected since commodity payments were calculated based on commodities
produced. Again, however, these correlations were not much stronger than the correlations
based on tenure and farm typology variables.
The regions with the highest location quotients of farms with payments per county
were the Heartland and the Northern Great Plains. As far as farms with payments are
concerned, the strongest correlations by Farm Resource Regions in the Heartland were
corn and soybean farms, and wheat farms in the Northern Great Plains. The regions with
the lowest location quotients of farms with payments were the Eastern Uplands, Basin
and Range, and Fruitful Rim. These regions were expected to be the lowest since they
had the weakest correlations of farms with payments to covered commodity farms.
Farms with commodity payments are dependent on commodity variables.
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The regions with the highest location quotients of commodity payments per
county were the Mississippi Portal, Heartland, and Southern Seaboard. The strongest
correlations to covered commodities in the Mississippi Portal were to soybean acres, as
well as upland cotton, rice, and sorghum acres. Corn acres were strongly correlated to
farms with payments in the Heartland. Commodity payments in the Southern Seaboard
were strongly correlated to upland cotton and peanut acres, and were not strongly
correlated to acres of corn, soybeans, or wheat as would be expected since these covered
commodities were found to be the most salient for the nation. The regions with the
lowest location quotients of commodity payments were the Basin and Range and the
Eastern Uplands. In the Basin and Range, commodity payments were strongly correlated
to wheat and barley acres, which was unexpected since it had the lowest location quotient
of commodity payments. Also, the Eastern Uplands had strong correlations between
commodity payments and corn and soybean acres. This is unexpected as well due to the
low location quotient of commodity payments. Therefore, commodity variables do not
seem to be as salient as other variables when it comes to the amount of commodity
payments farms receive, which is contrary to the hypotheses.
This can be broken down even further and analyzed on the county level basis. For
example, Johnson County, Tennessee, is in the Eastern Uplands. Its location quotients of
farms with commodity payments, commodity payments, and income dependence were all
well below normal. Its location quotients for all other salient variables discussed in this
paper were at or below normal. Queen Anne’s County, Maryland, is in the Southern
Seaboard. Its location quotients for farms with commodity payments and commodity
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payment dollars are both above normal. Location quotients for all salient variables were
at or above normal.
However, some counties do not fit the pattern, which is evident by looking at the
county level maps. Some counties have low government payment variables despite high
location quotients of other salient variables, and vice-versa. For example, Appling
County, Georgia, is in the Fruitful Rim. Location quotients for government payment
variables were well above normal, with commodity payment dollars at a 6.00 LQ.
Location quotients for corn, soybean, and wheat variables were all below normal.
Economic variable location quotients were above normal, as were very large farms, and
total cropland acres. Based on all of these analyses, the hypothesis was rejected.
Relation to Previous Studies
Monke (2004) pointed out that about 60 percent of acres enrolled in Farm Bill
programs are rented. That trend is supported by this study. Part owner and tenant farms,
farms with acres under operation that are rented, are strongly correlated to government
commodity payments. Based on the correlations, part owner farms and acres are more
strongly related to payment variables than tenant farms and acres. However, based on the
regional analysis, tenant farms are more strongly related than part owner farms. Despite
this discrepancy, this is still strong support of the previous studies that relate land rental
to Farm Bill program participation. This is not to say that there is an incentive to rent,
but that renters are more likely to participate in commodity programs. This is due to the
fact that rental prices are inflated by program payments (Burfisher & Hopkins, 2003).
Westcott & Young (2000) found that large farms, by sales class, receive higher
government payments. Hoppe & MacDonald (2001) stated that roughly half of all
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government payments went to large family, very large family, and nonfamily farms. By
contrast nonfamily farms were not found to be salient in this study. However, large
family farms, very large family farms, and very large family farm acres were strongly
correlated to commodity payments in this study based on correlations and location
quotient maps, as well. In addition, small family farming occupation higher sales farms
were also strongly related to commodity payments.
This same study by Westcott & Young (2000) found that large farms not only
earn more income, but also receive higher government payments. Four economic
variables were closely related to commodity payments. The correlations were all strong
when based on raw data, but only moderate when based on county level location
quotients. However, the correlation appears to be strong based on the regional analysis.
This study supports the Westcott & Young (2000) study.
Based on the 2007 Census, farm size and the number of farms seemed to remain
relatively stable since the Goodwin (2000) study. However, agricultural production grew
more concentrated. In 2007 only 32,886 farms, or 1.5 percent of farms, accounted for 50
percent of the market value of agricultural products sold in the U.S., down by roughly
31,000 farms from the Goodwin (2000) study. The number of farms receiving
government payments remained stable at 38 percent of farms in 2007, compared to 36
percent in 1997.
Commodity payment dependence has increased, however. In 2000, 40 percent of
net farm income on average came from government payments, including conservation
payments. In 2007, an average of 47 percent of net income came from commodity
payments alone, excluding conservation payments. This could be due to variations in
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income per year, but could also indicate a growing dependence among farmers on Farm
Bill subsidies, especially since less money was paid to farmers for conservation and
commodity programs in 2007 than in 2000.
Based on the calculations used in this study in contrast to the calculations used in
the study by Eathington & Swenson (2001), these numbers are not strictly comparable.
However, trends are noticeable between these two studies. In 1997, more than 37 percent
of all farms that received government payments (both commodity and conservation) were
in the Heartland (Eathington & Swenson, 2001). This study found that the Heartland had
the highest average of farms receiving commodity payments in 2007. According to the
2001 study, the Northern Great Plains had the highest mean percentage of farms
receiving government payments, and the Eastern Uplands had the lowest percentage.
Although this study looked only at commodity payments, the Heartland and Northern
Great Plains had the highest mean percentages per county, and the Basin and Range and
Eastern Uplands had the lowest mean percentages per county.
The Fruitful Rim had the highest mean amount of Farm Bill payments in 1997,
while the Eastern Uplands had the lowest (Eathington & Swenson, 2001). In 2007, by
contrast, the average county in the Mississippi Portal had the highest mean commodity
payment, while the Fruitful Rim came in fifth. The Eastern Uplands, however, still had
the lowest mean commodity payments in 2007. The Mississippi Portal had the highest
ratio of government payments to income in 1997 (Eathington & Swenson, 2001) as well
as the highest in 2007.
Based on economic variables, the Eathington & Swenson (2001) study found that
the Eastern Uplands had the lowest mean market value of agricultural products sold,
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while the Fruitful Rim had the highest. In 2007, based on the mean market value of
agricultural products sold by county, the Fruitful Rim was still the highest. The Eastern
Uplands had the second lowest mean market value of agricultural products sold,
following the Basin and Range region.
Broader Implications of the Farm Bill
Many people currently support the elimination or reduction of the Farm Bill to
curb government spending (Nixon, 2012). Commodity program payments alone totaled
$13.164 billion in 2002, reaching a high of $16.903 billion in 2006 and a low of $5.663
billion in 2008. Total spending for Farm Bill programs, including conservation,
commodities, crop insurance, and exports reached a high of $23.926 billion in 2006
(Monke & Johnson, 2010). Therefore, Farm Bill programs are on the table for cuts when
it comes to government spending appropriations (Imhoff, 2012b).
The Farm Bill is not only on the table for cuts due to government spending
concerns but also concerns about rising health care costs due to obesity, dwindling water
resources, environmental concerns, and local food movements (Imhoff, 2012b). If the
Farm Bill Commodity title were to be eliminated or reduced, environmental quality would
increase (Plantinga, 1996), government spending would decrease (Lingard, 2001), trade
and production distortions would decrease (Mayrand et al., 2003), and food prices would
increase (Fairchild, 2012).
The subsidies that would be the best to reduce for the environment are those most
directly tied to production and thereby encourage expanding acreage and yield, such as
DPs. Lowering or cutting these subsidies would lead to less intensive land use (Lingard,
2001) and reduce incentives to keep marginal land in production (Plantinga, 1996).
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Reducing the highly erodible land in production would decrease soil erosion,
sedimentation, and agro-chemical runoff (Plantinga, 1996). Cutting these programs
could mean that funding for land retirement programs will longer be necessary, as
farmers will no longer be cultivating marginal land (Plantinga, 1996).
It would also lead to reduced agro-chemical input (Lingard, 2001). One example
would be New Zealand. Subsidies here were reduced to almost no support from 1984 to
1987. Land prices fell by 60 percent as the added inflation in land prices from subsidies
crashed. Agro-chemical use, specifically fertilizer use, fell by 50 percent. By 1995, land
prices had recovered to 80 percent of the 1984 land prices (Lingard, 2001). The results in
New Zealand were mixed, but the agricultural economy recovered (Mayrand et al., 2003).
Cutting those programs that encourage increased acreages and yields are the best
programs to cut for the environment. However, there are less direct environmental
benefits of cutting these programs as well as government spending benefits. Agriculture
is a major source of pollution and other environmental problems that the farmer is not
responsible for mitigating. Therefore, the price of mitigating these problems is neither
included in the cost of the commodity produced, nor the true cost of consumption
(Mayrand et al., 2003). The consumer does not directly pay for the environmental price
at the grocery store. However, we as taxpayers pay to subsidize the cost of producing the
commodity before we buy it and pay to regulate and clean-up the environmental effects
of its production. For example, the Clean Water Act (CWA) created more stringent
regulations for livestock producers. Money from the Farm Bill, in the form of
Environmental Quality Incentives Program payments, can be used by the producer to
meet the new regulations imposed by the CWA (Mayrand et al., 2003). This can result in
124
up to $300,000 per operation owner in Farm Bill conservation dollars being spent on
meeting regulations (Imhoff, 2012b). Therefore, government spending would be reduced
by removing the subsidies paid to produce the commodity and the environmental
regulations and programs created to deal with the problems associated with intensive
agriculture (Lingard, 2001).
Reducing subsidies that are coupled with production would help both the
environment and world trade. Cutting these subsidies would precipitate a shift from high
intensity agricultural countries to low intensity countries. These high intensity countries
are bigger users of agro-chemicals, thereby reducing pollution. The lower intensity
countries are less developed nations, and this shift would increase economic activity for
these countries (Mayrand et al., 2003).
Fewer subsidies in the developed nations would lead to less trade distortion as
fewer cheap subsidized commodities flood world markets (Mayrand et al., 2003).
Thereby, world prices would increase (Borders & Burnett, 2006). The International
Monetary Fund estimates that ending subsidies in more developed countries would raise
global welfare by $100 billion (Borders & Burnett, 2006). Ending U.S. cotton support
could help stabilize the economies in sub-Saharan Africa. Burkina Faso could get an extra
$28 million in export revenue, Benin could see an increase of $33 million, and Mali
stands to gain the most at $43 million in export revenue (Borders & Burnett, 2006).
The increase in economies and global welfare would lead to a decreased need for
international food aid programs (Borders & Burnett, 2006), saving governments money,
and decreasing the need of subsidies to create surpluses of commodities. Once income
and economic development reach certain levels in less developed nations, it has been
125
shown that these countries spend more money on healthcare and environmental
protection (Borders & Burnett, 2006). Therefore, the governments of more developed
countries would need to spend less money not only on food aid programs but also
international healthcare and environmental programs.
If the Farm Bill were to be eliminated or reduced, the face of farm production
might change. One major impact of the Farm Bill is the reduced price of processed foods
and meat. If the Commodity title of the Farm Bill were to be eliminated or greatly
reduced, prices of meat and processed foods would increase greatly. The new prices
would more closely resemble the prices of organic or GMO free products currently.
Many farmers might switch to growing commodities that would gain them the most
income. In most cases, this would mean a switch to whole foods production. Fruits and
vegetables would increase in production, thereby reducing their prices to consumers.
Prices of other foods, such as meat and processed foods derived from flours and crop byproducts, would increase. Milk could double in price (Fairchild, 2012). This would
impact not only the income of farm families, but also the spending habits and food
security of most Americans.
Since price control was the original point of the Farm Bill, we might end up back
in the same situation that initiated the Agricultural Act of 1933 to begin with. Eliminating
the Farm Bill is not the answer as this would cause a drastic change in the American
economy. A gradual shift in the Farm Bill is needed to cover shifts in food
choices and the economy. Many Americans are shifting to local, organic, and GMO free
foods, but often cost is a major inhibitor to this shift in diet. A Farm Bill that begins to
support whole foods, such as fruits and vegetables, as well as organic and GMO free
126
foods would help ameliorate the problem of cost associated with healthier food choices.
This same legislation needs to begin scaling back on the commodity payments as they
currently are to help encourage this shift in the farmers without collapsing the farm
economy.
Payments could be made on an income-support or risk reduction method as
suggested by previous researchers (Gunderson et al., 2000; Lamb, 2003). These
programs would not provide an incentive to increase acreage or yield, and therefore
decrease environmental problems, but would still mitigate the financial risks inherent in
farming (Lingard, 2001). Lamb (2003) suggests a cut to commodity programs in favor of
a shift to supply and demand. The solution to cutting program payments is to offer a
‘buyout” program. In the 1980s the government provided payments to dairy farmers who
sold their entire herd without keeping their herd in production elsewhere, i.e., export or
slaughter. These producers could not be involved in dairy production for five years
following their buyout, effectively reducing the surplus of dairy products and stabilizing
dairy prices (Lamb, 2003). This buyout solution would allow farmers to exit production
with government assistance, thereby reducing surpluses and allowing market forces to set
commodity prices. Farmers who stay in production, generally the more industrialized
farms, will be able to continue without further government assistance (Lamb, 2003).
Gunderson et al. (2000) provided four approaches to government payments that
could replace commodity payments and crop insurance. The first method was to pay
farm operators the difference between their household income and the median nonfarm
household income in their Farm Resource Region. This would result in payments only to
those farmers with below median household income and would focus money into small
127
family farm typologies and away from large and non-family farms. The second method
was to pay farm operators the difference between their household income and 185
percent of the poverty level across the nation. This would result in establishing a
minimum farm household income for the entire U.S., without differentiating among Farm
Resource Regions. The third scenario was to pay farm households the difference between
their household income and the median household expenditures in their region. If
household expenditures in a Farm Resource Region are higher on average than a farmer’s
income, that farmer would receive a payment for the difference. Finally, the fourth
scenario is to ensure a farm household income equal to the average nonfarm selfemployed hourly wage by Farm Resource Region. Therefore, if farm income was below
a set level based on hourly wages, these farmers would be compensated for time up to the
average hourly wage for self-employed nonfarm workers (Gunderson et al., 2000).
All of these scenarios would result in a decline in payments to large family farms,
very large family farms, and nonfamily farms. The fourth scenario would overlook
limited resource and residential farms compared to the other three scenarios. All four
scenarios would change the distribution of government payments based on Farm
Resource Regions. For example, in 1997, based on these four scenarios, payments would
have been made to an average of 37 percent of farms in the Heartland, ranging from 21 to
62 percent. Payments in the Basin and Range would have been made to an average of 43
percent of farms, ranging from 22 to 75 percent, based on the scenario. In fact, payments
in 2007 ranged from 12 percent of producers in the Fruitful Rim to 55 percent of
producers in the Heartland (Table 1). These four different payment scenarios, on
average, close this gap between regions from 37 percent in the Fruitful Rim to 53 percent
128
in the Southern Seaboard (Gunderson et al., 2000). Not only would this solve the
environmental problems and world trade problems provided, but would also solve the
inequitable distribution of payments by Farm Resource Region.
Future Research
By using data from the upcoming 2012 Census of Agriculture, a comparative
analysis of the same study based on two Farm Bills would be possible. The data in the
2012 Census would cover payments made under the Food, Conservation, and Energy Act
of 2008. This would enable the researcher to compare the efficacy of changes made in
Bill over time. The new Farm Bill, due out in 2013, will be covered under the 2017
Census of Agriculture. Including this Census in the study would allow the analysis of
changes over three Farm Bills, spanning eleven years of government payments.
Future research would be needed to determine the effects of eliminating
commodity payments as they currently stand. What effects would the elimination of
these payments have on not only farm economies, but also on the economies of related
industries, such as livestock producers, processed food manufacturers, and agro-chemical
and seed suppliers? Would new economies emerge, such as heirloom seed companies
and organic fertilizer suppliers?
Future research could also include the effects of consumers on the Farm Bill
program development over the years. Have the changes in shopping and cooking habits
of Americans driven changes in the Farm Bill? Would a larger shift to organic, GMOfree, whole food diets create a change in subsidies toward smaller, niche market farms
that specialize in producing these products? Does the demand for cheap food from fast
129
food establishments drive the need to produce meat and commodities more cheaply and
reinforce the need for subsidies?
Future research could also include an analysis of public opinion in changes to the
legislation that would impact market prices of food. For example, would people be
willing to pay more per gallon of milk or box of snack cakes in order to cut federal
spending. Although the bulk of the changes that occur to the Farm Bill are encouraged
by the agricultural and agro-business lobbyists, pressure from constituents, if they support
eliminating Farm Bill programs, could create drastic changes to the Farm Bill. Nothing is
more important to a legislator looking to get reelected than the majority opinion of their
constituents.
Future research should also examine the environmental impacts of eliminating
those subsidies that encourage agricultural intensification. Modeling the impacts of
agricultural intensification on the environment has been successful before. These models
can be used to look at the impacts of eliminating different kinds of subsidies and the
impacts of eliminating subsidies all together in favor of income support payments like
those suggested by Gunderson et al. (2000).
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Farm Security and Rural Investment Act of 2002 (FSRIA). Public Law 107-171. May 13,
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Flinchbaugh, B., & Knutson, R. (2004). The agricultural policy outlook: Looking back
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Gardner, B. (1992). Changing economic perspectives on the farm problem. Journal of
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Goodwin, B. (2000). Instability and risk in U.S. agriculture. Journal of Agribusiness,
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133
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Appendix A
Kendall’s Tau Correlations of Independent to Government Payment Variables
135
Kendall’s Tau Correlations of Independent to Government Payment Variables
Raw
Data
Location
Quotient
Payment
(farms)
Payment
($)
Income
Dependence
Payment
(farms)
Payment
($)
Income
Dependence
Farms
.460**
.217**
-.049**
Land in Farms
.448**
.466**
.106**
Market Value
.507**
.529**
-.212**
.106**
.352**
.052**
Total Income
.528**
.559**
-.031*
.075**
.358**
-.069**
Production
Expenses
Net Income
.496**
.513**
-.188**
.025*
.315**
-.009
.501**
.555**
-.259**
.208**
.410**
.118**
Farming
Occupation
Other Occupation
.519**
.302**
-.083**
.297**
.175**
.133**
.382**
.136**
-.019
-.293**
-.171**
-.130**
Worked Off Farm
Any
Worked Off Farm
200+
American Indian/
Alaskan Native
Asian
.412**
.169**
-.046**
-.351**
-.208**
-.245**
.413**
.165**
-.050**
-.193**
-.085**
-.120**
-.036*
-.050**
-.011
-.197**
-.199**
-.088**
-.095**
-.053**
-.142**
-.197**
-.063**
-.131**
Black/ African
American
Multi-Race
-.162**
-.076**
.094**
-.068**
.021
.009
.050**
-.041**
.026
-.181**
-.194**
-.010**
White
.469**
.219**
-.055**
.240**
.090**
.057**
Spanish,
Hispanic, or
Latino Origin
Female
-.030*
-.022
-.012
-.243**
-.178**
-.120**
.246**
.067**
-.045**
-.348**
-.174**
-.220**
Male
.488**
.237**
-.045**
.351**
.178**
.224**
Family or
Individual (farms)
Family or
Individual (acres)
.440**
.187**
-.047**
-.187**
-.166**
-.115**
.469**
.447**
.106**
-.111**
-.169**
-.112**
136
Kendall’s Tau Correlations of Independent to Government Payment Variables
Raw
Data
Location
Quotient
Payment
(farms)
.499**
Payment
($)
.347**
Income
Dependence
.004
.374**
.476**
.139**
.139**
.173**
.174**
.181**
.169**
-.109**
.004
.086**
.004
.242**
.271**
-.097**
.019
.148**
.032*
.333**
.329**
-.123**
.109**
.168**
.045**
.300**
.391**
-.003
.109**
.121**
.032*
.377**
.321**
.003
.157**
.053**
.114**
.284
.312**
.055**
.025**
-.011
.056**
Limited Resource
(farms)
Limited Resource
(acres)
Retirement
(farms)
Retirement (acres)
.244**
-.001
-.025*
-.364**
-.266**
-.254**
.194**
.028*
.096**
-.402**
-.313**
-.297**
.289**
.058**
.004
-.342**
-.215**
-.164**
.237**
.110**
.146**
-.424**
-.291**
-.260**
Residential/Lifest
yle (farms)
Residential/Lifest
yle (acres)
Lower Sales
(farms)
Lower Sales
(acres)
Higher Sales
(farms)
Higher Sales
(acres)
Large Family
(farms)
.377**
.126**
-.024
-.250**
-.141**
-.119**
.328**
.193**
.138**
-.412**
-.290**
-.273**
.434**
.211**
-.038**
.024*
-.067**
-.064**
.298**
.219**
.110**
-.302**
-.361**
-.261**
.641**
.550**
-.049**
.540**
.309**
.310**
.439**
.427**
.078**
.369**
.125**
.208**
.631**
.602**
-.086**
.551**
.377**
.350**
Partnership
(farms)
Partnership
(acres)
Corporation Other
(farms)
Corporation Other
(acres)
Corporations
Family (farms)
Corporations
Family (acres)
Other (farms)
Other (acres)
Payment
(farms)
.224**
Payment
($)
.144**
Income
Dependence
.181**
137
Kendall’s Tau Correlations of Independent to Government Payment Variables
Raw
Data
Location
Quotient
Payment
(farms)
.476**
Payment
($)
.527**
Income
Dependence
.086**
.505**
.559**
-.182**
.429**
.386**
.322**
.455**
.607**
.043**
.452**
.458**
.361**
.417**
.365**
-.071**
.142**
.131**
.079**
.208**
.320**
.059**
-.065**
-.040**
-.051**
Full Owner
(farms)
Full Owner
(acres)
Part Owner
(farms)
Part Owner
(acres)
Tenant (farms)
.355**
.116**
-.049**
-.475**
-.245**
-.287**
.274**
.228**
.094**
-.485**
-.347**
-.329**
.593**
.352**
-.031*
.464**
.207**
.272**
.484**
.503**
.082**
.466**
.300**
.291**
.521**
.482**
.010
.321**
.248**
.234**
Tenant (acres)
.368**
.510**
.106**
.253**
.262**
.217**
Barley(farms)
Barley (acres)
.207**
.204**
.145**
.156**
-.062**
-.059**
.146**
.142**
.025*
.028
.017
.014
Barley (bushels)
.205**
.157**
-.060**
Beans (farms)
.125**
.158**
-.056**
.089**
-.011
.032**
Beans (acres)
.142**
.166**
-.046**
.108**
.014
.035**
Beans (cwt)
.142**
.166**
-.048**
Corn (farms)
.613**
.460**
-.003
.556**
.498**
.346**
Corn (acres)
.611**
.576**
.008
.498**
.560**
.350**
Corn (bushels)
.607**
.576**
-.003
**
**
.297**
.118**
.251**
.316**
Large Family
(acres)
Very Large
Family (farms)
Very Large
Family (acres)
Nonfamily
(farms)
Nonfamily (acres)
Upland cotton
(farms)
.053
.222
Payment
(farms)
.519**
Payment
($)
.319**
Income
Dependence
.336**
138
Kendall’s Tau Correlations of Independent to Government Payment Variables
Raw
Data
Location
Quotient
Payment
(farms)
Payment
($)
Income
Dependence
Payment
(farms)
Payment
($)
Income
Dependence
Upland cotton
(acres)
Upland cotton
(bales)
Oats
.071**
.264**
.321**
.143**
.313**
.353**
.070**
.265**
.319**
.429**
.318**
-.006
.336**
.211**
.186**
Oats (acres)
.427**
.353**
.031*
.298**
.233**
.182**
Oats (bushels)
.438**
.367**
.016
Peanuts
-.007
.110**
.198**
.055**
.120**
.201**
Peanuts (acres)
.019
.136**
.195**
.083**
.237**
.214**
Peanuts (pounds)
.019
.137**
.198**
Rice
.036*
.168**
.133**
.040**
.170**
.165**
Rice (acres)
.053**
.180**
.129**
.055**
.174**
.168**
Rice (cwt)
.054**
.182**
.132**
Sorghum (farms)
.265**
.311**
.189**
.292**
.192**
.337**
Sorghum (acres)
.269**
.341**
.217**
.298**
.204**
.358**
Sorghum
(bushels)
Soybeans (farms)
.272**
.343**
.215**
.574**
.453**
.042**
.547**
.545**
.376**
Soybeans (acres)
.561**
.501**
.064**
.488**
.561**
.380**
Soybeans
(bushels)
Sugarbeets
(farms)
Sugarbeets (acres)
.566**
.505**
.052**
.137**
.136**
-.044**
.107**
.022
.033**
.123**
.127**
-.050**
.094**
.016
.028*
Sugarbeets (tons)
Sugarcane (farms)
.123**
-.048**
.127**
.009
-.050**
-.022
-.061**
.025*
-.023
Sugarcane (acres)
-.028
.027
.003
-.030*
.036**
.003
Sugarcane (tons)
-.028
.027
.003
139
Kendall’s Tau Correlations of Independent to Government Payment Variables
Raw
Data
Payment
(farms)
.554**
.448**
.454**
.543**
Payment
($)
.519**
.504**
.516**
.278**
Location
Quotient
Income
Dependence
.127**
.164**
.144**
-.047**
Payment
(farms)
.553**
.463**
Wheat (farms)
Wheat (acres)
Wheat (bushels)
Total cropland
.472**
(farms)
Commodity
.639**
.195**
.484**
Payments
Income
.100**
.195**
.553**
Dependence
Note. Blank cells were not calculable based on location quotients.
*p < .05. **p < .01.
Payment
($)
.317**
.297**
Income
Dependence
.426**
.395**
.318**
.234**
-
.498**
.498**
-
Appendix B
Means and Standard Deviations of Variables by Farm Resource Region
141
Means and Standard Deviations of Variables by Farm Resource Region
Farms
No.
Land in
Farms
ac.
Market
Value
$
Total
Income
$
Total
Expenses
$
Net
Income
$
Farming
Occupation
No.
Other
Occupation
No.
Worked
Off Farm
Any
No.
Worked
Off Farm
200+
No.
No.
Female
Heartland
Northern
Crescent
Northern
Great Plains
Prairie
Gateway
Eastern
Uplands
M
823.3
748.2
539.7
824.1
777.8
SD
350.2
579.9
385.1
620.1
504.8
M
256,298.4
127,890.2
896,661.4
556,704.1
122,617.1
SD
118,333.3
112,361.5
566,449.4
340,518.2
105,700.0
M 132,582,011
78,855,418
106,170,464 114,997,404
42,012,873
SD 107,244,612
97,830,055
131,692,033 156,405,561
66,031,168
M
4,754,547
3,469,548
5,459,966
3,698,723
1,353,832
SD
3,292,904
3,533,330
4,638,124
3,133,097
2,260,960
M
99,427,878
63,758,953
82,748,067
100,525,096
36,701,522
SD
82,438,058
75,066,837
115,952,090
13,809,557
53,060,290
M
42,306,560
20,445,220
34,148,089
23,655,736
7,830,151
SD
33,078,859
28,242,901
27,647,556
25,043,290
15,159,893
M
388.3
352.6
301.2
354.9
317.3
SD
170.4
304.2
174.4
225.7
212.0
M
435.0
395.6
238.5
469.2
460.4
SD
212.0
291.4
227.1
404.9
298.6
M
524.1
476.6
306.0
541.9
516.2
SD
236.1
358.5
260.7
455.1
335.1
M
332.2
299.6
178.2
328.4
330.8
SD
157.0
228.7
157.1
270.2
218.2
M
82.0
113.4
64.8
113.5
95.7
SD
43.9
74.7
62.1
94.0
66.1
142
Means and Standard Deviations of Variables by Farm Resource Region
Farms
No.
Land in
Farms
ac.
Market
Value
$
Total
Income
$
Total
Expenses
$
Net
Income
$
Farming
Occupation
Other
Occupation
No.
No.
Worked
Off Farm
Any
No.
Worked
Off Farm
200+
No.
No.
Female
Southern
Seaboard
Fruitful
Rim
Basin and
Range
Mississippi
Portal
National
M
516.0
897.3
528.8
550.3
716.1
SD
413.9
961.2
535.4
337.5
564.4
M
99,326.5
388,738.6
541,200.3
177,725.3
297,859.0
SD
78,958.6
604,224.3
564,334.0
101,046.9
378,188.7
M
63,848,282
217,866,746 40,669,223
56,067,297
96,212,060
SD 107,637,607 472,200,809 55,355,182
53,664,342
181,462,549
M
1,918,280
6,000,168
2,071,345
2,146,152
3,392,874
SD
1,909,461
10,169,840
2,328,065
1,735,259
4,434,705
M
54,219,584
174,821,354 36,597,508
48,044,291
78,261,619
SD
82,804,596
366,348,125 45,979,386
42,805,406
143,314,856
M
13,936,245
54,105,846
8,964,660
14,508,467
24,794,277
SD
26,880,197
119,676,956 14,426,338
16,805,152
45,988,508
M
218.4
416.3
244.8
222.2
322.8
SD
173.8
473.7
289.9
130.9
263.1
M
297.6
480.9
284.0
328.2
393.3
SD
248.7
510.0
275.3
220.6
321.9
M
333.8
602.4
349.6
349.9
463.5
SD
281.1
656.1
334.2
224.8
382.6
M
204.0
347.8
197.8
214.3
284.6
SD
166.7
372.4
192.7
141.6
231.1
M
76.7
168.3
103.2
73.1
99.5
SD
65.1
208.2
198.9
51.0
106.6
143
Means and Standard Deviations of Variables by Farm Resource Region
No.
Male
American
Indian or
Alaskan
Native
No.
No.
Asian
Black or
African
American
No.
No.
Mutli-Race
No.
White
Spanish,
Hispanic,
or Latino
No.
Family or
Individual
No.
ac.
Partnership
No.
ac.
Heartland
Northern
Crescent
Northern
Great
Plains
Prairie
Gateway
Eastern
Uplands
M
741.3
634.8
474.9
710.6
682.1
SD
318.7
523.3
329.8
535.0
443.2
M
2.0
3.4
17.9
13.0
19.8
SD
2.9
3.5
44.0
25.0
55.3
M
1.6
2.8
1.6
2.8
4.3
SD
3.1
4.1
3.3
2.8
9.8
M
3.9
4.6
3.3
20.0
14.4
SD
8.7
10.8
4.5
37.0
21.8
M
2.6
2.7
2.8
7.9
8.8
SD
3.2
2.8
3.3
9.3
13.1
M
818.0
746.1
525.0
798.3
747.0
SD
348.2
575.2
381.4
592.3
477.4
M
3.6
5.5
4.6
36.0
5.8
SD
3.4
7.4
12.9
80.7
5.0
M
705.8
649.4
446.7
713.2
710.9
SD
307.7
517.0
319.9
575.6
464.0
M
195,543.7
102,873.8
612,268.9
379,708.9
105,705.6
SD
90,351.5
84,433.2
324,013.0
194,901.0
82,384.2
M
65.7
56.0
50.4
69.4
48.8
SD
30.7
45.8
41.2
40.9
35.8
M
36,732.4
21,197.5
143,894.0
108,594.4
15,178.0
SD
21,819.9
21,043.4
110,128.1
101,018.2
17,607.0
144
Means and Standard Deviations of Variables by Farm Resource Region
No.
Male
American
Indian or
Alaskan
Native
No.
No.
Asian
Black or
African
American
No.
No.
Mutli-Race
No.
White
Spanish,
Hispanic,
or Latino
No.
Family or
Individual
No.
ac.
Partnership
No.
ac.
Southern
Seaboard
Fruitful
Rim
Basin and
Range
Mississippi
Portal
National
M
439.3
729.0
425.6
477.2
616.6
SD
356.7
785.0
382.9
291.7
481.9
M
5.9
29.3
67.8
3.2
15.5
SD
19.7
192.4
381.2
3.9
123.8
M
3.0
26.0
3.6
1.6
7.2
SD
3.9
73.3
6.5
2.7
49.5
M
28.8
17.6
2.3
37.6
18.7
SD
35.7
31.7
3.7
45.6
32.0
M
3.5
7.1
3.6
2.5
5.1
SD
4.9
8.2
4.6
3.0
11.4
M
478.0
836.5
469.6
503.1
687.6
SD
391.1
889.8
414.7
328.7
533.6
M
8.1
96.6
34.2
5.5
21.0
SD
13.8
203.8
96.4
5.2
80.7
M
455.1
740.9
442.4
466.0
619.1
SD
377.1
792.5
490.0
308.5
498.0
M
82,575.3
177,200.5
239,523.5
108,428.5
201,617.2
SD
65,674.9
188,203.1
240,116.4
52,323.3
201,980.4
M
37.0
79.0
44.3
57.6
56.6
SD
28.4
106.0
34.1
35.9
49.0
M
16,314.4
86,635.8
100,679.5
56,373.9
54,641.9
SD
15,394.0
128,270.4
127,723.0
60,147.9
82,099.9
145
Means and Standard Deviations of Variables by Farm Resource Region
Heartland
Northern
Crescent
Northern
Great
Plains
Prairie
Gateway
Eastern
Uplands
3.3
3.7
1.8
2.7
1.7
3.3
4.1
2.5
2.7
2.4
M
1,280.5
996.6
2,984.5
2,415.0
305.3
SD
1,965.3
1,633.7
9,032.0
4,961.3
1,088.6
M
34.8
31.6
31.0
24.8
11.9
SD
24.3
26.6
37.1
17.6
12.3
M
27,547.8
13,989.9
153,854.5
57,807.0
5,876.3
SD
20,905.7
15,505.2
191,061.8
87,556.5
8,339.1
M
13.7
7.5
9.8
14.0
4.5
SD
10.0
7.7
9.4
10.2
6.0
M
3,100.3
1,834.0
25,400.7
9,250.1
1,302.3
SD
2,862.8
2,243.1
42,327.3
13,153.9
2,578.9
M
84.4
113.3
55.5
105.5
136.9
SD
52.0
78.9
43.7
97.5
88.5
M
8,212.0
10,458.2
25,733.2
25,121.5
14,588.4
SD
7,691.4
8,896.0
18,418.1
20,612.1
9,200.9
M
141.6
134.6
79.3
186.2
183.9
SD
75.9
94.2
66.4
176.4
119.1
M
19,745.3
14,992.3
48,896.9
60,588.7
27,159.8
SD
13,731.2
10,859.8
36,276.7
41,277.1
19,176.9
M
293.6
266.9
152.4
311.8
313.4
SD
148.3
205.8
161.1
290.9
209.3
Corporation No. M
Other than
SD
Family
Held
ac.
Corporation No.
Family
Held
ac.
Other
No.
ac.
Small
Family
Limited
Resource
No.
ac.
Small
Family
Retirement
No.
ac.
Residential/
Lifestyle
No.
146
Means and Standard Deviations of Variables by Farm Resource Region
Southern
Seaboard
Fruitful
Rim
Basin and
Range
Mississippi
Portal
National
2.4
8.6
3.2
2.9
3.3
3.1
13.2
3.9
2.9
5.5
M
784.9
10,514.9
7,463.2
2,082.7
2,701.3
SD
1,840.0
31,852.9
18,543.0
3,079.9
12,233.1
M
17.4
55.4
29.1
19.9
27.9
SD
15.6
70.5
28.5
15.9
32.5
M
9,428.7
60,910.3
107,571.5
16,681.4
40,025.4
SD
9,940.7
99,880.7
141,696.1
17,085.9
84,648.5
M
4.1
13.3
9.8
3.9
9.1
SD
4.5
19.7
8.9
4.7
10.6
M
1,481.1
74,363.4
66,194.3
1,802.8
15,971.7
SD
3,064.2
516,170.0
223,094.7
3,770.3
182,978.3
M
78.7
126.1
97.0
84.2
100.3
SD
65.8
152.7
204.8
60.9
100.5
M
8,144.7
14,496.5
20,953.1
9,138.7
13,953.8
SD
7,087.5
23,910.6
22,892.9
6,038.4
15,576.2
M
128.2
192.8
108.8
126.2
148.1
SD
114.3
209.4
107.7
89.1
129.9
M
19,770.1
31,109.1
38,173.0
21,950.1
29,378.9
SD
18,749.5
37,458.3
36,788.4
15,247.6
29,816.2
M
190.7
310.5
182.6
208.3
260.4
SD
168.8
331.1
178.7
144.8
221.4
Corporation No. M
Other than
SD
Family
Held
ac.
Corporation No.
Family
Held
ac.
Other
No.
ac.
Small
Family
Limited
Resource
No.
ac.
Small
Family
Retirement
No.
ac.
Small
Family
Residential/
Lifestyle
No.
147
Means and Standard Deviations of Variables by Farm Resource Region
Small
ac.
Family
Residential/
Lifestyle
No.
Small
Family
Lower
Sales
Small
Family
Higher
Sales
ac.
No.
ac.
Large
Family
No.
ac.
Very Large
Family
No.
ac.
Nonfamily
No.
ac.
Heartland
Northern
Crescent
Northern
Great
Plains
Prairie
Gateway
Eastern
Uplands
M
31,357.3
22,414.7
65,029.3
81,390.5
34,564.8
SD
18,392.3
18,033.0
47,990.3
50,444.8
24,121.0
M
91.7
97.5
78.0
96.6
86.0
SD
43.6
79.2
53.4
67.4
59.6
M
15,823.2
12,868.3
89,536.1
64,464.8
17,439.5
SD
10,786.5
10,492.8
77,789.9
57,089.0
14,681.8
M
60.6
49.0
60.7
32.6
12.6
SD
42.5
86.6
31.9
24.2
16.1
M
30,187.9
15,945.1
166,940.5
66,111.9
8,759.2
SD
19,444.0
18,157.7
138,998.3
55,860.6
10,245.2
M
57.8
32.4
48.0
27.1
11.5
SD
42.8
42.7
31.0
22.5
17.4
M
49,197.9
18,493.9
176,845.9
76,761.2
8,447.0
SD
31,298.5
19,975.3
122,953.6
54,967.6
12,096.0
M
56.8
27.3
43.3
29.2
17.0
SD
45.2
35.9
39.1
28.4
32.5
M
90,456.4
31,678.9
238,718.8
129,495.0
12,431.1
SD
58,967.3
37,796.3
171,026.3
113,475.6
22,942.7
M
36.9
27.3
22.5
35.2
16.6
SD
21.3
22.1
24.0
18.5
14.3
M
16,424.4
9,181.9
88,813.4
68,412.9
7,750.2
SD
11,155.6
8,313.9
125,030.7
112,196.3
15,978.9
148
Means and Standard Deviations of Variables by Farm Resource Region
Small
ac.
Family
Residential/
Lifestyle
No.
Small
Family
Lower
Sales
Small
Family
Higher
Sales
ac.
No.
ac.
Large
Family
No.
ac.
Very Large
Family
No.
ac.
Nonfamily
No.
ac.
Southern
Seaboard
Fruitful
Rim
Basin and
Range
Mississippi
Portal
National
M
23,023.1
43,399.7
50,222.1
29,239.7
39,792.1
SD
22,112.9
58,233.4
49,361.0
18,705.4
39,616.8
M
53.2
110.9
68.7
56.0
84.1
SD
44.0
121.6
78.1
38.7
70.5
M
9,855.7
35,457.8
53,165.0
11,651.0
29,186.6
SD
9,988.1
58,380.6
66,383.1
7,348.7
44,728.9
M
9.6
25.4
18.8
12.6
32.5
SD
10.5
38.6
20.8
11.9
45.8
M
7,780.0
31,259.3
70,632.4
10,361.2
38,133.7
SD
7,813.1
52,181.9
95,353.1
7,449.2
65,288.2
M
11.0
24.8
13.6
15.0
28.1
SD
14.5
40.8
16.4
13.8
35.1
M
9,177.7
36,785.1
72,665.1
19,174.9
45,559.9
SD
9,028.7
54,081.4
97,842.1
15,725.6
65,986.7
M
27.1
45.3
11.8
28.0
32.9
SD
47.1
86.0
16.9
29.9
46.3
M
21,023.3
95,439.2
96,826.4
70,240.7
76,868.7
SD
24,157.4
147,839.1
133,411.6
71,006.7
105,730.6
M
17.5
61.3
27.5
20.2
29.6
SD
14.5
90.9
23.4
13.7
35.7
M
9,072.4
126,168.6
147,925.7
19,894.0
42,841.6
SD
9,908.2
487,854.1
225,625.5
23,792.8
170,747.8
149
Means and Standard Deviations of Variables by Farm Resource Region
Full Owner
No.
ac.
Part Owner
No.
ac.
Tenant
No.
ac.
Barley
No.
ac.
bu.
Dry Edible
Beans
No.
ac.
Heartland
Northern
Crescent
Northern
Great
Plains
Prairie
Gateway
Eastern
Uplands
M
516.3
518.7
299.8
544.0
571.9
SD
252.4
390.7
273.7
472.7
372.9
M
66,638.3
50,269.1
238,460.5
188,661.3
66,432.3
SD
39,934.0
42,681.9
188,827.0
142,406.9
46,576.8
M
238.6
192.3
192.9
221.0
177.2
SD
113.6
159.5
104.4
142.6
122.2
M
165,869.1
76,091.5
588,565.5
312,936.6
54,370.9
SD
84,179.1
69,769.1
366,776.3
201,371.2
55,336.9
M
68.4
37.2
47.0
59.1
29.0
SD
44.1
58.3
34.6
32.9
22.0
M
25,118.9
6,644.1
69,635.5
55,035.6
5,340.9
SD
21,088.7
8,694.7
56,327.2
52,255.3
13,748.4
M
1.2
11.0
39.4
0.6
1.2
SD
3.6
32.0
56.8
1.5
5.1
M
40.0
362.6
13,273.1
44.3
22.9
SD
200.7
1,139.3
21,992.8
197.1
115.2
M
2,013.8
22,953.5
670,367.1
2,487.7
1,414.1
SD
10,457.9
77,985.4
1,166,759.3
11,545.1
8,418.3
M
0.3
3.6
14.5
0.7
0.0
SD
2.3
24.5
36.5
3.5
0.2
M
56.7
666.6
5,672.2
124.0
0.0
SD
576.2
4,585.8
15,314.8
882.8
0.2
150
Means and Standard Deviations of Variables by Farm Resource Region
Full Owner
No.
ac.
Part Owner
No.
ac.
Tenant
No.
ac.
Barley
No.
ac.
bu.
Dry Edible
Beans
No.
ac.
Southern
Seaboard
Fruitful
Rim
Basin and
Range
Mississippi
Portal
National
M
370.3
699.1
397.8
382.8
494.3
SD
300.9
798.0
463.6
264.5
431.9
M
50,487.8
178,608.6
243,803.1
63,252.3
107,934.9
SD
40,436.0
338,998.8
250,920.4
37,459.7
156,483.4
M
120.3
137.4
100.9
123.3
176.1
SD
104.5
134.6
89.4
75.8
131.6
M
46,975.0
161,041.2
273,073.0
79,104.2
166,612.0
SD
39,685.9
216,546.4
314,224.3
55,230.3
213,647.8
M
25.4
60.8
30.1
44.2
45.7
SD
22.5
74.5
29.5
32.1
48.3
M
6,133.5
47,445.9
40,499.0
35,385.4
27,415.8
SD
7,569.7
76,662.1
52,896.4
44,629.8
44,878.0
M
2.5
8.6
15.9
0.0
6.5
SD
8.6
26.7
34.8
0.3
24.0
M
186.1
2,429.0
3,550.0
2.2
1,326.8
SD
865.5
8,771.2
924.6
28.7
7,189.0
M
13,549.4
188,547.5
240,495.9
100.7
78,424.8
SD
67,468.9
711,780.2
686,787.2
1,289.6
430,585.4
M
0.0
3.5
3.5
0.0
2.2
SD
0.2
17.0
11.5
0.0
18.5
M
0.0
445.3
675.0
0.0
519.5
SD
0.5
1,810.1
3,365.6
0.0
4,507.2
151
Means and Standard Deviations of Variables by Farm Resource Region
Dry Edible
Beans
cwt.
Corn
No.
ac.
bu.
Upland
Cotton
No.
ac.
bale
Oats
No.
ac.
bu.
Peanuts
No.
Heartland
Northern
Crescent
Northern
Great
Plains
Prairie
Gateway
Eastern
Uplands
M
1,039.2
11,132.4
96,266.6
2,927.5
0.2
SD
10,483.8
80,653.3
259,389.3
20,791.9
3.9
M
339.9
179.2
90.9
67.6
35.3
SD
213.8
253.97.
106.8
97.2
60.1
M
95,835.3
24,903.1
37,582.5
31,773.2
3,208.4
SD
69,958.9
35,782.7
53,303.7
49,861.0
5,965.9
M 15,118,341
3,208,367
4,590,762
4,752,085
367,133
SD 12,553,742
5,054,506
7,045,153
8,263,123
708,809
M
0.9
0.0
0.0
17.2
0.9
SD
11.3
0.0
0.0
49.0
6.1
M
696.2
0.0
0.0
11,228.4
457.0
SD
8,493.0
0.0
0.0
37,653.8
3,424.7
M
1,329.7
0.0
0.0
19,932.8
472.3
SD
15,808.7
0.0
0.0
66,875.0
3,493.4
M
17.1
46.1
26.2
5.9
7.3
SD
30.8
69.5
29.0
9.0
33.1
M
501.5
1,174.8
2,623.4
414.3
132.9
SD
1,068.9
2,195.8
3,290.4
812.2
603.5
M
33,507.3
70,981.6
154,897.2
16,808.2
7,250.0
SD
65,233.6
145,312.5
199,657.3
33,332.1
35,448.8
M
0.0
0.0
0.0
2.2
0.0
SD
0.0
0.0
0.0
12.7
0.2
152
Means and Standard Deviations of Variables by Farm Resource Region
Dry Edible
Beans
cwt.
Corn
No.
ac.
bu.
Upland
Cotton
No.
ac.
bale
Oats
No.
ac.
bu.
Peanuts
No.
Southern
Seaboard
Fruitful
Rim
Basin and
Range
Mississippi
Portal
National
M
0.8
10,119.6
9,134.5
0.0
9,061.4
SD
16.4
43,567.7
44,839.6
0.0
78,097.6
M
36.7
15.2
5.4
43.3
113.0
SD
56.5
30.9
18.5
51.8
183.2
M
6,661.3
4,110.9
563.9
17,381.1
31,151.4
SD
11,462.9
10,231.1
1,952.1
23,638.1
53,118.3
M
701,650
590,260
99,364
2,561,041
4,657,411
SD
1,229,096
1,478,219
352,208
3,714,366
8,772,861
M
10.6
8.6
0.1
19.1
5.9
SD
22.2
25.1
1.3
28.5
23.6
M
4,674.8
3,856.8
1.5
15,381.0
3,304.4
SD
9,375.7
14,123.8
21.2
26,901.8
16,397.0
M
7,080.6
7,781.9
3.5
29,831.0
5,842.4
SD
15,210.7
30,467.1
48.9
54,404.3
30,084.8
M
3.7
3.5
4.8
0.9
13.8
SD
6.2
7.4
7.6
3.2
35.4
M
176.1
254.1
292.6
42.2
575.0
SD
403.2
703.4
695.1
213.8
1,507.6
M
9,375.8
19,905.5
18,525.1
3,856.8
34,379.1
SD
23,224.9
66,746.8
41,108.9
20,147.5
94,519.7
M
9.2
3.0
0.0
0.5
2.0
SD
22.5
11.1
0.0
1.7
11.0
153
Means and Standard Deviations of Variables by Farm Resource Region
Peanuts
ac.
lbs.
Rice
No.
ac.
cwt.
Sorghum
No.
ac.
bu.
Soybeans
No.
ac.
bu.
Heartland
Northern
Crescent
Northern
Great
Plains
Prairie
Gateway
Eastern
Uplands
M
0.0
0.0
0.0
534.1
0.4
SD
0.0
0.0
0.0
3,697.4
7.8
M
0.0
0.0
0.0
1,931,461.0
1,312.9
SD
0.0
0.0
0.0
14,250,520
26,388.4
M
0.8
0.0
0.0
0.2
0.1
SD
8.9
0.0
0.0
3.7
1.1
M
323.5
0.0
0.0
70.3
28.6
SD
3,615.1
0.0
0.0
1,329.2
307.5
M
22,164.8
0.0
0.0
4,446.2
1,754.4
SD
248,475.0
0.0
0.0
84,648.5
20,919.1
M
4.2
0.5
2.4
47.4
0.8
SD
11.2
1.3
10.0
65.1
3.2
M
480.2
10.1
810.8
12,755.9
80.6
SD
1,348.2
55.2
3,777.6
16,450.0
399.0
M
42,086.6
524.4
47,840.9
889,048.9
5,743.9
SD
120,803.9
3,479.7
231,829.2
1,247,380.4
30,213.9
M
310.9
106.6
71.6
52.0
14.8
SD
183.0
158.0
119.2
100.4
28.8
M
70,912.0
15,173.0
37,427.3
12,324.2
3,073.9
SD
42,160.3
23,483.7
67,890.0
22,514.4
6,762.0
609,422.9
1,347,236.9
467,877.1
86,045.2
1,007,823.8 2,386,952.9
928,607.7
200,831.2
M 3,125,342.7
SD 2,108,203.6
154
Means and Standard Deviations of Variables by Farm Resource Region
Peanuts
Rice
Sorghum
Soybeans
155
Means and Standard Deviations of Variables by Farm Resource Region
Sugarbeets
No.
ac.
ton
Sugarcane
No.
ac.
ton
Wheat
No.
ac.
bu.
Total
Cropland
No.
ac.
Heartland
Northern
Crescent
Northern
Great
Plains
Prairie
Gateway
Eastern
Uplands
M
0.7
2.2
9.5
0.1
0.0
SD
7.4
16.1
30.2
1.3
0.0
M
230.9
499.8
3,645.5
31.8
0.0
SD
2,744.4
3,810.0
12,909.1
334.8
0.0
M
5,211.6
12,240.9
85,269.8
796.9
0.0
SD
61,116.1
95,869.7
305,906.1
8,950.1
0.0
M
0.0
0.0
0.0
0.0
0.0
SD
0.0
0.0
0.0
0.0
0.0
M
0.0
0.0
0.0
0.0
0.0
SD
0.0
0.0
0.0
0.0
0.0
M
0.0
0.0
0.0
0.0
0.0
SD
0.0
0.0
0.0
0.0
0.0
M
66.8
53.0
154.7
119.7
9.1
SD
83.2
90.3
116.8
121.7
20.4
M
7,306.5
3,735.1
106,356.1
50,901.1
1,219.3
SD
11,492.7
6,734.7
98,100.2
57,237.8
3,894.7
M
364,296.0
230,977.8 3,805,778.3 1,666,559.1
40,789.7
SD
542,708.0
469,045.4 3,578,710.4 2,041,540.5
119,073.9
M
713.3
637.1
453.2
577.4
590.2
SD
304.8
508.9
306.8
393.8
381.4
M
207,227.4
85,057.2
381,285.7
211,425.6
45,492.9
SD 109,968.7
87,140.2
256,206.7
149,657.8
36,568.5
156
Means and Standard Deviations of Variables by Farm Resource Region
Sugarbeets
No.
ac.
ton
Sugarcane
No.
ac.
ton
Wheat
No.
ac.
bu.
Total
Cropland
No.
ac.
Southern
Seaboard
Fruitful
Rim
Basin and
Range
Mississippi
Portal
National
M
0.0
2.4
1.6
0.0
1.3
SD
0.0
11.0
8.1
0.0
10.9
M
0.0
756.9
313.6
0.0
407.0
SD
0.0
4,046.9
1,581.0
0.0
3,869.8
M
0.0
26,258.1
7,766.3
0.0
10,368.7
SD
0.0
139,888.0
39,147.3
0.0
97,047.6
M
0.0
0.8
0.0
2.8
0.2
SD
0.0
5.6
0.0
9.7
2.9
M
0.0
1,542.0
0.0
2,457.2
268.0
SD
0.0
18,303.9
0.0
8,609.2
5,816.6
M
0.0
56,981.4
0.0
85,372.4
9,612.3
SD
0.0
688,855.5
0.0
302,014.9
217,055.2
M
16.6
21.6
24.6
28.4
52.2
SD
26.3
47.4
58.4
38.5
86.8
M
3,282.5
12,117.4
19,240.3
9,590.8
19,132.9
SD
5,375.9
39,231.3
50,537.0
13,847.1
46,639.9
M
149,674.5
792,159.7
971,729.5
439,541.6
751,804.4
SD
256,018.7
618,728.0
1,842,768.5
M
356.7
593.8
352.8
392.8
547.4
SD
283.8
746.4
348.1
247.0
437.0
M
41,485.5
113,765.0
90,990.8
115,932.2
132,547.9
SD
35,782.2
162,519.6
125,671.8
98,657.7
149,407.8
2,291,997.5 3,006,848.0
157
Means and Standard Deviations of Variables by Farm Resource Region
Milk Cows
No.
ac.
Farms with No.
Commodity
Payments
Commodity
Payments
$
Income
%
Dependence
Heartland
Northern
Crescent
Northern
Great
Plains
Prairie
Gateway
Eastern
Uplands
M
21.5
87.3
8.0
6.6
18.8
SD
37.6
148.7
11.7
9.3
33.9
M
2,109.4
8,351.7
1,497.7
2,801.9
1,129.1
SD
3,853.9
12,197.1
6,610.7
10,084.3
2,045.6
M
453.2
239.1
291.9
276.7
129.1
SD
220.2
272.3
175.9
176.8
130.7
M
3,526,080
1,328,181
3,403,486
3,036,438
440,287
SD
2,318,028
1,443,872
2,460,797
2,912,230
712,447
M
21.1
12.9
12.4
33.6
17.3
SD
190.6
36.8
15.0
76.8
35.3
Means and Standard Deviations of Variables by Farm Resource Region
Milk Cows
No.
ac.
Farms with No.
Commodity
Payments
Commodity
Payments
$
Income
%
Dependence
Southern
Seaboard
Fruitful
Rim
Basin and
Range
Mississippi
Portal
National
M
5.5
16.8
7.8
3.9
22.7
SD
14.0
36.5
12.5
13.4
65.8
M
821.8
14,215.9
1,470.0
300.7
3,795.1
SD
2,654.5
46,897.5
4,016.1
1,377.4
15,888.4
M
97.2
110.0
82.9
194.8
223.3
SD
86.9
129.0
109.2
150.8
218.3
M
1,260,263
2,352,371
929,331
3,914,677
2,196,715
SD 1,733,375
4,396,800
1,538,519
4,338,473
2,742,667
M
47.7
20.3
74.2
378.4
47.1
SD
213.3
55.6
543.4
3,616.4
864.4
Appendix C
Location Quotients by Farm Resource Region
159
Location Quotients by Farm Resource Region
Farming
Occupation
Other
Occupation
Worked Off
Farm Any
Worked Off
Farm 200+
American
Indian/
Alaskan
Native
Asian
Black/
African
American
Multi-Race
White
Spanish,
Hispanic, or
Latino
Origin
Female
Male
Family or
Individual
(farms)
Family or
Individual
(acres)
Partnership
(farms)
Partnership
(acres)
Corporation
Other
(farms)
Corporation
Other
(acres)
H
NC
NGP
PG
EU
SS
FR
BR
MP
1.06
1.03
1.31
1.03
.90
.96
1.01
1.03
.95
.95
.97
.75
.96
1.08
1.04
.99
.97
1.04
.98
1.00
.85
.97
1.03
.99
1.03
1.02
.96
1.00
1.01
.80
.96
1.07
.99
.97
.94
.95
.15
.48
.58
1.50
2.53
.53
.80
.66
1.15
1.05
.66
1.75
1.47
4.90
3.58
1.57
36
.89
.36
.60
1.03
.70
.79
1.03
.44
.96
1.01
1.70
1.45
1.02
1.48
1.81
1.01
5.92
1.04
.96
3.20
1.47
.98
.67
1.20
.98
9.23
.78
.94
.18
.73
1.04
.31
1.26
.95
.26
.83
1.03
1.65
1.01
1.00
.29
.90
1.02
.52
1.12
.98
3.91
1.29
.95
2.61
1.25
.95
.49
.92
1.01
.99
.98
.95
.97
1.06
1.00
.95
.92
.94
1.36
1.32
1.25
1.32
1.58
1.40
1.07
.93
1.26
1.03
.93
1.19
1.23
.77
.98
1.13
1.29
1.50
.84
.82
.91
1.04
.62
.89
1.09
1.06
1.46
.90
1.44
.80
.90
.51
1.18
1.96
1.66
1.48
.45
.82
.88
.59
.25
.43
1.59
2.81
.32
160
Location Quotients by Farm Resource Region
Corporation
Family
(farms)
Corporation
Family
(acres)
Other Farms
(farms)
Other Farms
(acres)
Limited
Resource
(farms)
Limited
Resource
(acres)
Retirement
(farms)
Retirement
(acres)
Residential/
Lifestyle
(farms)
Residential/
Lifestyle
(acres)
Lower Sales
(farms)
Lower Sales
(acres)
Higher Sales
(farms)
Higher Sales
(acres)
Large
Family
(farms)
Large
Family
(acres)
H
NC
NGP
PG
EU
SS
FR
BR
MP
1.15
1.35
1.60
1.05
.40
1.08
1.67
1.71
1.35
.88
.92
1.21
.77
.32
.80
1.38
1.48
.85
1.30
1.44
1.41
1.68
.42
.70
1.24
1.77
.60
.45
.82
.88
.59
.25
.43
1.59
2.81
.32
.73
1.13
.74
.84
1.31
1.08
1.02
1.11
1.05
.83
2.07
.70
1.10
2.99
1.95
1.29
1.16
1.49
.84
.91
.66
1.00
1.17
1.19
1.06
.98
1.05
.93
1.42
.59
1.22
2.47
2.16
1.38
.97
1.59
.97
.96
.71
.94
1.10
.98
.96
.94
.98
1.03
1.50
.60
1.22
2.30
1.79
1.29
1.01
1.52
.95
1.16
1.29
1.04
.93
.90
1.07
1.08
.88
.68
1.17
.98
1.19
1.42
1.05
1.07
1.08
.77
1.60
1.12
2.82
1.21
.30
.51
.63
1.00
.56
.99
.88
1.56
1.03
.46
.59
.61
1.00
.47
1.81
.89
2.44
1.22
.31
.60
.65
.84
.81
1.36
.82
1.47
1.03
.37
.59
.60
.86
.66
161
H
Very Large
(farms)
Very Large
(acres)
Nonfamily
(farms)
Nonfamily
(acres)
Full Owner
(farms)
Full Owner
(acres)
Part Owner
(farms)
Part Owner
(acres)
Tenant
(farms)
Tenant
(acres)
Barley
(farms)
Barley
(acres)
Beans
(farms)
Beans
(acres)
Corn
(farms)
Corn (acres)
Upland
cotton
(farms)
Upland
cotton
(acres)
Oats (farms)
Oats (acres)
Peanuts
(farms)
Location Quotients by Farm Resource Region
NC
NGP
PG
EU
SS
FR
BR
MP
1.56
.66
1.77
1.19
.40
1.20
.98
.59
1.47
1.48
.80
1.28
1.02
.31
.83
.96
.64
1.35
1.11
1.18
1.08
1.34
.53
.94
1.56
1.55
1.18
.50
.64
.68
.81
.38
.67
1.57
1.89
.82
.90
1.02
.76
.90
1.06
1.04
1.09
1.05
.94
.80
1.24
.76
.96
1.57
1.50
1.47
1.35
1.15
1.18
.97
1.54
1.17
.92
.93
.70
.84
.96
1.17
.96
1.22
1.05
.77
.81
.73
.85
.78
1.34
.86
1.47
1.38
.61
.85
1.16
1.06
1.77
1.05
.57
.90
1.13
.43
.63
1.11
.83
1.96
.13
1.28
7.43
.13
.16
.61
1.51
3.17
.00
.03
.58
4.27
.02
.05
.50
2.11
1.86
.00
.11
1.35
8.03
.51
.01
.01
1.13
1.71
0
.08
1.73
5.28
.12
.00
.00
.63
1.01
0
2.61
3.72
1.07
1.35
1.09
.62
.74
.73
.27
.23
.57
.70
.15
.14
.05
.01
.56
.81
.29
0
0
3.68
.11
3.62
1.48
.09
5.59
.19
.92
1.06
0
2.60
4.59
0
2.62
2.41
1.94
.39
.52
.24
.40
.61
3.61
.42
.99
1.18
.21
.56
.00
.48
.51
6.23
.08
.14
0
0
0
1.32
.01
9.62
2.04
0
.41
162
H
Peanuts
(acres)
Rice (farms)
Sorghum
(acres)
Soybeans
(farms)
Soybeans
(acres)
Sugarbeets
(farms)
Sugarbeets
(acres)
Sugarcane
(farms)
Sugarcane
(acres)
Wheat
(farms)
Wheat (acres)
Total
cropland
(farms)
Total
cropland
(acres)
Milk cows
(farms)
Market Value
Total Income
Expenses
Net Income
Farms with
Commodity
Payments
Commodity
Payments
Income
Dependence
Location Quotients by Farm Resource Region
NC
NGP
PG
EU
SS
FR
BR
MP
0
.58
0
0
0
0
.69
.04
.00
.10
11.13
.03
4.80
1.86
0
.03
.16
18.23
.26
.01
.15
3.40
.06
.24
1.25
.00
2.20
3.01
.77
.92
.63
.12
.61
.05
.00
1.14
3.88
1.15
.86
.50
.27
1.11
.06
0
2.19
.62
.99
6.52
.15
0
0
1.83
1.52
0
.43
1.09
3.69
.03
0
0
.93
.11
0
0
0
0
0
0
0
3.25
0
42.96
0
0
0
0
0
0
3.35
0
34.95
1.16
.52
.71
.38
3.93
2.38
2.80
1.70
.13
.12
.62
.62
.36
.40
.61
.60
.90
.68
1.13
1.09
1.08
.95
.99
.91
.81
.85
.93
1.77
1.31
1.10
.95
.82
.95
.80
.49
1.29
.68
1.53
1.79
1.43
1.85
2.77
2.34
4.95
2.48
2.10
.44
.44
.67
.42
.56
.25
.69
.66
.74
.58
.67
.98
1.03
1.11
.73
.31
2.14
2.00
2.29
1.77
.51
3.49
2.65
2.53
2.51
.46
.44
.64
.66
.82
.15
.92
1.01
.98
.86
1.77
.81
1.78
1.37
.47
.73
.49
.52
1.15
1.94
1.15
.68
.89
.44
1.69
1.09
.30
2.43
1.35
.52
1.16
1.37
.46
.99
.62
.53
2.36