Implications of the 1996 Farm Bill on Cotton Acreage

A Research Paper Presented for the Master of Science in Agriculture and Natural Resources Degree

The University of Tennessee at Martin

Kimberly Ann Hall May 2013

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Dedication

As I come to the end of my educational career, I think about those who have stood beside

me throughout this journey. Three people in particular come to mind. These three individuals

have made personal sacrifices to ensure my success. These three are my parents, Ray and Mary,

and my husband, Brian.

College was never a question in my home during my high school years. My parents

supported me in all of my endeavors and truly believed that I could do anything that I set my

mind to. This theory was tested when I began college at UTM in August 2005. The transition

proved to be very difficult for both me and my parents. To this day, I share a close relationship

with both of my parents and being apart was a situation that did not set well. However, we all

knew that college provided the best future. Phone calls, cards, and weekends pulled me through

the first semester; the next two and a half years proved brighter. I am so thankful for their love

and support throughout my life. They have always been there no matter my need. I love them

and am proud to be their daughter.

Upon graduation in 2008, I decided to pursue my Master’s degree. After one semester, I

took a break. Brian and I were married in April 2009. In January 2010, I determined it was

either time to continue the degree or forget about it. Working forty plus hours a week, it has

been difficult to stay focused and motivated. That is where Brian has come in. Over the last

three years, he has encouraged me to keep pressing forward to reach my goal. He has picked up

my slack around the house to ensure ample time for my studies. When I have felt that the

situation was impossible, he has reassured me that I can do this and that he is behind me one

hundred percent. His devotion is unwavering; I only hope that I can show him the same support

in the years to come. I love him and appreciate all he has done.

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Acknowledgements

I would like to thank Dr. Scott Parrott for all of his help in completing this research

paper. I knew very little about the 1996 farm bill or cotton policy going into this project. I have

learned much from this study and appreciate all of Dr. Parrott’s guidance and insight in directing

me through the research and analysis.

I would like to thank Dr. Joey Mehlhorn for his help on this project and throughout my

college career. As my undergraduate advisor and agribusiness professor, he was always

available for help or advice. His great attitude and caring personality make him a great teacher

and mentor. I appreciate all of his help over the years.

I would also like to thank Dr. Barb Darroch for her continued support and

encouragement. She has received several phone calls and/or emails from a stressed-out grad

student over the last few years. After talking with her, though, the situation never seemed as bad

as it did in the beginning. She is also the reason that I finally understand statistics!

Finally, I would like to thank each educator and administrator who has played a part in

my education while at UT Martin. Each assignment, test, and class has been a stepping stone to

where I am today. A special thank you goes out to the Department of Agriculture and Natural

Resources. I truly believe this is the best group on campus!

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Abstract

Cotton has been an important row crop and a valuable commodity throughout the

evolution of American agriculture. Farm policy also evolved through the years to reflect

changes in the industry. In the early to mid-1900’s, the first farm bills created income and price

support, as well as deficiency payments for producers growing specific crops. However, in

1996, farm policy adopted a market focus in an attempt to remove government influence.

The Federal Agriculture Improvement and Reform Act of 1996 altered the provisions for

government payments. This legislation was also commonly called the Freedom to Farm Act by

producers. The end result was to have less government involvement in producer decision

making; producers would look to the market as a guiding tool. With the implementation of the

FAIR Act came questions as to the future of cotton acreage. Many felt that acres previously

devoted to cotton would be utilized for other commodities.

This study looked at planted cotton acreage in the United States from 1980 through 2011.

Several variables including planted cotton acreage, cotton price per pound, corn price per bushel,

and total government payments for cotton were placed in a multiple regression analysis to

determine whether or not they explained the variability of planted cotton acreage. Furthermore,

a Chow Statistical Test for Structural Change was used to find a breakpoint in the data. A

breakpoint was found between 1997 and 1998. Split regressions further examined the data to see

if producer decisions were altered by the introduction of the FAIR Act. The split regressions

produced higher R-square values (0.74 for 1980 through 1997 and 0.66 for 1998 through 2011),

indicating that they better explained the variability of planted cotton acreage than did the

multiple regression analysis including all years (R2 = 0.49). Lagged cotton acreage and lagged

corn price were significant coefficients in the 1980 through 1997 model (P = 0.04 and P = 0.06,

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respectively). Lagged cotton price and lagged corn price were significant coefficients in the

model for 1998 through 2011 (P = 0.03 and P = 0.09, respectively). Total government payments

for cotton proved to be significant (P = 0.003) in the model from 1980 through 1997 and not

significant (P > 0.10) from 1998 through 2011. This study provides evidence to suggest that the

initiation of the FAIR Act began to drive agricultural producers toward the use of free market

signals and away from government dependence when making decisions.

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Table of Contents  

Chapter 1: Introduction ...............................................................................................................1

The Role of Cotton .......................................................................................................................1

Chapter 2: Literature Review ......................................................................................................2

The Changing Face of Agriculture ...............................................................................................2

Pre-Freedom to Farm ................................................................................................................2

Freedom to Farm ......................................................................................................................4

Post-Freedom to Farm ..............................................................................................................6

Previous Studies .......................................................................................................................8

Objectives ...............................................................................................................................10

Chapter 3: Materials and Methods ...........................................................................................12

Data and Sources ........................................................................................................................12

Model and Statistical Analysis ...................................................................................................22

Chapter 4: Results and Discussion ............................................................................................24

Chapter 5: Conclusion ................................................................................................................31

References .....................................................................................................................................32

Appendix .......................................................................................................................................36

 

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List of Tables

Table 1. Planted acreage of cotton and corn for the United States during selected years both before and after the 1996 farm bill ..................................................................................3

Table 2. Planted acreage of cotton and corn for 1980 through 2011 ...........................................13

Table 3. Average price received for cotton, corn, and soybeans for 1980 through 2011 ............15

Table 4. Total input costs of cotton and corn for 1980 through 2011 ..........................................16

Table 5. Breakdown of input costs of cotton and corn for 2010 and 2011 ..................................17

Table 6. Total government payments for cotton and corn for 1980 through 2011 ......................18

Table 7. Total yield in pounds of cotton and bushels of corn for 1980 through 2011 .................19

Table 8. Government payments per pound of cotton and per bushel of corn for 1980 through 2011 ..................................................................................................................20

Table 9. Producer Price Index for 1980 through 2011 .................................................................21

Table 10. Price and production variables of cotton and its competitors used in the models .......22

Table 11. Statistical results and parameter estimates from the initial multiple regression analysis for 1980 through 2011....................................................................................24

Table 12. Statistical results of the Chow Statistical Test for Structural Change .........................25

Table 13. Statistical results and parameter estimates from the split regression analysis for 1980 through 1997 .......................................................................................................26

Table 14. Statistical results and parameter estimates from the split regression analysis for 1998 through 2011 .......................................................................................................27

Table A.1 Data used in the multiple regression analyses and Chow Statistical Test ...................36

 

 

 

 

 

 

 

 

 

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List of Figures

Figure 1. Comparison of planted acreage of cotton and corn for 1980 through 2011 .................14

Figure 2. Annual production of ethanol in the U.S. for 2001 through 2010 ................................28

 

 

 

 

 

 

 

 

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Chapter 1: Introduction

The Role of Cotton

Cotton, Gossypium hirsutum L., has been an important row crop in the United States for

many years, and it has helped to clothe the population. Cotton’s significance in the agricultural

industry has driven its production. However, producers face challenges and risks in their

profession, regardless of the crop grown. Because of these risks, government organizations and

policies are put into place.

One such organization is the United States Department of Agriculture (USDA). The

USDA creates many programs to benefit producers. These programs help to minimize market

risks, conserve natural resources, and recover from potential disasters (USDA-FSA, 2012c).

Today, more than 270 million acres of farmland are protected by crop insurance (USDA-ERS,

2012d). More than five billion dollars are dispersed annually in the form of government

payments to growers. Risk management is also accomplished through government farm bills. In

particular, the Federal Agriculture Improvement and Reform (FAIR) Act of 1996 played an

important role in farm policy (USDA-ERS, 1996). Upon its adoption on April 4, 1996, the FAIR

Act, also known as the Freedom to Farm Bill, altered the provisions for government support

payments. This aspect of the FAIR Act, along with others, may have changed production

decisions among American farmers (USDA-ERS, 1996). A review of acreage data might show a

shift from the production of cotton to the production of other commodities after the FAIR Act.

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Chapter 2: Literature Review

The Changing Face of Agriculture

Pre-Freedom to Farm

Farming practices evolved throughout the 1900’s. In the early years of the twentieth

century, agricultural production was labor intensive (Dimitri et al., 2005). Half of the population

of the United States was employed in agriculture, as were more than 20 million work animals. A

large number of small farms produced a variety of commodities. As the years passed, the

number of family farms began to decrease, and the size of remaining farms began to increase

(Dimitri et al., 2005). The average farm was valued at $4,000 in 1949 (Lanegran, 2004).

However, this value increased as farm size increased. In 1900, 41% of the workforce was

employed in agriculture-related jobs. By 1970, that percentage had dropped to four. During this

same period, the number of farms that required off-farm resources to remain productive rose

from about 30% to about 50% (Dimitri et al., 2005).

A number of factors may have contributed to the loss of small farms and farmers.

Growers with a small acreage lack the economic leverage of their larger counterparts

(Anonymous, 1984). Also, the land from smaller farms is being transformed into either larger

farms or urban development projects. Technological advancements are also directly linked to the

decrease in farm numbers and the increase in farm size (Dimitri et al., 2005). In 1930, there

were less than one million tractors in operation in the United States. By 1970, tractors had

practically replaced animal power, and harvesting had become a mechanical process (Dimitri et

al., 2005). Crop acreage varied slightly throughout the twentieth century (Table 1). The use of

3  

mechanical planting and harvesting techniques allowed producers to expand acreage, taking

advantage of economies of size.

Policy played a major role in agricultural production throughout the twentieth century.

The first official farm bill, the Agricultural Adjustment Act, was introduced in 1933 (Dimitri et

al., 2005). This act developed income and price programs that were tailored to specific

commodities. In 1965, the Food and Agricultural Act lowered price supports, and in 1973,

deficiency payments were introduced (Dimitri et al., 2005). The Food Security Act of 1985

allowed farm loans to be repaid at lower interest rates as market prices dropped (Dimitri et al.,

2005). This action prevented the government from holding large amounts of surplus grain.

The 1990 farm bill provided a glimpse of the future. The Food, Agriculture,

Conservation, and Trade Act of 1990 created opportunities for planting elasticity (Duffy et al.,

Table 1. Planted acreage of cotton and corn for the United States during selected years both before and after the 1996 farm bill.

Year Cotton Corn

-------------------------acres-------------------------

1975 9,477,600 78,719,000

1985 10,684,600 83,398,000

1995 16,931,400 71,479,000

1997 13,898,000 79,537,000

2007 10,827,200 93,527,000

2011 14,735,400 91,936,000

Source: USDA-NASS, 2012

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1993). Farmers were allowed to use the market on a limited basis to make production decisions.

Government payments were restricted as a result of acreage limitations (Duffy et al., 1993).

This, in turn, lowered the cost for farm programs.

Freedom to Farm

In 1996, a piece of legislation was passed that would change the course of agricultural

policy. The FAIR Act, or Freedom to Farm, altered the provisions placed on government

payments (Doering, 1999). Previously, when market prices were insufficient, price supports

and/or deficiency payments made up the difference. These payments were based upon the

quantity of acres or livestock owned by the producer (Doering, 1999). With the initiation of

Freedom to Farm, growers were granted payments based upon past involvement with similar

programs and previous payments received (Doering, 1999). Payments were determined by a

farm’s base acreage which was determined by a farm’s historical production of a particular crop

combined with the yield of that crop. Base acreage could be increased by increasing yield and/or

acreage; however, payments would continue to be based upon historical production (Anderson et

al., 2001-02). Farmers would no longer be restricted in their choice of crops (Doering, 1999).

Before the FAIR Act, farmers were forced to grow what had been previously grown on a certain

piece of land to be eligible for payments. Under Freedom to Farm, producers could decide the

crop and the number of acres without fear of losing subsidies (Doering, 1999). The bill locked in

direct payments from 1996 until 2002 (Nelson, 1996). It also provided funding for programs

involving conservation and rural development and lengthened funding for research and

extension.

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With the legislation came some risks. Farmers had to use the market as a tool for making

production decisions (USDA-ERS, 1996). However, some believe that the market is a better

source of price information (Parrott and McIntosh, 1996). Also, government payments would no

longer be fixed, creating greater fluctuations in income for producers (USDA-ERS, 1996).

Producers were required to enter into “production flexibility contracts” between the years of

1996 and 2002 (USDA-ERS, 1996). These contracts required that existing farms be maintained

for agricultural uses and in compliance with conservation and planting guidelines.

In the years prior to Freedom to Farm, cotton received more support payments than did

other commodities (Knutson et al., 1996). Cotton and rice had been the two most subsidized

commodities. With Freedom to Farm, pricing patterns for cotton became similar to those of

wheat, soybeans, and corn. Therefore, the subsidization amounts were more even among the

various crops (Knutson et al., 1996). Upon the introduction of Freedom to Farm, many

wondered how farmers would respond with their production decisions (Knutson et al., 1996).

An early analysis projected corn to remain king in the Midwest and to gain popularity in the

Great Plains. It was suggested that corn and a soybean/wheat combination would replace at least

a portion of the cotton acreage in Texas and the Southeast. In the West, corn, wheat, and hay

were predicted to become the leaders (Knutson et al., 1996).

A study performed through Texas A & M University provided real data from

representative farms in various regions of the United States to show production transitions from

before to after the Freedom to Farm Bill was passed (Anderson et al., 2001-02). Farms were

studied from eleven crop states: Washington, California, North Dakota, Iowa, Missouri,

Arkansas, Texas, Louisiana, South Carolina, Kansas, and Colorado. Production data were

gathered from these locations during 1990-1999 to determine if any changes were caused by

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Freedom to Farm (Anderson et al., 2001-02). Corn and soybean acreage showed no change in

the two Iowa farms studied. However, Missouri and Texas farms reported increases in corn

acreage of over 15% and 20%, respectively (Anderson et al., 2001-02). Some farms increased

their soybean acreage; others decreased it. Wheat acreage followed the same pattern (Anderson

et al., 2001-02). Cotton acreage did not show as much variation as was expected. Only one of

the Texas farms showed a decrease in cotton acreage during the years of the study (Anderson et

al., 2001-02).

Post-Freedom to Farm

Production agriculture has continued to evolve in the years since the Freedom to Farm

Bill was passed. Farms are now highly focused on one commodity or aspect and are restricted to

a small percentage of the land area of the United States (Dimitri et al., 2005). For example, the

Midwest specializes in corn production (Lanegran, 2004). The number of farms today is less

than two million; the total land area of those farms has declined by 40 million acres over the last

50 years (Lanegran, 2004). The average farm in 1997 was valued at over $100,000, much

greater than the average of $4,000 in 1949 (Lanegran, 2004). There are now about five million

tractors in operation, and less than two percent of the population is employed in agriculture

(Dimitri et al., 2005). It has become increasingly important for farming families to earn income

from sources off the farm. In 2000, 93% of all farming families earned income on the farm and

elsewhere (Dimitri et al., 2005).

The increasing use of precision agriculture (PA) products and techniques continues to aid

in agricultural specialization (Stombaugh and Shearer, 2000). Though the concept of PA has

been around for decades, the advanced equipment to implement its use has not been in existence

7  

for very long (Stombaugh and Shearer, 2000). Global positioning systems are being used more

frequently for crop management practices (Stombaugh and Shearer, 2000). These systems place

farmers and their equipment just where they need to be when transitioning from row to row,

allowing for the use of variable rate technology (VRT). VRT allows seed, chemicals, and other

inputs to be distributed at a needs-based rate across a given field (Stombaugh and Shearer, 2000).

Cotton remains one of the major commodities produced by farmers with 14.7 million acres of

cotton planted in 2011 (Table 1).

The most notable piece of legislation following Freedom to Farm was the 2002 farm bill.

Known as the Farm Security Act of 2002, this bill was initiated on May 13, 2002 (Young, 2008).

The bill initiated direct payments in association with a farm’s base acreage (USDA-FSA, 2012b).

These payments are based upon a crop’s yield per acre and are dispersed for a portion of a farm’s

base acreage. Among other implications, the bill incorporated counter-cyclical payments

(Young, 2008), which provide income to farmers when the effective price of a given commodity

is below the target price set by the USDA (USDA-ERS, 2012b). Both direct and counter-

cyclical payments are made in accordance with base acreage instead of actual crop production

(Qiu et al., 2009). Together, these two payment options form the Direct and Counter-Cyclical

Program (DCP).

The 2002 farm bill also promoted conservation practices on the farm (Young, 2008). The

Environmental Quality Incentives Program, created in 1996, received additional funding

(USDA-ERS, 1996). It also created a Conservation Security Program to compensate growers

who do their part to secure scarce resources. Programs were also established for the

conservation of wetlands and grasslands (Young, 2008).

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In 2008, the Food, Conservation, and Energy Act was passed (Kelhart, 2008), with a

budget of $300 billion spent to further such agricultural endeavors as research. The bill also

included provisions to further extension and education (Kelhart, 2008), and created a permanent

disaster program, along with fine-tuning crop insurance (USDA-ERS, 2008). Additional

assistance was initiated for those producers at a social disadvantage.

The 2008 farm bill also introduced the Average Crop Revenue Election (ACRE) program

(USDA-FSA, 2012a). Under this program, direct payments are made on only 80% of base acres.

Counter-cyclical payments would be non-existent should this program be utilized. This aspect

has made the program relatively unpopular with cotton producers. The benefits of ACRE are

comparable to those of an economical insurance policy (USDA-FSA, 2012a).

Previous Studies

Barnes (2010) examined cotton acreage data to determine if the policy changes of the

1996 farm bill altered the planted acreage of cotton. This paper focused on the southeastern

United States during the years following Freedom to Farm. Determinants considered in the

model included the per pound price of cotton, program returns, and alternative uses for the

cropland (Barnes, 2010). Barnes designed two separate models using multiple regression. One

of these models assumed that the producer would receive a complete subsidy for each acre,

whereas the second model decreased the subsidy amount. No significant relationship was found

among any of these determinants and cotton acreage (Barnes, 2010).

Though Barnes (2010) was very thorough in his study, he neglected to compare the

acreage data after Freedom to Farm to the acreage data before Freedom to Farm. This type of

9  

analysis would give a more accurate illustration of the relationship. It is necessary to examine

both time periods to determine the change, if any, in planted cotton acreage.

Other analyses of cotton acreage have also been performed. Duffy et al. (1987)

developed a model to show the acreage response of cotton. Prior to the study, policy

relationships had been heavily studied concerning only soybeans, corn, and wheat. The

document was presented as a means to calculate the acreage response of cotton and to determine

the elasticity of cotton acreage in terms of farm policy (Duffy et al., 1987).

Duffy et al. (1994) used an expected utility model to determine the responsiveness of

cotton to farm policy. The study focused on southeastern cotton, corn, and soybeans, but the

model seemed to fit the data for the latter two commodities only (Duffy et al., 1994). Another

study also used a regression model to study the acreage response of cotton (Parrott and

McIntosh, 1996). The model determined that the cash price of cotton is a larger determinant of

acreage response than is the price supplied by government programs.

Ramirez et al. (2004) explains strategies to better understand and initiate the modeling

process. They stress the importance of properly identifying the specific components of the

model and demonstrate how a probability distribution function can be used to analyze the

acreage response of cotton by using simulated data (Ramirez, 2004).

Liang et al. (2011) examined crop choice as directly influenced by risk by considering the

effects of both yields and prices upon crop supply. Liang et al. (2011) compares elasticity values

for cotton, corn, and soybeans to values from previous studies. These values show that cotton

and corn are more responsive to price changes than are soybeans (Liang et al., 2011).

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Liang et al. (2011) goes on to discuss implications of the calculated elasticity values.

Genetically engineered varieties of cotton, corn, and soybeans have the potential to increase

insect resistance, herbicide tolerance, and crop yields, while decreasing the costs incurred for

insecticides (Liang et al., 2011). However, seed manufacturers do charge technology fees in

connection with genetically engineered varieties; the benefits must be weighed against the costs.

In recent years, the production of ethanol has greatly increased the market prices of both corn

and soybeans (Liang et al., 2011). These attractive prices have caused many producers to shift

their acreage from cotton to corn and soybeans. Liang et al. (2011) also notes the increased

prices of fuel and fertilizer used for crop production. While the price of diesel rose more than

$2.50 between the years of 2003 and 2008, fertilizer prices have seen surges of up to $30 per

hundredweight in the same time period. These implications have both positive and negative

effects on crop yields and prices. Producers must examine all effects to determine the optimal

allocation of resources (Liang et al., 2011).

Objectives

Each of these previous studies represents reliable starting points for additional research

and analysis on cotton acreage. A thorough examination of acreage data from the two eras

before and after the 1996 farm bill might show responsiveness to farm policy and related factors.

This study evaluated planted cotton acreage from before and after the implementation of the

1996 farm bill and attempted to model the response of cotton acreage to a variety of related

variables. It also tested how well selected variables explain variability in planted cotton acreage.

Corn was chosen to represent the competition that cotton faces for acreage each year because

both cotton and corn can typically be planted within the same region. Market prices, as well as

regional and international supply, help to determine a producer’s crop choice for the year (Otte,

11  

2011). However, growing conditions also play a major role in the decision making process.

While both corn and soybeans are possible substitutes, it is corn that thrives on the same soil

types as does cotton (Flanders and Dunn, 2011), making it the prime cotton competitor in this

study.

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Chapter 3: Materials and Methods

Data and Sources

To accurately determine possible changes in planted cotton acreage before and after

1996, many factors have been taken into consideration. Available government payments for

cotton were obvious determinants. However, other factors such as input costs, market prices,

and production alternatives have been included to develop a model that provides true reasons for

crop choice.

This study included data on the United States as a whole. This provides a more uniform

illustration of any changes brought about by the 1996 farm bill compared to studying only one

region. However, cotton is most prominent within the southeast. Because of this regional

popularity, most of the cotton acreage discussed in this paper originated from the southeast.

Multiple data sources have been utilized to gather production, input, and program data. The

National Agricultural Statistical Service (USDA-NASS, 2013) has been a reliable starting point

for much of this data.

For this study, the variables utilized in the models were recorded for the years of 1980

through 2011. Planted acres of cotton and corn have been used (Table 2; Figure 1). Prices

received for cotton were documented in dollars per pound; prices received for corn and soybeans

were documented in dollars per bushel (Table 3). Input costs per planted acre have also been

considered for each crop (Table 4); descriptions of crop inputs have been included for 2010 and

2011 as an example (Table 5). In addition, total subsidy payments for cotton and corn were

included in the model (Table 6). Yield data (Table 7) were used to calculate payment data per

acre based on payment per pound or bushel (Table 8). A producer price index (Table 9) was also

used to adjust price data.

13  

Table 2. Planted acreage of cotton and corn for 1980 through 2011.

Year Cotton Corn ------------------------acres------------------------ 1980 14,533,800 84,043,000 1981 14,330,100 84,097,000 1982 11,345,400 81,857,000 1983 7,926,300 60,207,000 1984 11,145,400 80,517,000 1985 10,684,600 83,398,000 1986 10,044,600 76,580,000 1987 10,397,200 66,200,000 1988 12,514,800 67,717,000 1989 10,586,600 72,322,000 1990 12,348,100 74,166,000 1991 14,052,100 75,957,000 1992 13,240,000 79,311,000 1993 13,438,300 73,239,000 1994 13,720,100 78,921,000 1995 16,931,400 71,479,000 1996 14,652,500 79,229,000 1997 13,898,000 79,537,000 1998 13,392,500 80,165,000 1999 14,873,500 77,386,000 2000 15,517,200 79,551,000 2001 15,768,500 75,702,000 2002 13,957,900 78,894,000 2003 13,479,600 78,603,000 2004 13,658,600 80,929,000 2005 14,245,400 81,779,000 2006 15,274,000 78,327,000 2007 10,827,200 93,527,000 2008 9,471,000 85,982,000 2009 9,149,500 86,382,000 2010 10,974,200 88,192,000 2011 14,735,400 91,936,000

Source: USDA-NASS, 2012

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Figure 1. Comparison of planted acreage of cotton and corn for 1980 through 2011. Source: USDA-NASS, 2012

15  

Table 3. Average price received for cotton, corn, and soybeans for 1980 through 2011.

Year Cotton Corn Soybeans $/lb. -----------------------$/bu.----------------------- 1980 0.69 8.23 7.57 1981 0.67 6.00 6.07 1982 0.56 5.76 5.71 1983 0.63 7.03 7.83 1984 0.66 5.52 5.84 1985 0.56 4.52 5.05 1986 0.55 2.98 4.78 1987 0.60 3.72 5.88 1988 0.57 4.68 7.42 1989 0.60 4.15 5.69 1990 0.65 3.80 5.74 1991 0.66 3.79 5.58 1992 0.54 3.22 5.56 1993 0.54 3.77 6.40 1994 0.66 3.33 5.48 1995 0.77 4.64 6.72 1996 0.74 3.55 7.35 1997 0.68 2.60 6.47 1998 0.65 2.20 4.93 1999 0.52 1.89 4.63 2000 0.50 1.86 4.54 2001 0.39 1.89 4.38 2002 0.34 2.13 5.53 2003 0.52 2.27 7.34 2004 0.54 2.47 5.74 2005 0.43 1.96 5.66 2006 0.48 2.28 6.43 2007 0.50 3.39 10.10 2008 0.61 4.78 9.97 2009 0.49 3.75 9.59 2010 0.71 3.83 11.30 2011 0.88 6.02 12.50

Sources: NCC, 2013; ORNL-CTA, 2011; USDA-ERS, 2013c; USDA-NASS, 2012

16  

Table 4. Total input costs of cotton and corn for 1980 through 2011.

Year Cotton Corn ------------------$/planted acre------------------

1980 241.31 185.22

1981 300.00 207.37 1982 323.89 210.97 1983 312.21 202.07

1984 326.88 211.07 1985 323.09 197.92

1986 295.80 165.60 1987 326.08 158.30 1988 325.23 162.94

1989 329.53 173.84 1990 347.75 177.77

1991 323.50 183.04 1992 315.28 183.25 1993 324.42 177.89

1994 334.03 197.21 1995 360.42 207.33

1996 358.21 353.94 1997 516.27 363.73 1998 461.16 362.86

1999 488.07 364.73 2000 517.66 378.32 2001 530.52 348.53

2002 529.02 334.31 2003 496.74 354.41

2004 501.51 377.50 2005 544.23 386.88 2006 555.07 409.74

2007 666.39 443.97 2008 688.20 529.38

2009 690.49 550.70 2010 733.24 554.26 2011 748.85 616.45

Source: USDA-ERS, 2012a

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Table 5. Breakdown of input costs of cotton and corn for 2010 and 2011.

Cotton Corn

Item 2010 2011 2010 2011

Gross value of production: ---------------------$/planted acre---------------------

Primary product 639.88 475.74 688.47 836.58

Secondary product 100.94 112.27 0.92 1.19

Total, gross value of production 740.82 588.01 689.39 837.77

Operating costs:

Seed 80.98 96.61 81.58 84.37

Fertilizer 72.86 95.06 112.03 147.36

Chemicals 67.12 66.72 26.29 26.35

Custom Operations 22.88 23.40 16.36 16.77

Fuel, lube, and electricity 51.74 62.95 25.80 32.42

Repairs 34.84 35.86 23.96 24.79

Ginning 129.88 80.85

Purchased irrigation water 3.13 3.35 0.11 0.11

Interest on operating inputs 0.47 0.23 0.28 0.17

Total, operating costs 463.90 465.03 286.41 332.34

Allocated overhead:

Hired labor 14.60 14.77 2.96 2.92

Opportunity cost of unpaid labor 26.30 26.68 22.53 22.77

Capital recovery of machines and equip. 133.91 141.80 84.40 89.59

Opportunity cost of land 70.49 75.63 127.33 136.92

Taxes and insurance 7.75 8.15 9.61 10.14

General farm overhead 16.29 16.79 21.02 21.77

Total, allocated overhead 269.34 283.82 267.85 284.11

Total costs listed 733.24 748.85 554.26 616.45

Value of production less total costs listed 7.58 -160.84 136.13 221.32

Value of production less operating costs 276.92 122.98 402.98 505.43 Source: USDA-ERS, 2012a

18  

Table 6. Total government payments for cotton and corn for 1980 through 2011.

Year Cotton Corn ---------------------------dollars---------------------------

1980 172,000,000 382,000,000 1981 222,000,000 243,000,000 1982 800,000,000 713,000,000 1983 662,000,000 1,346,000,000 1984 275,000,000 367,000,000 1985 1,106,000,000 2,861,000,000 1986 1,042,000,000 5,158,000,000 1987 1,204,000,000 8,490,000,000 1988 924,000,000 7,219,000,000 1989 1,184,000,000 3,141,000,000 1990 441,000,000 2,701,000,000 1991 407,000,000 2,649,000,000 1992 751,000,000 2,499,000,000 1993 1,226,000,000 4,844,000,000 1994 826,000,000 1,447,000,000 1995 30,000,000 3,024,000,000 1996 807,484,252 2,119,059,177 1997 744,710,475 2,906,300,158 1998 1,317,974,839 5,064,623,703 1999 1,944,900,027 7,567,377,481 2000 2,067,504,066 8,058,490,168 2001 3,332,602,474 5,982,553,435 2002 1,950,393,714 2,498,438,680 2003 2,550,958,586 3,439,944,865 2004 2,229,214,063 5,308,631,480 2005 3,696,295,927 10,138,944,101 2006 2,979,753,309 5,796,967,433 2007 2,541,484,197 3,805,912,131 2008 1,582,403,328 4,194,188,347 2009 2,217,005,004 3,788,184,733 2010 834,884,295 3,517,922,685 2011 1,297,599,264 4,610,464,817

Sources: EWG, 2012; USDA-ERS, 2013b

19  

Table 7. Total yield in pounds of cotton and bushels of corn for 1980 through 2011.

Year Cotton Corn ----------lbs.---------- ----------bu.---------- 1980 5,338,608,000 6,639,369,000 1981 7,509,936,000 8,118,650,000 1982 5,742,096,000 8,235,101,000 1983 3,730,272,000 4,174,251,000 1984 6,231,264,000 7,672,130,000 1985 6,447,456,000 8,875,453,000 1986 4,670,928,000 8,225,764,000 1987 7,084,752,000 7,131,300,000 1988 7,397,520,000 4,928,681,000 1989 5,853,888,000 7,531,953,000 1990 7,442,592,000 7,934,028,000 1991 8,454,864,000 7,474,765,000 1992 7,784,880,000 9,476,698,000 1993 7,744,128,000 6,337,730,000 1994 9,437,760,000 10,050,520,000 1995 8,591,904,000 7,400,051,000 1996 9,092,160,000 9,232,557,000 1997 9,020,640,000 9,206,832,000 1998 6,680,736,000 9,758,685,000 1999 8,144,640,000 9,430,612,000 2000 8,250,384,000 9,915,051,000 2001 9,745,344,000 9,502,580,000 2002 8,260,128,000 8,966,787,000 2003 8,762,496,000 10,087,292,000 2004 11,160,336,000 11,805,581,000 2005 11,467,296,000 11,112,187,000 2006 10,362,144,000 10,531,123,000 2007 9,219,312,000 13,037,875,000 2008 6,151,344,000 12,091,648,000 2009 5,850,000,000 13,091,862,000 2010 8,689,968,000 12,446,865,000 2011 7,475,136,000 12,359,612,000

Sources: USDA-NASS, 2012; UNC, 2001

20  

Table 8. Government payments per pound of cotton and per bushel of corn for 1980 through 2011.

Year Cotton Corn $/lb. $/bu. 1980 0.03 0.06 1981 0.03 0.03 1982 0.14 0.09 1983 0.18 0.32 1984 0.04 0.05 1985 0.17 0.32 1986 0.22 0.63 1987 0.17 1.19 1988 0.12 1.46 1989 0.20 0.42 1990 0.06 0.34 1991 0.05 0.35 1992 0.10 0.26 1993 0.16 0.76 1994 0.09 0.14 1995 0.01 0.41 1996 0.08 0.24 1997 0.07 0.46 1998 0.10 0.36 1999 0.08 0.35 2000 0.07 0.32 2001 0.05 0.26 2002 0.20 0.28 2003 0.29 0.34 2004 0.20 0.45 2005 0.32 0.91 2006 0.29 0.55 2007 0.28 0.29 2008 0.26 0.35 2009 0.38 0.29 2010 0.10 0.28 2011 0.17 0.37

Sources: Barnes, 2010; EWG, 2012; UNC, 2001; USDA-ERS, 2013b; USDA-NASS, 2012

21  

Table 9: Producer Price Index for 1980 through 2011.

Year Producer Price Index

1980 88.0

1981 96.1

1982 100.0

1983 101.3

1984 103.7

1985 103.2

1986 100.2

1987 102.8

1988 106.9

1989 112.2

1990 116.3

1991 116.5

1992 117.2

1993 118.9

1994 120.5

1995 124.8

1996 127.7

1997 127.6

1998 124.4

1999 125.5

2000 132.7

2001 134.2

2002 131.1

2003 138.1

2004 146.7

2005 157.4

2006 164.8

2007 172.7

2008 189.7

2009 173.0

2010 184.7

2011 200.8 Sources: CDRPC, 2013

22  

In this study, thirteen independent variables (Table 10) were organized in a model to test

whether or not these variables are accurate determinants of planted cotton acreage. All

independent variables were lagged by one year within the model to imitate the decision making

process of the producer. A producer will use the previous year’s expenses and incomes to help

determine the current year’s production plan.

Model and Statistical Analysis

A multiple regression procedure was used to determine if a relationship exists between

the planted acreage of cotton and the other variables in the model. This procedure was also

selected to calculate the strength of any such relationship. Parrott and McIntosh (1996) provided

an example of this type of modeling for the determination of acreage response. As previously

Table 10. Price and production variables of cotton and its competitors used in the models.

Price and Production Inputs of Cotton

Price and Production Inputs of Competitors

Variable Abbreviation

Variable Abbreviation

Cotton Acreage (acre) Ctac

Corn Acreage (acre) Crac

Cotton Price ($) Ctpr

Corn Price ($) Crpr

Total Government Payments for Cotton ($)

Ctgt

Total Government Payments for Corn ($)

Crgt

Cotton Yield (lb.) Cty

Corn Yield (bu.) Cry

Government Payments per Yield ($)

Ctgy

Government Payments per Yield ($)

Crgy

Cotton Input Costs ($) Cti

Corn Input Costs ($) Cri

Soybean Price ($) Sbpr

23  

stated, all data were lagged by one year. Price data were also deflated in accordance with the

Producer Price Index (PPI) in Table 9. This step removed the effects of inflation over time so

that real prices were used instead of nominal prices. The data in this study were tested for

autocorrelation, as time series data were used. However, the results of this test were

inconclusive.

Statistical Analysis Systems (SAS) Enterprise Guide 4.3 (SAS Institute Inc., Cary NC)

was used for the analysis. Both the regression model and its parameter estimates were evaluated

at the one, five, and ten percent levels of significance. The R-square value was used to indicate

how well the independent variables explain cotton acreage. The adjusted R-square has been

included to measure the proportion of cotton acreage that is explained by the regression model.

In connection with the regression model, the Chow Statistical Test for Structural Change,

created by Gregory Chow, was used to divide the data into subgroups (Hansen, 2001). The

subgroups must be divided at a definite point in time for the test to be accurate. The test

determines whether the data should be considered as a whole or if a structural change separates

the data into two different eras (Hansen, 2001). If the data are structurally different, then split

regressions can be performed to test each group separately.

24  

Chapter 4: Results and Discussion

The initial multiple regression analysis combined all of the years of the study into one

model. Production economics theory and preliminary statistical results were used to reduce the

model parameters to those defined in Table 11. The overall model had an R-square value of 0.49

(P = 0.0012). This suggests that the model explains cotton acreage well. However, the adjusted

R-square value of 0.41 shows that only 41% of the variability in planted cotton acreage can be

explained by the regression model. Lagged cotton acreage and the lagged price of corn proved

to be the only significant (P < 0.05) parameter estimates (Table 11).

Both lagged corn price and total government payments for cotton have negative

coefficients in the model. These signs are consistent with the theories and/or practices of

agricultural production. As the price of corn increases, producers will choose to produce more

corn and less cotton. Therefore, the price of corn and the planted acreage of cotton have an

Table 11. Statistical results and parameter estimates from the initial multiple regression analysis for 1980 through 2011.

Analysis of Variance Parameter Estimates

Pr > F R-square Adj. R-square Variable Coefficient Pr > │t│

0.0012*** 0.4883 0.4096 LCA 0.5461 0.0022***

LCTP 5,889,983 0.1707

LCRP -666,798 0.0361**

CTP -0.000513 0.4602

*Significant at α = 0.10 LCA: Lagged Cotton Acreage **Significant at α = 0.05 LCTP: Lagged Cotton Price ***Significant at α = 0.01 LCRP: Lagged Corn Price CTP: Cotton Total Government Payments

25  

inverse relationship. The coefficient for total government payments for cotton may be negative

because of the payment-in-kind program (USDA-ERS, 1983). In the early 1980’s, agriculture

saw record-breaking harvests of crops such as cotton, corn, and wheat. These harvests created

surplus stocks, lowered farm prices, decreased farm incomes, and increased government

expenses. These issues, along with a weakened demand for agricultural commodities in the U.S.

and abroad, forced the government to implement the payment-in-kind program (USDA-ERS,

1983). This program compensated producers who temporarily took at least a portion of their

farmland out of production. As a result, both commodity prices and farm incomes were allowed

to rise (USDA-ERS, 1983). Therefore, the negative sign associated with this variable is

consistent with farm policy. The larger the number of farmers who were paid to idle their

farmland, the smaller the quantity of planted cotton acres.

Because one of the main objectives of this study was to test the theory that the FAIR Act

caused a structural change in how agricultural producers respond to market forces, the Chow

Test was used for this purpose. The Chow Test indicated that there was a break point (P =

0.0445) in the data (Table 12) and that the data should be divided into two subgroups. The first

group included the years of 1980 through 1997, and the second group included the years of 1998

through 2011.

Table 12. Statistical results of the Chow Statistical Test for Structural Change.

Test Break Point Num DF Den DF F-Value Pr > F

Chow 17 5 21 2.78 0.0445**

**Significant at α = 0.05

26  

Though one might expect the data to be divided at the point of implementation of the

1996 farm bill, the split created by the Chow Test is likely more relevant. It is also expected that

the effects of the 1996 farm bill were not felt until a couple of years after the policy changes

were implemented. Therefore, production decisions may not have been altered by the FAIR Act

until 1997 or 1998. Because the Chow Test was significant and a breakpoint was determined, a

split regression analysis was used to model cotton acreage for each time period separately.

The model for 1980 through 1997 had an R-square value of 0.74 (P = 0.0017; Table 13).

The adjusted R-square indicates that 65% of the variability in planted cotton acreage can be

explained by the model for this time period. Lagged cotton acreage, lagged corn price, and total

government payments for cotton were identified as significant parameter estimates. As in the

initial multiple regression analysis, lagged corn price and total government payments for cotton

have negative coefficients.

Table 13. Statistical results and parameter estimates from the split regression analysis for 1980 through 1997.

Analysis of Variance Parameter Estimates

Pr > F R-square Adj. R-square Variable Coefficient Pr > │t│

0.0017*** 0.7399 0.6531 LCA 0.3913 0.0354**

LCTP 6,604,943 0.4071

LCRP -680,785 0.0623*

CTP -0.00374 0.0027***

*Significant at α = 0.10 LCA: Lagged Cotton Acreage **Significant at α = 0.05 LCTP: Lagged Cotton Price ***Significant at α = 0.01 LCRP: Lagged Corn Price CTP: Cotton Total Government Payments

27  

The regression model for 1998 through 2011 produced an R-square of 0.66 (P = 0.0312;

Table 14). The adjusted R-square shows that 51% of the variability in planted cotton acreage

can be explained by the model. The significant parameter estimates in this model were lagged

cotton price and lagged corn price.

The coefficient for total government payments for cotton was significant (P = 0.0027) in

the model covering 1980 through 1997 (Table 13) but was not significant (P = 0.4503) in the

model covering 1998 through 2011 (Table 14). This information is consistent with the main

objective of the 1996 FAIR Act. The most prominent goal of the act was to allow agricultural

producers more freedom in crop choice and production decisions. Farmers were expected to use

the market as their main tool and rely less on government involvement (USDA-ERS, 1996).

According to the significance levels of government payments in the split regression models, the

1996 farm bill has driven agricultural producers toward market freedom and away from

Table 14. Statistical results and parameter estimates from the split regression analysis for 1998 through 2011.

Analysis of Variance Parameter Estimates

Pr > F R-square Adj. R-square Variable Coefficient Pr > │t│

0.0312** 0.6593 0.5079 LCA -0.5161 0.3861

LCTP 16,248,237 0.0323**

LCRP -6,211,425 0.0860*

CTP 0.0008218 0.4503

*Significant at α = 0.10 LCA: Lagged Cotton Acreage **Significant at α = 0.05 LCTP: Lagged Cotton Price ***Significant at α = 0.01 LCRP: Lagged Corn Price CTP: Cotton Total Government Payments

28  

government dependence. Government payments were no longer a significant variable in

explaining planted cotton acreage.

The coefficient of lagged cotton acreage was positive (P = 0.0354) in the model for 1980

through 1997 (Table 13) but was negative (P = 0.386) in the model for 1998 through 2011 (Table

14). One possible cause could be the increased production of ethanol. In recent years, ethanol

has become very popular as a fuel source (Figure 2), increasing corn prices and providing

incentives for additional corn acreage (USDA-ERS, 2013a). Many times, acreage is shifted from

the production of cotton to the production of corn. Because cotton inputs are more expensive

Figure 2. Annual production of ethanol in the U. S. for 2001 through 2010.

Source: USDE, 2011

0

2

4

6

8

10

12

14

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Bil

lion

Gal

lon

s

Year

29  

than corn inputs, the change is attractive to producers. However, these shifts come with a price.

Because cotton and corn cannot be harvested by the same machine, the initial costs to alter

production could rise into the hundreds of thousands of dollars. Therefore, some producers

would be likely to continue to raise cotton and forfeit a portion of the profit. Issues such as this

make lagged cotton acreage a difficult variable to predict. The results of the three regression

analyses of this study show that the split regressions contained higher R-square values. The split

regressions allowed like data to be examined together, producing more reliable results. The

parameter estimate values were fairly similar among the two split regressions; however, some of

the signs of those parameter estimates changed from the first model to the second model.

Overall, the split regressions were able to explain a larger percentage of the variability in planted

cotton acreage. This variability was explained by lagged cotton acreage, lagged cotton price,

lagged corn price, and total government payments for cotton.

Some outlying data points were observed in this study. For example, planted cotton

acreage saw a substantial decrease in 1983. Acreage dropped from 11,345,400 in 1982 to

7,926,300 acres in 1983. This decrease may have been caused by the payment-in-kind (PIK)

program introduced by the USDA in 1983 (USDA-ERS, 1983). Fluctuations in government

payments were also observed. Government payments dropped eight cents from 1994 to 1995,

from $0.09 to $0.01 per pound of cotton. Payment levels remained low through 2001. The

Uruguay Round Agreement on Agriculture, put in place by the World Trade Organization, may

have been to blame for this drop (USDA-ERS, 2012c). Under this agreement, ceilings were

placed on domestic support for agricultural commodities. Several countries, including the

United States, created this agreement to sustain international trade of agricultural products. Each

country involved was to inform the WTO of domestic support spending (USDA-ERS, 2012c).

30  

This study has been successful in fulfilling its objectives. The split regression models

produced R-square values that explain over half of the variability in planted cotton acreage. This

shows that the variables chosen for this study were reliable determinants, especially those found

to be significant. Furthermore, the study found that there was a difference in the effectiveness of

government payments as a determinant of planted cotton acreage between the two time periods.

Producer decisions have not been controlled as prominently by the promise of government

subsidization since the 1996 farm bill was adopted.

31  

Chapter 5: Conclusion

Cotton has long been a staple in agricultural production. The commodity has been grown

throughout the years and has seen many changes in the agricultural industry. The 1996 Federal

Agriculture Improvement and Reform Act brought with it many changes. Direct payments

replaced income and price programs of previous years. Farmers were given more freedom in

crop choice and production decisions. The market was expected to become the ultimate tool for

decision making.

This study examined planted cotton acreage in the United States from 1980 through 2011

and related cotton acreage to four of thirteen independent variables. The initial multiple

regression analysis contained all of the years of the study. A Chow Test then determined a

breakpoint in the data, and split regressions compared the two eras surrounding the 1996 farm

bill. The two split regression models were more successful in explaining the variability of

planted cotton acreage using the given data than was the initial regression. This study

determined that lagged cotton price, lagged corn price, lagged cotton acreage, and total

government payments for cotton have major roles in the producer decision making process.

Furthermore, this study also showed that total government payments for cotton do not impact

producer decisions as greatly today as they did before the 1996 farm bill was implemented. The

split regressions were able to account for up to 65% of variability in cotton acreage. Overall, the

models in this study have been fairly successful in explaining variability in planted cotton

acreage.

32  

References

Anderson, D.P., J.W. Richardson, and E.G. Smith. “Post-Freedom to Farm Shifts in Regional Production Patterns.” Working paper, Agricultural and Food Policy Center, Texas A & M University, 2001-02. Anonymous. “U.S. Small Farmers: An Endangered Species.” The Futurist 18.2(April 1984):68. Barnes, Jr. W. O. “Acreage Response of Southeastern Upland Cotton to Federal Policy in the Freedom to Farm Era.” Graduate research project, University of Tennessee, Martin, December 2010. Internet site: http://www.utm.edu/library.php Bureau of Labor Statistics—Capital District Regional Planning Commission (CDRPC) 2012. Producer Price Index. Internet site: http://www.cdrpc.org/CPI_PPI.html (Accessed March 21, 2013). Dimitri, C., A. Effland, and N. Conklin. The 20th Century Transformation of U.S. Agriculture and Farm Policy. Washington, DC: U.S. Department of Agriculture, Economic Research Service, Economic Information Bulletin No. 3, June 2005. Doering, O. “Farming’s Future.” Forum for Applied Research and Public Policy 14.3(Fall 1999):42-47. Duffy, P.A., D.L. Cain, and G.J. Young. “Incorporating the 1990 Farm Bill Into Farm-Level Decision Models: An Application to Cotton Farms.” Journal of Agricultural and Applied Economics 25.2(December 1993):119-133. Duffy, P.A., J.W. Richardson, and M.K. Wohlgenant. “Regional Cotton Acreage Response.” Southern Journal of Agricultural Economics (July 1987):99-109. Duffy, P.A., K. Shalishali, and H.W. Kinnucan. “Acreage Response under Farm Programs For Major Southeastern Field Crops.” Journal of Agricultural and Applied Economics 26.2(1994):367-378. Environmental Working Group (EWG) 2012. Farm Subsidies. Internet site: http://www.farm.ewg.org/region.php?fips=00000&progcode=total (Accessed February 16, 2013). Flanders, A., and K.C. Dunn. Structural Change in Rotation Response for Arkansas Cotton Acreage. Fayetteville, AR: Arkansas Agricultural Experiment Station, Research Series No. 602, 2011. Hansen, B.E. “The New Econometrics of Structural Change: Dating Breaks in U.S. Labor Productivity.” Journal of Economic Perspectives 15.4(Fall 2001):117-128.

33  

Kelhart, M.D. “A New Farm Bill, Research Structure at USDA.” Bioscience 58.7(July/August 2008):586. Knutson, R.D., J. Keeling, and D.E. Ray. “The 1996 Farm Bill: Implications For Farmers.” Farm Foundation, Increasing Understanding of Public Problems and Policies (1996):100-102. Lanegran, D.A. “The Changing Scale of American Agriculture.” Geographical Review 94.3(July 2004):412-414. Liang, Y., J.C. Miller, A. Harri, and K.H. Coble. “Crop Supply Response Under Risk: Impacts of Emerging Issues on Southeastern U.S. Agriculture.” Journal of Agricultural and Applied Economics 43.2(May 2011):181-194. National Cotton Council of America (NCC) 2013. Monthly Prices. Internet site: http://www.cotton.org/econ/prices/monthly.cfm (Accessed January 25, 2013). Nelson, F.J., and L.P. Schertz. Provisions of the Federal Agriculture Improvement and Reform Act of 1996. Washington, DC: U.S. Department of Agriculture, Economic Research Service, Agriculture Information Bulletin No. 729, September 1996. Oak Ridge National Laboratory (ORNL)—Center for Transportation Analysis (CTA) 2011. Biomass Energy Data Book. Internet site: http://cta.ornl.gov/bedb/feedstocks.shtml (Accessed January 26, 2013). Otte, John. “Cotton Competes for Crop Acres.” Carolina-Virginia Farmer (November 2011):42. Parrott, S.D., and C.S. McIntosh. “Nonconstant Price Expectations and Acreage Response: The Case of Cotton Production in Georgia.” Journal of Agricultural and Applied Economics 28.1(July 1996):203-210. Qiu, F., B.K. Goodwin, and J.P. Gervais. The Impacts of Government Programs on Farmland Rental Contract Choices: A Theoretical and Empirical Analysis. Department of Agricultural and Resource Economics, North Carolina State University, October, 2009. Ramirez, O.A., S. Mohanty, C.E. Carpio, and M. Denning. “Issues and Strategies for Aggregate Supply Response Estimation for Policy Analyses.” Journal of Agricultural and Applied Economics 36.2(August 2004):351-367. Stombaugh, T.S., and S. Shearer. “Equipment Technologies for Precision Agriculture.” Journal of Soil and Water Conservation 55.1(First Quarter 2000):6-11. University of North Carolina (UNC) at Chapel Hill 2001. U.S. Commercial Bushel Sizes. Internet site: http://www.unc.edu/~rowlett/units/scales/bushels.html (Accessed January 27, 2013).

34  

U.S. Department of Agriculture (USDA)—Economic Research Service (ERS) 1983. An Initial Assessment of the Payment-in-Kind Program. April 1983. U.S. Department of Agriculture (USDA)—Economic Research Service (ERS) 1996. 1996 FAIR Act Frames Farm Policy for 7 Years. Agricultural Outlook Supplement, April 1996. U.S. Department of Agriculture (USDA)—Economic Research Service (ERS) 2008. 2008 Farm Bill. Internet site: http://webarchives.cdlib.org/sw1vh5dg3r/http://ers.usda.gov/ FarmBill/2008/Overview.htm (Accessed October 20, 2012). U.S. Department of Agriculture (USDA)—Economic Research Service (ERS) 2012a. Commodity Costs and Returns. Internet site: http://www.ers.usda.gov/data- products/commodity-costs-and-returns.aspx (Accessed January 29, 2013). U.S. Department of Agriculture (USDA)—Economic Research Service (ERS) 2012b. Counter- Cyclical Payments. Internet site: http://www.ers.usda.gov/topics/farm-economy/farm- commodity-policy/program- provisions/counter-cyclical-payments.aspx (Accessed October 17, 2012). U.S. Department of Agriculture (USDA)—Economic Research Service (ERS) 2012c. Government Payments and the Farm Sector. Internet site: http://www.ers.usda.gov/topics/farm-economy/farm-commodity-policy/government- payments-the-farm-sector/us-wto-domestic-support-reduction-commitments-and- notifications.aspx (Accessed April 14, 2013). U.S. Department of Agriculture (USDA)—Economic Research Service (ERS) 2012d. Risk Management. Internet site: http://www.ers.usda.gov/topics/farm-practices- management/risk-management.aspx (Accessed October 18, 2012). U.S. Department of Agriculture (USDA)—Economic Research Service (ERS) 2013a. Corn. Internet site: http://www.ers.usda.gov/topics/crops/corn/background.aspx (Accessed April 20, 2013). U.S. Department of Agriculture (USDA)—Economic Research Service (ERS) 2013b. Farm Income and Wealth Statistics. Internet site: http://www.ers.usda.gov/data-products/farm- income-and-wealth-statistics.aspx (Accessed February 15, 2013). U.S. Department of Agriculture (USDA)—Economic Research Service (ERS) 2013c. Oil Crops Yearbook. Internet site: http://www.ers.usda.gov/data-products/oil-crops-yearbook.aspx (Accessed April 8, 2013). U.S. Department of Agriculture (USDA)—Farm Service Agency (FSA) 2012a. Average Crop Revenue Election Program Fact Sheet. Internet site: http://www.fsa.usda.gov/FSA/newsReleases?area=newsroom&subject=landing&topic=pf s&newstype=prfactsheet&type=detail&item=pf_20120123_insup_en_acre12.html (Accessed January 3, 2013).

35  

U.S. Department of Agriculture (USDA)—Farm Service Agency (FSA) 2012b. Direct and Counter-Cyclical Payment Program Fact Sheet. Internet site: http://www.fsa.usda.gov/FSA/newsReleases?area=newsroom&subject=landing&topic=pf s&newstype=prfactsheet&type=detail&item=pf_20120112_insup_en_dcp.html (Accessed January 4, 2013). U.S. Department of Agriculture (USDA)—Farm Service Agency (FSA) 2012c. Farm Programs. Internet site: http://www.fsa.usda.gov/FSA/webapp?area=about&subject=landing& topic=sao-dp (Accessed October 16, 2012). U.S. Department of Agriculture (USDA)—National Agricultural Statistics Service (NASS) 2012. Statistics by Subject. Internet site: http://www.nass.usda.gov/Statistics_by_subject/index.php (Accessed January 27, 2013). U.S. Department of Agriculture (USDA)—National Agricultural Statistics Service (NASS) 2013. NASS Home Page. Internet site: http://www.nass.usda.gov/ (Accessed April 28, 2013). U.S. Department of Energy (USDE) 2011. U.S. Ethanol Production, 2001-2010. Internet site: http://www1.eere.energy.gov/vehiclesandfuels/facts/2011_fotw681.html (Accessed April 29, 2013). Young, Edwin. The 2002 Farm Bill: Provisions and Economic Implications. Washington, DC: U.S. Department of Agriculture/Economic Research Service, Pub. No. AP-22, January 2008.

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Appendix

Table A.1. Data used in the multiple regression analyses and Chow Statistical Test.

Year Cotton Acreage

Cotton Price Per Pound

Corn Price Per Bushel

Cotton Payments

acres $/lb. $/bu. dollars 1980 14,533,800 0.69 8.23 172,000,000 1981 14,330,100 0.67 6.00 222,000,000 1982 11,345,400 0.56 5.76 800,000,000 1983 7,926,300 0.63 7.03 662,000,000 1984 11,145,400 0.66 5.52 275,000,000 1985 10,684,600 0.56 4.52 1,106,000,000 1986 10,044,600 0.55 2.98 1,042,000,000 1987 10,397,200 0.60 3.72 1,204,000,000 1988 12,514,800 0.57 4.68 924,000,000 1989 10,586,600 0.60 4.15 1,184,000,000 1990 12,348,100 0.65 3.80 441,000,000 1991 14,052,100 0.66 3.79 407,000,000 1992 13,240,000 0.54 3.22 751,000,000 1993 13,438,300 0.54 3.77 1,226,000,000 1994 13,720,100 0.66 3.33 826,000,000 1995 16,931,400 0.77 4.64 30,000,000 1996 14,652,500 0.74 3.55 807,484,252 1997 13,898,000 0.68 2.60 744,710,475 1998 13,392,500 0.65 2.20 1,317,974,839 1999 14,873,500 0.52 1.89 1,944,900,027 2000 15,517,200 0.50 1.86 2,067,504,066 2001 15,768,500 0.39 1.89 3,332,602,474 2002 13,957,900 0.34 2.13 1,950,393,714 2003 13,479,600 0.52 2.27 2,550,958,586 2004 13,658,600 0.54 2.47 2,229,214,063 2005 14,245,400 0.43 1.96 3,696,295,927 2006 15,274,000 0.48 2.28 2,979,753,309 2007 10,827,200 0.50 3.39 2,541,484,197 2008 9,471,000 0.61 4.78 1,582,403,328 2009 9,149,500 0.49 3.75 2,217,005,004 2010 10,974,200 0.71 3.83 834,884,295 2011 14,735,400 0.88 6.02 1,297,599,264