AGRICULTURE HAS BEEN A KEY ELEMENT

INTRODUCTION

History of Agriculture

Agriculture has been a key element in the development of mankind as well as the

socioeconomic development of humans. Agriculture is believed to have begun simultaneously

around the world. Archaeologists have uncovered evidence of animal domestication and plant

cultivation in the Middle East, Asia, and the “new world” that is now called the Americas. This

evidence has been dated as far back as 7000 BC, which proved the historically important nature

of this subject. The four stages of human development are hypothesized to be (1) huntergatherer,

(2) herdsman, (3) farming, and (4) civilization. The change to herdsman or farming marks the

beginning of early production agriculture. The early stages of production agriculture are a result

of human intensification of the food gathering or propagation process. The more food sources

that could be encouraged by humans along with the discouragement of un-useful plants and

animals within a given area, proved to be beneficial to the needs of increasing populations.

(Encyclopedia Britannica, 2007)

Domesticated crops such as peppers and avocados were being grown in the Americas as

early as 7000 BC. Villages prospered and became widespread after 3500 BC when the

production of maize, or corn, began (Encyclopedia Britannica, 2007). It was farming and the

production of maize (corn) that created a bond between the early settlers and the Native

Americans who inhabited the area that is now the United States of America. This production of

food was not only key to their nutritional survival in the new land but also to their ability to

create a strong economic foundation. As settlers moved west, the ability to feed their families

became as important to survival as to the early settlers. This involvement in production

agriculture and the surplus of goods above the family’s need also allowed trade among

individuals to evolve.

The ability to feed and clothe oneself became a structural stronghold of the United States

national foundation. This production of food and fiber also formed the basis of many other

industries. Later in the civilization of the country, farmers became able to produce more than

their families could consume. It was at this point when others were free to take on tasks other

than food acquisition. The ability to manufacture clothing and textiles was a relatively new idea,

yet was still dependent on production agriculture for the inputs needed. The mass processing of

food products also evolved during the industrial era. Although this industry was still dependent

on production agriculture it allowed the nation to feed itself and also to embark on trade

negotiations with other countries. (Encyclopedia Britannica, 2007)

The agricultural industry of today incorporates traditional production agriculture with the

newer processing and finishing industries as well. Production agriculture has grown to include

fewer individuals who produce a greater total amount of product. The amount of agricultural

product produced outweighs the domestic need and allows for global trade.

The number of individuals currently employed in agricultural support industries

outweighs those in production agriculture jobs by sixteen percent (Gilmore & Whatley, 2006).

This employment number should continue to grow as individuals become less self-sufficient and

more dependent on fast, readily available food sources. Although the number of individuals in

production agriculture has decreased in recent years, the total number of people that are

employed in the agricultural industry has grown over time.

Agriculture Defined

Agriculture is the production of food, feed, and fiber by the process of growing and

harvesting plants, animals, and other life forms from management, cultivation, or tillage

practices. The term Agriculture is derived from the Latin term “Agri” for field and “Cultura” for

cultivation (Merriam-Webster’s Collegiate Dictionary, 1996). It is described as an art or science

of farming: cultivating the soil, producing crops, and raising livestock. Although the practice of

agriculture has traditionally been geared toward crop cultivation and the raising of livestock, it

has expanded to include other related areas. The current field of agriculture encompasses many

subject matter areas including agronomic crops, horticulture, aquaculture, animal husbandry,

food science, environmental management, and many others.

Impact of Agriculture

The field of agriculture spans a subject area much wider than one may perceive. In order

to estimate the importance of the agriculture industry to the United States, researchers have

calculated the cost of producing commodities and preparing them for consumers. Agriculture

production has increased, including both the propagation of a commodity and its processing for

human consumption. This growth can be associated with increased production technology and

the growing demand for food and fiber.

It has been noted that an estimated thirty-six percent of the world’s population was

employed in some form of agriculture in the year 2006. It can also be noted that twenty percent

of the U.S. population was employed in some form of agriculture related job which equaled

approximately twenty two million jobs in 2005. Fewer than two million Americans, or ten

percent of the total population, are actively engaged in farming with the remainder of the

employment being in the fields of science, marketing, research, and communication. Even states

with very little production agriculture present report a substantial portion of their citizens’

employment is in the food and fiber system. Employment opportunities are projected to continue

to increase across all fields of agriculture and related industries. The number of jobs in this area

will be greater than fifty-eight thousand positions by 2010, which equals between twenty and

twenty-five percent of the total employment in the nation. (Gilmore & Whatley, 2006)

Even with this small number of producers in operation, the American farmer is efficient

enough to produce over sixteen percent of the world’s food supply (Gilmore & Whatley, 2006).

The quantity of food produced in the United States has a profound impact on the world market

and on individuals within the global countries. The number of individuals that are dependent on

the production of food and fiber in the United States is immeasurable by any normal standards

due to the vast span of agriculture markets around the globe. National changes in production,

marketing, and management of resources not only affects citizens within this country but can,

and does, impact individuals in many other countries around the world.

New Developments in Agriculture

Due to the growing agricultural industry that mirrors the global population which it

serves, many new developments have evolved in recent years. These issues range from

environmental stewardship, biofuels, and food safety. These issues are only a few of the new

developments and issues that the field of agriculture deals with daily.

Environmental stewardship has become a hot topic in the field of agriculture. Many

outside the agricultural sector have the misconception that all agricultural practices have a

negative impact on the surrounding environment. This can only be expected when common

agricultural practices disturb the natural structure and evolution of the soil, water, and even air

surrounding production areas. The ability of producers to maintain the natural structure of the

environment is to their benefit since these actions have a relationship to increased productivity of

the land, which is reflected in the amount of yield that is produced from a given commodity. The

term sustainable agriculture has been used to describe the production of food, feed, and fiber

indefinitely without causing damage to the ecosystem (Merriam-Webster’s Collegiate

Dictionary, 1996). Sustainable agriculture has three main goals: environmental stewardship,

farm profitability, and prosperous farming communities. These goals have been addressed from

both the standpoint of the producer and the consumer so it is easy to understand why individuals

gravitate toward this type of agriculture. Although all aspects of sustainability are not fully

known, issues like erosion, runoff, nutrient depletion from the soil, and crop waste are all issues

that are included in the plan utilizing viable sustainable practices.

In recent years agriculture has evolved into new forms such as organic production

methods, biotech seed development, and bio-fuel development. These areas are of increasing

concern to the public due to their relatively new nature and the uncertainty of their influence on

the perceptions of the general public. Globalization, food safety, and land use are also topics that

are currently involved in heavy discussions and can be expected to gain momentum for many

years to come.

Social research has shown that unsubstantiated claims made against agriculture have

become linkages or truths in the brain. Unless this misinformation is corrected with new

information, adaptation can not take place due to the fact that this issue is not properly

understood. This informational linkage is very important to those educating the public about

agriculture and its importance. (Agriculture Council of America, 2008) Misconceptions are

often wide ranging and cover numerous issues. They also affect wide ranges of individuals due

to the domestic and international issues with which they are associated.

Importance of Agricultural Literacy

Due to the number of individuals that are impacted by agriculture by both employment or

for food and fiber, agricultural literacy becomes an important factor. Literacy can be defined as a

base level of skill or knowledge that one possesses that allows them to competently complete a

task (Sticht, 1975). This can also be the ability to competently respond to information and make

educated decisions. For the purposes of this study, agricultural literacy is the level of agricultural

knowledge that enables a true understanding of the industry and allows good social decisions to

be made with regard to this subject area (Frick, Kahler, & Miller, 1991). Agricultural literacy

includes an understanding of agriculture’s current economic, social, and environmental

significance to all Americans. The understanding includes knowledge of food and fiber

production, processing, and domestic and international marketing (Agriculture Council of

America, 2008).

Agricultural literacy campaigns have set goals and objectives that steer their actions.

These goals include increasing knowledge of the agriculture industry, informing citizens about

the employment opportunities that exist in the field of agriculture, and highlighting the role that

agriculture plays in the history of the United States. (Agriculture Council of America, 2008) The

ability to properly inform members of society about the truth regarding agriculture enables

individuals to observe educated decisions being made for the betterment of society.

The Issue of Perception

Although the basis for all reactions, decisions, and rulings are hinged on the knowledge

that one has, the manner in which information is utilized by individuals is definitely in need of

discussion. The increase of proper knowledge is theorized to have a positive effect on the public

perception of an issue. The extent to which perception affects decisions and reactions is in need

of review.

Many of the issues that face the agriculture sector today are relatively new. Research on

these issues is in progress and current information is readily available, yet in many situations the

time that is needed to make definite decisions is not as readily available. This leaves many

uncertainties and unanswered questions. This lack of knowledge is thought to cause the

perceived effects of these issues to be misjudged. Many decisions have been made based on

statements that may not be true to the fullest extent. The problem arises when misinformed

individuals make these types of statements based on their perception of an issue and allow these

perceptions to guide others along a similar path of misinformation.

Today, information can be found on any topic instantaneously due to the use of the

internet. The problem with this wealth of information is that in many instances the information

may not be true. At the onset of any type of risk perceived by the public, attitudes toward that

topic may become negative. Research and scientific findings that support the facts will not

change the decisions that many have already made based on the original perceptions. (Frewer,

Howard & Aaron, 1998)

Statement of the Problem

In a time when perception is the driving force behind the actions of many who dictate

future agricultural policies, evaluating the perception of individuals with regard to agriculture

and the practice of growing food and fiber has become increasingly important. Elected

individuals rely on public opinion to guide the decision making process that they must undergo

while doing their job. The opinions of their voters and the perceptions that they have obtained

can drastically affect the manner in which these elected parties handle public policy issues.

Research that allows us to understand the relationship between knowledge and perception, while

understanding the outlying factors that affect both knowledge and perception is needed.

Purpose and Objectives

The purpose of this study was to determine the knowledge and perception of the animal,

plant, environmental, and food sciences by the adult residents of Louisiana. The evaluations of

the knowledge levels and the perception levels were also compared to determine if a relationship

existed between these two factors.

This study had the following objectives:

1. To describe adult residents of Louisiana on the following demographic

characteristics:

a. age,

b. gender,

c. ethnic background,

d. location of residence (in a rural area, on a farm, in a town, in a city),

e. parish of residence,

f. occupation of the head of household,

g. highest level of education.

2. To determine the knowledge of the adult residents of Louisiana regarding the

following selected aspects of the agriculture industry:

a. animal science,

b. plant science,

c. environmental science,

d. food science

3. To determine the perceptions of the agricultural industry among adult residents of

Louisiana.

4. To determine if a relationship exists between knowledge of selected aspects of the

agriculture industry (defined as animal science, plant science, environmental science,

and food science) and perceptions of the agriculture industry among adult residents of

Louisiana.

5. To determine if a relationship exists between perceptions of the agriculture industry

and the following demographic characteristics of adult members of the general public

in Louisiana:

a. age,

b. gender,

c. ethnic background,

d. location of residence (in a rural area, on a farm, in a town, in a city),

e. parish of residence,

f. occupation of the head of household,

g. highest level of education

6. To compare the perceptions of the agriculture industry among adult residents of

Louisiana by whether or not the respondent had completed a college degree in an

agricultural or related field.

7. To compare the perceptions of the agriculture industry among adult residents of

Louisiana by whether or not the respondent had prior agricultural training (defined as

whether or not the respondent indicated that they enrolled or participated in any

agriculture course(s) during high school or college, such as FFA, 4-H, or other

activities).

8. To compare the perceptions of the agriculture industry among adult residents of

Louisiana by whether or not the respondent had prior agricultural experience (defined

as whether or not the respondent indicated that they are currently a member of

Louisiana Farm Bureau).

9. To determine if a model exists explaining a significant portion of the variance in

perceptions of the agricultural industry among adult residents of Louisiana from the

following measures:

a. knowledge of adult residents of Louisiana regarding selected aspects of the

agricultural industry,

b. age,

c. gender,

d. ethnic background,

e. location of residence (rural, farm, town, or city),

f. parish of residence,

g. occupation of the head of household,

h. highest level of education

Significance of the Study

The findings of this study were used to clarify issues that affect perceptions of the

agriculture industry by the adult residents of Louisiana. Measuring knowledge, perceptions, and

the correlation between these two variables will be valuable to several different groups and

organizations including land grant universities, farm organizations, and youth agriculture

programs.

The groups that will benefit from these results the greatest are the farmers and ranchers of

Louisiana. The most important impact that this study may have is to enable those involved in

production agriculture to know how they are perceived by the general public. This will allow

members of the agriculture industry the ability to combat inaccurate perceptions and explain

important issues. They are able to correct local conflicts based on untruths and battle national

issues that affect the productivity of their industry. Since the United States legislative system is

based on bills written to meet the perceived needs of the American people, the ability to correct

misconceptions about the agriculture industry will enable producers to ensure that legislation that

affects the daily activities of the agriculture industry is based on factual information.

The findings will aid in the creation of new programming by agricultural organizations

that will address the needs found in this study. This study will also measure knowledge in the

areas of animal, plant, environmental, and food sciences and indicate which areas are in need of

promotion to increase agricultural literacy. The identification of specific areas where agriculture

knowledge held by respondents is lacking will aid in the development of agricultural literacy

programming. This programming will also be guided by the determination of select

demographic groups that are in need of such programs. The development of public relations

materials that combat this negative perception could be a result of the findings in this study. The

proof of need for public relations in this area will also assist in gaining funding for such projects.

Membership in the Farm Bureau organization could also be of benefit if there was a noted

positive impact on perception by the public relations activities produced by the organization.

Farmers and ranchers will be more apt to join an organization that has beneficial implications on

their occupations and livelihoods.

The impact on perception and the perceived need for university studies has a direct

correlation to the basic funding of these areas of study. The negative perception of the

agriculture industry may cause elected officials to vote against funding due to the pressure placed

on them by the general public. For this reason, institutions that have agriculture study areas are

in a constant state of flux where perception can mean the difference between progression and

deletion.

This study was to show if enrollment in agriculture courses, participation in agriculture

groups such as 4-H or FFA, or obtaining a degree in an agricultural area significantly affects

perception of the agricultural industry. The findings, showing a positive influence on perception,

could assist agriculture programs in high schools in recruitment of new members by showing the

increase of information that is disseminated to members. This could also assist university

agriculture programs in structuring their recruitment materials to change the agricultural

perception of certain demographic groups. These findings will aid in recruitment for these

occupational majors.

It has been said that “knowledge is power”. Agriculture knowledge based on factual

information is not only powerful but warranted in situations such as policy making, public

relations, and organizational development. This study will assist institutions and organizations

with each of these needs and clarify the perceptions of the agriculture industry at hand.

A limitation of this study is the use of phone interview technique. Phone surveys have

been categorized as secondary forms of gathering information in the past. Inability to reach

target audience groups and shorter than necessary surveys are reasons against the use of phone

and mail surveys. The inability to denote the physical reactions of respondents to survey

questions was noted by Dillman (2007) as yet another shortcoming. The movement toward

utilizing cell phones as a preferred method of phone communication is also a bias. Those who

are adapters of this new technology are at risk of being excluded from this study.

CHAPTER 2

REVIEW OF RELATED LITURATURE

Agriculture in the United States

Agriculture can be defined as the cultivation or tillage of the soil in order to produce food

or fiber for human use. (Merriam-Webster’s, 1996) Although the “tillage of the soil” has been

in existence for thousands of years, agriculture has become ever more important in recent years.

This is due in part to the importance of food and fiber production with regard to national

security. The increased publicity in the questioning of agriculture and its role in the maintenance

of soil, water, and air quality has also raised the individual concerns of citizens. Agricultural

subsidies within the United States have also raised concerns of citizens in times of financial

downfall due to the large volume of money that farmers are provided through government

payments each year. These concerns may be new but are just part of the evolution that farming

has gone through over time.

Even before the American Revolution, a large percentage of colonists were farmers by

trade. These farmers were not merely self-sufficient tillers of the land but marketed and traded

excess food and fiber to others in order to meet the demands of those who held other

occupations.

At the turn of the century the agricultural economy in the U. S. consistently grew. From

1897-1910, prices for agricultural commodities climbed higher each year (Cochrane, 1979). The

period that was to follow from 1910-1919 was to prove to be an even greater positive economic

time. This was a period of time in which the standard of living for farmers grew significantly

higher (Hurt, 2002). World War I marks the beginning of the golden age as it encouraged

farmers to plant more crops, develop more land, and create a surplus that could be exported to

other countries. The farmer was no longer a price taker, but for the only time in history he had

buying and selling power equal to that of other industries. This time period was called the

Golden Age of Agriculture. The end of the golden age coincided with the great depression. This

period witnessed many farmers losing everything that they owned due to decreases in prices and

demand for goods.

The need for new innovations was evident in the 1920s and beyond. The unveiling of

hybrid corn and the use of chemical fertilizers after World War I made a dramatic impact on the

Midwest. Fertilizers gave way to pesticides, both of which are commonly used today. The

adaptation of mechanical tools was also a major portion of the evolution. The labor shortage

created by World War II caused many to purchase tractors and other mechanized machinery. All

of these innovations caused an increase in production and surplus in the markets. These

surpluses caused exports to begin to increase and the farm economy to improve dramatically

(Cochrane, 1979). The ability to demonstrate the proper use of this technology was also

beginning to blossom.

FFA Organization

Founded in 1928, the Future Farmers of America (FFA) brought together students,

teachers and agribusiness to solidify support for agricultural education. The FFA program was

started in 1928 by vocational education students in Virginia. Later that same year in Kansas

City's Baltimore Hotel, 33 young farmers charted a course for the future by forming the national

Future Farmers of America organization (National FFA Organization, 2009). These young men

realized that there was a need for bonding between agriculture students to allow them to share

common ideas, new technologies, and interests. They could not have foreseen how the

organization would grow and thrive.

Since 1928, millions of agriculture students - no one knows exactly how many - have

donned the official FFA jacket and championed the FFA creed. FFA has opened its doors and its

arms to minorities and women, ensuring that all students could reap the benefits of agricultural

education. In 1950, the 81st Congress of the United States, recognizing the importance of the

FFA as an integral part of the program of vocational agriculture, granted a Federal Charter to the

FFA. The organization’s name changed in 1988 from “Future Farmers of America” to “The FFA

Organization”, reflect the expanding career field of Agricultural Education. Today, the National

FFA Organization remains committed to the individual student, providing a path to achievement

in premier leadership, personal growth and career success through agricultural education. Now,

the organization is expanding the nation's view of "traditional" agriculture and finding new ways

to infuse agriculture into the classroom. (National FFA Organization, 2009)

Cooperative Extension Service

Following the establishment of the land grant college system by the Morrill Act of 1862,

the need for education of farmers and ranchers beyond the traditional classroom setting was

reviewed. This need led to the birth of the Cooperative Extension Service. The history and

formation of the Cooperative Extension Service dates back to the implementation of The Hatch

Act of 1887. This legislative piece established a cooperative bond between United States

Department of Agriculture and the nation's existing land grant colleges allocating annual federal

funding for dissemination of research to the general public. This funding came from the sale of

land that was granted to the states or territories by the federal government. This funding

established the regional and local experiment stations that produced research based information

that was relevant to the population that the station would serve. This was one of the ways to

improve the productivity of the farms and by doing this, build up the economy and also help the

communities (Kile, 1921). It was the driving force for the land-grant colleges to meet

agriculture's needs with regard to regional research.

Congress also passed the Smith Lever Act in 1914. This legislation, signed by President

Woodrow Wilson on May 8, 1914, authorized an additional level of cooperative extension work

between the Land- Grant Colleges and the United States Department of Agriculture. The act

bears the names of the congressmen who introduced it, Senator Hoke Smith of Georgia and

Representative A.F. Lever of South Carolina. It provided for the establishment of what is now

titled the Cooperative Extension Service. As a result of the Smith Lever Act, there are now

extension offices which serve to “extend" information developed on teaching campuses and

research stations across the nation. The Smith-Lever Act of 1914 specified that the work would

consist of “instruction and practical demonstration in agriculture and home economics to persons

not attending or resident in said colleges in the several communities”. The legislation also stated

that this instruction would impart information on said subjects through field demonstrations and

publications. (Encyclopedia Britannica, 2009)

The need for demonstration leaders led to the formation of the group of individuals

known as county agents. The county agents and the experiment station personnel understood the

delivery of innovation in production agriculture and performed this task exceptionally well.

They also understood the need for organization in the areas of marketing and policy. American

Farm Bureau President James R. Howard (1919-1922) noted that the county agents would

become and should become the right arm of the American Farm Bureau Federation due to their

involvement with members of production agriculture and their in-depth understanding of the

need that this potential organization would fulfill. (Kile, 1921) For many years after the

establishment of the local farm bureaus, county agents were a driving force providing leadership.

This evident strength of the county agents within the county farm bureau organizations began to

weaken after the organizations began to gain momentum. This separation from the extension

service and the county agents was assisted by many attacks on the non-profit, industry oriented

organization of Farm Bureau and its use of individuals whose salaries were funded entirely by

the state and local tax base. Even so, the county agent still remains an active part of county farm

bureaus and are included in many county Farm Bureau activities.

Since its inception, the Cooperative Extension Service has worked with the farmers and

ranchers of Louisiana to create the finest food and fiber production systems in the world.

Cooperative extension played a huge role in the adoption of new machinery, pesticides, and seed

varieties. Test plots that were commonly used to demonstrate best agronomic practices utilized

these new innovations in order to visually educate the farmer on best management practices. The

formation of these test plots is largely due to the work of Seaman Knapp. Dr. Knapp believed

that communities should develop demonstration farms under the guidance of the Department of

Agriculture (Bailey, 1945). A suitable amount of money must be raised in order to cover any

losses that may be sustained by the owner and operator of the farm while under the supervision

of the Department. This type of operation has evolved to present day test plots on governmental

lands as well as those of individual farmers. The psychological key to unlock the door to the

farmer’s cooperation was found when the farmer involved in the first test plot reported to have

made profits significant enough to encourage the demonstrated practices on his entire farm.

Seaman Knapp stated, “What a man hears he may doubt what he sees he may possibly doubt, but

what he does himself he cannot doubt” (Bailey, 1945, p. 155).

This type of demonstration was in need of expansion to other members of the community

than solely farmers. The corn club for boys and the canning club for girls were established. The

demonstration club for boys was an unavoidable decision from the beginning. Knapp realized

that younger men and boys adapted to new practices easier than did their elder counterparts. The

ability to encourage this younger population to become active members of the agriculture

industry was a positive one (Bailey, 1945). This involvement with cutting edge agriculture

technology had significant impact on the perceptions of these youth with regard to the industry.

These organizations have evolved over time to what we presently know as the 4-H organization.

Although projects that members of this organization work on have a similar foundation to

those demonstration projects of the first corn club members, the activities of the organization

have expanded and evolved over time. Nearing its 50th anniversary, 4-H began to undergo

several changes. In 1948, a group participated in the first International Farm Youth Exchange.

Since then, 4-H has begun to extend into urban areas in the 1950's. Later, the basic 4-H focus

became the personal growth of the member. Life skills development was built into 4-H projects,

activities and events to help youth become contributing, productive, self-directed members of

society. The organization changed in the 1960's, combining 4-H groups divided by gender or race

into a single integrated program. The current 4-H program consists of 6 million young people

and 60 million alumni. 4-H'ers participate in activities supported by the latest research of land-

grant universities. The program’s three areas of focus are: Science/ Engineering/

Technology, Healthy Living, and Citizenship. (National 4-H Council, 2009)

While the cooperative extension service has enhanced the production side of agriculture,

the need for a combined voice to support legislation, marketing, and policy existed. This need

was similar to the one on which the members of mechanized industries based their unions. These

unions already existed and were utilized as models for communication and structure. The need

for cooperation among farmers that was noted by county agents across the state of

Louisiana was brought to the forefront when the Louisiana State University College of

Agriculture invited representatives to visit with Louisiana Farmers in 1920. Over one hundred

farmers from across the state of Louisiana met with the Dean of the College of Agriculture at

Louisiana State University and established the Louisiana Farm Bureau.

Louisiana Farm Bureau

After World War I, the prices of agriculture commodities plummeted due to the fiscal

depression that the country was experiencing and the ongoing drought that plagued the mid-west.

The independent nature of the farmer who was adventuresome and opinionated was faced with

many issues that were new to them. It became evident that in order to approach the new

problems with any type of success, they must align themselves together as one group and one

voice. The need for social recognition provided a push toward organization. (Kile, 1948)

Many started to leave the farm for jobs in cities during the industrial revolution. They formed

union groups to mediate their stand on job conditions, living expenses, and other issues that

affected them. This type of organization was later mirrored in a somewhat different form by the

agriculture sector. This flight from the farm caused an influx of immigrants to the United States

after World War I. This influx was due to the lenient immigration policy that the U.S. held after

the war (Woell, 1990). This was influential on the American agricultural scene due to the

number of these immigrants that became members of the farming population.

These immigrants brought with them the idea that farmers were no more than peasants

who worked long strenuous hours for little pay. The reference to all farmers as “just farmers”

was demeaning and untrue. Many native farmers also felt that they were termed in this manner

as well. The American farmer had dared when others had wavered. They had cleared the forest

and seeded the fields, while lesser men sought sheltered occupations. (Colby, 1968)

Social issues were not the only problem facing agriculture. In the 1920s Louisiana cotton

was king. Sugarcane and rice were following the trend of wheat and corn in the Midwest where

farmers were struggling to make ends meet. Farming proved to be as volatile as any market.

Farmers were often not able to set the price that they would ask for their goods. The agriculture

market is the closest example of a competitive market currently known. Because of this

structure, the farmer felt economic fluctuations more than any other individual business and

made survival extremely volatile. (Kile, 1948)

The newly formed farm organizations were of four principal types. These organizations

had (1) a central membership with farmers spread across the country that paid a membership fee,

(2) central organizations of delegates from townships and other local units, (3) representation

from other various farmer groups, (4) a dissociated farmers club without scattered about through

which the county agent could do his work. The main purpose of these new farm organizations

was to gather together a group of interested people who could plan projects that could be used

for demonstration teaching. The need for promotion of other outside agriculture interests was

also aided by the formation of this group. (Kile, 1948)

The name of “Farm Bureau” was coined by Byers H. Gitchell, secretary of the

Binghamton, New York Chamber of Commerce. On a tour of the rural portion of the state of

New York, the state Secretary of Agriculture stated his concerns with the abandonment and

decline in the number of farming operations. Mr. Gitchell had already created several other

bureaus to address issues in transportation and manufacturing. The logical name for an

organization that addressed issues with relation to the farm was the “Farm Bureau”. Although

this name and its mental association with governmental bureaucracy has not been extremely

beneficial, the establishment of this organization by this name at the local and state levels left

little choice for change. (Kile, 1948)

The first national meeting to discuss the formation and incubation of a Farmer

Organization took place in Ithaca, New York. The idea of a Farm Bureau grew rapidly and the

first state meeting was held on November 19, 1919 in Chicago, Illinois. In little more than a year

the leaders in the farming industry were organized into a group of over one million members.

This group was actively marketing their crops as one, buying train loads or supplies and

equipment through cooperative memberships, and influenced state and local laws like never

before. This Agrarian Crusade, as termed by Solon J. Buck, was more powerful than anyone

could have predicted. (Kile, 1921)

James R. Howard stated in the Birmingham news that the real birthplace of Farm Bureau

was Louisiana. He stated that the inception of the Extension Service by Seaman Knapp also

started the organization of farmer groups that discussed policy, marketing, and other needs above

the production practices taught by the county agents. James Howard was one of the early leaders

in the Farm Bureau movement and became the first President of the American Farm Bureau

Federation. The tie between the Farm Bureau and the Extension Service was evermore present.

Farm Bureaus and Extension Services were used by President Herbert Hoover in order to

strengthen his election campaign due to the tremendous social and political forces that these two

groups held as a combined effort. (Robertson, 1983)

American Farm Bureau has enhanced and influenced many factors that have changed the

course of American agriculture. This organization currently follows the objective that the

organization is to “develop, strengthen, and correlate the work of the state Farm Bureau

Federations of the Nation; to encourage and promote cooperation of all representative

agricultural organizations in every effort to improve facilities and conditions for the economic

production, conservation, marketing, transportation, and distribution of farm products; to further

the study and enactment of constructive agricultural legislation; to advise with representatives of

the public agricultural institutions cooperating with farm bureaus in the determination of

nationwide policies, and to inform farm bureau members regarding all movements that affect

their interests.” (Kile, 1921) The Louisiana Farm Bureau follows all of the above objectives but

states that their own objectives are to unite the farmers in a constructive organization, to serve as

a clearinghouse of information, coordinate efforts and aims of various agricultural agencies, and

to serve as an overall agricultural organization that fights to overcome the common problems to

agriculture throughout the state (Kemmerly, 1941). There are several subdivisions under these

objectives that include (1) representation of the farmer and farmer interests, (2) educating the

urban sector on the relationship of the farmer to other units in the social and economic setting

and to establish agriculture as the foremost industry on which all others depend, (3) safeguarding

the rights and interests of the farmer when dealing with legislative policy and defending the

farmer with regard to these policy decisions, and (4) protecting and extending marketing or

crops, extending new foreign markets, and reducing cost of necessities to production. (Woell,

1990) Louisiana Farm Bureau currently continues to uphold these objectives. The divisions of

Public Relations, Marketing, Commodity Policy, and Membership Development still closely

relate to the original areas of concern.

Farm Bureau was originally founded as a federation of members made up by full time

farmers and ranchers. Although membership has now been extended to the general public with

the payment of annual dues, full time farmers are the only individuals who can hold leadership

positions within this organization. These farmers are representative of each area of the state and

all major commodities.

Historically the active members of this group have been row crop, field grain, livestock

farmers. Although recent years have included those involved in horticulture, wildlife, and

aquaculture, the backbone of the agricultural sector remains the row-crop, field grain, and

livestock farmers. The first commodity committees at the national level started in 1944. These

groups included fruits and vegetables, livestock, dairy, poultry, and field crops. (Kile, 1948)

Historically, the commodity groups that were taken into consideration with regard to

legislative policy and marketing in Louisiana have included producers of the commodities of

cotton, corn, rice, sugarcane, sweet potato, forestry, poultry, beef, and dairy. (Kemmerly, 1941)

Louisiana Farm Bureau continues to support each of these groups at present time.

The national legislation dealing with agriculture production and economics, titled the

Farm Bill, limits the commodities to include field grains, cotton, corn, soybeans, dairy, beef,

poultry, rice, sweet potato, and sugarcane/ sugar beets (United States Department of Agriculture,

2008). For the purposes of this study we will include those fields of agriculture that involve the

production of the above mentioned commodities. We will continue to exclude the production of

the commodity forestry as well as any other portions of such industry due to the difference in

nature of production, manufacturing, the maturation time of this crop, and most importantly the

exclusion of this commodity from the Farm Bill. The inclusion of each of these areas as

mentioned above will be assumed when we describe agriculture as a whole.

Agricultural Literacy

Literacy is a phrase that has been utilized in combination with knowledge in many educational

situations. Literacy can be defined as a base level of skill or knowledge that one possesses that

allows them to competently complete a task (Sticht, 1975). This can also be the ability to

competently respond to information and make educated decisions. Just as the definition can take

on many uses, the level of knowledge needed to reach a certain skill level is measurable in one

instance but not necessarily generalizable to other situations. The level needed is relative and

without absolute standards. (Frick, Birkenholz, Gardner, and Machtmes, 1995c) The knowledge

needed to be literate on a given subject is only a minimum level and not to be misunderstood as a

complete understanding of the subject.

Knowledge with regard to agricultural information is commonly referred to as

agricultural literacy. Agricultural literacy is a concept that is founded on the idea that all

individuals should possess a basic level of understanding of the agriculture industry. It has been

stated recently that the broad range of American citizens are in general “agriculturally ignorant”

(Coon and Cantrell, 1985). Causes for the low level of agricultural literacy are broad in range.

The urbanization of the American population, the concern of social issues that involve

agriculture and misinformation about agricultural topics are all thought to be causes.

The first step in assessing agricultural literacy is to determine the current level. A

benchmark that verifies the level of agricultural knowledge and perception of agriculture should

be determined.

The second step is to address important areas of agricultural literacy. Frick (1990)

utilized seven concept areas in his benchmark Delphi study that include (1) global significance of

agriculture, (2) public policy, (3) relationship with the environment and natural resources, (4)

plant sciences, (5) animal science, (6) processed agriculture products, and (7) marketing and

distribution of agriculture products.

If agricultural literacy is to improve agricultural knowledge then perception of agriculture

should be assessed as effects to agricultural literacy in many differing situations with each of

these seven areas included. The Agriculture Council of America reemphasizes this by stating

that increased knowledge of agriculture allows individuals to make informed personal choices

(Agriculture Council of America, 2008).

Braverman, et al. (1991) noted that agricultural literacy is a major concern for adults in

our society. He stated that with increasing frequency and urgency, adults in American society are

called upon to make decisions about critical agriculture-related issues such as food safety, land

use, and water policy. In order to make informed decisions, the American public must have a

basic understanding of agriculture and its role in our society and economy. Braverman and Rilla

(1991) go on to say "Society often fails to recognize that agriculture encompasses the study of

economics, technology, politics, sociology, international relations and trade, and environmental

problems, in addition to biology" (Braverman and Rilla, 1991, p.4)

A study supported by grants from the W. K. Kellogg Foundation was established to

evaluate the effects that inclusion of agriculture in liberal arts college curricula had on

knowledge of agriculture issues in the context of society’s broad goals. The importance of

agriculture with regard to issues such as world hunger, international development, environmental

issues, and political actions were examples of the items incorporated into curricula. Ten liberal

arts colleges participated in this study and explored agricultural linkages to the liberal arts in a

combination of multiple interdisciplinary forms. This exploration included agriculture in the

curriculum, in public events held by the university, through field experiences, and library

resources. The study found that the importance of how knowledge is acquired was more

important in the educational process than deemed before. The inclusion of observation and

practical work experience complements and reinforces students’ studies. (Douglass, 1985)

Many states have expressed concern involving agricultural literacy. The California

Department of Education (2009) stated that the lack of knowledge and influence forces seriously

challenge American agriculture and education. These forces include changing demographics,

urbanization, and lifestyle changes; rapid increases in world-wide agricultural production needs;

domestic farm and trade policies; and global competition in high technology industries. The

application of knowledge available through use of sophisticated computers and technology such

as digital equipment, and biotechnological techniques can be modified by the agricultural

awareness of the general public. A statewide comprehensive program of agricultural literacy and

awareness provides infusion of agricultural topics and information into a broad range of

academic subject areas. Agricultural literacy and awareness supports a strong career preparation

program that not only meets the needs of a dynamic and competitive agricultural industry in

California, but also prepares a citizenry attuned to the health and economic importance of a very

productive industry (California Department of Education, 2009).

Arizona Cooperative Extension (2009) has also expressed support of agricultural literacy

programs. Their mission is to assist educators in the effective use of incorporating information

about agriculture into the subjects they already teach while educating consumers about the

agriculture industry in Arizona and its impact on the general public. A similar study by Pense

and Leising (2004) sought to assess the agricultural literacy of high school seniors in Oklahoma.

The study findings reported the participants to have low overall agricultural knowledge scores of

students. It was determined that the program completers who participated in the study were not

agriculturally literate. Boatner found similar results when agricultural literacy was measured in

Willamette Valley’s forth grade students. This study found the average score was found to be

6.12 questions correct out of 12 questions, or a 51% correct. The high scoring student answered

ten questions right and the lowest score was two questions.

Programs such as the Agriculture in the Classroom program have been implemented in

several states as a means to provide agriculture education to all school age children. A study by

Pense, Leising, Portillo, and Igo, (2005) sought to assess change in student agricultural

knowledge after implementing Agriculture in the Classroom (AITC) programs and to identify

strengths and weaknesses of student knowledge according to the five thematic areas. The study

included selected classrooms of kindergarten through sixth grade students that had AITC trained

teachers. These classes were in the states of Arizona, Montana, Oklahoma and Utah. Pre-test and

posttest mean score comparisons by grade groupings were made and in the five thematic areas.

The results reported greater agricultural knowledge in all four grade groupings with AITC

trained teachers. The study concluded that AITC training of teachers made a positive difference

in student acquisition of knowledge about agriculture.

Pals and Waitley (1996) developed a project to help the elementary school students in

Idaho learn more about agriculture. The project also helped in preparing a presentation and

developing writing skills. The curriculum was taken from the Idaho Agriculture in the Classroom

(AITC) which was created to focus on fourth grade students. The elementary school students

prepared presentations to present to fourth graders, which included hands-on, fun activities. The

project revealed that there are not enough instructors to teach the importance of agricultural

literacy to middle school students.

Other programs that produce agriculture materials include the Agriculture in Montana

School Program. Moore and Violett (1996) studied middle school agricultural education in the

state of Montana. For their study, they used the curriculum for eight Montana Schools which is

provided by Agriculture in Montana Schools. Moore and Violett discovered that there was a

tremendous amount of agricultural literacy material available to the middle school teachers, but

the problem seemed to be what part of agriculture to teach the students. The two schools used in

this study were from southeastern Montana, but had different student populations. One school

was in an isolated part of the state where agriculture is the main source of the economy. The

school enrolled 77 students from grades 7-12. The larger of the schools had 700 students from

grades 6-12 and was located in a suburb. The students were taught the importance of

agricultural literacy through game-type activities, so it would be fun and interesting for the

middle school students.

Studies that measure agricultural literacy in selected areas of agriculture have been

conducted. A study by Harbstreit and Welton (1992) measured high school agriculture students’

awareness about international agriculture in the areas of agricultural products, agricultural policy,

geography, and people and cultures. The study found that awareness is limited. The study also

found that agriculture students with higher grades possess more knowledge about international

agriculture than their counterparts with lower self-reported grades, student awareness about

international agriculture increases as advancement is made to the next high school class, and the

longer a student is a part of a high school agriculture program and involved with supervised

occupational experience, awareness about international agriculture increases.

Knowledge

Knowledge can be defined as the understanding of information and the ability to apply

and utilize this information in independent situations. Knowledge is what is known. There is not

a single definition of the term knowledge on which scholars agree, but rather numerous theories

and continued debate about the nature of knowledge.

Knowledge is part of the hierarchy made up of data, information, and knowledge. Data

are raw facts. Information is data with context and perspective. Knowledge is information with

guidance for action based on insight and experience or the result of applying data processing to

data, giving it context and meaning to yield knowledge. What is known by perceptual

experience and reasoning. For example, 1234567.89 is data; "Your bank balance has jumped

8087% to $1234567.89" is information; "No one owes me that much money" is knowledge; and

"I need to talk to the bank before I spend it” is the utilization of the knowledge gained.

Knowledge has been referred to as cognition, or the psychological result of perception and

learning and reasoning (Information, (data) information section, para. 1, n.d.).

Shapiro (2004) states that knowledge has a marked effect on learning outcomes.

Researchers try to control for that factor in learning research but are not always successful. This

study demonstrated that experimental controls are not successful in controlling knowledge

effects. The study included students who read texts about fictional places and events and

students who were asked to read several advanced texts. In both experiments, prior knowledge

accounted for a large portion of the subjects' test performance. It can be reasoned from this study

that knowledge could influence perception and the outcomes of perception.

If the reverse is true and knowledge is a resulting effect of perception or education then

the ability to adjust or sway perceptions may adjust the knowledge or assessments that

individuals may have with regard to the agriculture industry. With this relationship in mind, one

can see how the study of perception and knowledge should be done in conjunction with each

other in order to combine the effects of both as a whole.

Perception

Although it is difficult to explain how items, events, and individuals in the world can be

recreated in a mental form in our minds, the field of Psychophysics has evolved to study this

phenomenon. This field of study attempts to relate the physical properties of someone or

something to the reaction that we have with regard to these properties. This reaction is the direct

result of our perception of the situation. Both positive and negative perceptions are based on

one’s own experiences (Raab and Grobe, 2005).

Matlin defines perception as the study of the way you gather and interpret information

about the world around you. “Everything you know about the world in based on perceptual

information” (Matlin, 1983, p. 2). James J. Gibson (1950) suggested that in order to explain

perception we should explore the objects that we see, the sounds that we hear, and the feel of

objects in order to see which features cause the reactions and inevitably the perceptions that we

calculate with regard to these occurrences. The same principles can be used for the calculation

of responses to exposure to information and experiences. The awareness, recognition, or

experiences with a given subject are all elements that are used

Mowan (1995) develops the idea of perception utilizing stages which include exposure,

attention to information, and comprehension. These stages are also reflected in Matlin and

Foley’s (1992) three stages of sensation, perception, and cognition. The exposure or sensation

portion of this process is the stage in which individuals receive information from senses or

experiences. The attention or perception stage is the portion of the development that allows an

individual to process the individual sensing aspects of an encounter and mentally record these

factors. The comprehension or cognition stage includes the interpretation of the encounter or

event and drawing conclusions, making suggestions, or calculating the risks and benefits

associated with a given choice. Mowan (1995) also states that it can not be assumed that each

person will react in exactly the same manner to a stimulus or perceive it in the same manner due

to the influence of expectation, background, present knowledge, and historical occurrences.

Each of these alone or combined can cause varying degrees of reaction shifts. (Mowan,

1995) For example, farmers have been found to have a more positive attitude toward pesticides

due to their knowledge about the topic and their personal calculations of risk and benefits,

(Whitford, 1993) while the non-agriculture population is more apt to have a negative perception

due to their lack of knowledge about risk and potential value (Raab and Grobe, 2005). The same

type of reaction can be seen more recently in the purchasing choice of consumers with regard to

organic foods. Up to twenty-nine percent of Americans surveyed believe that the USDA

standards placed on organically grown foods in 2002 and the media associated with these

standards increased their knowledge in the area of organics and positively influenced their

decision to purchase these types of products. (Raab and Grobe, 2005) In order to understand

these cognitive shifts we must also understand the theory and causes behind these choices.

Discrimination and criterion are important factors in explaining perception. Criterion is a

term used to describe the ability of one to sense the situation and value the payoff or rewards

associated with a given response. Discrimination is the ability to determine the extent to which

the given situation will change. The determination as to which choice will change less or which

one will have the greatest payoff or reward is calculated. This determination of the balance

between benefit and risk is often viewed as separate calculation that must result in a conclusion

based on one outcome or the other. Whitford views the system as one calculated measure and

states that it should be viewed with the inclusion of benefits and risks as overlapping factors in

the decision making process. This coevaluation allows a truer analysis of the criterion to be

calculated. (Whitford, 1993)

Several terms have been utilized to describe the sensation of perception. These terms

have included “Awareness” (Wright, Stewart, & Birkenholz, 1994), “Attitude” or “Attitude

Formation” (Wiley, Bowen, Bowen, & Heinsohn, 1997), “Expected Impact” (Williams, 2000), or

“Opinion” (Nedley, 2006). Regardless of the phrase that is used, these are each examples of

perception theory that has similar research back grounding.

Knowledge and Perception of Agriculture Studies

Due to the impact that agriculture has on our society, economy, environment, and

personal health, it is vital that the general public be knowledgeable and has accurate perceptions

about agriculture. (Terry and Lawver, 1995) The need for a true understanding of agricultural

related issues will continue to increase as future generations take leadership roles and make

public policy decisions with regard to the agriculture industry. Brannon, Holley, & Key (1989)

measured the effect of the FFA program on people involved in community leadership. They

surveyed thirty communities in Oklahoma that had agriculture education programs in the local

school and found vocational agriculture/FFA as a contributing factor to community leader

success. The study revealed a mean rating of 3.98 out of 5.0 with 63 (17%) leaders indicating

that it had a great impact and 58 (16%) indicating that it had much impact. Community leaders

surveyed who had participated in vocational agriculture felt that their leadership activities were

effective in developing their leadership skills, contributed much to their success, and have been

of value in their careers regardless of occupation.

The need to address the problem of agricultural literacy, lack of agricultural knowledge,

and negative perceptions has become increasingly important as the general population becoming

more illiterate with passing generations (Frick, et al., 1995c). Frick, et. al. (1995c) reported that

the knowledge and perceptions of agriculture and related issues measured in rural high school

students were higher than those of urban students. The difference in these two groups is their

exposure to the subject matter which reemphasizes the relationship between agricultural literacy,

agricultural knowledge, and perception. In a similar study (Frick, et al., 1995b) that addressed

knowledge level of agriculture of rural and urban adults, it was found that twenty-nine and

onehalf percent of the respondents provided answers that were incorrect or answered that they

did not know the correct answer. Those that reported their residence to be rural held higher

knowledge levels of agriculture than their counterparts who reported their residence to be in an

urban area. Although both studies showed that both groups had some agricultural knowledge,

rural residents had higher knowledge levels. The low overall mean scores of agricultural

knowledge and perceptions of agriculture suggested that there is ample room for increased

education to raise agricultural knowledge levels.

The location of residence proved to be an important variable in two other studies

conducted. The first of these studies was conducted by Wachenheim and Rathge (2002) and

surveyed residents of the North Central region of the country. This study concluded that an

individual’s experience with and proximity to agriculture influences their perceptions. Frick, et

al. (1995a) also concluded that of the 4-H members surveyed, those who lived on a farm or in a

rural setting held higher knowledge and perception levels than those who did not possess this

trait. In a study of three high schools in the state of New York (Smith, Park, & Sutton, 2007), a

statistically significant difference was found between rural high school students and urban high

school students. The rural students held higher levels of agricultural literacy and their

knowledge and perception scores were in line with the actual state statistics.

Krueger and Riesenberg (1991) noted that the decline in enrollment in agricultural

programs in recent years is due to the misconceptions that students and the public have with

regard to the agriculture industry and agriculture careers. Perritt and Morton (1990) state that

most children in urban areas receive very little exposure to production agriculture. Unless the

current perceptions are understood, the errors in misinformation and lack of agricultural

knowledge can not be corrected (Newsom-Stewart and Sutphin, 1994). High school students in

California are unaware of the range of opportunities in agriculture. While high school juniors

and seniors list a stable and secure future as a top priority in choosing a career, they view

agriculture as outdoor, hard work, blue-collar, and insecure. They rated agriculture as lowest on

the qualities of providing a stable and secure future or earning a lot of money (Mallory and

Sommer, 2001).

A study conducted by Osborne and Dyer (2000) sought to describe the attitudes of

students and parents toward the agricultural industry and careers in agriculture. Although both

students enrolled in agriculture courses and their parents held more positive attitudes toward the

agriculture industry than those students who had no enrollment in agriculture courses, their

perceptions of agricultural careers varied. This variance was due to the amount of science

application included in the agriculture instruction that was administered. Those students and

parents who had more science application in their agricultural curricula had more positive

attitudes than those who did not have this application of science. A similar study by Dyer, Lacey,

and Osborne (1996) sought to understand enrollment trends in agriculture majors. The study

surveyed university freshmen enrolled in agriculture programs and found that the majority were

female, Caucasian, and held no agriculture background. The majority of the respondents found

high school agriculture to be good preparation for college and viewed agriculture as being both

scientific and technical.

These misconceptions with regard to the agriculture industry and agricultural careers are

extremely prevalent in minority populations. Given that the consumption of food is the primary

contact that many minorities have with agricultural sciences (Wiley et. al, 1997), many

minorities exhibit limited awareness of the science and business skills that are utilized in this

industry. The lack of knowledge that results in low perception of agriculture affects recruitment

into agriculture or related education and employment fields. The resulting small numbers of

individuals involved in the agriculture industry are maintained by ongoing perceptions that

agriculture is an industry focused on vocational skills and one meant for white males. (Wiley, et.

al., 1997) Findings from Beck and Swanson (2003) show Black and Hispanic graduate at levels

less than three percent for all degree levels in agriculture fields. The barriers that limit minority

enrollment were studied (Dobbins, King, Fravel, Keels, & Covington, 2002) and the major

reasons were reported to be hard jobs, long hours, low pay, and outdoors or just farming. Similar

findings by Krueger and Riesenberg (2001) found that students perceived an agricultural career

to be boring, hard work with poor pay, and involving more muscle than brain. Another study

(Esters and Bowen, 2004) found that the mother or female guardian has the most influence over

the choices and decisions that students make.

In similar studies it is reported that Black and Hispanic students are more likely to have a

negative perception of agriculture than students in other ethnic groups (Nichols and Nelson,

1993), which could relate to decreases in minority enrollment. Newsom-Stewart and Sutphin

(1994) found significant differences between the perception means. Means for white students

were higher than other ethnic groups for most items in the survey. This was also made obvious

by the limited appeal of agriculture to Asian students as a place for high school or college

graduates to work. Cultural differences in experience and differential socialization processes may

lead to the observed ethnicity differences in perception. Results suggest that educational

interventions are needed to encourage minority groups to better understand agriculture and

develop a more positive view of agriculture careers.

These misconceptions are not limited to minority groups. A focus group study of rural

and urban youth found that the participants equated agriculture with farming, but made no

connection to the technical or research-intensive aspects of agriculture. For example, farming

was perceived to be hard, physical labor and stressful because of machinery breakage, weather

uncertainties, and price variances. Youth, both rural and urban, tended to think of farmers as

wearing bib overalls and chewing on a straw. Most youth were generally aware of the

importance of agriculture to food production. The participants acknowledged that without

agriculture there would be no food. If agriculture disappeared, their personal lives, as well as

their community and state, would be affected. (Holz-Clause and Jost, 1995)

Understanding the relationship between knowledge and perceptions of agriculture, it can

be reasoned that education that increases knowledge of agriculture would have a positive impact

on perception as well. In all studies that measured knowledge and perception it was found that

those with higher agriculture education levels tended to possess higher scores in both areas.

Agriculture education has been the backbone of 4-H, FFA/ Ag Education classes. These and

other youth organizations tied closely to agriculture education have produced individuals with

extremely high levels of knowledge and perception with regard to the agriculture industry.

(Frick, et al., 1995c) Townsend (1990) believed that a pre-secondary agricultural education

program can build a positive attitude among students that will let them develop into positive

leaders. Riedel (2006) reported findings that supported this belief. The Riedel study found that

introductory courses in agriculture had a significant impact on agricultural knowledge. The

pretest score of overall agricultural knowledge and agricultural literacy was 20.99 or a

percentage score of 60%. The respondents’ final overall knowledge mean score was 24.13 on a

35-question literacy instrument or a percentage score of 69%. There was a nine percent increase

in overall literacy scores upon completion of the introductory agriculture course.

An increase in agriculture education directed toward groups that have no experiences in

agriculture programs should be implemented. These individuals have lower agricultural literacy

levels, more distance between themselves and rural areas, and lower educational levels.

Allowing this group the same information as those involved or surrounded by agriculture would

increase the average knowledge and perception of the general public. Dyer, Breja, and

Andreasen (1999) found that most college freshmen majoring in agriculture were white males

and had an agriculture background. He reported that the greatest influence on their decision to

major in agriculture was their high school agriculture teacher.

Studies in Kansas (Horn and Vining, 1986) and Virginia (Oliver, 1986) indicated a lack of

basic knowledge about agriculture among all elementary school students. Brown and Stewart

(1993) studied middle school students’ knowledge and attitudes before and after being exposed

to an agriculture curriculum. The results of this study indicated that there can be an improvement

in the agricultural attitude of middle school students in selected Missouri schools through

instruction about agriculture. This age group of students represents an important educational

stage for developing an increased understanding and appreciation about agriculture. This study

found that exposure to an agricultural education curriculum for a period as short as six weeks can

have an impact on middle school students’ agricultural knowledge. Although formal agriculture

education is referenced in several studies before mentioned, it is worth noting that Herren and

Oakley (1995) found that the influences that teachers with agriculture backgrounds have on

children can result in higher scores on agricultural knowledge tests than those children whose

teachers had no agriculture background. These results were based on the results of a survey

administered in sixteen classes of second grade children and twelve classes of fourth grade

students. Trexler and Meischen found that although teachers from rural areas demonstrated more

understanding of agriculture technology, overall the teachers did not possess requisite

understandings to help elementary students gain knowledge and understandings of, or concern

for the trade-offs found in the use of agricultural biotechnologies. A similar study (Trexler and

Suvedi, 1998) measured the impact of an e-program to increase science and agricultural literacy

in Sanilac County, Michigan. This study found that although principals were very positive about

teaching science through agricultural examples, the teachers held lower perceptions. Three years

later the perceptions of the teachers were high along with their comfort level of teaching science

through agriculture examples. This reinforces the need for agriculture education of all types for

school age children. Non-traditional programs should be developed at the elementary school

level to educate students about food, agriculture, and renewable resources (Trexler and Suvedi,

1998).

Measurement of the general public are needed to steer educational materials and public

relations activities. Bell (1995) studied the knowledge and awareness of members of civic

organizations with regard to agriculture. As an overall group, members of civic groups are not

very knowledgeable about general agriculture. This is based on the finding that their mean score

was 4.1 out of 15. Members of civic groups are much more aware about agriculture than they

are knowledgeable about it The mean score of 28.7 out of 35 possible in the general agricultural

awareness section points to a population which equals 65% of the respondents being somewhat

aware of the importance of agriculture and its activities. However, it should be noted that civic

groups, as a whole, are still unaware of many important agricultural activities. In a similar study

by Willits, Luloff, and James (2006) residents of Pennsylvania were studied to measure their

perceptions of agriculture. The survey showed that direct personal contact with farming and

visiting rural areas were the most important experiences associated with higher levels of

agricultural knowledge. The findings also showed that people who have greater agricultural

knowledge differ in their views and actions from those with less understanding of agriculture.

Lockaby and Ryan (1994) conducted a study that surveyed leaders in a Texas city where

agriculture has a great economic impact. The survey consisted of seventy items. Sixty items were

agricultural literacy questions which included agricultural knowledge, total awareness, and

awareness of High Plains and South Plains agriculture. The study found that city and government

leaders were not knowledgeable about agriculture in general. Those leaders who raised animals

or crops and/or took agricultural classes in high school had greater agricultural knowledge than

those who did not. Even though city and government leaders were not knowledgeable about

agriculture, they were aware of the problems that face agriculture.

A perception study conducted by the University of Florida comprising a statewide survey

of more than 300 registered voters in Florida about food and agricultural issues revealed that

more than 82% of those surveyed were confident that farming is safe for the environment, with

only 11% citing a lack of confidence, 80% of the respondents had favorable opinions of Florida

agriculture, and 98% of the people surveyed believed agriculture is important to Florida’s

economy. (Nedley, 2006) A similar result was found by Terry and Lawyer (1995) when they

surveyed university students on their perceptions of agriculture. They found that the overall

student perceptions about food safety and the impact of agriculture upon the economy and

environment were favorable. Gender and college major were both variables that impacted

perceptions. Males tended to have more positive perceptions as well as those with an

agricultural major.

Additional studies have found that the public’s overall knowledge and perceptions of the

topic of the environment and sustainable agriculture tend to be higher than those of other

agriculture topics. Williams (2000) found that perception of sustainable agriculture and the

expected positive impact that this practice has on the environment were of concern. Although

the group of agriculture education students held low knowledge levels of sustainable agriculture,

their perceptions of this practice were high. Similar results were found (Williams and Wise,

1997) when sixty teachers and their agriculture education classes were surveyed. This group

reported that they held high perceptions of sustainable agriculture and the environment. Terry

and Lawver (1995) found university students to have favorable perceptions about agriculture and

its impact on the environment. The Agriculture Institute of Florida (Nedley, 2006) reported that

eighty two percent of the respondents to an agriculture opinion survey were confident that

farming is safe for the environment. Wachenheim and Rathge (2002) reported a similar finding

for members of the north central region of the United States stated that they viewed farmers as

good environmental stewards and that current existing environmental regulations are appropriate.

Using Public Perception for Decision Making

The idea that personal experiences, observations, influences, knowledge, and values

about agriculture influence beliefs, intentions, and decisions has been studied over the past thirty

years (Fishbein and Ajzen, 1975). Although decisions can also be influenced by other notable

individuals within one’s life, it can be stated that knowledge can affect perception and attitudes

which can in turn affects decisions that are made.

Swanson (1972) developed a theory of assumed relationships among education,

knowledge, attitudes, and behavior. This theory is predicated on reasoning that that knowledge

and experiences, including education and first hand experiences, are precursors to attitudes and

behaviors. Swanson also suggests that the initial temporary perceptions often become more

permanent knowledge, leading to a change in attitude, which can govern ones behavior and

actions. An attitude may be such that it is not able to be put into words in order for it to be

expressed toward an issue or question, yet it may still affect behavior and actions. Fishbein

(1967) theorized that attitudes help individuals adjust to their environment providing

predictability in their behavior and aid in the understanding of others’ behavior.

A detailed description of the decision making process which included the science and

systematic approaches to making these assessments was referred to as the Analytic-Deliberative

Process by Stern and Fineberg (1996) in works titled Understanding Risk. This process takes

into account the affected parties, dialog, participation, and deliberation into every element of risk

analysis. Perceptual information gathered from individuals can be categorized into three main

classes: (1) deliberative methods which include conferences, juries, small planning groups, etc.,

(2) consultation methods that include group input from meetings, surveys, and focus groups, or

(3) referenda in which all people involved have a democratic vote. The utilization of all, or

combinations of the above mentioned three tools to gather information, increase the validity of

the information gathered.

A notable study reflected on the ability to make a decision that involved a surmountable

percentage of risk utilizing only the perception of individuals. The decision at hand dealt with

the commercialization of biotechnology. The question was whether open deliberation, focus

groups, and an interactive debate website would provide a simulative sample response similar to

that received by the traditional statistically representative survey. The answer to this question is

not due to the significant difference found between the two groups of responses. The plausible

explanation for the differences is that the open-debate activities primarily attracted extreme

members of the subject opinion group and the focus groups were not selected as a true

representation of the population to which this study is generalizable. It can be concluded by this

outcome that the manner in which data is collected may have great impact on the ability to utilize

true perception in a responsible way. (Pidgeon, Poortinga, Rowe, Jones, Walls, and O’Riordan,

2005)

When Americans express their opinion about animal cloning (Hallman and Condry,

2006), they are more likely to reflect their impressions of the topic rather than that indicative of a

portion developed over time that has been deliberated and supported by a foundation of factual

information. The effect of media coverage, public opinion of others, and lack of knowledge are

all effects that steer these perceptions. The same type of perception effect can be seen in other

areas of animal biotechnology. The general public has deemed recent production animal

agriculture as being controlled by corporate interests and motivated by profits rather than animal

care values (Fraser, 2001). Although agricultural organizations have rebuked these allegations,

the public is faced with two contradictory images. The public needs knowledge based research

and analysis to serve as their foundation for public policy and choice. Perceptions on such topics

could have public effects if the ability to continue this type of research is banned or altered by

legislative process, indirectly set into motion by the general public.

Perception has affected decisions within the agricultural industry for decades. Every four

years a group of representatives gather in Washington D.C. and make decisions about our

economic and social wellbeing with little personal knowledge or expertise about the topics at

hand. Hamlin (1962) noted that farm policy, i.e. the farm bill, is created, debated, and put into

action by a group of individuals that hold little expertise in the area of agriculture. The decisions

that are made could be the difference between the success and the demise of the agriculture

industry in the United States. Public policy is a result of ill advised representatives that are

steered by public goals and the perceptions that are many times joined with silent social or

political issues. The dangers that exist when dealing with this type of one way transfer of

information and the unknown underlying issues are constantly being studied and are growing

increasingly important in a time when communication and information are easily dispensable.

These decisions are often made without regard to economic stability or security and how these

decisions will affect the general American public. Lack of knowledge about agriculture translates

to a poorly informed public majority having input in policy decisions "that may affect the

agricultural industry's ability to function efficiently in an increasingly competitive world market"

(National Academy of Science, 1988).

The low level of individual knowledge with regard to the global agriculture industry was

noted by Harbstreit and Welton (1992). The effects of history and maturation may have assisted

the conclusion that involvement in agriculture programs and occupational experiences may have

increased the agricultural knowledge of individuals. The longer students were exposed to

international agriculture the higher their ratings on knowledge were. Regardless of the effect of

the education on knowledge levels, the preliminary knowledge of international agriculture was

found to be at a scored level of thirty-six percent which is well below the average that one should

have when making decisions or influencing choices on a given topic.

Public perception does not only affect production agriculture. Pesticide use, food safety,

and genetically modified food and fiber sources are all examples of issues with varying

perceptions of their effects. Research has shown that perception with regard to these issues, and

many others, can differ among individuals. The difference in public perception and research

outcomes becomes a conflict that fails to communicate the scientific facts. The public opinion

based on these false perceptions will not be easily changed. (Slovic, 1992)

Although agricultural literacy has become a growing concern, it should be classified as

more of a threat. Literacy on any subject lies at the root of people’s attitudes and their actions

(Barton, 2000). The inability to make informed and educated decisions based on factual and

proven information not only affects one aspect of life but can grow to the range of having a

detrimental effect on economics, society, and global relationships.

Summary

Agriculture can be defined as the cultivation or tillage of the soil in order to produce food

or fiber for human use. (Merriam-Webster, 1996) Although the “tillage of the soil” has been in

existence for thousands of years, agriculture has become ever more important in recent years.

Agriculture has evolved over time from a large percentage of American colonists who were

farmers by trade that marketed and traded excess food and fiber to others in order to meet the

demands of those who held other occupations. Current farmers utilize innovations was such as

hybrid seed for crops, chemical fertilizers, and pesticides, all of which are commonly used today.

The adaptation of mechanical tools for the planting and harvesting of crops was also a major

evolution.

Many agriculture organizations have been established. Founded in 1928, the

Future Farmers of America (FFA) realized a need for bonding between agriculture students to

allow them to share common ideas, new technologies, and interests. Today it strives to ensure

that all students reap the benefits of agricultural education. (National FFA Organization, 2009)

The Farm Bureau held its first state meeting was held on November 19, 1919 in Chicago,

Illinois. Since that first meeting has enhanced and influenced many factors that have changed

the course of American agriculture with marketing and legislative actions (Kile, 1948).

The Hatch Act of 1887 established a cooperative bond between United States Department

of Agriculture and the nation's existing land grant colleges allocating annual federal funding for

dissemination of research to the general public. Congress also passed the Smith Lever Act in

1914. This legislation provided for the establishment of what is now titled the Cooperative

Extension Service which serves to “extend" information developed on teaching campuses and

research stations across the nation. Since its inception originating with the Hatch Act of 1887,

the Cooperative Extension Service has worked with the farmers and ranchers of Louisiana to

create the finest food and fiber production systems in the world (Encyclopedia Britannica, 2009).

The realized that younger men and boys adapted to new practices easier than did their elder

counterparts led to the formation of the Corn Clubs. This involvement with cutting edge

agriculture technology had significant impact on the perceptions of these youth with regard to

the industry. These organizations have evolved over time to what we presently know as the 4-H

organization (Kile, 1921).

The idea of perception utilizing three stages which including exposure, attention to

information, and comprehension. The exposure or sensation portion of this process is the stage

in which individuals receive information from senses or experiences. The attention or perception

stage is the portion of the development that allows an individual to process the individual sensing

aspects of an encounter and mentally record these factors. The comprehension or cognition stage

includes the interpretation of the encounter or event and drawing conclusions, making

suggestions, or calculating the risks and benefits associated with a given choice (Mowan, 1995).

Knowledge can be defined as the understanding of information and the ability to apply

and utilize this information in independent situations. Knowledge is what is known. Knowledge

is part of the hierarchy made up of data, information, and knowledge. Data are raw facts.

Information is data with context and perspective. Knowledge is information with guidance for

action based on insight and experience. If the reverse is true and knowledge is a resulting effect

of perception or education then the ability to adjust or sway perceptions may adjust the

knowledge or assessments that individuals may have with regard to the agriculture industry.

With this relationship in mind, one can see how the study of perception and knowledge should be

done in conjunction with each other in order to combine the effects of both as a whole.

Literacy is a phrase that has been utilized in combination with knowledge in many

educational situations. Literacy can be defined as a base level of skill or knowledge that one

possesses that allows them to competently complete a task (Sticht, 1975). This can also be the

ability to competently respond to information and make educated decisions. If agricultural

literacy is to improve agricultural knowledge then perception of agriculture should be assessed as

effects to agricultural literacy in many differing situations with each of these seven areas

included. Braverman, et al. (1991) noted that adults in American society are called upon to make

decisions about critical agriculture-related issues such as food safety, land use, and water policy.

In order to make informed decisions, the American public must have a basic understanding of

agriculture and its role in our society and economy. California, Arizona, Montana, Utah, and

Oklahoma are only a few of the states that have noted efforts toward Agricultural Literacy.

The need to address the problem of agricultural literacy, lack of agricultural knowledge,

and negative perceptions has become increasingly important as the general population becoming

more illiterate with passing generations (Frick, et al., 1995c). The idea that personal experiences,

observations, influences, knowledge, and values about agriculture influence beliefs, intentions,

and decisions has been studied over the past thirty years (Fishbein and Ajzen, 1975). Although

decisions can also be influenced by other notable individuals within one’s life, it can be stated

that knowledge can affect perception and attitudes which can in turn affects decisions that are

made. Swanson (1972) developed a theory of assumed relationships among education,

knowledge, attitudes, and behavior. This theory is predicated on reasoning that that knowledge

and experiences, including education and first hand experiences, are precursors to attitudes and

behaviors. Swanson also suggests that the initial temporary perceptions often become more

permanent knowledge, leading to a change in attitude, which can govern ones behavior and

actions. An attitude may be such that it is not able to be put into words in order for it to be

expressed toward an issue or question, yet it may still affect behavior and actions.

Perception has affected decisions within the agricultural industry for decades. Every four

years a group of representatives gather in Washington D.C. and make decisions about our

economic and social wellbeing with little personal knowledge or expertise about the topics at

hand. Hamlin (1962) noted that farm policy, i.e. the farm bill, is created, debated, and put into

action by a group of individuals that hold little expertise in the area of agriculture. The decisions

that are made could be the difference between the success and the demise of the agriculture

industry in the United States. Although agricultural literacy has become a growing concern, it

should be classified as more of a threat. The inability to make informed and educated decisions

based on factual and proven information not only affects one aspect of life but can grow to the

range of having a detrimental effect on economics, society, and global relationships. (Barton,

1990)

CHAPTER 3

METHODOLOGY

Population and Sample

The target population for this study was defined as adult residents of the state of

Louisiana. The adult residents of Louisiana can be defined as all adult individuals, of the age of

eighteen or older, living or residing within the legal boundaries of the state. All adult individuals

that were residents of the state of Louisiana were included in the target population of this study.

While legal residency requires having a permanent residence, registered to vote, and payment of

taxes in the state of Louisiana, these lists are not always able to be utilized for the purposes of

phone surveys. For the purpose of this study residency was derived by the individual’s

registration of telephone service in their name at a residence in the state of Louisiana.

The accessible population was defined as the group of adult individuals in the defined

target population who had registered residential telephone numbers. The survey frame of the

accessible population was established by the current residential phone listings registered in the

state phone company databases. These listings were a combination of all residential listings by

telephone companies servicing any area in Louisiana. All multiple listings at a single residence

were deleted. Those types of duplicate numbers include children’s phone listings, fax line

listing, and multiple numbers listed for a single address. No random digit dialing techniques

were used that may allow for numbers associated with cellular telephones, businesses, or

educational institutions (Dillman, 1978). The use of cellular telephones as main lines of

communication for households may be a source of population bias. The bias against this group

of society may be due to the choice to have one phone number due to financial restrictions. This

is a limiting factor of telephone survey in this survey.

The sample was derived by randomly selecting residential telephone numbers from those

listed in public directories. The random selection of numbers was done by a computer aided

selection process that randomly chooses a number from the finalized residential telephone

listing. The minimum required sample size for this study was determined to be 384 using

Cochran’s (1977) sample size determination formula for continuous data with the following

computations:

N0= t2 (pq)

= (1.96)2(.25)

(.5)2

= (3.84) (.25)

(.0025)

= 384

Legend for Cochran’s sample size determination formula:

d = acceptable margin of error of +2%

(.02 X 5 point Likert-type scale)

t2 = risk willing to take

(t at .05 for N= greater than 4,000,000 is 1.96) pq = estimate of

variance in the population for a dichotomous variable

N = population size

N0 = unadjusted sample size

Criteria used in these calculations included an alpha level established “a’ priori” at the .05 level

(equivalent t value = 1.96); a conservative estimate of the variability in the population

established as .75 (equal to the product of .5 variance for each level of item); and an acceptable

margin of error (d = .05).

Due to the large defined target population, which was estimated to be 4,287,768 in 2006

(United States Census Bureau, 2008), the researcher determined that a larger sample size would

be desirable. The sample size for the current study consisted of a minimum of five hundred

responses. Data collection continued until the number of responses mandated was reached.

The sampling plan for the study included the following steps:

1. All telephone numbers registered with Louisiana telephone companies servicing an area

of the state were acquired.

2. Business and commercial listings that were included in the registered list were removed.

Multiple listings for the same address were also removed as duplicate listings.

3. Computer generated numbers provided at random from the remainder of the original

registered listing were contacted. Sample numbers were contacted only once. If no

response the next random number was called. This process was done until the minimum

number of responses (500) was met.

4. A usable response was defined as a complete list of responses to all questions in the

survey by an adult member of the household of which the information was requested.

Instrumentation

The instrument utilized in this study was based on a questionnaire found during the

review of related literature (Frick, et. al., 1995a) (See Appendix A). The instrument consisted of

fifty-five questions. This instrument consisted of three sections: demographic characteristics,

agriculture knowledge, and perception of agriculture (See Appendix B). The knowledge and

perception portions of this instrument were adapted from a similar questionnaire utilized by

Frick, et. al. (1995a). The researcher received written permission from Marty Frick, PhD, to use

and modify the original instrument for the purposes of this study (See Appendix C).

Instrument validity was examined in several ways. The original instrument was based on

eleven agriculture literacy concept areas previously identified in a Delphi study conducted by

Frick (1991). A national panel of agriculture literacy experts reviewed the instrument for content

validity and its compatibility with the key agriculture education target areas. The expert panel

found the instrument to be a valid tool for assessing the eleven agriculture concept areas. After

the pilot test associated with this previous study was conducted, the concept areas were collapsed

into seven areas based on the results of a factor analysis.

The objective of the first section of the instrument was to determine the demographic and

characteristic makeup of the population. The respondents were asked to give their answers to

questions regarding age, gender, ethnic background, location of residence (in a rural area, on a

farm, in a town, or in a city), parish of residence, occupation of the head of household and

highest level of education. The goal of the second section of the instrument was to determine the

knowledge of the agriculture industry of the respondents. Respondents were asked to indicate

whether they thought the statement was true or false. The last section of the instrument was

designed to determine the respondents’ perceptions of the agriculture industry. The respondents

were asked to indicate their level of agreement using a five point Likert-type scale. The response

scale includes the following response options: strongly disagree, disagree, neutral, agree,

strongly agree. Although the survey instrument sections that measure knowledge and perception

are similar to the original knowledge and perception survey instrument utilized by Frick, et. al.

(1995a), this section of the instrument varies from the original. The number of items utilized in

the current study was fewer than the number of items included in the original instrument. The

questionnaire was altered to include five knowledge questions within each of the four preselected

areas.

Four of the perception items were altered to reflect a connection with Louisiana. Each of

these items was altered to include a reference to the state of Louisiana. This alteration allowed

the development of a knowledge area that measured the level of knowledge of Louisiana by the

adult residents of Louisiana.

A pilot test of the survey instrument was conducted using three class sections of courses

at Louisiana State University during the spring semester of 2008. The three classes were

selected based on the diversity and cross enrollment of students from curricula across the

university and the enrollment level of the students that were enrolled in these classes. The three

classes included an upper level general studies course, a lower level course that is included in a

cross curricula minor, and a graduate level course that includes students with various ages and

backgrounds. The total number of students participating in the pilot study was eighty. The

pilot test participants were asked to make suggestions to the demographic portion of the

instrument that would allow the researcher to make changes to the instrument and make all

questions clear and understandable. The suggestions would also enable the final instrument to be

more user friendly and the results more reliable. The changes that were made from suggestions

made during the pilot study were adding more levels to the education level question, inclusion of

the age categories as seen in the pilot study, adding the option of “do not know/ uncertain” to the

knowledge portion of the survey, and a change in the perception section of the study to read

neither agree nor disagree in the place of neutral. Each of these changes was made to improve

the clarity of the items in the instrument.

Instrument reliability was assessed for both the perception and knowledge sections using

the data collected during the administration of the pilot test. The reliability was assessed by

calculating a Cronbach’s alpha for the 20 items utilized in the knowledge section of the survey

instrument and a separate Cronbach’s alpha for the 20 items included in the perception portion.

Cronbach's alpha measure of internal consistency as a reliability estimate of the knowledge scale

was determined to be α =.66. A Cronbach’s alpha measure of internal consistency computed to

measure reliability of the scale for the perception portion of this study was determined to be α=

.72, Similarly, reliability was assessed for the instrument upon completion of data collection for

the current survey. A Cronbach’s alpha measure of internal consistency of the knowledge scale

was determined to be α =.60 while the Cronbach’s alpha measure of internal consistency for the

perception scale was determined to be α =.61. According to Hair, Black, Babin, Anderson, &

Tatham (2006) these calculations were acceptable since these calculations were equal to or

greater than the minimum acceptable level of .60. The generally agreed upon lower limit for a

Cronbach’s alpha measurement is .70, although it may be decreased to .60 or lower if the

research is exploratory in nature. (Hair, et. al., 2006)

Data Collection

Data was collected using the telephone interview technique procedures suggested by

Dillman (1978). Phone surveys have been found to increase the ability to reach samples of

larger geographical populations. They have also been found to provide results at a much faster

rate than traditional methods due to the instantaneous turn around time on responses and data

collected. It can be stated that in a society where individuals are more mobile, have higher forms

of communication, and appreciate instantaneous reaction; surveys done verbally are more likely

to be completed. Time has become a valuable commodity in recent years. The time that it takes

one to fill out a paper form or fill out and submit a digital copy of a survey has become an

obstacle that surveyors are faced with. Verbal correspondence has been noted to increase

response at a significant rate (Dillman and Salant, 1994).

Phone and mail surveys have been categorized as secondary forms of gathering

information in the past. They have been described as an inferior method to the face to face

method of data collection. Low response, inability to reach the target audience group, and

shorter than necessary surveys, are all reasons against the use of phone and mail surveys. The

inability to denote the physical reactions of respondents to survey questions was noted by

Dillman (2007) as yet another shortcoming. However, the positive aspects of a phone survey

outweigh the negatives for the purpose of this study.

Researchers involved in the implementation of phone surveys must be very careful not to

mislead respondents in certain directions. Only statements and questions provided in the

instrument were utilized by the researcher. Approval for implementation of the study was

obtained from the Louisiana State University Institutional Review Board for Human Subject

Protection prior to initiation. Permission from IRB to conduct this survey was granted due to the

minimal amount of risk to respondents from participating in this survey. The study was granted

approval #E3891 (See Appendix D).

The survey was conducted during the month of February, 2008. This time period was

chosen due to the lack of activity in production agriculture in the state of Louisiana. Field grains

of rice, cotton, corn, grain sorghum, sweet potatoes, and soybeans are planted in mid spring

through early summer and are harvested in early fall. The winter commodities such as sugar

cane are planted in early fall and harvested in late fall of the following year with completion

around the new year. Other winter grain crops such as wheat are planted in mid fall and not

harvested until late spring. During the month of February all activity within the arena of

production agriculture is at a minimum. This limits the amount of recent first hand experiences

that the respondents may have with production agriculture which would affect their responses.

An instance where tractors deposited mud on roads where respondents traveled that day or ash

being deposited on home and personal belongings whose source was a sugar cane field being

burned are both examples of daily experiences which could affect the participants’ responses to

the survey.

The surveys were conducted randomly over the course of a week. The survey process

was conducted by a professional data collection service. The administrators were provided with

detailed scripts that included the fifty five items utilized in this study. The time frame of this

study included both weekdays and two weekend days. The time period that the respondents were

contacted ranged from 8:00 am- 8:00 pm each day during the selected week.

Data for this study was collected using the following steps:

1. The randomly drawn number from the sample list was called. If the researcher was

unable to make contact with the respondent or they were unwilling to participate, another

number was selected using the same random selection technique as described in the

population and sample section of this chapter.

2. Once the researcher made contact with the respondent, introduction of the researcher and

verification that the correct number was contacted was completed.

3. Verification that the number was a private residence and that an adult member of that

household was being interviewed was done.

4. The researcher provided the respondent a brief overview of the survey and its goals and

requested participation from that individual.

5. If the respondent was willing to participate and met all of the qualifications set forth in

the population and sample portion of this chapter, the researcher recorded responses to the

survey questions into a database established for the purpose of recording the responses to

the survey instrument.

6. If the respondent was willing to respond and met all of the qualifications set forth

previously, but was unable to respond at the time of the initial contact, the researcher

made note of a more appropriate time to call again. A call was returned at the appropriate

time provided by the respondent.

7. If the respondent did not qualify to answer the questionnaire the researcher contacted

another number from the population utilizing the same random selection techniques

described previously.

CHAPTER 4

FINDINGS

The purpose of this study was to determine the knowledge and perception of animal

science, plant science, environmental science, food science, processing, and policy by the adult

residents of Louisiana. The evaluations of both knowledge and perception were compared to

determine if a relationship exists between these two factors. Five hundred and forty seven

individuals participated in the study. Findings are reported in this portion of the study and are

organized by research objectives.

Objective One

The first objective was to describe the adult residents of Louisiana on selected

demographic characteristics. The respondents were asked to give personal information on the

following demographic characteristics: age, gender, ethnic background, location of residence (in

a rural area, on a farm, in a town, or in a city), parish of residence, occupation of the head of

household, and highest level of education completed.

Each of the 547 respondents was asked to report their age as of their last birthday. Five

hundred and thirty three participants responded while 14 respondents declined to provide

information regarding their age. The mean age of the respondents was 53.31 years (SD= 16.0).

The reported ages ranged from a low of 18 years to a maximum of 89 years. To further

summarize the information on age of respondents, the researcher grouped the respondents into

the following categories of age: 18-29, 30-44, 45-59, and 60 or more. These categories were

selected based on their use in previous research conducted that studied perceptions of agriculture

(Birkenholz, 1993). The age category which was reported by the largest number of participants

was the 60 or more years of age category (n= 199, 37.3%). The age category that was reported

by the smallest group of respondents was 18-29 (n=37, 6.9%). Respondents within age

categories increased with ascending age brackets (See Table 1).

TABLE 1 Age Reported by Adult Residents of Louisiana Categorized

in Age Categories

Age Category n %

18-29 37 6.9

30-44 131 24.6

45-59 166 31.2

60+ 199 37.3

Total 533 100.0

Note: Mean Age = 53.31, SD = 16.06

Fourteen respondents did not provide information regarding their age.

The findings of the current study differed from the demographic finding for “age” by the

United States Census Bureau (2008). In the 2000 census the largest age group in among

Louisiana residents was 35-44 years of age with 691,966 people, representing 15.5 percent of the

total population. The second largest group was 25-34 years of age with 601,162 people,

representing 13.5 percent of the population.

It was noted that people in these the ages ranging from 36 to 54 were primarily born

during the post-World War II “Baby Boom” and were a major cause of the large number of

respondents in these age categories.

Regarding respondents’ gender, 47.2% (n= 258) reported that they were male and 52.8%

(n=289) reported that they were female. All study participants responded to this item. These

findings are similar to those by the United States Census Bureau (2008) which states that the

Male population of the Louisiana was 48.4% (n= 2,162,903) and the Female population was

51.6% (n= 2,306,073).

With regard to ethnic background the majority of the respondents (n= 387, 70.7%)

indicated that they were Caucasian (See Table 2). The second largest group was those who

indicated their ethnic background as African American (n=143, 26.1%) All other ethnic

backgrounds including Hispanic, Native American, Asian, and Other were reported by less than

10% of the respondents.

TABLE 2 Ethnic Background Reported by Adult Residents of Louisiana

Ethnic Background n %

Caucasian

387

70.7

Hispanic

1.5

African-American

143

26.1

Native American

1.3

Asian

Otherº l .2

Total 547 100.0

º = The “Other” response was not specified by the respondent.

These findings were similar to those reported by the United States Census where the

largest ethnic background group in Louisiana was Caucasian (63.9%), while the second largest

ethnic background group was African American (32.5%). All other ethnic background groups

listed were Asian (1.2%), American Indian (.6%), Pacific Islander (<.001), and Other (.7%).

Participants were asked whether they considered the location of their residence (physical

location of their home) to be on a farm, in a rural area, in a town, or in a city. The responses to

this question can be found in Table 3. The category that was reported by the smallest number of

respondents was “on a farm” (n=23, 4.2%). The category that was reported by the largest number

of respondents (n=235, 43.4%) included those who considered their residence to be “in a city”.

Five participants did not respond to this item.

TABLE 3. Location of Residence Reported by Adult Residents of Louisiana

Location of Residence

n %

In a City

235

43.4

In a Rural Area

174

32.1

In a Town

110

20.3

On a Farm

4.2

Total

542

100.0

Note: Five respondents did not reply to this portion of the survey instrument

When asked to provide their parish of residence, all study participants responded. All

parishes in Louisiana were represented by at least one respondent, with the exception of

Cameron Parish which had no respondents in this study. The parish which was reported by the

largest number of respondents was East Baton Rouge (n=64, 11.7%). The parishes of Catahoula,

East Carroll, Red River, St. Helena, Tensas, West Feliciana, and West Carroll were each reported

by one respondent. A complete presentation of the parish of residence of respondents is

presented in Appendix E.

To further summarize information on parish of residence and to facilitate subsequent data

analysis, the parishes were grouped into regions of the state. These regions were established to

reflect the regions that were utilized in previous research conducted by Louisiana Farm Bureau

(2000). The regions consisted of Acadiana, North Louisiana, Orleans, and the Florida Parishes.

Specific information regarding which parishes are included in each of the regions can be found

in Appendix E. The region with the largest number of respondents was “Acadiana” with 181

(33.1%) participants reporting a parish of residence that was located in this region. The region

with the smallest number of respondents was “Orleans” with 46 (8.4%) participants reporting a

parish of residence that was located in this region (See Table 4).

TABLE 4 Geographic Region of Residence reported by Adult Residents of Louisiana

Geographic Region

Acadiana

181

33.1

Florida Parishes

161

29.4

Orleans Area

8.4

North Louisiana

159

29.1

Total

547

100.0

When asked whether or not the respondent considered themselves to be the head of

household, 338 (61.8%) reported that they were the head of household. The remaining 209

(38.2%) respondents reported that they were not the head of household. The head of household

was defined as the primary wage earner for the household. All study participants responded to

this item. These findings differed from those of the United States Census Bureau (2008) that

reported the number of “Householders” to be 1,656,053 or 31.7%.

Regardless of their status as head of household, respondents were asked to report the

primary occupation/ profession of the head of the household. Due to the nature of this item, this

question was asked in a categorical manner with available responses falling into four categories.

The four response categories provided to participants were based on categories found in previous

studies conducted by the Louisiana Farm Bureau (Kennedy, 2004). These categories included

Laborer, Sales/ Clerical/ Technical, Administrative/ Professional, and Other. The category of

“Other” was provided for those participants who felt that the occupation of their head of

household did not fit into any of the three other options. All of the participants did not respond

to this question with 30 respondents leaving the item blank.

The largest number of respondents (n = 262, 50.7%) reported their head of household’s

occupation would be most appropriately described as “Laborer”. In addition, 215 (41.6%)

respondents reported their head of household’s occupation would most appropriately be

described as “Sales/ Clerical/ Technical”. These two categories combined to total 92.3% (n=477)

of the participants responses. Table 5 includes the number of respondents in each category.

TABLE 5 Occupation of Head of Household Reported by Adult Residents of

Louisiana

Occupation/ Profession of the Head of Household

Laborer

262

50.7

Sales/ Clerical/ Technical

215

41.6

Administrative/ Professional

5.2

Other

2.5

Total

517

100.0

Note: Thirty respondents did not reply to the question regarding

occupation/profession of the head of household

When asked to report their highest level of education completed, respondents were asked

to answer with one of the seven categories provided. These categories included “Less than High

School”, “High School Graduate”, “Some College”, “College Graduate- Non-Agriculture

Degree”, “College Graduate- Agriculture or Related Degree”, “Post Graduate- Non-Agriculture

Degree”, or “Post Graduate- Agriculture or Related Degree”. Six respondents did not identify

their educational level completed. The highest level of education completed by the largest group

of participants (n=142, 26.3%) was “College Graduate- Non-Agriculture Degree”. Results

showed 356 (65.8%) respondents reported that they had obtained an educational level of some

college or higher as their highest level of education completed. Those who had obtained a

degree in agriculture were reported in two groups and totaled 18 respondents (3.3%). These two

groups were “College Graduate- Agriculture or Related Degree” (n=14, 2.6%) and “Post

Graduate- Agriculture or Related Degree” (n=4, .7%). A detailed listing of the educational levels

and the number of respondents reporting each can be found in Table 6.

TABLE 6 Highest Level of Education Completed Reported by Adult Residents

of Louisiana

Highest Level of Education Completed

Less than High School

8.1

High School Graduate

141

26.1

Some College

137

25.3

College Graduate- Non-Agriculture Degree

142

26.3

College Graduate- Agriculture or Related Degree

2.6

Post Graduate- Non-Agriculture Degree

10.9

Post Graduate- Agriculture or Related Degree

Total

541

100.0

Note: Six respondents did not identify their education received

Objective Two

The second objective of the study was to determine the knowledge of the adult residents

of Louisiana regarding five areas of agriculture industry. To accomplish this objective,

participants were asked to respond to 20 items designed to measure their knowledge of

environmental science, plant science, animal science, processing, and policy. A listing of the

twenty items used to measure the respondents’ knowledge in these areas with the correct answer

identified is provided in Appendix F. Respondents were asked to indicate that each item was

either “true”, “false”, or that they “do not know/ uncertain”. Each item was scored as either

correct or incorrect. Responses of “do not know/ uncertain” were scored as incorrect. A

summary of the number of correct and incorrect responses to the items are presented in Table 7.

Among the twenty items, the statement that was responded to correctly by the largest

number of respondents was the statement “Hamburger is made from the meat of pigs”. The

correct response was noted by 92.5% (n= 506) of the respondents. The item that was

TABLE 7 Knowledge of Adult Residents of Louisiana Regarding Selected Aspects

of Agriculture

Item Correct Incorrect Total

n % n % n %

15. Hamburger is made from the

meat of pigs.

506 92.5

41 7.5

547 100.0

18. Processing increases the cost of

food products.

483 88.3

64 11.7

547 100.0

16. Food Safety is a major concern

of the food processing industry.

457 83.5

90 16.5

547 100.0

7. Farming and Wildlife can not

survive in the same geographic area.

431 78.8

116 21.3

547 100.0

9. Louisiana Farmers participate in

voluntary programs that support

environmental quality and

conservation.

429 78.4

118 21.5

547 100.0

8. The use of pesticides has

increased the yield of crops.

420 76.8

127 23.2

547 100.0

10. Animal wastes are used to

increase soil fertility.

413 75.5

134 24.5

547 100.0

4. Many farmers use tillage practices

that conserve the soil.

409 74.8

138 25.2

547 100.0

14. Biotechnology has increased the

pest resistance of plants.

405 74.0

142 26.0

547 100.0

5. Louisiana laws and regulations

have little effect on farmers.

395 72.2

152 27.8

547 100.0

12. Animals can be a valuable source

of medical products.

391 71.5

156 28.5

547 100.0

19. U.S. agriculture policies

influence food prices in other

countries.

384 70.2

163 29.8

547 100.0

6. Government subsidies payments

to farmers are used to stabilize food

prices.

340 62.2

207 37.9

547 100.0

20. Very little grain produced in the

U.S. is exported.

326 59.6

221 13.4

547 100.0

13. The commercial fishing industry

produces over fifty percent of all

seafood in the U.S.

323 59.0

224 41.0

547 100.0

(table con’t.)

3. One out of every five jobs in the

U.S. is related to agriculture.

322 58.9

225 41.1

547 100.0

21. Using crops grown in Louisiana

for fuel production reduces the U.S.

dependency on foreign oil.

319 58.3

228 41.7

547 100.0

11. Animals eat foodstuff that can

not be digested by humans.

317 58.0

230 42.0

547 100.0

22. Forestry is the leading

agricultural industry in the state of

Louisiana.

254 46.4

293 53.6

547 100.0

17. Homogenizing kills bacteria in

milk with heat.

116 21.2

431 78.8

547 100.0

Note: Items receiving a response of “do not know/ uncertain” were recorded as incorrect

responded to correctly by the second largest number of respondents was the statement

“Processing increases the cost of food products” (n=483, 88.3%). When asked to indicate

whether the statement “Food safety is a major concern of the food processing industry” was true

or false 457 (83.5%) of the respondents answered correctly with true as their response. This item

received the third largest number of correct responses.

The items that received the smallest number of correct responses were: “Homogenizing

kills bacteria in milk with heat” (n=116, 21.2%) and “Forestry is the leading agricultural industry

in the state of Louisiana” (n=254, 46.4%).

To further summarize the information on knowledge of agriculture among adult residents

of Louisiana, the researcher computed an overall knowledge score for each participant in the

study. To compute this score, the researcher coded each correct response as “1” and each

incorrect response as “0”. The responses to the 20 items on the scale were then summed for each

respondent. Therefore, the possible overall agriculture knowledge scores ranged from a low of 0

(no correct responses) to 20 (all correct responses). The calculated scores ranged from a low of 5

to a high of 20. The overall mean agriculture knowledge scores of adult residents of Louisiana

was 13.60 (SD=2.743).

In addition to an overall knowledge score, the data from the knowledge scale was

summarized into five scales designed to be measured in the measuring instrument. These

subscales included environmental science, policy, plant science, animal science, and processing.

The subscales utilized were five of the seven predetermined areas of agricultural knowledge as

proposed by Birkenholz (1993). Four questions were asked within each subscale area. Four

questions were also asked in order to measure the knowledge of respondents on agricultural

subject areas specifically associated with the state of Louisiana, bringing the number of subscales

to six. These Louisiana questions were overlapped with the five predetermined areas previously

mentioned and were utilized in the calculations of both the predetermined subscale scores as well

as in the Louisiana subscale score. A detailed list of the questions associated with each of the

five predetermined areas with the number of correct and incorrect responses to each item can be

found in Table 8.

A subscale score was computed for each respondent in each agricultural knowledge area

measured. The sub-scales were defined as the total number of correct responses in each

TABLE 8 Knowledge of Adult Residents of Louisiana Regarding Selected Aspects

of Agriculture by Predetermined Aspects of the Agriculture Industry

Animal Science

Correct

Responses

Incorrect

Responses

Total

Responses

15. Hamburger is made from the

meat of pigs.

506

92.5

41 7.5

547

100.0

12. Animals can be a valuable

source of medical products.

391

71.5

156

28.5

547

100.0

(table con’t)

13. The commercial fishing

industry produces over fifty

percent of all seafood in the

U.S.

323

59.0

224

41.0

547

100.0

11. Animals eat foodstuff that

can not be digested by humans.

317

58.0

230

42.0

547

100.0

Environmental Science

7. Farming and Wildlife can not

survive in the same geographic

area.

431

78.8

116

21.3

547

100.0

9. Louisiana farmers participate

in voluntary programs that

support environmental quality

and conservation

429

78.4

118

21.5

547

100.0

10. Animal wastes are used to

increase soil fertility.

413

75.5

134

24.5

547

100.0

4. Many farmers use tillage

practices that conserve the soil.

409

74.8

138

25.2

547

100.0

Plant Science

8. The use of pesticides has

increased the yield of crops.

420

76.8

127

23.2

547

100.0

14. Biotechnology has

increased the pest resistance of

plants.

405

74.0

142

26.0

547

100.0

22. Forestry is the leading

agricultural industry in the state

of Louisiana.

254

46.4

293

53.6

547

100.0

20. Very little grain produced in

the U.S. is exported.

326

59.6

221

13.4

547

100.0

(table con’t.)

Policy

5. Louisiana laws and

regulations have little effect on

farmers.

395

72.2

152

27.8

547

100.0

19. U.S. agriculture policies

influence food prices in other

countries.

384

70.2

163

29.8

547

100.0

6. Government subsidies

payments to farmers are used to

stabilize food prices.

340

62.2

207

37.9

547

100.0

3. One out of every five jobs in

the U.S. is related to agriculture.

322

58.9

225

41.1

547

100.0

Processing

18. Processing increases the cost

of food products.

483

88.3

11.7

547

100.0

16. Food Safety is a major

concern of the food processing

industry.

457

83.5

16.5

547

100.0

21. Using crops grown in

Louisiana for fuel production

reduces the U.S. dependency on

foreign oil.

319

58.3

228

41.7

547

100.0

17. Homogenizing kills bacteria

in milk with heat.

116

21.2

431

78.8

547

100.0

Louisiana

9. Louisiana farmers participate

in voluntary programs that

support environmental quality

and conservation.

429

78.4

118

21.5

547

100.0

5. Louisiana laws and

regulations have little effect on

farmers.

395

72.2

152

27.8

547

100.0

(table con’t.)

21. Using crops grown in

Louisiana for fuel production

reduces the U.S. dependency on

foreign oil.

319

58.3

228

41.7

547

100.0

22. Forestry is the leading

agricultural industry in the state

of Louisiana.

254

46.4

293

53.6

547

100.0

subscale. A summated score was used since all subscales consisted of the same number of items.

The mean subscale score was then computed for each of the six defined subscales across all

respondents. This information is presented in Table 9 including the mean and standard deviation

for each subscale as well as the minimum and maximum respondent score for each of the

measurements.

Analysis of the computed mean subscale scores revealed that the respondents had the

highest level of knowledge in the subscale of environmental science (M = 3.07, SD= .959) and

the lowest reported level of knowledge in the subscale of processing (M = 2.51, SD= .828).

TABLE 9 Summated Subscale Knowledge Scores of Adult Residents of Louisiana

Regarding Selected Aspects of Agriculture

Knowledge Subscale Statistics M SD Minimum Maximum

Environmental Science 3.07 .959 0 4

Animal Science 2.81 .906 0 4

Policy 2.62 1.028 0 4

Plant Science 2.57 1.006 0 4

Louisiana 2.55 .992 0 4

Processing 2.51 .828 0 4

Overall Knowledge Score 13.60 2.743 5 20

Objective Three

Objective three of this study was to determine the perceptions of the agriculture industry

among adult residents of Louisiana. Participants were asked to respond to a 20 item scale

(Birkenholz, 1993) designed to measure perceptions of agriculture. Study participants were

asked to indicate their level of agreement or disagreement with each of the scale items using a

five-point Likert-type scale with the following response values: Strongly Disagree=1,

Disagree=2, Neither Agree nor Disagree=3, Agree=4, Strongly Agree=5. The mean response

value for each of the items was calculated and is presented in Table 10. To interpret the responses

to the items the researcher designed an interpretive scale based on the scale response values as

follows: Strongly Disagree =1-1.5, Disagree =1.51-2.5, Neither Agree nor Disagree =2.51- 3.49,

Agree = 3.50-4.49, Strongly Agree =4.5-5.

The item with the highest level of agreement was “Not all land is suitable for farming”

(M = 4.22, SD = 1.002). This mean response value was classified in the “agree” category using

the researcher designed interpretive scale. The item with the second highest level of agreement

was “Louisiana farmers should develop new innovative marketing strategies” (M = 4.16, SD =

.839). This item was also classified in the “agree” interpretive category.

The item which had the highest level of disagreement was “farmers earn too much

money” (M = 1.61, SD = .898). This item was classified in the “disagree” interpretive category.

Overall, nine items were classified in the “agree” category, six were classified in the “neither

agree nor disagree” category, and five were in the “disagree” category. See Table 10.

In order to further summarize the information regarding the respondents’ perception of

agriculture the scale was factor analyzed to determine if any underlying constructs exist in the

scale based on responses provided. The method used was the principal components analysis with

a varimax rotation.

TABLE 10 Perceptions of Adult Residents of Louisiana Regarding the

Agriculture Industry

Statements Ma SD Response Categoryb

31. Not all land is suitable for farming.

4.22

1.002

Agree

36. Louisiana farmers should develop

new innovative marketing strategies.

4.16

.839

Agree

39. Farm grains are becoming an

important energy source in the U.S.

3.98

1.053

Agree

32. Farmers take good care of animals.

3.96

1.105

Agree

41. Biotechnology has increased the

yield of crops in developing countries.

3.80

1.052

Agree

26. U.S. Citizens spend a higher

percentage of their income on food than

in other countries.

3.79

1.297

Agree

23. Agriculture employs a large number

of people in Louisiana.

3.76

1.216

Agree

35. Raising hybrid plants results in

higher yields.

3.69

1.109

Agree

29. Pesticides can be used safely when

producing food.

3.51

1.314

Agree

33. Confinement is an acceptable

practice when raising livestock.

3.34

1.310

Neither Agree nor

Disagree

40. People in Louisiana are moving

away from rural areas due to changes in

agriculture.

3.27

1.416

Neither Agree nor

Disagree

25. Farmers have no control over food

prices.

3.25

1.429

Neither Agree nor

Disagree

38. The U.S. should allow free trade

with other countries for food products.

3.12

1.391

Neither Agree nor

Disagree

30. Only organic methods should be

used to produce food.

2.7

1.403

Neither Agree nor

Disagree

37. A strong agriculture industry is more

important than military power.

2.63

1.341

Neither Agree nor

Disagree

42. Agriculture practices in Louisiana

are harmful to the environment.

2.44

1.248

Disagree

27. The government should exert more

control over farming.

2.33

1.341

Disagree

28. Agriculture is the greatest polluter of

our water supply in Louisiana.

2.33

1.359

Disagree

(table con’t.)

34. Animals have the same rights as

people.

2.22

1.281

Disagree

24. Farmers earn too much money.

1.61

.898

Disagree

aResponse Scale. Strongly Disagree =1, Disagree =2, Neither Agree nor Disagree =3, Agree =4,

Strongly Agree =5 bResearcher designed Interpretive Scale. 1.00-1.50= strongly disagree, 1.51-

2.5= disagree, 2.51- 3.49= neither agree nor disagree, 3.5- 4.49= agree, 4.5- 5.0= strongly agree.

Several of the questions were designed such that a ‘disagree’ response indicated a more

positive perception of agriculture. For this reason, the researcher reversed the scale on these

items prior to the identification of subscales so that for all scale items a higher response value

indicated a more positive perception of the agriculture industry. For example, the more positive

response to the statements “Agriculture is the greatest polluter of our water supply in Louisiana”

and “Farmers earn too much money” was the response of “Strongly Disagree”. All of the

perception items are listed in Appendix G with the answer exhibiting a more positive perception

of agriculture noted.

Prior to conducting the planned factor analysis, the researcher examined the cases-

tovariable ratio (28.7:1) which met the minimal cases-to-variable ratio recommended by Hair et

al.,

(2006). A review of the anti-image correlation matrix revealed measures of sampling adequacy

(MSA’s) all above the 0.5 threshold. Furthermore, a Kaiser-Meyer-Olkin (KMO) Measure of

Sampling Adequacy was conducted and calculations revealed a KMO value of .636. KMO

values above 0.5 determine sampling to be adequate (University of Newcastle Upon Tyne, 2006).

In addition, a Bartlett’s Test of Sphericity was performed to test the hypothesis that the variables

in the population correlation matrix are uncorrelated. The strength of the relationships between

variables was found to be strong and acceptable for factor analysis based on results of this test

(X2(df=190, n=20) = 918.100, p < .001), University of Newcastle Upon Tyne, 2006).

All measures indicated that the data from this scale were adequate and appropriate for calculation of

a factor analysis and well exceeded the minimal sample size and the minimal cases-to-variable ratio

(Hair, et al., 2006).

After the determination that the data was adequate for completing an exploratory factor

analysis, the next step in conducting the test was to determine the number of factors to be

extracted from the perception scale. The researcher used a combination of the latent root

criterion, the scree test criterion, and the percentage of variance explained to make this decision.

When the scree test was analyzed, the number of factors was interpreted to be four, five, or six.

The researcher examined each of these factor grouping models and determined the four factor

solution to be the most conceptually meaningful and contained the least amount of cross loadings

for the items in the survey. Hair, et al. (2006) states that factor loadings are reflective of the

sample size and for a sample size of 350 or greater a factor loading of .30 is significant. Each

factor was significant with a loading of at least .30, with the exception of one statement. The

statement “A strong agriculture industry is more important than military power.” did not meet the

minimal statistical loading strength of .30 in the four factor model in any of the four identified

factors with an actual loading of .19. This survey item was excluded from the scale due to the

low loading strength. A detailed description of the factor loadings of each item can be found in

Table 11.

TABLE 11 Factor Analysis of Perception of the Agriculture Industry Among

Adult Residents of Louisiana

Attitude Toward Farming Subscale

Item

Factor 1

Factor 2

Factor 3

Factor 4

24. Farmers Make Too Much Money

.628

-.158

-.122

42. Agriculture practices in Louisiana are

harmful to the environment

.622

.156

-.108

(table con’t)

28. Agriculture is the greatest polluter of our

water supply in Louisiana.

.615

-.178

27. The government should exert more

control over faming.

.534

.230

.320

34. Animals have the same rights as people.

.397

.381

Issues Relating to Food Supply Subscale

Item

Factor 1

Factor 2

Factor 3

Factor 4

35. Raising hybrid plants results in higher

yields.

.572

.142

.291

41. Biotechnology has increased the yield of

crops in developing countries.

.570

.120

-.102

31. Not all land is suitable for farming.

.330

.458

-.163

.149

40. People in Louisiana are moving away

from rural areas due to changes in

agriculture.

.206

-.456

.101

.180

36. Louisiana farmers should develop new

innovative marketing strategies.

.430

.126

23. Agriculture employs a large number of

people in Louisiana.

-.110

.370

.348

Farming Practices Subscale

Item

Factor 1

Factor 2

Factor 3

Factor 4

29. Pesticides can be used safely when

producing food.

.138

.108

.687

.132

33. Confinement is an acceptable practice

when raising livestock.

.507

32. Farmers take good care of animals.

.379

.477

-.164

38. The U.S. should allow free trade with

other countries for food products.

.184

-.160

-.447

Food Prices Subscale Item

Factor 1

Factor 2

Factor 3

Factor 4

26. U. S. Citizens spend a higher percentage

of their income on food than in other

countries.

.127

.642

39. Farm grains are becoming an important

energy source in the U.S.

.252

.126

-.616

25. Farmers have no control over food

prices.

.137

.147

-.394

30. Only organic methods should be used to

produce food.

.367

-.195

.368

.391

Note: * = Factor Loadings <.10 Four subscales were identifiable and were determined to be

underlying constructs of the perceptions of agriculture. The four factor model explained 35.24%

of the total explained variance. The researcher labeled the four subscales as follows: “Attitude

Toward Farming”, “Issues Relating to Food Supply”, “Farming Practices”, and “Food Prices”.

The first factor identified was labeled by the researcher to be “Attitude Toward

Farming”. This factor included items related to farmers income, negative effect of

farming on the environment, governmental control over faming, and animal rights; A

total of five items with loadings ranging from .628 to .397 were included in this factor,

and it explained 11.8% of the total variance in the scale. The second factor identified in

the scale was labeled by the researcher as “Issues Relating to Food Supply”. This factor

included six items related to changes in crop yields, land usage for farming, employment

within the agriculture sector, new markets for crops, and the geographic movement of

citizens. The factor loadings for this subscale ranged from a high of .572 to a low of .370

and explained 9.056% of the overall scale variance. The third factor identified items that

reflected perceptions of “Farming Practices” and explained 7.811 percent of the total

variance. The loadings ranged from .687 to .447 and included four items: pesticide use,

practices of raising livestock, organic farming practices and the utilization of free trade in

commodity markets. This factor was labeled by the researcher as “Farming Practices”.

The fourth factor added an additional 6.569% of explained variance and had factor

loadings ranging from .642 to .391. This factor included items that discussed income

spent on food, grains as an energy source, and farmer control over prices. This factor was

labeled as “Food Prices” by the researcher.

In order to more adequately describe the four subscales identified from the factor

analysis, the researcher computed subscale scores for each of these constructs. Each of the

subscale scores are defined as the mean of the items included in each respective subscale.

The mean score was chosen over a summated measure due to the varying number of items

in each factor. The computed mean scores for the various factors were found to range

from a high of 3.81 for the factor titled “Attitudes Toward Farming” to a low value of 3.14

for the factor labeled “Food Prices”. Each of the factor subscales had a possible minimum

value of one and a possible maximum value of five. The subscale scores are presented in

Table 12.

TABLE 12 Perceptions of the Agriculture Industry Among Adult Residents

of Louisiana Subscale Scores

Subscales

SD Response Categoryb

Attitude Toward Farming

547

3.81

.73 Agree

Issues Relating to Food Supply

547

3.72

.49 Agree

Farming Practices

547

3.39

.65 Neither Agree nor Disagree

Food Prices

547

3.14

.68 Neither Agree nor Disagree

aResponse Scale. Strongly Disagree = 1, Disagree = 2, Neither Agree nor Disagree = 3,

Agree = 4, Strongly Agree = 5

bResearcher designed Interpretive Scale: 1.00-1.50 = strongly disagree, 1.51- 2.5 = disagree,

2.51- 3.49 = neither agree nor disagree, 3.5- 4.49 = agree, 4.5- 5.0 = strongly agree.

Objective Four

Objective four of the study was to determine if a relationship exists between knowledge

of selected aspects of the agriculture industry and perceptions of the agriculture industry

among adult residents of Louisiana. In order to accomplish this objective, Pearson’s

Product Moment Correlations were calculated to determine the strength and direction of the

relationship between the knowledge of agriculture subscale scores measured in the study

and the perception of agriculture subscale scores. Davis (1971) descriptors of

association were used to describe the bivariate correlations. These descriptors included .70

or higher = “very strong association”; .50- .69 = substantial association”; .30 - .49 =

“moderate association”; .10 - .29 = “low association”; and .01 - .09 = “negligible”. The

alpha level for significance of correlations was established “a’ priori” as .05.

Many of the relationships between knowledge and perception were significant and

were described as low to moderate based on Davis’ (1971) descriptors. A complete

presentation of the correlations between the knowledge and perception subscales can be

found in Table 13.

When examining the relationship between the knowledge subscale “Policy”, and the

perception subscales, the highest association was with the perception subscale “Issues

Relating to Food Supply” (r= .21, p= <.001). The nature of this relationship was such that

higher levels of knowledge regarding “Policy” were associated with more positive

perceptions regarding “Issues Related to Food Supply”. Two additional perception

subscales were found to be significantly related to the “Policy” subscale score. These

include “Farming Practices” (r=.11, p=.01) and “Food Prices” (r=.09, p=.03).

In examining the relationship of the knowledge subscale “Environmental Science”

with the perception subscale scores, all of the correlations were found to be statistically

significant. The highest association was with the “Issues Relating to Food

Supply” (r= .23, p<.001). The next highest association was with the “Attitude Toward

Farming” subscale (r=.22, p<.001). Based on Davis’ (1971) descriptors, all of the

relationships between the “Environmental Science” knowledge subscale and the perception

subscale scores were described as “Low Associations”. Additionally, all of these associations

were positive, indicating that higher levels of knowledge in the “Environmental Science”

subscale were associated with more positive perceptions of agriculture in each of the

identified subscales (See Table 13).

TABLE 13 Correlation Between Agricultural Knowledge Subscale Scores and

Perception of Agriculture Subscale Scores Among Adult Residents of

Louisiana Subscales

Knowledge

Subscale

Items

Perceptio

n Subscale

Items

Policy

Descriptorsb

Environmental

Science

Descriptorsb

Plant

Science

ra p

Descriptorsb

Animal

Science

Descriptorsb

Processing

Descriptorsb

Louisiana

Descriptorsb

Attitude

Toward

Farming

r= .02/ p=

.69

Negligible

r= .22/

p= <.001

Low

r= .13/

p= .002

Low

r= -.01

p= .86

Negligible

r=- .04

p= .39

Negligible

r= -.09

p= .04

Negligible

Issues

Relating to

Food

Supply

r= .21/

p= <.001

Low

r= .23/

p= <.001

Low

r= .20/

p= <.001

Low

r= .12

p= .01

Low

r= .09

p= .03

Negligible

r= .12

p= .01

Low

Farming

Practices

r= .11/ p=

.01

Low

r= .16/

p= <.001

Low

r= .14/

p= <.001

Low

r= .02

p= .61

Negligible

r= -.04

p= .39

Negligible

r= -.10 p=

.03

Low

Food

Prices

r= .09/

p= .03

Negligible

r= .14

p= <.001

Low

r= .09/

p= .03

Negligible

r= .01

p= .90

Negligible

r= .07

p= .11

Negligible

r= .17

p= <.001

Low

Note: n= 547

a= Pearson’s Product Moment Correlation Coefficients, alpha= .05, 2-tailed test b=

Davis’ Descriptors (1971) including .70 or higher = very strong association; .50-.69 =

substantial association; .30- .49 = moderate association; .10- .29= low association; and

.01- .09= negligible association.

Examination of the relationship between the knowledge subscale “Plant Science” and

perception subscale scores revealed that all of the correlations were statistically significant.

The highest association was with the subscale “Issues Relating to Food Supply” (r=.20,

p<.001) (See Table 13). Based on Davis’ (1971) descriptors, all four of the relationships

between the “Plant Science” knowledge subscale and the perception subscales were

described as “Low Associations”. These four perception subscales include “Issues Relating

to Food Supply”, “Attitude Toward Farming”, and “Farming Practices”. All of the

relationships with “Plant Science” were positive such that a higher level of knowledge in the

“Plant Science” subscale was associated with a more positive perception of agriculture.

In examining the relationship of the knowledge subscale “Animal Science” with

the perception subscale scores, one of the correlations was found to be statistically

significant. That association was with the perception subscale “Issues Relating to Food

Supply” (r= .12, p= .01). Based on Davis’ (1971) descriptors, the association between the

“Animal Science” knowledge subscale and the “Issues Relating to Food Supply”

perception subscale score was described as a “Low Association”. This association was

also positive indicating that a higher level of knowledge in the “Animal Science” subscale

was associated with a more positive perception of agriculture in the subscale “Issues

Relating to Food Supply” (See Table 13).

In examining the relationship between the knowledge subscale “Processing” and the

perception subscale scores, one correlation was found to be statistically significant.

This association was with the perception subscale “Issues Relating to Food Supply” (r=.09,

p=.03) (See Table 13). Based on Davis’ (1971) descriptors, this relationship was described

as a “Negligible Association”.

Examination of the relationship of the knowledge subscale “Louisiana” with the

perception subscale scores revealed statistically significant correlations with each of the four

perception subscales. The highest association was found between the knowledge subscale

“Louisiana” and the perception subscale “Food Prices” (r=.17, p=<.001). The second

highest association was with the perception subscale “”Issues Relating to Food Supply” (r=

.12, p= .01) (See Table 13). Based on Davis’ (1971) descriptors, the relationships between

the “Louisiana” knowledge subscale and the perception subscales “Food Prices”, Farming

Practices”, and “Issues Relating to Food Supply” were described as “Low Associations”.

Additionally, these associations were positive indicating a higher level of knowledge on the

“Louisiana” subscale was associated with more positive perceptions of agriculture in the

subscales “Food Prices”, “Farming Practices”, and “Issues Relating to Food Supply”. The

relationships between the “Louisiana” knowledge subscale and the perception subscale

“Attitude Toward Farming” was described as a “Negligible Association”. This association

was also positive indicating that a higher level of knowledge in the “Louisiana” subscale

was associated with more positive perceptions of agriculture in the identified subscale

“Attitude Toward Farming”.

Objective Five

Objective five was to determine if relationships exist between perceptions of the

agriculture industry and selected demographic characteristics of adult residents of

Louisiana. The demographic characteristics included in this objective were age, gender,

ethnic background, location of residence, parish of residence, occupation of the head of

household, and highest level of education received. The statistical test used to measure

the association between these demographic characteristics and each of the perception

subscales was selected based on the appropriateness of the test for the level of

measurement of each variable as well as to maximize the interpretability of the results.

In examining the relationship between perceptions of the agriculture industry and

selected demographic characteristics, the researcher utilized the mean scores associated

with each of the subscales previously identified. These mean scores were computed on

the subscales “Attitude Toward Faming”, “Issues Relating to Food Supply”, “Farming

Practices”, and “Food Prices”.

The first demographic characteristic examined for relationships with the perceptions

of agriculture was age. The statistical procedure used to measure these relationships was

the Pearson’s Product-Moment Correlation Coefficient. Two of the perceptions of

agriculture subscale scores were found to be significantly related to the age of

respondents. The highest association identified was with the “Food Prices” subscale (r =

.16, p <.001). This correlation was classified as a “Low Association” using Davis’

descriptors (Davis, 1971). The nature of the relationship was such that respondents who

were older tended to have more positive perceptions of agriculture on the “Food Prices”

subscale.

The other subscale that was found to be significantly correlated with age was the

“Issues Relating to Food Supply” score (r= -.12, p= .004). This correlation was also

classified as “Low” (Davis, 1971). The nature of this relationship was such that younger

respondents tended to have more positive perceptions of agriculture on the “Issues Relating

to Food Supply” score. The other two perception subscale scores were not significantly

related to the age of respondent (See Table 14).

Due to the dichotomous nature of the variable gender, the relationship between the

perception of agriculture in Louisiana and the variable gender was determined using the

independent t-test. Results from this test indicated there was a statistically significant

difference between males and females in their perception of agriculture related to the

subscales “Attitude Toward Farming”, “Issues Dealing with Food Supply”, and “Farming

TABLE 14 Correlation Between Age and Perceptions of the Agriculture Industry

Among Adult Residents of Louisiana

Perception Subscales

n ra

p Descriptorsb

Food Prices

533 .16

<.001 Low

Issues Relating to Food Supply

533 -.12

.004 Low

Attitude Toward Farming

533 -.01

.712 Negligible

Farming Practices

533 .01

.830 Negligible

a = Pearson Product Moment Correlation Coefficient

b = Descriptors based on Davis’ (1971) including .70 or higher = very strong association;

.50-.69 = substantial association; .30- .49 = moderate association; .10- .29= low

association; and .01- .09= negligible association.

Practices”. Each of these three differences were significant at p <.001. Additionally, the

perception subscale “Food Prices” was slightly significantly different between males and

females (p=.049). The nature of all of the significant differences was such that male

respondents tended to have more positive perceptions of agriculture than the female

respondents (See Table 15).

TABLE 15 Comparison of Perceptions of the Agriculture Industry by Gender

Among Adult Residents of Louisiana

Perception Subscale Gender

t Sig. t

Issues Relating to Malea

3.848

.495

5.507 <.001

Food Supply Femaleb

3.618

.478

Farming Practices Malea

3.529

.653

4.530 <.001

Femaleb

3.279

.635

Attitude Toward Malea

3.929

.717

3.535 <.001

Farming Femaleb

3.708

.739

Food Prices Malea

3.207

.725

1.972 .049

Femaleb

3.091

.645

Note: df for all tests = 545

a n= 258 b n= 289

In examining the relationship between the perception of agriculture among adult residents

of Louisiana and the variable ethnic background, a t-test was utilized. This test was

selected based on the use of only the two ethnic groups African American and Caucasian.

The groups of Hispanic, Asian, Native American, and Other were not included in this

measurement due to the small number of respondents in each of these categories (Hispanic,

n=8; Native American, n=7; Asian, n=1; Other, n=1). The means, standard deviations, and

significance for each of the perception subscales can be found in Table

16.

TABLE 16 Comparison of Perception of the Agriculture Industry by Ethnic

Background of Adult Residents of Louisiana

Perception

Ethnic

Subscale

Background

SD df t Sig. t

Attitude Toward

Caucasianb

3.91

.6776 219.36c 4.900 <.001

Farming

African Americana

3.54

.8109

Issues Relating to

Caucasianb

3.77

.4864 528 3.621 <.001

Food Supply

African Americana

3.60

.4951

Farming Practices

Caucasianb

3.46

.6776 307.274c 4.059 <.001

African Americana

3.22

.5549

Food Prices

Caucasianb

3.19

.7050 528 2.399 .017

African Americana

3.02

.6542

a n= 143 b n= 387 c = df with equal

variances not assumed

It should be noted that the Levene’s Test for Equality of Variance revealed that the

Ethnic Background groups (African American and Caucasian) had significantly different

variances for two of the perception subscale scores, “Attitude Toward Farming” (F =

4.078, p = .044) and “Farming Practices” (F= 9.682, p = .002). Therefore, for these two

subscales, the t-value used was with equal variances not assumed.

Results of the t-tests indicated that there was a significant difference between the two

ethnic groups with relation to all of the perception subscales (See Table 16). These

differences were such that those respondents indicating their Ethnic Background to be

Caucasian tended to have more positive perceptions of agriculture than those respondents

that indicated their Ethnic Background to be African-American in each of the subscales.

The greatest difference between the two Ethnic backgrounds was found in the perception

subscale “Attitude Toward Farming” (t219.36 = 4.90, p, .001).

Examining the relationship between the perception of agriculture in Louisiana and

the location of residence (on a farm, in a rural area, in a town, or in a city) among adult

residence of Louisiana was accomplished by utilizing one way analysis of variance tests.

Results of the analysis of variance tests comparing perception of agriculture subscale scores

by location of residence indicated that at least one significant difference existed among the

four groups on the subscale “Food Prices” (F= 2.961, p= .032) (See Table 17).

A Tukey’s Post-hoc test was used to identify the specific group means that were

significantly different. A detailed listing of this difference can be found in Table 18.

TABLE 17 Comparison of Perceptions of the Agriculture Industry by Location

of Residencea of Adult Residents of Louisiana

Variable

Sig.

Food Prices

3. 538

1.366

2.961

.032

Attitude Toward Farming

3. 538

.755

1.398

.243

Farming Practices

3. 538

.473

1.101

.348

Issues Relating to Food Supply

3. 538

.235

.946

.418

a Location of Residence defined as self reported member of one of the following categories:

On a Farm, In a Rural Area, In a Town, In a City.

TABLE 18 Analysis of Variance of Perceptions of the Agriculture Industry

“Food Prices” Subscale Scores by Location of Residenceº of Adult

Residents of Louisiana

Perception Subcategory df

Food Prices

Sig.

Between 3

1.366

2.961

.032

Within 538

.461

Total 541

a Location of Residence defined as self selection of one of the following categories: On a

Farm, In a Rural Area, In a Town, In a City.

Although the Analysis of Variance test showed a significant F value (F= 2.961, p=

.032) when Tukey’s Post Hoc multiple comparison test was applied to the data no

significant differences were found to exist. Results of the Tukey Post Hoc test can be

found in Table 19.

TABLE 19 Analysis of Variance Post Hoc Comparisonsa of Perceptions of the

Agriculture Industry “Food Prices” Subscale Scores by Location of

Residence Among Adult Residents of Louisiana

Subset 1

Location of Residence

M/ SD

In a Rural Area

174

3.04/ .7411

In a City

235

3.15/ .6194

In a Town

110

3.23/ .6582

On a Farm

3.39/ .8566

Note: No Significant Differences shown in Tukey Post-hoc

Test at .05 level/ Differences seen in ANOVA Table. Items

not included in both subsets are significantly different a

Tukey’s Post Hoc multiple comparison test was utilized

To examine the relationship between parish of residence and perception of agriculture

among adult residents of Louisiana, the researcher grouped the parishes into geographic

regions. This grouping was needed since the number of respondents in each parish was

insufficient to permit individual parish comparisons. These areas are a reflection of the

geographical areas that are utilized by farm bureau in order to separate areas into logistically

accessible work regions. Analysis of the relationship between the perception of agriculture

and the parish of residence was accomplished by comparing the four perception subscale

scores by geographic region (as a measure of parish) using one way analysis of variance

tests. No significant differences were found among the four geographic regions with regard

to their perceptions of the agriculture industry in

Louisiana. These differences are found in Table 20.

TABLE 20 Comparison of Perceptions of the Agriculture Industry by Parish of

Residence Among Adult Residents of Louisiana

Perception Subscales

Sig.

Issues Relating to Food Supply

3, 543

.902

2.114

.097

Farming Practices

3, 543

.723

1.541

.203

Food Prices

3, 543

.325

1.308

.271

Attitude Toward Farming

3, 543

,544

1.002

.391

Another variable that was examined to determine if it was related to the perceptions of

the agriculture industry was the occupation of the head of household. This variable was

self reported in four categories which included “Laborer”, “Sales/ Clerical/ Technical”,

“Administrative/ Professional”, and “Other”. To measure this relationship, the researcher

chose to compare the perception sub-scale scores by categories of the occupation of head

of household using the one-way analysis of variance procedure. Of the four sub-scale

scores compared, a statistically significant difference was found in one of the scores. A

Significant F test indicated that there was at least one significant difference among the

groups on the sub-scale score “Attitudes Toward Farming” (see

Table 22). No significant difference was found in the other three sub-scale sores (see

Table 21).

TABLE 21 Comparison of Perceptions of the Agriculture Industry by

Occupation of the Head of Household Among Adult Residents of

Louisiana

Perception Subscales

Sig.

Attitude Toward Farming

3.523

2.194

4.122

.007

Food Prices

3.523

.588

1.249

.291

Issues Relating to Food Supply

3.523

.318

1.277

.281

Farming Practices

3.523

.261

.599

.616

TABLE 22 Analysis of Variance of Perception of the Agriculture Industry

“Attitude Toward Farming” Subscale scores by Occupation of

the Head of Household

Perception Subscales

Attitude Toward Farming

Sig.

Between

2.194

4.122

.007

Within

523

.532

Total

526

To identify specifically which groups of the occupation of head of household were

significantly different on the “Attitude Toward Farming” perception subscale score, the

researcher used the Tukey’s Post Hoc analysis test. Results of this test (See Table 23)

revealed that the group “Laborer” (M = 3.69, SD = .8257) was significantly different

from the “Sales/ Clerical/ Technical” group (M = 3.89, SD = .6591) and the

“Professional/ Administrative” group (M = 4.03, SD = .6625). The nature of this

difference was such that both “Sales/ Clerical/ Technical” group and the “Professional/

Administrative” group had more positive perceptions of the agriculture industry in the

“Attitudes Toward Farming” sub-scale that the “Laborer” group.

TABLE 23 Analysis of Variance Post Hoc Comparisonsa of Perception of

the Agriculture Industry “Attitude Toward Farming” Subscale

Scores by Occupation of the Head of Household of Adult Residents

of Louisiana

Subset 1

Subset 2

Occupation of the Head of Household N M/SD

M/SD

Professional/ Administrative 37 4.03/ 4.03

Sales/ Clerical/ Technical 262 3.89/ 3.89

Other 13 3.89/ 3.89

3.89/ 3.89

Laborer 215

Note: Items not included in both subsets are significantly different

The mean difference is significant at the .05 level a

Tukey’s Post Hoc multiple comparison test was utilized

3.69/ 3.69

subscale scores by categories of the highest level of education completed variable. When

the subscale scores were compared, two were found to have significant F values indicating

that at least one significant difference existed among the categories of highest level of

education completed for each of these two scores (See Table 24).

TABLE 24 Comparison of Perceptions of the Agriculture Industry by Highest

Level of Education Completeda Among Adult Residents of Louisiana

Perception Subcategories

Sig.

Attitude Toward Farming

6.534

1.831

3.475

.002

Food Prices

6.534

1.011

2.176

.044

Food Supply

6.534

.442

1.796

.096

Farming Practices

6.534

.665

1.546

.161

º = Levels of Education Compared include the following: Less than High School,

High School Graduate, Some College, College Degree- Agriculture, College Degree-

Non-Agriculture, Post Graduate Degree- Non-Agriculture.

The subscale which was found to have the highest level of significance among the

education categories was “Attitude Toward Farming” (F= 3.475, p= .002) (See Table 25).

TABLE 25 Analysis of Variance of Perceptions of the Agriculture Industry

“Attitude Toward Farming” Subscale Score by Highest Level of Education

Completed by Adult Residents of Louisiana

Subscale

Attitude Toward Farming

Sig.

Between

1.831

3.475

.002

Within

534

.527

Total

540

A Tukey’s Post Hoc multiple comparison test was used to identify the specific

groups which were significantly different (See Table 26). This test revealed that

participants who reported their highest level of education completed as “Post Graduate-

Agriculture or Related Degree” had significantly more positive perceptions on the items

in the “Attitude Toward Farming” subscale than those who reported their highest level of

education as “Less than High School”. The second subscale that was found to have a

significant difference among the education categories was “Food Prices” (F= 2.176, p=

.044) (See Table 27). A Tukey’s Post Hoc multiple comparison test was used to identify

the specific groups which were significantly different. Although the Analysis of

Variance test showed a significant F value, when Tukey’s Post Hoc multiple comparison

test was applied to the data no significant differences were found to exist (See Table 28).

TABLE 26 Analysis of Variance Post Hoc Comparisons of Perceptions of the

“Attitude Toward Farming” Sub Scale Scores Among Adult

Residents of Louisiana by Highest Level of Education Completed

Subset 1 Subset 2

Educational Levels

M/ SD M/ SD

Less than High School

3.40/ .8996

High School Graduate

College Graduate- Agriculture

141

3.74/ .8297 3.74/ .8279

or Related Degree

3.84/ .6477 3.84/ .6477

(table con’t.)

Post Graduate- Agriculture

or Related Degree 4 4.20/ .7118

4.20/ .7118

Some College 137

3.84/ .6275

College Graduate- Non Agriculture 142

3.91/ .6650

Post Graduate- Non Agriculture 59

Note: Items not included in both subsets are significantly different

Items not included in both subsets are significantly different

3.91/ .6859

TABLE 27 Analysis of Variance of Perceptions of the Agriculture Industry

“Food Prices” Subscale Score by Highest Level of Education Completed

by Adult Residents of Louisiana

Subscale

Food Prices

Sig.

Between

1.011

2.176

.044

Within

534

.465

Total

540

TABLE 28 Analysis of Variance Post Hoc Comparisonsa of Perceptions of

the “Food Prices” Sub Scale Scores Among Adult Residents of

Louisiana by Highest Level of Education Completed

Subset 1

Educational Levels

M/ SD

College Graduate- Agriculture or Related Degree

2.85/ .8544

Post Graduate- Agriculture or Related Degree

2.91/ .5000

Some College

137

3.08/ .6321

College Graduate- Non Agriculture

142

3.08/ .6585

High School Graduate

141

3.17/ .7470

Less than High School

3.23/ .6611

Post Graduate- Non Agriculture

3.37/ .6640

Note: No Significant Differences shown in Tukey Post-hoc

Test at .05 level/ Differences seen in ANOVA Table. Items

not included in both subsets are significantly different a

Tukey’s Post Hoc multiple comparison test was utilized

Objective Six

Objective six was to compare the perceptions of the agriculture industry among adult

residents of Louisiana by whether or not the respondent had completed a college degree in

an agricultural field. Of the respondents, 18 (3.3%) stated that they had received a college

degree in an agriculture or related field while the remaining 523 (95.6%) stated that they

had not. Of those respondents that stated they obtained a college degree in an agriculture

or related field, 14 (2.6%) obtained an undergraduate degree while 4 (.7%) obtained a

graduate level degree in agriculture or related field.

Independent t-tests were used to accomplish this objective due to the dichotomous

nature of the independent variable. Each of the perception subscales scores were

compared by levels of the independent variable. None of these tests revealed a significant

difference between the group who indicated that they had completed a college degree in

an agriculture or related field and the group that indicated that they had not completed a

college degree in an agriculture field. (See Table 29)

TABLE 29 Comparison of the Perceptions of the Agriculture Industry Among

Adult Residents of Louisiana by Whether or Not They Had

Completed a College Degree in an Agriculture Field

Perception Subscales Degree

M SD t

Sig. t

Food Prices Agriculture or Relateda

3.143 .6838 1.721

.086

Non Agricultureb

3.143 .6888

Farming Practices Agriculture or Relateda

3.356 .6717 .865

.388

Non Agricultureb

3.427 .6474

Issues Relating to Agriculture or Relateda

3.774 .4794 -.736

.462

Food Supply Non Agricultureb

3.699 .5088

Attitude Toward Agriculture or Relateda

3.915 .6671 -.645

.519

Farming Non Agricultureb

Note: df for all test =

539 a n = 18 (3.3%) b n =

523 (95.6%)

3.742 .7722

Objective Seven

Objective seven was to compare the perception of the agriculture industry among adult

residents of Louisiana by whether or not the respondent had prior agricultural training

(defined as whether or not the respondent indicated that they enrolled or participated in

any agriculture course(s) during high school or college, such as FFA, 4-H, or other

activities). This was measured by whether or not the respondent held membership in FFA

and/or 4-H.

Of the five hundred forty seven individuals who provided complete data, three hundred

twenty two (58.87%) respondents stated that they did not have prior agriculture training

such as participation in either FFA, 4-H, or other activities. Two hundred twenty five

(41.13%) respondents stated that they had prior agriculture training such as membership in

at least one of the organizations.

Independent t-tests were used to accomplish this objective due to the dichotomous

nature of the independent variable, prior agricultural training. Each of the four perception

subscale scores were compared by the levels of the independent variable. None of these

tests revealed a significant difference in the perception subscale scores between those

with prior agricultural training and those that did not have this type of training. Complete

results of the t-tests can be found in Table 30.

TABLE 30 Comparison of the Perception of the Agriculture Industry by

Adult Residents of Louisiana by Whether or Not Respondents

Had Prior Agricultural Training

Prior

Perception Subcategory Ag Training M

SD t

Sig. t

Issues Relating to Yesa 3.763

.4611 -1.463

.144

Food Supply Nob 3.700

.5230

Attitude Toward Farming Yesa 3.865

.7220 -1.427

.154

Nob 3.774

.7454 (table con’t.)

Farming Practices

Yesa

3.424

.6609 - .841

.401

Nob

3.377

.6513

Food Prices

Yesa

3.171

.7238 - .746

.456

Note: df for all tests = 545

a n = 225 (41.13%) b n =

322 (58.87%)

Nob

3.127

.6593

Objective Eight

This objective was to compare the perceptions of the agriculture industry among adult

residents of Louisiana by whether or not the respondent had prior agriculture

experience (defined as whether or not the respondent indicated that they are currently a

member of Louisiana Farm Bureau). Five hundred and forty respondents reported their

membership, or lack thereof, in Farm Bureau while seven respondents did not answer this

item. It was noted that 56 (10.0%) respondents acknowledged that they are currently

members of Louisiana Farm Bureau, while 484 (90.0%) of the respondent group stated that

they were not members of Farm Bureau.

In order to accomplish this objective independent t-tests were used due to the

dichotomous nature of the independent variable “Prior Ag Experiences”. Each of the four

perception subscale scores were compared by the levels of the independent variable.

These tests revealed two significant differences in respondent perceptions of agriculture.

These differences were in the perception subscales “Issues Relating to Food Supply” (t=

2.350, p= .019) and “Food Prices” (t= 2.306, p= .022). The differences in perception with

regard to prior agriculture experience as measured by current membership in Farm

Bureau can be seen in Table 31.

These significant differences were such that a more positive perception for “Issues

Relating to Food Supply” and “’Food Prices” was held by those respondents who held

membership in the Farm Bureau Organization.

TABLE 31 Comparison of the Perception of the Agriculture Industry Among

Adult Residents of Louisiana by Whether or Not Respondents Held

a Current Membership in Louisiana Farm Bureau

Farm Bureau

Perception Subscale

Member

Sig. t

Issues Relating to

Yesa

3.875

.506

2.350

.019

Food Supply

Nob

3.709

.497

Food Prices

Yesa

3.345

.806

2.306

.022

Nob

3.122

.669

Farming Practices

Yesa

3.492

.730

1.130

.259

Nob

3.388

.648

Attitude Toward

Yesa

3.889

.839

.791

.429

Farming

Note: df for all tests = 538

a n = 56 (10.0%)

b n= 484 (90.0%)

Nob

3.807

.724

Objective Nine

Objective nine was to determine if a model exists explaining a significant portion of the

variance in perceptions of the agriculture industry among adult residents of

Louisiana from selected measures.

To accomplish this objective, the researcher used the multiple regression analysis

statistical procedure. A multiple regression analysis was performed separately for each of

the perception of agriculture subscale scores derived during the factor analysis. The

subscale scores were defined as the mean of the items included in each of the identified

factors. The selected demographic variables and knowledge subscale scores were used as

independent variables in each analysis. These variables were entered into the analysis

using stepwise entry of the variables due to the exploratory nature of the influence that

these variables had on the perception of agriculture subscale scores.

The following measures were entered as independent variables into the regression

analysis:

a. knowledge of adult residents of Louisiana regarding selected aspects of the

agricultural industry.

b. age

c. gender

d. ethnic background

e. location of Residence (in a rural area, on a farm, in a town, in a city)

f. parish of Residence

g. occupation of the head of household

h. highest Level of Education

The five independent variables that were measured as categorical data were

recoded to create a dichotomous variable from each level of the variable. The recoded

variables included ethnic background, location of residence, parish of residence,

occupation of the head of household, and highest level of education completed. Gender

was a naturally dichotomous variable and did not need recoding. The independent variable

of age was continuous in nature and also did not need recoded.

The first independent variable that needed to be recoded as a series of dichotomous

variables was ethnic background. This variable included six different ethnic background

responses from the study participants. However, all but two of these categories included

very small numbers of respondents (less than ten). Therefore, the researcher decided to

use recoding procedures to establish two ethnic background variables. These variables

were African American and Caucasian, and each of them was defined as whether or not

the respondent was identified as being a member of that ethnic background. For example,

the variable African American was defined as whether or not the respondent identified

himself as a member of this group, and all respondents were classified as either African

American or not African American. The same procedure was used to establish the variable

Caucasian.

For those categorical variables with three or more response categories available,

each respondent was coded as either having or not having the trait represented by each of

the available response categories. The variable location of residence had four response

categories. A separate variable was created for each of the four response categories (in a

rural area, on a farm, in a town, and in a city) with participants classified as having

reported that their residence either was or was not classified as each of the categories. For

example, a variable was created for the category “On a Farm” with all participants who

responded to this item classified as reporting that they resided or did not reside on a farm.

Each of the variables was entered into the analysis utilizing stepwise entry.

The variable “Level of Education” was naturally categorical in nature. A separate

variable was created for each of the seven response categories (Less than High School, High

School Degree, Some College, College Graduate- Non Agriculture, College

Graduate- Agriculture or Related Field, Post Graduate- Non Agriculture, Post Graduate-

Agriculture or Related Field). Each of the respondents was recoded as either having or

not having each of the six educational levels as their highest educational level achieved.

Due to the small number of respondents that indicated their highest level of education

completed to be “Post Graduate- Agriculture or Related Field” (n = 4, .07%), this variable

was removed from the analysis. Each of the remaining variables was entered into the

analysis utilizing stepwise entry.

Similarly, the respondents were provided with four head of household occupation

categories. A separate variable was created for each of the four response categories

(Sales/ Clerical/ Technical, Professional/ Administrative, Laborer, and Other) with

participants classified as having reported that their occupation was or was not classified as

belonging to that category. The responses for each of these variables were entered into

the analysis utilizing stepwise entry.

The independent variable Parish of Residence was previously categorized into four

geographical areas as recognized in previous studies commissioned by the Louisiana

Farm Bureau. The four regions included “Orleans Area”, “Florida Parishes”, “Acadiana”,

and “North Louisiana”. These four regions were recoded into four established separate

dichotomous variables. Each respondent was classified as either residing or not residing

in a geographic region. Each of the four dichotomous variables was then entered into the

regression analysis.

The first dependent variable to be analyzed in this portion of the study was the

perception subscale “Attitude Toward Farming”. The first step in the analysis was the

researcher’s examination of the data for the presence of excessive multicollinearity among

the independent variables in the analysis. This was accomplished through the

examination of the tolerance values and the variance inflation factor (VIF) for the data

included in the analysis.

The independent variable “Ethnic Background- African American” held the lowest

tolerance (.148) and the highest variance inflation factor (VIF= 6.780). The tolerance

values ranged from .148 to .992, and the VIF values ranged from 6.780 to 1.008 (see

Table 32). Hair, et al., (1998) indicated that, “A common cutoff threshold is a tolerance

value of .10”. A tolerance value of .10 would correspond to a VIF of 10.0.

Since the tolerance values and the VIF values were within acceptable ranges, the researcher

concluded that no incidences of excess co- linearity were found in the data.

TABLE 32 Co-linearity Diagnostic Measures for the Regression of Perception

Subscale “Attitude Toward Farming”

Variable Inflation

Variables Tolerance Factors (VIF)

Education- College Graduate- Agriculture .992 1.008

Location of Residence- In a Town .991 1.009

Parish of Residence- Orleans Region .988 1.013

Location of Residence- In a Rural Area .981 1.019

Education- Post Graduate- Non Ag .972 1.029

Gender .969 1.032

Knowledge Subscale- Animal Science .963 1.039

Knowledge Subscale- Processing .963 1.038

Location of Residence- On a Farm .961 1.041

Ethnic Background

- Caucasian .960 1.042

Location of Residence- In a City .960 1.042

Parish of Residence- North Louisiana .955 1.040

Occupation of the head of household

- Sales/ Clerical/ Technical .954 1.048

Parish of Residence- Florida Parishes .951 1.052

Parish of Residence- Acadiana .948 1.055

Education- Some College .935 1.070

Age .930 1.075

Knowledge Subscale- Environmental Science .921 1.086

Education- Less than High School .917 1.091

Knowledge Subscale- Policy .911 1.098

Education- College Graduate- Non Agriculture .907 1.102

Knowledge Subscale- Plant Science .891 1.123

Occupation of the head of household

- Professional/ Administrative .886 1.129

Knowledge Subscale- Louisiana .830 1.204

Education- High School Graduate .784 1.276

Occupation of the head of household

- Laborer .392 2.551

Ethnic Background

- African American .148 6.759

Two way correlations between factors used as independent variables in the regression and

the dependent variable are presented for descriptive purposes. These correlations can be seen

in Table 33. The correlations between the twenty seven variables used as independent

variables in the analysis and the dependent variable “Attitude Toward Farming” perception

sub-scale score were examined and thirteen were found to be statistically significant. Of

these thirteen variables, six variables were highly significant (<.001) and included

Knowledge Subscale- Environmental Science, Gender,

Ethnic Background- African American, Ethnic Background- Caucasian, Education Level-

Less Than High School, and Education Level- High School Graduate. These associations are

found in Table 33.

TABLE 33 Relationship Between the “Attitude Toward Farming” Perception

Subscale Score and Selected Agriculture Knowledge and

Demographic Characteristics Among Adult Residents of Louisiana

Variable r p

Ethnic background- Caucasian .217 <.001

Ethnic background- African/ American - .211 <.001

Knowledge Subscale- Environmental Science .231 <.001

Educational Level- Less Than High School - .168 <.001

Gender a - .159 <.001

Educational Level- High School Graduate - .145 <.001

Occupation of the head of household

- Laborer - .136 .001

Knowledge Subscale- Plant Science .134 .001

Occupation of the head of household

- Sales/ Clerical/ Technical .122 .002

Parish of Residence- Orleans - .107 .007

Knowledge Subscale- Louisiana .092 .017

Educational Level

- College Graduate- Non Agriculture .080 .033

Occupation of the head of household

- Professional/ Administrative .072 .048

Location of Residence- In a Town -.070 .053

Educational Level- Post Graduate- Non Agriculture .059 .089

Parish of Residence- Florida Parishes .052 .114

Knowledge Subscale- Processing - .050 .123

Location of Residence- In a Rural Area .046 .147

Location of Residence- On a Farm .037 .197

Parish of Residence- Acadiana .036 .202

Educational Level- Some College .032 .230

(table con’t.)

Educational Level

- College Graduate- Agriculture

or Related Degree .030 .246

Parish of Residence- North La - .025 .286

Knowledge Subscale- Policy .022 .309

Age - .013 .382

Knowledge Subscale- Animal Science - .012 .390

Location of Residence- In a City .001 .490

Note: One-tailed Significance, n= 531

For all recoded dichotomous variables: 1 = presence of the trait and 0 = absence of the trait

a Male = 1, Female = 2

A stepwise regression analysis was conducted utilizing the probability of F to enter at

.05 and the probability of F at .10 to be removed from the equation. The variables were

entered into the analysis using the stepwise method. The first variable to enter the model

was the Knowledge Subscale- Environmental Science, and it explained 5.3% (F change =

29.690, p= <.001) of the variance in the perception subscale score “Attitude Toward

Farming”. Additionally, the variable “Ethnic Background- Caucasian” explained 3.4% (F

change = 19.432, p = <.001) of the variance, the variable Gender explained 1.4% (F

change = 8.166, p = .004) of the variance, and the variable Occupation of the head of

household- Sales, Clerical, Technical explained 1.3% (F change = 7.583, p = .006). The

remainder of the variables accounted for less than 1.0% of the variance each with Parish

of Residence- Orleans explaining 0.9% (F change = 5.530, p = .019) and Educational

Level- Less Than High School explaining 0.8% (F change = 5.010, p = .026) of the

variance in the model. Combined these six variables explained 13.1% of the variance in

the perception subscale “Attitude Toward Farming”.

The nature of the influence of these variables that entered the model was such that

respondents with higher subscale scores in the knowledge area of “Environmental Science”,

denoted their ethnic background to be “Caucasian”, or stated their Head of household’s

occupation to be in the category of “Sales/ Clerical/ Technical” tended to have higher scores

in the perception subscale “Attitude Toward Farming”. The influence of the variable

gender was such that Males tended to have higher scores in the perception subscale

“Attitude Toward Farming”. Additionally, the influence of the variables that entered the

model was such that respondents denoting their Parish of Residence in the Orleans region

or their highest educational level received as “High School Graduate” tended to have lower

scores in the perception subscale “Attitude Toward Farming”. Table 34 presents the results

of the multiple regression analysis utilizing the perception subscale “Attitude Toward

Farming” as the dependent variable.

TABLE 34 Multiple Regression Analysis of the Perception of Agriculture

Subscale “Attitude Toward Farming” by Selected Knowledge of Agriculture and

Selected Demographic Characteristics

Source of Variation

F p

Regression

37.408

6.235

13.167 <.001

Residual

248.112

524

.473

Total

285.520

530

Model Summary

Standardized

Sig F Coefficients

Model Cumulative Change

Change

Change Beta

Knowledge Subscale - .053 .053 29.690 <.001 .155

Environmental

Science

Ethnic .087 .034 19.432 <.001 .165

Background-

Caucasian

Gender .101 .014 8.166 .004 -.129

Occupation of the .113 .013 7.583 .006 .100

Head of household-

Sales/Clerical/Technical

(table con’t.)

Parish of Residence- .123 .009 5.530 .019 -.101

Orleans

Educational Level- .131 .008 5.010 .026 -.095

Less than

High School

Variables Not in the Equation

Variables t Sig. t

Knowledge Subscale- Processing -1.953 .051

Location of Residence- In a Town -1.877 .061

Knowledge Subscale- Policy -1.549 .122

Educational Level- High School Graduate -1.310 .191

Knowledge Subscale- Animal Science -1.292 .197

Occupation of the head of household

- Professional/ Administrative 1.165 .245

Occupation of the head of household

- Laborer -1.116 .265

Ethnic Background- African American - .703 .482

Parish of Residence- North Louisiana - .979 .328

Parish of Residence- Acadiana .959 .338

Location of Residence- In a Rural Area .781 .435

Age .690 .491

Location of Residence- In a City .606 .545

Educational Level- Some College .590 .555

Knowledge Subscale- Plant Science .571 .568

Location of Residence- On a Farm .515 .607

Educational Level- Post Graduate- Non Agriculture .438 .661

Educational Level

- College Graduate- Agriculture

or Related Degree .388 .699

Educational Level- College Graduate- Non Agriculture .379 .705

Knowledge Subscale- Louisiana - .092 .926

Parish of Residence- Florida Parishes - .012 .990

The second dependent variable to be analyzed in this portion of the study was the

perception subscale “Issues Relating to Food Supply”. The first step in the analysis was the

researcher’s examination of the data for the presence of excessive multicollinearity among

the independent variables in the analysis. This was accomplished thought the examination

of the tolerance values and the variance inflation factor (VIF) for the data included in the

analysis.

The independent variable “Ethnic Background- African American” held the lowest

tolerance (.147) and the highest variance inflation factor (VIF= 6.792). The tolerance

values ranged from .147 to .992, and the VIF values ranged from 6.792 to 1.009 (see

Table 35). Hair, et al. (1998) indicated that, “A common cutoff threshold is a tolerance

value of .10” (p.193). A tolerance value of .10 would correspond to a VIF of 10.0. Since

the tolerance values and the VIF values were within acceptable ranges, the researcher

concluded that no incidences of excess co-linearity were found in the data.

TABLE 35 Co-linearity Diagnostic Measures for the Regression of Perception

Subscale “Issues Relating to Food Supply”

Variables Tolerance Variable Inflation Factors (VIF)

Knowledge Subscale

- Environmental Science .897 1.115

Parish of Residence- Acadiana .992 1.009

Location of Residence- In a Rural Area .985 1.016

Location of Residence- In a Town .984 1.016

Location of Residence- On a Farm .978 1.023

Gender .970 1.031

Educational- College Graduate

- Agriculture or Related Degree .967 1.034

Ethnic Background- Caucasian .967 1.034

Educational- High School Graduate .949 1.054

Location of Residence- In a City .945 1.058

Parish of Residence- Orleans Region .942 1.061

Knowledge Subscale- Processing .937 1.068

Occupation of the head of household

- Professional/ Administrative .936 1.069

Age .925 1.081

Knowledge Subscale- Animal Science .924 1.082

Educational- Post Graduate

- Non Agriculture .923 1.084

Occupation of the head of household

- Sales/ Clerical/ Technical .912 1.096

Knowledge Subscale- Policy .899 1.112

Knowledge Subscale- Plant Science .892 1.122

Occupation of the head of household

- Laborer .884 1.131

Educational- Less than High School .796 1.256

Educational- Some College .787 1.271

(table con’t.)

Parish of Residence- North Louisiana .782 1.279

Parish of Residence- Florida Parishes .781 1.280

Knowledge Subscale- Louisiana .760 1.316

Educational- College Graduate

- Non Agriculture .757 1.322

Ethnic Background- African American .147 6.792

Two way correlations between factors used as independent variables in the regression

were analyzed for descriptive purposes. These correlations between independent variables

can be seen in Table 36. The twenty seven variables were examined and thirteen were

found to have significant two-way associations with the

Perception. Those variables found to have significant associations were Knowledge

Subscale-Policy; Knowledge Subscale-Environmental Science; Knowledge SubscalePlant

Science; Knowledge Subscale-Animal Science; Knowledge Subscale-Processing;

Knowledge Subscale-Louisiana; Age; Gender-Female; Ethnic Background-African

American; Ethnic Background-Caucasian; Parish of Residence-North Louisiana;

Educational Level-Less Than High School; and Educational Level-High School Graduate.

TABLE 36 Relationships Between the “Issues Relating to Food Supply”

Perception Subscale Score and Selected Agriculture Knowledge and

Demographic Characteristics among Adult Residents of Louisiana

Variable r

Knowledge Subscale- Environmental Science .234

<.001

Gender a - .217

<.001

Knowledge Subscale- Policy .192

<.001

Knowledge Subscale- Plant Science .183

<.001

Ethnic background- Caucasian .160

<.001

Ethnic background- African American -.142

.001

Educational Level- High School Graduate -.130

.001

Knowledge Subscale- Louisiana .119

.003

Age .118

.003

Education Level- Less Than High School -.109

.006

Knowledge Subscale- Animal Science .091

.018

Knowledge Subscale- Processing .090

.019

(table con’t.)

Parish of Residence- North La -.086

.024

Parish of Residence- Acadiana .069

.056

Location of Residence- On a Farm .069

Occupation of the head of household

.057

- Professional/ Administrative .065

.067

Educational Level- Some College .056

.098

Parish of Residence- Orleans -.055

.105

Educational Level- Post Graduate- Non Agriculture .050

.124

Parish of Residence- Florida Parishes .048

.137

Educational Level- College Degree- Non Agriculture .048

Occupation of the head of household

.137

- Sales/ Clerical/ Technical .041

Educational Level

.175

- College Degree- Agriculture or Related Field .039

Occupation of the head of household

.185

- Laborer -.028

.258

Location of Residence- In a Town -.022

.309

Location of Residence- In a City -.013

.384

Location of Residence- In a Rural Area .015

.361

Note: One-tailed Significance, N= 531

For all recoded dichotomous variables: 1 = presence of the trait and 0 = absence of the trait

a Male = 1, Female = 2

A stepwise regression analysis was conducted utilizing the probability of F to enter

at .05 and the probability of F at .10 to be removed from the equation. Each variable was

entered into the analysis using a stepwise regression method. The variable

“Knowledge Subscale- Environmental Science” entered the analysis and explained 5.5%

(F change = 30.594, p= <.001) of the variance in the perception subscale score “Issues

Relating to Food Supply”. When the remaining variables were entered into the analysis,

the variable “Gender” explained 3.4% (F change = 19.737, p = <.001) of the variance, the

variable “Knowledge Subscale-Policy” explained 1.5% (F change = 8.941, p = .003) of

the variance, and the variable “Ethnic Background-Caucasian” explained 1.4% (F change

= 8.165, p = .004). The remainder of the variables accounted for less than 1.0% of the

variance each with “Parish of Residence- Acadiana” explaining 0.7% (F change = 3.989, p

= .046) and “Educational Level-High School Graduate” explaining 0.6% (F change =

3.886, p = .049) of the variance in the model. Combined these six variables explained

13.1% of the variance in the perception subscale “Issues Relating to Food Supply”.

The nature of the influence of these variables that entered the model was such that

respondents with higher subscale scores in the knowledge areas of “Environmental

Science” or “Policy”, denoted their ethnic background to be “Caucasian”, or stated their

Parish of Residence as Acadiana tended to have higher scores in the perception subscale

“Issues Relating to Food Supply”. Additionally, those respondents who denoted their

Gender to be “Male” tended to have higher perception scores on the subscale” Issues

Relating to Food Supply”. Those respondents who denoted and their educational level to

be “High School Graduate” tended to have lower scores in the perception subscale “Issues

Relating to Food Supply” due to their inverse relationship. Table 37 presents the results

of the multiple regression analysis utilizing the perception subscale “Attitude Toward

Farming” as the dependent variable.

TABLE 37 Multiple Regression Analysis of the Perception of Agriculture

Subscale “Issues Relating to Food Supply” by Selected Knowledge of Agriculture

and Selected Demographic Characteristics

Source of Variation SS DF MS F p

Regression 17.045 6 2.841 13.135 <.001

Residual 113.330 524 .216

Total 130.375 530

Model Summary

Standardized

R2 R2 F

Sig F Coefficients

Model Cumulative Change Change

Change Beta

Knowledge Subscale- .055 .055 30.594

Environmental

<.001 .153

Science

Gender .089 .034 19.737

<.001 -.174

(table con’t.)

Knowledge Subscale- .104 .015

Policy

8.941 .003

.113

Ethnic .118 .014

Background-

Caucasian

8.165 .004

.116

Parish of Residence- .124 .007

Acadiana

3.989 .046

.088

Educational Level- .131 .006

High School Graduate

Variables Not in the Equation

3.886 .049

-.082

Variables

Sig. t

Knowledge Subscale- Plant Science

1.838

.067

Age

-1.534

.126

Knowledge Subscale- Processing

1.292

.197

Parish of Residence- Florida Parishes

1.222

.222

Location of Residence- On a Farm

1.044

.297

Educational Level- College Graduate- Non Agriculture - .859

.390

Parish of Residence- Orleans - .766

.444

Parish of Residence- North Louisiana - .702

Educational Level

.483

- College Graduate- Agriculture or Related Field .635

.526

Educational Level- Some College .570

.569

Location of Residence- In a Rural Area .504

.614

Location of Residence- In a Town - .446

.656

Educational Level- Post Graduate- Non Agriculture .390

.697

Ethnic Background- African American .371

.711

Educational Level- Less Than High School - .333

.739

Knowledge Subscale- Animal Science .326

Occupation of the head of household

.744

- Laborer .294

Occupation of the head of household

.769

- Sales/ Clerical/ Technical .270

.787

Location of Residence- In a City - .254

.800

Knowledge Subscale- Louisiana - .046

Occupation of the head of household

.963

- Professional/ Administrative - .037

.970

The third dependent variable to be analyzed in this portion of the study was the

perception subscale “Farming Practices”. The first step in the analysis was the researcher’s

examination of the data for the presence of excessive multicollinearity among the

independent variables in the analysis. This was accomplished thought the examination of

the tolerance values and the variance inflation factor (VIF) for the data included in the

analysis. The independent variable “Ethnic Background- African

American” held the lowest tolerance (.148) and the highest variance inflation factor (VIF=

6.740). The tolerance values ranged from .148 to .998, and the VIF values ranged from

6.740 to 1.002 (see Table 38).

Hair, et. al. (2006) indicated that, “A common cutoff threshold is a tolerance value

of .10” (p.193). A tolerance value of .10 would correspond to a VIF of 10.0. Since the

tolerance values and the VIF values were within acceptable ranges, the researcher

concluded that no incidences of excess co -linearity were found in the in the data.

TABLE 38 Co-linearity Diagnostic Measures for the Regression of Perception

Subscale “Farming Practices”

Variables Tolerance Variable Inflation Factors (VIF)

Parish of Residence- North Louisiana .998

1.002

Parish of Residence- Acadiana .997

1.003

Location of Residence- In a Rural Area .996

1.004

Location of Residence- In a Town .996

Education- College Graduate

1.004

- Agriculture or Related Field .994

1.006

Education- Post Graduate- Non Agriculture .993

1.007

Location of Residence- On a Farm

.993

1.007

Parish of Residence- Orleans Region

.992

1.008

Location of Residence- In a City

Occupation of the head of household

.991

1.009

- Sales/ Clerical/ Technical

.990

1.010

Parish of Residence- Florida Parishes

.987

1.013

Education- High School Graduate

.984

1.016

Education- Some College

.981

1.019

Gender

Ethnic Knowledge Subscale

.976

1.025

- Animal Science

Occupation of the head of household

.975

1.026

- Laborer

Background

.974

1.027

- Caucasian

.974

1.027

(table con’t.)

Education- College Graduate

- Non Agriculture

Occupation

.971

1.020

- Professional/ Administrative

.970

1.031

Knowledge Subscale- Processing

Knowledge Subscale

.969

1.032

- Environmental Science

.957

1.045

Education- Less than High School

.952

1.050

Age

.947

1.056

Knowledge Subscale- Policy

.928

1.078

Knowledge Subscale- Plant Science

.899

1.112

Knowledge Subscale- Louisiana

Ethnic Background

.833

1.200

- African American

.148

6.740

Two way correlations between factors used as independent variables in the

regressions were analyzed for descriptive purposes. These correlations between

independent variables can be seen in Table 39. The twenty seven variables were examined

and nine were found to have significant two-way associations with the

Perception. Those variables found to have this significant association included

Knowledge Subscale-Policy, Knowledge Subscale-Environmental Science, Knowledge

Subscale-Plant Science, Knowledge Subscale-Louisiana, Gender, Ethnic BackgroundAfrican

American, Ethnic Background-Caucasian, Location of Residence-In a City, and Educational

Level-Less Than High School.

A stepwise regression analysis was conducted utilizing the probability of F to enter at

.05 and the probability of F at .10 to be removed from the equation. Each variable was

entered into the analysis using a stepwise regression method. The variable “Gender”

entered the analysis and explained 3.6% (F change = 19.894, p= <.001) of the variance in

the perception subscale score “Farming Practices”. When the remaining variables were

entered into the analysis, the variable “Ethnic Background-Caucasian” explained 1.9% (F

change = 10.732, p = .001) of the variance, and the variable

“Knowledge Subscale-Environmental Science” explained 1.4% (F change = 7.795, TABLE

39 Relationship Between the “Issues Relating to Food Supply”

Perception Subscale Score and Selected Agriculture Knowledge and

Demographic Characteristics Among Adult Residents of Louisiana

Variable

Gender a

-.190

<.001

Knowledge Subscale- Environmental Science

163

<.001

Ethnic background- Caucasian

151

<.001

Ethnic background- African/ American

-.148

<.001

Knowledge Subscale- Plant Science

.143

<.001

Knowledge Subscale- Policy

.109

.006

Knowledge Subscale- Louisiana

.102

.009

Education Level- Less Than High School

-.087

.022

Location of Residence- In a City

-.081

.031

Location of Residence- In a Rural Area

.066

.064

Parish of Residence- North La

-.060

.082

Parish of Residence- Orleans

-.060

.085

Educational Level- College Degree- Non Agriculture

-.059

.086

Parish of Residence- Florida Parishes

Occupation of the head of household

.052

.117

- Professional/ Administrative

.048

.136

Parish of Residence- Acadiana

.044

.157

Educational Level- High School Graduate

Educational Level

.040

.180

- College Degree- Agriculture or Related Field

-.039

.185

Knowledge Subscale- Processing

-.037

.198

Location of Residence- In a Town

Occupation of the head of household

.029

.255

- Sales/ Clerical/ Technical

-.024

.290

Educational Level- Some College

.022

.303

Knowledge Subscale- Animal Science

Occupation of the head of household

.019

.334

- Laborer

.016

.353

Age

.012

.387

Location of Residence- On a Farm

.006

.446

Educational Level- Post Graduate- Non Agriculture

-.005

.451

Note: One-tailed Significance, N= 531

For all recoded dichotomous variables: 1 = presence of the trait and 0 = absence of the trait

a Male = 1, Female = 2 p = .005). Combined these three variables explained

6.9% of the variance in the perception subscale “Farming Practices”.

The nature of the influence of these variables that entered the model was such that

respondents with higher subscale scores in the knowledge areas of “Environmental

Science” or denoted their ethnic background to be “Caucasian” tended to have higher

scores in the perception subscale “Farming Practices”. Additionally, those respondents that

denoted their Gender to be “Male” tended to have higher scores in the perception subscale

“Farming Practices”. Table 40 presents the results of the multiple regression analysis

utilizing the perception subscale “Farming Practices” as the dependent variable. The

fourth dependent variable to be analyzed in this portion of the study was the perception

subscale “Food Prices”. The first step in the analysis was the researcher’s examination of

the data for the presence of excessive multicollinearity among the

TABLE 40 Multiple Regression Analysis of the Perception of Agriculture Subscale

“Farming Practices” by Selected Knowledge of Agriculture and

Selected Demographic Characteristics

Source of Variation

SS DF

F p

Regression

15.891 3

5.297

13.062 <.001

Residual

213.709 527

.406

Total

229.600 530

Model Summary

Standardized

R2 R2

Sig F Coefficients

Model

Cumulative Change

Change

Change Beta

Gender

.036 .036

19.894

<.001 -.164

Ethnic

Background-

Caucasian

.055 .019

10.732

.001 .121

Knowledge Subscale- .069 .014

Environmental

Science

7.795

.005 .120

(table con’t.)

Variables Not in the Equation

Variables t

Sig. t

Educational Level- College Graduate- Non Agriculture -1.946

.052

Location of Residence- In a City

-1.803

.072

Educational Level- High School Graduate

1.674

.095

Location of Residence- In a Rural Area

1.639

.102

Knowledge Subscale- Plant Science

1.489

.137

Knowledge Subscale- Policy

1.374

.170

Parish of Residence- Acadiana

1.321

.187

Parish of Residence- Orleans

-1.205

.229

Parish of Residence- North Louisiana

-1.199

.231

Knowledge Subscale- Louisiana

Educational Level

1.121

.263

- College Graduate- Agriculture or Related Field

-1.060

.289

Age

1.051

.294

Educational Level- Less Than High School

-1.006

.315

Knowledge Subscale- Processing

- .961

.337

Location of Residence- In a Town

Occupation of the head of household

.672

.502

- Laborer

Occupation of the head of household

.613

.540

- Sales/ Clerical/ Technical

- .594

.553

Educational Level- Some College

.591

.555

Parish of Residence- Florida Parishes

.570

.569

Educational Level- Post Graduate- Non Agriculture - .545

.586

Knowledge Subscale- Animal Science - .434

.665

Ethnic Background- African American - .423

.672

Location of Residence- On a Farm - .350

Occupation of the head of household

.726

- Professional/ Administrative .178

.859

independent variables in the analysis. This was accomplished thought the examination of the

tolerance values and the variance inflation factor (VIF) for the data included in the analysis.

The independent variable “Location of Residence-In a Town” held the lowest

tolerance (.220) and the highest variance inflation factor (VIF= 4.539). The tolerance

values ranged from .220 to .897, and the VIF values ranged from 4.539 to 1.013 (see Table

41). Hair, et al. (1998) indicated that, “A common cutoff threshold is a tolerance value of

.10” (p.193). A tolerance value of .10 would correspond to a VIF of 10.0. Since the

tolerance values and the VIF values were within acceptable ranges, the researcher

concluded that no incidences of excess co-linearity were found in the in the data.

TABLE 41 Co-linearity Diagnostic Measures for the Regression of Perception

Subscale “Food Prices”

Variables Tolerance Variable Inflation Factors (VIF)

Education- Post Graduate- Non Agriculture .994 1.006

Education- College Graduate- Agriculture .987 1.013

Knowledge Subscale- Louisiana .983 1.017

Parish of Residence- Acadiana .979 1.022

Ethnic Background

- Caucasian .975 1.026

Ethnic Background

- African American .968 1.034

Parish of Residence- North Louisiana .968 1.033

Parish of Residence- Orleans Region .968 1.033

Gender .968 1.033

Age .967 1.034

Occupation of the head of household

- Sales/ Clerical/ Technical .965 1.036

Parish of Residence- Florida Parishes .963 1.038

Education- Less than High School .962 1.039

Knowledge Subscale- Animal Science .953 1.049

Education- Some College .943 1.060

Occupation of the head of household

- Professional/ Administrative .913 1.095

Occupation of the head of household

- Laborer .905 1.105

Education- College Graduate-

Non-Agriculture or Related Field .887 1.128

Education- High School Graduate .882 1.134

Location of Residence- On a Farm .861 1.161

Knowledge Subscale- Policy .843 1.186

Knowledge Subscale- Processing .815 1.227

Knowledge Subscale

- Environmental Science .800 1.250

Knowledge Subscale- Plant Science .772 1.296

Location of Residence- In a City .631 1.584

Location of Residence- In a Rural Area .628 1.592

Location of Residence- In a Town .220 4.539

Two way correlations between factors used as independent variables in the regressions

were analyzed for descriptive purposes. These correlations between independent variables

can be seen in Table 42. The twenty seven variables were examined and thirteen were found

to have significant two-way associations with the Perception subscale “Food Prices”.

Those variables found to have this significant association included Knowledge

Subscale-Policy, Knowledge Subscale-Environmental Science, Knowledge SubscalePlant

Science, Knowledge Subscale-Louisiana, Age, Gender, Ethnic Background-African

American, Ethnic Background-Caucasian, Location of Residence-On a Farm, Location of

Residence-In a Rural Area, Parish of Residence-Acadiana, Parish of Residence-Florida

Parishes, and Educational Level-Post Graduate- Non Agriculture.

A stepwise regression analysis was conducted utilizing the probability of F to enter at

.05 and the probability of F at .10 to be removed from the equation. Each variable was

entered into the analysis using a stepwise regression method. The variable

“Knowledge Subscale-Louisiana” entered the analysis and explained 3.3% (F change =

17.915, p= <.001) of the variance in the perception subscale score “Food Prices”. When the

remaining variables were entered into the analysis, the variable “Age” explained 2.8%

TABLE 42 Relationship Between the “Food Prices” Perception Subscale Score

and Selected Agriculture Knowledge and Demographic

Characteristics Among Adult Residents of Louisiana

Variable r

Knowledge Subscale- Policy .122

.002

Knowledge Subscale- Louisiana .181

<.001

Age .160

<.001

Knowledge Subscale- Environmental Science .152

<.001

Location of Residence- On a Farm .105

.008

Educational Level- Post Graduate- Non Agriculture .102

.009

Location of Residence- In a Rural Area -.096

.014

Knowledge Subscale- Plant Science .094

.015

Ethnic background- African American -.094

.015

Ethnic background- Caucasian .093

.016

Gender a -.091

.018

(table con’t.)

Parish of Residence- Florida Parishes -.087 .022

Parish of Residence- Acadiana .077 .038

Location of Residence- In a Town .074 .043

Educational Level

- College Degree- Non Agriculture -.071 .051

Knowledge Subscale- Processing .069 .055

Occupation of the head of household

- Laborer -.068 .060

Educational Level

- College Degree- Agriculture

or Related Field -.053 .110

Educational Level- Some College -.052 .115

Occupation of the head of household

- Professional/ Administrative .051 .122

Education Level- Less Than High School .042 .169

Educational Level- High School Graduate .047 .139

Location of Residence- In a City -.013 .382

Knowledge Subscale- Animal Science .012 .393

Parish of Residence- North La .011 .397

Parish of Residence- Orleans -.007 .440

Occupation of the head of household

- Sales/ Clerical/ Technical .002 .483

Note: One-tailed Significance, N= 531

For all recoded dichotomous variables: 1 = presence of the trait and 0 = absence of the trait

a Male = 1, Female = 2

(F change = 15.600, p = <.001) of the variance, the variable “Location of Residence-In a

Rural Area” explained 1.5% (F change = 8.613, p = .003), and the variable “Gender”

explained 1.3% (F change = 7.332, p = .007) of the variance in the perception subscale score

“Food Prices”. The last two items entered the analysis explaining less than 1.0% of the

variance. “Educational Level-Post Graduate- Non Agriculture” explained 0.8% (F change =

4.772, p = .029) of the variance while “Location of Residence-In a City” explained 0.8% (F

change = 4.664, p = .031) of the variance in the perception subscale as well. Combined

these six variables explained 10.5% of the variance in the perception subscale “Food

Prices”.

The nature of the influence of these variables that entered the model was such that

respondents with higher subscale scores in the knowledge areas of “Louisiana”, are older in

age, or denoted their highest level of education to be “Post Graduate- Non Agriculture”

tended to have higher scores in the perception subscale “Food Prices”. Additionally, those

respondents that denoted their Gender to be “Male” tended to have higher scores in the

perception subscale “Food Prices”. Those respondents who indicated that their location of

residence was “In a Rural Area” or “In a City” tended to have lower scores in the perception

subscale “Food Prices”. Table 43 presents the results of the multiple regression analysis

utilizing the perception subscale “Farming Practices” as the dependent variable.

TABLE 43 Multiple Regression Analysis of the Perception of Agriculture

Subscale “Food Prices” by Selected Knowledge of Agriculture and Selected

Demographic Characteristics

Source of Variation SS

DF MS F p

Regression 25.704

6 4.284 10.193 <.001

Residual 220.241

524 .420

Total 245.945

530

Model Summary

Standardized

R2 R2

Sig F Coefficients

Model

Cumulative Change

Change

Change Beta

Knowledge

.033 .033

17.915

<.001 .195

Subscale-

Louisiana

Age

.061 .028

15.600

<.001 .192

Location of

Residence-

Rural

.076 .015

8.613

.003 -.179

Gender

.088 .013

7.332

.007 -.116

(table con’t.)

Educational .097 .008 4.772 .029 .092

Level- Post Graduate

Non Agriculture

Location of .105 .008 4.664 .031 -.112

Residence-

City

Variables Not in the Equation

Variables t Sig. t

Knowledge Subscale- Policy 1.941 .053

Knowledge Subscale- Environmental Science 1.805 .072

Educational Level- High School Graduate 1.655 .099

Parish of Residence- Acadiana 1.573 .116

Educational Level

- College Graduate- Agriculture or Related Field -1.551 .121

Parish of Residence- Florida Parishes -1.358 .175

Educational Level- Less Than High School 1.272 .204

Educational Level- College Graduate- Non Agriculture -1.176 .240

Ethnic Background- African American -1.139 .255

Occupation of the head of household

- Laborer -1.307 .192

Ethnic Background- Caucasian 1.243 .214

Location of Residence- On a Farm .943 .346

Location of Residence- In a Town - .933 .351

Knowledge Subscale- Plant Science - .897 .370

Knowledge Subscale- Animal Science - .633 .527

Educational Level- Some College - .555 .579

Occupation of the head of household

- Professional/ Administrative .439 .661

Knowledge Subscale- Processing .228 .819

Parish of Residence- North Louisiana - .200 .842

Parish of Residence- Orleans - .143 .887

Occupation of the head of household

- Sales/ Clerical/ Technical .027 .978

CHAPTER 5

SUMMARY

Summary of Purpose and Specific Objectives

The purpose of this study was to determine the knowledge of animal science, plant

science, environmental science, food science, processing of food, and Louisiana by the

adult residents of Louisiana. The second purpose of this study was to determine the

perception of “Attitude Toward Farming”, “Issues Relating to Food Supply”, “Farming

Practices”, and “Food Prices” by the adult residents of Louisiana. The evaluations of

both knowledge and perception were also compared to determine if a relationship exists

between these two factors.

This study had the following objectives:

1. To describe adult residents of Louisiana on the following demographic

characteristics:

a. age,

b. gender,

c. ethnic background,

d. location of residence (in a rural area, on a farm, in a town, in a city),

e. parish of residence,

f. occupation of the head of household,

g. highest level of education.

2. To determine the knowledge of the adult residents of Louisiana regarding the

following selected aspects of the agriculture industry:

a. animal science,

b. plant science,

c. environmental science,

d. food science.

3. To determine the perceptions of the agriculture industry among adult residents of

Louisiana.

4. To determine if a relationship exists between knowledge of selected aspects of the

agriculture industry (defined as animal science, plant science, environmental

science, policy, and processing) and perceptions of the agriculture industry among

adult residents of Louisiana.

5. To determine if a relationship exists between perceptions of the agriculture industry

and the following demographic characteristics of adult members of the general

public in Louisiana:

a. age,

b. gender,

c. ethnic background,

d. location of residence (in a rural area, on a farm, in a town, in a city),

e. parish of residence,

f. occupation of the head of household,

g. highest level of education

6. To compare the perceptions of the agriculture industry among adult residents of

Louisiana by whether or not the respondent had completed a college degree in an

agricultural field.

7. To compare the perceptions of the agriculture industry among adult residents of

Louisiana by whether or not the respondent had prior agricultural training

(defined as whether or not the respondent indicated that they enrolled or participated

in any agriculture course(s) during high school or college, such as

FFA, 4-H, or other activities).

8. To compare the perceptions of the agriculture industry among adult residents of

Louisiana by whether or not the respondent had prior agricultural experience

(defined as whether or not the respondent indicated that they are currently a

member of Louisiana Farm Bureau).

9. To determine if a model exists explaining a significant portion of the variance in

perceptions of the agricultural industry among adult members of the general public

in Louisiana from the following measures:

a. knowledge of adult residents of Louisiana regarding selected aspects of the

agricultural industry,

b. age,

c. gender,

d. ethnic background,

e. location of residence (in a rural area, on a farm, in a town, in a city),

f. parish of residence,

g. occupation of the head of household,

h. highest level of education

Summary of Methodology

The target population for this study was residents of the state of Louisiana. For the

purposes of this study, included were all adult individuals that were residents of the state

of Louisiana. This study residency was derived by the individual’s registration of

telephone service in their name at a residence in the state of Louisiana. The accessible

population was defined as the group of adult individuals in the defined target population

who had registered residential telephone numbers. The accessible population was 547

adult residents of Louisiana. The frame of the population was established by the current

residential phone listings registered in the state phone company databases.

The instrument utilized in this study was based on a questionnaire found during a

review of related literature. The instrument consisted of fifty-five questions. This

instrument consisted of three sections: demographic characteristics, agriculture knowledge,

and perception of agriculture. The knowledge and perception sections of this instrument

were adapted from a similar questionnaire utilized by Frick, et al. (1995b).

Data was collected using phone interview techniques. The data was collected in the

month of February in the year 2008. The surveys were conducted randomly over the

course of a week.

Summary of Major Findings

The major findings of this study are discussed by objective.

Objective One

This objective was to describe adult residents of Louisiana on selected demographic

characteristics.

Of the 547 participants in this study, there were more females (n=289, 52.8%) than

males (n= 258, 47.2%). With regard to age, the largest response was from the age group

of 60+ years of age (n= 199, 37.3%) while the age group with the smallest number of

respondents was the 18-29 years of age category (n= 37, 6.9%). The majority of the

respondents stated that their ethnic background was “Caucasian” (n= 387, 70.7%) while

the ethnic background of African-American (n= 143, 26.1%) represented the second most

frequent response.

The largest group of respondents (n= 142, 26.3%) indicated their highest level of

education completed as “College Graduate- Non Agriculture”. Only four (.7%) of the

respondents indicated their highest level of education completed as “Post Graduate-

Agriculture or Related”, making this group the smallest response group to this item.

More of the respondents considered themselves to be the Head of Household (n= 338,

61.8%) than those who did not consider themselves to be the Head of Household (n= 209,

38.2%). A majority of the respondents were classified in the occupation of the head of

their household as “Laborer” (n= 262, 50.7%). Those who classified the occupation of the

head of their household to be “Other” (n= 13, 2.5%) were the smallest group of

respondents.

Logistically, the greatest number (n= 64, 11.7%) of respondents stated that their

parish of residence was East Baton Rouge while seven other parishes only recorded one

respondent (.001%) per parish. When grouped regionally, the area with the greatest

number of respondents was Acadiana (n= 181, 33.1%) while the area with the smallest

number of respondents was Orleans (n= 46, 8.4%). The majority of respondents (n= 235,

43.4%) stated that they lived in what they considered to be the “In a City”. Additionally,

the smallest group (n= 23, 4.2%) indicated that they considered their location of residence

to be “On a Farm”.

Objective Two

This objective was to determine the knowledge of the adult residents of Louisiana

regarding the agriculture industry. Participants were asked to answer true or false to

twenty statements. The statement that was responded to correctly by the largest number of

respondents was the statement “Hamburger is made from the meat of pigs” (n= 506,

2.5%). The statement that received the smallest number of correct responses was the

statement “Homogenizing kills bacteria in mild with heat” (n=116, 21.2%). The

researcher computed a knowledge score for each participant in the study by coding each

correct response as “1” and each incorrect response as “0”. These calculated scores

ranged from a low of 5 to a high of 20. These scores were averaged for the group and

the overall mean agriculture knowledge scores of adult residents of Louisiana was

13.60 (SD= 2.743).

The scale was further analyzed utilizing five of the seven predetermined areas of

agricultural knowledge proposed in previous research. The five categories were

environmental science, plant science, animal science, policy, and processing. Four

questions were assigned to each subscale with one question from each of the

predetermined areas overlapping into a sixth subscale associated with the state of

Louisiana. A subscale score was computed for each of the six subscales, defined as the

total number of correct responses in that subscale. The computed subscale scores

revealed that the respondents had the highest level of knowledge in the subscale of

environmental science (m= 3.07, SD= .959) and the lowest reported level of knowledge in

the subscale of processing (m= 2.51, SD= .828).

Objective Three

This objective was to determine the perceptions of the agriculture industry among

adult residents of Louisiana. Participants were asked to respond to twenty questions in

order to indicate their level of agreement or disagreement using a five-point Likert-Type

scale. The researcher designed an interpretive scale in order to interpret the response

items. The statement with the highest level of agreement was “Not all land is suitable for

farming” (m= 4.16, SD= 1.002) and was classified in the “agree” category. The statement

with the highest level of disagreement was “Farmers earn too much money” (m= 1.61,

SD= .898) and was classified in the “disagree” category.

Several statements were designed such that a “disagree” response indicated a more

positive perception of agriculture. The researcher reversed the scale on these items prior

to the identification of subscales. Four subscales were identified to be underlying

constructs of the perceptions of agriculture. The four factor model explained 35.24% of

the total explained variance. The four subscales included “Attitude Toward Farming”,

“Issues Relating to Food Supply”, “Farming Practices”, and “Food Prices”. The

researcher computed a subscale score for each of the constructs that are defined as the

mean of the items included in each respective subscale. The computed mean scores for

the various factors ranged from a high of 3.81 for the factor titled “Attitude Toward

Farming” to a low value of 3.14 for the factor labeled “Food Prices”.

Objective Four

This objective was to determine if a relationship exists between knowledge of selected

aspects of the agriculture industry and perceptions of the agriculture industry among adult

residents of Louisiana. Pearson’s Product Moment Correlations were calculated to

determine the direction and strength of this relationship. Davis’ (1971) descriptors were also

used to describe these correlations.

The highest relationship between the knowledge subscale “Policy” and the perception

subscales was with the perception subscale “Issues Relating to Food Supply” (r= .21,

p<.001). This relationship was such that higher levels of knowledge regarding

“Policy” were associated with more positive perceptions regarding “Issues Relating to

Food Supply”. The highest relationship between the knowledge subscale “Environmental

Science” and the perception subscales was with the perception subscale “Issues Relating to

Food Supply” (r= .23, p<.001). This was a “Low Association” based on Davis’ (1971)

descriptors. The relationship was such that higher levels of knowledge regarding

“Environmental Science” were associated with more positive perceptions of agriculture.

The highest relationship between the knowledge subscale “Plant Science” and the

perception subscale scores was found to be with the “Issues Relating to Food Supply”

subscale (r= .20, p<.001). The highest relationship found between the knowledge

subscale “Animal Science” and the perception subscale scores was with the “Issues

Relating to Food Supply” subscale (r= .12, p=.01). The most significant relationship

found between the knowledge subscale “Processing” and the perception subscales was the

perception subscale “Issues Relating to Food Supply” (r= .09, p= .03). In examination of

the relationship between the knowledge subscale “Louisiana” and the perception subscale

scores, the most significant relationship was found with the perception subscale “Food

Prices” (r= .17, p=<.001).

Each of these significant relationships was positive and such that higher levels in the

knowledge subscales were associated with a more positive perception of agriculture.

Objective Five

This objective was to determine if relationships exist between perceptions of the

agriculture industry and selected demographic characteristics of adult residents of Louisiana.

Mean scores previously identified in each of the subscales were utilized in examining these

relationships.

When examining the relationship between perceptions of agriculture and the

demographic characteristic age, two perception subscales scores were found to be

significantly related. The highest association identified was with the “Food Prices” subscale

(r= .16, p<.001) and was associated as a “Low Association”. This association was such that

respondents who were older tended to have more positive perceptions of agriculture on

“Food Prices”. The other significantly correlated subscale was with the “Issues Relating to

Food Supply” (r= -.12, p= .004) and was classified as a “Low Association”. The nature of

this relationship was such that younger respondents tended to have higher perceptions

related to the “Issues Relating to Food Supply” subscale. Results from the t-test that

examined the relationship between gender and perception of agriculture revealed a

significant difference in the subscales “Attitude Toward Farming”, “Issues Dealing with

Food Supply”, and “Farming Practices” all at p=<.001, and “Food Prices” at p= .049. The

nature of all of these differences were such that male respondents tended to have more

positive perceptions of agriculture on the subscales “Attitude Toward Farming”, “Issues

Dealing with Food Supply”, “Farming Practices”, and “Food Prices”.

In examining the relationship between the perceptions of agriculture and the variable

ethnic background, two ethnic groups were utilized. These included African

American and Caucasian. The ethnic groups of Hispanic, Asian, Native American, and

Other were not included in this measurement due to the small number of respondents in

each of these categories (Hispanic, n=8; Native American, n=7; Asian, n=1; Other, n=1).

The Leven’s Test for Equality of Variance revealed that the ethnic groups of African

American and Caucasian had significantly different variances for two of the perception

subscales “Attitude Toward Farming” (F= 4.078, p= .044) and “Farming Practices” (F=

9.682, p= .002). The t-test results showed significant differences between the two ethnic

groups and all of the perception subscales. The greatest significant difference was found in

the perception subscale “Attitude Toward Farming”. The differences were such that those

indicating that their ethnic background was Caucasian tended to have more positive

perceptions of agriculture than those who indicated their ethnic background to be African-

American.

Examination of the relationship between perceptions of agriculture and the variable

“Location of Residence” revealed one significant difference on the perception subscale

“Food Prices” (F= 2.961, p= .032). Although the Analysis of Variance test showed a

significant F value, when Tukey’s Post Hoc test was applied to the data no significant

differences were found.

When examining the relationship between perceptions of agriculture and the variable

“Parish of Residence”, the parishes indicated by the respondents were grouped into

geographic regions. This was done due to the insufficient number of respondents in all

parishes needed for individual parish comparisons. No significant differences were found

among the four geographic regions in Louisiana.

Examination of the relationship between perceptions of agriculture and the

demographic variable “Occupation of Head of Household”, the occupations were grouped

into four categories. These categories included “Laborer”, “Sales/ Clerical/ Technical”,

“Administrative/ Professional”, and “Other”. A significant difference was found among

the occupation groups on the perception subscale “Attitude Toward

Farming” (F= 4.122, p= .007). Tukey’s Post Hoc test revealed that the group “Laborer”

(M= 3.69, SD= .8257) was significantly different from the “Sales/ Clerical/ Technical”

group (M= 3.89, SD= .6591) and the “Professional/ Administrative” (M= 4.03, SD= .6625)

group.

In order to examine the relationship between perceptions of the agriculture industry

and the demographic variable “Highest Level of Education” the researcher used a one

way analysis of variance test. This test found two significant F values in the perception

subscales of “Attitude Toward Farming” (F= 3.475, p= .002) and “Food

Prices” (F= 2.176, p= .044). With regard to the perception subscale “Attitude Toward

Farming”, Tukey’s Post Hoc test revealed significant differences between the educational

level “Less than High School” (M= 3..40, SD= .8996) and three other educational levels

including “Some College” (M= 3.84, SD= .6275), “College Graduate- Non Agriculture” (M=

3.91, .6650), and “Post Graduate- Non Agriculture” (M= 3.91, SD= .6859). With regard to

the perception subscale “Food Prices”, although the analysis of variance test showed a

significant F value, the Tukey’s Post Hoc test found no significant differences

Objective Six

This objective was to compare the perceptions of the agriculture industry among adult

residents of Louisiana by whether or not the respondent had completed a college degree in

an agriculture or related field. The respondents indicated that 18 (3.3%) had received a

college degree in an agriculture or related field while the remaining 523 (95.6%) had not.

Of the respondents who stated that they had received a college degree in an agriculture or

related field, 14 (2.6%) obtained an undergraduate degree while four (.7%) obtained a

graduate level degree.

When the perception subscales were compared by whether or not they completed a

college degree in agriculture or related degree, no significant differences were found in

any of the subscales.

Objective Seven

This objective was to compare the perceptions of the agriculture industry among adult

residents of Louisiana by whether or not the respondent had prior agricultural training. This

training was defined as whether or not the respondent indicated that they enrolled or

participated in any agriculture course(s) during high school or college, such as FFA, 4-H, or

other activities. Of the 547 who responded, 322 respondents stated that they did not have

prior agricultural training while 225 respondents stated that they had received prior

agricultural training.

Each of the four perception subscales was compared by the independent variable. No

significant differences were revealed in the perception subscales scores between those with

prior training and those that did not have this type of training.

Objective Eight

This objective was to compare the perceptions of the agriculture industry among adult

residents of Louisiana by whether or not the respondent had prior agriculture experience.

This experience is defined as whether or not the respondent indicated that they are

currently a member of Louisiana Farm Bureau. Of the five hundred and forty respondents

to this item of the survey, 56 (10.0%) respondents indicated that they were currently

members of Louisiana Farm Bureau while 484 (90.0%) of the respondent group stated that

they were not currently members of Louisiana Farm Bureau.

Each of the four perception subscale scores was compared by the two levels of the

independent variable. A t-test was used due to the dichotomous nature of the independent

variable. These tests revealed two significant differences in the perception subscales

“Issues Relating to Food Supply” (t= 2.350, p= .019) and “Food Prices” (t= 2.306, p=

.022). These differences were such that respondents who had prior agriculture experience

(current membership in Louisiana Farm Bureau) tended to have more positive

perceptions of agriculture in the subscales areas of “Issues Relating to Food Supply” and

“Food Prices”.

Objective Nine

This objective was to determine if a model exists explaining a significant portion

of the variance in perceptions of the agriculture industry among adult residents of

Louisiana from selected measures. These measures included knowledge of adult residents

of Louisiana, age, gender, ethnic background, location of residence (in a rural area, on a

farm, in a town, in a city), parish of residence, occupation of the head of household, and

highest level of education. Dependent variables consisted of the perception subscales

determined previously by factor analysis. This objective was accomplished by using a

multiple regression analysis. The mean score for each of the perception subscales was

based on the information from the items lading into each subscale. The selected

demographic variables and knowledge subscale scores were used as independent variables

in the analysis and were entered into the analysis using stepwise entry. The variables that

were nominal or ordinal were recoded to make all of the variables dichotomous in nature.

Each dependent variable was examined for the presence of multicollinearity among the

independent variables in the analysis. This was accomplished through examination of the

tolerance values and the variance inflation factor (VIF) for the data included in the

analysis. All tolerance values were within the acceptable range above the .10 threshold.

The first dependent variable to be analyzed in this portion of the study was the perception

subscale “Attitude Toward Farming”. The independent variable “Knowledge Subscale-

Environmental Science” explained 5.3% (F change= 29.690, p= .001) of the variance in the

perception subscale. When the remaining variables were entered into the analysis, the

variable “Ethnic Background- Caucasian” explained 3.4% (F change=

19.432, p= <.001) of the variance, the variable “Gender” explained 1.4% (F change=

8.166, p= .004) of the variance, and the variable “Occupation of the Head of Household-

Sales/ Clerical/ Technical” explained 1.3% (F change= 7.583, p= .006). Combined, these

six variables explained 13.1% of the variance in the perception subscale “Attitude Toward

Farming”.

The nature of the influence of these variables was such that respondents with higher

subscale scores in the knowledge subscale “Environmental Science”, denoted their ethnic

background to be “Caucasian”, stated their head of household’s occupation to be in the

category of “Sales/ Clerical/ Technical”, or indicated their gender to be “Male” all tended

to have higher perception subscale scores relating to “Attitude Toward Farming”.

Additionally, those indicating their parish of residence to be within the “Orleans” region

or their highest educational level received as “High School Graduate” tended to have a

lower subscale scores in the perception subscale “Attitude Toward Farming”.

The second dependent variable to be analyzed in this portion of the study was the

perception subscale “Issues Relating to Food Supply”. The independent variable

“Knowledge Subscale- Environmental Science” entered into the analysis and explained

5.5% (F change= 30.594, p= <.001) of the variance in the perception subscale score

“Issues Relating to Food Supply”. When the remaining variables were entered into the

analysis, the variables “Gender- Female” explained 3.4% (F change= 19.737, p= <.001) of

the variance, “Knowledge Subscale- Policy” explained 1.5% (F change= 8.941, p=

.003) of the variance, and the variable “Ethnic Background- Caucasian” explained 1.4% (F

change= 8.941, p= .003). Combined, these six variables explained 13.1% of the variance

in the perception subscale “Issues Relating to Food Supply”.

The nature of the influence of these variables that entered the model was such that

respondents with higher subscale scores in the knowledge subscale area “Environmental

Science” or “Policy”, denoted their ethnic background to be “Caucasian”, denoted their

gender to be “Male”, or stated their parish of residence to be in the “Acadiana” region tended

to have higher subscale scores in the perception subscale “Issues Relating to Food Supply”.

Additionally, those respondents who denoted their highest educational level received to be

“High School Graduate” tended to have lower subscale scores in the perception subscale

“Issues Relating to Food Supply”.

The third dependent variable to be analyzed in this portion of the study was the

perception subscale “Farming Practices”. The variable “Gender- Female” entered the

analysis and explained 3.6% (F change = 19.894, p= <.001) of the variance in the

perception subscale score “Farming Practices”. When the remaining variables were

entered into the analysis, the variable “Ethnic Background- Caucasian” explained 1.9% (F

change = 10.732, p = .001) of the variance, and the variable “Knowledge Subscale-

Environmental Science” explained 1.4% (F change = 7.795, p = .005). Combined these

three variables explained 6.9% of the variance in the perception subscale “Farming

Practices”.

The nature of the influence of these variables that entered the model was such that

respondents with a higher subscale score in the knowledge area of “Environmental

Science”, or denoted their Gender to be “Male”, or denoted their ethnic background to be

“Caucasian” tended to have higher subscale scores in the perception subscale “Farming

Practices”.

The fourth dependent variable to be analyzed in this portion of the study was the

perception subscale “Food Prices”. The variable “Knowledge Subscale- Louisiana” entered

the analysis and explained 3.3% (F change = 17.915, p= <.001) of the variance in the

perception subscale score “Food Prices”. When the remaining variables were entered into the

analysis, the variable “Age” explained 2.8% (F change = 15.600, p = <.001) of the variance,

the variable “Location of Residence- In a Rural Area” explained 1.5% (F change = 8.613, p =

.003), and the variable “Gender” explained 1.3% (F change = 7.332, p = .007) of the variance

in the perception subscale score “Food Prices”. The last two items entered the analysis

explaining less than 1.0% of the variance. “Educational LevelPost Graduate- Non

Agriculture” explained 0.8% (F change = 4.772, p = .029) of the variance while “Location of

Residence-In a City” explained 0.8% (F change = 4.664, p =

.031) of the variance in the perception subscale. Combined these six variables explained

10.5% of the variance in the perception subscale “Food Prices”.

The nature of the influence of these variables that entered the model was such that

respondents with higher subscale scores in the knowledge areas of “Louisiana”, were older

in age, denoted their Gender to be “Male”, or denoted their highest level of education to be

“Post Graduate- Non Agriculture” tended to have higher subscale scores in the perception

subscale “Food Prices”. Those respondents who indicated that they considered their

location of residence to be “In a Rural Area” or “In a City” tended to have lower subscale

scores in the perception subscale “Food Prices”.

Conclusions, Implications, and Recommendations

Conclusion One

Adult members of the general public of Louisiana have a moderately high level of

knowledge with regard to agriculture. This conclusion is based on the overall mean

agriculture knowledge score of adult residents of Louisiana for the twenty items included in

the survey instrument equaling 13.60 (SD = 2.743) out of 20, or 68% of the knowledge items

were answered correctly.

These findings were similar to the results found by Frick, et al. (1995b) that surveyed

456 adults from rural areas and 428 adults from urban areas and asked 35 knowledge based

questions. The mean knowledge score for the respondents from rural areas was 24.25

(69.3%) while the mean knowledge score for the respondents from urban areas was 24.69

(70.5%). This similarity is also seen in the results found by Frick, et al. (1995a) who

surveyed 550 4-H members asking 35 knowledge-based questions. This research found

their mean knowledge score to be 23.07 (65.9%).

These findings are different from the findings of the study by Frick, et al. (1995c)

who surveyed 668 rural high school students and 453 urban inner-city high school

students. The overall knowledge score for both of these groups combined was 56%.

Although these two groups only included high school students, the mean knowledge scores

for each of these groups was below the mean knowledge score that was found in this study.

The findings of this study are also different from a similar study by Wright, Stewart, and

Birkenholz (1994) that reported knowledge scores for 435 eighth grade students, 164

enrolled in agriculture and 371 not enrolled in agriculture. The mean knowledge scores for

each of these groups was18.08 (52.6%) which was lower than the mean found by the

researcher.

Implications of this conclusion include efforts in educating the public, such as Ag

in the Classroom and commodity promotional materials, may have been successful in

increasing the knowledge levels of adult residents. The knowledge level found in the

adult population of Louisiana shows that the members of this group have a strong base for

developing further their knowledge levels.

Based on this conclusion, the researcher recommends continuation and expansion in

agriculture education efforts. Mass media promotion utilizing billboards, television ads,

newspaper articles, and web postings should be continued and increased. Additionally,

the researcher recommends that the use of “YouTube”, blog sites, and group networking

sites such as “Twitter” should be utilized in order to expand the population reached by

such efforts.

The researcher further recommends increasing publication of classroom agriculture

education materials designed to reach various audiences. Materials developed and

distributed by the Ag in the Classroom program are an example of such publications. The

researcher also recommends that commodity promotion boards continue to revamp

current educational publications as well as increase publication of classroom educational

materials for all commodities. One example of such a publication is the material

produced by the Rice Promotion Board in 2009. It is also recommended that the Ag in

the Classroom program expand to in-service teachers and distribute materials to educators

at the school level.

Conclusion Two

Adult residents of Louisiana have the highest levels of knowledge in the

Environmental Science area among the agricultural content areas addressed in this study.

The computed mean concept area scores revealed that adult members of the general public

of Louisiana reported the highest level of knowledge for the items in the “Environmental

Science” factor with a mean knowledge score of 3.07 (SD= .959) which fell into the

interpretive category of moderately high and was 76.8% of the items answered correctly.

These findings were similar to the results of the study by Frick, et al. (1995c) that

surveyed rural and inner-city high school students and rural and urban adult residents. The

study found Natural Resource knowledge scores, similar to the Environmental Science

knowledge concept area, to be higher than all other knowledge scores for concept areas.

The mean knowledge scores for the Natural Resources concept areas were 3.96 (79.2%)

and 3.90 (78.0%) in the study by Frick, et al. (1995c).

Implications of this conclusion are such that the high knowledge level seen in the

environmental science area is evidence that the general public of Louisiana is concerned

about the environment and its potential benefits. This is seen in the importance that the

general public has placed on the environment with the increased growth of programs such

as recycling, water conservation using household fixtures that use less water, and

decreased air emission programs. These programs are examples of the expanding

emphasis placed on the environmental sector. The increases in youth organizations

involvement in environmental projects such as the adopt-a-highway program are

increasing awareness while allowing individuals to become directly involved in

environmental issues. The increased exposure that the Louisiana coastline has been given

over the past five years has placed greater emphasis on the natural environment. This

exposure has given rise to interest in environmental changes and their role in the

increased risk of catastrophic events. The attention given to these areas has aided greatly

in the public relations aspect of increasing knowledge among the public with regard to

environmental issues.

The researcher recommends further research to determine adult resident’s

involvement in environmental activities such as participation in wildlife conservancies or

membership in wildlife habitat organizations such as Delta Waterfowl. This study should

also include exploratory research into the amount of time devoted to environmental

programs. This measurement should also determine extracurricular activities that include

some type of environmental connection, such as visits to state parks or conservatories such as

the Bluebonnet swamp in Baton Rouge.

The researcher also recommends an expanded study to measure the environmental

science concept area in more detail. For the purposes of the current study, each concept

area was limited to four questions. A more extensive study to measure knowledge of

environmental issues should include multiple questions on several areas associated with the

environment. Multiple questions on air quality, water quality, soil quality, urban impact,

agricultural impact, impact of industry, and conservation should be explored.

Due to the high level of knowledge reported in the environmental science area, the

researcher requests support of environmental organizations that assist in the continued

growth of this knowledge base such as the Sierra Club, Delta Waterfowl, and the

Nature Conservancy.

Conclusion Three

Adult members of the general public of Louisiana have more positive perceptions of

agriculture with regard to the “Attitude toward Farming” and “Issues Related to Food

Supply”. This conclusion is based on the mean perception scores of adult residents of

Louisiana for the perception concept areas being equal to 3.81 (SD= .73) and 3.72 (SD= .49),

respectively. These concept area scores were derived from the responses to the five items

included in each concept area of the survey.

These findings are similar to multiple studies on sustainable agriculture practices. Those

studies include results reported by Williams and Wise (1997) who surveyed 41 teachers

educating 464 eleventh and twelfth grade students involved in agriculture education on their

perceived impact of sustainable agriculture practices. The results showed a composite mean

across all items listed in the survey to be 3.82 (SD= .39). A similar study by Williams

(2000) surveyed 386 eleventh and twelfth grade student enrolled in agriculture education on

their expected impact from sustainable agriculture.

The mean across all items utilized in this survey was found to be 3.42. A study by

Gammon and Scofield (1998) also showed composite means for “younger” and “potential”

agricultural producers to be 3.49 (SD= .45) and 3.65 (SD= .44) respectively with regard to

the perceived results of sustainable agriculture practices. This data was collected over a

four year period from participants on the campus of Iowa State

University during the winter program and included 188 respondents.

The findings of the current study, however, differ from results reported by Frick,

et al. (1995c). Low perception totals were observed for rural and urban adults and rural

and urban high school students. The total perception scores for these four demographic

groups range from 73.97 (SD= 12.97) or 42.2% to 85.79 (SD= 15.42) or 49.0%. These

reported perception scores are lower than those seen in the current study and show a

lower level of perception of agriculture among rural and urban adults and high school

students than the level of perception of agriculture of adult residents in Louisiana.

The implications of this study are different for the two sets of perception of

agriculture concept areas. The perception concept areas “Attitude toward Farming” and

“Issues Related to Food Supply” are more positive in nature. The production of our food

and fiber and the activity that provides the public these goods are thought of as a

necessity. Necessities are held in higher regard than luxuries and for this reason it can be

reasoned that perception scale attitudes toward farming and the activities included in this

process are more positive.

The ability to purchase food and have it readily available for the public’s consumption

can be taken for granted. In recent years the general public of Louisiana has dealt with

multiple natural disasters that have altered the ability to transport, refrigerate, and

produce these goods. Many of the retail food chains were empty for weeks following the

fall hurricanes of 2008 and 2005. The general public was able to realize the importance

of their food supply. This reality, coupled with the ongoing publicity for world hunger

brings food supply to the forefront. We have seen the faces of starving children, the

helicopters delivering airdropped food supplies, and the advertisements here to sponsor

starving children around the world. This type of media attention to food supply, and lack

thereof, could be an explanation of why the food supply subscale is more positive.

The perception concept areas “Farming Practices” and “Food Prices” are both

ambivalent in nature. It can be reasoned that individuals are always questioning prices of

any purchase that is made, food purchases being no exception. With the economic issues

that we have seen on the cost of production, processing, transportation, and on the final

cost of the product purchases are made with caution due to the rising costs. For this reason

it is expected that the perception of the “Food Prices” concept area would not be

considered less positive. Similarly, it is to be expected that the perception of agriculture

concept area “Farming Practices” would also be interpreted as being less positive.

Negative media influences can play a huge role in the perception of the general public.

Headlines that highlight soil erosion due to runoff, application of harmful chemicals, and

use of large amounts of ground water are only a few of the headlines that the general public

may witness. For this reason, the use of best management practices is constantly

questioned.

The researcher recommends that all agriculture producers make deliberate efforts to

be in compliance with rules and regulations governing farming practices. Initiatives such

as the LSU AgCenter’s Master Farmer Program should be encouraged and supported by

the Louisiana legislature. Support for this program via correspondence to local legislators

is encouraged.

The researcher also recommends that Louisiana Farm Bureau and the LSU AgCenter

continue and expand positive media messages giving factual information regarding

farming practices. Support of programming including “This Week in Louisiana

Agriculture” should be encouraged. Short web based video clips depicting factual

agriculture information should be expanded and made more readily available to the

general public. Piazza (2009) states that information presented on the LSU AgCenter’s

website spans gender, geographic location, and age. This portal could potentially impact

nearly every citizen in the state of Louisiana. This portal, as well as those of other

agricultural organizations, should be maintained to include factual information on all

aspects of agriculture.

Similarly, the researcher encourages the promotion of “agritourism” endeavors.

Agritourism allows members of the general public to visit farms and ranches which

increases their knowledge and allows individual perceptions to be made. These perceptions

are void of the influences placed by negative and misleading media. The researcher also

encourages the protection of those individuals engaged in coordination of agritourism. It

has been suggested in prior research (Wright, et al, 1994) that positive perceptions are a

prerequisite to the development of good policy decisions related to agriculture. Louisiana

law 9:2795.5., passed in 2008, limits the liability of producers involved in agritourism

activities. Support of legislation similar to this is encouraged and recommended.

Conclusion Four

Agricultural knowledge and perception of agriculture are related. This conclusion is

based upon the 17 significant correlations between the six knowledge concept areas and

the four perception concept areas. Each of these correlations was significant at the .05

level or higher.

These findings are similar to those of Frick, et al. (1995c) who concluded that more

positive perception s might result if the agriculture literacy level was enhanced. This

study surveyed 1121 high school students that were residents of both rural and inner-city

areas. This conclusion was based on the significantly higher knowledge scores for both

rural and inner-city students that were reflected in the higher perception scores reported

for both groups.

These findings are also similar to the relationship between knowledge and perception

reported by Wright, Stewart, and Birkenholz (1994). This study surveyed 435 eleventh

grade students that were either enrolled in agriculture education or not enrolled in

agriculture education. They reported a weak relationship between agriculture knowledge

and perceptions of agriculture (r= .174).

The implications of this study are such that an increase in agricultural knowledge

may result in a more positive increase in perceptions of agriculture. The more positive the

perception that an individual holds with regard to an issue, the more likely they are to

support that issue both socially and financially. For this reason, more positive perceptions

are also related to legislative support in times of hardship, economic downslides, and

catastrophic events. It has been stated in prior research that positive knowledge and

perceptions about agriculture are a prerequisite to the development of good policy

decisions (Wright, et al., 1994). This support could result in the minimization for

adversity surrounding decisions that are made governing all aspects of agriculture, from

production to marketing. This increase in knowledge and perception will also provide

individuals the basis upon which to feel secure. This security may lead to the willingness

of individuals to be open to new techniques in production, processing, and marketing of

agricultural goods.

Promotion by use of billboards, television ads, newspaper articles, and web postings

should be continued and increased. The use of new web based media outlets is also

encouraged due to their growing popularity.

Conclusion Five

Caucasians have more positive perceptions of agriculture than African Americans.

This conclusion is based on the significant differences between the two ethnic

background groups of Caucasian and African American with regard to the four perception

concept areas. These concept areas were derived from the twenty perception items

utilized in the study. The differences between the two ethnic background groups was

such that respondents indicating their ethnic background to be Caucasian tended to have

more positive perceptions of agriculture than those respondents who indicated their ethnic

background to be African-American.

These findings are similar to the results reported by Newsom-Stewart and Sutphin

(2000) who surveyed 925 tenth grade students in the state of New York. This study observed

ethnic differences in perceptions of agriculture between “white” or Caucasian students and all

other ethnic groups in six of the thirteen perception descriptors. These descriptors include

“importance to the economy”, “importance to the future”, “politically important”, “a place for

high school graduates to work”, “high tech”, and “a place for college graduates to work”.

This study also observed significant differences between white students and those of other

ethnic backgrounds in twelve of the thirteen perception descriptors utilized in this study.

These findings are also similar to the results by Mendoza (2006) who found that

respondents who indicated their race to be Caucasian were more willing to participate in

environmental programs as opposed to those respondents who indicated their race to be

African-American. It can be reasoned that individuals would likely not participate in

activities without positive outcomes. For this reason, willingness to learn is directly related

to positive perception.

The findings from the current study are also similar to the results reported by Frick, et

al. (1995b) who studied 668 rural high school students and 453 inner-city high school

students. The rural high school student group consisted of primarily white students

(88.5%) while the inner-city high school group consisted of primarily black students

(86.3%). The overall perception score was not significantly different and only two of the

seven perception scores for the concept areas were significantly different between the two

groups. Rural high school students received higher mean scores in both the “Plants” and

“Animals” concept area subscores.

Implications of this conclusion are such that the lower perceptions of agriculture

scores for the ethnic background group “African- American” show that more effort is

needed to influence perceptions of this demographic group. Given that the consumption of

food is the primary contact that many minorities have with agricultural sciences, many

minorities exhibit limited awareness of the science and business skills that are utilized in

this industry. Minimal advertising has been seen on television stations that target minority

populations that present the agriculture industry in a factual manner. This presentation is

needed to shift the “negative images of agriculture by minorities” toward one based on the

scientific and business dimensions of the agriculture industry. The low perception affects

recruitment into agriculture related education and employment fields. The resulting small

numbers of individuals involved in the agriculture industry are maintained by ongoing

perceptions that agriculture is an industry focused on vocational skills and one meant for

white males. (Wiley, et. al., 1997)

The researcher recommends targeted public relations that deliver positive messages or

presents the current traits of the field of agriculture accurately on television stations, in

magazines and printed materials, and via web postings that are frequented by individuals

with the ethnic background “African American”. The ability to communicate to all ethnic

groups the highly diverse and scientific nature of agriculture should become a priority.

Expansion of recruitment that targets members of the “African American”

population for both educational and occupational endeavors is highly recommended.

Efforts to include all demographic groups in agriculture fields would become a goal of all

individuals currently involved in the agriculture industry. Programs such as the Penn

State College of Agriculture Sciences “Food and Agricultural Sciences Workshop” (FAS)

should be adopted by other universities. This program consists of five days of instruction

taught by university faculty to expose academically talented minority students to non

vocational curricula and career opportunities in the agriculture sciences. Wiley, et al.

(1997) studied the effect of the FAS program on the perceptions of 44 students enrolled in

the program during the summer of 1994. The findings showed stability among the

participants’ attitudes before and up to a year post involvement in the FAS program. The

findings did show a positive shift in the posttest means in the areas that indicated

participants associated agricultural careers with more than production agriculture and held

a greater understanding about agricultural jobs. (Wiley, et. al., 1997)

The researcher recommends further research that includes a more detailed study to

measure the impact of ethnic background on the knowledge of agriculture concept areas.

A study that would examine correlations between ethnic background and knowledge

would expand the understanding of the correlations that are noted between several

knowledge and perception concept area scores in the current study.

Understanding the perception of minority groups with regard to agriculture would enable

this industry to educate this demographic in a more productive manner. This greater

understanding could allow education to be placed in concept areas that are in more need of

resources.

Conclusion Six

There is no difference in perceptions of agriculture between those adult residents of

Louisiana who have a college degree in an agriculture related field and those adult

residents of Louisiana who do not have a college degree in an agriculture related field.

This conclusion is based on the findings of the study that no significant differences

between the two groups were found in the four perception concept areas by whether or not

the respondents indicated that they had completed a college degree and that their degree

was in an agriculture related field. These concept area comparisons included:

“Food Prices” (t539 = 1.721, p = .086); “Farming Practices” (t539 = 0.865, p = .388);

“Issues Relating to Food Supply” t539 = -0.736, p = .462); and “Attitude toward

Farming” (t539 = -0.645, p = .519).

Results of the current study are in contrast to those reported by Brown and Stewart

(1993) when they examined 264 middle school students enrolled in an agriculture

curriculum. Brown and Stewart utilized a pre and post test design to examine the effects

of enrollment in an agriculture course on a student’s knowledge and attitude toward

agriculture. Their findings showed a change in the knowledge of and attitudes toward

agriculture after being enrolled in the course.

Several possible explanations exist for the findings regarding the lack of effects from

having received direct instruction in agriculture. One such explanation for the current

study’s outcome is the low number of participants that indicated they held a college degree

in agriculture or a related field. Eighteen respondents of the five hundred and forty seven

total respondents indicated that they obtained a college degree in an agriculture or related

field. This low number of respondents was too small to provide a valid comparison

between the two groups.

An additional explanation is evidence of the evolution that colleges of agriculture

have seen over time. A greater percentage of agriculture colleges contain degree programs

that are not considered to be traditional agriculture programs related to production

agriculture. Areas of study such as textile design, dietetics, workforce education,

landscape design, and environmental science are all examples of non traditional

agriculture programs. Although these areas of study are highly related to the agriculture

industry of today, they attract a more diverse group of individuals into their programs that

are more likely to have no agriculture background.

Due to this lack of agriculture background in students enrolled in agriculture one of

the growing responsibilities that agricultural educators face is to develop a positive

association with agriculture in the public sector (Perritt and Morton, 1990). The researcher

recommends continuation of universal agriculture programming that allows all students

(agriculture and non agriculture) to gain exposure to basic agriculture knowledge. Courses

such as the Agriculture 1001 taught at Louisiana State University are examples of general

agriculture courses that expose all participants to the broad range of agriculture areas and

the connection between these areas. Expansion of this type of program to include

information on basic areas of agriculture such as animal sciences, plant sciences, food

science, agricultural business, and environmental science is also recommended by the

researcher. This expansion provides a mode of delivery for the knowledge base needed to

change perceptions toward the positive.

Conclusion Seven

No differences in perception of agriculture were identified between those adult residents

of Louisiana who had prior agriculture training (defined as whether or not the respondent

indicated that they enrolled or participated in any agriculture courses such as FFA, 4-H, or

other agriculture activities) and those who did not have this type of prior agriculture

experience.

This conclusion is based on the lack of significant statistical differences between the

four perception subscale scores. These subscale comparisons included: “Food Prices” (t454

= -.746, p = .456); “Farming Practices” (t454 = -.841, p = .401); “Issues Relating to Food

Supply” t454 = -1.463, p = .144); and “Attitude Toward Farming” (t454 = -1.427, p = .154).

Results of the current study are different than those reported by Dyer, Lacey, and

Osborn (1996). Dyer, et al, surveyed 495 college of agriculture freshmen enrolled at the

University of Illinois during the academic year 1994-1995. These findings were such that

students who completed high school agriculture courses displayed different attitudes toward

the field of agriculture than students who were not high school agriculture program

participants. These two groups were significantly different on ten of the twenty one construct

areas. Overall, this study found that students who participated in high school agriculture

programs held a more positive attitude of agriculture with regard to agriculture as a career

field, high school agriculture programs, and university agriculture programs.

These findings were also in contrast to those found by Frick, et al. (1995a). Frick et al

reported that the overall mean perception score for 4-H members was high. The perception

score in the this study was increased by the demographic characteristic “enrolled in high

school agriculture education” and showed positive relationship to the perception scores

with an F value of 7.74.

Possible explanations exist for the findings regarding the effects from having

participated in 4-H, FFA, or other agriculture activities. One explanation is that the lack

of differences in perception between those with prior agriculture experience and those

with no prior agriculture experience are a potential cause of the evolution of the 4-H and

FFA Programs. The evolution of these programs has allowed the inclusion of

nontraditional instruction that was geared more toward professional and personal

development of the membership. Although this diversification has increased the diversity

of the membership and the ability to reach the interests of more of the population, the

small level of mandated instruction on traditional agriculture skills within the FFA and 4H

programs has also had an effect on the level of perception scores. The placement of both

4-H and FFA in the school setting could also be a reason for this evolution due to these

programs having to justify their existence with purpose measurable in a similar manner to

all other curricula.

The researcher recommends the continued inclusion of traditional agriculture skill

training. This training provides participants hands on agriculture activities that they are able

to base their perceptions upon. The marriage of both traditional training and the non-

traditional training that encourages personal development are also recommended.

Conclusion Eight

Differences were identified in perceptions of agriculture for respondents with prior

agricultural experience (defined as whether or not the respondent indicated that they are

currently a member of Louisiana Farm Bureau) and the perceptions of agriculture for

those respondents who did not have prior agricultural experience (defined as whether or

not the respondent indicated that they are currently a member of Louisiana Farm Bureau).

This conclusion is based on the findings of the study that significant differences

were found in two perception subscale areas by whether or not the respondent indicated

that they were currently a member of Louisiana Farm Bureau. The significant differences

found were in the perception of agriculture concept areas “Issues Relating to Food Supply”

(t538 = 2.350, p = .019) and “Food Prices” (t538 = 2.306, p = .022).

To date, no prior research on the effect that membership in a general farming

organization such as Farm Bureau has on the perception of agriculture of adults has not

been conducted. Mission statements of various state Farm Bureau organizations include the

Enhancement of the public perception of agriculture (Texas Farm Bureau, 2009) and the

desire to communicate to and inform individuals and organizations that influence

perceptions of agriculture (Kansas Farm Bureau, 2009)

One possible explanation for the differences in perceptions of agriculture could be

farm bureau members’ involvement in production agriculture. Membership in Farm

Bureau is usually acquired in two ways; as a pre-requisite to obtaining insurance through the

subsidiary company, or by requesting outright membership due to interest in protecting the

rights and lifestyles of farm and rural Americans. Most individuals only realize that they are

“members” of the Farm Bureau organization if they obtain membership in the second manner

due to their vested interest in the actions of the organization. For this reason, it can be

reasoned that those who have vested interest in production agriculture would be the same

individuals who declared membership in Farm Bureau.

Individuals that are involved in production agriculture would have more positive

perceptions of agriculture in both perception concept areas; “Issues Relating to Food

Supply” and “Food Prices”. The more positive perceptions of agriculture are due to their

involvement in or close relationship to the production of food. The knowledge that they

have with regard to the production safeguards, amount of labor needed for food production,

and knowledge of world commodity prices gives farm bureau members the ability to derive

a more fact based perception.

The researcher recommends increased promotion of agriculture to members of the

general public. Information on production agriculture that would allow members of the

general public the ability to base perceptions upon need to be placed into mass media

markets.

The researcher also recommends increased promotion to the membership that is not

actively engaged or related to production agriculture but holds membership in Farm

Bureau. Depiction of this group and information targeted directly toward them would

allow information to be delivered to an already captive audience due to their link to the

organization via the insurance sector of the Farm Bureau. This type of education is

encouraged and recommended.

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