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THE SUCCESS OF AGRICULTURE IN MICHIGAN COUNTIES:
A WEAK TEST OF SUSTAINABILITY

By

Cristin Popelier Hosmer

A THESIS
Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of
MASTER OF SCIENCE

Agricultural Economics

2009

ABSTRACT

THE SUCCESS OF AGRICULTURE IN MICHIGAN COUNTIES:
A WEAK TEST FOR SUSTAINABILITY

By
Cristin Popelier Hosmer

Due to growing concerns about issues such as population growth, resource
depletion, and social inequity, the concept of sustainability has received signiﬁcant
attention in academic, environmental and policy circles. In the context of agriculture,
sustainability implies that three main objectives (economic, environmental, and social
sustainability) are compatible and synergistic. To date, little effort has been devoted to
developing an analytical framework for evaluating this goal in agriculture. Despite a
growing body of literature about sustainability, there is no framework for testing for the
sustainability of agriculture at the local level. Therefore, this thesis proposes a Weak
Test of Sustainability for Michigan, as it relates to short-term agricultural economic
sustainability.

This thesis research advances the concept of agricultural sustainability by
estimates a series of equations and tests for complimentarity and substitutability between
economic, environmental and social indicators. The ﬁnding that increased environmental
and social performance does not necessarily diminish economic performance suggests
that the goal of sustainability is feasible. Results suggest that there are structural
differences in the ability of various regions in Michigan to achieve short-term economic

sustainability.

ACKOWLEDGEMENTS

‘What a long strange trip it has been’ since I ﬁrst began this masters’ degree
journey. It has been challenging, although extremely worthwhile. I truly appreciate the
support of the numerous individuals who have helped me along the way. However, space
limitations dictate that I only mention a few. I would ﬁrst like to thank my loving
parents, Mark and Carol Popelier, for a lifetime of encouragement, support, and
friendship. I would also like to express a deep sense of gratitude to my husband, Brian
Hosmer. I would have never made it this far without you and I look forward to our future
in agriculture.

I would like to acknowledge the generous support and guidance of Dr. Soji
Adelaja, my advisor and mentor. I am grateful for his insights, not only on this thesis
effort, but also with respect to my future career goals and plans. I would also like to
thank my committee members, Jim Bingen, Patricia Norris, and John Warbach for their
patience and interest in my research and my future.

I would like to express my appreciation to the Land Policy Institute (LPI) for
being a place to learn, grow, and be challenged. LPI folks I would like to especially
thank include Mary Beth Graebert, Chuck McKeown, Malika Chaudhuri and Ben Calin.
The data and mapping assistance of Chuck McKeown, Ben Calin and Manuel Colunga-

Garcia are greatly appreciated.

iii

TABLE OF CONTENTS

LIST OF TABLES .................................................................................... v
LIST OF FIGURES ................................................................................. vi
Chapter I: Introduction .............................................................................. 1
1.1 Background ....................................................................................... 1
1.2 Research Objectives ........................................................................... 7
1.3 Organization of the Study .................................................................... 8
Chapter H: Review of Literature .................................................................. 10
2.1 Origin of the Sustainable Development Concept ........................................ 10
2.2 The Application of Sustainability to Agriculture ........................................ 14
2.3 Measuring Sustainability ................................................................... 22
2.3.1 Past Modeling Efforts ...................................... . ............................ 24

2.4 Summary ...................................................................................... 26
Chapter III: Conceptual Framework ............................................................. 28
3.1 The Agro-Economic Sustainability Models Conceptualized .......................... 28
3.2 Summary ..................................................................................... 33
Chapter IV: Analytical Framework, Data and Estimation .................................... 34
4.1 Analytical Framework for WTS .......................................................... 34
4.2 Framework for Comparing the Different Models ....................................... 39
4.3 Deﬁnitions and Calculations of the Data and Variables ............................... 40
4.4 Summary ..................................................................................... 49
Chapter V: Empirical Results ..................................................................... 50
5.1 Results of the Standard Short-Term Economic Sustainability Model ............... 50
5.2 Results of the Environmentally Augmented Model .................................... 54
5.3 Results of the Socially Augmented Model .............................................. 56
5.4 Results of the Fully Augmented Model .................................................. 58
5.5 Comparison of the Models via F-Tests & Predicted Values ........................... 59
5.6 Summary ..................................................................................... 61
Chapter VI: Conclusions & Recommendations ................................................. 62
Appendix 1: Tables .................................................................................. 66
Bibliography ......................................................................................... 76

iv

LIST OF TABLES

Table 1: Historical Highlights of Michigan Agriculture ....................................... 16
Table 2: Results of the Joint F-Tests .............................................................. 60
Table A1: Deﬁnitions of the Independent Variables and Sources ............................ 66
Table A2: Parameter Estimates for Short-term Sustainability on Michigan Farms:

Models 1-4 ........................................................................................... 70
Table A3: Correlation Coefﬁcient Matrix ....................................................... 71
Table A4: NFI__ac and Predicted Values for each County in Models 1-4 ................... 72

LIST OF FIGURES

Figure 1: Various Deﬁnitions of Sustainable Development ................................... 12
Figure 2: Concentric Representation of the Multiple Objectives of Sustainability. 13
Figure 3: Interpretations of Sustainable Agriculture ........................................... 17

vi

Chapter I: Introduction

1.1 Background

Agriculture is a key industry in the United States. It provides numerous
economic, environmental, and social beneﬁts to the public. Not only does agriculture
provide American consumers with eighty-three percent of their food and ﬁber needs
(USDA-ERS, 2006), it is also a major conduit for environmental and cultural resources
(Hellerstein and Nickerson, 2002; Boody and Krimke, 2000). Activities in the
agriculture and food-processing sector represent the largest economic sectors in many
states.

In Michigan, the subject state for this study, the agri-food system, which includes
farming and allied activities, generates $70 billion in economic activity annually,
provides 1.05 million jobs and has made $8.6 billion in capital investments over the past
ﬁve years (Knudson and Peterson, 2009; Peterson et.al., 2006). Agriculture also serves
as the primary steward of forty-six percent of the land in the US (USDA-ERS, 2005). Its
amenity beneﬁts include open space preservation and a ‘rural character’ that contributes
to tourism, employment, and quality of life (“Understanding the N on-Economic Impacts
of Agriculture,” 2006). Agriculture also contributes other non-pecuniary beneﬁts such as
wildlife habitat, water and air recharge, and greenery (Kline and Wichelns, 1996).

Despite the importance of these beneﬁts, their non-pecuniary nature makes it
difﬁcult to capture their values monetarily. This is a signiﬁcant issue especially at a time
when the agricultural sector is predicted in 2009 to experience declining commodity

prices, increasing input prices and disrupted credit markets, (Morehart and Johnson,

2009). Even though we have seen record levels of farm income being set in the past 10
years, many states agricultural sectors are experiencing an increase in the consolidation
of farms, regulation, and urban pressures, along with a decline in the farm population, all
of which have had adverse impacts on domestic agriculture (Morehart and Johnson,
2009; Adelaja and Sullivan, 1998; USDA-ERS, October, 2005; Gardner, 1992). The
proﬁt squeeze in agriculture is also affecting the industrial organization of the agricultural
industry. Mid-sized farms have been particularly hard hit by the changing business
climate of agriculture.

According to the USDA, agricultural land is being converted because farmers can
produce more with less land, higher land values, and increased population pressure
(USDA-ERS, October, 2005). The economic well-being of farm households has
historically been largely dependent on farm-related income. However, farmers are
increasingly relying on off-farm income to supplement farm income (Fernandez-Comejo
et.al., 2007). In fact, the percentage of the labor force employed in farming continues to
decline and is now close to 1 percent (US Census Bureau, 2009; USDA-NASS, 2007).

Simultaneously, agriculture is the subject of increasing environmental and social
welfare concerns (Feenstra, 1997). The off-farm environmental impacts of agriculture,
such as erosion and excess nutrients that impair surface and ground water, have led to
regulation that is more stringent (Feenstra, 1997). The rapid conversion of farmland to
development has also spurred greater desire by the public to stop growth. Various
growth management tools are emerging; however, these tools may limit the ability of
farmers to proﬁt from their rising land values (Adelaja and Gottlieb, 2009).

Increased competition, rapid consolidation and the concentration of farms near

processors, has also driven smaller farms out of the market and raised a host of other
economic, environmental, and social problems (MacDonald et.al., 1999). Other budding
concerns include the limited diversity in the agricultural labor force and limited access to
the amenities provided by agriculture, despite the community, human, and physical
resources that support agriculture. The ability of the farm household enterprise and the
adjacent communities to sustain themselves (or its viability), in other words, is
increasingly problematic.

Agriculture’s success is not dependent solely on the economic performance of the
industry. It is also intrinsically linked to the overall social and environmental climate
within which it exists. Farmers must coexist with non-farmers and neighbors. In fact, the
social and environmental beneﬁts that agriculture conveys affect the climate in which
agriculture must exist, and in-turn affect the economic bottom-line. Increasingly, the
term sustainability is used to describe the modern optimization challenges of agriculture.
Farmers must be economically, environmentally, and socially sustainable to be
successful. While the market has not been well developed for farmers to capture some of
the beneﬁts of their positive environmental extemalities positive feedback through the
regulatory environment and business climate could offer indirect rewards to farmers’
positive externalities.

Sustainable agriculture is an alternative philosophy to conventional agriculture. It
suggests that farmers can indeed capture some of the potential returns associated with
their positive extemalities (i.e. do economically well, by doing good). Because
sustainable agriculture considers the natural environment and seeks to minimize negative

environmental and social extemalities of agricultural production practices such as

excessive nutrient and water use, it is often expected to be more proﬁtable for farmers
(USDA-ERS Amber Waves, 2004; Schaller, 1993). The conservation of natural
resources on farmland can improve the environment while providing ﬁnancial beneﬁts in
the form of increased efﬁciency and a technological competitive advantage (USDA-ERS
Amber Waves, 2004). Considering the growing importance of policy and regulation,
sustainable and responsible farming can also yield dividends in terms of public support at
the local, state, and national levels, which can translate into long-term survival and
viability (Bird and Ikerd, 1993).

The key to understanding the concept of agricultural sustainability is to recognize
that farmers need a nurturing environment and business climate to be able to maintain
viable operations. Agricultural sustainability is tantamount to long-term viability, not
just short-term economic viability, as typically evaluated by economists. For agriculture
to be sustainable long-term, it must be economically sustainable in the short run. Since
long—term viability is tied to long-term policy support and the regulatory environment,
agriculture’s survival is a multi-dimensional problem. Farming must be economically,
environmentally, and socially compatible with the objectives of the public in order to
thrive. By deﬁnition, therefore, a sustainable agricultural system is one that is: (1)
economically viable, (2) enhances the environmental quality of the resource base, and
(3) enhances the quality of life and social objectives of farmers and society as a whole
(Schaller, 1993).

The best understood aspect of agricultural sustainability is economic viability.
Numerous studies have examined economic viability by identifying its determining

factors through regression analysis. The determinants identiﬁed in previous studies fall

into four broad categories: farm ﬁnancial characteristics, location and structural factors,
operator characteristics and policy climate factors. The key determinants include farm
ﬁnancial characteristics such as household income from farm and non-farm activities,
debt service, asset holdings, and diversity of the revenue stream. Operator characteristics
including age, experience, and education are also key determinants. Community and
location variables including urban inﬂuence, access to markets and input services, and
regional factors also impact the viability of farms. Socio-demographic and attitudinal
characteristics including beliefs about regulations, right-to-farm conﬂicts and the degree
of optimism are also viability factors (Adelaja and Rose, 1988; Adelaja and Sullivan,
1998).

Despite the fact that agriculture does not exist in a vacuum and its survival may
be linked to its compatibility with non-farm objectives, few, if any, of these studies have
looked at the relationship between economic viability and social and environmental
viability. There is little empirical evidence, to date, that relates the environmental and
social aspects of sustainability to the proﬁtability of agriculture in the short-term. While
Adelaja and Sullivan, (1998) included a few environmental and social variables, such as
farmers’ political involvement in the community and chemical use, they did so without
acknowledging the concept of sustainability and the critical nature of the interaction
between elements of sustainability and farm survival. Furthermore, it is expected that
previously deﬁned models, which do not include such factors, are not adequately
speciﬁed. The failure of economists to account for environmental and social factors
implies that the standard viability model suffers from model speciﬁcation error. Because

some of these concepts are unobservable, conceptual indicators need to be developed as a

precursor for measuring and modeling the affect of the environmental and social aspects
of sustainability on farm success.

Considering that economic viability, environmental stewardship and social
compatibility constitute the holistic elements of sustainability, it is important to sort out
how they interact with each other. This thesis seeks to understand the interactions
between the elements of sustainability in agriculture by estimating the relationship
between farm sector proﬁtability and some key sustainability factors. Secondly, in the
absence of an existing framework for linking agriculture’s success to economic, social,
and environmental variables, a novel framework labeled a weak test of sustainability
(W TS) has been developed.

WTS involves comparing the economic base model for short-term agro-economic
sustainability to three augmented models through a series of model speciﬁcation tests and
F-tests. The augmented viability models speciﬁed herein are hypothesized to improve
speciﬁcation in comparison to the base sustainability model. A positive relationship
between proﬁtability and sustainability factors suggests that sustainability is (in a weak
sense) possible and synergies between the three components of sustainability exist.
Alternatively, a strong test of sustainability would not be a test on the proﬁtability of
agriculture in the short-term, but instead a test of the long—term holistic concept of
sustainability, a concept that this point to too large and too complex to measure and
therefore outside of the scope of this thesis.

The general objective of this thesis research is to improve the knowledge base
surrounding sustainability by evaluating the effect of economic, environmental, and

social sustainability factors on net farm income. The conceptual framework used herein

will help improve the perception of proﬁtability and help to clarify the roles of
environmental and social factors in enhancing agriculture’s success, as well as identify
the monetary tradeoffs of pursuing the multiple goals of sustainable agriculture. At a
minimum, this thesis will contribute to a better understanding of model speciﬁcation and
determinants of short-term agro-economic sustainability. Furthermore, this thesis
research proposes that a comprehensive sustainability index, generated from the predicted

values of the augmented viability models, is possible.

1.2 Research Objectives

The prime research objective of this thesis is to explore and investigate the
concept of short-term economic sustainability as it applies to local (county-level)
agriculture. By investigating the relationship between the three aspects of sustainability,
which are economic viability, environmental stewardship and social compatibility, the
hope is to gain a better understanding of the monetary trade-offs associated with pursuing
multiple agricultural objectives. Because the framework for modeling and measuring
viability and proﬁtability is well documented, it is used as a framework for developing a
Weak Test of Sustainability (WT S). Alternatively, if an actual direct measure (dependent
variable) for sustainability could be identiﬁed one could carry out a strong test for
sustainability. Unfortunately, we do not have a precise and clearly deﬁned measure of
sustainability in agriculture; therefore, we must rely on the WTS.

Net farm income per acres (NFI_ac) is a good indicator (dependent variable) of
short-term sustainability because if a farm is not proﬁtable, it will go out of business (and
is not sustainable). The basic framework is based on the following philosophy: ‘if

sustainable principles and practices are implemented and proﬁtability is not impacted,

then it sufﬁces to say that farmers can sustain themselves and the environment and
socially coexist within the community by doing well environmentally and within the
community’. Using Michigan agriculture as a case study, WTS (Weak Test of
Sustainability) is the test in this thesis for assessing whether increasing levels agro-
economic sustainability detracts from proﬁtability.

The speciﬁc research objectives are operationalized by estimating a series of
conceptual models and their predicted values. First, a standard model of economic
sustainability (or viability) is estimated by regressing economic factors against net farm
income per acre. Next, three augmented models of economic sustainability are estimated.
The ﬁrst includes environmental factors. The second includes social factors. The third
includes both environmental and social factors. The signs of the coefﬁcients of the
augmented models provide insight on sustainability. The efﬁcacy of the augmented
models can further be compared to the standard viability model via Speciﬁcation error
testing to determine appropriateness. The predicted values serve as a county—level index
or benchmark of short-term agro-economic sustainability, which can be used as a broad
assessment tool for counties interested in the parameters that effect proﬁtability in the

agricultural sector.

1.3 Organization of the Study

Chapter Two is the review of literature in which the economic viability and
sustainability literature are explored. Relating to the concept of sustainability, literature
on environmental stewardship and social compatibility are also reviewed.

Chapter Three presents the conceptual framework for a series of agricultural

economic sustainability models; one standard and three augmented models are included.

Chapter Four presents an empirical framework and describes the data and
estimation processes for the standard and augmented viability models speciﬁed for
Michigan. Chapter Four also highlights the joint F-Test the basis of the weak test of
sustainability in agriculture.

Chapter Five summarizes the empirical ﬁndings of this research.

Chapter Six summarizes and draws conclusions on the research and provides

recommendations for future research in the area.

Chapter II: Review of Literature

As mentioned in Chapter I, the goals of this study are to review the literature on
sustainability in agriculture, develop a framework for short-term economic sustainability
and a weak test of sustainability, and identify the determinants of sustainability as it
applies to the success of the agricultural sector in Michigan. In this chapter, the literature
on sustainability and other related agricultural concepts is reviewed. A good starting
point is to highlight the broader paradigm of sustainability and its numerous deﬁnitions.
Literature on the application of the sustainability concepts in the agricultural sector is

also reviewed. A brief discussion of past modeling efforts is also included.

2.1 Origin of the Sustainable Development Concept

The concept of sustainability is a broad and encompassing paradigm that has
allowed interested parties in the academic, public policy, and business communities to
assess and address issues related to growth and development patterns, intergenerational
fairness, and resource use. Sustainable development is an intrinsic framework that links
survival to various economic, environmental, and social objectives. It is essentially a
triple-bottom-line approach to development, which addresses the modern challenges of
meeting the needs of a growing worldwide population. In fact, many of the discussions
surrounding sustainability tend to focus on the role of environmental, natural, and cultural
resources in sustaining economic well-being into the future (Woodward, 2000).

The concept of sustainability is deeply rooted in society’s ethical and moral
obligation to the commons (air, land, and water) (Perrnan et.al, 2003). The concept

emerged partly because of concerns about population growth, the farmers’ limited ability

10

to feed a growing population, resource constraints, and other land use problems. Early
thinkers, such as Mill, Malthus, and Ricardo ﬁrst identiﬁed the limits of the Earth’s
support system to sustain human consumption and growth in the 19th and 20th Century
(Perman et.al., 2003; Pinstrup-Andersen, 2001). These works tended to focus on the
extemalities of grazing on common lands, the feasibility of continuing long-run economic
growth given unfettered population growth, diminishing returns, the scarcity of ﬁnite
resources, environmental degradation, and declining human welfare (Penman et.al.,
2003). In addition, the Progressive Conservation Movement, led by, Aldo Leopold, and
Wendell Berry in the United States, further developed the underpinning concepts of
sustainable development (Hernandez, 1997: Batie, 1989; Berry, 1987; and Leopold,
1949).

The sustainable development ideology gained signiﬁcance in the international
policy arena with the World Commission on the Environment and Development Report,
Our Common Future (Brundtland and Khalid, 1987). Better known as the United
Nation’s Brundtland Report, it deﬁned sustainable development as development which
meets the needs of current generations without compromising the ability of future
generations to meet their own needs (UNECE, 2005). This particular deﬁnition
introduced the concept of intergenerational balance and optimization and brought the
worldwide community together in an effort to make sustainability a public policy goal
(Goodland, 1995). The idea, later adopted as the theme of the UN Conference on
Environment and Development (the Rio Conference), inspired the international

community to attempt to deﬁne and measure sustainability (Munasinghe and Shearer,

ll

1995). However, the challenge of deﬁning and observing sustainability in practice has

proven difﬁcult. A list of a few such deﬁnitions follows in Figure 1.

Figure 1: Various Definitions of Sustainable Development

 

“Ecologically sustainable economic development can be thought of as the process of
related changes of structure, organization and activity of an economic-ecological system,
directed towards maximum welfare, which can be sustained by the resources to which
that system has access” (Bratt (1991) in Kuik and Verbruggen, 1991).

“Sustainable development is development without growth in throughput of matter and
energy beyond regenerative and absorptive capacities” (Goodland and Daly, 1996).

“Sustainable development can mean a multitude of things to different people but it is
generally used to reﬂect concerns about the living standards of future generations and
their right to inherit a natural resource base of undiminished value” (Stoneham et.al.,
2003).

“Biogeophysical sustainability is the maintenance and/or improvement of the integrity of
the life-support system on Earth. Sustaining the biosphere with adequate provisions for
maximizing future options includes provision for maximizing future options. . .and
requires planning and action at the local, regional and global scales. . .”(Munasinghe and
Shearer, 1995).

 

There are numerous deﬁnitions of sustainability, some more useful than others and none
universally applicable.

Expanding on the deﬁnition in Figure 1, Munasinghe and Shearer (1995) suggest
that when developing a deﬁnition of sustainable development, one should not freeze the
current state, but instead deﬁne the boundaries, while considering the number of
variables, ease of measurement, generality, applicability, and ﬂexibility. Bratt (1991)
suggests that many of the deﬁnitions of sustainable development tend to focus on welfare
optimization and the self-imposed obligation to maximize welfare in the present. The
breadth and scope of this movement is not easily captured in a simple deﬁnition, which

has allowed the concept to be used so broadly.

12

The sustainable development literature has tended to focus on three main
components. As stated earlier, sustainability promotes a triple bottom-line approach that
collectively accounts for economic, environmental, and social objectives and goals.
Visually, as shown in Figure 2, it has been represented as a triangle, three-legged stool

and as three concentric circles (Goodland and Daly, 1996).

Figure 2: Concentric Representation of the Multiple Objectives of Sustainability

 

 

 

 
   
   
 

   
   
  

  
   
   

 

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Figure 2 shows the overlapping objectives or components of sustainability, such as
economic return and growth, environmental health and biodiversity, and social
empowerment and persuasion.

The center of Figure 2 represents the area where the three objectives are
overlapping and synergistic. This particular representation shows how a sustainable
system is related by introducing a simple framework that integrates the three components
of sustainability. Relating the sustainability elements to real world issues however is

more complex than the diagram portrays. In agriculture, for instance, in recognizing that

it is not just a productive land-based industry, but also a connection for the non-farm

13

population to natural resource amenities, sustainability has become a central farm policy

issue. The following section takes an in-depth look at sustainable agriculture.

2.2 The Application of Sustainability to Agriculture

As stated above, earlier concepts of sustainability were ﬁrst developed in response
to the extemalities of agriculture such as overgrazing and erosion. The paradigm of
agricultural sustainability has been applied to the survival and economic success of the
farm sector, the role of farmers in the stewardship of the land, and the necessity of
farming within the greater economy (Feenstra, 1997). A deeper assessment of the
concept suggests that a transformation in agricultural production and the way our society
views agriculture is needed.

The notion of sustainable agricultural development is essentially a systems
approach to the production of food and ﬁber that accounts for economic, environmental,
and social elements. In essence, sustainable agriculture seeks to afﬁrrn that the
proﬁtability of agriculture is inextricably linked to the productivity of the land and
welfare of farms and the surrounding community. According to Bird and Ikerd (1993),
sustainability in agriculture comes from maintaining an adequate number of owner-
operated farms, utilizing family-farm labor, creating partnerships between families and
laborers, diversifying crops and markets, and utilizing on-farm resources and site-speciﬁc
decision-making.

Changes in the farm sector have spurred many emerging sustainability goals.
Trends show that the capacity to produce agricultural products in a sustainable fashion is
rapidly deteriorating: farmers are losing money and going out of business, environmental

impacts are affecting ecosystem health, and social equity disparity is increasing. In

14

Michigan, the case study of this thesis, there were 17% fewer farms in 2002 then there
were in 1974 and total farm production expenses increased by the nominal value of
393%. Table 1 (below) highlights some of the structural changes in Michigan
agriculture. The issues identiﬁed by Bird and Ikerd (1993) with respect to the changes in
agriculture include a declining number of farms, consolidation, dependence on off-farm
inputs and energy imports, increased environmental and human health risks, and a
declining level of direct interaction between the farm and off-farm population.
Therefore, the ability of agriculture to sustain current production levels into the future

may be at risk.

15

 

 

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Sustainable agriculture proponents claim that it represents a more diverse,
versatile, and less intense form of agriculture than its conventional counterpart (Hansen,
1996; Allen, 1993). The US National Academy of Sciences highlights the incorporation
of natural systems, increased use of biological and genetic material, and improved
cropping practices as relevant sustainability goals (Allen, 1993). In agriculture,
sustainability is a bold end goal that is not necessarily a set of distinct practices that are
applicable to all farms. It is instead the center hub of farmer productivity, economic and
population demands, healthy/safe food, adequate farm incomes, and social equity
(Benbrook and Groth, 1996).

As with the sustainable development concept, there are many interpretations of
sustainable agriculture. Hansen (1996) based on the work of Thompson (1992) draws a
distinction between goal-prescribing and system-describing sustainability in agriculture.
Goal-prescribing deﬁnitions of sustainability are related to the ideological and or
management approach to agriculture. System-describing deﬁnitions relate to the ability
of agriculture to fulﬁll a diverse set of goals or the ability to continue in the future. Many
of the deﬁnitions can be placed into these two categories based on their underlying goals;

a policy interpretation has also been included in Figure 3.

Figure 3: Interpretations of Sustainable Agriculture

 

Goal Prescribing Interpretation
Sustainability as an ideology is: ‘an approach or philosophy. . .that integrates land
stewardship with agriculture. Land stewardship is the philosophy that land is managed
with respect for use by future generations’ (Neher, 1992 in Hansen 1996).

Sustainability as a set of strategies: ‘. . .(a) the development of technology and practices
that maintains and/or enhances the quality of land and water resources; and (b) the
improvements in plants and animals and the advancement in production practices that
will facilitate the substitution of biological technology for chemical technology’ (Ruttan,
1990 in Hansen, 1996).

 

17

 

Figure 3 (con’t)

 

System Describing Interpretation
Sustainability as the ability to fulﬁll a set of goals: ‘. . .an agrifood sector that over the
long term can simultaneously (1) maintain or enhance environmental quality, (2) provide
adequate economic and social rewards to all individuals and ﬁrms in the production
process, and (3) produce a sufﬁcient and accessible food supply’ (Brklacich et.al., 1991
in Hansen, 1996). '

Sustainability as the ability to continue: ‘...the maintenance of the net beneﬁts
agriculture provides to society for present and future generations’ (Gray, 1991 in Hansen,
1996).

Policy Interpretation
Sustainability as a policy is: ‘an integrated system of plant and animal production
practices, having site-speciﬁc application that will over the long-term satisfy human food
and ﬁber needs, enhance environmental quality, make the most efﬁcient use of non-
renewable resources, integrate natural biological cycles, sustain the economic viability,
and enhance the quality of life for farmers and society as a whole’ (FACT Act, 1990).

 

While no one widely accepted deﬁnition of agricultural sustainability has emerged,
people have been applying the term to agriculture.

As discussed in the previous section, Figure 3 highlights the many deﬁnitions and
applications of sustainability to agriculture. These concepts, Hansen (1996) suggests, can
be used to motivate the adoption of alternative approaches to agricultural production. It
can also be used as criteria for guiding agriculture, as it responds to changes in the
surrounding environment. The three components of agricultural sustainability are
discussed in the remainder of this section.

The economic component is the ﬁrst component of agricultural sustainability.
The economic objective, which gets to the root of farm sector survival and the
proﬁtability of agriculture, is focused on the growth, efﬁciency, and returns of farming.
Technically, a farm is economically viable when it generates adequate revenues to cover
all variable and ﬁxed costs, family living expenses, and replacement costs (Adelaja and
Rose, 1988). As one of the components of sustainability, it is the most important to

farmers and banks as a predictor of farm sector strength. Unfortunately, Adelaja and

18

Lake (2007) suggest that, in Michigan, agricultural viability may be threatened by 1) a
lack of competitiveness in international markets, 2), increased right-to-farm and
environmental compliance issues, 3) a lack of resources to support agricultural
innovation, and 4) fragmentation and diseconomies of scale in agricultural support
industries.

With respect to agricultural viability, even though the rise in productivity and
technical efﬁciency in the farm sector has been substantial in the past few decades,
farmers are still struggling to make a living wage from farming. Farmers, in turn, either
exit farming or rely on off-farm employment to cover family living expenses (e. g. health
and property insurance, household debt and educational expenses) (Femandez-Comejo
et.al, 2007). Identiﬁed as the “farm problem,” the reduced demand for labor, asset ﬁxity,
adjustment costs, low returns, and the elasticity of export demand generate less viable
farms (Garder, 1992). Hughes et. al (1985) suggest that farm ﬁnancial stress is caused
by a host of macroeconomic policies and individual management decisions which
generate a less resilient, less productive and more concentrated farm sector.

The second component of agricultural sustainability is environmental. It is
concerned with the agro—ecosystem biodiversity and carrying capacity. The agricultural
environment, which provides many non-pecuniary and intangible beneﬁts such as
conservation of wildlife habitat and open space, is being increasingly targeted for
preservation for future generations. The extemalities of agriculture (i.e. chemical, energy
and erosion issues) however, raise doubts about the sustainability of agricultural
production practices. Therefore, alternative strategies for improving the agro-ecosystem

have been identiﬁed as relevant agricultural sustainability objectives. These strategies

19

include: 1) managing the ecosystem to maximize the economic and ecological functions
of agricultural land, 2) maintaining the diversity of land uses, crops, and families, and 3)
protecting the land, air, and water from undue harm, according to the Canadian
International Development Agency (1992).

Indeed, farm stewardship practices can affect the viability of the farm itself.
Goodland (1995) has identiﬁed a strong link between the proﬁtability of agriculture, the
health of the environment, and the scarcity of natural resource inputs (land, energy, and
water). Adelaja, Sullivan, and Govindasamy (1999) suggest that the potential of
sustainable agricultural practices to increase farm viability will lead to a win-win
situation between the agricultural economy and environment.

The third component of agricultural sustainability is social. It relates objectives,
like empowerment, participation, and welfare, to the survival of agriculture. Social and
cultural support for farming in rural areas that enhance the bottom-line of a farmer’s
income are imperative to the survivability of the agricultural sector in the future.
However, issues such as consolidation and income inequality threaten to drive small and
medium farms out of the industry. Goldschmidt (1978) ﬁrst recognized the link between
“industrialized” farming communities and social viability, suggesting that a more
concentrated industry, with larger farms, adversely affects the vitality and welfare of a
rural community. Therefore, improving the social sustainability of agriculture through its
social contributions has been put forth as a goal not only for agriculture but also for rural
economic development (Allen, 1993).

Some suggest that the farm sector could enhance social sustainability and rural

economic development through public investments in human capital, education,

20

innovation, and a growth-from—within approach (Bird and Ikerd, 1993). Stoneham et.al.
(2003) suggest that the key elements of social sustainability ensure the free movement of
labor and capital between regions and maintain the existing character of rural
communities. The key elements of social sustainability, such as equity, the distribution
of income, connectedness, governance, cultural diversity, and quality of life, will in turn
maintain and enhance the long-term sustainability of agriculture (Barron and Gauntlett,
2002; CIDA, 1992). In addition, enhancing and improving community cohesion,
institutional support, and moral and human capital, will further the sustainability of a
rural community (Goodland, 1995).

In conclusion, agricultural sustainability is equated to a positive and growing
system, where the farmers are proﬁtable, stewards of their environment and an integral
part of the community. It has been shown that farmers who make more money generally
spend more money on environmental protection and are more involved in their
community (Adelaja and Sullivan, 1998). Furthermore, expenditures on agro-
environmental stewardship may increase the proﬁtability of a farm and its ability to
withstand the ﬂuctuating market (Batie and Horan, 2001). Local food systems (i.e. direct
sales to institutions, farmers’ markets, and community-supported agriculture) keep
resources in the community and provide agro-tourism opportunities, suggesting that there
is overlapping energies between the three objectives of agricultural sustainability.

Interdependency between the numerous economic, environmental, and social
relationships exists, which adds to the complexity of understanding the makeup of a
sustainable and balanced system (MAFF, 2000). Nevertheless, farmers, policy-makers,

and academics desire an increased understanding of the relationship between short-term

21

economic sustainability and its economic, environmental, and social factors. Empirical
evidence, concluding that short-term agricultural economic sustainability is
complementary to economic, social, and environmental objectives, would provide
evidence of the interconnectedness of the multi-faceted goals of sustainability. The

following section includes a discussion about measurement.

2.3 Measuring Sustainability

Although it is difﬁcult, measuring sustainability has its beneﬁts. For example, it
could be used to inform farmers and policy-makers on how to evaluate agriculture’s
progress towards a sustainable state. Information on progress towards farm sustainability
will aid in the collective assessment of the interrelated sustainability aspects. In lieu of a
direct measure for sustainability, many organizations have sought to develop
measurement schemes, such as indicators, to assess the health of economic,
environmental, and social aspects of agro-economic sustainability. This section delves
into the topic of measuring agricultural sustainability.

Given that sustainability is a big concept to measure, simple proxy or indicator
schemes have been developed to highlight the state of agriculture. Indicators are the key
assessment tools created for farmers, academics, and policy-makers to help build
consensus and implement sustainable policy (HSD, 2006). The Ministry of Agriculture
Fisheries and Food in the United Kingdom (MAFF), for example, has developed regional
and local reference indicators with the intention of

1) Tracking the impact of agriculture over time to show how agriculture

is becoming more or less sustainable,

22

2) Providing policy makers with the tools to assess the effects of current
policies, identifying new policy options and appraising the merit of
such policies,

3) Raising awareness about the extemalities of agriculture and its
contribution to sustainable development, and

4) Effectively inﬂuencing the international debate about indicators and

sustainability targets (MAFF, 2000).

Furthermore, Zhen and Routray (2003) believe that the international desire for tools that
aid in the assessment and implementation of sustainability for farming are becoming
more important at local and national levels.

Quantitative measures of sustainability, such as indicators, are also an important
prerequisite to legislative action intending to enhance sustainable agriculture (Zhen and
Routray, 2003). Indicator development programs, like the ones being developed in the
UK, Netherlands, and Australia, measure progress towards economic, environmental, and
social sustainability. Through such programs, the documentation of sustainability and the
respect for indicators, as a measurement tool, have been greatly enhanced.

There is much debate about what makes a good indicator. Scientists, policy
makers, producers, and researchers agree that relevant, easily understandable, and
measurable indicators are the best (OECD, 1999). They should also overlap in time so
that they capture the majority of interactions in agriculture. A good indicator should be
spatially and temporally relevant to the study area in question and should be able to

integrate the economic, environmental, and socio-economic elements of sustainability.

23

Indicators can be conceptualized for many purposes. For instance, indicators can
be used for planning or communication purposes. According to Kuik and Verbruggen
(1991) they can be retrospective to establish a baseline and contribute to our
understanding of changes in the agricultural system. They can also be predictive and
help us understand what the future agricultural system will look like.

Indicators provide an assessment tool for the individual components of
sustainability, but could also provide useful information for a comprehensive look at
sustainability through economic modeling. A number of sustainability modeling
approaches are discussed in the following section. Notably, the economic viability
literature (one component of sustainability and some would say the most important to

farmers) is the most abundant and the starting point of the modeling discussion.

2.3.1. Past Modeling Efforts

The framework for modeling and measuring agricultural viability and proﬁtability
is well documented. Historically, the general approach to viability modeling was to
develop proxy measures of viability, such as debt—to-asset, viability, and cash ﬂow or net
income (NFI) approximations and to regress these on hypothesized causal factors. The
four measures of economic viability have been used interchangeably. Intuitively farms
with negative cash ﬂows and high debt to asset ratios should be less ﬁnancially stable.

There have been several inherent ﬂaws in these previous models. First, the
proxies used to estimate economic viability are only indicators of proﬁtability and are not
direct measures. Second, some of the previous models may be incorrectly speciﬁed and
biased, given that the relationship between viability and its determinants seems to be

simultaneous (Adelaja and Rose, 1988). The possible causality of the debt-to-asset ratio

24

in the viability equation an issue. It has been argued that debt-to-asset ratio may be a
determinant of economic viability rather than an endogenous variable (Adelaja and Rose,
1988). Jolly et.al. (1985) imply that farm ﬁnancial stress is related to a high debt to asset
ratio and negative cash ﬂows. Viability ratio deﬁned as the ratio of net household
income to ﬁnancial obligations, may be inadequate because it is a scaled measure and not
in actual dollar terms (Adelaja and Rose, 1988). Viability is best approximated in terms
of NFI given the inherent ﬂaws in previous proxies and the fact that cash ﬂow and the
impact of the determinants are measured in actual dollar terms.

Hosts of agricultural economists have attempted to identify the explanatory
variables that affect the short-run economic viability of farms (Jolly and Doye, 1985;
Adelaja and Rose, 1988; Tanewksi et.al., 2000). The studies that have previously
attempted to pinpoint the factors that determine agricultural viability do so by estimating
single equation models. From these models the determinants of farm viability can be
assessed using proxy measures for farm ﬁnancial characteristics, structural factors and
demographic characteristics (Adelaja and Rose, 1988; Adelaja and Sullivan, 1998).
Previous studies have included ﬁnancial variables (gross farm income, off-farm income,
debt, depreciation, farm asset values, and revenue from asset sales), structural factors
(commodities, region, and acreage), and demographic factors (operator age, experience,
and education). Empirical ﬁndings from New Jersey, using a simultaneous cash-ﬂow
model, suggest farm economic viability is positively related to size, gross farm income,
off-farm income, operator’s education and experience, and the number of adult operators,
but it is negatively impacted by debt and the age of the operator (Adelaja and Rose,

1988). Attitudinal characteristics, such as planning horizons, the farmer’s beliefs with

25

respect to the business and regulatory climate, and the Farm Bill, were also found to
inﬂuence viability (Adelaja and Sullivan, 1998).

Despite the prevalence of economic viability studies, there is still a limited
knowledge of the structure of farm proﬁt and how it relates to the environmental and
social components of sustainability. The absence of knowledge about viability’s
relationship to environmental and social variables is especially confounding. It has not
been well documented in the literature. Attempts to understand the relationship between
farm income and the extemalities associated with agriculture, the dependence on federal
price support programs and social equity are imperative to sound farm policy and market
intervention (Gardner, 1992). It has been suggested that the factors that drive many
national agricultural intervention policies aimed at aiding the viability of farm sector, are
likely to perpetuate the farm problem if a broadened view of economic viability is not
taken (Gardner, 1992; Hughes et.al., 1985; and Jolly et.al., 1985). Viability studies that
do not factor in the environment and social dimensions of agriculture are not likely to
explain what viability and sustainability in agriculture truly entails. One of the objectives
of this thesis is to test whether or not the speciﬁcation of the four agricultural viability
models presented in Chapters 3 and 4 are improved by the addition of environmental and

social indicators of sustainability.

2.4 Summary

In summary, Chapter H provides a broad overview of the origin of the
sustainability concept in the 19th and 20‘h centuries. The paradigm of sustainable
agriculture, its deﬁnition, and components are also discussed in detail. In addition, the

current framework for measuring sustainability is highlighted.

26

The lack of a detailed framework for understanding the relationship between
agro-economic, environmental, and social objectives for agriculture limits one’s
understanding of the complex relationship between NFI and other sustainability factors.
This thesis develops a novel approach to modeling economic sustainability that
incorporates many of the indicators put forth by the OECD, MAFF and others. Chapter

III highlights the conceptual framework for modeling short-term economic sustainability.

27

Chapter III: Conceptual Framework

In Chapters I and II, it was suggested that the concept of agricultural sustainability
is important and relevant to maintaining a viable agricultural sector, short-term. The
issue that this thesis focuses on is how to develop and apply a conceptual framework for
evaluating the monetary trade-offs associated with pursuing the multiple elements of
agricultural sustainability in counties across Michigan. One indicator of short-term
sustainability is the proﬁtability or viability of the agricultural sector, which is well
documented in the economic viability literature. In this thesis, this framework is
expanded to account for environmental and social factors. Net farm income per acre
(dependent variable) is therefore decomposed via simple linear cash ﬂow models in an
effort to study the contributions of economic, environmental, and social factors to short-
terrn agro-economic sustainability. The ﬁrst, economic viability, has been well addressed
in the literature.

The WTS is a test for the county level agro-economic success based on the
sequential inclusion of economic, environmental, and social sustainability factors. It is
called the WTS because it is simply a one-sided test on the proﬁtability of the farm-
sector, not a direct measurement of an observed variable for agricultural sustainability.

This is further explained in the rest of Chapter III.

3.1 The Agro-Economic Sustainability Models Conceptualized

As shown in Chapter H, the proﬁtability of agriculture has been well studied. At
a minimum, NFI must be greater than zero in order to satisfy the very basic conditions for

sustainability (Zhen and Routray, 2003). Leaning on the work of Adelaja and Rose

28

(1988); Adelaja, Derr and Rose-Tank (1989); Adelaja and Sullivan (1998); Jolly et.al.
(1985) and Zhen and Routray (2003), this thesis develops a conceptual framework for
assessing short-term agricultural sustainability. With the purpose of identifying the
determinants of economic sustainability and estimating the impacts of those determinants
on net farm income per acre, the model begins by accounting for many of the factors
found in previous studies.

Following previous studies, the economic component of sustainability can be
explained by farm sector (1) ﬁnances, (2) structural assets and resources, and (3) socio-
demographic characteristics. Adelaja and Rose (1988) suggest that farm ﬁnancial
determinants include gross farm income, off-farm income, debt, proﬁtability ratio, total
assets (equipment and land), revenue from asset sales and acreage. Structural farm
resources include the type of commodities grown, changes in amount of agricultural
acreage, tenanted/rented acres, and the location of the farm (Adelaja and Rose, 1988;
Jolly et.al, 1985). Socio-demographic characteristics, such as the number of operators,
age of farmers, the number of farm residences, and operator experience, are also
identiﬁed in the literature as determinants of viability (Adelaja and Sullivan, 1998). The
EU Commission on Agriculture and the Environment (2001) suggests that indicators of
farm income include the net worth of resources, ﬁnancial stress, and the gross value
added to production. The ﬁnancial, structural, and social-demographic characteristics
mentioned here are hypothesized to be determinants of agricultural economic
sustainability.

The implicit function for agro—economic sustainability incorporates ﬁnancial,

structural, and Operator characteristics, and helps to explain what it takes to maintain

29

adequate proﬁts in the farm sector. The basic relationship for short-term agro—economic

sustainability is such that sustainability (Yi) is speciﬁed as follows:
Yi = Yi (Wi, Xi, Zr) = Yi (Vi) ( 1)

where Yi is agro-economic sustainability, measured as net farm income per acre
(dependent variable) of ith county in Michigan, Wi is a vector of variables depicting the
ﬁnancial characteristics of farms in the ith county, X; is a vector of county structural
factors for farms and Z is a vector of socio-demographic characteristics for farms in the
ith county. Collectively Wi, Xi and Z account for the economic viability (Vi) component
of the agricultural sector. A simple linear relationship is assumed.

The conceptual model framework is based on the notion that an agricultural
economic viability model that includes economic variables alone is incomplete, because
it does not consider the effect of environmental and social factors on farm income. It is
also based on the notion that previous models of viability are insufﬁcient indicators of
sustainable agriculture. This study is different from others in that it seeks to evaluate the
effect of additional environmental and social factors on farm income. The rationale for
including the environmental and social sustainability variables is that they are critical
determinants of economic viability itself. In other words, if proﬁtability is
environmentally and socially compatible, it should be reﬂected in the economic bottom-
line.

The environmental resources (e. g. soil and water conditions) of a farm, and the
management of those resources for agricultural production should affect the sustainability
of the natural environment on and off the farm. The indicators identiﬁed in the literature,

such as crop diversity, are also expected to affect the short-term viability of the farm, by

30

increasing resilience and productivity (OECD, 2001). The health and state of farm
resources, such as the nutrient content of soil, inﬂuence the farm ecosystem and could
raise environmental degradation concerns. Policy responses to the extemalities of farm
production practices, i.e. conservation payments, have the ability to improve the agri-
environment and proﬁtability of farms through incentive payments. These environmental
sustainability factors, which include the management of farm resources, the extemalities
of harmful production practices, and the response to those practices, are hypothesized to
affect the short-term economic sustainability and the bottom-line of a farm. However, a
farm that is economically sustainable and simultaneously is environmentally sustainable
in the short-term is likely to be more sustainable that one that is only economically
sustainable. This is the essence of the WTS.

In Equation 2, environmental sustainability variables are added to economic
viability variables. They are assumed to impact upon short-term agro-economic

sustainability, such that:
Yi* = Yi* (Vi, Bi) (2)

where (E) is a vector of environmental measures to be added to Vi, to test whether or not
they improve estimates of short-term sustainability. Vi characteristics are deﬁned above
in Equation 1 and the environmental (Ei) factors in Equation 2 are secondary. It is
hypothesized that the speciﬁcation of the short-term economic sustainability model is
enhanced by including a vector of environmental determinants.

Agriculture affects the society in which we live by altering the social fabric of
rural and urban places and changing the roles of individuals. One goal of this thesis is to

capture the effects of societal interaction in a sustainable agricultural system, such as the

31

population pressures at the urban/rural fringe, direct sales to consumers, farm agro-
tourism opportunities, and access to input suppliers. The economic development
potential of agricultural jobs, incubators for agri-business start-ups, and ag-based festivals
is of particular interest to local governments and economic development organizations
looking to spur sustainable agricultural development.

Subsequently in Equation 3, social sustainability variables are also added and

assumed to impact upon short-term economic sustainability, such that:
Yi** = Yi** (Vi, Si) (3)

where (Si) is a vector of social sustainability indicators added to Vi. Augmenting the
agricultural sustainability equation with the hypothesized indicators of social
sustainability is expected to improve the speciﬁcation of the equation. Note that the E,
vector is missing from Equation 3. This is to allow for the independent assessment of the
effects of environmental and social factors.

Given the theoretical model of short-term agro-economic sustainability and the
vectors of environmental and social indicators described above, this thesis suggests that
agricultural economic sustainability is collectively deﬁned by economic, environmental,
and social sustainability determinants. Therefore, farm income can be treated as a
dependent variable that is affected by a host of economic viability determinants as well as
environmental and social sustainability measures. The model that captures the collective

effect of economic, environmental, and social vectors on net farm income is such that:
Y***r = Yi***(Via Ea, Si) (4)

In Equation 4, agricultural economic sustainability (Yi) is better estimated by including

the effect of the economic (Vi), environmental (Bi) and social (8,) factors in estimating

32

the net farm income equation. It is hypothesized that the collective effect of factors
capturing the objectives of sustainability will improve model speciﬁcation when
comparing Equation 1-4. If Equation 4 is better speciﬁed than Equation 1, then
sustainability in a weak sense (WT S) is possible. By providing evidence that the
movement towards a more sustainable agricultural system does not detract from short-
term economic sustainability, one can conclude that economic, environmental, and social

objectives are compatible.

3.2 Summary

Chapter HI presents a conceptual framework for a Weak Test of Sustainability,
which is expected to expand the knowledge surrounding the potential economic trade-off
between economic, environmental, and social sustainability objectives in agriculture.
The series of additive linear equations described in Chapter III are expected to improve
the speciﬁcation of the short-term economic sustainability model. Chapter IV presents
the empirical framework used in this thesis to test the hypotheses that previous models
are incomplete because they fail to include environmental and social determinants, that
model speciﬁcation is improved by including environmental and social factors of
sustainability, and that the objectives of sustainability are not separate from one another

but inextricably linked.

33

Chapter IV: Analytical Framework, Data and Estimation

The analytical framework put forth in this thesis is to identify the determinants of
agro-economic sustainability and to estimate a series of economic sustainability
functions. The framework for examining the monetary trade-offs associated with the
three independent but interrelated goals of (economic, social, and environmental)
sustainability in agriculture in Michigan counties is proposed as a Weak Test of
Sustainability (WT S). Chapter IV highlights the ﬁndings of previous studies of farm
viability and speciﬁes four models of agro-economic sustainability. The comparative
framework for choosing the best model is also deﬁned, which involves comparing a
standard economic sustainability model to three additional augmented models, through a
series of F-tests and the creation of benchmark index based on the predicted outcomes of
the models. Finally, a section on the data and estimation procedures used in this thesis is

presented.

4.1 Analytical Framework for WTS

The analytical framework for the WTS in Michigan includes four equations. All
four equations include the economic determinants of sustainability. The ﬁrst model
includes only economic factors. The other three allow for testing the individual and
collective effects of environmental and social sustainability variables on farm income.
This section describes the results of previous studies that provide the rationale for
including many of the variables in the economic sustainability model. It also speciﬁes

the equations used to perform the WTS.

34

Recall from Chapter 1H that the basic agro-econornic sustainability (Yi) model
includes the economic characteristics (Vi), which is comprised of farm ﬁnancial
characteristics (W i), county structural factors (Xi) and operator characteristics (Zi).

Previous studies have identiﬁed the following farm ﬁnancial variables (W i) as
important to enhancing economic sustainability, proﬁtability, and viability in the
agricultural sector: gross farm income, off farm income, proﬁtability, assets value, and
farm acreage or size (Zhen and Routray, 2003; Duffy, 2002; Adelaja and Sullivan, 1998;
and Adelaja and Rose, 1988). For example, in their study of farm viability at the urban
fringe, Adelaja and Sullivan (1998) showed that total and per acre gross farm income
affects viability, indicating that larger farms with more intense production practices are
more viable.

Duffy (2002) estimated a proﬁt index for small farms in Iowa, concluding that,
farms that used land and machine resources more efﬁciently were more proﬁtable.
Financial factors that have been shown to detract from farm viability include high debt
service and the high cost of production (Adelaja and Sullivan, 1998 and Adelaja and
Rose, 1988). Another variable, considered, but not been found to be signiﬁcant, is the
revenue from land sales. On one hand, such revenue can help farmers pay the bills
during slow market times. On the other hand, such sales are a warning sign that a farm
may be going out of business.

County structural characteristics (X) that may affect the viability of the farm
sector. have also been identiﬁed in the literature. Adelaja and Rose ( 1988) show that fruit
and vegetable farms in New Jersey are generally more viable than grain farms because

they can utilize more land with fewer assets. Regional variables included in a study by

35

Adelaja and Sullivan (1998) suggests that farms in urbanized areas are less viable.
Reductions in farmland acreage have been shown to detract from viability, indicating that
suburbanization further erodes the agricultural sector (Adelaja and Sullivan, 1998).
However, when farming choices and prices are factored in, previous studies show
suburbanization increases the proﬁtability of the vegetable sub-sector, (Lopez et. al.,
1988).

The characteristics of farm operators (2;) also affect the sustainability of farms.
The number of operators per farm, experience, education, and the number of members in
a household have been shown to enhance viability (Duffy, 2002 and Adelaja and Rose,
1988). Farming experience was empirically shown to enhance the viability of farms
because more experienced and older farmers have less debt (Adelaja and Rose, 1988).
However, Duffy (2002) found that the owners of successful small farms in Iowa are
younger and better educated. Education was shown to provide a substantial beneﬁt and
returns to a college degree could be well over 200% by Adelaja and Sullivan (1998).
Past studies also show that farms with appropriate managerial, ﬁnancial, and marketing
practices are more viable (Duffy, 2002).

Recall from Chapter II, that the literature highlights many of the environmental
factors (Ej) of agricultural sustainability. A few of these environmental variables have
been included in previous farm viability, proﬁtability, and sustainability studies.
Empirical results from New Jersey show that a reduction in the use of chemicals does not
detract from farm viability, indicating that lower input and sustainable agriculture
practices are compatible with economic viability (Adelaja and Sullivan, 1998). Other

suggested environmental indicators of sustainability include soil nutrient content and the

36

use of fertilizers/pesticides (Zhen and Routray, 2003). MAFF (2000) includes indicators
like the direct energy consumption by farms and characteristic features of farmland.

Social (8,) sustainability factors are important elements to incorporate into short-
term sustainability models. A number of social variables have been included in previous
viability studies. For instance, how extensively a farmer utilizes information, such as
extension and the Department of Agriculture in New Jersey, ultimately enhances farm
viability (Adelaja and Sullivan, 1998). The skill and knowledge base available to farmers
is expected to be important to the survivability of the farm sector (Smith and McDonald,
1998). Attitudinal indices were also included in the New Jersey study and the marketing
and supply indices were both signiﬁcant (Adelaja and Sullivan, 1998). This suggests that
farmers, who feel that the marketing environment is not conducive to selling their
products or that it is difﬁcult to procure supplies, are less viable. Zhen and Routray
(2003) suggest that social sustainability for agriculture should include access to resources
and support services.

To facilitate econometric estimation, the empirical models conceptualized in this

thesis are a series of additive equations. The following Equations 5-8 were speciﬁed:

1? =00 +2 “fo +8i =00 +: “f“? + Z “fo + Z “fo +63 (5)

f=1 f=1 f=n+l f=m~l
n h
Y]. =a0+; afvf+Zl ﬂjEj+ej (6)
= J:
Y, =aO+Z afo +2 7,5,+e, (7)
f=l r=l

37

n h P
f=1 j=l r=l

where, Yi.j.r.k is the dependent variable net farm income per acre for counties in
Michigan, Vf is the vector of economic variables, E is the vector of environmental
variables, St is the vector of social variables, and the 3i.j.r,k are the errors terms assumed to

be normally distributed with means of zero and constant variances. The variables
included in the vectors of the four models are deﬁned in Table A1: Deﬁnitions of the
Independent Variables and Source in the appendix and discussed below. The Equations
to be estimated are 5-8. Equation 8 is expected to exhibit the least speciﬁcation error or
the highest adjusted R-square. Equations 6 and 7 include either environmental or social
indicators and are therefore expected to be less than optimal in terms of speciﬁcation.
Equation 5 is expected to be the least efﬁcacious because it totally ignores environmental
and social factors.

Consistent with the literature on economic viability and sustainability, nine
economic (Vf) factors are included as independent variables in the estimation of Equation
5-8. Wf contains off-farm income (OFI_ac), equipment assets (Equip_ac), revenue from
asset sales (RAS_ac), and operating expenses (opEx_ac). Xf contains access to key
services (AKS_ac), diversity of farms by size (SizeDiv), and MSU Extension Educators
(EXT_ac). Zf contains the average age of farmers (avg_age) and young farmers
(Farms25_34).

Equation 6, added to Equation 5, is augmented with a list of environmental (Ej)
variables based on available data. They include land diversity (LDiv), chemical

dependence (CD_ac), conservation acreage (ConA_ac), and N RCS management acres

38

(NRCS). The determinants of environmental sustainability are expected to enhance the
model speciﬁcation of the short-term agro-economic sustainability equation when
collectively estimated.

Equation 7 is estimated with social (8,) variables added to the economic variables
in Equation 5. They include direct sales to consumers (Direct_ac), migrant labor capacity
(MLC), population interaction index (PH), farm opportunities (Fopps_ac), and project
fresh coupons granted (PFresh_ac). The inclusion of these social sustainability factors
are expected to enhance the speciﬁcation of the short-term sustainability model.

Equation 8 is the fully speciﬁed model that is meant to augment the standard
model with environmental and social determinants introduced in Equation 6 and 7. The
expected outcome of the comprehensive Equation 8 is an enhanced agro-economic
sustainability model that includes economic, environmental, and social variables that both
enhance and detract from the proﬁtability of the sector. The WTS provides a collective
scheme for incorporating economic, environmental, and social variables, which is
expected to enhance the predictive capabilities of the model, increase the signiﬁcance of
included variables, and increase the adjusted R-square statistics. The following section

explores the framework for comparing the models.

4.2 Framework for Comparing the Different Models

The basic framework for comparing the four models involves subjecting the
results to a series of F-tests. It is expected that model speciﬁcation will improve with the
inclusion of environmental and social sustainability factors. Short—term agricultural
sustainability is concerned with the monetary tradeoffs associated with economic,

environmental, and social variables. The Chow F—test is used here to jointly test the

39

signiﬁcance of the subset of environmental and social of variables on viability, labeled
here as the WTS. The F—test requires the comparison of a full (or unrestricted) model
with a restricted model. In this analysis, the restricted model is the standard economic
sustainability model. The three augmented speciﬁcations are the unrestricted models.
The F-test is speciﬁed as follows:

F = (RZUR—RZR)/q
q’N—k (1- RZUR )/(N -k) (9)

 

In Equation 9, q is the number of restrictions, N is the number of the variables,
and k is the number of parameters in the unrestricted model (Pindyck & Rubinfeld,
1998). This is a test on the R2. The three null hypotheses are that all of the coefﬁcients
of the environmental and social variables in Equations 6-8 are jointly equal to zero. The
three alternative hypotheses are that the added coefﬁcients in the models speciﬁed herein
are non-zero. A positive economic relationship between the economic and other
environmental and social sustainability factors would suggest that short-term economic
sustainability is consistent with social and environmental sustainability. If the groups of
environmental and social variables signiﬁcantly affect proﬁtability then the WTS is
passed. The predicted values of Equations 5-8 are used as a tool for assessing the

benchmark structural difference in county’s agricultural sector.

4.3 Definitions and Calculations of the Data and Variables

The county is the unit of analysis in this study. County level data is collected on
all dependent and independent variables included in the models. There are 83

observations given the number of counties in Michigan. The year of analysis was 2002,

40

with a few exceptions where some non-census data was obtained from other sources for
which matching census year data were unavailable. The basic assumption is that
variability across counties in the dependent variable (net farm income) can be explained
by variability in the independent variables. If the added variables, described in this
section, exhibit signs that suggest that improved environmental and social compatibility
contribute to short-term sustainability, then one can conclude that agriculture passes the
WTS.

In this case study of Michigan counties, in addition to data from the US Census of
Agriculture, other data sources include the Bureau of Economic Analysis, many USDA
sources like the Economic Research Service (ERS) and the Natural Resource
Conservation Service (NRCS), and others such as The Michigan Department of
Agriculture (MDA), and the Michigan State University Extension (MSUE). Table A1,
found in the Appendix, provides a list of the independent variables used in estimating
Equations 5-8. It includes a descriptor for each variable, the description of the variable,
the source of data, and the mean and the variance of the data. In Table A1, these
variables are listed in the order of modeling. Presented ﬁrst are the standard economic
determinants, second are the environmental variables, and last are the social sustainability
variables. Given that the data used in the analysis are for a single year, the resulting
parameter estimates are reﬂective of the effects of causal factors on short-term
sustainability.

Net farm income per acre (N FI_ac) of farm operations in Michigan is the
dependent variable in Equations 5-8. NFI_ac is a measure of the proﬁtability per acre of

farms in a county. Eight counties in Michigan experienced negative net income in 2002,

41

while the others experienced positive NFI (Census of Agriculture, 2002). In Michigan,
57% of farms lost money in 2002, with animal-based commodities faring the worst
(Census of Agriculture, 2002; Adelaja and Lake, 2007).

Oﬁ‘ farm income per acre (OFI_ac), measures the degree of dependence of the
farm sector on the non-farm economy. Income from outside sources has become
increasingly ﬁnancially important to sustaining farms in the US. The additional income
from off-farm employment helps farm families pay down debt and make additional
capital investments in farming operations. Some farmers may also use off-farm jobs to
cover their health insurance and retirement beneﬁts. On the other hand, off-farm
employment may be necessary for farms that cannot make ends meet or may decrease a
farmer’s interest in farming. Information on OFI_ac was collected from the US Bureau
of Economic Analysis, which reports off-farm job income for farmers (US—BEA, 2002).
OFI_ac is expected to be positively related to NFI_ac.

Equipment assets (Equip_ac) are measures of capital investment and machinery
infrastructure per acre. It has been shown that farming operations with high liquidity and
returns on assets are more proﬁtable (Adelaja and Rose, 1988). If equipment assets
adequately reﬂect the level of capital intensity then they should be positively relate to
NFI_ac.

The revenue from the sale of assets (RAS_ac) provides a measure of the potential
internal source of capital generated by the county’s farms. This variable is included
because (arguably) the revenue can be spent on investment in new technology. RAS_ac
is calculated by multiplying the change in total acreage (from 1997 to 2000) by the

average easement value for the county and dividing it by the number of harvested acres.

42

The statewide average easement value of agricultural land in Michigan, estimated by
Adelaja, et.al. (2006), suggests that the value of farmland minus the cost of buildings is
$2,078. RAS_ac is expected to be positively related to NFI_ac.

Operating expenses (opEx_ac) are a measure of the size of the farming budget, an
alternative measure of size and a measure of efﬁciency. High opEx_ac indicates that the
farms in the county are not efﬁcient. In fact, most of Michigan is experiencing losses in
efﬁciency, given that opEx_ac in Michigan have increased by over 400% in the last 4
decades, as shown in Chapter H. The effect of opEx_ac is expected to be negative.

Services key to agriculture are necessary to maintaining a viable farm sector.
These services include licensed nursery, grain, pesticide application business and
wholesale potato dealers (MDA, 2006). The total number of businesses in each county
across all of these services divided by the number of harvested acres creates the access to
key services per acre (AKS_ac) indicator. Higher levels of AKS_ac indicate that there
are more key support services available, which will contribute to a more viable
agricultural sector. The relationship between AKS_ac and NFI_ac is expected to be
positive.

A Simpson’s Index for farm size diversity (SizeDiv) was created for each county
based on the size make-up of farms in the county. The value between {0,1} indicates the
level of diversity. The closer the value is to { 1} the more diverse the sizes of farms in a
county are (Bastian and Stienhardt, 2002). Physically, larger farms can operate more
efﬁciently by taking advantage of economies of scale in equipment and land. However,
physically smaller farms can quickly adapt to changing market conditions and tend to

focus on niche crops. More physically diverse areas are expected to be more proﬁtable.

43

Michigan State University Extension (MSUE) provides many valuable education
and research services in each county of the state. Adelaja and Sullivan (1998) found that
the returns to research and the extent to which farmers utilize information contribute to
viability. The Extension Educators (EXT_ac) in the county per acre are an invaluable
support network for the farm community. Using MSU’s Extension Web Portal, an
indicator for agriculturally related Extension Educators was created from all of the listed
staff members (MSUE, 2006). The Extension coefﬁcient is expected to be positively
relates to N FI_ac.

The average age of farmers in a county (avg_age) and the number of young
farmers (Farms25_34) are two age variables included in the model. The literature
suggests that there is a positive relationship between experience or the number of years in
business and viability (Adelaja and Schilling, 1998; Adelaja and Rose, 1988). The
expectation is that more experienced farmers have less debt and interest and are therefore
more proﬁtable. On the other hand, young farmers tend to be educated and enthusiastic
entrepreneurs who are willing to pursue direct and niche markets, adding to the success
of the farm. Therefore, avg_age and Farms25_34 are expected to have a positive impact
on NFI_ac.

The environmental indicators included in the second and fourth augmented
viability models are calculated from various data sets, such as the USDA Census of
Agriculture and the USDA-NRCS PRMS Database. Many of the environmental
variables included in this thesis have not been empirically tested in previous models,

making a clear judgment about their expected relationship with NFI_ac difﬁcult. The

collective effect, measured via F—test, however is expected to enhance the speciﬁcation of
the short-term sustainability model.

Michigan ranks second in the nation for the diversity of agricultural products
grown. There are fruit and vegetable belts along the western coast of Michigan, grain
farms across the central interior and cattle, dairy and tree farms in the north. Farmers
who grow different types of crops in different parts of the state are expected to face
different market conditions, business circumstances, growing conditions, and external
pressures. Land Diversity (LDiv) is a Herﬁndahl index calculated from the share of land
by major land classes (pastureland, cropland, idle, failed, summer fallow, woodland,
rangeland, houses, CWRP, organic, enrolled in federal programs, vegetables, orchards).
It measures the diversity and concentration of land uses. The statewide average is 69.85
indicating that agro-ecosystem is relatively unconcentrated. A small index means that the
county is diverse and a large index (greater than 1,800) means that the agricultural sector
in a county is dominated by one or more commodity. LDiv is expected to contribute to a
more diverse ecosystem and may enhance the bottom line of farms in a county by
diversifying income sources and harvest times.

The use of natural resources and synthetic materials for the production of
agricultural commodities can have a lasting effect on the surrounding environment, create
dependencies in the ecosystem by making it less resilient, and affecting the bottom-line
of the farm operation when expenses become insurmountable. Chemical dependence
(CD_ac) includes acres treated with chemical, fertilizer, lime, and other soil conditioners

per harvested acre. The impact on NFI_ac is expected to be negative.

45

Conservation acreage (ConA_ac) like the land Conservation Reserve and
Wetlands Program (CRWP), certiﬁed organic, and the land fertilized with manure is
expected to enhance the sustainability of the agro-ecosystem. CRWP enrolled land
restores, protects, and enhances the function of the ecosystems services provided by
agricultural land. Organically produced farm products use no chemicals, are the fastest
growing market segment of the food economy, and through marketing efforts fetch a
higher price premium for farmers. Manure is expected to inﬂuence the bottom-line of a
farm though the substitution of manure-related nutrients for expensive chemical-based
nutrients. ConA_ac is expected to contribute to NFI_ac.

Natural Resource Conservation Service total enrolled program acres including,
nutrient, pest, residue management (NRCS_ac), improve not only the ecological
environment surrounding the farm, but also the proﬁtability the farming enterprise
because they enhance the money available for agri-environmental expenditures. The
data, collected with the aide of N RCS State Economist June Grabemeyer, was obtained
from the USDA-NRCS PRMS Database. NRCS_ac is expected to contribute to the
sustainability of farms in a county.

The social characteristics of an agricultural community include population
interaction variables, support services, and agro-tourism opportunities. Social
characteristics are hypothesized to inﬂuence the viability of farms in a county, although
relatively few studies, if any, provide evidence of their sign or signiﬁcance. Data sources
of the indicator variables used herein include the USDA-ERS, MDA, and others. The
collective effect of the social sustainability indicators/variables that are included in

Models 3 and 4 are expected to enhance viability positively.

46

Direct sale per acre (Direct_ac) is the indicator used to measure how important
direct marketing is to farm success. Value of agricultural products sold directly to
individuals for human consumption is the total income from roadside stands, farmers’
markets, pick-your-own sites (Census of Agriculture, 2002). Adelaja and Sullivan (1998)
found that direct sales combined with the use of a dummy variable for innovative
marketing techniques are positive aspects in the viability equation, although not
consistently signiﬁcant. Direct_ac is expected to be positively related to N FI_ac.

Migrant Labor Capacity (MLC), or the number of beds in certiﬁed migrant labor
sites, provide short-term migrant laborers with housing for the short growing season
when many of Michigan’s crops such as asparagus, blueberries, and grapes must be hand
harvested. Given the debate in the Unites States right now about migrant labor and
security at the border, knowing how MLC inﬂuences viability in agriculture will become
increasingly important. There are over 4,500 labor-housing units according to the MBA
2006 Licensed Migrant Labor Housing Sites (by County) (MDA, 2006). MLC is
expected to inﬂuence N FI_ac positively, especially in areas with many hand-harvested
crops.

Population Interaction Index (PH) is an index that estimates the potential
interaction between urban populations and agriculture, across the US, in ﬁve-kilometer
grid cells (USDA-ERS, 2005). Any PH that exceeds the rural threshold is then classiﬁed
as a low, medium, or high population—interaction zone, depending on the population
nearby (USDA-ERS, 2005). On one side of the fence, Lopez et.al. (1988) argues that the
nearby population can enhance revenues at the urban fringe, especially through direct

markets. On the other side of the fence, the proximity of a highly urbanizing are can

47

cause right-to—farm conﬂicts and trespassing issues. Nonetheless, the relationship
between PH and NFI_ac is expected to be negative.

There are numerous opportunities (Fopps_ac) to visit on-farm markets, u-pick
operations, and agricultural tourism operations in the state of Michigan, where families
can participate in everything from apple pressing for cider to strawberry picking (MDA,
2006). The number of u-pick operations, Community Supported Agriculture (CSA’s)
and on-farm markets per county was obtained from the online MDA Farm Market, U-
Pick & Ag Tourism Directory and Localharvestorg. Wineries represent an ever-growin g
segment of the beverage and tourism market. It is estimated that wineries in Michigan
generate $800 million in sales per year. The wine indicator in this thesis is the number of
wineries per county and the data was obtained from the 2006 Michigan Wine County
Magazine. The effect of Foops_ac on NFI_ac is expected to be positive.

Fresh fruit and vegetables are essential to a well balanced diet. However, they are
out of reach for some consumers given the expense. Project Fresh, a W1C education
program, seeks to put the fresh fruit and vegetables sold at local farmers markets into the
hands of those most in need. The data on the total amount granted (for 71 counties =
$774K) of Project Fresh spending per county (PFresh_ac) was obtained by Viki Lorraine
of the Michigan Department of Community Health (MDCH, 2006). PFresh_ac not only
contributes to the social well-being of the community by putting fresh fruits and
vegetables into the hands of those in need, but it also puts money directly into the hands

of farmers. The relationship is expected to be positive.

48

4.4 Summary

A series of models for measuring short-term agro-economic sustainability, in
Equations 5-8, are speciﬁed in Chapter IV. The novel WTS introduced in this thesis is
expected to improve upon previous model. The WTS implies that three conditions are
met, (1) that the standard model that excludes environmental and social factors is not
appropriate speciﬁcation for short-term sustainability, (2) that the inclusion of
environmental and social factors in the model improves speciﬁcation, and (3) that the
nature of observed relationships between the different aspects of sustainability are
complimentary to proﬁtability. Chapter V highlights the results of the empirical models

deﬁned in this chapter using the above-mentioned data.

49

Chapter V: Empirical Results

As indicated in Chapters IH and IV the conceptual and analytical frameworks for
exploring the issue of sustainability in Michigan agriculture is to evaluate the effect of the
determinants of short-term agro-economic sustainability on proﬁtability (measured by
NFI_ac) in a series of additive equations that include economic, environmental, and
social sustainability variables. Equations 5-8 were estimated using the ordinary least
squares regression technique. The results from estimating the four models are discussed
in Chapter V. The parameter estimates for the four models are shown in Table A2 in
Appendix I. The results of the F—tests, which are used to test the efﬁcacy of the collective
inclusion of environmental and social sustainability factors, are also included in this
chapter. The basic premise of the tests is that if improved environmental and social
performance enhances short-term economic sustainability, then, proﬁtability is not
compromised by the pursuance of other sustainability objectives.

The ﬁrst step is to estimate Equation 5, including only economic determinants.
The next step is to successively add environmental and social factors individually and test
for their impact. The ﬁnal Equation includes the entire suite of all economic,

environmental, and social factors.

5.1 Results of the Standard Short-Term Economic Sustainability Model

Equation 5 was estimated using the OLS technique. This equation allows a
comparison to previously speciﬁed models. Table A2 shows the parameter estimates and
signiﬁcance levels of the coefﬁcients of the variables included in Equation 5 under the

heading of Model 1. The results from estimating this restricted economic equation are

50

similar to results found in previous studies; however, this study relies on cross-section
data. The adjusted R-square in Model 1, which is 0.5771, indicates that the included
causal factors explain the dependent variable fairly well, considering the cross-section
nature of the data.

To investigate the presence of multcollinearity correlation matrices for all
included variables were examined and included in Table A3 in Appendix I.
Multicollinearity creates biased standard errors, thus leading to inaccurate conclusions in
the test of statistical signiﬁcance (Pindyck and Rubinfeld, 2003). Dropping collinear
variables or increasing the sample size are two ways to eliminate the problems associated
with multicollinearity. The latter was not possible in this study given that Michigan has
83 counties. Therefore, GFI_ac was dropped. GFI_ac is highly correlated with NFI_ac,
the dependent variables, and many of the independent variables like opEx_ac, AKS_ac,
and PPI. Given that opEx_ac captures much of the same data, excluding GFI_ac is not
necessarily problematic. The resulting Model 1 is a near optimum subset of the
originally proposed economic viability indicators.

The estimated coefﬁcients of most of the ﬁnancial (Vi) characteristics were
statistically signiﬁcant. Furthermore, the operator characteristic variables were
statistically signiﬁcant. Structural variables, which depict the role of location and size,
were also signiﬁcant. In fact, sixty-six percent of the coefﬁcients (6 out of 10 economic
indicators) in the restricted model were signiﬁcant at the 1, 5 and 10 percent levels.

Examine ﬁrst the effects of the farm ﬁnancial (W i) variables on viability. Many
of these variables had statistically signiﬁcant coefﬁcients. The coefﬁcient of off farm

income per acre (OFI_ac) in Model 1 was negative and signiﬁcant at the a = 0.05 level.

51

This is inconsistent with the previous result of Adelaja and Sullivan (1998). For every
one-dollar increase in OFI_ac, NFI declines by $1.44. Therefore, the two are at odds.
The implication of this is that OFI detracts from viability. This may reﬂect the fact that
OFI may decreases the interest in farming, the inability of the farm to maintain cash ﬂow
without off farm employment, or the tendency of part-time farmers to pursue less
intensive, and therefore less proﬁtable, farming techniques.

The coefﬁcient for the asset value of machinery and equipment per acre
(Equip_ac) is positive and signiﬁcant at the a = 0.05 level in Model 1. This suggests that
capital intensity contributes to farm viability. Note that this coefﬁcient is not signiﬁcant
in Equation 3 and 4.

The coefﬁcient for potential revenue from land sales per acre (RAS_ac) is
negative and statistically signiﬁcant at the a = 0.05 level in all 4 equation. This supports
the ﬁndings of Adelaja, Derr and Tank (1989) and Adelaja and Sullivan (1998) that
revenues generated from land sales enhance viability. It should be noted that RAS_ac is
the potential value of sales (easement value * change in harvested acreage) and may
undervalue the true value of land asset sales sold.

The coefﬁcient of operating expenses per acre (Opex_ac) is positive and
signiﬁcant at the a = 0.01 level, contradicting the expectation of a negative sign for this
coefﬁcient. This suggests that for every additionaldollar per acre of operating expenses,
net income increases by 22 cents. Opex_ac is essentially a measure of cost, which could
reﬂect efﬁcacy or the type of commodity grown. For example, vegetable farms should
have higher opex_ac than grain farms, but also generate greater gross farm income per

acres. The fact that the effect on NFI_ac is positive may suggest that such costs create a

52

more viable farm sector. Tree fruit, nursery, and ornamental farms also fall into the
category of high rate crop farms.

Many of the county structural (Xi) variables included in Model 1 are statistically
signiﬁcant. Critical support services such as licensed nurseries, grain dealers, pesticide
applicators, and wholesale potato dealers are necessary to agricultural viability. The
access to key services (AKS_ac) indicator is positive and signiﬁcant at the a = 0.01 level.
Moreover, AKS_ac has the largest coefﬁcient in the models, ranging from $5,791 in
Model 1 to $12,191 in Model 4. This is a very important ﬁnding, suggesting that a
‘critical mass’ of agricultural support infrastructure is necessary to a viable agricultural
future.

The SizeDiv coefﬁcient is positive and signiﬁcant at the a = 0.05 level. In
counties where a more diverse set of farm sizes exists NFI_ac increase by $786.
Therefore, it can be inferred that in areas where a more diverse set of farm sizes exists the
farm sector is more successful. Michigan agriculture is one of the most diverse in the
nation. This thesis supports that its diversity is an asset.

The results of the standard model presented here are consistent with ﬁndings from
previous studies. When applied to Michigan however, some of the coefﬁcients were
found to be insigniﬁcant including, EXT_ac, avg_age, Farm525_34. This may be a
function of the Michigan marketplace or a function of the exclusion of sustainability
considerations. The effects of environmental and social factors are discussed in the next

sections.

53

5.2 Results of the Environmentally Augmented Model

One of the objectives of this thesis was to test whether or not environment
sustainability variables enhance or detract from proﬁtability. To test the joint effect of
four environmental factors, Model 2 was estimated via OLS. The results for the inclusion
of environmental variables in the viability equation are discussed in this section. Table
A2 in the Appendix I highlights the parameter estimates under the heading of Model 2.

Model 2 conﬁrms that environmental stewardship contributes to our
understanding of economic viability in the farm sector. For Equation 6, which includes

environmental stewardship indicators, the adjusted R-square value increased from 0.5771

 

to 0.5986. This is an improvement over Model 1, suggesting that environmental
variables should be included in models of economic viability. However, only two of the
four coefﬁcients for the environment included in Model 2 are statistically signiﬁcant,
LDiv and CD_ac.

The coefﬁcient of land diversity (LDiv) is negative and signiﬁcant at the a = 0.10
level in Model 2. This suggests that farm in areas with greater levels of agricultural land
use diversity (i.e. measured via Herﬁndahl index) and therefore more land application
diversity are less proﬁtable by 38 cents per acre, all else equal. LDiv is not the same as
production diversity, for instance the LDiv variable includes uses such as land left fallow,
wetlands, Christmas trees, pastureland, woodland, ect. This variable essentially measures
land use compatibility or dissimilarity. It is possible that the negative coefﬁcient reﬂects
the growth challenges faced by farms in dealing with non-farmers when there is not a

dominant land use in the community.

54

The coefﬁcient of chemical dependence (CD_ac) measured as acres treated with
(insecticides, herbicides, fungicides and other pesticides) per harvested acre, is negative
and signiﬁcant in Model 2. This is a major ﬁnding and an addition to the literature.
Decreasing the amount of acres treated with chemicals implies greater environmental
stewardship. The ﬁnding that chemical treatments decrease proﬁtability suggests that
increased stewardship would enhance the success of farms and implies that sustainability
is possible as conceptualizes through the weak test of sustainability. Farmers can reduce
chemical treatments and still enhance their economic viability. This issue is at the very
heart of sustainability decisions in the US.

The insigniﬁcance of the coefﬁcients for conservation acreage (ConA_ac) and
total nutrient, residue, and pest management acres (NRCS_ac) supports the ﬁnding that,
at the least, environmentally friendly farming does not detract from proﬁtability. For
instance, the coefﬁcient for ConA_ac is not statistically signiﬁcant, indicating that
farmers pursuing environmentally friendly farming objectives are not adversely affected,
economically. The conservation variable does not affect proﬁtability signiﬁcantly.
Therefore, farmers can implement NRCS conservation practices without negatively
affecting the success of their operation, which is often cited as a speciﬁc concern relating
to voluntary conservation programs. This also indicates to policymakers that the
USDA’s NRCS Conservation Programs that increase environmental stewardship do not
detract from short-term economic sustainability.

In short, while half of the environmental coefﬁcients are insigniﬁcant, results
indicate that E, belongs in the equation. The short-term sustainability model is improved

(adjusted R-squared increases) by the inclusion of environmental variables. Moreover,

55

these environmental factors included at least do not detract from proﬁtability, indicating
that economic and environmental objectives are not at odds and therefore (somewhat)

complimentary to one another.

5.3 Results of the Socially Augmented Model

To test the joint effect of the ﬁve social considerations, Model 3 was estimated by
adding these ﬁve variables to the nine economic variables. The coefﬁcients of three of
the ﬁve social indicators are signiﬁcant. The contribution of social factors is also
conﬁrmed given the increase in the adjusted R-squared value and the cumulative effect of
social variables, which is positive. In fact, the adjusted R-squared increased to 0.5771 to
0.7467, indicating that social factors enhance the explanation of short-term sustainability.
The speciﬁc ﬁndings are discussed next.

The coefﬁcient of direct sales per acre (Direct_ac) is positive and signiﬁcant at
the a = 0.01 level in both Models 3 and 4. Farms that sell their agricultural products
directly to consumers are more proﬁtable. In fact, for every extra dollar of Direct_ac,
NFI_ac increases by $5.73 in Model 3 and $5.78 in Model 4, respectively. This is an
extremely important ﬁnding, which suggests that roadside stands, farmers ’ markets, and
u-pick sites contribute signiﬁcantly to short-term sustainability in agriculture.

Given the newly renewed Federal debate over migrant labor and border security,
access to migrant labor, which is expected to relate to the success of farming, is
becoming more challenging. A large number of seasonal farm workers are needed for
planting, cultivating, harvesting and packaging the many labor-intensive crops in
Michigan. Migrant Labor Housing Capacity (MLC) allows Michigan to secure about

23,000 seasonal workers (although other estimates suggest that there could be more like

56

100,000 seasonal workers). The coefﬁcient of MLC is found to be insigniﬁcant,
suggesting that the social services programs in place that help to secure an adequate labor
supply neither enhance nor detract from short-term sustainability in Michigan.

The coefﬁcient of population interaction index (PH) is negative and signiﬁcant at
the a = 0.01 level in Model 3 & 4. PH measures the interactions between urban
population and agriculture based on the potential interaction between nearby urban—
related population and agricultural activities. Increased PH was expected to enhance
proﬁtability because farmers in close proximity to higher value retail opportunities (such
as restaurants and specialty markets) face more Opportunities to direct market and educate
consumers (such as farmers markets and u-picks). The estimated negative effect may
suggest that right to farm conﬂicts, competing land uses, rising land values, losses in
agricultural support services, and farmers declining political clout cancel out the possible
positive effects of proximity.

The coefﬁcient of the summed contributions of the number of u-pick farms, on-
farm markets or roadside stands, cider mills, pumpkin patches, Christmas Trees, CSA’s,
and wineries (wine) per harvested acre, labeled Fopps_ac, is signiﬁcant at the a = 0.01
level in Model 3 & 4. Farmers who offer on-farm agro-tourism opportunities may be
making as much as $2.60 more per acre. One thing is for sure, direct market
opportunities are related to the success of the farm sector and a major contributor to the
social compatibility of agriculture within a community.

Project Fresh coupons’ (Pfresh_ac) coefﬁcient is not signiﬁcant. This means that

farms in counties with greater levels of WIC funding are no more or less viable. This

57

may suggest that social programs meant to increase access to fresh fruits and vegetables
for low-income residence do not detract from the success of the agricultural community.
Similar to the ﬁndings from the inclusion of environmental factors, the necessity
of the inclusion of social sustainability factors in conﬁrmed. The overall performance of
the model, in comparison to the restricted model, is enhanced. This suggests that social

compatibility is a signiﬁcant contributor to the short-term sustainability of farms.

5.4 Results of the Fully Augmented Model

Model 4 combines the economic, environmental, and social determinants and
allows the testing of the combined effects of all categories of sustainability objectives on
NFI_ac. When estimated collectively, in Model 4, the adjusted R-Square increases
further to 0.7373. This suggests that economic, environmental, and social factors when
estimated in concert provide a better explanation of agricultures success in Michigan.
Therefore, Model 4 provides a better overall estimate of short-term agro-economic
sustainability than the three subsequent models.

Eight of the eighteen variables included in Model 4 are signiﬁcant. Signiﬁcant
and positive coefﬁcients include opEx_Ac, AKS_ac, SizeDiv, Direct_ac, and Fopps_ac.
The negative and signiﬁcant coefﬁcients, which detract from viability include, OFI_ac,
RAS_ac, and PH. Many of the implications resulting from the sign of these coefﬁcients
are discussed in the previous sections.

On the positive side the ﬁnding, the signiﬁcance of AKS_ac has huge
implications for farmers that rely on local support services in a time when agriculture and

its many support industries are failing. Farms with greater access are more proﬁtable, but

58

as access dwindles so will viability. Dir_ac impact viability greatly, in Model 4 results
suggests the Dir_ac enhance NFI_ac by over $5.

On the negative side, OFI_ac and PPI remain negative. This is not surprising.
OFI_ac is negative and signiﬁcant in all four models, suggesting that the pursuance of
OFI_ac deﬁnitely attracts from the success of the farm sector. PH is remains signiﬁcant
and negative in Model 4, further showing that higher levels of PH detract from
proﬁtability. This has important implications for farmers at the urban fringe.

Overall, Model 4 suggests that the economic, environmental, and social
sustainability concerns are related, compatible, and synergistic. It is therefore safe to
purport that previously deﬁned models, which have not included environmental and
social aspects, are less appropriate and that future models should include such
considerations. The results of the four models are further compared via F-test and those

ﬁndings are highlighted in the next section.

5.5 Comparison of the Models via F-Tests & Predicted Values

As shown above, the inclusion of environmental and social factors of
sustainability improves the adjusted R-Squared values. Furthermore, more of the
environmental and social variables detract from viability. However, this thesis is more
concerned with the collective effect of the environmental and social variables versus the
individuals sign and signiﬁcance of the eighteen coefﬁcients. The F-test, which involves
comparing the resulting R-square of the restricted model (standard viability model,
Model 1) to the three unrestricted models (Models 2-4) using Equation 9 described in
Chapter IV, is the tool for analyzing joint effects of the 4 environmental and 5 social

variables and therefore speciﬁcation efﬁcacy. Table 2 below shows the results.

59

Table 2. Results of the Joint F-Tests

 

 

 

 

 

 

 

 

 

 

R URI UR2 UR3
Model 1 Model 2 Model 3 Model 4

R-Squared 0.6235 0.6607 0.7899 0.795
q restrictions 0 4 5 9
N observations 83 83 83 83
K-l parameters 9 13 14 18
N - K 73 69 68 64

F q, N-K 1.89 10.77* 5.95*

 

 

 

 

 

* F-Statistic is significant at the 1 percent level
The restricted (R) Model 1 is compared to the unrestricted (UR 1-3) Models 2-4 via F-
Test.

In Table 2, the reported F-Statistic tests the hypothesis that the model in question
signiﬁcantly enhances speciﬁcation. In other words, when the F-Statistic is statistically
signiﬁcant, then the coefﬁcients of the added variables (environmental, social, or both)
belong in the equation and should therefore not be dropped.

The signiﬁcance of the F-tests on the R2 values in the UR Models 3 & 4 suggests
that the coefﬁcients of environmental and social variables in the later two of the UR
Models are not jointly equal to zero. The F—test for Model 3 & 4, in comparison with the
restricted model, are positive and signiﬁcant at the a = 0.01 level. This supports the
hypotheses that the subsets of added coefﬁcients in the augmented models speciﬁed
herein are non-zero. However, the joint effect of the environmental variables alone is
insigniﬁcant suggesting that the environmental variables alone do not sufﬁciently add
value unless one includes the full slate of environmental and social variables.

Overall, a positive relationship exists between short—term sustainability and the
groups of the environmental and social sustainability factors. Therefore, the Weak Test

of Sustainability is passed, sustainability is possible, and synergies exist between the

three objectives of sustainability: proﬁtability, environmental stewardship, and social

60

compatibility in agriculture. The results of the F-tests prove that joint effects of social
and environmental factors are complementary to, rather than detrimental to the
proﬁtability of the agricultural economy.

The predicted values of Models 1-4 for each of Michigan’s 83 counties are shown
in Table A4 in Appendix 1. Each county can be compared via NFI_ac and the resulting
ﬁtted values of the Models estimated. Lake County fares the worst (losses of $60-90 per
acre) when it comes to short-term sustainability while Oakland County fares the best
(gains of $800-1100 per acre). In Allegan County, for instance, the predicted values for
the four short-term sustainability models (with the exception of Model 1) increased in

comparison to NFI_ac.

5.6 Summary

The empirical results of the four models speciﬁed in this thesis were highlighted
in Chapter V. Results indicate that the fully augmented model is the best speciﬁcation
and that the environmental and social factors individually and collectively improve the
speciﬁcation of the Models. The results of the F-tests conﬁrm that agriculture in
Michigan passes the Weak Test of Sustainability. Hence, sustainability is possible,
because holistically environmental and social factors do not detract from the viability of

farms in a county. Conclusions and Recommendations are provided in Chapter VI.

61

Chapter VI: Conclusions & Recommendations

Agriculture is a key industry in the United States; it provides food, ﬁber, a conduit
to the natural environment, income to farmers and farrn-related businesses, and jobs to
both urban and rural constituents. As shown in Chapter I, Michigan’s agri-food system,
the subject of this study, generates $70 billion in economic activity annually from
farming and allied activities, provides 1.05 million jobs and has made $8.6 billion in
capital investments over the past ﬁve years (Knudson and Peterson, 2009 and Peterson
et.al., 2006). However, agriculture’s success is not dependent solely on its economic
performance. It is also intrinsically linked to the overall social and environmental climate
within which it exists. Increasingly, the term sustainability is used to describe the
modern optimization challenges of agriculture.

The concept of agricultural sustainability has been put forth as an alternative to
conventional agriculture and it implies that three main objectives economic,
environmental and social sustainability are compatible and synergistic. To date, little
effort has been devoted to an analytical framework for evaluating this goal in agriculture.
This thesis research advances the concept of short-term sustainability by estimating the
effects of environmental and social sustainability factors on proﬁtability measures in
agriculture in Michigan. The Weak Test of Sustainability (WT S) used here is based on
the notion that the inclusion of environmental and social variables enhances the
speciﬁcation of proﬁtability models. This is an alternative to a Strong Test of
Sustainability, based on an actual measure of sustainability, which is currently not

possible due to data and deﬁnitional limitations.

62

The WTS involves comparing a standard viability model to three other augmented
models using F-tests. Results suggest that the three augmented viability models speciﬁed
herein improve the speciﬁcation when compared to the standard viability model. In
addition, the acceptance of hypothesis that relationship between economic viability and
the subset of environmental and social sustainability is non-zero suggests that
sustainability is (in a weak sense) possible and that synergies exist between the three
components of sustainability.

Many of the determinants of economic, environmental, and social sustainability
are found to be complementary to proﬁtability, suggesting that short-term sustainability is
a realistic goal for farmers. The factors shown to have a positive effect on NFI_ac
include opEx_ac, AKS_ac, SizeDiv, Direct_ac, and Foops_ac. The factors shown to
detract from NFI_ac include OFI_ac, RAS_ac, and PH. The sign of these latter factors
supports the notion that if farms were sustainable in the ﬁrst place they would not be
working off farm, selling land or competing with alternative land use. Results suggest
that future models would have greater efﬁcacy if they account for such economic,
environmental, and social sustainability factors.

Farmers are in the business of making money from agricultural production. The
empirical ﬁndings from this thesis suggest that farmers can pursue the environmental and
social objectives of sustainability while simultaneously enhancing (or at least not
detracting from) their bottom line. The beneﬁts associated with agriculture are much
more than just economic and many synergies between the three aspects of sustainability
exist. For instance, farmers that have access to key services and sell directly to

consumers are more proﬁtable. Farmers with a reliance on off farm income and greater

63

population pressures are less proﬁtable. Therefore, if farmers wish to maximize the
short-term sustainability of farming, they must take a broader view of agriculture.

The positive and signiﬁcant relationships between economic, environmental, and
social aspects of agriculture shown here suggest that future studies should include such
measures. The data and analytical systems currently used for assessing agriculture in the
US focus on economic variables. It is recommended that greater emphasis be placed on
documenting environmental and social variables that quantify agriculture for the rest of
society. As shown in this thesis we can no longer rely on comer solutions in agriculture,
where the focus is on just one aspect of agricultural viability. Therefore, assessing
economic, environmental, and social sustainability objectives simultaneously is
important.

Given that the information collected and tested in this thesis is for a single year,
results are indicative of short-term viability. Dynamic efﬁciency, as it relates to
intergenerational transfer and long-term viability and sustainability, was ignored. It is
recommended that future studies attempt to include time series data. One possible way to
do this is through the creation of a comprehensive (20 year) database of viability
variables. Due to the lack of data that is packaged correctly and readily available, this
type of analysis was not possible.

Information about land tenure and longevity, farm production opportunities and
the business climate, farmland preservation and zoning, eco and agro-tourism, the current
regulatory framework, animal damage, right to farm issues, communication and computer
use, agency support and informational services, training programs, farm management and

farmers’ attitudes are all necessary viability data components that are not currently

collected. These types of questions should be added to current survey tools, such as
Census of Agriculture, which are consistently updated. It is recommended that state-level
monetary support be made available for such attempts.

Another recommendation following from this research is the potential of a
simultaneous cash ﬂow equation model and its ability to explain further the many
interactions in agricultural viability. Previous research ﬁndings from Adelaja and Rose
(1988) suggest that a simultaneous cash ﬂow equation (SECF) may further explain some
of the interactions and correlations within the three factions of sustainability. The SECF
model allows for further quantiﬁcation of the effect of causal variables on viability, in
terms of cash ﬂow (Adelaja and Rose, 1988).

Lacking a clear measure of sustainability makes the concept difﬁcult to
implement in the real world; therefore, policies aimed at sustainable agriculture are
limited. One recommendation is that a clear deﬁnition of sustainability be adopted for
Michigan agriculture. It makes sense that sustainability is a broader concept than farm
proﬁtability and that many studies to date have been focused on deﬁning the concept of
sustainability, generating priorities, strategies and indicators and obtaining buy-in from
policy makers and society (Allen, 1993). To date, the academic, environmental and
policy circles interested in sustainability have been unsuccessful in generating adequate
measures of environmental stewardship or social equity, which clearly limits our analysis
of sustainability. It is recommended that we put these limitations behind us and move
forward. Given the ﬁndings from this thesis, the need to create a statewide sustainability
measurement initiative has never been more necessary. A round table may be one way to

do this.

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