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TRADE-OFFS, INCENTIVES AND THE SUPPLY FOR
ECOSYSTEM SERVICES FROM CROPLAND

presented by

MARIA CHRISTINA B. JOLEJOLE

has been accepted towards fulfillment
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Master of degree in Agricultural, Food and

 

 

Science I Resource Economics

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TRADE-OFFS, INCENTIVES AND THE SUPPLY OF ECOSYSTEM SERVICES
FROM CROPLAND

By

Maria Christina B. Jolejole

A THESIS

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

MASTER OF SCIENCE
Agricultural, Food and Resource Economics

2009

ABSTRACT

TRADE-OF F S, INCENTIVES AND THE SUPPLY OF ECOSYSTEM SERVICES
FROM CROPLAND

By

Maria Christina B. Jolejole

Agriculture is a managed ecosystem. The decisions of its managers, the farmers,
drive the mix of ecosystem services (ES) that it produces. The thesis is divided into two
essays. Essay 1 develops tradeoff analysis between profitability and selected
environmental indicators for different types of cropping systems using data from
agronomic field trials. The tradeoff frontiers developed in the study are profit vis-a-vis
global warming potential (GWP) and nitrate leaching. Both reveal that the conventional
com-soybean-wheat rotation treatment is dominated. The organic treatment is dominated
unless certified organic prices are used. The no-till cropping system shows potential as an
efficient choice for the farmer, as does alfalfa for its GWP. The tradeoffs between no-till
and alfalfa for GWP and no-till with certified organic imply that there are opportunity
costs to changing cropping systems in order to provide more nonmarketed ES.

Essay 2 uses survey data to examine farmers’ willingness to enroll in a program
that compensates them for adopting environmental stewardship. Results show that
Michigan farmers’ acreage enrollment decisions depend consistently on farm size and the
perception of environmental improvements from the practices. For farms over 500 acres,
the payment offered was also a significant inducement to acreage enrollment in all
systems examined. The second essay advances the literature on adoption of agro-
environmental practices by developing a supply function for crop acreage managed for
environmental stewardship. Like prior studies of environmental technology adoption in
agriculture, we find that environmental attitudes and affiliations, age, education and
current farming practices are influential. But we also find that the low cost suppliers of
environmental services are the largest farms. Agricultural policies based on payment for
environmental services that aim for cost-effective environmental impact will likely

achieve most of their impact from larger farms.

© Copyright by
Maria Christina B. Jolejole
2009

To God be the glory.

ACKNOWLEDEGEMENTS

This thesis is the end of my longjoumey in obtaining my Masters Degree in
Agricultural Economics. There are a lot of people who made this journey easier with
words of encouragement and more intellectually satisfying by offering help to expand my
theories and ideas.

I first want to thank my assistantship supervisor and thesis committee chair, Dr.
Scott M. Swinton. He gave me the confidence and support from the very first day I
started my Masters Program. He provided me direction and guidance all throughout the
process. He challenged me to set my benchmark even higher and gave me the confidence
to pursue my dreams of moving on to pursue my PhD. I learned to believe in my future,
my work and myself.

I would also like to thank the members of my committee, Dr. Frank Lupi and Dr.
Phil Robertson for their research guidance and for the valuable contributions they made
to improve my work and support they have given me along the way.

I am also grateful to the support of Kellogg Biological Station’s Long-Term
Ecological Research and the members of the National Science Foundation’s Human and
Social Dynamics program. I would especially like to thank Lenisa Vangel, Robert Shupp,
Sara Parr, and Sven Bohm for providing me with ideas and help with data. I greatly
appreciate their time and efforts. Also for the National Agricultural Statistics Service
staff especially Vince Matthews for help with the survey design and sampling. Also,
Natalie Rector for her valuable comments on the survey design.

I would like to express my appreciation to the faculty and staff of the Department

of Agricultural, Food and Resource Economics for all their help and support throughout
my time here.

Many thanks to my friends and fellow graduate students who helped me with
survey work. In particular, Lara de Villa, Feng Song, Huilan Chen, Xuan Wei, Alexandra
Peralta, Yong J iang, Shan Ma, Minh Chen, Jane Zhang and Ping Yuan for all the help
with the signing, labeling and mailing of questionnaires. Michelle Corzine, .loleen
Hadrich, Nicole Olynk, Marcus Coleman, Jake Ricker-Gilbert and Lee Schulz for the
help with the survey design. For the undergraduates who worked with me, thank you for
keeping up with me with the deadlines.

I would like to thank my good friends in the MSU Filipino Club, most especially
Carmille Bales and .on Gordoncillo who have brought me so much happiness and
support, especially during the stressful survey work and writing stage. Also to my second
family here in the US, to the Glatz family who have always welcomed me in their home
and treated me like a family for the past years I have been here in the US.

And last but not the least, for all my loved ones. My family, for the love and
support that overcome the distance. My parents, Nick and Dollie, and my sisters Tricia,
Therese and Trina, it may have cost us a lot of phone cards but all the encouragement and
inspiration you give me is priceless; and for Michael James Foreman for being there for

me all throughout, supporting me and believing in me.

vi

List of Tables

TABLE OF CONTENTS

 

 

List of Figures ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,

List of Acronyms

Introduction

oooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo

References

oooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo

Essay 1: Profitability and Environmental Stewardship for Row Crop
Production: Are There Trade-offs?
1.1 Introduction

1.2 Objectives of the Study ____________________________________________________________________________

1.3 Conceptual Framework: Environment-Profit Tradeoffs __________________________
1.4 Background of the Study: Site and Experimental Treatments _________________
1.5 Types and Sources of Data _______________________________________________________________________
Agronomic Data _____________________________________________________________________
Price Data and Cost Data

Environmental Data

1.5.1
1.5.2
1.5.3

1.6 Methods ____________________________________________________________________________________________________

1.6.1

1.7 Results a

References

oooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo

1.5.3.1 Global Warming Potential ..........................................
1.5.3.2 Nitrate Leaching ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,

Profitability Us

ing Enterprise Budgets ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,

1.6.1.1 Relative Profitability ..................................................
1.6.1.2 Crop Prices and Sensitivity Analysis .........................
1.6.2 Trade-Off Frontiers and Efficiency Determination ,,,,,,,,,,,,,,,,

nd Discussion

1.8 Summary and Conclusion ________________________________________________________________________

oooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo

Essay 2: Incentives to Supply Enhanced Ecosystem Services from Cropland

2.1 Introduct

2.3.1
2.3.2

2.3.3
2.4 The Data
2.4.1

ion

oooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo

2.2 Objectives of the Study ____________________________________________________________________________
2.3 Conceptual Model: The Supply of Environmental Service

by Farm Households ______________________________________________________________
Multi-attribute Utility Function ______________________________________________
Economic Model of Willingness to Accept

and Environmental Supply _____________________________________________________
Farmers’ Decision Model

vii

12
l3
l3
14
14
15
I6
l6
16
18
19
20
20
24
38

43
45

46
46

47
49
51
51

2.4.2 The Questionnaire Design ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
2.5 Methods

2.5.1 Econometrics of WTA ............................................................
2.5.2 Variable Specification and Working Hypothesis ..................
2.6 Results and Discussion .............................................................................
2.6.1 Descriptive Results ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
2.6.2 Participation Decision ...........................................................
2.6.3 Acreage Decision __________________________________________________________________

2.6.4 Payment Effects By Stratum ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,

 

 

2.6.5 An Approximation to the Supply Curve ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,

2.7 Summary and Conclusion ........................................................................

References ......................................................................................................

Conclusion ............................................................
Appendices

A.l Enterprise Budgets For the Cropping Systems Used in Essay 1 _____________
A.2 Overview, Goals, Study Design, Collection Procedure and Data

Management for the Crop Management and Environmental

Stewardship Survey ____________________________________________________________________________
A.3 Questionnaire Design ______________________________________________________________________________
A.4 Sampling Design ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
A.5 Bid Design _______________________________________________________________________________________________
A.6 Experimental Design and Versions _________________________________________________________
A. 7 Pre-Notice, Cover Letters, Reminder Post Card and

The Questionnaire ___________________________________________________________________________________

A.8 Survey Returns, Response Rate and Completion Status _________________________
A.9 Survey Weights _______________________________________________________________________________________
A.10 Some Descriptive Results from the Farm Survey (Essay 2) _________________

viii

52
54
54
58
62
62
62
65
67
68
69
88

94

96

107
112
115
117
120

123
144
147
151

Table 1.1

Table 1.2

Table 1.3

Table 1.4

Table 2.1

Table 2.2

Table 2.3

Table 2.4

Table 2.5

Table 2.6

LIST OF TABLES

Description for the Different Treatments at KBS-LTER ................... 25
Input Prices and Sources ___________________________________________________________________ 26
Output Prices and Sources .................................................................. 27

Mean Values for Revenue Above Selected Costs and Different
Environmental Indicators ___________________________________________________________________ 28
Specific Practices of the Cropping Systems presented in the
Questionnaire _____________________________________________________________________________________ 73

Descriptive Statistics for the Variables used in Regression
Analysis, weighted by farm size stratum, 1688
Michigan corn or soybean farmers, 2008 ___________________________________________ 74

Participation Decision in the Environmental Stewardship
Practices (Probit Regression), weighted by stratum, by Cropping
Systems, 1688 Michigan corn or soybean farmers, 2008 __________________ 76

Acreage Decision in the Environmental Stewardship Practices
(Truncated Regression), weighted by stratum, by Cropping
Systems, 1688 Michigan corn or soybean farmers, 2008 __________________ 79

Participation Decision in the Environmental Stewardship Practices
with Payment Effects, by Farm size stratum and by Cropping
Systems, 1688 Michigan corn or soybean farmers, 2008 __________________ 82

Acreage Decision in the Environmental Stewardship Practices with
Payment Effects,by Farm size stratum and by Cropping Systems,
1688 Michigan corn or soybean farmers, 2008 __________________________________ 85

Figure 1.1

Figure 1.2

Figure 1.3

Figure 1.4

Figure 1.5

Figure 1.6

Figure 1.7

Figure 1.8

Figure 1.9

Figure 2.1

Figure 2.2

LIST OF FIGURES

Production Possibilities Frontiers with Profits and Environmental
Quality and Indifference Curve for Farmer, i _________________________ 29

Production Possibilities Frontiers with Profits and Environmental
Damage and Indifference Curve for Farmer, i __________________________________ 30

Mean Revenue Above Selected Costs Across Treatments,
KBS-LTER, Hickory Comers, Michigan, 1993-2007* ,,,,,,,,,,,,,,,,,,,,, 31

Mean Revenue and Costs That Vary Across Treatments
KBS-LTER, Hickory Comers, Michigan, 1993-2007* _____________________ 32

Mean Yields for Corn, Soybean and Wheat for the

Cropping Systems, KBS-LTER, Hickory Comers, Michigan,
1993-2007 ___________________________________________________________________________________________ 33
Proportion of Inputs By Type, KBS-LTER, Hickory Comers,
Michigan, 1993-2007* _______________________________________________________________________ 34

Sensitivity of Profits Based on Crop Prices, KBS-LTER,
Hickory Corners, Michigan, 1993—2007* ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 35

Tradeoffs between Global Warming Potential and Revenue Above
Selected Costs, KBS-LTER, Hickory Comers, Michigan,

l993-2007* ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 36
Tradeoffs between Nitrate Leaching and Revenue Above Selected
Costs, KBS-LTER, Hickory Comers, Michigan, 1993-2007* ___________ 37

Predicted Farm-level Supply Curves of Acreage Enrolled
by Cropping System, 1688 Michigan Corn or Soybean Farms,

Predicted F arm-level Supply Curves of Acreage Enrolled by
Strata and by Cropping System, 1688 Michigan Corn or
Soybean Farms, 2008 _________________________________________________________________________ 72

KBS
LTER
ES
NMES
MRT
MRS
CSW
CS
1PM
PVIFA
WTA
WTP
CRP
CSP
EQIP
MEAEP
NASS
ERS
USDA
PES

IPCC

LIST OF ACRONYMS

Kellogg Biological Station

Long Term Ecological Research
Ecosystem Services

Non Marketed Ecosystem Services
Marginal Rate of Transformation

Marginal Rate of Substitution
Corn-Soybean-Wheat Rotation
Com-Soybean Rotation

Ideal Point Method

Present Value Interest Factor for Annuity
Willingness to Accept

Willingness to Pay

Conservation Reserve Program
Conservation Security Program
Environmental Quality Incentives Program
Michigan’s Agriculture Environmental Assurance Program
National Agricultural Statistics Service
Economic Research Service

United States Department of Agriculture
Payment for Ecosystem Services

Intergovernmental Panel on Climate Change

xi

Introduction

Agriculture is the world’s largest terrestrial ecosystem (Millennium Ecosystem
Assessment, 2005). Managed to provide food, fuel and fiber to meet humans’ market and
subsistence demands, it simultaneously affects and depends on the biophysical and
economic settings in which it operates. In so doing, it generates non-marketed ecosystem
services (ES), like carbon sequestration into soil or trees, and disservices, like lake
eutrophication from phosphorus runoff. These non-marketed ES and disservices come as
joint products or byproducts with the intentional food, fiber and fuel products. Braden
and Lovejoy (1990) stressed that these residuals and by-products are difficult to quantify
and have rarely been priced.

Unlike natural ecosystems, agriculture is a managed ecosystem. The decisions of
its managers drive the mix of ES that it produces. Farmers play an important role as
ecosystem managers in that they balance their decisions regarding land and other
agricultural inputs for production and modify their practices to adjust the positive and
negative impacts to the environment (Wossink and Swinton, 2007). By their choices of
production inputs and management practices, farmers shape their impacts on the
environment.

This thesis is divided into two essays. Essay 1 develops tradeoff analysis between
profitability and selected environmental indicators for the different cropping systems
from experimental treatment plots located at the Kellogg Biological Station. The
objectives of this study are: (1) ) to compare the profitability of the cropping systems by

constructing enterprise budgets for all the cropping systems, (2) to construct trade-off

frontiers between profitability and selected environmental indicators for all the cropping
systems and (3) to identify preferred cropping systems from the trade-off frontiers.

Essay 2 examines farmers’ willingness to accept compensation to adapt to
environmental stewardship practices in Michigan based on the analysis of survey data.
Moreover, it advances the literature on adoption of agro-environmental practices by
developing a supply function for crop acreage managed for environmental stewardship.
The objectives of this paper are: (1) to identify farmer’s willingness to accept (WTA)
payments for environmental services to adopt to environmental stewardship practices; (2)
to investigate the determinants of their willingness to adopt those practices, and the
relative importance of these factors; and (3) to estimate empirically the supply curves for
acreage enrollment in hypothetical environmental stewardship programs which implicitly

expresses how much ecosystem services are farmers willing to produce.

References

Braden, 1.3. and SB. Lovejoy, eds. 1990. Overview in Agriculture and Water Quality:
Agriculture and Water Quality International Perspectives. Lynne Rienner

Publishers, Boulder, CO, 1-37.

Millennium Ecosystem Assessment. 2005. Ecosystems and Human Well Being:
Systhesis. Reid. Island Press, Washington DC.

Wossink A. and SM. Swinton. 2007. Jointness in Production and Farmers' Willingness
to Supply Non-marketed Ecosystem Services. Ecological Economics 64(2):297-
304.

Essay 1:
Profitability and Environmental Stewardship for Row Crop Production:
Are There Trade-offs?

1.1 Introduction

Farmers play an important role acting as ecosystem managers that help maintain
the natural supporting ecosystem services that make agriculture productive (Swinton, et
a1., 2006). Moreover, they make choices that can change the type, magnitude and relative
mix of services provided by the ecosystems (Rodriguez et a1, 2006). By their choice of
inputs and management practices, they face important trade-offs such as those between
agricultural production and ecosystem services such as biodiversity, water and soil
quality.

Careful selection of crop systems involves examining trade-offs between
profitability and environmental impact. Gebremedhin and Schwab (1998) provided an
extensive literature review on the effects of crop rotations on profitability and the
environment. For example, they pointed out that less dependence on external inputs, i.e.
less dependence on fertilizer and chemicals, can reduce the costs for the farmers and at
the same time using less chemicals is beneficial for environment. Cover crops incur
planting costs for the farm but can also improve soil structure, increase soil organic
matter, water percolation, beneficial insect population, suppress weeds, reduce soil
erosion and fix residual N after the grain is harvested (Gebremedhin and Schwab, 1998;
Jones and Ritchie, 1996). Dhuyvetter et a1 (1996), points out how conservation tillage
reduces operation costs as it reduces expenses for labor, fuel, oil, and machinery use costs

and at the same time increases water infiltration and water loss from evaporation.

lnvariably, the environmental objectives conflict with one another and farmers’ choices
involve significant tradeoffs.

But what lies behind the farmers’ decisions are the incentives they have for doing
a particular practice. Empirical studies in the soil conservation literature have shown that
the most important motive for adoption is the “selfish”, financial—economic concern, or
profits including financial attributes in some sense (Chouinard et al, 2008). Cary and
Wikinson (1997) and Honlonkou (2004) found that adoption of conservation practices
depends on financial economic indicators such as profitability. Graafland (2002) modeled

the trade off between profit and stewardship centering upon the profit maximization

principle. In a farmer focus group1 conducted in south-central and central Michigan,
several farmers expressed their commitment to environmental stewardship, but felt that
profitability and business viability had to come first. One of the farmers said, “I always
try to choose practices that have environmental benefits but if it’s going to cause me to
lose money then I can’t take that choice.”

On the other hand, a category of literature focuses on social and attitudinal issues
in agricultural production, including stewardship motives. Wunderlich (1991) examined
the evolution of the concept of stewardship among agricultural producers and stressed
that farmers view themselves as stewards and that their farming is a way of life rather
than a business to maximize profit. Ryan, Erickson and De Young (2003) examined the
motives for protecting biodiversity and water quality in the Midwest. They discovered

that an important factor in motivating conservation is attachment to the land, and that

 

l . .
S.M. Swmton, N. Rector, G.P. Robertson, C.B. Jolejole and F. Lupi. July, 2007. “Ecosystem
Services from Farmland: What do farmers think?”. Unpublished manuscript.

producers are more likely to engage in a practice that makes their farm appear well
managed.

Clearly, the literature shows that there are economic and non-economic
conservation incentives. An integrated analysis of economic and environmental indicators
of alternative cropping systems can be done using a multi-objective approach grounded in
multi-attribute utility theory. Antle et a1. (2004) discussed trade off frontier analysis as a
modeling system for agricultural and environmental policy analysis. Trade off analysis
quantifies the relationship between key economic and environmental indicators at the
level of a farm field. For policy analysis, results may be aggregated on a bigger scale.

New crop production technologies have been studied in light of this growing
concern for environmental stewardship practices. In particular, Kellogg Biological
Station’s Long-term Ecological Research (KBS-LTER) project evaluated the
environmental benefits from low input crop rotations. The LTER program is a
fundamental ecological research network funded by the National Science Foundation. It
started in 1980 and now supports more than two dozen field sites in North America,
Antarctica and Polynesia. The KBS-LTER founded in 1988 is the site focused on
agricultural ecology. It has developed a cropping system that offers comparable yields
with less pesticides and fertilizers applied than conventional systems in the northern
Combelt. Despite the environmental benefits, few farmers have adopted this crop system.

This paper looks at the profitability of the different cropping systems including
the low-input crop rotation that KBS developed. Moreover, the paper develops trade off
frontiers between profitability and environmental performance using enterprise budgets

from Michigan research trials and selected environmental indicators from the KBS. It

contributes to the growing body of knowledge about the economic and environmental
impact of alternative cropping systems while trade off analysis allows stakeholders to
make informed decisions concerning the dual goals of agricultural production and

safeguarding the environment.

1.2 Objectives of the Study

The objectives of this study are: (l) to compare the profitability of the cropping
systems by constructing enterprise budgets for all the cropping systems, (2) to construct
trade-off frontiers between profitability and selected environmental indicators for all the
cropping systems and (3) to identify preferred cropping systems from the trade-off

frontiers.

1.3 Conceptual Framework: Environment-Profit Trade Offs

The concept of trade off is fundamental to economics and derives from the idea
that resources are scarce. Consequently, to obtain more of one scarce good, an individual
or society collectively must give up some amount of another good. Trade off analysis
applies these principles to derive information about sustainability of agricultural
production systems, by quantifying the inter-relationships among environmental
indicators implied by the underlying processes and the economic behavior of profit
maximization.

The integrated economic and environmental systems have multiple objectives.

Thus the idea of a multi-attribute utility function is fitting in assessing these trade offs

where a general efficiency rule is used that applies to all decision makers who generally
care about the different attributes (King and Robison, 1984).

Following the framework by Chouinard et al (2006), we build on the model of a
farmer behavior by integrating environmental attributes from a multi-attribute utility
function to determine dominance and production possibilities function (PPF) to determine
technical efficiency. It is worth noting that in reality, farmers do not think in terms of
production functions rather they think of production technologies and farm practices.
Farmers identify a specific combination of inputs and outputs, i.e. practices, as a farm
technology.

We start with a multi-attribute utility function. We assume that the farmers would

want to maximize a utility function that is increasing in profits 71' and environmental

quality E.

MaxU=U(7r,E); E7>O;_6E—>O (1)

Where,

6E 6E
E:e(anxP); _>O’a__<0 (2)

. .5Q
flszQ—pxx—CO; 5—Q—>O’5;:>O,ax_>0 (3)

Environmental quality, E , is an increasing function of environmental enhancing

inputs, x15 , and decreasing with polluting inputs, xp . Also profit, 77 , is a function of

output Q , input x , fixed costs co , output prices p Q and input prices px .

Figure 1.1 shows a generically shaped PPF. The PPF shows how a fixed resource
such as land can be allocated most efficiently between two different outputs. Although

traditionally outputs are marketed, they can also include non-marketed services like

environmental quality, E . Anything lying inside the frontier is considered a technically
inefficient choice. PPF therefore determines technical efficiency.

The slope of the PPF is the marginal rate of substitution between the two outputs.

on

So that the slope, a E , shows the marginal rate of technical substitution or the change in

profit, 72' , per unit change in environmental quality, E . This is the implied cost of to the

farmer of increasing environmental services provision to improve environmental quality.

A particular farmer, l , maximizes utility where indifference curve, L1 is

tangent to the PPF (in particular point A) and produces corresponding profit and

. 0
environmental quality. For farmer 1 any point above the indifference curve (I, would

be preferred.
Even among individuals whose utility fits the assumptions in Equation (1), the

shape of indifference curves for different individuals may differ, meaning that they have

different relative preferences between profit, fl , and environmental quality, E . This

makes this type of analysis appealing because it covers wider type of individuals

 

including policy makers so long as their utility fits this common assumption (King and
Robison, 1984).

The shaded area represents points that would be preferred over point A by any
farmer whose utility function meets the general assumptions in Equation (1), because it
allows one to increase profit and/or decrease environmental damage at the same time.
The area could be called the area of profitability-environmental quality dominance
relative to point A.

This study makes use of two environmental indicators data on global warming
potential (GWP) and nitrate leaching which both exhibit negative environmental effect.

From this point forward to the end of the section, we will denote to this as environmental

damage (ED) . Figure 1.2 presents a diagram with measures of environmental damage

and profit on the axes.

King and Robison, (1984) noted that an efliciency criterion divides the decision
alternatives into two mutually exclusive sets: efficient set and inefficient set. The
efficient set contains the choice of every individual whose preferences conform to the
assumptions associated with the criterion. No element in the inefficient set is preferred by
decision makers with the preferences assumed. Thus, inefficient alternative choices are
no longer considered in the decision.

More formally, profit-environmental quality efliciency criterion is stated in terms
of these two conditions, I and 2: Outcome distribution] ith profit 72-1 and

environmental damage E131 , dominates outcome distribution 2 with profit 72-2 and

environmental damage ED2 , if 7T] 2 772 and EH S EDZ and ifone ofthese two

10

inequalities is strict. Efficiency criteria are useful in cases where preferences are not

known directly but we do observe technology characteristics (King and Robison, 1984).

In Figure 1.2, point A’ is where farmer l maximizes utility. The shaded region
represents points that are profit-environmental quality dominant over initial point A’
because it allows one to increase profit and/or decrease environmental damage at the
same time. Points B and D on the other hand represents tradeoffs relative to point A.
Point B is a dominated choice relative to point A because even though it allows the
individual to increase profit, environmental damage increases at the same time. The same
goes for point D because even though environmental damage is decreased, profit is also
decreased. Point C is simply an inefficient choice because it gives lower utility to anyone
whose preferences fit equation 1.

The procedure for building trade off frontiers is analogous to risk efficiency with
two variables, such as mean-variance efficiency (King and Robison, 1984). The basic
idea is to increase the good and decrease the bad (i.e. increase the mean and decrease the
variance). Likewise, the farmer tries to increase profit and decrease environmental
damage. Efficiency determination involves mapping alternative practices or policies and
evaluating their efficiency in the sense of giving the best profitability for a given level of
environmental performance, or the best environmental outcome at a given profitability
level. Efficient choices will lie on a frontier, where there is a trade-off between improving
profitability and environmental performance. The slope of the trade off frontier represents
the opportunity cost of environmental choices in terms of reduced farm income (Antle,
Capalbo and Crissman, 1998). The steeper the slope, the greater is the opportunity cost

for improving the environmental stewardship measured by the foregone profit. Thus, the

11

an
slope, TED ,

represents the implicit cost of foregone income from changing the systems
to decrease environmental damage.

Moreover, the influence of the exogenOus factors can be seen on the shape of the
frontier curves and can also be considered as drivers of the production system that could
result in a shift of the frontier. This can be referred to as change in system’s exogenous
drivers as policy, technology or resource change scenarios (Weersink et al, 2002). There
are several studies that constructed trade off frontiers. Kelly, Lu and Teasdale (1996) did
a simulated analysis of long-term impacts of different cropping systems including trade
off analysis of net return and different components of environmental quality. Van der

Veeren and Lorenz (2002), looked at the cost effectiveness, spatial equity and

sustainability and constructed trade off curves to show relationships among the three.

1.4 Background of the Study: Site and Experimental Treatments

In this study, the trade offs between profitability and some environmental
indicators for several cropping systems were constructed and analyzed. The KBS-LTER
main experimental site was the source of data for this study. It is a 60 hectare site divided
into six different treatments, each one replicated into six one-hectare plots. Four of these
seven systems are annual crop rotations and two are perennial crops, namely, alfalfa and

poplar.

The annual crops are corn-soybean-wheat rotations (CSW) with four treatments.
The conventional cropping system uses university extension recommended chemical

inputs and chisel plowing. The no-till system uses conventional chemical inputs and uses

 

no-tillage management. The low-input system uses 2/3 of the chemical inputs as the
conventional, banded herbicide and tillage to control weeds, and a winter cover crop in 2
of 3 years. In the organic system, no chemical inputs or manure are used. Mechanical

cultivation is used for weed control, and cover crops are used.

Perennial systems include alfalfa and poplar. The poplar treatment is a fast
growing Populus clone that is fertilized only once when established. It is harvested every
9-10 years and allowed to coppice or regrow from stems. On the other hand, the alfalfa
stands are fertilized with phosphorus, potassium, boron and lime according to soil tests
and university recommended rates. The alfalfa is harvested 3-4 times per year and
replanted every five to six years. Table 1.1 summarizes the differences among the

treatments.

The experimental plots for the study are located at WK. Kellogg Biological
Station (KBS) in Hickory Comers, Michigan. The site is located at the northern end of
the US. Combelt, 50 km east of Lake Michigan (42° 24’N, 85° 24’W) on soils developed
from glacial outwash deposited 12000 years ago. Annual rainfall averages 890 mm y'l
with about half falling as snow and potential evapotranspiration (PET) exceeds
precipitation for about 4 months of the year. Mean annual temperature is 9.7 °C. (More

information is at htlp://lter.kbs.msu.edu).

 

1.5 Types and Sources of Data
The data include agronomic farm data (site specific production and input data),
external price data and environmental data (site-specific experimental data for calibration

of biophysical models). Agronomic farm data and prices were used in constructing the

enterprise budgets to measure profitability. Environmental data were used together with

the computed profits to construct trade off analysis.

1.5.1 Agronomic Data
Agronomic data include yields, fertilizer and herbicide application rates, seeding
rates, and tillage activities from the KBS-LTER agronomic log. For the cropping

systems,15 years of data from 1993 to 2007, equivalent to five complete crop rotations of

corn, soybean and wheat was used.2 While for the perennial systems, the poplar data
covered a complete ten-year cycle from 1989 to 1998 and the alfalfa data covered a three

complete five-year cycles from 1989 to 2003.

1.5.2 Price and Cost Data
Cost data for this study were collected from a variety of secondary sources in an
effort to represent actual prices observed in Michigan. The input and output price

sources are presented in tables 1.2 and 1.3, respectively. The price data includes the

1978-2008 National Agricultural Statistics Service (NASS) agricultural prices3, average

organic prices for 2008 from the Economic Research Service of the United States
Department of Agriculture (ERS-USDA) website4, price of fertilizer and herbicides from

an agricultural input vendor in Michigan as of April 20085, average organic certification

 

http://lter.kbs.msu.edu/datasets
http://www.nass.usda.gov/QuickStats/index2.jsp

2
3
4 http://www.ers.usda.gov/Data/OrganicPrices/
5

Jorgensen Farms Elevator, Pers. Comm., April 24, 2008, Fax request for Input Prices

14

costs from Institute of Food and Agricultural Sciences of University of Florida, and the
cost of tillage operations from the custom machine work rates in the Saginaw Valley and

on Iowa State University custom rate survey.

1.5.3 Environmental Data

Crop management practices directly affect the mix of ecosystem services
generated. Some environmental indicators, namely global warming potential and nitrate
leaching data collected at the Kellogg Biological Station, were used in the trade off

analysis.

1.5.3.] Global Warming Potential

The data used in the study was taken from the Robertson et al (2000) paper
measuring the global warming potential of different treatments. Robertson et al (2000)
reported that, globally, agriculture is responsible for 20% of the terrestrial greenhouse gas
emissions. In particular, the major greenhouse gases coming from agriculture are
methane (CH4), nitrous oxide (N20) and carbon dioxide (C02).

A complete understanding of agriculture’s impact on global warming was
performed by field-level analysis of all greenhouse gas emission rate fluctuations to
derive the net global warming potential (GWP) for the different cropping systems
(Robertson et a1, 2000). Robertson et a1 (2000) performed greenhouse gas accounting in
which the different gases that come from agriCulture were given weighted values
according to their "potency as a greenhouse gas". This potency of a gas is referred to by

the lntergovemmental Panel on Climate Change (IPCC) as GWP.

15

1.5.3.2 Nitrate Leaching and Runoff

The data used came from Syswerda (2009) which looked at long-term nitrate loss
for different treatments. They sampled soil at different depths, and nitrogen content was
measured and recorded to measure leaching. Agricultural nitrogen comes from a wide
variety of sources but primarily from inorganic fertilizer, animal waste, and nitrogen
fixing plants. The KBS-LTER site includes no animal wastes.

Most crops only take up 50% of nitrogen applied (Syswerda, 2009; Robertson,
1997). The other 50% is subject to loss to the environment including leaching into
groundwater (Syswerda, 2009; Fenn et al., 1998; Sanchez et al, 2004). This can impact
human health when ingested. Leached nitrates can reach surface water leading to
eutrophication and algal blooms, which harm or kill fish and other wildlife (Garrett and
Buck, 1997). Ribaudo (2003) estimated the cost of mitigating U.S. water quality

impairment due to nitrate in the tens of billions of dollars.

1.6 Methods
The first part of this section presents analysis of enterprise budgets to look at
profitability and the second part explains the trade off frontier analysis using

environmental data from the KBS-LTER experiments.

1.6.1 Profitability Using Enterprise Budgets

At the farm level, optimizing farmers choose the best cropping system among the

technically feasible alternatives. As mentioned earlier, the conservation literature has

16

shown that the most important motive for adoption is the financial-economic concern, or
profits.

Profitability is a function of yield, output prices and operation costs which include
seed costs, fertilizer, herbicides, insecticides and custom operations costs. Different
cropping systems can have different operation costs and yields, thus it is important to use
a common index for their comparison — a measure of net financial return to the farm.

This study compares the profitability of different production systems by
calculating an annualized net return (annual payment stream with cumulative value equal
to the net present value) for each system. The annual systems included the 4 cropping
systems and the annualized present value was calculated assuming a balanced rotation
where a third of available land is planted to each crop in each year. For the perennial
systems, alfalfa and poplar, the analysis assumes three five-year and one ten-year
complete cycles respectively. An annualized value was computed by dividing the present
values by a present value interest factor for an annuity (Weston and Copeland, 1986,
Appendix A.4). The discount rate chosen for this study was 5%, to reflect a real, risk free
rate of return.

Enterprise budgeting is one of the most basic production economic tools available
(Roberts and Swinton, 1996). It is relatively simple compared to other methods but can
still provide a detailed, in-depth analysis. Enterprise budgets represent the estimates of
receipts (income or gross returns), costs (fixed and variable costs), and profits associated
with the production of agricultural products for an enterprise. They can be used to
evaluate how one crop or activity can contribute to the profitability of a certain cropping

system and to compare the contributions to profitability of the same crop or practice

17

under different rotations (Gebremehedhin and Schwab, 1998; Christenson et al,, 1995;
Jones and Ritchie, 1996). Enterprise budgets can be used to rank the profitability of the
different systems.

In order to conduct a profitability analysis of different cropping systems, a clear
description of each system and its associated practices becomes essential (Table 1.1). If
the differences are limited to only part of the farm operation, gross margin analysis of
revenues minus costs that vary among treatments suffices for comparison across
treatments (CIMMYT, 1988). In this case, the differences among the cropping systems
are on the use of cover crops, amount of chemical use and tillage.

This study constructed enterprise budgets as shown in Appendix Tables 1 to 7 for
the different treatments in Table 1.1. Gross margins cover selected cash expenses such as
seed costs, fertilizer costs, herbicides costs, tillage costs and custom costs. The budgets
omit costs that are unchanging across treatments such as land. Hence, although they do
not fully measure profitability, they offer a complete measure of profitability differences
across treatments. For Treatment 4, the no chemical treatment, two enterprise budgets
were constructed, one assuming non-organic prices and the other assuming certified

organic farm prices.

1.6.1.1 Relative Profitability
Among the cropping systems, the comparatively more profitable would always be

preferred by a profit maximizing producer. Thus, selecting the optimal technology

involves two stages: computing the profit for each treatment, t , then comparing across

the T number of treatments.

Mathematically, profitability across is given by:

Px) " Co (4)

 

7ft : pQ'Q(t|anxP) " C(t
Where 72' is the profit or the revenue above selected costs. Q is the output which

is a function of treatment, I , conditional on factors that contribute to output such as

input used which includes both the polluting and environmental enhancing inputs,

xp and x E . C is the variable which accounts for the costs that vary, which is a

function of production technology or treatment, t , conditional on input prices, p x .

While cO accounts for the fixed costs which are the same for the treatments.

1.6.1.2 Crop Prices and Sensitivity Analysis

Choi and Helberger (I993) looked athow sensitive are crop yields to price
changes and farm programs. Moreover, Houck and Gallagher (1976) using time series
for 1951-1972 estimated the corn yield changes with respect to changes in corn price.

In this paper, we also look at the sensitivity of profitability changes in response to
changes in crop prices. A reasonable time series price data from National Agricultural
Statistics Service (NASS) was used (1978-2008). Crop prices were deflated to year 2008
using the 2008 Economic Report of the President for the producer price index for farm
products.

Standard deviations were computed to determine a low price scenario, and a high

price scenario relative to the baseline. The low price scenario is computed by subtracting

 

the computed standard deviation from the baseline price while the high price scenario is

computed by adding the computed standard deviation to the baseline price.
Thus, expected profit can be written in terms of both price scenario, J , and

technology treatment, t ,

7th =ij-Qr(tlx59xp)_ct(tlpx)] (5)

where subscript J represents the price scenario (low, mean, or high price scenario).

1.6.2 Trade-Off frontiers and Efficiency Determination

This study illustrates the tradeoffs between the economic and environmental
sustainability of different agricultural systems. The joint distribution of outcomes is
presented in a graphical form with environmental measures on the y-axis and revenue
over selected costs or gross margin on the x-axis. A given point on the graph represents
the joint environmental-economic outcome for a given type of technology or cropping
system adopted. Each different KBS-LTER treatment (conventional, no-till, low-input,
organic, alfalfa, poplar and successional plots) generates a new point. Connecting the
points via ideal point method (1PM) forms a frontier.

Using 1PM idea, we look for at least one point that dominates the other points.
Generally, along the frontier, the idea is that gains in one area cause losses in the other
one. If there is a win-win situation, then one of the points must have been inefficient. As
we can recall in previous section, the tradeoff curve represents the joint distribution of

economic-environmental outcomes that are efficient.

20

1.7 Results and Discussion

Appendix Tables 1 through 7 present the enterprise budgets for the different
treatments presented in Table 1.1. The information is summarized in bar charts in Figures
1.3 and 1.4 where revenue above selected costs and gross revenue vis-a-vis costs across
treatments are presented. The no chemical or “organic” treatment under certified organic
selling prices generated the highest revenue followed by the no-till treatment, the low
input treatment and the conventional treatment. Organic prices have been high, thus
generating highest profits. When the same treatment was evaluated using non-organic
prices, the profit was lowest among the four cropping systems. This could be explained
by the low yield performance of this treatment. The mean yields in Figure 1.5 show that
the organic treatment did not perform well for corn, soybean or wheat. No-till performed
best in yields followed by low input and conventional treatment. Thus, at non-organic
selling prices, the no-till treatment generated the highest profit. An interesting issue is
given the profitability of organic treatment with large premium, Michigan Department of
Agriculture reports that only 140 farms out of 53,000 farms across the state are currently

certified as organic farms under USDA’s National Organic Program, which is less than

. . 6 . .
1% of all Michigan farms. One poss1ble reason why more farmers are not adopting
organic practices is the transaction and time cost of procuring a certification. During the
three-year adjustment, farmers suffer lower yields without higher prices. Moreover, if

most farmers switch to organic farming, the large price premiums might cease to persist.

 

6

Sattleberg, .1. 2008. “Getting to Organic.”
http://74.125.95. l32/search?q=cache:zh8DUGkYZdUJ:www.moffa.org/f/Getting_to_Organic_20
08.pdf+organic+fanners+in+michigan&cd=1 l&hl=en&ct=clnk&gl=us '

21

Figure 1.6 shows a stacked bar graph of proportion of inputs by treatment. Tillage
cost was high for the organic treatment and low input treatments. While chemical cost
was highest for the no till treatment, the low input treatment had low cost on
agrichemicals but had the highest cost for tillage due to reliance on tillage for weed
control. The no-till treatment had zero tillage cost but the highest agrichemical cost for
weed control. Figure 1.6 shows that there are increased herbicide costs with the no-till
treatment. Thus, a no-till budget may appear less expensive in terms of tillage costs, but
agrichemical costs are increased.

The annualized revenue and costs for the perennial systems, alfalfa and poplar,
are also included in the analysis. Looking at the stacked bar graph on proportion of input
costs, alfalfa generated the highest custom costs, like hay baling, but all in all annualized
total costs for other things are low for alfalfa and poplar. Annualized revenue for poplar
and alfalfa were also low.

The effects of changes in crop prices are also subjected to crop price sensitivity
analysis, as shown in Figure 1.7. This shows how changes in crop prices could impact
profitability. With this fact in mind the study calculated the net return for three different
price scenarios (high, mean and low) by taking the average of the deflated prices from
1978-2008 and computing standard deviations from the actual prices observed presented
in Appendix Table 8. Ranking of systems by relative profitability does not seem to
change regardless of crop price scenario. This shows that ranking is robust to output
prices making the information meaningful for managerial decisions.

Table 1.4 summarizes the revenue above selected costs together with

environmental indicators namely, global warming and nitrate leaching. Figures 1.8 and

22

1.9 show the plotted points and estimated tradeoff frontiers between nitrate leaching and
revenue above selected cost and between GWP and revenue above selected costs.
respectively.

By using the efficiency criteria discussed earlier, we know that the ideal direction
for both environmental indicators would be moving toward southeast direction in both
XY space as indicated by the arrow on the lower right of the diagram. That is because
moving towards that direction would mean improved profits and less negative
environmental effect.

By selecting efficient points we see that in Figure 1.8 for global warming
potential as environmental indicator, alfalfa and certified organic treatments dominate the
rest. Anything lying to the left or above that solid line is dominated in the sense that there
is a chance to increase the profit or decrease negative environmental effects or both by
moving towards the frontier. Excluding the certified organic prices from the analysis
yields a different frontier which includes no till and alfalfa, as shown by the dashed line.
The slope for the dashed tradeoff frontier is steeper thanthe tradeoff frontier with a solid
line which implies that the farmer can improve GWP at a lower unit cost in reduced
profitability.

Regarding the nitrate leaching, the certified organic and no-till treatments
dominate the rest as shown by the solid line in Figure 1.9. Excluding certified organic
yields a tradeoff frontier that only includes only the no-till treatment. In this case, no-till

treatment is a comer solution meaning one technology exists in the efficient set.

23

1.8 Summary and Conclusion

In both the trade off frontiers, the conventional treatment is dominated. Also, the
organic treatment is dominated unless certified organic prices are used. This shows that
the conventional treatment is a dominated choice, which leads to the question of why
farmers are still using this technology. Based on the trade off frontiers, the no-till
cropping system shows a potential as an efficient choice for the farmer. With the method
presented in this study, it was shown that tradeoffs exist as farmers make choices
between environmental and economic goals. Trade-off curves represent a convenient
means of summarizing the information for policy makers and form the basis for

conceptualizing sustainability policies.

24

Table 1.1 Description for the Different Treatments at KBS-LTER

 

 

Treatment Description

Conventional Standard chemical input CSW rotation, chisel plowed

No-Till Standard chemical input CSW rotation no-tilled
Low chemical Input CSW rotation conventionally tilled

Low In ut (ridge till for 1993), with Cover Crops, banded

p herbicide, starter N at planting, additional post plant

cultivation
Zero chemical input CSW rotation conventionally tilled

Organic (ridge till for 1993), With Cover Crops, additional post
planting cultivation (rotary hoe)

Poplar Populous clones on Short Rotation (9—10 years) harvest
cycle

Alfalfa Continuous Alfalfa, replanted every 6-7 years

 

Source: KBS-LTER Website, http://lter.kbs.msu.edu/about/experimental_design.php

25

 

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27

Table 1.4 Mean Values forRevenue Above Selected Costs and Different Environmental indicators

 

Revenue Above Global Warming Potential
Selected Costs Nitrate Leaching (g of carbon dioxide
Treatment (Slacre) (Mean kg NoB-N/acre) equivalents/m-Z)

Conventional 122 6.07 1 14

No-Till 140 -l .54 14

Low-Input 134 0.12 63

Organic in Non-organic Prices 83 0.12 41

Organic in Organic Prices 182 0.12 41

Poplar 18 0.07 -20

Alfalfa 36 1.09 -105

 

Sources: Revenues and costs from enterprise budgets; environmental indicators from G.P. Robertson et al
(2000), Syswerda (2009)

28

Figure 1.1 Production possibilities frontiers with profits and environmental quality
and indifference curves for Farmer i

E

 
  

 

 

29

Figure 1.2 Production possibilities frontiers with profits and environmental damage
and indifference curve for Farmer i

ED

   
 

 

 

30

Figure 1.3 Mean revenue above selected costs across treatments, KBS-LTER,
Hickory Corners, Michigan, l993-2007*

i
l
l
I

Mean $/acre

200
180
160
140
120
100

40

80 l A
60 »

20$

 

* Except for alfalfa:l989-2004; poplar:1989-1998.

31

Figure 1.4 Mean revenue and costs that vary across treatments, KBS—LTER,
Hickory Corners, Michigan, 1993-2007*

   

3001 - , — ~-'-— -—~
1
250 3 - , s
l
i 3 200 » _, 7* 7— lRevenue
1 g 1 '
l \ I 1.
‘ ‘2 150 1 fl- -
‘ 3 ‘ g IHTotaICost
I 5 100 1"." ~-——7
1 :
l ; 3
l 50 i H
1 0 i, ;:
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o e
4; 60
0“"

1, , ,, , 7.2 , 7* 7 __A,,,,, #7. . , ‘v _, ,_,_ c__“

* Except for alfalfa: 1 989-2004; poplar:1989-1998.

32

Figure 1.5 Mean yields for corn, soybean and wheat in the annual cropping systems,
KBS-LTER, Hickory Corners, Michigan, 1993-1997

I Corn Yield

1 2
‘ 8
‘ E [EISoybean Yield
1 U
a f;

a

.n

l Wheat Yield

  

33

Figure 1.6 Proportion of inputs by cropping system, KBS-LTER, Hickory Corners,
Michigan, l993-1997*

300'*’~--—-v— 777777~7H~A_,
1

Custom Costs

 

I Certification
I22: Tillage Costs
I Herbicides

o Fertilizer

Mean $/acre

\\ Cover Crop

= Seed/cuttings

 

* Except for alfalfa: 1 989-2004; poplar: 1989-1998.

 

Figure 1.7 Sensitivity of profits based on crop prices for the annual cropping system
treatments, KBS-LTER, Hickory Corners, Michigan, 1993-1997

   

250 ; , , ,, ,,
200 . . 1
I High Scenario 1
l
i
150 . 1
ll Base Scenario ‘
ca ‘ ‘
E
U
m
«7» 100 l -
l Low Scenario
1
so ;
I I
o 1
Conventional No-Till Low Input Organic at Organic at i
Non-Organic Organic Prices J
Prices i

35

Figure 1.8 Tradeoffs between Global Warming Potential and revenue above
selected costs, KBS-LTER, Hickory Corners, Michigan, 1993-2007 *

 

 

 

 

 

150
Convegtional
100
Uncertified Low input Certified
50 Organic ’ Organic
‘0
iQC)11T//////////////.
”,x.

150 200

 

-50

Global Warming Potential (g/rn-Z)
O

 

-1OO

 

Alfalfa

 

-150

 

Revenue Above Selected Costs (S/acre)

 

 

 

* Except for alfalfa: 1989-2004; poplar:1989-l998.

36

Figure 1.9 Tradeoffs between nitrate leaching and revenue above selected costs,

kbs-lter, Hickory Corners, Michigan, 1993-2007*

 

 

 

 

 

 

 

 

 

 

 

Conventional

5 Q
1?
g s
m
g 4
3’
5 3
0
5
g: 2
E Alfalfa

1 j
0 Poplar Uncertified Organic Low Input Certified Organic
E O .— 7 9 r 9 r L 3
g c: 50 100 150/ 200

-1

Mfr."
-2

 

 

Revenue Above Selected Costs (Slacre)

 

* Except for alfalfa: 1989-2004; poplar:1989-l998.

37

 

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42

Essay 2:
Incentives to Supply Enhanced Ecosystem Services from Cropland

2.1 Introduction

Agriculture is a managed ecosystem. The decisions of its managers drive the mix of
ecosystem services that it produces. Farmers play an important role as ecosystem
managers in that they balance their decisions regarding land and other agricultural inputs
for production and modify their practices to adjust the positive and negative impacts to
the environment (Wossink and Swinton, 2007). By their choices of production inputs and
management practices, farmers shape their impacts on the environment. Thus, agriculture
offers a special opportunity for ecosystem service management because ecosystem
services are produced simultaneously with agricultural products.

The policy challenge is to develop incentives for farmers to produce ecosystem
services while meeting the demand for food (Hodge, 1991; Hanley and Oglethorpe,
1999). Important policy questions from this growing body of research are:

0 What are the incentives that will make the farmers provide ecosystem
services?

0 Are farmers willing to change their land management practices in
exchange for a payment, and if so, how much?

0 Which farmers are willing to change their practices and should future
policies be targeted toward specific groups of farmers?

The literature concerning what motivates producers to adopt environmentally

sound practices has been growing. Empirical studies of conservation farming have found

43

that the most important motives for conservation adoption are “selfish”, financial-
economic concerns (Chouinard et al, 2006). Cary and Wikinson (1997) showed that the
best way to increase the use of conservation practices is to make them profitable.
However non-financial factors also play a role in conservation decisions because
producers may gain direct personal satisfaction from the improved environmental quality
(Chouinard et al, 2006).

Understanding crop farmers’ willingness to supply nonmarketed ecosystem
services calls for understanding the effects of changed cropping systems on both profit
and personal satisfaction. The late 19905 saw the emergence of literature on the supply
side of environmental improvements from agriculture (Bonnieux and Rainelly, 1995;
Bateman et al, 1996; Kazenwadel et a1., 1998). These authors focused on the farmers’
willingness to adopt new practices and on the factors influencing their participation
decisions. Some studies were based on actual scenarios and some were based on
contingent data or hypothetical scenarios. For example, Purvis et a1 (1989) studied
farmers’ willingness to participate in a filter strip program using a contingent valuation
survey. The study concluded that farmers’ participation decisions are determined by the
yearly payment offered to participants, fanners' perception on environmental change, and
their opportunity costs. For example, farmers would be more likely to participate in a
filter strip program if the rules allow haying or other economic uses of the enrolled
cropland.

This study also uses stated preference survey methods, but it differs from the
others in three major respects. First, it takes into account the potential that the attitude

and behavior of farmers are influenced not only by farmer and farm characteristics, but

44

also by the characteristics of the required practices or cropping systems. This implies that
participation could vary across different types of programs. In order to test this, we
evaluated the behavior of the same group of respondents toward four different sets of
distinct cropping practices. Second, the paper introduces a subsidy program to make
direct payments to the farmers for adopting cropping practices that are known to produce
environmental services rather than as a cost sharing program. Finally, this analysis goes
beyond participation to address acreage enrollment. In so doing, it becomes possible to
estimate fann-level supply functions for land providing specified suites of ecosystem

services.

2.2 Objectives of the Study

The objectives of this paper are: (1) to identify farmer willingness to adopt
environmental stewardship practices in exchange for payment (willingness to accept or
WTA) to; (2) to investigate the determinants of their willingness to adopt those practices
and the relative importance of these factors; and (3) to estimate empirical supply curves
for acreage enrollment for hypothetical environmental stewardship programs that
correspond to ecosystem service levels that could be produced.

The rest of the paper is organized in two broad sections. The next section
introduces the conceptual framework, the research design and the methods of data
collection and analysis. The final section summarizes the findings and discusses their

policy implications.

45

2.3 Conceptual Model: The Supply of Environmental Services by Farm Households
2.3.1 Multi-attribute Utility Function

A basic premise of the neoclassical economic theory is that rational producers
make choices about production inputs and technology (e.g. cropping practices).
Following Dupraz et a1 (2003), farmer behavior is motivated by utility maximization,
where utility is increasing in consumption goods and environmental services.
Consumption is constrained by net income, which depends on agricultural product

revenue minus costs. Thus, farmer behavior can be formalized as follows:

AggxwgflS) (1)
g S 7r(p,es) + NF] (2)
es 2 0. (3)

The parameters of the utility function are household consumption denoted by g and the

quantity of environmental service, es, that is co-produced by farming activities. The

utility function is assumed to be increasing, concave and differentiable in g and es. The
household consumption goods, g cannot exceed the sum of the farm income, 72' ( p, 6S) ,

and exogenous non-farm income, NFI . The profit function, 72' ( p, es) , is assumed to be

convex and is a function of prices of factors and products, p and environmental service,

€S_ The solutions to this utility maximization model are denoted as: g*, es * and

g*,eS*,U* = U(g*,eS*).

From equations 1 and 2, we see that apart from marketed agricultural products,

two kinds of ecosystem services, ES, matter in this model: ES in the utility function and

46

supporting ES in the profit function that substitute for cash inputs in the agricultural
production (e.g., soil quality, biological control of crop pests). Thus we expect the
demand for these supporting ES to be a derived input demand for ES that depends upon

the prices of products and inputs.

2.3.2 Economic model of Willingness to Accept and Environmental Supply

The microeconomic concept of “willingness to accept” (WTA) is helpful to
specify the supply of environmental service. WTA is defined as the minimum amount of
income that the farm household would require to supply a given amount of environmental
service. WTA is classically formalized by using an expenditure function to provide
theoretical structure for welfare estimation. WTA can be represented as the change in
expenditure levels of the farm household in response to change in the level of ecosystem
services produced, given that their utility is kept the same.

Following Dupraz et al’s (2003) derivation for the definition of WTA, we assume

that the farmer is invited to increase the environmental service supply,es, by a fixed
quantity such that: ABS = es, - 830 > 0. The expenditure function, e(p,es, U0) ,
represents the minimum amount of exogenous income which in this case is represented

by , NF] = g ‘ ”(P, es) , that is needed to produce a fixed quantity of ecosystem
service Aes while maintaining constant utility. Specifically,

9(P,€S,U0)= Min[g—7r(p,es);U(g,es)2U0] (4)

WTA =e(p,es,,U0)—e(p,eSO,U0)=€(P,eSon)—eo (5)

47

where equation 5 expresses the minimum payment that the farmer requires to increase ES

production from 630 to 6‘51 , while maintaining utility level U0 . Letting g‘(p,es,Uo)

denote the solution of the cost minimization problem in equation 4, the expression in

equation 5 becomes:

WTA = [7419.680 ) - ”(17.65. )1 -1g*(p,eso,Uo)- {(19,68on )1 (6)
The first term in brackets in equation (6) is the farm’s foregone profit. The second term is
the amount that the household is willing to pay for an increase in environmental service.
In other words, the willingness to accept equals the foregone profit offset by the
monetary value of change in the farmer’s utility from producing more ecosystem service.

This equation can be restated as:
WTA = [fl(p,eSO)-7r(p,eS.)l-MVU(.IAeSl), (7)

where the function M V U(.) represents the monetary value of the utility from switching
from the current technology to the alternative technology. This variable shows the utility
from producing more ecosystem service expressed in monetary terms via consumption

goods

*
. . . . e
The changes In expenditure for changes In ecosystem servrces, 5;- , traces out
s

the farmers supply function for the non-marketed ecosystem service. The area below the
supply curve represents the WTA to produce ecosystem services under any given

technology.

48

2.3.3 The Farmer’s Decision Rule

In this study, farmers were not directly asked the minimum amount they would be
willing to accept in order to adopt certain cropping systems. Rather they were asked how
many acres they would enroll in a program that offers to pay “3” dollars per acre and
requires them to adopt a set of practices known to produce ecosystem services at some
transaction cost involved with participation, denoted TC. Thus, the net payment to
farmers for enrolling “a” acres in the program is sa - TC.

The logical condition for farmer enrollment behavior is that for any per acre
payment, 3 , farmers with WTA less than or equal to the net payment from participation
are willing to participate in the program (implying a > 0), and those with WTA greater
than net payment from participation are not willing to participate. Based on the definition

of WTA in equation (7), this participation condition can be written:
a > O ifl. [7r(p,es0)—tr(p,esl)— MVU(p,es,)] S sa — TC (8)
Now, consider a farmer that manages N total acres. Let 8S1 correspond to the

ecosystem service produced from some portion of land, a out of N acres, that is

I
devoted to an alternative cropping system Q , and let 6S0 correspond to the initial

level of ecosystem service produced from devoting all land, N , to the initial cropping

0
system Q . Transforming the equation into an acreage based decision model that

allows farmers to allocate their land to a hypothetical program that requires them to do a

particular cropping system, equation 8 could be rewritten as:

49

7r(N,QO) S 7r(N—a,p,Q0)+rr(a,p,Q')+sa + MVU(Q',Z)—TC(a,Z)(9)

Where Iris the profit function; N is total land acreage that the farmer manages; a is the
amount of land allocated to production under an alternative technology or cropping

system where they are given a subsidy or payment per acre, 3. The function,

Q0 = f(S, 650, Z), is the currently employed production technology, which depends on

a combination of systems in the vector S which conditions the choice of inputs in the
production function, ecosystem services, es, and farmer/farm characteristics Z. The
combination of system, S, entails crop choice, rotation tillage, fertility and pest
management. Z is a vector of parameters that captures characteristics of the farmer that
govern his or her preferences for environmental benefits, but also farming experience and

willingness to adopt new technologies such as age and education. On the other hand,

Q' = jl-S', 65, , Z) is an alternative production technology that depends on some other

combination of systems thus defining a new set of inputs and new outcome level of
ecosystem services; while TC (a. Z) captures various transaction, monitoring and
enforcement costs related to participation in the payment for environmental services
prOgra.m that effectively reduce the total size of the subsidy.

The right hand side of equation (9) corresponds to the farmer’s profit from re-
allocating (1 acres of land to an alternative technology under the subsidy scheme: the first
term is the profit generated from N — a acres under the current technology; the second
term is the profit generated from a acres under the alternative technology; the third term
is the effective (or expected) subsidy payment; and the fourth term is the monetary value

of utility from switching to an alternative technology; and the fifth negative term is the

50

transaction costs, TC . The left hand side of equation (9) is simply the farmer’s profit
under the current technology. Thus, the farmer will have an incentive to allocate land to
an alternative land-management system if the combined benefits under the subsidy
scheme are valued at least as much as the farmer’s current profit.

This decision rule takes into account not only direct costs but also the opportunity
cost of deviating from profit maximizing mix of inputs. The farmer’s preferences and
resource constraints also affect this decision. Thus, participation depends on the cropping
systems’ relative profitability (Valentin et a1., 2004), transaction costs of being involved
in the program and general attitudes towards adoption (McCann and Easter, 1999).

2.4 The Data
2.4.1 Data Collection

The study asked farmers about their willingness to adopt selected practices from
corn, soybean and wheat cropping systems related to ones studied by scientists since
1989 at the long-term ecological research project in agro-ecology at the Kellogg
Biological Station (KBS-LTER) near Kalamazoo, Michigan. The payment vehicle drew
upon traits of existing U.S. farm programs that pay farmers for providing environmental
services. Specifically, the questionnaire offered respondents specified payments if they
would participate in a hypothetical farm program that paid them by the acre to adopt
specified cropping practices. Farmers who expressed willingness to participate were
asked how many acres they would enroll in the program.

The data on farmers’ potential supply of enhanced ecosystem services was
collected using a mail survey sent to a random sample of 3,000 corn and soybean growers

in Michigan in mid-February of 2008. The survey used a four contact version of the

51

tailored design method (Dillman, 2000) consisting of l) a prenotice letter, 2) a
questionnaire and one dollar incentive, 3) a postcard reminder, and 4) a replacement
questionnaire. The survey achieved a net response rate of 56.4% after adjustment for
refusals, undeliverables and deceased recipients (details in Appendix 8). The survey
design and questionnaire development were preceded by a series of farmer focus groups
and pre-tests to ensure validity and clarity of the questions as well as an appropriate range
of payment offers for those cropping practices. Six farmer focus groups were conducted
during February and March of 2007, while in-person questionnaire pre-tests were
conducted in January of 2008.

The sample was obtained from the 2007 agricultural census mailing list of the
National Agricultural Statistics Service (NASS) office in East Lansing, Michigan. NASS
provided the project with a 4—tier, acreage-stratified random sample of 3,000 corn and
soybean farmers in Michigan. The four strata represent farmers with 0 to 100, 101 to 500,
501 to 1000 and 1000 and more acres. This method was chosen to allow for comparison
across strata to ensure that the farmer population is well represented and that it is linked
to the behavioral model on acreage based decision of farmers. In the analyses that follow,
weights were used to appropriately correct for the stratification (see Jolejole, 2009,

Appendix Table 9).

2.4.2 The Questionnaire Design
The survey instrument presented farmer respondents with a series of four corn-
soybean-based cropping systems. The four systems differ in their degree of cropping

practices involved, offering increasing levels of ecosystems services compared to a

52

baseline corn-soybean system. The first, System A, was a corn-soybean crop rotation
with chisel plow tillage, pre-sidedress nitrate test in corn, and all agrochemicals broadcast
in the field according to Michigan State University recommendations or pesticide label
instructions. System B was identical except that a cover crop was added during winter.
System C added winter wheat to the crop rotation after soybean, in addition to the winter
cover crops after corn and wheat. Finally, System D was identical to System C except
that fertilizers and pesticides were applied in bands over the row resulting in a 1/3
reduction in chemical applications. Table 2.1 presents the specific practices for each
cropping system.

An orthogonal design framework was constructed to combine the various program
attributes and payment levels for the cropping systems into different questionnaire
versions (Jolejole 2009, Appendix 5). There were six variables: sequence of cropping
systems, payment provider, and the four cropping systems described above, each with 4
levels of prices. The design resulted in 16 versions of the questionnaire, which were
randomly assigned within each stratum (details in Jolejolc 2009, Appendix 6). The
payment levels for each of the cropping systems were set by deriving the bids associated
with the 20‘“, 40th, 60th and 80tln percentiles of the distribution of participation predictions
that were computed from pilot models that used data from the farmer focus groups held
in 2007. Other factors that varied in the framing of the proposed transaction were the
payment provider (government or non-government organization) and the sequence of
cropping practice questions presented (increasing effort [from system A to D] or

decreasing effort [from system D to A]).

53

Respondents were asked a variety of attitudinal and background questions in
order to assess farmer preferences about the environment, the cost of changing practices,
and levels of household and farm resources, (Jolejole 2009, Appendices 3 and 7). The
stated preference questions were preceded by a full description of how the program
works along with instructions on what varied across the questions. The enrollment
question was presented as follows: “If a program run by [the government or a non-
governmental organization] would pay you $[X] per acre each year for 5 years for using
cropping system [Y], how many acres of land would you enroll in this program? (If you
would not enroll, please write zero).” Terms in square brackets were varied across

questionnaire versions.

2.5 Methods
2.5.] Econometrics of WTA

Farmer respondents were asked to make two decisions with regard to their
willingness to accept payments to adopt environmental stewardship cropping systems: (I)
Will they participate in the program? (2) If yes, how much of the land area will they
devote to environmental stewardship? The econometric hurdle model allows for the
possibility that these two decisions are affected by different sets of variables.

The model, originally due to Cragg (1971), has been applied in a variety of areas.
Applications include Burton, Dorsett and Young (1996) and Newman (2001), who
modeled household expenditure on meat; Jensen and Yen (1996) who modeled US. food
expenditure outside the home; Yen (1997) who applied the model to alcohol consumption

and Jones (1997) who examined U.S. household consumption of cheese. The model has

54

rarely been used in willingness to accept studies. Some exceptions would be Goodwin et
al. (1993), Yen et al. (1997) and Reiser and Sheeter (1999).

The hurdle model is a parametric generalization of the tobit model, in which the
decision to participate in the program and the level or degree of participation (e.g.,
acreage enrollment) are determined by two separate processes. This approach allows the
two decisions to have different variables or different coefficients with the same variables.
This study employs a hurdle model where the probability of participation in the program
is estimated as a separate function from the number of acres supplied. The two stages of
the hurdle model will be called the participation model and acreage decision model,
respectively.

A probit model is used to estimate the initial participation decision. The probit

relates choice probability to explanatory factors the program, farm, and farmer

characteristics. We let 0t stand for acres enrolled. The following probit model is used to

estimate the probability of participation (i.e., Ot>0):

fl'p xi
Pr(a > le) = CD 7 (10)

where CI)() is the standard normal cumulative distribution function, x i is an

S X] vector of farm and farmer characteristics for farmer l , and flp is the vector of

coefficients from the participation model and standard deviation, 0p .

55

The second step of the hurdle is a truncated regression model to account for the

acreage enrollment conditional on participation. We first assume a latent acreage variable

* .
a i that IS generated by:
* _ l
06,- -flax,-+8a.- (11)
where xi is a S x 1 vector of farm and farmer characteristics for farmer i , and [Ba is the

vector of coefficients for acreage decision and Eat are disturbance terms from acreage

decision assumed to be independently and normally distributed with mean zero and

p
' 0'
variance a .

We observe enrolled acres a]. only if a,* > 0 so that the expected value of acres is,
E(a. Ia,- > 0) = 5'. x.- + 01(7) (12)

where

¢(l/) _xfla

,1 = =
(7) 1413(7) and}, o- (13)

where ¢() is the standard normal probability density function and (D() is the

standard normal cumulative distribution function. Equation (13) is the truncated
regression for positive values of the continuous decision of how many acres to

enroll (at > 0) . Note that for observed acres,

56

_ * * ~
ai — ai ai > O Truncated Normal. (14)

The hurdle model allows the participation decision and acreage enrollment decision to
have different coefficients, i.e. coefficients in equations 10 and 12 are different because
they arise from separate stochastic models. If they are the same, then a tobit model arises
(Lin and Schmidt, 1984). The truncation correction accounts for the fact that only a
portion of the distribution is observed (i.e. only the participants), and, therefore, the mean
is only calculated based upon what is observed, i.e. participation.

The results from both probit and truncated regressions are important in predicting
acreage enrollment, i.e., estimating the supply of land contributing ES. The acreage
supply prediction can be computed by multiplying the probability of participation

(Equation 10) by the predicted acreage conditional on participation (Equation 12):

PREDICTACRES = Pr(a,. > 0|x) * E(a,-la,- > 0) (is)

The predicted supply of land contributing ES is traced by systematically increasing the

payment variable upward from zero while holding other variables at their mean values.

57

2.5.2 Variable Specification and Working Hypotheses

For the participation model, a dichotomous dependent variable for participation
indicates whether or not a farmer is willing to accept the offered payment to adopt the
environmentally friendly practices (participation=l , nonparticipation=0). For the acreage
model, a continuous dependent variable measures the number of acres that the farmer
agreed to enroll.

The independent variables are hypothesized to be associated with the adoption of
environmentally friendly measures that implicitly links to prior studies on the theoretical
derivation of WTA in Equation (1 I), and the particularity of the farming systems of the
study area. The potential explanatory variables that are hypothesized to influence
farmers’ willingness to adopt to environmental measures are the following:

Payment or subsidy (s). The adoption of changed cropping practices is assumed to
cause the farmer to incur additional costs for labor and/or material inputs. As a result,
subsidy payments to farmers to adopt stewardship measures are expected to have a
positive effect on participation.

Descending sequence. The cropping systems differed in their degree of changes
relative to a typical com-soybean rotation. This variable is a dummy variable that
accounts for the manner the cropping systems were presented. (l-descending sequence
and 0-ascending sequence) This accounts for the “anchoring effect” of questionnaire
versions. Previous studies suggest that it is ideal for this variable to have no effect on
participation decision.

Government. This variable is a dummy variable which accounts for the payment

mechanism (1 -government and O-non-govemmental organization). It might reflect

58

perceived transaction costs involved in participation. One person in the farmer focus
groups was adamant that farmers have a higher transaction cost when dealing with the
government. It might also measure aversion to government programs or a general
political philosophy. Thus, this variable is expected to have a negative effect on
participation.

Perceived Environmental Improvement (Monetary Value of Utility from
Ecosystem Services, M VU). This variable was measured through a series of 5 point Likert
scale questions (1 for strongly disagree, 2 for disagree, 3 for neutral, 4 for agree and 5 for
strongly agree) that measure how much the farmer perceives that the proposed cropping
system would outperform their current system in terms of environmental qualities such as
soil organic matter, soil conservation, phosphorus surface runoff, nitrate leaching, global
warming potential and pesticide risk. The answers for all these environmental services
were averaged to derive one variable to measure perceived environmental improvement
offered by each cropping system. Lynne et al., (1988) suggest that while economic
incentives will increase effort, responsiveness will differ with strength of conservation
related attitudes and perceptions. Other empirical studies show that farmers with a
generally positive attitude towards new technologies are keen on undertaking and
maintaining environmental measures (Shiferaw and Holden, 1998; Abera, 2003). Also,
according to a paper by Bonnieux (1998), positive environmental attitude influence
adoption of conservation practices. Hence, in this study, a high value of perceived
environmental improvement is hypothesized to have a positive effect on participation.

Total Land Area Managed (N) refers to the total area of cropland managed by the

farmer at the time of the survey. Empirical studies have found that large farms are more

59

likely to use conservation technology than small farms (Norris and Batie, 1987; Bekele
and Drake, 2003). Therefore, it is hypothesized that area of the cropland is positively
related with participation.

Current Practices (Q = f (S,eso , Z) ). This category consists of several variables

that show what the farmers are currently doing on their farms. It includes whether they
have wheat in rotation, type of tillage they use, and cover crop use. The proposed new
practice may involve costs, but if the farmer is currently doing something similar to the
cropping system being offered, the marginal cost of participation will be low and it is
expected that they will be more likely to participate.

Biophysical variables (part of Z, farm characteristics) in this study refer to
dummy variables for soil texture. Clay soils may be more fertile but less well-drained
than the loam soil baseline, whereas sandy soils are less fertile but better drained due to
loser particles. Biophysical variables have been found to have a mixed effect on the
adoption of environmental measures (Ervin and Ervin, 1982; Norris and Battie, 1987;
Pattanayak and Mercer, 1998; Pender and Kerr, I998). Particularly in this study, adoption
of cropping system D which requires less use of chemicals is expected to be positively
related to clay soil which is classified to be more fertile than sandy soil and silty soil.
Cropping system B, C and D, on the other hand, all of which requires the use of cover
crops over winter is expected to be positively related to sandy soil.

Future Price Expectations (p, expected output prices). This category includes
expected harvest time prices of com, soybean and wheat. Wheat-to-com price ratio and

wheat-to-soybean price ratios were also derived. Both are expected to be positively

60

related on cropping systems that require wheat, namely cropping systems C and D and
may be negative for cropping systems A and B.

Experiential Variables (Environmental Program Experience; part of Z,
farm/farmer characteristics). This consists of several dummy variables that indicate any
form of experience with the conservation programs, such as Michigan’s Agriculture
Environmental Assurance Program (MAEAP) and the federal Conservation Reserve
Program (CRP), Environmental Quality Incentives Program (EQIP) and the Conservation
Security Program (CSP). Empirical studies have shown that prior membership in
conservation programs is positively correlated with conservation practice adoption and
effort (Ervin and Ervin, 1982; Norris and Batie, 1987; Sureshwaran et al., 1996;
Pattanayak and Mercer, 1998; Bekele and Drake, 2003).

Farmer demographics (Z). This category includes farm and farmer characteristics.
Dupraz et al. (2000) found that environmental stewardship programs are more likely to be
adopted by farmers with higher education. According to Bonnieux (1998), there is a
significant age effect, with younger farmers more likely to adopt conservation practices.
Drake (1992) stressed that neighboring farms applying environmental measures, older
farmers, higher education and previous participation have positive effects on adoption.
Vanslembrouck et al. (2002) found that larger farms, agricultural education, participating

neighbors and younger farmers are more likely to adopt.

61

2.6 RESULTS AND DISCUSSION
2.6.1 Descriptive Results

The variables to be included in the regression analysis, their units of measurement
and weighted means are presented in Table 2.2. Additional descriptive results are
provided in Appendix 10 (Jolejole, 2009). To avoid problems of multicollinearity
(Greene, 1997), several variables were dropped from the models, based on F-tests. The
final set of variables includes dummies for non-government provider, and descending
sequence of cropping system complexity, as well as continuous variables for subsidy
payment, perception that the new system being introduced offers more environmental
services and total acreage. Other variables included in the analysis are biophysical
variables on the most common soil texture for a farm with loam soil as the baseline;
current farming practices, including tillage, cover crops and wheat in rotation; expected
price of wheat relative to other crops (since wheat is the only crop added in the
hypothetical program introduced); experiential variables, and age and education.

Based on the mean values, most of the respondents farm mostly clay soils and
practice conservation tillage. Only 9% of the land is planted with wheat and 7% with
cover crops. Approximately 15% of the respondents have participated in government
programs like EQIP and CRP. Farmers’ average age is 54, which is equal to the state

average for corn-soy growers (USDA-ERS, 2000).

2.6.2 The Participation Decision

The results of the probit models for adoption of the four proposed cropping systems are

presented in Table 2.3. They include parameter estimates, corresponding standard errors

62

and some regression diagnostics. The pseudo R2 measure of goodness of fit (McFadden,
1973) ranged from 0.18 to 0.26 for the 4 cropping systems. The p-value associated with
each coefficient estimate is the probability that the 2 test statistic would be observed
under the null hypothesis that the particular regression coefficient is zero, given the rest
of the predictors in the model. The Wald test was used as an alternative to the likelihood
ratio test of whether all the predictor regression coefficients in the model are
simultaneously zero. For all the models, the null hypothesis that all the regression
coefficients are simultaneously zero was rejected.

The results show that the participation decision in all cropping systems is
significantly influenced by the payment, perceived environmental improvement from the
system being introduced and the total land acreage operated. Hence, farmers are willing
to produce ecosystem services at some subsidy. Perceived environmental improvements
from the proposed cropping system and greater total land acreage both contribute to
willingness to participate, as expected.

Other factors varied in significance depending on which cropping system is
offered. Sandy soil is negative and significant for cropping system D as expected.

Moldboard tillage is negative and significant in all cropping systems. The
hypothetical program requires chisel plowing. The results suggest that if the farmer is
moldboard plowing, he or she is less likely to participate, which likely reflects the fact
that switching from one practice to another adds capital costs.

Wheat acres with respect to total land was positive and significant in cropping
system C and negative for cropping system A. The ratio of cover cropped land to total

land area was positive and significant in cropping system B. These results suggest that if

63

the hypothetical program requires the farmer to do a practice that they already do, they
are more likely to participate, which validates the hypothesis we made in the previous
section.

The ratio of wheat price to corn price was positive and significant only for
cropping system A while wheat price to soybean price was positive and significant for
cropping systems B and C. The only result consistent with the previous hypothesis that
expected output prices have positive effect on participation would be the positive
participation effect on cropping system C.

The USDA Environmental Quality Incentives Program (EQIP) offers financial
and technical help to assist farmers to install or implement structural and management
practices on eligible agricultural land. Previous experience with EQIP favored
participation. This is consistent with the hypothesis that previous experience in similar
programs tends to increase participation.

Age was negative and significant for cropping systems A, B and C. This shows
that younger farmers are more likely to adopt cropping systems that supply more
ecosystem services. The government program provision variable was insignificant for all
cropping systems which suggests that farmers do not necessarily view the transaction
costs of dealing with the government to be different from those of an unspecified non-
governmental organization. The descending sequence variable was negative and
significant for cropping systems A and C, which would suggest that farmers are less
likely to enroll if the cropping systems are presented in a descending manner. This

pattern suggests an anchoring effect.

64

2.6.3 Acreage Decision

To capture the second decision faced by the farmer on how many acres to enroll
in the program, truncated regression is used to model acres supplied conditional on
participation in the program. Respondents who did not participate were not included in
this regression. Table 2.4 shows the results. The coefficients in the truncated regressions
can be interpreted as the change in underlying latent acreage enrollment for every unit
change in the variable, and they have a related effect on the conditional acreage amounts
(see equation I 1).

For all cropping systems, the amount of acreage enrolled is positive and
significantly affected by the total land area managed and relative perception of
environmental improvement. The payment offer for adopting the cropping systems is
significant and positive for cropping systems A, C and D, but somewhat surprisingly, was
not significant for system B.

Other factors varied in significance, depending on which cropping system is
offered. Sandy soil is positive and significant for cropping systems B and C as expected.
Clay soil, on the other hand exhibits a positive and significant effect on acreage offered in
all cropping systems. Clay soil’s positive effect on cropping system D is consistent with
the hypothesis.

Moldboard tillage reduced acreage enrolled in cropping systems A, C and D,
while no-till and conservation tillage undermined acreage committed to cropping system
A. The proportion of wheat acres with respect to total land increases acreage enrolled in
cropping systems C and D. As hypothesized, the more similar the practices in the

cropping system offered to the farmer’s current system, the more likely the farmers are to

65

participate which is likely due in part to the cost involved in switching to a different
cropping system.

The wheat to corn price ratio has a negative and significant effect on acreage
enrolled in cropping systems A and B but a positive effect on cropping system D, which
is consistent with the hypothesis. Wheat-to-soybean price ratio showed positive and
significant effects on acreage enrollment in all cases. The wheat-to-soybean price effect
on cropping systems C and D is consistent with the hypothesis.

MAEAP certification had a surprising negative effect on acreage enrolled,
although only for cropping system A. MAEAP offers farmers a certification that their
crop management practices are consistent with generally approved agricultural practices
in the state. The negative sign means that farmers who are MAEAP certified are less
likely to enroll acreage in system A.

Farmer age had a negative and significant effect on acreage enrolled in cropping
systems A and C. Education on the other hand, increased acreage enrollment in cropping
system D. This shows that younger and more educated farmers tend to enroll more acres.
The government program provision variable was negative and significant for cropping
systems A and D, which suggests that acreage enrollment decreases when the
government handles the program. The sequence variable or the way the cropping systems

were presented in the questionnaire was insignificant in all cropping systems.

66

2.6.4 Payment Effects By Stratum

Patterns of participation and acreage enrollment in the environmental stewardship
cropping systems program varied significantly by farm size stratum. As mentioned in the
previous section, the four strata used include stratum 1 representing the 0-100 acre farms;
stratum 2 for 101-500 acre farmers, stratum 3 for 501-1000 acre farms and stratum 4 for
farms over 1000 acres.

Table 2.5 shows the participation decision with payment effects by stratum. The
stratum dummy equals 1 if the farm is in that size stratum and 0 otherwise. The stratum
dummies are interacted with the payment or subsidy variable. On the participation
decision, strata 4 and 3 exhibited positive and significant payment by stratum interaction
effects in all cropping systems. On the other hand, for stratum 2 the interaction is positive
and significant only for cropping systems C and D and for stratum 1 it is insignificant in
all cropping systems.

Table 2.6 shows the acreage decision with payment effects by stratum. Strata 4
and 3 exhibited positive and significant payment by stratum interaction effects on the
acreage enrollment decision for all cropping systems. On the other hand, in Stratum 2 the
interaction is not significant in all cases and in stratum 1 it is negative and significant but
only for cropping systems A and B.

An unusual result is the negative and significant effect of payments on the acreage
decision for stratum l in cropping systems A and B. This means that an increase in
payment in cropping system A and B will cause farmers to enroll fewer acres of land.
This counterintuitive result may be explained by labor time and physical capital barriers

for the small farms to be able to meet the required practices. In many instances, adoption

67

of the proposed practice requires new equipment (e.g., band chemical applicator or chisel
plow), which could dramatically increase the marginal cost of increasing acreage on
small farms. In other cases, the practice may require new knowledge or added work,
which may be too demanding for a part-time farm. Either of these effects could mean
that the marginal cost of switching from current cropping system to a new one might be
very large for the small farms.

Both the probit and truncated regressions indicate that the payment level strongly
affects the participation and acreage decisions only for farms over 500 acres, i.e. strata 3
and 4. Smaller farms do not respond to increasing subsidy levels by increasing acreage
enrolled. Again, this may be linked to physical capital and time availability barriers to

change from their normal operation.

2.6.5 An Approximation to the Supply Curve

Using the participation and acreage enrollment equations, we adopt the approach
of Lee and Helmberger (1985) and McIntosh and Shideed (1989) in predicting program
acreage response. The approximated supply curves for acreage enrollment for each
cropping system are shown in Figure 2.1. The values used to predict this curve come
from the probit and truncated regression results in Tables 2.3 and 2.4 using equation 15.
Plotting predicted acreage enrollment for different subsidy levels yields the supply for
land for cropping systems that are known to yield ecosystem services.

The first striking pattern in the supply curves is the decline in elasticity with the
complexity of the proposed cropping practices. In Figure 2.1, as one moves from

Cropping System A (simpler system) to Cropping System D (more complex), the slope of

68

the supply curve becomes steeper, meaning that acreage enrollment becomes less
responsive to the increasing payments being offered. This result suggests that more
farmers are likely to respond to payment offers for doing cropping system A, which is
close to the conventional system and less likely to respond to a payment offer to
participate in more complicated cropping system D.

The second striking result is the far greater elasticity of response among larger
farms. Figure 2.2, shows the supply curves for acreage enrollment by stratum and
cropping system. In all cropping systems, we see that the small farms in stratum 1 have
the steepest slope, while the large farms in stratum 4 have the gentlest slope — implying

the greatest elasticity of acreage response to payments.

2.7 Summary and Conclusion

Besides private market goods, agriculture jointly produces a number of public
goods that are provided as externalities. This paper examines the incentives of farmers to
participate in hypothetical programs to promote cropping systems that would increase
production of these nonmarket ecosystem services. Based on a survey of Michigan corn
and soybean farmers, we examine stated willingness to adopt sets of cropping practices
that embody increasing levels of environmental stewardship. Farmer willingness to adopt
these practices is a function of the payment offered, the farmer’s perception of
environmental improvements from the new cropping system, and total land acreage
operated. The amount of acreage farmers would be willing to enroll depends consistently
on farm size and the perception of environmental improvements from the practices.

Among farms over 500 acres, the payment offered was also a significant inducement to

69

enrolling acreage in these environmentally beneficial cropping programs. We find that
under a payment for environmental service program, large farms are the low cost
providers of the ecosystem services associated with the cropping systems we studied.
This paper advances the literature on adoption of agro-environmental practices by
developing a supply function for crop acreage managed for environmental stewardship.
Like prior studies of environmental technology adoption in agriculture, we find that
environmental attitudes and affiliations, age, education and current farming practices are
influential. But we find that the marginal contribution of environmental services —like
most food— is likely to come from the largest farms. These are the ones that exhibit the
greatest price elasticity of acreage supply. Notwithstanding the image of the small farmer
as environmental steward, future agro-environmental policies that aim for cost-effective

environmental impact will likely achieve most of their impact from larger farms.

70

Figure 2.1 Predicted Farm-level Supply Curves of Acreage Enrolled by Cropping

System, 1688 Michigan Corn or Soybean Farms, 2008.

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92

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93

Conclusion

Agriculture is a managed ecosystem. The decisions of its managers, the farmers,
drive the mix of ecosystem services (ES) that it produces. Essay 1 developed tradeoff
analysis between profitability and selected environmental indicators for different types of
cropping systems from research field trials. The tradeoff frontiers developed in the study
are profit vis-a-vis global warming potential (GWP) and nitrate leaching. Both revealed
that the conventional treatment is dominated. The organic treatment was dominated
unless subject to certified organic prices. The no-till cropping system showed potential as
an efficient choice for the farmer. With the method presented in this study, it was shown
that tradeoffs exist as farmers make choices between environmental and economic goals.
These tradeoffs imply that there are costs involved in changing cropping systems and
many farmers would need to be compensated to adopt these practices. Trade-off curves
represent a convenient means of summarizing the information for policy makers and form
the basis for conceptualizing sustainability policies.

Essay 2 used survey data to examine farmers’ willingness to enroll in a program
that compensates them for adopting environmental stewardship. Based on a survey of
1,688 Michigan corn and soybean farmers, stated willingness to adopt sets of cropping
practices that embody increasing levels of environmental stewardship was examined.
Farmer willingness to adopt these practices was found to be a function of the payment
offered, the farmer’s perception of environmental improvements from the new cropping
system, and total land acreage operated. The results showed that Michigan farmers’

willingness to enroll acreage in an environmental stewardship program depends chiefly

94

 

on farm size and the perception of environmental improvements from the practices. For
farms over 500 acres, the payment offered was also a significant inducement to acreage
enrollment in all systems examined.

The second essay also developed a supply function for crop acreage managed for
environmental stewardship, using the participation and acreage enrollment equations
estimated. It was found that the low cost suppliers of environmental services are the
largest farms as exhibited by their greater price elasticity of acreage supply.
Notwithstanding the image of the small farmer as environmental steward, future agro-
environmental policies that aim to have the most cost-effective environmental impact will

likely achieve most of their impact from larger farms.

95

APPENDIX ONE: Enterprise Budgets For the Cropping Systems Used in Essay 1

96

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97

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99

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101

Appendix Table 6. Enterprise Budget for Perennial Crop Poplar based on a single ten-year
cycle, adjusted to reflect Net Present Value

 

 

 

 

Price
per
Unit
Quantity Unit ($) 1989 1990 1991 1992 1993 1994 1995
1 2 3 4 5 6 7
Revenue Source§
Wood yield 2.60 ton 45.00 0 0 0 0 0 0 0
Total Revenue 0 0 0 0 0 0 0
gash Expenses
Planting
Cutting 1100.00 cutting 0.20 220.00 0 0 0 0 0 0
Oats 3.00 Bu 8.19 25.00 25.00 0 0 0 0 0
Fertilizers
Nitrogen 109.73 lbs/acre 0.71 77.91 0 0 0 0 0 0
Pest Control
Lorox 55.84 gal/acre 133.67 55.84 0 O O O 0 0
Princep 6.93 gal/acre 16.60 6.93 0 0 O 0 0 O
Roundup 12.71 gal/acre 33.80 0 0 O 0 0 0 0
Custom Costs
Disking 1acre 7.55 7.55 O O O 0 0 0
Plowing 1acre 3.54 3.54 0 0 0 0 O 0
Harvest
Cutting/Hauling 2.60 ton 18.00 0 0 0 0 0 0 0

ngi Cash Expenses

Revenue Above Selected Cash Expenses

 

Note: This analysis uses a single ten-year cycle, and average ton/year is based on annual average of
total growth over ten years in previous studis on short rotation poplar (Miller, 2008; Miller, 2002;
Dickmann, 2001 ). Profit from P0plar is a one-off event at year 10, and this is reflected in the
annualized present value of the profitability of the system.

Source: Application rates from KBS—LTER project; prices are at current prices (see input prices and
out prices table sources)

* annuity is computed at present value of an annuity of t years (PVA) at an interest rate r=5°/o and time-
t, divided by the present value interest factor for an annuity (PVIFA) whose value was taken from table
A.4 Appendix A of Weston and Copeland. Managerial Finance, 1986 at PVIFA for a 10 year 5%
interest rate

102

Continued Appendix Table 6. Enterprise Budget for Perennial Crop Poplar based on a single
ten-year cycle, adjusted to reflect Net Present Value

 

Present
Price Value (at
per 5%
Unit interest

Quantity Unit (6) 1996 1997 1998 Total rate) Annualized"

 

8 9 1O

 

Revenue Sources

Wood yield 2.60 ton 45.00 0 0 26.00
Total Revenue 0 0 1170.00 1170.00 718.30 93.02
Cash Expenses
Planting
Cutting 1100.00 cutting 0.20 0 0 0 220.00 209.52 27.13
Oats 3.00 Bu 8.19 0 0 0 49.00 46.49 6.02
Fertilizers
Nitrogen 109.73 lbs/acre 0.71 0 0 O 78.62 74.20 9.61
Pest Control
Lorox 55.84 gal/acre 133.67 0 0 0 55.84 53.18 6.89
Princep 6.93 gal/acre 16.60 0 0 0 6.93 6.60 0.86
Roundup 12.71 gal/acre 33.80 0 12.71 0.00 12.71 8.19 1.06
Custom Costs
Disking 1 acre 7.55 0 0 0 7.55 7.19 0.93
Plowing 1 acre 3.54 0 0 0 3.54 3.37 0.44
Harvest
Cutting/Hauling 2.60 ton 18.00 0 0 46.89 46.89 28.79 3.73
Total Cash Expenses 437.54 56.66
Revenue Above Selected Cash Expenses 280.76 36.36

 

 

Note: This analysis uses a single ten-year cycle, and average ton/year is based on annual average of
total growth over ten years in previous studis on short rotation poplar (Miller, 2008; Miller, 2002;
Dickmann. 2001). Profit from Poplar is a one-off event at year 10, and this is reflected in the
annualized present value of the profitability of the system.

Source: Application rates from KBS-LTER project; prices are at current prices (see input prices and
out prices table sources)

* annuity is computed at present value of an annuity of t years (PVA) at an interest rate r=5% and
time-t, divided by the present value interest factor for an annuity (PVIFA) whose value was taken from
table A.4 Appendix A of Weston and Copeland, Managerial F inance, 1986 at PVIFA for a 10 year 5%
interest rate

103

Appendix Table 7. Enterprise Budget for Perennial Crop Alfalfa based on a 3 five-
year cycle, adjusted to reflect Net Present Value (at current prices)

 

 

 

1989 1990 1991 1992 1993 1994 1995 1996 1997
1 2 3 4 5 1 2 3 4
Revenue Sourees
Alfalfa Haylage 0 6.60 5.84 4.56 3.25 1.65 5.39 3.05 4.30
Total Revenue 0 257.34 227.65 177.79 126.61 64.26 210.26 118.93 167.61

Cash Exeenses
Planting

Seed
Fertilizers
K20
0-46-0
boron
lime
Pest Control
Ambush
Sevin
Concentrate
Dimate
Poast Plus
2, 4-D
Roundup
Field Operations
Plowing
Cultivating
Disking
Raking
Harvest
Cutting Hay
Baling
Chopping Silage
Flail Mowing
Haybine

Total Cash Expenses

52.19

0000

0000000

13.70

20.50
13.90
5.65

0
1.00
0
0

000

16.95

1.50
0
10.70

000

22.60

0
2.00
0
0

18.40 36.80 36.80

Revenue Above Selected Cash Expenses

0 0
9.92 19.33
0 0
0 0
0 0
0 0
0 0
0 0
0 O
0 0
0 3.32
0 17.43
0 0
0 0
0 0
5.65 11.30
0 20.50
0.50 0.50

0 107.70
0 0
36.80 27.60

80.30
13.39

000

3.06
1.90
15.06
0
0

0
20.50
0
11.30

0
1.00
35.90
0
18.40

0 0 0
6.77 24.79 29.75
0 21.60 0
0 4.76 4.25
3.08 3.08 3.08
O 0 0
0 12.40 0
0 0 0
0 3.70 0
0 0 17.60

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

1.00 0.50 0
71.80 71.80 71.80
10.70 0 10.70

9.20 27.60 0

 

Source: Application rates from KBS-LTER project; prices are at current prices (see input prices and

out prices table sources)

* annuity is computed at present value of an annuity oft years (PVA) at an interest rate r=5% and
time-t. divided by the present value interest factor for an annuity (PVIFA) whose value was taken
from table A.4 Appendix A of Weston and Copeland, Managerial Finance, 1986 at PVIFA for a 17

year 5% interest rate

104

Continued Appendix Table 7. Enterprise Budget for Perennial Crop Alfalfa based on a 3 five-
year cycle, adjusted to reflect Net Present Value (at current prices)

 

 

 

 

Present
Value
1998 1999 2000 2001 2002 2003 2004 Total (at 5% \nnualized'
interest
Jate)
5 1 2 3 4 5 1
Revenue Soureee
Alfalfa Haylage 3.84 0.85 0.13 0.08 5.36 4.79 4.59 58.36
Total Revenue 149.81 33.16 4.90 2.99 208.93 186.98 178.94 2276.14 1524.37 140.65
ceeh Exeeneee
Planting
Seed 0 40.59 0 0 0 O 52.20 225.28 161.00 15.50
Fertilizers
K20 6.77 0 0 0 0 41.64 0 211.84 139.00 13.38
0-46-0 O 0 0 0 43.20 21.60 0 123.25 72.14 6.94
boron 0 0 O 0 0 7.87 0 24.83 13.00 1.25
Iime 3.08 4.01 0 0 O 0 0 16.35 11.00 1.06
Pest Control
Ambush 0 0 0 0 0 0 0 21.95 19.00 1.83
Sevin 0 0 0 0 0 0 0 13.60 9.48 0.91
Concentrate 0 0 0 3.68 0 0 0 6.74 4.24 0.41
Dimate O 0 0 0 0 0 0 5.60 3.92 0.38
Poast Plus 0 O 0 13.21 0 O 13.21 74.14 48.65 4.68
2, 4-D 0 0 0 3.88 0 0 O 7.20 4.66 0.45
Roundup 0 12.55 0 8.16 0 0 0 38.14 25.32 2.44
Field Operations
Plowing O 0 0 0 0 0 0 13.70 13.05 1.26
Cultivating 0 O 10.25 0 0 0 0 51.25 40.53 3.90
Disking 0 13.90 0.0 0 0 0 0 27.80 21.37 2.06
Raking 0 0 O 0 5.65 16.95 0 101.70 75.68 7.29
Harvest
Cutting Hay 10.25 10.25 0 20.50 10.25 0 0 82.00 48.87 4.70
Baling 0.50 0.50 0 0 0.50 1.00 0 11.00 8.00 0.77
Chopping Silage O 71.80 0 0 71.80 35.90 0 574.40 368.00 35.43
Flail Mowing 0 0 0 10.70 32.10 32.10 0 117.70 66.20 6.37
Haybine 9.20 O 0 0 O 0 0 220.80 179.19 17.25
T tal a h Ex enses 1332 128.26
Revenue Above Selected Cash Expenses
192.07 18.49

 

Source: Application rates from KBS-LTER project; prices are at current prices (see input prices and out prices

table sources)

* annuity is computed at present value of an annuity oft years (PVA) at an interest rate r=5% and time—t, divided
by the present value interest factor for an annuity (PVIFA) whose value was taken from table A.4 Appendix A of
Weston and Copeland, Managerial Finance, 1986 at PVIFA for a 17 year 5% interest rate

105

Appendix Table 8. Crop Prices Standard Deviations and Mean computed
from Prices (1978-2008; deflated to 2008)

 

 

Crop Standard Deviation Mean
Corn 0.74 2.66
Soybean 1.61 6.09
Wheat 0.9 3.30

 

1 06

APPENDIX TWO: Overview, Goals, Study Design, Collection Procedure and Data
Management for the Crop Management and Environmental Stewardship Survey

107

OVERVIEW

The survey about Crop Management and Environmental Stewardship was
conducted as a mail survey with funding from the National Science Foundation under
Human and Social Dynamics Grant No. 0527587 and Long—term Ecological Research
Grant No. 0423627. It looks into the incentives of producing and consuming ecosystem
services from low-input cropping systems. Questionnaires were sent to a stratified
random sample of 3,000 corn or soybean producers in Michigan.

Respondents answered about their current crop management practices, opinions
about links between the agriculture and environment, views on the importance of
different possible environmental benefits linked to agriculture and their willingness to
adopt several farming practices. Mailing and data collection were conducted from
February 8 to March 14, 2008. Questionnaires were completed by 1688 individuals. The

net response rate was 56.36%.

GOALS

The main goal of the survey about the Crop Management and Environmental
Stewardship was to look into farmers’ willingness to accept payment for environmental
services from agriculture. The results of the survey will be used to help understand
farmers’ views about the costs and benefits of adopting low-input cropping practices.
This knowledge will potentially help to shape future policies and programs to benefit

Michigan farmers.

108

STUDY DESIGN

The study of Crop Management and Environmental Stewardship was conducted
as a mail survey by the Department of Agricultural Economics at Michigan State
University. The highest standards of quality survey research were employed in
conducting this project.

The administrative coordination of the project was provided by Professor Scott
Swinton. The graduate research assistant, Christine B. Jolejole, and was responsible for
questionnaire design, data collection, coding and editing, and writing the methodology
part as part of her Masters thesis. She was also responsible for ensuring data accuracy and
conversion of raw Excel data into a STATA system file format for analysis. Professor

Frank Lupi also actively advised the survey activities in Essay 2.

DATA COLLECTION PROCEDURES

The procedures used by the project for this mail survey were based on Mail and
Telephone Surveys, by Don A. Dillman (Dilman, 1999). Mailing and data collection for
the survey about Crop Management and Environmental Stewardship were conducted
from February 8 to August 15, 2008.

The first mailing was sent on February 8, 2008. This was a personally signed
prenotice letter informing the farmer that they would receive the questionnaire in a week.
Michigan State University (MSU) embossed paper and envelops were used. All mailings

were sent first class postage.

109

The second mailing was on February 15, 2008 and included the following: (1) a
cover letter that invited the farmer to participate in the survey and was a part of a very
small sample in the state, printed on Michigan State University letterhead and signed by
Dr. Scott Swinton as the head of the project, (2) a survey instrument, a sixteen page
questionnaire; (3) a dollar bill as incentive; and (4) a business reply envelope.

The third mailing consisted of a reminder postcard that was sent out to the entire
sample on February 22. The postcard thanked the individuals if they had already filled
out the questionnaire, and asked them to take time to complete the survey if they had not
already done so.

On March 14, a fourth mailing was sent to all individuals who had not yet
returned their survey. This mailing was identical procedurally to the second mailing
except that it did not include the dollar bill; it included a copy of the questionnaire, a
reminder cover letter, and a business reply envelope.

After the fourth mailing, by May 15, the response rate was deemed sufficient so

no further mailing was done.

DATA MANAGEMENT

Editing and coding included the completion of two major tasks. First all the
surveys were checked for response clarity to eliminate dual responses when single-
answer responses were sought, or to create a separate category for multiple responses.
Second, the coders transcribed all responses to open-ended and “other specify” questions.
Data entering involved the use of double data entry, and merging the files to detect out-

of-range values.

110

Finally, merging the two files, checking for inconsistencies and editing were done
by one individual who was familiar with the questionnaire and the purpose of the study.
Unclear or ambiguous responses were directed to the same person for resolution. In
addition, conducted quality control was conducted and coded/edited surveys were
reviewed throughout this phase. Once a complete file of the questionnaire was
constructed, it was examined systematically to remove data errors. In addition, the excel
spreadsheet itself was examined manually to identify the cases with paradoxical and

inappropriate responses.

111

APPENDIX THREE: Questionnaire Design

112

QUESTIONNAIRE DESIGN

The initial draft of the questionnaire was provided to Professor Scott Swinton in
November 2007. A total of 23 pretests were done from January 9 to January 29, 2008 and
changes were subsequently made to the survey. The first pre-test session was conducted
with five undergraduate students and six graduate students with crop and soil science
backgrounds. The second pre-test session was conducted with agricultural extension
agents. And finally eight face to face pre-testing and interviews were conducted with
eight farmers during the corn and soybean research pest management meeting in January
2008. Professors Scott Swinton and Frank Lupi approved the final questionnaire prior to
start of data collection. The questionnaire was divided into six sections.

Section one asked about the farmers’ current crop management practices. This
includes the acreage of crops they planted in 2007, the cropping rotation, soil texture,
types of tillage, use of cover crops, soil tests, pre-sidedress nitrate test (PSNT) and price
expectations.

Section two asked about the farmers’ opinion about links between agriculture and
the environment. it consisted of Likert scale questions assessing respondents’ opinions.

Section three looked at the farmers’ views on the importance of different possible
environmental benefits linked to agriculture. They were asked about the importance of
this environmental benefit to them vis-a-vis importance to society.

Section four asked about farmers’ adoption of individual farming practices. They
were asked whether they are currently using them, previously tried and somehow

abandoned or if they have never tried them before.

113

 

Section five included the cropping systems payment offer questions. Four
cropping systems were described in terms of the crop rotation, cover crops, tillage, soil
test, fertilization and pesticide application rate. Farmers were then asked about their
views on the environmental effects of this cropping system if any. They were also asked
to consider the payment and write the number of acres they would enroll in this program.

Finally, section six asked for the demographic questions. They were asked about
their farming history and intentions (year they started farming, for how many years they
plan to continue farming) and background information (zip code, year born, and highest

level of education completed).

114

 

APPENDIX FOUR: Sampling Design

115

SAMPLING FRAME DESIGN

Questionnaires were sent to a stratified random sample of corn and soybean
growers in Michigan. The sample was obtained from the 2007 agricultural census mailing
list of National Agricultural Statistics Service (NASS) office in Michigan. NASS
provided the project with a stratified random sample of 3,000 corn and soybean farmers
in Michigan.

Table below provides strata and number of respondents within it.

Appendix Table 9. Sampling Frame Design

 

 

Number of Sampled
Strata Acres Number of Farmers Farmers within each Cluster
1 0-100 9849 301
2 101-500 5545 1050
3 501-1000 1361 770
4 1000+ 879 879

 

The survey sampling frame provided by NASS was a proportional allocation
based on acreage of soybean and/or com. The sample size was chosen to allow for
comparison across strata. Within each stratum, the sample was a systematic random

sample ofthe population.

116

APPENDIX FIVE: Bid Design

117

BID DESIGN

There are several approaches in estimating the range of bids for use in surveys.
The common approach is to compute for the average or the mean. Another is to use the
50th percentile or the median from prior WTP estimates. Since the goal was to be able to
derive four different prices for each cropping system, the 20‘“, 40th, 60th and 80th
percentile of the distribution were computed from the data gathered from the farmer
focus groups conducted during February and March 2007.

The percentiles were computed from the predicted participation rates at various
payment levels for each of the four crop systems based on the forecasts of the
participation probit models estimated from the focus group data (Lupi et al, 2007).

To ensure that the percentiles of bids to be used in the survey were realistic, we
asked Kim Wieber, one of the program coordinators of Conservation Security Program
(CSP) of Natural Resource Conservation Service (N RCS) to give a price estimate for our
suggested cropping systems in the survey.

Appendix Table 10 provides the percentiles for the bid design.

118

Appendix Table 10 Payment Level to Achieve Participation

(Based on Probit Particflaation Models using Focus Group Data)

 

 

 

Cropping System
A B C D
Percentile 10 n/a 1 1 n/a 14
20 n/a 17 10 31 +
30 3 21 22 42
40 6 25 32 52
50 9 28 41 62
60 12 32 50 71
70 15 36 60 81 '
80 18 40 72 93 E
90 23 46 89 109 '

 

References:

Lupi, F., R. Shupp, S.M. Swinton and L. Vangjel. 2007. Farmers’ Willingness to Accept
Payments for Providing Environmental Services. Michigan State University, East
Lansing, Michigan. Unpublished manuscript.

119

APPENDIX SIX: Experimental Design and Versions

120

 

 

EXPERIMENTAL DESIGN

The experimental design determined the combinations of the varying factors
within the questionnaire. There were 6 variables. Sequence of cropping system difficulty,
which correlated positively with payment level (2 levels: ascending or descending),
payment vehicle (Federal government or a non-governmental organization) and four
cropping systems each with 4 levels or prices. Using a factorial design, the experimental
design was drawn from 202*404 full factorial.

A main effects orthogonal array from this factorial design is composed of 16
alternatives under conditions. One condition is that no individual survey has decreasing
prices moving from system A to system D. That condition was imposed on the design to
truncate bids to prevent this from occurring. The resulting design matrix is balanced in
the two binary variables and has low correlation among the variables except for two pairs

B,C and CD which have modest correlation but below 0.5.

VERSIONS

The sample was selected with 16 replicates within each stratum. Each replicate was a
sample of the population. Each of the 16 questionnaire versions was assigned to a
replicate. The questionnaire varied and has sixteen different versions (versions A-P).
Variations include the cropping systems payment levels in section five (for details, see
bid design), the sequence in which the cropping systems were proposed, and the payment
vehicle or the funding agency for the cropping systems payment. Appendix Table l 1

provides range of identification numbers and the particular version mailed.

121

 

 

 

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122

APPENDIX SEVEN: Pre Notice, Cover Letters, Reminder Postcards and the
Questionnaire

123

 

coutct OF
AGRICULTURE MID
NATURAL
RESOURCES

“Mimi
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MwSueUrimity
emu-comm

East Lamng. Ml

46824 1039

Fax 517(432-1811

Wet: mummoocm

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OWN-W? WW

MICHIGAN STATE
0 N l v E R s l T Y

February _, 2008

Dear Valued Producer,

1n a few days you will receive a questionnaire in the mail about Crop Management and
Environmental Stewardship Survey. When the questionnaire arrives, please fill it out
and mail it back promptly. Michigan State University in collaboration with the National
Science Foundation is conducting this survey and chose your address, not you
personally, as part of a randomly selected sample from Michigan Agricultural Statistical
Service (MASS) survey mailing list..

Your farm is one of a small number whose managers are being asked to express their
opinions. Thus, it is very important that you reply so that the results give the clearest
possible picture of how herbicide choices are made in Michigan.

Thank you in advance for your help.

If you have questions about the research 1 would be most happy to answer any question
you might have. You can e-mail me at swintons@msu.edu or call me at 1-517-353-7218

Sincerely,

Scott M. Swinton
Professor

124

 

CO“!!! or
AGRICULTURE MID
NATURAL
RESOURCES

WUW
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In 5173412 1333'

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MICHIGAN STATE

U N l V E R S l T Y
February _, 2008

Dear Valued Producer.

We would like to ask about your views on cropping practices and the environment. So we ask for a
few minutes of your time to complete the attached questionnaire. This study is being conducted by
Michigan State University with support from the National Science Foundation in order to understand
better farmers’ views on the appeal of adopting various low-input cropping practices. The results of
this research will be analyzed and written up for publications that range from farm newspapers to
academic journals. There are no right answers, because everyone farms different ground and has
different management strategies and marketing plans.

Your farm is one of a small number whose managers are being asked to express their opinions. It was
drawn in a random sample of corn and soybean growers from the entire state. In order that the results
will truly represent the thinking of Michigan farmers, it is important that each questionnaire be
completed and returned. It is also important that we get the views of the decision makers of the farms.
Thus, we request that the questionnaire be completed by the person who mains most decisions on
cropping practices on your farm.

You may be interested to know that we researchers sending you this questionnaire will not have access
to your name and contact information. The Michigan Agricultural Statistical Service manages the
survey mailing list. it does not permit the researchers to have a copy of the address labels or to link
your identity to the identification number assigned to (so we will know if you have replied so we can
stop sending you questionnaires) fi'om (MASS) for mailing purposes only. So your name will never be
placed on the questionnaire and we researchers at MSU will not have any access to it. Your individual
views will be completely confidential, and your privacy will be protected to the maximum extent
permitted by law. Also, your participation in the survey is voluntary. You may choose not participate
at all, refuse to answer certain questions, or end the survey any time.

if you have any questions or concerns regarding your rights as a study participant, you may contact
Peter Vasilenko. Ph.D., Director of Human Subject Protection Programs at Michigan State University
by phone: (517) 355-2180, email: irh’rrrmsucdu.

 

If you have questions about the research 1 would be most happy to answer any question you might
have. You can e-mail me at swintnns (111130.ch or call me at 1-517-353-7218.

 

Thank you for your assistance.

Sincerely,

Scott M. Swinton
Professor

125

 

 

 

MICHIGAN STATE
0 N l v E R s I T Y

Crop Management
and Environmental Stewardship

 

A SURVEY OF YOUR OPINIONS

 

 

This research aims to understand farmers’ views on adopting various
low-input cropping practices. There are no right or wrong answers
because everyone farms different ground and has diflerent
management strategies and marketing plans.

Your opinions matter!
By completing this questionnaire you are helping to

inform the design of future policies that better reflect the
views and concerns of Michigan farmers.

 

126

 

 

CROP MANAGEMENT AND ENVIRONMENTAL
STEWARDSHIP STUDY

 

ll SECTION A: Your current crop management practices H

Al. In 2007, did you plant corn or soybean?

D No 12> If you answered NO, you do not need to continue filling out this
[:1 Yes questionnaire. Please return it in the envelope provided. Thank
you for your time.

A2. Are you the main decision maker for annual crop management on your farm?

I] No :1) If you answered N0, please direct the questionnaire to the person
1:] Yes who makes cropping decisions on the farm.

A3. In 2007, what were the main crops you planted and how many acres of each did you plant?
(Please write the corresponding number of acres in the right column.)

 

 

 

 

 

 

 

 

 

 

CROPS in 2007 ACRES
Corn ACRES
Soybeans ACRES
Wheat ACRES
Alfalfa ACRES
Oats ACRES
Other (please specify):
ACRES
ACRES
ACRES

 

 

127

 

A4. Over the last 3 years, what was the crop sequence on most of your corn and soybean fields?
I:l Continuous com
[I Continuous soybean
Cl Com-soybean
El Corn-corn-soybean
I: Corn-soybean-wheat
El Others (please specify):

 

A5. How would you describe the most common soil texture on your farm?
El Sand
Cl Silt
El Loam
I:l Clay
El Clay-loam
El Silty-loam
El Sandy-loam
El Others (please specify):

 

 

A6. Did you grow soybeans in 2007?

El No (please go to A8)
[I Yes

A7. What kind(s) 01' primary tillage did you chiefly practice in fall and spring when going into
2007 soybeans? (Please mark the acres of each type below. There may be more than
one answer.)

 

 

 

 

 

 

 

 

 

TILLAGE FALL 2006 SPRING 2007
GOING INTO SOYBEANS M007
Moldboard plow ACRES ACRES
No-till ACRES ACRES
Chisel plow ACRES ACRES
Disc ACRES ACRES
Strip, zone or row tillage ACRES ACRES

 

 

Other (please specify):

ACRE ACRE

 

 

 

128

A8. Did you grow corn in 2007?

El No (please go to A10)
I] Yes

A9. What kind(s) of primary tillage did you chiefly practice in fall and spring when going into

2007 corn? (Please mark the acres of each type below. There may be more than one

 

 

 

 

 

 

 

 

 

 

answer.)
TILLAGE FALL 2906 SPRING 2907
GOING INT ORN in 2 07

Moldboard plow ACRES ACRES
No-till ACRES ACRES
Chisel plow ACRES ACRES
Disc ACRES ACRES
Strip, zone or row tillage ACRES ACRES
Other (please specify):

ACRES ACRES

 

 

 

 

A10. Did you plant a cover crop after corn, soybeans or wheat in 2007?

D No (please go to All)
[I Yes

Allla. If YES, please specify what main crop it followed and how many acres of
cover crops you planted. (If none, please mark zero acres planted)

 

Here are some examples of cover crops:
Legume Cover C raps: C lovers, Hairy Vetch, Field Peas, Annual Medic, Alfalfa and Soybean
Non-legume Cover Crops: Rye, Oats, Wheat, Forage Turnips, Oilseed Radish and Buckwheat

 

 

 

 

 

 

MAIN CROP COVER CROP (S) ACRES
Corn ACRES
Soybeans ACRES
Wheat ACRES

 

 

129

A l l.
2005?

A12.

Did you have soil tests done on your corn, soybean or wheat land since the beginning of

D No

[I Yes 12:) If YES, how many acres of crop land did you soil test?
ACRES

Did you do pre-sidedress nitrate testing (PSNT) in 2007?

D No

[I Yes :1) If YES, on how many acres did you use the PSNT?
ACRES

A13. Did you {practice organic farming in 2007? (C heck all that apply.)

Cl No

El Yes, Certified, on ACRES

D Yes, Not currently certified, on ACRES

A14.

A15.

Which of the following types of farm products accounted for more than 10% of your farm
revenues in 2007? (Please check all that apply).

[3 Field crops (including grains, oilseeds, sugarbeets, silage and hay)
L—J Fruit, nut and vegetable crops (including potato)

El Flowers, omamentals and live plants (nursery crops)

El Milk and dairy products

[I Livestock and animal products other than dairy

D Other (please specify):

 

If you were to grow corn, soybean and wheat this year, what prices would you expect to
receive for your 2008 harvest? (Please answer for all three crops.)

 

 

 

EXPECTED HARVEST
CROP PRICE RECEIVED
Corn $/BUSHEL
Soybeans $/BUSHEL
Wheat $/BUSHEL

 

130

 

 

SECTION B. Your opinions about links between agriculture and the environment

l=‘

Bl. Please check the boxes that best represent your agreement with the following statements.

Less tillage is good for soil conservation.

Winter cover crops are good for soil conservation.

Cover crops increase soil fertility.

Incorporating manure and fertilizer into soil reduces
phosphorus runoff into waterways compared with surface
application.

Environmental stewardship only makes sense on my farm if
it also contributes to income.

Less tillage on my farm reduces global warming by storing
carbon in soil.

Applying nitrogen fertilizer based on a pre—sidedress nitrate
test (PSNT) generally reduces nitrate leaching into water
supplies.

Applying nitrogen fertilizer based on a pre-sidedress nitrate

test (PSNT) generally reduces global warming by reducing
greenhouse gas emissions linked to nitrogen.

Nature provides services that improve my crop production.

By their choice of cropping practice, farmers can improve or
harm environmental quality.

I am familiar with the MSU recommended fertilizer rates for
my farm.

131

Strongly
Agree

[:1

Agree

Neutral

Disagree

Strongly
Disagree

[:1

 

 

SECTION C. Your views on the importance of different possible environmental
benefits linked to agriculture

 

C 1. Please rate the following environmental effects on how important they are for YOU as an
individual. (Check the box of your choice.)

 

Highly Somewhat Not
Important Important Important

to Me to Me to Me
Increasing soil organic matter I] [j [:1
Increasing soil conservation [3 1:] [:1
Reducing phosphorus surface runoff D [:1 [:1
Reducing nitrate leaching E] [:1 1:]
Reducing global warming [:1 [Z] 1:]
Reducing pesticide risk to human health E] [j C]

C2. Now, please rate the same environmental effects on how important YOU believe they are to
socieg. (Check the box of your choice.)

Highly Somewhat Not
Important Important Important
to Society to Society to Society

Increasing soil organic matter {:1 D D
Increasing soil conservation
Reducing phosphorus surface runoff
Reducing nitrate leaching

Reducing global warming

DECIDE]
DECIDE]
ClElElElCl

Reducing pesticide risk to human health

132

 

SECTION D. Your adoption of farming practices on your corn-soybean acres

C URREN TL Y USING if you 're currently practicing it right now on any corn-soybean land
TRIED .-l.\"D .‘lBANDONED ifyou previously used the practice but gave it up
NEVER TRIED Ifyou 've never tried it at any time

 

 

 

DI. Please mark your experience using the practices below.

 

Currently
using
Previously
tried and
abandoned
Never tried

PRACTICES ON CORN SOYBEAN LAND

No tillage for 4 or more consecutive years.

 

No tillage in some years.

Reduced tillage (compared to moldboard plow).

Apply manure.

Include wheat in corn-soy rotation.

Plant any cover crop before corn.

Plant legume cover crop before corn.

Use PSNT to guide nitrogen application rate.

Band apply N fertilizer over rows at MSU recommended rates,
reducing total fertilizer use to 2/3 of full field rate.

Apply only post-emergence herbicides (no pre-emergence).

Scout for insect pests to guide pesticide decisions.

Band apply herbicide and insecticide over rows at label rates.
reducing total pesticide use to 2/3 of full field rate.

DEIDEJDCICICJDEICIU
DDDDDDDDDDDD
DDDDDDDDDDDD

133

 

 

 

SECTION E: Four specific cropping systems

Background:

We will show you four specific cropping systems.

We ask your views on environmental effects for the four cropping systems. We also ask about
your willingness to accept payments to adopt the cropping systems.

The basic idea is that these systems may have some environmental benefits compared to
conventional farming. Hence, there may be a reason to compensate farmers for any losses
that they might incur by changing fiom their current practices to these practices.

We know there are actual programs that may be similar to the ones we describe, but we ask
you to consider only the scenario we are proposing for each of the four cropping systems.

The Program:

Imagine a program run by a non-governmental organization that would pay you each year
for a 5-year commitment to adopt a particular cropping system. The practices that are
required for each system are described. For a given set of practices, you choose the number
of acres, if any, to enroll in the program.

i Key Points in answering:

0 When selecting the acres to enroll in a cropping system, consider each program
separately -- as if the payment programs for the other three systems are unavailable.

0 For crop rotations enrolled:
o Corn-soybean-wheat rotation means '/3 of the acreage in each crop every year.
0 C orn-soybean rotation means ’/2 of the acreage in each crop every year.

0 If cover crops are included, they are on all enrolled fields during winter.
0 Tillage refers to the principal tillage method on the enrolled acres.

0 PSN T or pre-sidedress nitrate test is for the corn crop. Nitrogen fertilizer applied
following PSN T is split rate (starter at planting and most at sidedress time).

 

134

 

 

 

 

 

HOW TO ANSWER THE CROP SYSTEM QUESTIONS

 

This page provides an example of what you will see and how to answer

 

(STEP 1: Read the
description of this
cropping system.
Each page has a
different system!

C

 

EXAMPLE OF A PAGE DESCRIBING THE CROP SYSTEMS

 

 

STEP 2: Give your
views on any
environmental

e_ ects.

 

 

(STEP 3:

Consider the
payment and write
the acres you
would enroll in
this system.

If you would not
enroll, write “0
acres " rmn'

 

 

 

(‘ropping System I! I: ( urn-soy 1min mtalion

Rotation: ( uni-Soy hem rotation

('oyci t lti'h.’ \oiic

lill.i'__-c: ('hiscl l’luw with culliyatiiiii i|\ ll‘y‘tIL'tI

Soil lest: I'I'C’\I\ICLIIC>\ \Itnitc lentii'\\l I

lentil/Jill)": Broadcast Iciiili/cis at lull \lfsl rates and split \itroacn based on I’SN I

Pcslnnlc Rgilc: Broadcast pesticides .11 .i label 1‘.th

 

 

 

Ll. If you switch from your current cropping system in cropping system “I. how do you llilflly the
following myironiiicntiil cffccty would change? it'lmw {lull llh‘ luv. I/mi tit-vi It'I'lL'u'lllt ium
.Ign-cIIII-ut w ith ”VIII/lint mg; ‘ItI’L'HII'HHI

it — ‘7‘
s; t s 5 2%..
° 3 3 a T s e
5 I a 55
a \uil organic matter incrcincs with 1th crown; a) stem D D D D D
h \iiil tnmen aliiui increases with this {nlm‘lllf 9‘1““ D D D D D
c I‘ll-whims surfs-.- niiiut‘l'iy minced with Ihly yy «cm [1 [j [] LT] [ J
J \Iimlc Ira-Jung l‘ rnhk ed with Ilil‘ cropping \) stem [ l D L] D [ ‘I
c (IN‘N' warming Is reduced with tho cropping yy ylcm L] [j C] [j [j
l I'L‘IILILL' mi. i~ rcduud \Iilh Ihh cropping sy \lcm [] D { J D [ ]
13.2 ”It program run by a nonguycrnnicntiil organization would pay you 8' dollars
per were each year for 5 years for using cropping system it 1. how many acres of
land would you enroll in this program? (It iou lIrlllII/IINI tum/l p/mw lllllt :. Int
.’\( R18

1.5. If you answered Icro acres. which ofthe following best describes your situation?
Cl l \_yuiild not enroll Ill this progniiii no matter how high the piiyiiicnt was.

Cl I would enroll in this program ii the my mcnt wcrc higher.

(Olilioiiul) \\ hat was your reason for enrollment or Iion-cllrolliiii-Iit in the program?

\

 

 

 

Io

 

 

135

 

 

 

 

Cropping System # l: Corn-soybean rotation

Rotation: Com-Soybean rotation

Cover Crops: None

Tillage: Chisel plow with cultivation as needed

Soil Test: Pre-sidedress Nitrate Test (PSNT)

Fertilization: Broadcast fertilizers at full MSU rates and split Nitrogen based on PSNT
Pesticide Rate: Broadcast pesticides at a label rate

 

 

El. If you switch from your current cropping system to cropping system #1, how do you think the
following environmental effects would change? (Please check the box that best represents your
agreement with the following statements).

Compared to my current cropping system, 2% g g E E5

a. Soil organic matter increases with this cropping system.
b. Soil conservation increases with this cropping system.
c. Phosphorus surface runoff is reduced with this system.
d. Nitrate leaching is reduced with this cropping system.

c. Global warming is reduced with this cropping system.

DECIDED
DECIDED
DUDDUD

DUUUDD
UDUUUD

f. Pesticide risk is reduced with this cropping system.

E2. If a program run by a non-governmental organization would pay you $4 dollars per
acre each year for 5 years for using cropping system #1, how many acres of land would
you enroll in this program? (If you would not enroll, please write zero).

AC RES

 

E3. If you answered zero acres, which of the following best describes your situation?
E] I would not enroll in this program no matter how high the payment was.

E] I would enroll in this program ifthe payment were higher.

(Optional) What was your reason for enrollment or non-enrollment in the program?

 

 

136

 

 

 

Cropping System #2: Corn-soybean rotation with cover crop

Rotation: Com-Soybean rotation

Cover Crops: Any type present over winter

Tillage: Chisel plow with cultivation as needed

Soil Test: Pre-sidedress Nitrate Test (PSNT)

Fertilization: Broadcast fertilizers at full MSU rate and split Nitrogen based on PSNT
Pesticide Rate: Broadcast pesticides at a label rate

 

 

 

E4. If you switch from your current cropping system to cropping system #2, how do you think the
following environmental effects would change? (Please check the box that best represents your

agreement with the following statements).

Strongly
Agree
Agree

Compared to my current cropping system.

3. Soil organic matter increases with this cropping system.
b. Soil conservation increases with this cropping system.
c. Phosphorus surface runoff is reduced with this system.
d. Nitrate leaching is reduced with this cropping system.

c. Global warming is reduced with this cropping system.

DDUDDD
DDDDDD
DDDDDD
DDDUDD
ClClElClElEl

f. Pesticide risk is reduced with this cropping system.

E5. If a program run by a non-governmental organization would pay you $10 dollars per
acre each year for 5 years for using crapping system #2, how many acres of land would
you enroll in this program? (If you would not enroll, please write zero).

ACRES

 

E6. If you answered zero acres, which of the following best describes your situation?
C] I would not enroll in this program no matter how high the payment was.

E] I would enroll in this program if the payment were higher.

(Optional) What was your reason for enrollment or non-enrollment in the program?

 

 

l37

 

Cropping System # 3: Corn-soybean—wheat rotation with cover crop

Rotation: Corn-Soybean-Wheat rotation

Cover Crops: Any type present over winter

Tillage: Chisel plow with cultivation as needed

Soil Test: Pre-sidedress Nitrate Test (PSNT)

Fertilization: Broadcast fertilizers at full MSU rate and split Nitrogen based on PSNT
Pesticide Rate: Broadcast pesticides at a label rate

 

 

 

E7. If you switch from your current cropping system to cropping system #3, how do you think the
following environmental effects would change? (Please check the box that best represents your

agreement with the following statements).

Compared to my current cropping system,

Strongy
Agree
Agree

a. Soil organic matter increases with this cropping system.
b. Soil conservation increases with this cropping system.

0. Phosphorus surface runoff is reduced with this system.

 

d. Nitrate leaching is reduced with this cropping system.

c. Global warming is reduced with this cropping system.

DDDDDD
DECIDED

DDUDDU
ElElClElElCl
ElElDClElCl

f. Pesticide risk is reduced with this cropping system.

E8. If a program run by a non-governmental organization would pay you $15 dollars per
acre each year for 5 years for using cropping system #3, how many acres of land would
you enroll in this program? (If you would not enroll. please write zero).

ACRES

 

E9. If you answered zero acres, which of the following best describes your situation?
El 1 would not enroll in this program no matter how high the payment was.

E] 1 would enroll in this program if the payment were higher.

(Optional) What was your reason for enrollment or non-enrollment in the program?

 

 

138

 

Cropping System # 4: Corn-soybean-wheat with cover crop and reduced rates
of fertilizer & pesticides

Rotation: Corn-Soybean-Wheat rotation

Cover Crops: Any type present over winter

Tillage: Chisel plow with cultivation as needed

Soil Test: Pre—sidedress Nitrate Test (PSNT)

Fertilization: Band apply fertilizers over row at MSU rate and split Nitrogen based on PSNT
Pesticide Rate: Band apply pesticides over row at label amount

 

 

 

E10. If you switch from your current cropping system to cropping system #4, how do you think the
following environmental effects would change? (Please check the box that best represents your

agreement with the following statements).

E]
Cl
C]
E]
E]
El

Strongly
Agree
Agree

Compared to my current cropping system,

a. Soil organic matter increases with this cropping system.
b. Soil conservation increases with this cropping system.
c. Phosphorus surface runoff is reduced with this system.
d. Nitrate leaching is reduced with this cropping system.

c. Global warming is reduced with this cropping system.

DECIDED
ClDClClElCl
DDDUUD
DECIDED

f. Pesticide risk is reduced with this cropping system.

Ell. If a program run by a non-governmental organization would pay you $20 dollars per
acre each year for 5 years for using cropping system #4, how many acres of land would
you enroll in this program? (If you would not enroll. please write zero).

ACRES

 

E12. If you answered zero acres, which of the following best describes your situation?
Ci 1 would not enroll in this program no matter how high the payment was.

Cl 1 would enroll in this program if the payment were higher.

(Optional) What was your reason for enrollment or non-enrollment in the program?

 

 

I39

 

SECTION F: This last section asks for background information to help identifi» patterns among
different kinds of farms. Your answers will be kept completely confidential.

 

 

 

 

Fl. What is your age? YEARS
F2. What is your gender?
El Male
El Female
F3. How long have you been farming? YEARS

 

F4. ‘Other than yourself, how many members of your household are in each of these age groups?
Members under l8

Members Ages 18 to 30

Members Ages 31 to 64

Members Ages 65 and over

 

| l

 

F5. ls farming the main source of income for your household?
l] Yes
D No :1) If NO, what is your main source of income?

 

F6. What was the total household income of the principal operator in 2007? (Include net income
from farming, wage or salary income from all sources, social security and investment income.)

Less than $20,000

$20,000 to $39,999

$40,000 to $59,999

$60,000 to $79,999

$80,000 to $99,999

$100,000 and above

DDUEIEICI

F7. How much cropland did you farm during 2007? And how much was irrigated?
Owned land: (ACRES) of which irrigated = (ACRES)
Rented land: (ACRES) of which irrigated = (ACRES)

 

 

F8. Is your farm certified under the Michigan Agricultural Environmental Assurance Program
(MAEAP), including farming system and cropping system?

El No
C] Yes

F9. What is the zip code of your farm?

NEXT PAGE :>

 

140

 

F 10. Have you ever participated in any of the following conservation program?

 

a. Environmental Quality Incentives Program (EQIP)

b. Conservation Reserve Program (CRP)

 

 

 

 

13:11:13
DUB;

c. Conservation Security Program (CSP)

 

F11. What is the highest level of education you have completed?
Less than 12 years

High school diploma

Technical training beyond high school

Some college (including AA, AS degrees)

4-year college degree

Some graduate work

DDUDDDD

Graduate degree

F12. ls land use in any of your farm fields restricted? (Check all that apply.)
D Unrestricted
El Restricted, Agricultural (PA 116, conservation easement, purchase of development rights. etc.)
[3 Restricted, Residential
El Restricted, Industrial
F13. What is your farm’s form of business organization?
l:l Sole proprietorship
El Partnership
[:1 Limited Liability Corporation (LLC)
El Small Corporation (subchapter S)
El Corporation (subchapter C)

F14. How many more years do you expect to continue farming? YEARS

F15. Do you intend to pass on the farm to a family member or a close friend?

D No
D Yes

THANK YOU!

if you have questions, please contact Scott A. Swinton at 1-517-353-7218, by e-mail at swintons@msu.edu, or by
postal mail at Department of Agricultural Economics. Michigan State University, East Lansing, Ml 48823-1039.

141

 

Crop Management and Environmental Stewardship Survey

Department of Agricultural Economics MICHIGAN STATE
306 Agriculture Hall —--——'
Michigan State University U N I V E R S I T Y
East Lansing. MI 49924 -9904

FARMER NAME
Address

 

February _, 2008

Last week a questionnaire about crop management and environmental stewardship was
mailed to you. Your name was drawn from a random sample of corn farmers in Michigan.

If you have already replied, thank you very much. If not, please do so today. We have sent
the questionnaires to only a small but representative sample of Michigan com-soybean
growers. It is very important that you reply so that the results give the clearest possible
picture of how decisions on crop management and environmental stewardship practices
are made in Michigan.

If by any chance you did not receive the questionnaire. or it was misplaced. please call me
collect at 517-353—7218. and 1 will replace it right away.

Sincerely,

Scott Swinton
Project Coordinator

142

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March 14, 2008

Dear Michigan Corn and Soybean Producer,

About four weeks ago, I wrote to ask your opinions on cropping practices and the
environment. As of yesterday 1 had not yet received your completed questionnaire.

We have undertaken this study to ensure that the design of future agro-environmental policies
is informed by the views and concerns of Michigan farmers.

1 am writing to you again because each questionnaire really matters for this study. Your farm
is one of the small number of farms drawn from a scientific sampling process. In order for the
results of this study to fairly represent the opinions of all Michigan farmers, it is very
important that each farmer in the sample return their questionnaire. As mentioned in our last
letter, the questionnaire the person who makes the crop management decisions on the farm.

in the event that your questionnaire has been misplaced, I enclose a replacement.

As I mentioned in the earlier letter, your individual views will be completely confidential and
your privacy will be protected to the maximum extent permitted by law. Also, your
participation in the survey is voluntary, and you may refuse to answer certain questions. If you
have any questions or concerns regarding your rights as a study participant, you may contact
Peter Vasilenko, Ph.D., Director of Human Research Protection Programs at Michigan State
University by phone: (517) 355-2180, or by email: irbit‘i‘msuedu.

1 would be happy to answer any questions you might have. Just call me at 1-517-353-7218 or
email me at swintons@msu.edu.

Spring is not far off. 1 know you have lots to do, but 1 do hope that you can carve out 20
minutes to let us know your thoughts. Thanks in advance for your cooperation on this.

Sincerely,

Scott Swinton
Professor

PS. If your farm no longer grows corn and soybean, please answerjust the first question and
return the questionnaire in the prepaid envelope.

143

APPENDIX EIGHT: Survey Returns, Response Rate and Completion Status

144

 

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Q

SURVEY RETURNS, RESPONSE RATE AND COMPLETION STATUS

Returned surveys were counted to track sample status and response rate. Peak
survey returns occurred a few days after each mailing and illustrate the importance of
multiple mailings to ensure a high response rate. Figure 1 shows the number of returned
surveys each week.

Appendix Figure 1. Number of Returned Surveys By Week

LEGEND:

 

7001 *** ** * A-Feb16
B-Feb22
C-Feb27,28
D-Mar6,7
E-Mar11,14
F—Mar19,21

G - Mar 24, 26, 28,

 

 

 

 

H-May16

Questionnaires were completed and returned by 1747 individuals. An additional
59 individuals returned the survey but refused to answer, 1194 surveys were not returned,
and 5 were eliminated from the sample for the reasons listed in Appendix Table 12 next

page. The net response rate was 56.36 %.

145

Appendix Table 12. Final Status of the Survey about Crop Management and

Environmental Stewardship

 

 

 

 

 

 

 

 

 

 

Status Number Percent

Surveys returned
1747 58.23 %
(Returned and answered)
Refusals (Returned but
59 1.97 %

refused to answer)
Surveys not returned 1.194 39.80 o/o
Eliminated:
Undeliverable 2 0.07 %
Deceased 3 0.10 %
TOTAL SENT 3000 100 %

 

 

 

COMPLETED QUESTIONNAIRES = Surveys Returned — Refusals

NET RESPONSE IRATE

= Completed Questionnaires x 100
Total Sent-eliminated

146

11

1688

56.36%

 

 

APPENDIX NINE: Survey Weights

I47

 

SURVEY WEIGHTS

Since each stratification level is representative of an unequal number of farmers,
the survey design results in unequal selection of probabilities. In order to estimate
descriptive statistics that are representative of the target population and avoid sample
bias, each observation must be appropriately weighted.

The sample weight corrects for the difference in selection probabilities by

stratum. To determine the sample weight, observe that each stratification level i (where i
= l to 4) represents a separate subsample, S ,- from the total sample, S , and that the total
population represented by each stratum is given by n,- from the total population of

N (Deaton, 1997). We can define the weight, W, , for farmer l as the ratio of true shares

to sample shares,

ni/N
w:—
' si/S (1)

148

 

where the true shares, ’7,- / N , and sample shares, Si / S , represent the selection

probabilities from a simple random sample and from our stratified sample. These survey
weights ensure that the sample should sum to the sample size and average to one (where
these sums and averages are taken across the 1688 cases). Appendix Table 13 presents

the sample selection and sample weight calculation by stratum.

Reference:

Deaton, A. 1997. The Analysis of Household Surveys: A Microeconometric Approach to
Development Policy. The World Bank: Washington, DC.

149

 

 

 

 

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APPENDIX TEN: Some Descriptive Results from the Farm Survey (Essay 2)

 

151

Adoption of several individual cropping practices is shown in Appendix Table 14.
A large portion of farmers has never tried no tillage during 4 or more consecutive years,
although a large portion are currently using no tillage in some years. Moreover, large
percentages of farmers are currently using reduced tillage, manure application, wheat in
rotation, post-emergence herbicides and scouting for pests. Large percentages of farmers
have never tried cover crops, PSNT to guide nitrogen application rate and band

application of herbicides.

Appendix Table 14. Corn and Soybean Land Practices Adoption (unweighted)

 

 

Previously
Currently Tried and
Using Abandoned Never Tried

No tillage for 4 or more consecutive years 31% 15% 53%
No tillage in some years 55% 19% 26%
Reduced tillage (compared to moldboard plow) 82% 12% 6%
Apply manure. 56% 17% 27%
Include wheat in com-soy rotation 65% 18% 17%
Plant any cver crop before com 19% 26% 55%
Plant legume cover crop before com 15% 19% 65%
Use PSNT to guide nitrogen application rate. 19% 16% 65%
Band apply N fertilizer over rows at MSU recommended
rates. reducing total fertilizer use to 2/3 full field rate. 22% 10% 69%
emergence) 55% 25% 20%
Scout for insect pests to guide pesticide decisions. 87% 4% 9%
Band apply herbicide and insecticide over rows at label
rates, reducing total pesticide use to 2/3 of full field rate. 21% 24% 55%

 

 

Source: Crop Management and Environmental Stewardship Survey

Some of these practices are bundled into 4 different cropping systems as

presented in section Table2.1 and used in the survey questionnaire.

152

Reasons for taking part and not taking part in the proposed programs are given in
Appendix Table 15 and Appendix Table 16. An optional open ended question on reasons
for non-enrollment was presented after the dichotomous questions. Clearly, based on the
frequencies in Appendix Table 15, profitability of the system or the financial and
practical reasons seem to be most important for non-enrollment. Farmers said that some
of the practices in the cropping system offered. to them are not feasible due to lack of

physical capital.

Appendix Table 15. Frequency of Reasons for not taking part in the countryside stewardship
measures (unweighted)
Cropping Cropping Cropping Cropping
I did not enroll because System A System B System C System D
I do not find it profitable. 462 401 349 314

I do not agree with activity(s) in this system. It is

not feasible in my case (I.e. lack of physical

capital, etc.) 326 296 257 381
This practice does not do any good for the

environment. I do not believe that I am a direct

contributor for global warming! 13 2 3 6
There's too much control and too much hassle. 46 36 32 51
I do not want to deal with the government. 19 16 20 32
This is risky. 9 4 ll 20
I am about to retire. 14 9 9 14
I need more information. 16 17 20 21

 

Source: Crop Management and Environmental Stewardship Survey

Appendix Table 16. Frequency of Reasons for taking part in the countryside stewardship
measures (unweighted)

Cropping Cropping Cropping Cropping

 

I enrolled because... System A System 8 System C System D
I am currently doing this. 93 34 42 37
Ijust want to try it out. 35 90 107 63
This is really good, economically and
environmentally. 14 5 16 35

 

Source: Crop Management and Environmental Stewardship Survey

153

 

A small number of farmers also did not recognize the link with protecting the
environment or indicated that they do not believe that their farming is a direct contributor
to global warming as shown in Appendix Table 15.

Appendix Figure 2 presents the frequencies on how the farmers saw the
importance of different ecosystem services for the individual and the society. Using a 3
point likert-scale of importance where I-highly important, 2- somewhat important and 3-
not important; so when difference (society rating minus individual rating) were taken,
negative values meant more important to individual point than to society. The ecosystem
services were increasing soil organic matter, increasing soil conservation, reducing
phosphorus surface run off, reducing nitrate leaching, reducing global warming and
reducing pesticide risk to human health. Farmers rated the increased soil organic matter,
enhanced soil conservation, reduced phosphorus runoff, reduced nitrate leaching and
reduced pesticide risk more important to themselves than society with 0.01 significance
levels for paired difference t-tests. By contrast, reduced global warming was found to be
much more important to society than to themselves. Benefits from soil organic matter and
soil conservation contribute directly to crop productivity. Reduced global warming has

the most diffused benefits of all.

154

 

 

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155

From the reasons listed in Appendix Table 15, some farmers find that there is too
much in the type of program being offered and a portion of farmers even said that they
did not want to deal with the government. In fact a number of farmers mentioned that
they are enrolling a small portion of their land just to try the program out. They feared
less flexibility if enrolled in the program. In Appendix Table 16, farmers who enrolled in
the program are either doing almost the same cropping system or they believed in the
benefits of the system.

Although Appendix Tables 14, 15 and 16 were unweighted numbers and
frequencies, they clearly illustrate that there is a portion of farmers who are willing to

adopt, but the follow up question on reasons for decision revealed that there is also a

portion of farmers who are still not convinced about the appeal of such measures. The big

difference in reaction to both questions — the higher non-participation and negative
reactions — and the link between the farmers’ characteristics- are further analyzed in the
probit model and truncated models in the text.

Appendix Table 17 shows the weighted mean estimates the two categories of
sample respondents differ in various aspects. It also presents also the p-values for an

equality mean t-test between participants and non-participants.

156

 

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