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METHODOLOGY FOR ECONOMIC AND ENVIRONMENTAL
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Tracy Lynn Irwin

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METHODOLOGY FOR ECONOMIC AND ENVIRONMENTAL ASSESSMENT OF
CROPPING SYSTEMS: A MICHIGAN CASE STUDY

By

Tracy Lynn Irwin

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

Department of Agricultural Economics

1992

ABSTRACT

METHODOLOGY FOR ECONOMIC AND ENVIRONMENTAL ASSESSMENT OF
CROPPING SYSTEMS: A MICHIGAN CASE STUDY

By

Tracy Lynn Irwin

Since the Food Security Act of 1985, farmers’ decision making environment has
changed significantly. Farmers need more information on the economic and environmental
tradeoffs of alternative cropping systems to make efficient decisions. This project developed a
method for designing and simulating economic returns and environmental characteristics of
alternative production systems. An interdisciplinary team, including a farmer, was active in
all stages of project development. The SMART-FRMS system was used to simulate the
environmental characteristics and a Net Present Value model was constructed for economic
comparisons between systems. This case study included comparisons of conventional and no-
tillage systems, hairy vetch and anhydrous as nitrogen sources, and the impact of including an
alternative crop in a rotation. The no-till systems were found more profitable with less
environmental impact than the conventional system. Anhydrous was determined to be the
least expensive nitrogen source, and the alternative crop reduced the profitability of the

system.

11

ACKNOWLEDGEMENTS

This thesis would not have been possible without the help and support of many
individuals. I would like to thank Dr. Luanne Lohr, my major professor, for her insightful
guidance and encouragement throughout my graduate program and thesis. I would also like
to thank Dr. Richard I-Iarwood, Endowed Chairman of Sustainable Agriculture at MSU, for
his financial support of this research project, and for his commitment to interdisciplinary
research. I am also grateful to Dr. Gerald Schwab for his patience and encouragement while
I studied farming methods and terminology. Special thanks to the participating farmer, his
family and employees for their active participation and cooperation throughout this project.
Their insight and enthusiasm made this project particularly rewarding. Many thanks to other
members of the interdisciplinary team including Dr. Vernon Meints, Sue Ellen Johnson, Tim
Pruden and Anne Conwell.

Above all however, I thank my husband, family and friends for their understanding,

support and love throughout my graduate school days.

iii

TABLE OF CONTENTS

Chapter 1 Introduction ............................................. 2
1.0 Problem statement and background ............................ 2

1.2 Objectives of this study ............................... 5

1.3 Sustainability defined ................................ 6

1.4 Introduction to the study .............................. 6

1.5 What is unique about this approach? ..................... 7

1.6 Outline of the thesis ................................. 8

Chapter 2 The Literature Review ...................................... 8
2.0 Introduction to the chapter .................................. 8

2.1 Computer based information systems ..................... 8

2.2 Optimization models ................................. 10

2.3 Net present value analysis ............................. 11

2.4 Participatory on-farm research ......................... 12

2.5 How this study incorporated these ideas ................... 13

Chapter 3 The Model .............................................. 15
3.0 Introduction to the chapter .................................. 15

3.1 Farmer decision making ............................... 15

3.2 Net revenue specification .............................. 17

3.3 Introduction to the net present value analysis (NPV) ......... 18

3.4 The basic NPV equation .............................. 21

3.5 A model for rotations of unequal length ................. 22

3.6 The transition period and the NPV model ................. 24

3.7 Calculation of the annuity: the basis for rotation comparisons . . 26

3.8 Summary of the NPV model ........................... 26

Chapter 4 Data Collection ........................................... 28
4.0 Introduction ............................................. 28

4.1 The project setting .................................. 28

4.2 Farm characteristies .................................. 28

4.3 Field and cropping system selection ...................... 29

4.4 Field descriptions ................................... 31

4.5 Rotation descriptions ................................. 33

4.6 Other assumptions ................................... 36

4.7 Collecting the data .................................. 37

4.8 Soil information ..................................... 38

4.9 Field treatment information ............................ 38

4.10 Nitrogen credit information ........................... 41

4.11 Yield information .................................. 41

4.12 Price information ................................... 44

,4.13 Other input information .............................. 46

4.14 Summary comments on the data collected ................ 46

iv

Chapter 5 Simulation Methods ........................................ 50
5.0 Introduction to the chapter .................................. 50

5.1 FIELD MANAGER ................................. 50

5.2 Introduction to SMART-FRMS ......................... 51

5.3 BUDGETOR ...................................... 52

5.4 PLANETOR ....................................... 55

5.5 Concluding comments on the SMART-FRMS system ......... 58

Chapter 6 Results of the Simulations ................................... 60
6.0 Introduction to the chapter .................................. 60

6.1 Field I ............................................ 61

6.2 Field 11 ........................................... 66

6.3 Field 111 .......................................... 72

Chapter 7 Conclusions .............................................. 78
7.0 Introduction to the chapter ................................. 78

7.1 Alternative cropping systems ........................... 78

7.2 Farmer participation in university research ................. 80

7.3 SMART-FRMS as a practical tool ....................... 81

7.4 Areas for future research .............................. 83

List of References .................................................. 85

1
Chapter 1 Introduction

1.0 Problem statement and background

Since 1985, farmers have been faced with a new set of economic constraints for
decision making. Given these changes, they need more information to make efficient
decisions. The purpose of this project was to develop a method for designing and simulating
economic returns and environmental characteristics of alternative production systems.

The pre-1985 decision making environment faced by farmers was distinctly different
than the current situation. Before 1985, soil conservation measures were not imposed on
farmers. The basic objectives of federal farm legislation during this period were to support
farm income and to secure a stable and reasonably priced food and fiber supply
(Reichelderfer, 1990). This was implemented by supporting commodity prices and farm
income, and by controlling the quantities of the commodities produced. However, these
policies had unintended consequences on environmental quality.

In 1984, the Environmental Protection Agency declared agriculture to be the largest
non-point source polluter of surface water in the United States (US. Environmental Protection
Agency, 1984). Surface and ground water were increasingly found to be contaminated with
inputs to and byproducts of agricultural production, such as nutrients, sediments and
agrichemicals. Farmers and nonfarrners began to question the amount and type of chemicals
used in farming, as well as farming practices and their relationship to the safety of drinking
water, pesticide residues on food and the rate of topsoil depletion. In response to these
charges, the Food Security Act of 1985 introduced the strongest environmental components of
any Farm Bill.

The Conservation Title in the Food Security Act of 1985, required farmers with

highly erodible land to submit a farm plan of Best Management Strategies if they wanted to

2

participate in the federal farm programs (Farnsworth, 1988). Certain states are supplementing
the federal program with their own provisions. In Michigan, for example, the Right to Farm
Act (1982, amended 1987) offers protection to farmers from liability for environmental
damages under several state regulations if farmers follow state approved Best Management
Guidelines for fertilizers, pesticides and manure. Other states are increasing the pressure to
restrict and ban the use of some agrichemicals (Reichelderfer, 1990; Aiken, 1991).

Another venue that impacts farmers’ decision making is environmental regulation.
The Environmental Protection Agency’s 1987 Water Quality Act provides voluntary
guidelines that would restrict farmers’ use of ground and surface water for waste disposal of
farm chemicals and sediment from run-off (Reichelderfer, 1990). Currently, these guidelines
are voluntary. However, some farmers anticipate that these may soon be required practices.

Public opinion about farmers has also changed. The public is concerned about
erosion and associated surface water quality degradation. In a national survey with 604
respondents interested in public issues (Guither and Seibold 1990), 90 percent supported
conservation compliance measures. In the same survey, 84 percent of the respondents
supported increased regulation of certain farming practices and land use to reduce pollution of
surface and ground water. Other public concerns from the survey include food safety and the
structure of farming (corporate vs. the family farm).

In standard economic theory, the farm is analyzed as a profit-maximizing firm.
However, we hypothesize that farmers are different because they live on the production unit
and in the rural conununity. Hence, they share many of the public’s concerns. Many
farmers feel as though they are stewards of the land and worry about issues like erosion,
worker health and food safety. Yet, unlike the general public, farmers and their families are
personally dependent on both the economic and environmental viability of the farm. They

worry that more restrictions on production may affect their family income. Consequently,

3

farmers were analyzed as utility-maximizers, where income is only one component of their
objective functions. Farmers were hypothesized to make decisions based on individual farm
family objectives (Hildebrand and Waugh, 1986), which may include full employment of the
family, worker safety and occupational choice.
1.1 Implications of these changes

The changing regulations and public opinion have had several impacts on farm
production decisions. First, farmers are uncertain about the availability and price of
production inputs (e. g., fossil fuels, agrichemicals and labor). Second, farmers with highly
erodible land, or land associated with vulnerable watersheds or groundwater supplies, may
need to make changes in production methods. This may necessitate a change in the chemicals
and tillage used, or the crops grown on the land. Production changes can be expensive
especially if new machinery and training in its use are required to make the change. Finally,
these new and complex regulations may discourage farmers from participating in the federal
farm programs. Since commodity programs are coupled with environmental requirements,
nonparticipants may have less incentive to comply with environmental provisions in the Farm
Bill.
1.2 Objectives of this study

This study has three objectives. The primary objective is to develop a method for
designing and simulating the economic returns from alternative production systems specified
through farmer-researcher collaboration. This approach exploits the farmers’ self-identified
constraints, whether monetary or nonmonetary, without requiring explicit modeling of the
constraints. The second goal is to provide this economic information to farmers to help
address their changing decision making environment. The third objective is to assess the
SMART-FRMS system, a farm-level environmental-economic planning software package, for

use by extension educators and farmers.

4

To achieve the first objective, a farm in Michigan was selected for a case study and
the farmer’s input was solicited to develop hypothetical alternative cropping systems. These
were characterized in economic and environmental terms. The economic characterizations
were done with a net present value model that permitted ranking the alternatives as
intertemporal investments. The alternatives were compared using the annuity equivalent of
their cash flows.

The environmental attributes of the systems were characterized using the experimental
SMART-FRMS (Center For Farm Financial Management, 1990b) software package. The
environmental indicators were expected soil loss and potential for run-off and leaching. This
component is important because environmental factors may alter farmers’ production methods.
Finally, economic and non-economic tradeoffs between the alternatives were evaluated. This
approach implicitly addresses both economic and noneconomic factors that enter the farmer’s
objective function, as detemiined by the farmer.

Under the second objective, the farmer was provided with the results of the
comparative analysis and encouraged to select the best system based on these characterizations
and his/her internal constraints. Internal constraints are farm objectives that may be difficult
or impossible to quantify and incorporate into a typical profit-maximization model.

The third objective, to assess the SMART-FRMS system for use by extension
educators and farmers, was accomplished by testing it throughout the project. Data needs and
collection methods were developed and the software was evaluated for ease of use. A final
section in Chapter 6 discusses this in detail.

1.3 Sustainability defined

In this study, sustainability was analyzed from the farmer’s perspective. In this

context, sustainable agriculture was defined as:

"farming that ensures an adequate net farm income to support an acceptable standard

5

of living for farmers while also underwriting the annual investments needed to

improve progressively the productivity of soil, water, and other resources." (Benbrook

p. 12, 1991)

This definition was used because it can be interpreted to include both monetary and
nonmonetary farm objectives. An acceptable standard of living depends on income and other
farm objectives, such as worker safety and full employment of family labor. Environmental
concerns, such as erosion and water quality, are also implicit in this definition and important
considerations in this project.

1.4 Introduction to the study

This study was part of an interdisciplinary research project designed to analyze
sustainable agriculture in Michigan. The (environmental-economic) analysis occurred as
agronomic field studies were being conducted by other members of the research team in
Michigan State University’s Crop and Soil Sciences Department.

The economic component of the research project focused on a field level analysis of
alternative rotations selected by the participating farmer in collaboration with researchers.
Actual field data was collected and organized into a computerized field record keeping system
owned by the farmer. More detailed economic and financial information for the farm was
collected and put into the SMART-FRMS system. Next, a present value model was
constructed and used to simulate budgets for the hypothetical cropping systems to make
intertemporal comparisons of the alternative rotations. Then, the environmental
characterizations of the rotations were derived using SMART-FRMS simulation capabilities.
Finally, the results were summarized for presentation of alternatives to the farmer.

1.5 What is unique about this approach?
There are Several factors that make this approach unique when it is compared to other

economic studies of farm production. Most notably, this model is non-optimizing.

6

Optimization models are restrictive because they usually do not account for farmers’ internal

constraints. Nonmonetary considerations may be difficult to quantify, but may be significant
in farmer decision making. If not captured by the model, these internal constraints may limit
the usefulness of optimization approaches.

The economic model used in this study explicitly recognizes the transition period,
often referred to as the "start-up" period. This is the time it takes to reach a biological and
management equilibrium after converting to a new production system. Simulation and
optimization models of alternative farming systems usually assume this equilibrium has
already been reached. By including the transition period in the analysis, more realistic
economic results fer the intertemporal comparisons are possible.

This analysis provides foundations for a whole-farm system on a field-by-field basis
that closely resembles the actual decision making process for a farmer. This incremental
approach is often adopted by farmers because it reduces the economic risk of a transition from
one system to another. This is particularly true in Michigan where soil type varies widely
across fields, thus results across the farm may be mixed.

Environmental factors are used to characterize the rotations and the on-farm cost of
erosion is included in the present value model. Rausser (1980) argues for a model that
includes the on—farm, economic tradeoffs associated with erosion. He also states that such

measures "should be generated endogenously. In this project, the type of farming practice
dictates the amount of the erosion penalty' (Slacre) which is included in the variable
production costs. This expands the costs, usually considered in systems comparison, beyond

simply production cost components.

 

‘ The erosion penalty is calculated by multiplying the physical quantity of expected
erosion (tons/acre) by the value of the nutrients lost in a ton of erosion. In this study, we
assumed the value to be $6.00/ton (discussed further in Chapter Four).

7

Finally, there was significant farmer-researcher interaction and interdisciplinary effort
among agricultural economists and agronomists in the development, planning and analysis of
the alternatives. Farmer participation is rarely solicited in research design, since the objective
of typical agricultural research is to develop recommendations.

1.6 Outline of the thesis

Chapter Two describes the literature reviewed in the course of this project: computer
based information systems, optimization models, present value analysis and participatory
research. Chapter Three presents the net present value model used for the intertemporal
simulation of the rotation budgets. It also includes the derivation of the annuity which is used
for the economic comparisons. In Chapter Four, the data collection process is discussed.
This includes the techniques developed to aid in the collection and organization of the data,
and a discussion of the computer based tools used throughout the project. Chapter Five
outlines the results of the analysis on a field-by-field basis. This chapter also includes tables
that summarize the economic and environmental results of the analysis. Finally, Chapter Six
includes the summary, a discussion of the major conclusions of the project, and areas of

future research.

8
Chapter 2 The Literature Review

2.0 Introduction to the chapter

The four main bodies of literature that were relevant in the development of this
project are computer based information systems (economic and agronomic), optimization
models in agricultural production, present value analysis, and participatory research. Each is
treated separately in the following sections.

2.1 Computer based information systems

Computer based information systems have historically been designed for farm level
use and have been problem specific. They usually answer either an economic question, or an
agronomic/biological question and are used as inputs to the farmer decision making process.
FINPACK (University of Minnesota) and TELFARM
2 are examples of programs that answer farm level economic questions. FINPACK conducts
short and long-run financial analysis that helps farmers make production decisions. It does
this from a strictly economic perspective, without consideration of environmental or
agronomic impacts on the system. TELFARM is an accounting and financial record keeping
system used for general business analysis. It was not developed for long-run planning and it
also fails to provide an environmental analysis of the system.

FIELD MANAGER (Horizon Computer Systems) is an example of a field record
system. It is a data base used to hold all production information on a field-by-field basis. It
is designed to receive both economic and agronomic information, such as the costs and
quantities of inputs used and crop yields. It does not calculate or record any environmental

information.

 

2 Organized by: The Department of Agricultural Economics, Michigan State University,
East Lansing, MI. Serviced through the Cooperative Extension Service.

9

Other types of problem specific computer programs designed for farmers include PIG
PLAN, SOYSTEM, SOYHERB, CORNSTEM and CORNHERB (all developed at Michigan
State University). Each of these programs calculates important information for farmers, such
as feeding and seeding rates given specific biological parameters and farm goals. However,
these programs do not conduct financial analysis or alert the farmer to potential environmental
problems given the cropping systems chosen.

There are several computer programs that analyze chemical movement in the soil.
Two examples of these are GLEAMS (Leonard et al., 1987), and LEACHMP (Wagenet and
Hutson, 1987). GLEAMS simulates pesticide transport and LEACHMP simulates pesticide
leaching in the soil given specific environmental parameters. These are important
environmental indicators; however, neither of these programs do any type of economic
analysis or erosion calculation given a cropping system or rotation.

The problem specific approach fails to answer some of the most difficult questions
that farmers face. Farmers want and need information on the simultaneous impact of their
decisions on several aspects of their farm operations. They need information on the economic
effects, the agronomic effects, and the environmental effects of their decisions. Once they
have this information, they can utilize problem specific information systems to answer
questions about particular options.

The SMART-FRMS system is different from the problem specific systems previously
mentioned. It can be used to show the tradeoffs between alternative cropping systems in
economic and environmental terms. It measures the components of a system by multiple
criteria, allowing the farmer to make an informed decision using multiple farm goals. The
SMART-FRMS program was selected for use in this study because it provides this

information.

10
2.2 Optimization models

The optimization models discussed in this section are nonclassical models, also
referred to as mathematical programming models (Chiang, 1984). They are used to maximize
a specified objective function subject to a set of constraints and may include stochastic
elements. Optimization models in this context use mathematical programming to simulate the
effects of alternative production decisions and select the choice that best meets the objective
function and constraints.

In farm level applications, these models generally assume that the objective of the
decision maker is to maximize profits, and that all constraints and objectives can be quantified
and included in the model or are summarized in stochastic unknown error terms. The more
elements of the objective function that are unknown, the greater the error in the results of the
optimization.

There are several types of mathematical programming. Linear programming has
linear constraints and objective functions. Nonlinear formulations may require quadratic,
separable, or nonlinear programming methods (Pfaffenberger and Walker, 1976). Multiple
goal programming allows more than one known criteria to be specified as an objective.
Dynamic programming is used to account for changes over time. It separates a problem into
a sequence of interrelated subproblems, each of which may be solved from the knowledge
gained by solving'its predecessors in the sequence (Pfaffenberger, 1976; Kennedy, 1986). If
elements of the objective function or constraints are not deterministic, stochastic optimization
methods may be specified.

All these categories of mathematical programming select a "best" choice, based on
optimization of the objective subject to constraints. Researchers use these outcomes to
describe recommended strategies. Examples in agricultural production include El-Nazar and

McCarl (1986), Burt (1981) and Kennedy (1986).

11

According to Ellis et ai. (1991), firm-level programming models have difficulty
predicting producer behavior. They attribute this to two main factors: there are alternative
producer goals or constraints that cannot be incorporated into the model, and that the relevant
resource constraints are incorrectly specified. These goals and constraints are often excluded
from the analysis because of modeling difficulties. As a result, profit maximization becomes
the objective of the analysis by default. Mathematical programming techniques offer a single
optimal solution, usually without explicit consideration of noneconomic goals and constraints
(e. g. environmental).

This analysis is based on a hypothesized objective function, but does not use an
optimization model because the goal is to provide information about the alternative production
systems rather than a recommendation. It is assumed that the farmer makes the best decision
given his/her constraint set. This avoids specification errors by researchers trying to model
the farmers’ decision process, yet still provides an important role for the research process.
This approach facilitates decisions, rather than dictating them.

2.3 Net present value analysis

Net present value analysis is a procedure widely used for intertemporal investment
analysis (Robison, unpublished). It was developed because decision makers needed a
systematic procedure for valuing cash flows that occur at different times in the life of an
investment. The mechanism designed for this valuation is discounting. Discounting is based
on the assumption that $1.00 today is worth more than $1.00 received one year later (this
reflects the opportunity cost). Net present value analysis uses discounting, via a discount
rate’, to convert future cash flows into current dollars. If a decision maker is considering

two (or more) investments of equal duration, their cash flows (revenue less variable costs) can

 

3 The discount rate is not equivalent to the interest rate. In this context it refers to the
risk-free investment rate, or the best alternative use of funds for the farmer.

12

be discounted and their net present values (NPV) can be compared. The investment with the
largest NPV is the one that provides the highest return on the investment. In this sense, NPV
analysis is an optimization tool.

This discounting effect is particularly important when analyzing alternative cropping
systems. When converting to a new production system there is often a significant initial cost
incurred‘. For reasons discussed above, the selection of the discount rate could determine
whether an alternative system is more profitable than a conventional system.

If a decision maker is interested in comparing investments of unequal length, NPV
analysis can still be applied. One technique is to repeat the investments an infinite number of
times, extending the cash flows into perpetuity, and then comparing their annualized NPV.
Otherwise, the longer investment is likely to have an inherent advantage over the shorter
investment, since, if net returns are positive in each period of the investment, more periods
will accrue to the investment with the longer duration. If both investments are repeated an
infinite number of times and their cumulative NPVs annualized, it is possible to avoid errors
in conclusions due to summing NPVs across unequal numbers of years.

NPV analysis can be readily applied to investments that meet criteria other than profit-
maximization as an objective of the decision maker. The NPV model in this study was
applied to an intertemporal comparison of alternative rotations to provide information rather
than recommendations.

2.4 Participatory on-farm research
As the agricultural production industry becomes more complex, there is an increasing

need for participatory on-farm research teams. Francis et al. argue that a "team" approach is

 

‘ The fixed costs of converting to a new system are not included in the model (Chapter
Three). However, a section in Chapter Six discusses how they can be incorporated into
future analyses.

l3

needed for objectives that are "too broad for solution by a single scientist or discipline. "
(1982, p.43). Laughlin (1991) comments that "[b]y posing problems and solving them
together, we have the opportunity to reach a new level of progress.” Participatory on—farm
research is defined here as a research project with significant farmer-researcher interaction
and interdisciplinary collaboration in the development, planning and analysis of the research
problem.

Lockeretz and Anderson (1990) argue that this approach is particularly important for
research on alternative production systems and sustainable agriculture because many of the
innovations used in alternative production systems originated with farmers. Consequently,
farmers help researchers ask the right questions and better specify the research model. Also,
sustainable agriculture is commonly thought to involve more active and sophisticated
information management by farmers. If so, the greater the degree of farmer involvement the
more empowered he/she becomes (Laughlin, 1991).

Other models of participatory research, (Tripp, 1982; Harwood, 1973; Rhodes and
Booth, 1982; and Chambers and Ghildyal, 1985)’, have different objectives, but as a group,
they differ from traditional laboratory models of farm research. The participatory research
models include a farm level research component and are characterized by sharing values and
information (Laughlin, 1991).

2.5 How this study incorporated these ideas

This study used an integrated computer information system to compare crop rotations
at the field level. Field budgets were constructed with the BUDGETOR component of the
experimental SMART-FRMS software and a NPV model was used for intertemporal

comparisons of the alternative production systems. The environmental impact assessment was

 

5 These models were cited in Laughlin (1991).

l4
done with the PLANETOR component of the SMART-FRMS program. This produced

information for simultaneous consideration of economic and environmental factors to aid in
selecting a production system.

This study does not implement an optimization model in order to derive an optimal
solution. Instead, it provides a characterization of the alternative rotations so that the farmer
can make the best decision given his/her internal and external constraints. An underlying
hypothesis of producer utility maximization is suggested, but only as a basis for the economic
model.

Active farmer participation and a cohesive interdisciplinary team were expected to be
key to the success of this project. The farmer and the interdisciplinary team collaborated in

the development, planning, implementation and analysis of the alternative cropping systems.

15
Chapter 3 The Model

3.0 Introduction to the chapter

This chapter presents a theoretical model of farmer decision making consistent with
the hypothesis that internal constraints play a major role in the process. The basis of decision
making was assumed to be utility maximization, rather than profit maximization. It was also
assumed that the individual farmer can identify the preferred alternative production system
using information about economic returns and environmental indicators, as well as his/her
own internal constraints.

The economic-environmental information was generated from a net present value
model of alternative cropping systems and from the SMART-FRMS simulation software. The
NPV model is developed in this chapter, with discussion of the computer simulation deferred
to Chapter 4.

3.1 Farmer decision making

As discussed in Chapter 2, most models of farmer decision making assume
optimization of deterministic or stochastic objective functions specifying maximization of net
revenue or expected net revenue. This approach assumes the farmer is a firm manager
operating under neoclassical profit maximization. We hypothesize that the farmer is
maximizing utility, and that monetary returns are only one component of that utility
specification. In addition, the farmer considers nonmonetary factors that are difficult to

quantify, but influence the choice of alternatives. The implicit form of this model is:

max E(U) =f(M;N)
s.t. M 2 I

(l)

where: E(U) is expected utility
M is a vector of variables representing monetary returns from farming

16

N is a vector of variables representing nonmonetary returns from farming
I is a minimally acceptable level of income from farming.

This model states that the farmer maximized expected utility, which is a function of both
monetary and nonmonetary considerations, subject to a constraint on income from farming.
The level of I is determined by family income needs and other sources of income available to
the family.

Taking total derivative of E(U) yields the first-order condition for maximization of
expected utility:

QEL‘DBL.Mif.=o . (2)
3f aM af M

The monetary component is hypothesized to be the traditional net revenue formulation, with
the inclusion of on-farm costs of environmental degradation, in this case, erosion. The
nonmonetary component is assumed to be known only to the farmer. In this case, it included
considerations such as full employment of family members on the farm, lack of availability of
skilled labor, interest in collaboration with university researchers and desirability of practicing
good land stewardship.

By assuming that only the farmer is capable of assigning weights to the nonmonetary
considerations, it is not possible to specify the explicit form of the objective function and to
optimize it. Therefore, this study focussed on generating economic and environmental
information for the farmer to use in making a decision among cropping alternatives. To this
end, a net present value model was formulated and simulated for each production system

option.

17
3.2 Net revenue specification

A net present value (NPV) model was developed to facilitate intertemporal analysis of
the alternative cropping systems selected in this project. The basis of this model is the net
revenue, defined as total revenue less costs, that accrues in each period.

Consider a single-year crop model where net revenue is defined as:

NetRevenue =(Pm)Y— C. - CI
(3)
where C. = C" + e Ce

where:

Pm is the market price of the crop ($/unit)

Y is the yield of the crop (units/acre)

C, is the variable cost associated with producing the crop ($lacre)

Cf is the annual fixed cost associated with producing the crop ($Iacre)

C, is the variable input cost of production ($lacre), excluding erosion

CC is the on—farm cost of erosion (Slton) in lost nutrients

e is the physical amount of erosion (tons/acre)
Total revenue is equal to the product of market price (Pm) and yield (Y), and is measured in
dollars per acre. Variable input costs (Co) are not assumed to be a function of yield. They
include all costs association crop production, such as seed, fertilizer, fuel, repairs, etc.

The on-farm cost of erosion, Cc, is measured in temis of the value of nutrients lost
with the soil, in dollars per ton. This cost is multiplied by the quantity of soil lost (measured
in tons per acre), to generate cost in dollars per acre for a specified cropping system.
Although soil loss may have negative effects on long-term field productivity, there are not
reliable indicators of the correlation between erosion and productivity changes over time.
Rausser (1980) noted that economic tradeoffs should have relevance to farmers, so nutrient
loss was used to represent year-to-year effects of soil loss from the farmer’s perspective. The
soil loss measure was generated endogenously in the environmental simulation used for this

study, and depended on soil characteristics and cropping practices selected, as suggested by

Rausser (1980).

18

By incorporating on-farm erosion costs, the monetary component of the utility
function in the previous section includes a visible effect of productivity loss, that is, nutrient
reduction. This is an extemality of production that is internalized by the farmer.
Nonmonetary private extemalities, including stewardship considerations and desire to improve
the farm for heirs, were assumed to be calculated implicitly by the farmer, and were not
specified in the net revenue model.

The total variable costs, C,, are composed of the sum of production input costs, Cu,
and on-farm erosion cost, C,. If the level of erosion increases, so do the total variable costs
of production. However, variable input costs are independent of the quantity of erosion. The
production input costs are assumed to change only with the crop and production methods
used, not with changes in soil loss. The annual fixed costs (Cf) are the payments to debt
reduction and overhead made each year. These might include payments on capital equipment
specific to a desired production system.

To do an intertemporal analysis of net revenue, the NPV model was used.

3.3 Introduction to the net present value analysis (NPV)

The NPV approach was chosen because it allows for an intertemporal comparison of
the cash flows associated with alternative cropping systems. This project treats cropping
decisions as a series of investments with multiple-year planning required for some
alternatives. NPV models convert future cash flows into current dollars, so that the net
revenues over the entire planning period may be counted. The mechanism for this conversion
is discounting.

When principal is invested at a given interest rate, it accumulates value according to
the period of compounding. If interest is compounded annually, then each year the principal
grows by an amount equal to the interest rate multiplied by the principal that existed at the

end of that period. In subsequent years, the interest is based on this accumulated principal.

l9

Discounting is the reverse of compounding. The relationship between the two for a one—

period investment is given by:

 

V(1 + r) = P
P = (4)
(l + r)
where: V is the present value of the investment

P is the principal accumulated

r is the market interest rate in the first equation and the private discount rate
in the second. As mentioned, the interest rate and the discount rate are not always equal.
However, unless an individual provides information about his/her discount rate, the rate is
usually assumed to be close to the market rate of interest. As a simple example, suppose
principal equal to $100 were invested at a market interest rate of 5 percent, compounded
annually. In one year, the investment would be worth $105. The present value, $100, in
addition to the accumulated interest, 5 percent multiplied by $100, gives the principal, $105.
Conversely, suppose at the end of next year, $100 in principal would have accumulated as a
result of an investment made now at the same market interest rate. At a discount rate of 5
percent, the investor would value that principal at $95.24 now. The principal at the end of
the year, $100, is divided by (1+r),a sum representing the investor’s time value of money,
105 percent (1.05 in decimal terms), to get the present value.

For multi-period investments, compounding and discounting may be accumulated by

summing across all periods, allowing for the increase in principal (compounding) or decrease

20

in present value (discounting) that occurs as the time period increases. With discounting, the
farther into the future the analysis is extended, the greater the accumulation of discount

factors :

 

 

. P1
Onepertod: = V
(1 + r)
P P
Twoperiods: l + —2— = V (5)
(1 + r) (l + r)2
, P, P, P
npenods: + —— + " =

 

OH) (1 +r)2 + (1 +r)"

where the subscripts on P and the superscripts on (1 + r) represent the period being
discounted, relative to the current decision period. The general form for the present value

model is:

R P;

(6)
1;: (l + r)i

 

where all terms are as previously described. This model was adapted for use in this analysis.

The alternative cropping practices specified by the farmer-researcher team relied on
crop rotations. A rotation is a sequence of annual crops planted on the same field over a
period of years. Farming in rotations using different crops has advantages in terms of
breaking up pest cycles and improving nutrient availability. Disadvantages may include
greater year-to-year income variation, relative to a monoculture, and greater management and
equipment demands. When the entire sequence is completed, it is repeated.

Typically, one thinks of an entire farm being divided into as many parcels as there are
years in the rotation, with the sequence being repeated across the farm as well as on the

individual parcels over time. Thus, all years in the rotation are represented on the farm in the

21

same year. For the single-field approach adopted for this study, the field was assumed to be
entirely planted to a single crop, following the rotation sequence.

Present value analysis can be done with either discrete or continuous discounting.
The discrete method is used in this model because discounting is done annually. In Michigan,
fields produce only one crop per year; subsequently, there is one cash flow accounting period
per year, rather than a continuous change in investment value. In this case, the discrete
model more accurately represents the system. However, in mathematical terms, discrete
models can always be converted to continuous models (Chiang).
3.4 The basic NPV equation

The basic net present value equation used to analyze the alternative cropping systems

is shown below:

m)Y'n,'(C +eieiC +C)
NPV=;::O(P"'(1;r)‘ (7)

 

where:

i marks the year in the rotation sequence
1 is the length of the rotation sequence

P». is the market price of the crop in the 1‘" year ($lunit)
Y". is the annual yield of the crop in the i"' year (units/acre)
C are the variable input costs in the 1’” year ($lacre)

e is the amount of erosion per year, in the 1" year (tons/year)
C is the on—farm erosion cost of lost nutrients, in the 1" year ($lton)

C is the annual fixed cost payment in the f" year ($lacre)
,- is the farmer’s discount rate

This equation represents the net present value of one complete rotation on a single field.
The cash flow each period is calculated by subtracting total cost from total revenue.
This is divided by the discrete discounting factor shown in the denominator. The cash flows

are discounted each year of the rotation and their discounted values are summed over the

22

length of the rotation.

This model can be used to compare rotations of equal length. The rotation with the
highest NPV offers the highest accumulated dollar return in discounted temis. If one rotation
is longer than the other, and if net revenue is positive in each period, the longer rotation may
have an implicit advantage over the shorter rotation because the accumulation of net revenue
occurs over a longer period.

3.5 A model for rotations of unequal length

In the basic equation, where discounted revenue is summed over the length of the
rotation, the longer rotation may appear more profitable than the shorter rotation. One way
to compensate for this advantage is to extend the rotations into perpetuity and then compare
their NPVs. The single rotation model may be extended into perpetuity by modeling infinite
repetition of the rotation crop sequence. The net revenue for the ith year of the rotation, R,,
is:

(8)
Let R, = (PM, + Pd!) Y, - (Cm + e, C,‘ + Cf)

Where all terms are as previously defined. For a single rotation, the discounted net revenue,

NPV, is:

' R. (9)
NPV = __'_
1;] (1 + r )‘

Where r is the farmer’s discount factor, and r is the number of years in the rotation sequence.
When this rotation is repeated into perpetuity, the discounted net revenue may be compared to
other rotations, whether of equal or unequal rotation length. If repeated an infinite number of

times, the net revenue obtained from the rotation is:

23

on r R (10)
NPV=2 [2 I ](1+r)-nr

n-O '-l (1+r)‘

 

Equation (10) describes two steps: 1) the annual cash flows are discounted and summed for
the single rotation, and 2) the net present value of each complete rotation is discounted and
summed over the infinite series of repetitions.

The discount factor for the infinite series, (1 + r)”, has the same form as the
discount factor for the single rotation, (l + r)‘, except for the exponents. For the single
rotation, the discount factor is applied to cash flows at the end of each crop year in the
rotation, i = l,...,r, since net returns are accrued once in each period, when the crop is
harvested and sold. When the rotation sequence is repeated, the N PV for the complete crop
sequence is discounted at the end of the rotation, in year 1, and is summed over perpetuity, n
= 01', 11', 21,...,oor.

Expanding the sum in equation 10 gives:

(11)

NPV=[: Ri ] [1+(l+r)"+(l+r)’2’+(l+r)'3’+...]

i-1(1+I')i

The infinite series converges to:

I R. 1 (12)
NPV = '
1:1 (1 + r)i [ 1 -(1 + r)”]

 

 

Or, by substitution:

 

 

' (P,)Y,. - (C,+e,.c,+cf) 1 (13)
NPV: . . .

12.1 (1H)i 1-(1+r)"

24

Equation (13) allows rotations of unequal length to be meaningfully compared. However, this
model assumes the rotation has reached a steady state equilibrium. That is, penalties in yield
or cost due to agroecosystem adjustments or management errors while learning new practices
are not explicitly accounted.

3.6 The transition period and the NPV model

When a new cropping system is adopted, it is expected to take some time to reach a
steady state equilibrium. This period, before an equilibrium is reached, is referred to as the
transition period. The two main reasons for this transition period are natural perturbations
from weed and pest populations, as they adjust to the new cropping pattern and management
errors while learning the new system.

The transition period may be any length of time required to reach equilibrium. The
steady state may be achieved within a single rotation, or may require more than one rotation.
Once the transition is completed, all subsequent complete rotations are assumed to be
identical, and are discounted from that point in time to perpetuity, as in Equation (13). Prior
to equilibrium, from the beginning to the end of the transition, each rotation in the transition
period is separately discounted, to account for the changing yield and cost penalties as pest
populations stabilize and as farmers learn more about the cropping system. To accurately
represent the cropping systems, the transition period was incorporated into Equation (13).

For this study, the main changes from existing production systems were the institution
of rotations of varying lengths and crop sequences, and the planned used of no-till cultivation.
With little agronomic data to support a hypothesis about the length of transition period, the
farmer estimated steady state could be achieved within one rotation.

In the following equation, the transition period is assumed to be one complete
rotation, of 7 years. Once equilibrium is reached, the transition period is never repeated.

The model with a one-rotation transition period is:

25

. (Pm)Y. - Cn+e.Ce+C)
NPV=2 ,1 (' .” ’
j-I (1+r)’

 

(14)

T (Pm,)Yt' — (Cn,+eiCe,+Cf) (Id-7)-,

+ 2

1.1 (1+r)i 1-(1+r)"

 

where j marks the year in the transition rotation, i marks the year in the equilibrium rotation,
extended into perpetuity, and all other terms are as previously defined. The lengths of the
rotation in both the transition and equilibrium periods are the same, as is the crop sequence.
Although aggregate yield effects of similar production changes on all farms could alter
the market price for the crop, this analysis focussed on the field level so prices were
exogenous to the model. Costs were also assumed to be independent of yield, since the
farmer was assumed to follow the same input use patterns whether the field was in the
transition period or the equilibrium period. In reality, with the cropping systems used in this
model, input use should be sensitive to pest pressures and management expertise. However,
without modelling these changes explicitly in a dynamic system, it is not possible to model
responses to them. The erosion rate, e, depends on the cropping system selected, and
changes with the year in the rotation, since crops and tillage may differ by year. The erosion
rate was generated exogenously by the SMART-FRMS simulation of environmental impact.
Costs and prices were assumed to be constant over time. This assumption was due to
inability to predict prices and costs though the next Farm Bill, let alone through perpetuity,
rather than because it is a maintained hypothesis. Since the goal of this project was
comparison of alternative cropping systems, and all systems were subject to the same relative

price and cost differences, this was a reasonable simplification for analysis.

26
3.7 Calculation of the annuity: the basis for rotation comparisons

Equation (14) can be used to compare rotations of unequal length while accounting for
the influence of the transition period on NPV. The rotation with the highest cumulative NPV
is the one that offers the highest discounted net return.

Although this is often calculated in economic analyses of alternative projects, the NPV
of a cropping sequence is not commonly used in farm analysis. Farmers are used to thinking
about a cropping system in terms of the expected annual cash flows. To make the analysis
more familiar to the farmer, the annualized value of the NPVs were calculated for the
comparison. An annuity approximates a constant cash flow equivalent to the NPV.

The annuity does not represent the actual discounted net returns that accrue in each
year, or in each rotation. Annual returns differ as various crops are grown, and rotational
returns differ when the system moves from the transition period to the equilibrium period.
However, the analytical approach is still valid, because it provides a mechanism for
comparing the alternative cropping systems in terms with which the farmer would be more
familiar, namely, annualized returns. Recognizing the simplification being made, the annuity,
was calculated as the NPV over all rotations to infinity, multiplied by the discount rate used
to determine the NPV. This annuity equivalent was used to make economic comparisons of
the alternative cropping systems.

3.8 Summary of the NPV model

NPV analysis was used to address the monetary component of farmers’ expected
utility which was assumed to be only one of the criteria for decision making. The other
component of the utility function depends on nonmonetary returns from farming which were
assumed to be known only by the farmer, therefore; they could not be included in an
optimization model.

The NPV model is needed to do an intertemporal comparison of the rotations. The

27
SMART-FRMS system helps organize and simulate much of the data used in the NPV model

but it does not provide information to make an economic comparison of the alternative
cropping systems.

The standard equation (7) can be used to compare rotations of equal length.
However, it might bias results in favor of a longer rotation over a shorter one when
comparing rotations of unequal length. Equation (13) compensates for this by extending the
rotations into perpetuity before comparing their NPVs. The transition period is incorporated
into the model with equation (14). This period only occurs once as the system is being
established, and was assumed to be one rotation long in this analysis.

Finally, the NPV was converted into its armuity equivalent for comparison with other

rotations.

28
Chapter 4 Data Collection

4.0 Introduction

This chapter describes the project environment, the selection and description of fields
and cropping systems for study and data collection. The project setting, farm characteristics
and farmer objectives were in large part determined the study design. Two separate software
programs were used to collect data and simulate partial budgets for the fields and cropping
systems, as well as environmental impacts.

4.1 The project setting

The study site for the project was Ingham County, Michigan. The US. Census
Bureau, (Census, 1987) reported that in this county there are 859 farms with harvested
cropland. This accounts for 48 percent of the total county acreage (Michigan Department of
Agriculture, 1990). The average farm size in the county is 152 acres and 50 percent of the
farmers there are full time farmers (Census, 1987).

In 1987, 89 percent of the harvested cropland was planted to four main crops: com
(42 percent), hay (20 percent), soybeans (17 percent), and wheat (10 percent). There are
yearly fluctuations in the crop mix due to price expectations, and structural changes every five
years due to the commodity provisions in the Farm Bills.

The soil in this region is highly variable across farms and across fields. The topsoil is
generally considered loamy with 27 to 35 percent clay in the subsoil (Hicks, 1992). The
predominant soil in the region is Capack Loam at 58,000 acres.

4.2 Farm characteristies

The farm that was the subject of this study has over 1000 acres, a trait shared by less

than 10 percent of the farmers in the county (Census, 1987). Three family members,

including the owner-operator, work full-time in farming with some of the labor applied to a

29

beef cattle operation that is not part of this study.

The main crops grown on the farm are similar to the county profile. In 1992, corn
was grown on 58 percent of the tillable land, soybeans on 25 percent, alfalfa 10 percent,
canola 3 percent, and 4 percent of the land in set aside. Currently, rotations of corn,
soybeans, and alfalfa are being established; however, before 1991 rotations were not used.

The predominant soils on the fields selected for the analysis are typical of the county.
They are Capack Loam, Marlett Fine Sandy Learn and Owosso Marlett Sandy Loam.

Capack Loam has land use classification code Ilw and is in management group 2.58. Its
average slope is 0 to 3 percent with average slope length of 100 feet. This soil is limited by
excess water. The US. Soil Conservation Service recommends tiling this soil for better
drainage as it tends to be wet. Marlett Fine Sandy Loam is the third most common soil type
in the county and is classified as a moderately-, to well-drained soil. Its land use capability
class is He, management group 2.5A. The average slope is 2 to 6 percent, with average slope
length of 100 feet. This soil is limited by erosive conditions. Owosso Marlett Sandy Loam is
the seventh most common soil in the county and is considered to be a moderately- to well-
drained soil. Its land use capability is He, management group (3/2)a. The average slope is 2
to 6 percent with average slope length of 100 feet. This soil is also subject to erosion
problems.

4.3 Field and cropping system selection

The fields and cropping systems used in the analysis were selected in a group meeting
before the 1992 planting season. This interdisciplinary group was made up of the farmer, his
private consultant, three representatives from Michigan State University’s (MSU) Department
of Agricultural Economics and three representatives from MSU’s Crop and Soil Sciences
Department. From Agricultural Economics, there was a farm management specialist, an

environmental and public policy specialist and a graduate researcher. From Crop and Soil

3O

Sciences there was a sustainable agriculture and nutrient specialist, a field technician, and a
graduate researcher.

The interdisciplinary team was assembled to provide recommendations and answer
questions that the farmer may have had in deciding on the systems for the analysis. The
agronomic and economic—environmental analyses were to proceed simultaneously, so field and
cropping system selection needed to meet requirements for the farmer, the economists and the
agronomists. However, the farmer was the final decision maker.

Existing fields were reviewed for possible inclusion in the study. There were three
major considerations in the field selection, all related to potential influence on environmental
quality. First, the fields were selected to represent different environmental conditions
(potential for run-off and leaching) that are typical of the farm. Second, they were to
represent various levels of erodibility. This potential was estimated by considering soil type,
average slope and average slope length on each existing field. The final consideration in field
selection was proximity to the cattle operation and possible nitrate problems. Originally, the
study planned to integrate the fields and cattle operation; however, this component was
cancelled because there was a delay in the cattle purchase.

The farmer expressed substantial interest in crop rotations and "no-till" farming as
alternative system attributes. Logan (1990) defines no-till as a production system where the
soil is left undisturbed prior to planting. Planting is completed in a narrow seedbed about 2
to 8 centimeters wide. Given the farmer’s interest in the economic and environmental
viability of no-till systems, the rotations designed for analysis were no—till; one conventionally
tilled system was included as a control.

There were five criteria for selecting the rotations for the analysis. First, they
represented rotations that the farmer was interested in comparing in economic and

environmental terms. Second, they answered questions the farmer had about new practices

31

or crops. Third, they reflected the research needs of the researchers and farmer. Fourth,
they represented rotations of crops common in mid-Michigan. Finally, they were feasible
given the farm’s labor and equipment constraints. Three fields and eight different rotations
were selected for analysis: three rotations for field 1, five rotations for field 11, and two
rotations for field 111. Some rotations on these fields included the same crop sequence, but
different assumptions about nitrogen carryover.
4.4 Field descriptions

Two tables summarize the field and rotation descriptions described in this section.
Table 1 describes the attributes of the three fields selected for this study, including size,
predominant soil series, percent organic matter, average slope, average slope length and
available field history. Field I is 24 acres, while field 11 is about 124 acres and field III is 60
acres. The field boundaries followed the farmer’s existing designations. The soil types
represent the major soil series present on the farm. As discussed, drainage is a restriction on
the Capack Loam, which is the main soil on field II, while erosion may limit practices on the
Owosso Sandy Loam and the Marlett Fine Sandy Loam making up fields I and 111,
respectively.

Organic matter is important in this study because it influences plant growth in many
ways.
”The greatest benefits of organic matter in soil are its water-holding capacity; the manner in
which it alters soil structure to improve soil tilth; its high exchange capacity for binding and
releasing some mineral nutrients; and its mineralization to nitrogen, phosphorus, and sulfur.
The cycling of mineral nutrients between living organisms and dead organic components of
the soil system provides an important reservoir of the elements needed in plant growth."
(National Research Council, p.143, 1989)
This attribute is varied across the three fields. Field I has the highest level at 3.3 percent.
Field 11 has 2.8 percent organic matter and field 111 was estimated to have 1.5 percent, based

on default information contained in the SMART-FRMS data base.

32

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are... 2 383

 

 

 

 

 

 

 

 

 

 

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8.03 on money 03% 59.0. 98225 98803 3:8: motomv $0.53
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33

Average slope and slope length are similar across the three fields, with the format
ranging from three to four percent, and the latter estimated at 100 feet. The steeper and
shorter the slope, the more likely a field is to experience erosion problems. The three fields
in this study are not very steep on average, but have fairly short slopes. However, these
values are typical for the county Michigan (Hicks, 1992).

The field history gives an idea of soil condition and of the nutrient and pest carryover
that may be expected for the transition rotation. Field I was in alfalfa for eight years prior to
the study. Field 11 was set aside in compliance with federal commodity program requirements
in 1989, then planted to corn in 1990 and 1991. Field 111 was in corn in 1990, and was set
aside and seeded to alfalfa in 1991. There could be nitrogen carryover from Fields I and 111
where alfalfa, a legume, was previously grown. Field H may be easier to plant into, but is
likely to have lower, if any, nitrogen carryover, and potentially greater insect problems if
corn is planted for a third year. These considerations affected the rotation selection and input
decisions.

4.5 Rotation descriptions

Table 2 shows the cropping sequences selected for the rotations on each field. As
mentioned, the rotations selected were primarily no-till systems. The corn, soybeans, and
wheat are no-till and the canola and the alfalfa are conventionally tilled. Crop sequences were
selected for comparison of environmental and economic objectives, as well as for agronomic
interest.

Field 1: The first rotation assumed for field I was a six-year rotation - com, com,
soybeans, and three years of alfalfa - designated IA. The second rotation, IB, was identical to
the first one except that the first-year corn was inter-seeded with hairy vetch. This legume
was assumed to provide a 20-pound nitrogen that substituted for chemical fertilizer as an

available source of nitrogen. The third rotation, IC, was the same as the second except that

34

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£32 £980 30:3 maggom E00 500 £52 £32 £32 5:
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25%
30:3 mcaonxom Eco 5.023 500 Q:
356
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R055 mca0§om E00 :80 m:
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35% :29
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358 :20.»
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a 30> w 50> N. 30> o 30> m 50> v 50> m 23> N 30> L 50> 8.533—

8238: 05 c8 3.02% 88038 3:520 05. .Né 03$.

35

the hairy vetch was assumed to provide a 90-pound nitrogen credit.

The objective of designing these rotations was a comparison of the effects of including
hairy vetch in the rotation as a nitrogen source and a soil cover under differing assumptions
about nitrogen value (IB vs. IC). Exclusion of the legume (IA) provides a baseline for the
comparisons.

Field II: The first rotation designed for field 11, designated IIA, was a traditional
two-year corn, soybean rotation. The second rotation, IIB, was com, com, soybeans, wheat.
The third rotation, IIC, was identical to HB except that hairy vetch was interseeded into the
first-year corn and assumed to provide a 20-pound nitrogen credit. The fourth rotation, IID,
was the same as IIC except that the vetch was assumed to give a 90-pound nitrogen credit.
Finally, The fifth rotation, IIE, was com, com, soybeans, wheat, canola, and was designed to
explore the use of canola as part of a rotation system. A final rotation on this field, HZ,
included only corn and soybeans. It differed from rotation 11A in that it assumed a
conventional tillage system. This crop sequence and tillage method are common in southern
Michigan.

The objectives of these alternative systems were; 1) to evaluate an alternative crop
(canola) and its affect on the rotation (IIB vs. IIE), 2) to examine the economic and
environmental effects of including hairy vetch in the rotation at varying nitrogen credit levels
(IIC vs. 11D), 3) to compare rotations typical of the region under different tillage systems.

Field 111: The first rotation on field 111, designated IIIA, was a seven year rotation:
com, com, soybeans, wheat, alfalfa, alfalfa, alfalfa. The second rotation, IIIB, was the same
except canola followed the wheat in year eight: com, com, soybeans, wheat, canola, alfalfa,
alfalfa, alfalfa. The objective of this comparison was to evaluate the potential benefits of

including canola in a relatively long rotation (IIIB vs HIA).

36
4.6 Other assumptions

Since production methods may vary by region, assumptions had to be made about
specific systems used in this region and study. In Michigan, only one crop is harvested from a
field in a given year due to weather constraints. Corn and soybeans were assumed to be
planted in the spring and harvested in the fall. Wheat and canola were considered frost
seeded and harvested mid-summer. In this system, we assumed that the alfalfa was seeded in
the spring, which allows for two cuttings in the establishment year and three cuttings in the
two post-establishment years.

Timing of production activities was assumed to fall within optimal windows of
activity. For most crops, planting and harvesting activities are weather dependent, so yield
penalties for lateness could result. Lack of information on rotations prevented analysis of
these timing effects. Weed and insect pressures were assumed to be those of an average year.

Hairy vetch is not typically included in rotations in this region. However, it was
included in the four rotations in this study because researchers in the MSU Crop and Soil
Sciences Department believe that it may be a valuable source of nitrogen and soil conditioning
agent. Assumptions about hairy vetch were based on existing literature and expert opinions,
as data on performance in these rotation systems was not available.

Hairy vetch was assumed to be aerial seeded at 30 lbs per acre into 12 to 14 inch first
year corn. The seed cost was assumed to be $31.50 per acre, plus $5.58 per acre for the
application fee (Lohr et al., 1991). The nitrogen it produces and fixes in the soil benefits the
second-year corn. Expert opinion is divided on the amount of nitrogen credit attributable to
the vetch. As result, the only difference between rotations, IIC and HD, and IB and IC, is
that the nitrogen credit from the vetch was assumed to be 20 pounds per acre for [B and IIC
and 90 pounds per acre for IC and IID. This credit reduces the amount of anhydrous

ammonia application on a nitrogen-equivalent basis at side-dressing time.

37

By including hairy vetch in the rotation, the herbicide program that the farm used
was also assumed to change. In the first-year corn, one banded application of pre-emergence
herbicide was assumed at a cost of $6.12 per acre and one mechanical cultivation for weed
control at $8.30 per acre (Woods, 1992). The following year, before the second-year corn is
planted, the vetch must be killed with a contact herbicide.

The C—factor, calculated for use in the Universal Soil Loss Equation (USLE), is also
affected by the hairy vetch. Since hairy vetch was assumed to remain on the field after the
corn was harvested, it added to the field cover over the winter (the C-factor is higher). This
helped reduce erosion, included here as an on-farm variable cost of production.

Finally, it is critical to note that in this study, hairy vetch was valued only as a
nitrogen source. There may be other benefits of including hairy vetch in a rotation, such as
its contribution to organic matter, and soil nutrients. However, until those benefits can be
measured and quantified, they are difficult to incorporate into an economic analysis.

4.7 Collecting the data

The Period, 1987 through 1990, immediately preceded this research project. During
this time, production and financial records were kept by the farmer on a series of ledger
sheets. Gaps in the information were filled in by personal interviews with the owner, the
farm manager and the farmer’s consultant.

The production and financial data for the period, 1991 to 1992, was collected during
the course of this project. The primary venues for data collection from the farmer and
bookkeeper were planning meetings, telephone interviews, personal interviews, collected field
notes, and FIELD MANAGER, a computerized record keeping system, which was maintained
by the farm after assistance from MSU.

Some of the information needed to construct the budgets for the cropping systems was

not available from existing farm records. When this occurred, estimates of this information

38

was gathered from experts familiar with the project and with the farm’s production history.
Expert opinion based on existing literature and personal experience, was used when empirical
data was not available. The method of collecting the expert opinion relied heavily on the
cooperation of the interdisciplinary team and specialists in other departments at MSU.

Six main subjects required expert opinion for completeness: soil attributes, field
treatments, nitrogen credits, yields, prices and variable costs. The soil information was the
only topic not supplemented with a form specifically designed to aid in data collection. At
least one expert research collaborator was selected to provide information in each of the six
areas requiring supplemental data. When opinions differed, a third collaborator was consulted
and the more conservative estimates were used. This approach avoided exaggerated claims
for the hypothetical systems. Some of the forms were used in an interview setting; others
were mailed to the collaborators with a letter of explanation. When necessary, follow up
telephone contacts were used to clarify descriptions.

4.8 Soil information

The soil information came primarily from a personal interview with the District
Conservation Officer (DSO) for Ingham County. The DSO was familiar with specific soil
information on this farm and in the fields used in this project. He provided the necessary
information on the predominant soil type, the average slope and slope length, and the drainage
characteristics for each field. He also calculated the C-factors for all the rotations used in this
study, based on accepted SCS methods (Hicks, 1992).

The DSO also provided a figure for the on-farm costs of erosion. Based on his
discussions with farmers in the county, the cost of erosion to the farmer in terms of lost
nutrients was assumed to average $6.00 per ton. This value could fluctuate depending on soil

characteristics and carryover nutrients available in the soil at the time the erosion occurred.

39
4.9 Field treatment information

The field treatment information was collected in a personal interview with the acting
farm manager. The objective of this interview was to determine inputs used, by practice
applied, in order to allocate field treatment costs on a per-acre basis. This required
information on the basic components of production: seedbed preparation, planting, weed
control, insect control, harvest and drying, where applicable. The information targeted by the
interview was the specific tillage, the seeding rates, the application rates for fertilizer,
herbicides, and pesticides, the type of labor used for each activity, the specific farm
machinery used for each operation, and the expected time required to complete a treatment.

A sample of the form designed for use in this interview is shown in Figure 4-1. The
form listed the crop of interest (com), the field name used by the farmer (GR-12), the size of
the field (24 acres) and the type of tillage assumed (no-till). The collaborator was requested
to allocate the costs for each activity among machinery, labor and material inputs.
Information was also requested on special treatments necessary due to the previous crop, such
as when com follows corn or alfalfa, as in rotations IA, IB and IC. This information was
necessary for all of the crops proposed in the alternative rotations. The acting farm manager
received this form as well as information on the order of the crops in the rotations and the
field history, including previous yields.

Where actual field data was limited, estimates were received and discussed with the
farmer’s crop consultant. He provided specific information on seeding and chemical
application rates, based on field history, and assuming an average farming year. The
fertilizer recommendations were based on actual field tests when possible. This information

was used to generate per acre field level budgets.

4O

 

 

 

 

 

 

 

 

CORN
BUDGET FOR GR-12 24 ACRES
NO-TILL
ACTIVITY MACHINERY LABOR MATERIALS
' HRS. $/HR. QUANTITY $/UNIT
Swdbed
Preparation
Planting
Weed Control
Insect Control
Harvest
Dry
SPECIAL TREATMENTS FROM ROTATION
Com-Com
Alfalfa-Corn

Figure 4-1.

Sample field treatment form

41
4.10 Nitrogen credit information

The nitrogen credit information was solicited from two research collaborators using a
form which described the rotations and the fields’ history. The collaborators, a field
technician in MSU’s Crop and Soil Sciences Department and the farm’s private consultant,
were sent the form with a letter of explanation. The goal was to get expert opinion on the
amount of nitrogen carryover to expect in. the soil given the previous crop, soil type and
tillage regime. Figure 4-2 shows a sample form for collecting data on nitrogen carryover. A
definition of nitrogen credits was provided, reminding the collaborators that both green
manure and previous nitrogen-fixing crops were to be included and that the value should
reflect leaching and runoff losses prior to planting. The three study areas were described
using the farmer’s field names and the crop sequence in each rotation (examples given).
Collaborators were asked to estimate nitrogen credits in pounds per acre under both no-till
and conventional tillage systems. This information was then used to estimate the potential for
excess nitrogen in the system.

4.11 Yield information

The yield information was solicited from two research collaborators using a form
which described the rotations and the fields’ history. The same collaborators who responded
to the nitrogen credit request were sent the form with a letter of explanation. The goal of this
form was to get expert opinion on how the tillage and rotation would affect crop yields. The
collaborators were asked to give an estimate of the expected yield, the optimistic yield and the
pessimistic yield based on tillage, crop and yield history.

A sample of the form used to collect yield data is shown in Figure 4-3. Estimates of
three yield levels were requested: expected, percent above and percent below. Since the data
was to be used in the SMART-FRMS simulation, the definitions provided in the SMART-

FRMS software manual were provided for the collaborators (Center for Farm

42

SMART Rotation Budgets

Nitrogen Credits from the previous crop includes both green manure and credits from a previous
nitrogen fixing crop. The amount entered should be a net value adjusted for losses from leaching and
runoff that might occur before planting time.

PLEASE ENTER THE NITROGEN CREDITS (lbs. of N) FROM PREVIOUS CROP

FIELD GR-12 A,B,C Rotation one: alfalfa, com, com, soybeans

 

 

 

 

 

 

 

 

 

NITROGEN Year 1 Year 2 Year 3 Year 4 Year l-Alfalfa

Field history: Hay alfalfa Corn Corn Soybeans This begins the

8 yrs in alfalfa. rotation again

No-Till.

Conventional Till. II
m =

 

 

FIELD GR-IZ A,B,C Rotation two: alfalfa, com, com (inter-seeded with vetch), soybeans

 

 

 

 

 

 

 

 

 

 

 

 

NITROGEN Year 1 Year 2 Year 3 Year 4 Year l-Alfalfa
Field history: 8 yrs Hay alfalfa Corn inter-seeded Corn Soybeans This begins the
in alfalfa. with vetch rotation again
No—Till.
Conventional Till.

FIELD ITO-02 Rotation one: com, soybeans
NITROGEN Year 1 Year 2 Year l-Com
Field history: 1989 Corn Soybeans This begins the

set aside
1990 com
1991 corn

rotation again

 

No-Till.

 

 

Conventional Till.

 

 

 

 

 

Figure 4-2. Sample nitrogen credit form

43

SMART Rotation Budgets
Definition (according to the SMART-BUDGETOR system):

Exmted Yield - This is the average or ”normal" yield expected based on tillage.

fl Above - This is the percent above me expected yield you would likely achieve one year out of six. It is not the highest yield
ever experienced.

Z Belgw - This is the percent below the expected yield you would likely have in one year out of six. It is not the lowest yield
ever experienced.

PLEASE ENTER THE YIELD INFORMATION BASED ON THE ROTATION AND TILLAGE REGIME

 

FIELD GR-12 A,B,C (24 acres) Rotation One: alfalfa. com, com. soybeans

 

Field History: Hay Alfalfa Corn Corn Alfalfa
8 yrs. in alfalfa (Tons) (Bu.) (Bu.) . This begins the

TILLAGE REGIME YIELD Year 1 Year 2 Year 3 Year l-Hay
rotation again

 

II—

 

Expected Yield

NO-TILL. % Above
(optimistic)

 

 

% Below
(pessimistic) II

Expected Yield

 

 

CONVENTIONAL 95 Above
TILL. (optimistic)

 

% Below

J Li a... t ..=._

 

 

 

 

 

 

 

 

 

FIELD GR-12 A,B,C (24 acres) Rotation Two: alfalfa, com, com (inter-seeded with vetch), soybeans

 

Field History: Hay Alfalfa Corn Corn Soybeans Alfalfa
8 yrs. in alfalfa ('I'ons.) (Bu.) inter-seeded with (Bu.) This begins the
vetch (Bu.) rotation again

 

” TILLAGE REGIME YIELD Year 1 Year 2 Year 3 Year 4 Year l-Hay

I Expected Yield

 

NO—TILL. % Above
(optimistic)

 

% Below

J: I (pessimistic)
Expected Yield
CONVENTIONAL % Above
TILL. (optimistic)

% Below
(pessimistic)
fir 1=l=n=

Figure 4-3. Sample yield form

 

 

 

 

 

 

 

 

 

 

 

 

 

 

44

Financial Management, 1990a). Field names, sizes, field history and crop sequences were
listed for each site. The tillage regimes included no—till and conventional. The yield figures
implicitly included an element of probability in that the percent above and percent below
categories requested an estimate of the percentage below expected yield that would be likely
in one out of six years, or about 17 percent of the time the crop was planted. The integrative
effects of yield due to preceding crops were explicitly considered in estimation of yields.

This information was used in all calculations of revenues.

4.12 Price information

The price information was solicited from one research collaborator, a farm
management specialist in Agricultural Economics. He used a MSU price simulation model to
predict the market prices of the crops in the rotations, in appropriate years matching rotation
lengths in real terms. The purpose of this form was to obtain price expectations incorporating
expected market changes.

Figure 4-4 shows a sample form for collecting expected price information. Estimates
of prices for each crop, for each year of the rotation were requested. In addition, the
collaborator was asked to suggest percent above and percent below the expected price that
would be likely one out of six years. The definitions of expected price, percent above and
percent below were taken from the BUDGETOR manual (Center For Farm Financial
Management, 1990a).

Expected prices are perhaps the most difficult data to estimate, because they rely on
market responses to price changes, both on the supply and demand sides. Several of the
crops in the rotations are eligible for price support under federal commodity programs in the
1990 Farm Bill, but future policy changes may alter these programs. Since all of the crops
except alfalfa are traded on international markets, prices may fluctuate in response to world

production and market conditions.

Definitions (according to the SMART-BUDGETOR system):

45

SMART Rotation Budgets

NOTE: For program crops use either the target or the market price, depending on which price will be higher.

Exflted Price - This is the long range average price for the commodity.

Optimistic Price - This is not the highest price ever experienced. Rather, a price this high or higher would be expected one out
of six years. Express the optimistic price as a percentage of the expected price. For example. if the optimistic price is 110% of

the expected price, then enter +10%.

Pessimistic Price - This price is not the lowest price ever experienced. Rather, a price this low or lower, would be expected one
out of six years. Express the pessimistic price as a percentage of the expected price. For example, if the pessimistic is 90% of

the expected price, then enter -10%.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

PRICE: 1990 Farm Bill
CORN 1995
Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9

Expected

% Above % Below

(Optimistic price) (Pessimistic Price)
PRICE: 1990 Farm Bill
SOYBEANS 1995

Year I Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9
= =

Expected

% Above % Below

(Optimistic Price)

(Pessimistic Price)

Figure 4-4. Sample price form

46

The model used by the collaborator accounts for some of these effects.

It was recognized that actual prices might be quite different from those predicted.
After considering the implications for incorrect valuation of perpetual rotations using this
data, it was decided that constant 1992 prices would be used. While perhaps less realistic,
this approach avoided the problems inherent in predicting prices beyond the current Farm
Bill. Table 4-3 shows the prices assumed for outputs, in 1992 dollars. These prices were
used in all calculations of revenue.

4.13 Other input information

This information was solicited directly from the participating farmer using a form
during an interview. Two weeks before the interview, the form was sent to the farmer to
give him an idea of the information needs and allow him time to access records required. At
the interview, specific questions were answered by the farmer and the form was completed by
the graduate researcher. The object of this form was to collect data needed to calculate the
per acre costs for items such as repairs, supplies and miscellaneous expenses.

A sample form for collecting input information not described in the field treatment
forms is shown in Figure 4—5. For a list of materials and expenses, the per unit cost and the
description of the units were requested. The farmer was asked to quantify production acres
(land actually being farmed) for comparison with tillable acres (land that could be farmed).
This permitted allocation of the variable input costs to be made on the same basis as the costs
quantified on the field treatment forms.

The information received was actual values for 1991 and predicted values for 1992.
This information was used to complete the description of per acre costs.

4.14 Smnmary comments on the data collected
Ideally, actual field and cost data for all of the categories above would have been

collected. However, since the rotations had not been established on the farm, estimates

47

Table 4—3. Prices used in the budgets and economic analysis, in 1992 dollars

 

 

 

 

 

 

CROP PRICE ($lunit)
Corn 2.25/bushel
Soybeans 5.50/bushel
Wheat 2.75fbushel
Canola 0.09/pound
Alfalfa 62.00/ton

 

 

 

 

48

OTHER CASH OPERATING COSTS-FOR CROP PRODUCTION
Inputs for PLAN ETOR

UNITS

DryingFuel

 

Direct Crop Labor

Fuel

Repairs

-annual repair costs/machine
-includes oil changes etc.

Supplies
-variable costs (e. g. tires)

Miscellaneous

Operating Interest Expense

Total Production Acres

SIUNIT

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

PHYSICAL

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Total Tillable Acres

' Operating Interest Expense refers to the interest expense per acre on operating loans for the above expenses as
well as expenses from seed, fertilizer, crop chemicals, and scouting. Enter the estimated operating loan interest (or interest
on accounts payable) that would be paid on loans incurred to pay the listed expenses. For example. if it is estimated that
$80 will be borrowed per acre for 10 months at 12% interest. the operating interest expense will be: $80 x 10/12 x .12 =

$8.00

Figure 4-5. Sample variable cost form

49

were needed in several areas. In every case, the estimates were taken from the farmer,
experts in the appropriate areas, or the best alternative information source.

In some cases where actual farm data was not available, or where farm data was not
believed by the farmer or farm consultant to represent a typical year on the farm, information
was collected from published sources for similar farm conditions. Two such sources of
information were crop and livestock budgets generated by MSU (Nott et al., 1992) and
machinery cost estimates from the University of Minnesota (Fuller, et al., 1992). Both Of
these sources are considered reliable sources of costs estimates for the parameters specified.
These published sources complemented actual data and expert Opinion. Taken together, these

sources provided the data for the study.

50
Chapter 5 Simulation Methods

5 .0 Introduction to the chapter

This chapter describes the simulation methods used to compile financial and farm data
and generate budgets and environmental impact information for the analysis. Three software
packages were used for this project. FIELD MANAGER (Harvest Computer Systems, 1986),
a farm record keeping system, was used to organize existing farm data. SMART-FRMS
(Center For Farm Financial Management, 1990a) was used to generate budget information
and produce measures of potential for chemical leaching and soil erosion. QUATTRO PRO
(Borland, 1991), a spreadsheet package, was used to simulate the NPV for each field.

SMART-FRMS is an experimental program being evaluated by the University of
Minnesota for use by extension agents and farmers. Testing this program was one of the
objectives of this research. Although it contains several modules, only two - BUDGETOR
and PLANETOR - were needed for this project. These modules, as well as FIELD
MANAGER, are discussed in the following sections.
5.1 FIELD MANAGER

FIELD MANAGER is a computer based record keeping system that was owned by
the farmer before the research project began. It keeps detailed records Of each field’s
production history including soil tests, yields, costs, and application rates of other farm
inputs. FIELD MANAGER can also determine the costs of production on a field-by-field
basis and conduct breakeven analysis using actual or projected figures. Prior to this project,
the software was not in use. Though the farm wanted the program running, the time
constraints of implementation were too great given their available labor.

One of the first steps of this research project was assisting the farm’s bookkeeper in

setting up FIELD MANAGER for farm use. Records from the previous four years for all

51
fields were converted for inclusion in the FIELD MANAGER database at MSU. The

program and files were then installed on the farm’s computer for corrections and additions by
the farm’s bookkeeper.

This process accomplished two ends. First, it permitted the researchers to become
familiar with previous field treatments and farm activities. Second, it established a rapport
with the farmer and farm support staff, and demonstrated to them the commitment to provide
research that could be of use on the farm. Compilation of records for input to the program
required substantial effort on the part of the farmer and his Office staff, as previous records
had been kept on paper and tapes in several locations. The farmer, not the researchers,
decided that use of FIELD MANAGER would be beneficial to his operation.

5 .2 Introduction to SMART-FRMS

The SMART-FRMS system is an experimental software program developed by the
University of Minnesota, and funded jointly by the Extension Service and the Cooperative
State Research Service of the U.S. Department of Agriculture. The acronym stands for
"Sustaining and Managing Agricultural Resources for Tomorrow-Farm Resource Management
System. " (Center for Farm Financial Management, 1990c) The goal of the software was
”simultaneous consideration of resource conservation, environmental protection, productivity
and profitability,.." (Ikerd p. 104, 1991). This ultimately leads to multiple goal planning.

SMART—FRMS, version 1.4, was chosen for this research project because it informs
farmers of the economic and environmental implications of selecting a production system.
This helps farmers make decisions on the tradeoffs between economic and environmental
Objectives. In this study, it was used to simulate budgets and environmental impacts of the
alternative rotations. The main components of the system are BUDGETOR, the budgeting
component, and PLANETOR the whole farm planning component, which simulates the

environmental effects of different rotations.

52
5 .3 BUDGETOR

BUDGETOR was used to develop the budgets for the rotations selected for this
project. Actual economic and environmental farm data were used to complete the simulation.
Default values, generated annually by MSU researchers (Nott et al., 1992), were used only
when necessary.

Most Of the data came from files compiled for FIELD MANAGER. Other data was
collected from collaborators or taken from published sources, as described in the last chapter.
The level of detail required by BUDGETOR necessitated an extremely time consuming data
collection process, even with a farm as well staffed as the one in this study. The input -
output table shown in Figure 5-1. lists the information needed for this part of the project and
the output generated.

Actual field data was preferred for all of the items listed in Figure 5-1. However,
estimates provided by extension or university researchers were used when necessary. This
was particularly relevant for items such as nitrogen credit from the previous crop, soil type,
and organic matter.

The BUDGETOR outputs shown in Figure 5-1 include excess nitrogen, highest
chemical rating, value of expected yield, labor requirements and total cash operating costs, or
variable costs of production. BUDGETOR calculates these values based on the inputs
provided, some of which are qualitative, like chemical names, and some of which are
quantitative. The following definitions are paraphrased from the BUDGETOR manual
(Center for Farm Financial Management, 1990a).

"Excess nitrogen" (pounds per acre) refers to the average nitrogen (N) available
(pounds per acre), minus the average N removed (pounds per acre). Average N available is

the

53

 

INPUT'

OUTPUT

 

Credit green manure (lbs/acre)
Credit other sources (lbs/acre)
Applied nitrogen (lbs/acre)
Expected yield (units/acre)
Optimistic yield (units/acre)

Excess nitrogen (pounds)
with expected yield
with optimistic yield

 

Herbicides used on the field
Pesticides used on the field

Highest chemical rating (High, Medium, Low)
as measured by:

toxicity

leaching potential

run-Off potential

 

Expected yield (units/acre)
Expected price ($lunit)

Value of expected yield ($lacre)

 

Total labor hours required
(hours/acre)

Labor requirement (hours/acre)

 

Costs/acre for:
Seed

Fertilizer

Crop chemicals
Crop insurance
Drying fuel
Irrigation energy
Water assessment
Custom hire
Direct crop labor
Fuel

Repairs
Packaging
Supplies
Miscellaneous
Operating interest expense

Total cash operating costs ($lacre)
(variable costs)

 

 

Total return ($lacre)
Total direct cost ($lacre)

 

Return over direct costs ($lacre)

 

 

 

' There is other information that can be processed by BUDGETOR. The information shown
here is what was actually used in this study.

Figure 5-1. BUDGETOR - input/output table

54

N available in the soil, plus N from other sources, such as manure and chemical applications.
Average N removed is the amount of N the crop removes per unit of production (pounds per
unit), times the yield (units per acre).

"Highest chemical rating" is an overall rating that indicates the potential for off-site
transportation of farm chemicals. There are three possible ratings (High, Medium, and Low)
that depend on three major sub-components: leaching potential, run-off potential, and toxicity.
BUDGETOR assigns the rotations a rating for each of the sub-components which are averaged
to obtain the overall rating (highest chemical toxicity). The inputs required to obtain these
ratings are the chemicals used on the fields and the excess nitrogen in the system. These
ratings are independent of the soil type to which the chemical is applied, but do rely on
cropping practices. Interpretation of these ratings is more accurate when using the ratings in
the PLAN ETOR component, described in the next section, which includes the influence of
soil type in the analysis.

The "value of expected yield” ($ per acre) is per acre value, or total revenue, of the
crop assuming a typical yield. It is calculated by multiplying the expected price of the crop ($
per unit) and the expected yield of the crop (units per acre).

"Labor requirement" (hours per acre) shows the labor requirements per acre for all
field activities on a monthly basis. this output is useful in planning labor needs throughout
the year.

”Total cash operating costs " ($ per acre) are the variable costs of production
calculated on a per acre basis. This measures the cost of seed, fertilizer, crop chemicals,
repairs, labor, etc.

”Retum over direct costs " ($ per acre) is net revenue, per acre return minus the
variable costs. It is calculated by multiplying the expected yield by the expected price and

then subtracting the variable costs.

55
Other outputs produced by BUDGETOR were not used in this research project.

These include diesel (gallons) and BTU (millions) requirements per year, total return risk
factor (+/-), corn equivalents produced (bushels), hay equivalents produced (tons), silage
equivalents produced (tons) and Animal Unit Months (AUM) produced. For more
information on these other areas the reader is referred to the BUDGETOR manual (Center for
Farm Financial Management, 1990a).

5.4 PLANETOR

PLANETOR was designed to be the whole farm planning instrument of the SMART-
FRMS system. However, since this project is not a whole farm planning exercise, it was
used to simulate the environmental impacts of certain fields and provide the environmental
characteristics of the field-rotation combinations. PLAN ETOR’s environmental information
combines crop and soil information to describe the rotations in terms of erosion, water quality
and pesticide toxicity.

In this version of PLANETOR the environmental information was provided as
"High", "Medium" or "Low" ratings of a potential problem in the given area. These ordinal
rankings are not assigned a specific probability of risk in this version of the program.
Instead, they refer to physical characteristics of the chemicals, the soil, and the cropping
system. Erosion and excess nitrogen are the only environmental indicators that are calculated
in both qualitative and quantitative terms. For example, the Soil Conservation Service has
determined a tolerable annual soil loss, or T-value (tons per acre per year) for soil types in
the United States. If the soil loss predicted by the Universal Soil Loss Equation (USLE), in
tons per acre, for a given field-rotation combination is less than 90 percent of the T-value for
that soil, the combination gets a "Low" soil loss rating. If the predicted level is between 90
percent and 200 percent of the T-value the combination rates " Medium". Likewise, if the

predicted level is over 200 percent of the tolerable level the system receives a "High" rating.

56

Figure 5-2. shows an input/output table of the information needed to run the
PLAN ETOR component of the SMART-FRMS system and the results generated. Erosion is
calculated with the USLE. The specific field information required for the calculation includes
soil type, organic matter, average slope and slope length on the field, the type of practice
(contour plowing, terracing, standard), and the C-factor. The C-factor is a measure Of the
cover remaining on a field throughout the year. It is calculated by the Soil Conservation
Service based on the crop grown and the tillage system used on the field. Each rotation has a
unique C—Factor.

The water quality rating is the overall rating based on three factors; excess nitrogen,
chemical leaching, and chemical run-off. PLANETOR determines the rating by assessing the
soil characteristics (organic matter and soil depth) to determine leaching and run-off potential.
It combines this information with the leaching and run-off characteristics of the chemicals
(soil adsorption, water solubility, persistence) used in production to determine an overall
water quality rating. The information needed to run this simulation includes soil type, organic
matter, total nitrogen available, yield and crops grown, total nitrogen applied, and the
herbicides and pesticides used on the fields. This rating is independent of weather factors that
affect the probability of environmental problems.

Figure 5-2. shows that all of the information required to determine pesticide toxicity
is already in the program through the BUDGETOR component. Pesticide toxicity measures
the human toxicity of a chemical. The ratings come directly from the chemical labels rating,
stored in a database prepared by the Agricultural Research Service. This information is
independent of the soil type and handling practices.

Other output provided by PLANETOR but not used in this research project include:

gross farm income, total farm expenses, net worth change, monthly labor balance, energy

57

 

INPUT

Soil type (series name)
Organic matter (%)

Average slope length (feet)
Average slope in the field (%)
Practice (contour, terrace, etc.)
C-factor

OUTPUT

Erosion (tons/acre)

 

II Soil type (series name)
Organic matter (%)

Total nitrogen avail. (lbs/acre)a
Total nitrogen applied (lbs)'
Crop planteda

Expected yield‘

Herbicides used on the field“
Pesticides used on the field‘

Water quality (High, Medium, Low)
as measured by:

excess nitrogen

chemical leaching

chemical runoff

 

Herbicides used on the fields‘
Pesticides used on the fields8

 

 

Pesticide toxicity (High, Medium, Low)

 

LP

 

 

‘ This information was already put into the program via BUDGETOR. It does not need to be

re-entered in PLANETOR.

Figure 5-2. PLANETOR - input/output table

58
summary, water summary, manure summary and the production summary. This information
is relevant for a whole farm study; however, it is not appropriate for a field level study of
alternative rotations.
5 .5 Concluding comments on the SMART-FRMS system

What separates SMART-FRMS from traditional farm software packages is that it
facilitates simultaneous consideration of the economic and enviromnental implications of
alternative cropping rotations. A field can be analyzed with any number of rotations to see
which one best satisfies the farmers objectives. This type of information is extremely
important given the changing parameters that farmers have to operate within.

However, there are three specific areas of the program that are troublesome. The
first concerns the water quality indicators. These indicators (High, Medium, Low) do not
change as the amount of the chemical used on the field changes. Thus, a farmer who uses
two quarts of Roundup per acre will generate the same rating, as a farmer who uses four or
even ten quarts per acre.

Second, the ratings are not correlated with a quantified probability related to erosion,
water quality or pesticide toxicity. Similar systems tend to get the same ratings which makes
ranking similar systems very difficult without using an external model that simulates chemical
movement such as GLEAMS (Leonard, 1987) or LEACHMP (Wagenet, 1987).

Finally, there is no component in the system for an intertemporal comparison of
rotations. Without an intertemporal comparison it is difficult for a farmer to decide which
system offers the highest economic return over time. This is the reason for using the output
from SMART-FRMS in a NPV simulation. The NPV analysis was conducted using a
spreadsheet package and the discounting methods explained in Chapter 3. Output from
BUDGETOR and PLAN ETOR were used to obtain net revenue, accounting for the on-farm

cost of erosion, for each period in the rotations outlined in Chapter 4. The results of the

analysis are presented in the next chapter.

59

60
Chapter 6 Results of the Simulations

6.0 Introduction to the chapter

As stated in Chapter 3, the rotations were extended into perpetuity before the
annuities of their NPVs were calculated. The discount rates used in the analysis were three,
five and seven percent. These rates are close to current interest rates for Treasury bills (about
three percent) and for consumer credit (about seven percent).

The economic basis of comparison was the annuity value of the rotation. The
annuity, including the transition period, was calculated for the rotations with the on—farm
erosion costs equal to $6.00 per ton in lost nutrient value (Hicks, 1992).

Prices and costs used, correspond to those outlined in Chapter 4. The environmental
basis of comparison was erosion, .water quality and pesticide toxicity. The systems were
assumed to be primarily no-till. Corn, soybeans, and wheat are no-tilled in this project while
canola and alfalfa remained conventionally tilled. Each of the fields and their rotations are
discussed in subsequent sections. The focus was on the economic and environmental
characteristics of the field-rotation combinations, including the annuity values calculated at all
discount rates, with erosion valued on-farm, and the environmental indicators used in this
study.

For comparison, an extra rotation was included in field III’s analysis. This rotation,
"Z" , was a conventionally tilled com-soybean rotation typically used in Michigan. The
variable costs for this rotation were taken directly from the 1992 Michigan Crop Budgets
(Nott et al., 1992). The yield was assumed to be 120 bushels per acre and the same prices

were used as for the other rotations.

61
6.1 Field I

Field I had three alternative cropping systems developed for it, including two rotations
that differed only by the nitrogen carryover assumptions from hairy vetch. The rotations
were shown in Table 4.1. As mentioned, the objective of designing these rotations was a
comparison of the effects of including hairy vetch in the rotations as a nitrogen source and
soil cover under differing assumptions about nitrogen value (IB vs. IC). The vetch was
excluded from IA to provide a baseline for the comparisons.

Table 6-1 shows the variable production costs for the three rotations on field 1. Fixed
costs were not included in the NPV analysis due to the difficulty in determining which
assumptions to use about the state of existing production-specific equipment. A discussion of
this problem is included in Section 7.4. As mentioned, variable production costs consist of
actual farm data, expert opinion and published data. Due to confidentiality, the costs for
individual inputs were not included here.

The summary costs showed that there were slight differences between the rotations for
all years, except two and three, which corresponded to corn production. Rotation IA, which
acts as the control, was assumed to have constant costs across both years of corn production
($119.08 per year). Rotation IB and IC had higher first year corn costs because of the hairy
vetch establishment; however, their second year costs were lower than the first year due to
the nitrogen benefits of the vetch. The summary table shows that including the vetch costs an
additional $39.00 per acre, in year one, and saves between $2.00 and $15.00 per acre in year
two.

Table 6-2 shows the annuity of the NPV calculated for each rotation, at each discount
rate. Rotation IA had the highest annuity at $140.00 per acre. This was $4.00 per acre more
than [B which includes hairy vetch and assumes a 90 pound nitrogen credit. Rotation IC had

an annuity of $133.00 per acre assuming a 20 pound nitrogen credit. The annuity figures

62

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imply that including the hairy vetch in the rotation, given current prices, costs the farmer
between $4.00 and $7.00 per acre per year. This difference was related to the relative price
differences of a pound of nitrogen fertilizer provided by hairy vetch and by anhydrous
ammonia.

If application costs were assumed equal for anhydrous ammonia and hairy vetch, the
cost of a pound of nitrogen from the vetch was $1.53 per pound when a 20 pound nitrogen
credit was assumed, and $0.35 per pound when a 90 pound nitrogen credit was assumed. In
contrast, a pound of nitrogen from anhydrous cost approximately $0.13 per pound. So even
with an optimistic assumption on hairy vetch’s actual contribution, it was still three times
more expensive than anhydrous. Factors that may cause their relative prices to change
include; state regulation on chemical fertilizers, a fertilizer tax, or a significant increase in
the price Of fossil fuels.

Table 6-3 shows the environmental indicators for field I. These indicators - erosion,
water quality, and pesticide toxicity - were described in Chapter 5. The rotations on field I
each achieved an overall rating of "Low" potential for erosion problems. PLAN ETOR
predicted that IA would generate, on average, 0.43 tons of erosion per acre per year. This
was higher than the rotations that included the vetch as a cover crop, 0.35 tons. Vetch helped
reduce erosion by maintaining a cover on the field after the other crops were harvested. The
tolerable soil loss for this field was 5 tons per acre per year, significantly higher than
predicted values.

The potential threat to surface and groundwater from agricultural chemicals and
nitrogen was described by the water quality indicator in PLANETOR. Each rotation was
given a "High" rating based on soil and chemical characteristics of the field-rotation

combinations. The current version of the program generated the ratings independent of the

65

 

 

 

 

 

 

 

 

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66

amount of the chemical applied or the application method. Subsequently, it was difficult to
distinguish between similar rotations based on the overall water quality rating.

The excess nitrogen component of the water quality rating was one way to distinguish
between similar rotations. For example, rotation IA produced an average of 46 excess pounds
of nitrogen per acre per year, and rotations IB and IC produced 44 excess pounds. This
result was probably due to the assumed nitrogen carryover from the previous crop.

The pesticide toxicity indicator gave each of the rotations a "Medium" rating. The
nature of this indicator, that is, label—dependent, makes it somewhat unreliable as a measure
of actual health benefits.

6.2 Field 11

Field 11 had six alternative cropping systems, including the conventional com-
soybeans rotation. The rotations were shown in Table 4-1. As mentioned, the objectives of
this comparison were: 1) to evaluate an alternative crop (canola), 2) to examine the economic
and environmental effects of including hairy vetch in the rotation at varying nitrogen credit
levels, and 3) to compare rotations typical of the region under different tillage systems.

Table 6-4 shows the variable production costs for all rotations in field 11 by year of
the rotation. Fixed costs were not included in the NPV analysis due to the difficulty in
determining which assumptions to use about the state of existing equipment. A discussion of
this problem is included in Section 7.4. As mentioned, variable production costs consist of
actual farm data, expert opinion and published data. Due to confidentiality, the costs for
individual inputs were not included here.

The summary costs show that rotation [12 has significantly higher variable costs than
the other rotations. This was largely attributed to differences in labor, fuel and erosion costs

between tillage systems. The most interesting tillage comparison was between [IA and Ill

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because they were both two year corn-soybean rotations. Labor costs were $43.00 per acre
higher for corn production in rotation 112, than IIA. Most of this difference was caused by
the increased seedbed preparation needed in rotation [12. The extra tillage also caused an
increase in fuel and erosion costs, $7.00 and $11.00 per acre, respectively. These higher
costs make rotation HZ less profitable than rotation IIA, especially since herbicide costs were
assumed to be approximately the same across tillage systems.

Rotations IIB, IIC, IID compared the use of hairy vetch in the rotation. Where
rotation IIB’s costs are constant across the two-year corn cycle, the costs in rotation HC and
11D are higher in the first year as the vetch was established, and then lower in the second year
as the vetch provides nitrogen to the system. Otherwise, the costs between the two rotations
were the same. Canola was analyzed in rotation IIB and HE. Production costs were the same
in the first four years of the rotation then, in year five, IIE’s costs were lower than IIB’s
costs. This occurred as IIB began its rotation again with corn. Differences in variable costs
for the same crop across rotations were due to assumptions made about nitrogen credits and
other integrative effects discussed in Chapter 4.

Table 6—5 shows the annuity of the NPV calculated for each rotation, at each discount
rate. Rotation IIA had the highest annuity at $122.00 per acre. This was $18.00 higher than
Rotation IIE which was the rotation with the next highest annuity. Rotations IIB and HE,
which differ only by canola, had almost the same annuity value, $104.00 and $105.00,
respectively. The five-year rotation (IIE) that included canola provided $1.00 per acre more
than four year rotation (IIB) without the canola. Depending on commodity programs and
world markets, inclusion of canola could be profitable.

The economic value of including hairy vetch in the rotation was analyzed by looking
at Rotations IIB, IIC, and IID. Rotation IIB did not have hairy vetch in the rotation and had

a annuity of $104.00. Rotation IIC assumed a 20-pound nitrogen credit for the vetch and had

69

 

 

 

 

 

 

 

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70
an annuity of $95.00. Rotation IID, with a 90-pound credit for the vetch, had an annuity of

$98.00. Including hairy vetch in these rotation as a N-source cost the farmer between $6.00
and $9.00 per acre, depending on the assumed nitrogen credit. This difference may be
attributed to the relative prices of buying and applying hairy vetch seed and anhydrous
ammonia, the chemical nitrogen source. If fossil fuel prices increased, the hairy vetch might
become competitive as an N-source.

Rotation [IA and rotation [12 were both corn-soybean rotations. However, rotation
IIA was assumed to be no-till and rotation HZ was conventionally tilled. Rotation IIA
returned an annuity of $121.00 per acre, while rotation HZ generated $90.57 per acre, a
difference of $20.32 per acre. A significant portion of this difference was due to the labor
and fuel requirements of the systems. No—till requires less of each if the timing of chemical
applications is optimal, as was assumed for this analysis. Nonoptimal management could
result in conventionally tilled systems being more profitable.

Table 6-6 shows the environmental indicators for field II. These indicators - erosion,
water quality and pesticide toxicity - were described in Chapter 5. All rotations on field II
achieved an overall erosion rating of "Low". PLANETOR predicted that rotation IIA would
produce, on average, 0.25 tons of erosion per acre. Rotation IIB would produce 0.37 tons,
rotation IIC and 11D would generate 0.31 tons, rotation IIE, 0.5 tons, and rotation 112 would
produce 2.04. By comparing IIB, IIC, and IID, it was apparent that the rotations that
included vetch produced less erosion than the same rotation without the vetch. Vetch
performs a soil-retaining function.

The tolerable soil loss for this field was five tons per acre per year. This explains
why all the rotations received a "Low" rating. As expected, the conventionally tilled rotation

produced significantly more erosion than the rotations that were no—tilled. The no-till effect

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72

over shadowed the soil holding contribution of the hairy vetch. The vetch effect might be
more important in a conventionally tilled system. The effect of valuing erosion on-farm was
to make conventional tillage less profitable. ‘

The potential threat to surface and ground water from chemicals and nitrogen was
described by the water quality indicator. Each of the rotations received a "High” rating for
potential water quality problems. PLAN ETOR determined these ratings based on the soil and
chemical characteristics of the rotation-field combinations. The current version of the
program does not distinguish between chemical application rates and techniques, or the effect
they have on the potential for water quality problems. Subsequently, it was difficult to
distinguish between the rotations based on the overall water quality rating.

The excess nitrogen component gave each of the rotations a "Medium" rating for the
amount of expected excess nitrogen. Rotation 11A had the lowest average excess nitrogen at
35 pounds per acre and Rotation 112 had the highest average at 46 pounds per acre. This
result was probably due to the assumed nitrogen carryover from the previous crop.

The pesticide toxicity indicator assigned rotations 11A, IIB, IIC, HD, and [IE a
" Medium" rating and rotation [12 "High" rating. The "High" indicator in 112 was triggered
by an insecticide not needed in the other rotations. The nature of this indicator, that is, label-
dependent, makes it somewhat unreliable as a measure of actual health effects.

6.3 Field 11]

Field IH had two alternative cropping systems developed for comparison. The
rotations were outlined in Table 4-2. As mentioned, the objective of this comparison was to
evaluate the potential benefits of including canola in a relatively long rotation.

Table 6-7 shows the annual variable production costs for rotations IIIA and 1118.
Fixed costs were not included in the NPV analysis due to difficulty in determining which

assumptions to use about the state of existing machinery, discussed in section 7.4. The

73

variable production costs consisted of actual farm data, expert opinion and published data.
The costs for individual inputs were not included here to maintain farm confidentiality.

The summary variable costs show little annual difference between the rotations,
although IIIB was slightly higher in every period. This difference was directly equal to the
difference in annual on-farm erosion costs. Since canola was assumed to be conventionally
tilled in these rotations, its inclusion in the rotation increases annual expected erosion.

Table 6—8 shows the annuity of the NPV calculated for both rotations at each discount
rate. Rotation IIIA has the highest annuity at $135.00 per year, which was $3.00 per acre
more than rotation IIIB. This difference was probably caused by the additional erosion costs
associated with rotation IIIB, and by timing changes. For example, adding canola to the
rotation delays, by one period, the beginning of the second repetition of the rotation. In this
rotation, it means delaying a return to the crop with the highest net revenue, which could
cause a lower annuity value.

Table 6-9 shows the environmental indicators for field 1H. These indicators - erosion,
water quality, and pesticide toxicity - were described in Chapter 5. PLANETOR assigned
both rotations a "Low" rating for the potential for erosion problems. The tolerable soil loss
for this field is 4.0 tons per acre per year, which is significantly higher than rotation IIIA,
0.29, and rotation IIIB, 0.50. Though they both receive "Low" ratings, IIIB produces almost
twice as much erosion as rotation IIIA.

PLANETOR’S water quality indicator estimated the potential threats to surface and
ground water contamination from agricultural chemicals and nitrogen. Both rotations received
a "High" rating by the program based on the soil and chemical characteristics of the field-
rotation combinations. As mentioned, the current version of the program generates the

ratings independent of the amount of the chemical applied or the application method. This

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makes it difficult to distinguish between similar systems based on the overall water quality
rating.

The excess nitrogen estimates provided actual estimates and can be used to distinguish
the rotations. Though both of the rotations received a "Medium" rating, they produced
different amounts of excess nitrogen. Rotation IIIA generated 44 pounds of excess nitrogen
per acre and rotation IIIB generated 42 pounds of excess nitrogen. These differences are
likely caused by the assumed nitrogen carryover from the previous crop in the different
rotations.

The pesticide toxicity indicator assigned both of the rotations a "Medium" rating. As
mentioned, the nature of this indicator, that is, label-dependent, makes it somewhat unreliable

as a measure of actual health effects.

78
Chapter 7 Conclusions

7 .0 Introduction to the chapter

The primary objective of this project was to develop a method for designing and
simulating the economic returns and environmental characteristics of alternative productions
systems, specified through farmer-researcher collaboration. This approach exploited the
farmers’ self-identified constraints, whether monetary or nonmonetary, without requiring
explicit modeling of these constraints. The second goal was to provide this economic and
environmental information to farmers to address their changing decision making environment.
The final objective was to assess the SMART-FRMS system, a farm-level environmental-
economic planning software package, for use by extension educators and farmers.

This chapter comments on the results of the hypothetical alternative cropping systems
and factors that affected these conditions, farmer participation in interdisciplinary university
research, the experimental SMART-FRMS software package, and suggests several areas for
future research.

7 .1 Alternative cropping systems

The economic and environmental ranking factors included the annuity, erosion, excess
nitrogen, water quality, and pesticide toxicity. These factors were used to account for a range
of conditions simulated on the three fields. However, only the first three distinguish the
alternative cropping sequences because water quality and pesticide toxicity are the same across
rotations. Two of the cropping sequences are ranked first, in two of the three factors.
Rotation HA is the most profitable and produces the least excess nitrogen on field 11, and
rotation IIIA is the most profitable and generates the least erosion on field 111. These results
would be expected to differ if erosion was not incorporated into variable cost analysis.

The primary comparisons across the three fields were between vetch and anhydrous

79

ammonia as nitrogen sources, no-till and conventional as tillage systems, and rotations with
and without canola as an additional crop in the sequence. The three fields differed in size and
soil type, among other factors, representing a variety of conditions on the farm.

The rotations that included hairy vetch as a nitrogen source were found to have lower
annuities than the same rotation fertilized with anhydrous. This can be attributed to the
significant difference in the cost of a pound of nitrogen supplied by vetch and by anhydrous.
The results indicate that even with an optimistic assumption on hairy vetch’s actual
contribution, it is still three times more expensive than anhydrous. However, several factors
could cause the relative prices to change.

Some states have considered banning, taxing and/or limiting the amount of inorganic
fertilizer (i.e. , anhydrous) that can be used on agricultural land. If any of these restrictions
occur, hairy vetch and other nitrogen fixing organic compounds would have to be re-evaluated
to determine the most profitable fertilization technique. The price of fossil fuel could also
influence the relative prices of the vetch and the anhydrous, since inorganic fertilizer’s
production and distribution depend on fossil fuel.

Finally, hairy vetch could become profitable if it had measurable value in addition to
being a nitrogen source. Many soil scientists believe that organic nitrogen (Le. , hairy vetch)
has benefits beyond nitrogen contribution, such as adding to soil tilth, soil biota“, and organic
matter. If these benefits could be reliably measured and valued in terms of their contribution
to yield and/or the variable costs of production, then hairy vetch may be profitable given
existing prices. Agronomists are conducting research to determine these relationships.

No-till rotations were consistently determined to be more profitable than the

conventional system. The no-till systems had significantly higher annuity values, as well as,

 

" Soil biota is defined as all species and plants occurring in the soil.

80

lower potential environmental risks than the conventional system. In reaching this conclusion,
the assumption of optimal herbicide application was critical. This assumption is probably
realistic in the equilibrium period of the rotation; however, it is less realistic during the
transition period. Little scientific data exists on how yields are affected by slight deviations
from the optimal spraying time. Although, observations suggest the potential for significant
financial losses.

The environmental conditions of the no-till system will almost always be better than
the conventional system assuming that herbicides are correctly applied. Fewer nutrients leave
the field in a no—till system since there is significantly less erosion than in a conventional
system.

Finally, fixed costs are important in analyzing the no-till system. As mentioned, the
fixed costs were not included in the NPV analysis in Chapter 5. Farm size and the state of
the farmer’s machinery, have significant influence on the annual fixed cost payments per acre,
and thus, profitability. A decision maker must analyze the fixed cost requirements based on
farm size and existing machinery before making a production decision. This is discussed in
section 7.4.

Canola is a relatively new crop in mid-Michigan. After it became subsidized under
the federal farm programs, the participating farmer wanted information on its profitability and
environmental impact. The rotations of field 111 were designed for this analysis. As
previously mentioned, the rotation without the canola, IIIA, had a higher annuity than with
the canola, IIIB. However, canola was the last crop in a 8 year rotation. Since all future
benefits are'discounted to the present, canola may have been disadvantaged by being placed 8

years in the future.

81
7 .2 Farmer participation in university research

As mentioned in earlier chapters, there was significant farmer-researcher interaction
and interdisciplinary effort among agricultural economists and agronomists in the
development, planning and analysis of the alternative rotations. This level of cooperation and
collaboration was successful only because it was well planned and executed.

From the initial planning stages, team members, including the farmer, acknowledged
that a inter-disciplinary team was crucial for a successful research project. Individuals were
selected from all appropriate areas of expertise to build a strong human resource base. This
created efficiency in research as the interdisciplinary members relied on one another for
information outside their respective disciplines. For example, the economic-environmental
analysis relied on information from several team members, as mentioned in the text. When
the information needs were extensive, as in the hypothetical budgets, forms were designed to
facilitate information exchange between members. These forms effectively and successfully
collected relevant information from the other team members.

The participating farmer had previous experience with researchers and was familiar
with the research process. This experience better enabled him to participate in all stages of
project development. In group discussions, he stated that his interest in sustainable and
alternative agriculture grew as environmental awareness increased, and as environmental
regulation of agricultural production seemed more likely. In fact, he initiated contact with
researchers in sustainable agriculture about a potential on-farm research project, hoping to
gradually convert to more sustainable farming practices. He attributed his involvement in
interdisciplinary on-farm research to two main reasons: access to information on alternative
production methods relevant for his farm, and that he and his family enjoy working in a

research environment. He felt that on-farm research kept him in contact with the most

82

current farming research, and gave him credibility with other researchers and extension
educators.
7.3 SMART-FRMS as a practical tool

The SMART-FRMS system is important because it was developed in response to
farmers’ changing needs. It recognized that farmers needed more information about
production systems in order to make the best decision. Specifically, farmers needed a system
to simultaneously analyze economic and environmental objectives through farm simulation.
This version provides farmers with valuable planning information on the consequences of their
production systems from both perspectives.

The environmental component of the program is relatively easy to manipulate and
provides useful information on important environmental indicators; however, more
sophisticated analysis will be needed in the future. As mentioned in Chapter 5, the water
quality indicators are triggered by chemical and soil properties, independent of the application
rate. The application rate should be tied into the environmental analysis to more accurately
model the system. Another weakness of the program is that it only calculates water-related
erosion for comparison with the SCS determined "tolerable level" of erosion. Since the
tolerable level is a measure of both wind and water erosion (Hicks, 1992), the erosion ratings
of the rotations are skewed downward.

The economic information calculated with the program applies only to a whole farm
analysis. It is also easy to manipulate and understand once the crop budgets are established.
However, the time and effort required to establish the budgets is potentially prohibitive. The
current version of the program requires that extension educators construct soil specific, default
budgets, for a given region. In Michigan, where soil types are highly variable, hundreds of
budgets would need to be developed for all of the crop-soil combinations. The budgets used

in this study were developed for three fields on a specific farm. Eighteen budgets had to be

83

constructed to account for the crop-soil combinations. Hypothetical budgets were developed
for cropping sequences not yet implemented on the farm. Finally, a component for
intertemporal analysis of the rotations would aid farmers in selecting among rotations. It is
difficult to select the most profitable rotation when comparing unequal cash flows received in
different time periods. The present value model constructed for this analysis was designed to
meet this need.

Many of these suggestions were being addressed in the next version of the program.
In particular, researchers were trying to incorporate a chemical transport model into the
program which would provide more specific information on the risks to water quality from
specific farming practices. Other work was being done to assign probabilities to the
environmental ratings.

The experimental SMART-FRMS computer system was an important step forward in
decision support programs. It addressed farmers’ changing information needs. Once the
system is established, farmers can easily compare different cropping systems across their farm
or on a single field.

7 .4 Areas for future research

The economic and agronomic analysis occurred simultaneously over the course of this
project. This was important in the overall project design; however, the data for economic
analysis was limited as field trials were still underway. There were two areas of agronomic
research that would be helpful in further analysis of these alternative cropping systems:
nitrogen credits from the previous crop, and the effects of no-till on soil properties and crop
production.

Conclusive information on nitrogen credits from one year to next, and their
relationship to tillage, has important implications on variable production costs and water

quality. At the farm level, nitrogen credit data could change farmer’s variable costs as they

84

substitute nitrogen carryover for chemical fertilizer. Off-farm changes in water quality are
possible as existing resources ( Le. , organic nitrogen) are used more efficiently.

Agronomists know that no-till production changes soil properties in ways that affect
plant growth (National Research Council, 1989). However, the direction and magnitude of
these changes are ambiguous, and need to be quantified so they can be included in economic
analyses of alternative cropping systems. Three specific areas are listed. First, research is
needed to investigate hypothesized yield penalties associated with no-till production, in both
the transition period and the equilibrium period of the system. Next, more information is
needed on the potential yield variability of no-till systems. This should address the impact of
timing and herbicide applications on yield, and downside yield risk (Harwood, 1992).
Finally, research is needed to determine the effect of tillage on herbicide and pesticide
applications which effect both the economic and environmental components of the system.
The agronomists at MSU are conducting field studies that address many of these issues.
Carefully constructed experiments on farms and research plots will eventually provide
important information for economic analyses of alternative cropping systems.

A whole farm economic study is needed to fully understand the implications of
alternative systems on profitability and environmental quality. This would require more
information on equipment and labor scheduling across the farm, risk from weather and
management errors, and fixed cost analysis. For example, fixed costs were included in the
model in Chapter 3 but were not used in the actual calculations. This was due to difficulties
in determining which assumptions to use about the state of existing production-specific
equipment. In agricultural production, a change in production will cause a change in annual
fixed cost payments, as different equipment is required for different systems. The magnitude
and direction of the change in annual fixed cost payments depends on the state of existing

production-specific equipment. If the farmer’s existing equipment is new, the annual fixed

85

cost payments of converting to a new system will be higher than if the existing equipment is
fully amortized with zero salvage value. This difference can be attributed to the additional
cost to the farmer of idling new equipment over old equipment. Further studies on fixed
costs are needed to determine an optimal conversion point for farms of all sizes at various

discount rates.

LIST OF REFERENCES

87

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