• TABLE OF CONTENTS
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 Front Cover
 Copyright
 Preface
 Acknowledgement
 Table of Contents
 Introduction
 The partial budget
 Marginal analysis
 Variability
 Index






Group Title: From agronomic data to farmer recommendations : an economics training manual
Title: From agronomic data to farmer recommendations
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Permanent Link: http://ufdc.ufl.edu/UF00077490/00001
 Material Information
Title: From agronomic data to farmer recommendations an economics training manual
Physical Description: v, 79 p. : col. ill. ; 26 cm.
Language: English
Creator: CIMMYT Economics Program
Publisher: CIMMYT Economics Program
Place of Publication: México D.F. México
Publication Date: 1988
Edition: Completely rev. ed.
 Subjects
Subject: Agriculture -- Economic aspects -- Research -- Methodology   ( lcsh )
Agriculture -- Research -- On-farm   ( lcsh )
Farm management -- Study and teaching   ( lcsh )
Genre: non-fiction   ( marcgt )
 Notes
General Note: Includes index.
Funding: Electronic resources created as part of a prototype UF Institutional Repository and Faculty Papers project by the University of Florida.
 Record Information
Bibliographic ID: UF00077490
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: oclc - 20594487
lccn - 89100617
isbn - 9686127186

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Table of Contents
    Front Cover
        Page i
    Copyright
        Page ii
    Preface
        Page iii
    Acknowledgement
        Page iv
    Table of Contents
        Page v
    Introduction
        Page 1
        Overview of economic analysis
            Page 1
            Page 2
            Page 3
            Page 4
            Page 5
            Page 6
            Page 7
            Page 8
            Page 9
            Page 10
            Page 11
            Page 12
    The partial budget
        Page 13
        Costs that vary
            Page 13
            Page 14
            Page 15
            Page 16
            Page 17
            Page 18
            Page 19
        Gross field benefits, net benefits, and the partial budget
            Page 20
            Page 21
            Page 22
            Page 23
            Page 24
            Page 25
            Page 26
            Page 27
            Page 28
            Page 29
    Marginal analysis
        Page 30
        The net benefit curve and the marginal rate of return
            Page 30
            Page 31
            Page 32
            Page 33
        The minimum acceptable rate of return
            Page 34
            Page 35
            Page 36
            Page 37
        Using marginal analysis to make recommendations
            Page 38
            Page 39
            Page 40
            Page 41
            Page 42
            Page 43
            Page 44
            Page 45
            Page 46
            Page 47
            Page 48
            Page 49
            Page 50
            Page 51
            Page 52
            Page 53
            Page 54
    Variability
        Page 55
        Preparing experimental results for economic analysis: Recommendation domains and statistical analysis
            Page 55
            Page 56
            Page 57
            Page 58
            Page 59
            Page 60
            Page 61
            Page 62
        Variability in yields minimum returns analysis
            Page 63
            Page 64
            Page 65
            Page 66
            Page 67
            Page 68
            Page 69
            Page 70
        Variability in prices : Sensitivity analysis
            Page 71
            Page 72
            Page 73
            Page 74
            Page 75
        Reporting the results of economic analysis
            Page 76
            Page 77
            Page 78
    Index
        Page 79
Full Text
An Ecnmc Trinn Manua


From Agronomic Data to
Farmer Recommendations


C


M


M


Y


T


6CNMC PROGRAM











I
































The International Maize and Wheat Improvement Center (CIMMYT)is
an internationally funded, nonprofit scientific research and training
organization. Headquartered in Mexico, the Center is engaged in a
worldwide research program for maize, wheat, and triticale, with
emphasis on food production in developing countries. It is one of 13
nonprofit international agricultural research and training centers
supported by the Consultative Group on International Agricultural
Research (CGIAR),which is sponsored by the Food and Agriculture
Organization (FAO) of the United Nations, the International Bank for
Reconstruction and Development (World Bank). and the United
Nations Development Programme (UNDP).The CGIAR consists of 40
donor countries, international and regional organizations. and private
foundations.

CIMMYT receives support through the CGIAR from a number of
sources, including the international aid agencies of Australia, Austria,
Brazil. Canada. China, Denmark. Federal Republic of Germany.
France, India, Ireland, Italy, Japan. Mexico. the Netherlands. Norway.
the Philippines. Saudi Arabia. Spain, Switzerland, the United Kingdom
and the USA, and from the European Economic Commission, Ford
Foundation. Inter-American Development Bank. International
Development Research Centre. OPEC Fund for International
Development. Rockefeller Foundation. UNDP. and World Bank.
Responsibility for this publication rests solely with CIMMYT.

Correct Citation: CIMMYT. 1988. From Agronomic Data to Farmer
Recommendations: An Economics Training Manual Completely
revised edition. Mexico. D.F.


ISBN 968-6127-18-6










This document is a completely revised version of the
Preface CIMMYT Economics Program manual, From Agronomic
Data to Farmer Recommendations: An Economics
Training Manual, written by Richard Perrin, Donald
Winkelmann, Edgardo Moscardi, and Jock Anderson.
Since its publication in 1976 that manual has been
through six printings and has been translated into six
languages. The manual has been used by countless
students and researchers for learning a straightforward
method of analyzing the results of on-farm agronomic
experiments and making farmer recommendations.

We approach the revision of such a successful manual
with considerable caution. Our work over the past
decade has given us a chance to present this material,
in the classroom and in the field, to agricultural
researchers in a wide variety of settings all over the
world. This experience has led us to propose and test
some new ways of explaining and presenting key
concepts. We gradually began to consider the possibility
of incorporating some of those ideas in a revised
manual.

One of the first steps in the process was to introduce a
set of exercises for classroom teaching, developed by
Larry Harrington. Later, Robert Tripp and Gustavo Sain
developed further exercises and methods of presentation
which they tested in training courses. Tripp and Sain
wrote the first draft of the present document and guided
its review by the entire staff of the CIMMYT Economics
Program.

Just as this revised manual has built on the experience
of hundreds of researchers with the original version, we
hope that those who use this new version will provide
suggestions for its improvement. We believe it will be
useful in the classroom as well as for individual study
and reference. A book of exercises has been developed to
accompany this manual and can be obtained from
CIMMYT. We hope that the new version of the manual
will find an acceptance as wide as that of its
predecessor.


Derek Byerlee
Director
CIMMYT Economics Program











Acknowledgements


Many people have contributed to the production of this
manual. Jock Anderson and Richard Perrin, two of the
authors of the original manual, were kind enough to
read the final draft of this revised version and to offer
detailed comments and suggestions. Miguel Avedillo,
Carlos Gonzalez, Peter Hildebrand, Roger Kirkby,
Stephen Waddington, and Patrick Wall also read the
final draft and provided valuable ideas. In addition,
participants in the courses and workshops presented by
the CIMMYT Economics Program over the past decade
have made significant contributions.

The document passed through several drafts, which
would not have been possible without the superb
organization and typing of Maria Luisa Rodriguez.
Editing was in the very competent hands of Kelly
Cassaday and design was skillfully directed by Anita
Albert. Typesetting, layout, and production were
carefully managed by Silvia Bistrain R., Maricela A de
Ramos, Miguel Mellado E., Rafael De la Colina F., Jose
Manuel Fouilloux B., and Bertha Regalado M.









Contents


Part One:
Introduction

Part Two:
The Partial
Budget



Part Three:
Marginal
Analysis






Part Four:
Variability








Part Five:
Summary


Chapter One
Overview of Economic Analysis

Chapter Two
Costs That Vary
Chapter Three
Gross Field Benefits,
Net Benefits, and the Partial Budget

Chapter Four
The Net Benefit Curve and
the Marginal Rate of Return
Chapter Five
The Minimum Acceptable
Rate of Return
Chapter Six
Using Marginal Analysis
to Make Recommendations

Chapter Seven
Preparing Experimental Results for
Economic Analysis: Recommendation
Domains and Statistical Analysis
Chapter Eight
Variability in Yields:
Minimum Returns Analysis
Chapter Nine
Variability in Prices: Sensitivity
Analysis

Chapter Ten
Reporting the Results
of Economic Analysis

Index


13

20



30


34


38



55



63


71



76



79





Sart oeIron




This manual presents a set of procedures for the
Chapter One economic analysis of on-farm experiments. It is intended
Overview of for use by agricultural scientists as they develop
Economic Analysis recommendations for farmers from agronomic data.
Developing recommendations that fit farmers' goals and
situations is not necessarily difficult, but it is certainly
easy to make poor recommendations by ignoring factors
that are important to the farmer. Some of these factors
may not be very evident.

A recommendation is information that farmers can use
to improve the productivity of their resources. A good
recommendation can be thought of as the practices
which farmers would follow, given their current
resources, if they had all the information available to the
researchers. Farmers may be able to use a
recommendation directly, as in the case of a particular
variety. Or they may adjust it somewhat to their own
conditions and needs, as in the case of a fertilizer level
or storage technique. The agronomic data upon which
the recommendations are based must be relevant to the
farmers' own agroecological conditions, and the
evaluation of those data must be consistent with the
farmers' goals and socioeconomic circumstances.


On-Farm Research
The stages of an on-farm research program are
shown in Figure 1.1. The first step is diagnosis. If
recommendations are to be oriented to farmers, research
should begin with an understanding of farmers'
conditions. This requires some diagnostic work in the
field, including observations of farmers' fields and
interviews with farmers. The diagnosis is used to help
identify major factors that limit farm productivity and to
help specify possible improvements.

The information from the diagnosis is used in planning
an experimental research program that includes
experiments in farmers' fields. The on-farm experiments
should be planted on the fields of representative
farmers. After the first year, the experimental results
form an important part of the information used for
planning research in subsequent crop cycles. Other
diagnostic work continues during the management of
the experimental program as researchers continue to
seek information about farmers' conditions and
problems which will be useful in planning future
experiments.





Sart oeIron




This manual presents a set of procedures for the
Chapter One economic analysis of on-farm experiments. It is intended
Overview of for use by agricultural scientists as they develop
Economic Analysis recommendations for farmers from agronomic data.
Developing recommendations that fit farmers' goals and
situations is not necessarily difficult, but it is certainly
easy to make poor recommendations by ignoring factors
that are important to the farmer. Some of these factors
may not be very evident.

A recommendation is information that farmers can use
to improve the productivity of their resources. A good
recommendation can be thought of as the practices
which farmers would follow, given their current
resources, if they had all the information available to the
researchers. Farmers may be able to use a
recommendation directly, as in the case of a particular
variety. Or they may adjust it somewhat to their own
conditions and needs, as in the case of a fertilizer level
or storage technique. The agronomic data upon which
the recommendations are based must be relevant to the
farmers' own agroecological conditions, and the
evaluation of those data must be consistent with the
farmers' goals and socioeconomic circumstances.


On-Farm Research
The stages of an on-farm research program are
shown in Figure 1.1. The first step is diagnosis. If
recommendations are to be oriented to farmers, research
should begin with an understanding of farmers'
conditions. This requires some diagnostic work in the
field, including observations of farmers' fields and
interviews with farmers. The diagnosis is used to help
identify major factors that limit farm productivity and to
help specify possible improvements.

The information from the diagnosis is used in planning
an experimental research program that includes
experiments in farmers' fields. The on-farm experiments
should be planted on the fields of representative
farmers. After the first year, the experimental results
form an important part of the information used for
planning research in subsequent crop cycles. Other
diagnostic work continues during the management of
the experimental program as researchers continue to
seek information about farmers' conditions and
problems which will be useful in planning future
experiments.











Figure 1.1. Stages of on-farm research


Policy
National goals.
input supply.
credit, markets.
etc.


Choose
target
farmers
and
research
priorities


Identify
policy
issues


On-Farm Research


1. Diagnosis
Review secondary
data, informal and
formal surveys

I
2. Planning
Select priorities for
research and design
on-farm experiments


1
3. Experimentation
Conduct
experiments in
farmers' fields to
formulate improved
technologies under
farmers' conditions

1
4. Assessment
-Farmer assessment
-Agronomic evaluation
-Statistical analysis
-Economic analysis

1
5. Recommendation
Demonstrate improved
technologies to
farmers


New
components
for on-farm
research


Experiment
Station
Develop and
screen new
technological
components


Identify
problems
for station
research










The results of the on-farm experiments must be
assessed. There are several elements in such an
assessment. First, researchers must discuss the results
with farmers to get their opinions of the treatments they
have seen in their fields. The farmers' assessment is
very important. The experimental results must also be
subjected to both an agronomic evaluation and a
statistical analysis. Finally, an economic analysis of the
results is essential. The economic analysis helps
researchers to look at the results from the farmers'
viewpoint, to decide which treatments merit further
investigation, and which recommendations can be made
to farmers. The procedures for carrying out such an
economic analysis are the subject of this manual.

The results of an assessment of on-farm experiments
can be used for several purposes. First, they may be
used to help plan further research. Some experiments
will have as their goal the clarification of production
problems: Is production limited by the availability of
phosphorus? Will improved weed control give an
important increase in yields? The answers to such
questions provide researchers with information for
further work. As Figure 1.1 shows, that information can
be used to plan subsequent experiments. It also may
help orient work on the experiment station.

Second, the results may be used to make
recommendations to farmers. Some experiments will
compare possible improvements to farmers' current
practices: Which level of phosphorus should be applied?
Which weed control method gives the best results? The
answers to these questions provide information to guide
farmers in their management decisions.

Finally, the results of on-farm experiments may
sometimes be used to provide information to
policymakers regarding current policy toward such
matters as input supply or credit regulations.
Experimental results can be used to help analyze policy
implementation: Given a significant response to
phosphorus, is the appropriate fertilizer available? Do
local credit programs allow farmers to take advantage of
new weed control methods? Although the emphasis in
this manual will be on the economic analysis of on-farm
experiments for guiding further research and making
recommendations to farmers, at several points links
between on-farm research and policy implementation
will be mentioned.










Goals of the Farmer
In order to make recommendations that farmers will
use, researchers must be aware of the human element
in farming, as well as the biological element. They must
think in terms of farmers' goals and the constraints on
achieving those goals.

In the first place, many farmers are primarily concerned
with assuring an adequate food supply for their families.
They may do this by producing much of what their
family consumes, or by marketing a certain proportion
of their output and using the cash to obtain food. Farm
enterprises also provide other necessities for the farm
family, either directly or through cash earnings. In
addition, the farm family is usually part of a wider
community, towards which it may have certain
obligations. To meet all of these requirements, farmers
often manage a very complex system of enterprises that
may include various crops, animals, and off-farm work.
Although the procedures of this manual concentrate on
the evaluation of improvements in particular crop
enterprises, it is essential that these new practices be
compatible with the larger farming system.

Second, whether farmers market little or most of their
produce, they are interested in the economic return.
Farmers will consider the costs of changing from one
practice to another and the economic benefits resulting
from that change. Farmers will recognize that if they
eliminate weeds from their fields they will likely benefit
by harvesting more grain. On the other hand, they will
realize that they must give up a lot of time and effort for
hand weeding, or that alternatively they must give up
some cash to buy herbicides and then expend some
time and effort to apply them. Farmers will weigh the
benefits gained in the form of grain (or other useful
products) against the things lost (costs) in the form of
labor and cash given up. What farmers are doing in this
case is assessing the difference in net benefits between
practices-the value of the benefits gained minus the
value of the things given up.

As farmers attempt to evaluate the net benefits of
different treatments, they usually take risk into account.
In the weed control example just mentioned, farmers
know that in the case of drought or early frost they may
get no grain, regardless of the type of weed control.
Farmers attempt to protect themselves against risks of
loss in benefits and often avoid choices that subject










them to these risks, even though such choices may on
average yield higher benefits than less risky choices do.
That farmers may prefer stable returns to the highest
possible returns is referred to as risk aversion.

Another factor in farmers' decision making, related to
risk aversion, is the fact that farmers tend to change
their practices in a gradual, stepwise manner. They
compare their practices with alternatives, and seek ways
of cautiously testing new technologies. It is thus more
likely that farmers will adopt individual elements, or
small combinations of elements, rather than a complete
technological package. This is not to say that farmers
will not eventually come to use all the elements of a
package of practices, but simply that in offering
recommendations to farmers it is best to think of a
strategy that allows them to make changes one step
at a time.


Characteristics of On-Farm Experiments
What are the characteristics of agronomic experiments
that will allow an appraisal of alternative technologies in
a way that parallels farmer decision making? The
following are five requirements of on-farm experiments
that must be fulfilled if the procedures described in this
manual are to be useful:

SThe experiments must address problems that are
important to farmers. It may be that farmers themselves
are not initially aware of a particular problem (e.g., a
nutrient deficiency or a disease), but if research does not
lead to possibilities for significantly improving farm
productivity, it will neither attract the interest of
farmers, nor merit assessment. Thus the experiments
demand a good understanding of farmers' agronomic
and socioeconomic conditions.

2 The experiments should examine relatively few factors
at a time. An on-farm experiment with more than, say.
four variables will be difficult to manage and may be
inappropriate given farmers' stepwise adoption behavior.

3 If researchers are to compare the farmers' practice with
various alternatives in order to make a recommendation.
then the farmers' practice should be included as one of
the treatments in the experiment. The farmers will want
to see this comparison in any case.





























































Once this work has been
done, and there is evidence
that farmers will adopt the
new insect control method.
it could be used as a
nonexperimental variable in
the fertilizer experiments.
as long as it is understood
that the fertilizer
recommendation to be
developed from such trials
depends on the farmers first
adopting the insect control
method.


SThe nonexperimental variables of an experiment should
reflect farmers' actual practice. It is sometimes tempting
to use higher levels of management for the
nonexperimental variables to increase the chances of
observable responses to the experimental variables. This
type of experiment may certainly be justified in some
cases, but the results usually cannot be used to make
recommendations to farmers.

An example may be useful. Assume that researchers
wish to carry out a fertilizer experiment in an area
where insects cause crop losses but farmers do not
control insects. There are four possibilities:

Carry out the fertilizer experiment with good insect
control. The experiment will give interesting
information on fertilizer response but will probably
not provide a relevant fertilizer recommendation for
farmers who do not use insect control. An analysis
of this experiment using the procedures in this
manual will give misleading results.

Carry out the fertilizer experiment without insect
control (the farmers' practice). The results can be
analyzed, using the procedures in this manual, to
decide what level of fertilizer is appropriate, given
farmers' current insect control practices.

If insects are indeed a very serious problem, it may
be better to experiment first with insect control
methods before experimenting with fertilizer. The
diagnosis and planning steps of on-farm research are
meant to help set these priorities. The methods of
this manual could then be used to help identify an
appropriate insect control method for
recommendation to farmers.1/

If insects and fertility are both serious problems,
then an experiment can be designed which takes
both insect control and fertilizer as experimental
variables. As long as one treatment represents the
farmers' practice with respect to both insect control
and fertility, the experiment can be analyzed using
the procedures in this manual.

5 Finally, not only must the management of
nonexperimental variables be representative cf farmers'
practice, but the experiments must be planted at
locations that are representative of farmers' conditions.



































Recommendation domain


If most of the farms are on steep slopes, the results of
experiments planted on an alluvial plain will probably
not be relevant. Similarly, if most farmers plant one
crop in rotation with another, experiments from fields
that have been under fallow may provide little useful
information. More will be said in the following section
about selecting locations.


Experimental Locations
and Recommendation Domains
The development of recommendations for farmers must
be as efficient as possible. The conditions under which
farmers live and work are diverse in almost every
respect imaginable. Farmers have different amounts and
kinds of land, different levels of wealth, different
attitudes toward risk, different access to labor, different
marketing opportunities, and so on. Many of these
differences can influence farmers' responses to
recommendations. But it is impossible to make a
separate recommendation for each farmer.

As a practical matter, researchers must
compromise by identifying groups of farmers who
have similar circumstances and for whom it is
likely that the same recommendation will be
suitable. In this manual such a group of farmers is
called a recommendation domain. Recommendation
domains may be defined by agroclimatic and/or by
socioeconomic circumstances. The definition of the
recommendation domain depends on the particular
recommendation. For example, a new variety may be
appropriate for all farmers in a given geographical area,
whereas a particular fertilizer recommendation may be
appropriate only for farmers who follow a certain
rotation pattern or whose fields have a certain type of
soil. Thus the recommendation domain for variety
would be different from the recommendation domain
for fertilizer.

Recommendation domains are identified, defined, and
redefined throughout the process of on-farm research.
They may be tentatively described during the first
diagnosis. Experimentation adds precision to the
definition of domains. The final definition may not be
developed until the recommendation is ready to be
passed to farmers.










When interpreting agronomic data to make their
recommendations, researchers must have a fairly clear
idea of the group of farmers who will be able to use this
information. Researchers must consider not only the
agroclimatic range over which the results will be
relevant, but also whether such factors as different
management practices or access to resources will be
important in causing some farmers to interpret the
results differently from others.

For the purposes of this manual, it is important that the
on-farm experiments be planted at locations that are
representative of the recommendation domain. The
economic analysis is done on the pooled data from a
group of locations of the same domain. The economic
analysis of results from a single location is not very
useful because, first, researchers cannot make
recommendations for individual farmers, and second,
one location will rarely provide sufficient agronomic
data to be extrapolated to a group of farmers. Thus all of
the examples in this manual will represent data from
several locations of one recommendation domain.


Introduction to Basic Concepts

To make good recommendations for farmers,
researchers must be able to evaluate alternative
technologies from the farmers' point cf view. The
premises of this manual are:

Farmers are concerned with the benefits and costs of
particular technologies.

2 They usually adopt innovations in a stepwise
fashion.

SThey will consider the risks involved in adopting
new practices.

These concerns will be treated in subsequent sections of
the manual. Part Two describes the construction of a
partial budget, which is used to calculate net benefits.
Part Three presents the techniques cf marginal analysis.
This is a way of evaluating the changes from one
technology to another by comparing the changes in
costs and net benefits associated with each treatment.
Part Four describes ways of dealing with the variability
that is characteristic of farmers' environments.
Variability in results from location to location and from










year to year, and in the costs of the inputs and prices of
crops, is of concern to farmers as they make production
decisions. Part Five summarizes the first four sections
and provides general guidelines for reporting research
results.

The following sections offer a brief overview of
these topics.


The Partial Budget
Partial budgeting is a method of organizing
experimental data and information about the costs and
benefits of various alternative treatments. As an
example, consider the farmers who are trying to decide
between their current practice of hand weeding and the
alternative of applying herbicide. Suppose that some
experiments have been planted on farmers' fields, and
the results show that the current farmer practice of
hand weeding results in average yields of 2,000 kg/ha,
while the use of herbicides gives an average yield of
2,400 kg/ha.


Table .1.. Example of a partial budget

Hand
weeding Herbicide

Average yield (kg/ha) 2,000 2,400
Adjusted yield (kg/ha) 1,800 2,160
Gross field benefits (S/ha) 3,600 4,320

Cost of herbicide ($/ha) 0 500
Cost of labor to apply
herbicide (S/ha) 0 100
Cost of labor for hand
weeding (S/ha) 400 0

Total costs that vary (S/ha) 400 600

Net benefits (S/ha) 3,200 3,720


Table 1.1 shows a partial budget for this weed control
experiment. There are two columns, representing the
two treatments (hand weeding and herbicide). The first
line of the budget presents the average yield from all
locations in the recommendation domain for each of the
two treatments. The second line is the adjusted yield.










Although the experiments were planted on
representative farmers' fields, researchers have judged
that farmers using the same technologies would obtain
yields 10% lower than those obtained by the
researchers. They have therefore adjusted the yields
downwards by 10% (yield adjustment will be discussed
in Chapter 3).

The next line is the gross field benefits, which values
the adjusted yield for each treatment. To calculate the
gross field benefits it is necessary to know the field price
of the crop. The field price is the value of one kilogram
of the crop to the farmer, net of harvest costs that are
proportional to yield. In this example the field price is
$2/kg (i.e., 1,800 kg/ha x $2/kg = $3,600/ha).2/

Farmers can now compare the gross benefits of each
treatment, but they will want to take account of the
different costs as well. In considering the costs
associated with each treatment, the farmers need only
be concerned by those costs that differ across the
treatments, or the costs that vary. Costs (such as
plowing and planting costs) that do not differ across
treatments will be incurred regardless of which
treatment is used. They do not affect the farmers'
choices concerning weed control and can be ignored for
the purpose of this decision. The term "partial budget"
is a reminder that not all production costs are included
in the budget-only those that are affected by the
alternative treatments being considered.

In this case, the costs that vary are those associated
with weed control. Table 1.2 shows how to calculate
these costs. Note that they are all calculated on a per-


Table 1.2. Calculation of costs that vary

Price of herbicide $250/1
Amount used 2 1/ha
Cost of herbicide $500/ha

Price of labor $50/day
Labor to apply herbicide 2 days/ha
2/ The use of the S sign in this Cost of labor to apply herbicide $100/ha
manual is not intended to
represent any particular
currency, and several Price of labor $50/day
different currencies are
assumed in the examples Labor for hand weeding 8 days/ha
tat follow. Additional Cost of labor for hand weeding $400/ha
abbreviations used in this
manual are: hectare (ha),
kilogram (kg), and liter (1).










hectare basis. The total costs that vary for each
treatment is the sum of the individual costs that vary.
In this example, the total costs that vary for the present
practice of hand weeding is $400/ha, while the total
costs that vary for the herbicide alternative is $600/ha.

The final line of the partial budget shows the net
benefits. This is calculated by subtracting the total costs
that vary from the gross field benefits. In the weed
control example, the net benefits from the use of
herbicide are $3,720/ha, while those for the current
practice are $3,200/ha. Net benefits are not the same
thing as profit, because the partial budget does not
include the other costs of production which are not
relevant to this particular decision. The computation of
total costs of production is sometimes useful for other
purposes, but will not be covered in this manual.

A partial budget, then, is a way of calculating the total
costs that vary and the net benefits of each treatment in
an on-farm experiment. The partial budget includes the
average yields for each treatment, the adjusted yields
and the gross field benefit (based on the field price of
the crop). It also includes all the costs that vary for each
treatment. The final two lines are the total costs that
vary and the net benefits.


Marginal Analysis
In the weed control example, the net benefits from
herbicide use are higher than those for hand weeding. It
may appear that farmers would choose to adopt
herbicides, but the choice is not obvious, because
farmers will also want to consider the increase in costs.
Although the calculation of net benefits accounts for the
costs that vary, it is necessary to compare the extra (or
marginal) costs with the extra (or marginal) net benefits.
Higher net benefits may not be attractive if they require
very much higher costs.

If the farmers in the example were to adopt herbicide, it
would require an extra investment of $200/ha, which is
the difference between the costs associated with the use
of herbicide ($600)and the costs of their current
practice ($400). This difference can then be compared to
the gain in net benefits, which is $520/ha ($3,720-$3,200).

In changing from their current weed control practice to
a herbicide the farmers must make an extra investment
of $200/ha; in return, they will obtain extra benefits of










8520/ha. One way of assessing this change is to divide
the difference in net benefits by the difference in costs
that vary ($520/$200 = 2.6). For each $1/ha on average
invested in herbicide, farmers recover their $1, plus an
extra $2.6/ha in net benefits. This ratio is usually
expressed as a percentage (i.e., 260%)and is called the
marginal rate of return.

The process of calculating the marginal rates of return
of alternative treatments, proceeding in steps from the
least costly treatment to the most costly, and deciding if
they are acceptable to farmers, is called marginal
analysis.


Variability
In addition to being concerned about the net benefits of
alternative technologies and the marginal rates of return
in changing from one to another, farmers also take into
account the possible variability in results. This
variability can come from several sources, which
researchers need to consider in making
recommendations.

Experimental results will always vary somewhat from
location to location and from year to year. An agronomic
assessment of the trial results will help researchers
decide whether the locations are really representative of
a single recommendation domain, and can therefore be
analyzed together, or whether the experimental
locations represent different domains. This type of
agronomic assessment helps refine domain definitions
and leads to more precisely targeted recommendations.

Another source of variability in experimental results
derives from factors that are impossible to predict or
control, such as droughts, floods, or frosts. These are
risks that the farmers have to confront, and if the
experimental data reflect these risks, they should be
included in the.analysis.

Finally, farmers are also aware that their economic
environment is not perfectly stable. Crop prices change
from year to year, labor availability and costs may
change, and input prices are also subject to variation.
Although such changes are difficult to predict with
accuracy, there are techniques that allow researchers to
consider their recommendations in view of possible
changes in farmers' economic circumstances.





Part Tw Th atude
U U SS


Chapter Two
Costs That Vary


Costs that vary


Opportunity cost












Field price (of an input)


The first step in doing an economic analysis of on-farm
experiments is to calculate the costs that vary for each
treatment. Costs that vary are the costs (per
hectare) of purchased inputs, labor, and machinery
that vary between experimental treatments.
Farmers will want to evaluate all the changes that are
involved in adopting a new practice. It is therefore
important to take into consideration all inputs that are
affected in any way by changing from one treatment to
another. These are the items associated with the
experimental variables. They may include purchased
inputs such as chemicals or seed, the amount or type of
labor, and the amount or type of machinery. These
calculations should be done before the experiment is
planted, as part of the planning process, to get an idea
of the costs of the various treatments that are being
considered for the experimental program.

In developing a partial budget, a common measure is
needed. It is of course not possible to add hours of labor
to liters of herbicide and compare these with kilograms
of grain. The solution is to use the value of these
factors, measured in currency units, as a common
denominator. This provides an estimate of the costs of
investment measured in a uniform manner. It does not
necessarily imply that farmers spend money for labor or
receive money for grain. Neither does it imply that
farmers are concerned only with money. It is simply a
device to represent the process that farmers go through
when comparing the value of the things gained and the
value of the things given up.

An important concept in these calculations is that of
opportunity cost. Not all costs in a partial budget
necessarily represent the exchange of cash. In the case
of labor, for instance, farmers may do the work
themselves, rather than hire others to do it. The
opportunity cost can be defined as the value of any
resource in its best alternative use. Thus if farmers
could be earning money as laborers, rather than
working on their own farms, the opportunity cost of
their weeding is the net wage they would have earned
had they chosen not to stay on the farm and weed. The
concept of opportunity cost will be discussed at several
points in the following sections.

The field price of a variable input is the value
which must be given up to bring an extra unit of
input into the field. The field price is expressed in
units of sale (e.g., $ per kilogram of seed, liter of
herbicide, day of labor, or hour of tractor time).





Part Tw Th atude
U U SS


Chapter Two
Costs That Vary


Costs that vary


Opportunity cost












Field price (of an input)


The first step in doing an economic analysis of on-farm
experiments is to calculate the costs that vary for each
treatment. Costs that vary are the costs (per
hectare) of purchased inputs, labor, and machinery
that vary between experimental treatments.
Farmers will want to evaluate all the changes that are
involved in adopting a new practice. It is therefore
important to take into consideration all inputs that are
affected in any way by changing from one treatment to
another. These are the items associated with the
experimental variables. They may include purchased
inputs such as chemicals or seed, the amount or type of
labor, and the amount or type of machinery. These
calculations should be done before the experiment is
planted, as part of the planning process, to get an idea
of the costs of the various treatments that are being
considered for the experimental program.

In developing a partial budget, a common measure is
needed. It is of course not possible to add hours of labor
to liters of herbicide and compare these with kilograms
of grain. The solution is to use the value of these
factors, measured in currency units, as a common
denominator. This provides an estimate of the costs of
investment measured in a uniform manner. It does not
necessarily imply that farmers spend money for labor or
receive money for grain. Neither does it imply that
farmers are concerned only with money. It is simply a
device to represent the process that farmers go through
when comparing the value of the things gained and the
value of the things given up.

An important concept in these calculations is that of
opportunity cost. Not all costs in a partial budget
necessarily represent the exchange of cash. In the case
of labor, for instance, farmers may do the work
themselves, rather than hire others to do it. The
opportunity cost can be defined as the value of any
resource in its best alternative use. Thus if farmers
could be earning money as laborers, rather than
working on their own farms, the opportunity cost of
their weeding is the net wage they would have earned
had they chosen not to stay on the farm and weed. The
concept of opportunity cost will be discussed at several
points in the following sections.

The field price of a variable input is the value
which must be given up to bring an extra unit of
input into the field. The field price is expressed in
units of sale (e.g., $ per kilogram of seed, liter of
herbicide, day of labor, or hour of tractor time).










The field cost is the field price multiplied by the
l cosquantity of the input needed for a given area. Thus
field costs are usually expressed in $ per hectare. If the
field price of herbicide is $10/1, and if 3 1/ha are
required, then the field cost of the herbicide is $30/ha.
In both cases, the emphasis is on the field, i.e., what the
farmers pay to obtain the input and to transport it to
their farms. Such field prices may be quite different
from official prices.


Identifying Variable Inputs
To identify which inputs are affected by the alternative
treatments included in an experiment, researchers must
be familiar with farmers' practices as well as the
practices used in the experiment. They must then ask
themselves which operations change from treatment to
treatment and make a list of these.

For example, consider an experiment in which two
different fungicides (A and B) are being tested, along
with the farmers' practice of no fungicide application.
There are thus three treatments in the experiment. The
list of variable inputs is as follows:

Fungicide A
Fungicide B
Labor to apply each fungicide
Labor to haul water for mixing with each fungicide
Rental of sprayer to apply each fungicide

This list includes purchased inputs (the fungicides),
labor, and equipment (a sprayer). The following sections
explain how to calculate the costs for all of these.


Purchased Inputs
Purchased inputs include such items as seed, pesticides,
fertilizer, and irrigation water. The best way to estimate
the field price of a purchased input is to go to the place
where most of the farmers buy their inputs and check
the retail price for the appropriate size of package. For
instance, if farmers normally purchase their insecticide
in 1-kg packets in a rural market, that is the price that
should be used, rather than the price of insecticide in
25-kg sacks in the capital city.










In some situations, the farmers will be selecting seed
from their previous crop, rather than buying seed. This
seed is not without cost. The best way to estimate the
opportunity field price of the farmers' own seed is to use
the price that farmers pay if they buy local seed, either
from each other or at the market.

The next step is to find out how the farmers get the
input to the farm. Such inputs as insecticides and
herbicides, which are not. bulky, can be carried by the
farmers and transportation costs are probably not
important. But for fertilizer and perhaps seed, this is not
the case. Usually the farmers must use a truck or
an animal to get the input to the farm. If this is so, a
transportation charge must be added to the retail price.
As many farmers pay others to transport such items for
them, it is not difficult to learn what the normal charges
are. In general, it is best to be guided by the practice
that is followed by the majority of farmers in the
recommendation domain.

For example, if a 50-kg sack of urea costs $375 in the
market, and it costs $25 to transport the 50 kg to the
farm, then the field price of urea is calculated as follows:

$375 cost of 50 kg urea in market
+ $ 25 cost of transporting 50 kg to farm
$400 field price of 50 kg urea

or $400
0 k= $8/kg, field price of urea
o50 kg

Very often fertilizer experiments, especially those in the
early stages of investigation, use single-nutrient
fertilizers. The treatments are usually expressed in
terms of amounts of the nutrient (e.g. 50 kg N/ha or 40
kg P205/ha). In these cases, it is useful to go one step
further and calculate the field price of the nutrient. This
can be done by simply dividing the field price of the
fertilizer by the proportion of nutrient in the fertilizer. In
the case of urea, which is 46% nitrogen,

$8/kg urea
= $17.4/kg N, field price of N
0.46 kg N/kg urea

The field cost of 50 kg N in a particular treatment using
urea would be 50 x $17.4, or $870/ha.










This should be done only when working with single-
nutrient fertilizers, and it presumes that the field price
of nitrogen (for instance) is roughly the same in any
nitrogen fertilizer available. If it is not, researchers
should of course be aware of this and take these
differences into account when considering which
fertilizer to experiment with and recommend.

A final point about purchased inputs is in order. This
discussion has assumed that the inputs in the
experiments are available in local markets, or can be
made available. If this is not the case, then the
economic analysis of experiments involving such inputs
may be of little immediate use to farmers. The results
may be used, however, to communicate to policymakers
the possible benefits of making a particular input
available.


Equipment and Machinery
Some experimental treatments may require the use of
equipment not required by other treatments. It is then
necessary to estimate a field cost per hectare for the use
of the equipment.

The easiest way to estimate the per-hectare field cost of
equipment is to use the average rental rate in the area.
For example, if farmers rent their sprayers for $20/day
and if the sprayer can cover 2 ha in one day, then the
field cost may be taken as $10/ha. When estimating the
field cost of tractor-drawn or animal-drawn implements,
or small self-powered implements, the average rental
rate in the area can also be used. This is especially
appropriate if most farmers are renting the implements
anyway, but even for farmers who own their equipment.
the rental rate is a good estimate of the opportunity field
cost. In certain cases a pro-rated cost per hectare can be
calculated, using the retail price of the equipment and
its useful lifetime, but this calculation involves factors
such as repair costs, fuel costs, and the possibility that
the equipment will have other uses on the farm. If a pro-
rated field cost is to be calculated, it is best to consult
an agricultural economist who is familiar with the
equipment and costing techniques.


Labor
It is very important to take into consideration all of the
changes in labor implied by the different treatments in
an experiment. Estimates of labor time should come










from conversations with farmers and perhaps direct
observation in their fields. Information about labor use
derived from the experimental plots is not very useful,
however, if small plots are used in the experiments. The
best way to get this information is to visit with several
different farmers. Each will have an opinion as to the
time required for a given operation, but a number close
to the average of these opinions will provide a good
estimate. Not all farmers take the same amount of time
for a given task, so the estimates will only be
approximate. For new activities with which farmers are
completely unfamiliar, some educated guesses will have to
be made until more reliable estimates can be developed.

If farmers hire labor for the operations in question, the
field price of labor is the local wage rate for day laborers
in the recommendation domain, plus the value of
nonmonetary payments normally offered, such as meals
or drinks. This wage rate can be estimated by talking to
several farmers. The field cost of labor for a particular
treatment is then the field price of labor multiplied by
the number of days per hectare required.

When members of the farm family will do the work, it is
necessary to estimate the opportunity cost of family
labor. This is the value which is given up to do the work
and thus represents a real cost. For example, if farmers
must take a day off from working in town to do extra
weeding, they will give up a day's wages in town. This
opportunity cost is just as real as paying a laborer to do
the work. And even if the farmer has nothing to do but
sit in the shade, the opportunity cost is not zero, as
most people put some value on being able to sit in the
shade rather than work in the sun.

The best place to start in estimating an opportunity field
price for family labor is the local wage rate (plus
nonmonetary payments). It is not unusual to find the
rate higher during some periods of the year than others,
and this must be taken into account.

It is sometimes difficult to estimate an opportunity cost
of family labor, especially if local labor markets are
poorly developed. Labor availability may vary
seasonally, or across different types of farm households.
Labor availability and labor bottlenecks are two of the
most important types of diagnostic information that aid
in selecting appropriate treatments for experiments and
in defining recommendation domains. If labor is scarce
at a particular time, extreme caution must be used in
experimenting with technologies that further increase






































Total costs that vary


the labor demand at that time. In cases such as this, it
is reasonable to set the opportunity cost of labor above
the going wage rate. On the other hand, if additional
labor is to be used during a relatively slack period, an
opportunity cost below the going wage rate might be
appropriate. But in no case should the opportunity cost
of labor be set at zero.

In situations where most farm labor is provided by the
family, and where the new technologies being
considered change the balance between cash
expenditures (i.e., for inputs) and labor, special care
must be taken in estimating labor costs. If a particular
treatment involves a large change in labor input,
relatively small differences in the opportunity cost of
labor will have significant effects on the estimation of
the cost of the treatment.


Total Costs That Vary
Once the variable inputs have been identified, their field
prices determined, and the field costs calculated, the
total costs that vary for each treatment can be
calculated. The total costs that vary is the sum of
all the costs that vary for a particular treatment. A
description of a weed control by seeding rate experiment
is provided in Table 2.1; the calculation of costs that
vary is shown in Table 2.2; and the calculation of the
total costs that vary is shown in Table 2.3.


Table 2.1. Weed control by seeding rate experiment wheatl


Treatment
1a/
2
3
4
a/ Farmers' practice


Weed control


No weed control
Herbicide (2 1/ha)
No weed control
Herbicide (2 1/ha)


Seeding rate
120 kg/ha
120 kg/ha
160 kg/ha
160 kg/ha


Data


Field price of seed
Field price of herbicide
Field price of labor
Field price of sprayer
Labor to apply herbicide
Labor to haul water


$20/kg
$350/1
$250/day (local wage rate)
$75/day (rental rate)
2 days/ha
One laborer can haul 400 I/day
(200 1 water/ha are required for
the herbicide)










Table 2.2. Calculation of costs that vary


Cost of seed Treatments 1 and 2: 120 kg/ha x $20/kg = $2,400/ha
Treatments 3 and 4: 160 kg/ha x $20/kg = $3,200/ha
Cost of herbicide Treatments 2 and 4: 2 1/ha x $350/1 = $700/ha
Cost of labor to apply herbicide Treatments 2 and 4: 2 days/ha x $250/day = $500/ha

Cost of labor to haul water Treatments 2 and 4: 200 1 required x $250/day = $125/h
400 I/day

Cost of sprayer Treatments 2 and 4: 2 days/ha x $75/day = $150/ha


Table 2.3. Total costs that vary for weed control by seeding rate experiment

Treatment

1 2 3 4
Seed (S/ha) 2,400 2,400 3.200 3.200
Herbicide (S/ha) 0 700 0 700
Labor to apply herbicide (S/ha) 0 500 0 500
Labor to haul water (S/ha) 0 125 0 125
Sprayer ($/ha) 0 150 0 150
Total costs that vary (S/ha) 2,400 3,875 3,200 4,675

The perceptive reader will have noticed that not all of
the costs that vary have been treated in this chapter.
There are two important exceptions. Costs associated
with harvest and marketing are discussed in the next
chapter, where they are included in the field price of the
crop. Costs associated with obtaining working capital,
such as interest rates, are discussed in Chapter 5.











Chapter Three
Gross Fleld Benefits,
Net Benefits,
and the Partial
Budget


There are several steps involved in calculating the
benefits of the treatments in an on-farm experiment:

Step 1. Identify the locations that belong to the same
recommendation domain. The economic
analysis is done on the pooled results of an
experiment that has been planted in several
locations for one recommendation domain.

Step 2. Next, calculate the average yields across sites
for each treatment. If the results of these
experiments are agronomically consistent and
understandable, do a statistical analysis of the
pooled results. If there is no reasonable
evidence of differences among treatment yields,
researchers need only consider the differences
in costs among the treatments. But if there are
real yield differences, then researchers should
continue with the partial budget.

Step 3. Adjust the average yields downwards, if it is
believed that there are differences between the
experimental results and the yield farmers
might expect using the same treatment.

Step 4. Calculate a field price for the crop and multiply
by the adjusted yields to give the gross field
benefits for each treatment.

Step 5. Finally, subtract the total costs that vary from
the gross field benefits to give the net benefits.
With this calculation the partial budget
is complete.


Pooling the Results From the
Same Recommendation Domain
The first line of a partial budget is the average yield for
each treatment for a particular experiment for all
locations for a recommendation domain. Recall that a
recommendation domain is a group of farmers whose
circumstances are similar enough that they will all be
eligible for the same recommendation. Tentative










identification of recommendation domains begins during
the diagnostic and planning stages of on-farm research.
This tentative identification is used for selecting
locations for planting experiments. The recommendation
domain for a fertilizer experiment, for example, might
be defined in terms of farmers who plant the target
crop, whose fields have certain types of soil, and who
follow a particular crop rotation. Locations for the
experiments are chosen to represent farmers with those
particular circumstances. Upon analyzing the results it
may be found that a factor not previously considered,
such as the slope of the field, is responsible for different
results between locations. In such a case, the
experiments from the tentative domain would not all be
combined for economic analysis. Instead, they would be
divided into two domains (further defined by slope, in
this case), and two separate analyses would be carried
out. More detail on how and when to pool experimental
results is presented in Chapter 7.

It should be noted here that although locations can be
eliminated from analysis if it can be demonstrated that
they do not belong to the recommendation domain in
question, this does not hold for locations where trials
were severely damaged by drought, flooding, or other
environmental factors that are not predictable. Such
locations must be included in the economic analysis
because they are outcomes that farmers will experience,
too. Further discussion of risk analysis is to be found in
Chapter 8.


Assessing Experimental
Results Before Economic Analysis

Before doing an economic analysis of the pooled results
of an experiment for a particular recommendation
domain, researchers must assess the experimental data
to verify that the observed responses make sense from
an agronomic standpoint. Researchers must also review
the statistical analysis of the experimental data.
Performing an economic analysis on experimental data
that researchers do not understand, or do not have
confidence in, is a misuse of the techniques presented in
this manual.

If statistical analysis of the results of an experiment
indicates that there are no relevant differences between
two treatments, then the lower cost treatment would be
preferred. If researchers have evidence that treatment











yields are probably about the same, the gross benefits
for these treatments will also be similar, and the lowest
cost method of achieving those benefits should be
chosen. If two methods of weed control give equivalent
results, for instance, the method with the lower costs
that vary should be chosen (for further experimentation
or for recommendation) and no further economic
analysis is needed.

More details on the relation of statistical analysis to
economic analysis are given in Chapter 7.


Average Yield

When the recommendation domain for a particular
experiment has been established and agronomic and
statistical assessments have indicated that it is
worthwhile to proceed with a partial budget, the average
yields of each treatment are entered on the first line of
the partial budget.

Table 3.1 shows the results from five locations in one
recommendation domain of the weed control by seeding
rate experiment described in Tables 2.1-2.3. There were
two replications at each location. Note that the results
from location 5, which was affected by drought, are
included in the average.3/


Table 3.1. Yields (kg/hal for weed control by seeding rate experiment

Treatment
I
No weed control
120 kg seed/ha
(farmers' practice)

Replication
Location 1 2 Avg.

1 2,180 2.220 2,200
3/ Note that the individual 1 2,180 2220 2200
treatment yields are 2 2,800 2.640 2.720
reported to the nearest
10 kg/ha, to reflect the 3 1,720 1,880 1,800
reliability of the data. It
should be remembered that 4 2,680 2,620 2,650
neither the average yields 5a/ 530 670 600
nor any of the results of
calculations done with them
can be more precise than
the original yield data on Average yield 1,994
which they are based. Thus
the final digit reported in
the average yields is not a/ Affected by drought
significant and is
maintained in the partial
budget for convenience
only.


















Adjusted yield


The average yields for the four treatments are reported
on the first line of the partial budget (Table 3.2, p. 27).


Adjusted Yield
The next step is to consider adjusting the average
yields. The adjusted yield for a treatment is the
average yield adjusted downward by a certain
percentage to reflect the difference between the
experimental yield and the yield farmers could
expect from the same treatment. Experimental
yields, even from on-farm experiments under
representative conditions, are often higher than the
yields that farmers could expect using the same
treatments. There are several reasons for this:


1 Management. If they manage the experimental
variables, researchers can often be more precise and
sometimes more timely than farmers in operations such
as plant spacing, fertilizer application, or weed control.
Further bias will be introduced if researchers manage
some of the nonexperimental variables.

2 Plot size. Yields estimated from small plots often
overestimate the yield of an entire field because of errors
in the measurement of the harvested area and because



in one recommendation domain


Treatment
2
Herbicide (2 I/ha)
120 kg seed/ha


Replication
1 2


3,030
3,090
2,200
3,270
860


2,570
3,410
2,180
3,090
740


Avg.

2,800
3,250
2,190
3,180
800

2,444


Treatment
3
No weed control
100 kg seed/ha


Replication
1 2


2,440
2,790
1,820
2,950
700


2,180
3,010
1,680
2,770
500


Avg.


2.310
2.900
1,750
2,860
600

2,084


Replication
1 2


3,200
3,410
2,410
3,400
620


3,060
3.510
2,230
3,480
680


Treatment
4
Herbicide 12 I/ha)
160 kg seed/he


Avg.

3,130
3,460
2,320
3,440
650

2,600










the small plots tend to be more uniform than
large fields.

Harvest date. Researchers often harvest a crop at
physiological maturity, whereas farmers may not
harvest at the optimum time. Thus even when the
yields of both researchers and farmers are adjusted to a
constant moisture content, the researchers' yield may
be higher, because of fewer losses to insects, birds,
rodents, ear rots, or shattering.

Form of harvest. In some cases farmers' harvest
methods may lead to heavier losses than result from
researchers' harvest methods. This might occur, for
example, if farmers harvest their fields by machine and
researchers carry out a more careful manual harvest.

Unless some adjustment is made for these factors, the
experimental yields will overestimate the returns that
farmers are likely to get from a particular treatment.
One way to estimate the adjustment required is to
compare yields obtained in the experimental treatment
which represents farmers' practice with yields from
carefully sampled check plots in the farmers' fields.
Where this is not possible, it is necessary to review each
of the four factors discussed earlier and assign a
percentage adjustment. As a general rule, total
adjustments between 5 and 30% are appropriate. A
yield adjustment of greater magnitude than 30% would
indicate that the experimental conditions are very
different from those of the farmers, and that some
changes in experimental design or management might
be in order. Many cf these problems regarding yield
adjustment are eliminated if the farmers manage the
experiment. Decisions regarding experimental
management will depend on several factors, but where
possible the farmers should certainly manage the
nonexperimental variables. As the experimentation
moves into later stages, farmers should also manage the
experimental variables.

In the case of the weed control by density experiment in
wheat, researchers estimated that their methods of
seeding and of herbicide application were more precise
than those of the farmers, and so estimated a yield
adjustment of 10% due to management differences. Plot
size was also judged to be a factor, and a further 5%
adjustment was suggested. Since the plots were
harvested at the same time as those of the farmers, no
adjustment was needed to account for differences in










harvest date. However, the plots were harvested with a
small combine harvester, while the farmers used larger
machines, and the difference in harvest loss was judged
to be about 5%.Thus the total yield adjustment for this
experiment was estimated to be 20%. The second line of
the partial budget (Table 3.2) thus adjusts the average
yields downwards by 20%. For instance, the average
yield for Treatment 1 is 1,994 kglha and the adjusted
yield is 80% x 1,994 or 1,595 kglha.

It is obvious that this type of adjustment is not precise,
nor does it pretend to be. The point is that it is much
better to estimate the effect of a factor than to ignore it
completely. As researchers gain more experience in an
area they will have better estimates of the differences
between farmers' fields and the experiments, and yield
adjustments will become more accurate. The yield
adjustment, although approximate, should not be looked
upon as a factor to be applied mechanically. Each type
of experiment, each year, should be reviewed before
deciding on an appropriate adjustment. If this is done,
researchers will be able to make decisions about new
technologies with a realistic appreciation of farmers'
conditions.


Field Price of the Crop

Field price (of output) The fieldprice of the crop is defined as the value
to the farmer of an additional unit of production in
the field, prior to harvest. It is calculated by
taking the price that farmers receive (or can
receive) for the crop when they sell it, and
subtracting all costs associated with harvest and
sale that are proportional to yield, that is, costs
that can be expressed per kilogram of crop.

The place to start is the sale price of the crop. This is
estimated by finding out how the majority of the
farmers in the recommendation domain sell their crop,
to whom they sell it, and under what conditions (such
as discounts for quality). Crop prices often vary
throughout the year, but it is best to use the price at
harvest time. It is the amount that the farmer actually
receives, rather than the official or market price of the
crop, that is of interest.

Next, subtract the costs of harvest and marketing that
are proportional to yield. These may include the costs of
harvesting, shelling, threshing, winnowing, bagging, and
transport to the point of sale. These costs have to be

25










estimated on a per-kilogram basis. In the case of
harvesting or shelling, for instance, this may require
collecting data on the average amount of labor
necessary to harvest a field of defined size and yield, or
shell a given quantity of grain. Again, these may be
cash costs or opportunity costs.

* If farmers sell maize to traders for $6.00/kg,

* and they incur harvesting costs of $0.30/kg,

shelling costs of $0.20/kg,

* and transport costs of $0.20/kg,

* then the field price of an additional unit of maize is:
$6.00 ($0.30 + 0.20 + 0.20) = $5.30/kg.

It is important to account for these costs because they
are proportional to the yield; the higher the yield of a
particular treatment, the higher the cost (per hectare) of
harvesting, shelling, and transport. That is, the cost of
harvesting, shelling, and transporting 200 kg is almost
exactly twice the cost of performing the same activities
for a harvest of 100 kg. As these costs will differ across
treatments (because the treatment yields are different),
they must be included in the analysis. It is convenient
to treat these costs separately from the costs that vary
(described in Chapter 2) because. although they do vary
across treatments, they are incurred at the time of
harvest and thus do not enter into the marginal analysis
of the returns to resources invested. That is, farmers
have to wait perhaps five months to recover their
investment in purchased inputs, but only a few days to
recover harvest-related costs.

If there are costs associated with harvest or sale that do
not vary with the yield, then these should not be
included in the field price, nor in the partial budget. In
the example of the weed control by seeding rate
experiment, the farmers sell their wheat in town for
$9/kg. The harvesting is done by combine, and the
operators charge on a per-hectare basis (regardless of
yield), so harvest cost is not included in the calculation
of field price.

* There is a bagging charge of $O.10/kg,










* transport charge of $0.50/kg,


and a market tax of $0.40/kg,

so the field price of the wheat is:
$9.00 ($0.10 + 0.50 + 0.40) = $8.00/kg.

The gross field benefits for each treatment is
Gross field benefits calculated by multiplying the field price by the
adjusted yield. Thus the gross field benefits for
Treatment 1 is 1,595 kg/ha x $8/kg = $12,760/ha.

Although the field price is based on the sale price of the
crop, the concept can normally be used even in
situations where farmers do not produce enough for
their own needs. An alternative would be to calculate an
opportunity field price for the crop, based on the money
price the farm family would have to pay to acquire an
additional unit of the product for consumption (see note
5, p. 35). But under most conditions use of the field
price is adequate for estimating the value of the product
to farmers, even when the product is not sold, and this
is the approach that will be followed in this manual.


Table 3.2. Partial budget, weed control by seeding rate experiment

Treatment
1 2 3 4
Average yield (kg/ha) 1,994 2,444 2,084 2,600
Adjusted yield (kg/ha) 1,595 1,955 1,667 2,080
Gross field benefits (S/ha) 12,760 15,640 13,336 16,640
Cost of seed (S/ha) 2,400 2,400 3,200 3,200
Cost of herbicide (S/ha) 0 700 0 700
Cost of labor to apply
herbicide (S/ha) 0 500 0 500
Cost of labor to haul
water ($/ha) 0 125 0 125
Cost of sprayer
rental (S/ha) 0 150 0 150

Total costs that vary (S/ha) 2,400 3,875 3,200 4,675
Net benefits (S/ha) 10,360 11,765 10,136 11,965











Net Benefits


Net benefits


4/ It is important to remember
that the net benefits do not
have greater precision than
the original yield data
(which in this case were
reported to three significant
digits in Table 3.1). When
using a calculator for
further operations (such as
calculating the marginal
rates of return), it is
convenient to take the
numbers as they appear in
the partial budget, but for
final reporting researchers
may wish to round the net
benefits (e.g.. $1 1.800
instead of $11,765 in
Treatment 2).


Table 3.2 is a partial budget for the weed control by
seeding rate experiment. The final line of the partial
budget is the net benefits. This is calculated by
subtracting the total costs that vary from the
gross field benefits for each treatment.4/


Including All Gross Benefits in the Partial Budget

The examples discussed above have assumed that a
single product is the only thing of value to the farmers
from their fields. This is often not the case. In many
regions crop residues have considerable fodder value, for
instance. The procedure for estimating the gross field
benefit for fodder is exactly the same as that for
estimating the value of grain. First estimate production
(by treatment) and adjust the average yields. Then
calculate a field price. Of course "harvesting" becomes
"collecting," "shelling" becomes "baling," and so forth.
It is important to consider each activity that is
performed (for instance, is maize fodder chopped?).
Multiplying the field price of the fodder by the adjusted
fodder yield gives the gross field benefit from fodder,
and this should be added to the gross field benefit
from grain.

Another important example is that of intercropping. If
the majority of farmers in the recommendation domain
intercrop, then experiments should reflect that practice.
(Intercropping experiments may of course include
individual treatments with a single crop as well, if that
is considered a possible alternative.) It may be that the
experimental variables affect only one crop, but if
farmers intercrop maize and beans, for instance, then a
fertilizer experiment on maize should include beans, or
a disease control experiment on beans should be planted
with maize. The yields of both crops should be
measured, since treatments may have a direct or
indirect effect on the associated crop. The partial budget
would then have two average yields, two adjusted
yields, and two gross field benefits.

The total costs that vary would be subtracted from the
sum of the two gross field benefits to give the net
benefits. Table 3.3 gives an example.











Table 3.3. Partial budget for an experiment on bean density and phosphorus application In a
maize-bean intercrop

Treatment

1 2 3 4

Bean density (plants/ha) 40.000 60,000 80,000 80.000
Phosphorus rate (kg P205/ha) 30 30 30 60


Average bean yield (kg/ha) 650 830 890 980
Average maize yield (kg/ha) 2,300 2,020 1.700 1,790

Adjusted bean yield (kg/ha) 553 706 757 833
Adjusted maize yield (kg/ha) 1.955 1,717 1,445 1,522

Gross field benefits,
beans (S/ha) 17.143 21,886 23,467 25,823
Gross field benefits,
maize (S/ha) 14.663 12,878 10,838 11,415

Total gross field benefits (S/ha) 31.806 34,764 34.305 37.238

Cost of bean seed (S/ha) 900 1,350 1,800 1,800
Cost of labor for planting
beans ($/ha) 450 675 900 900
Cost of fertilizer (S/ha) 1,050 1.050 1.050 2.100

Total costs that vary (S/ha) 2.400 3.075 3.750 4,800

Net benefits (S/ha) 29.406 31,689 30.555 32.438





P a r T h e M a g i a A n l s i


Chapter Four
The Net Benefit Curve
and the Marginal
Rate of Return


Dominance analysis


In the previous chapter a partial budget was developed
to calculate the total costs that vary and the net benefits
for each treatment of an experiment. This chapter
describes a method for comparing the costs that vary
with the net benefits. This comparison is important to
farmers because they are interested in seeing the
increase in costs required to obtain a given increase in
net benefits. The best way of illustrating this
comparison is to plot the net benefits of each treatment
versus the total costs that vary. The net benefit curve
(actually, a series of lines) connects these points. The
net benefit curve is useful for visualizing the changes in
costs and benefits in passing from one treatment to the
treatment of next highest cost. The net benefit curve
also clarifies the reasoning behind the calculation of
marginal rates of return, which compare the increments
in costs and benefits between such pairs of treatments.
Before proceeding with the net benefit curve and the
calculation of marginal rates of return, however, an
initial examination of the costs and benefits of each
treatment, called dominance analysis, may serve to
eliminate some of the treatments from further
consideration and thereby simplify the analysis.


Dominance Analysis
Table 4.1 lists the total costs that vary and the net
benefits for each of the treatments in the weed control
by seeding rate experiment from the previous chapter.

Notice that the treatments are listed in order of
increasing total costs that vary. The net benefits also
increase, except in the case of Treatment 3, where net
benefits are lower than in Treatment 1. No farmer would
choose Treatment 3 in comparison with Treatment 1,
because Treatment 3 has higher costs that vary, but
lower net benefits. Such a treatment is called a
dominated treatment (marked with a "D" in Table 4.1),
and can be eliminated from further consideration. A
dominance analysis is thus carried out by first
listing the treatments in order of increasing costs
that vary. Any treatment that has net benefits
that are less than or equal to those of a treatment
with lower costs that vary is dominated.

This example illustrates that to improve farmers'
incomes it is important to pay attention to net benefits,
rather than yields. Notice (from Table 3.2) that the
yields of Treatment 3 are higher than those of
Treatment 1, but the dominance analysis shows that the





P a r T h e M a g i a A n l s i


Chapter Four
The Net Benefit Curve
and the Marginal
Rate of Return


Dominance analysis


In the previous chapter a partial budget was developed
to calculate the total costs that vary and the net benefits
for each treatment of an experiment. This chapter
describes a method for comparing the costs that vary
with the net benefits. This comparison is important to
farmers because they are interested in seeing the
increase in costs required to obtain a given increase in
net benefits. The best way of illustrating this
comparison is to plot the net benefits of each treatment
versus the total costs that vary. The net benefit curve
(actually, a series of lines) connects these points. The
net benefit curve is useful for visualizing the changes in
costs and benefits in passing from one treatment to the
treatment of next highest cost. The net benefit curve
also clarifies the reasoning behind the calculation of
marginal rates of return, which compare the increments
in costs and benefits between such pairs of treatments.
Before proceeding with the net benefit curve and the
calculation of marginal rates of return, however, an
initial examination of the costs and benefits of each
treatment, called dominance analysis, may serve to
eliminate some of the treatments from further
consideration and thereby simplify the analysis.


Dominance Analysis
Table 4.1 lists the total costs that vary and the net
benefits for each of the treatments in the weed control
by seeding rate experiment from the previous chapter.

Notice that the treatments are listed in order of
increasing total costs that vary. The net benefits also
increase, except in the case of Treatment 3, where net
benefits are lower than in Treatment 1. No farmer would
choose Treatment 3 in comparison with Treatment 1,
because Treatment 3 has higher costs that vary, but
lower net benefits. Such a treatment is called a
dominated treatment (marked with a "D" in Table 4.1),
and can be eliminated from further consideration. A
dominance analysis is thus carried out by first
listing the treatments in order of increasing costs
that vary. Any treatment that has net benefits
that are less than or equal to those of a treatment
with lower costs that vary is dominated.

This example illustrates that to improve farmers'
incomes it is important to pay attention to net benefits,
rather than yields. Notice (from Table 3.2) that the
yields of Treatment 3 are higher than those of
Treatment 1, but the dominance analysis shows that the










value of the increase in yield is not enough to
compensate for the increase in costs. Farmers would be
better off using the lower seed rate, provided they are
not using herbicide.


Table 4.1, Dominanoe analyila, weed control by seeding rate
experiment


Weed
Treatment control


None
None
Herbicide
Herbicide


Seeding
rate
(kg/ha)

120
160
120
160


Total costs
that vary
($/ha)

2,400
3,200
3,875
4,675


Net
benefits
($/ha)

10,360
10,136 D
11,765
11,965


Net benefit curve


Net Benefit Curve
The dominance analysis has eliminated one treatment
from consideration because of its low net benefits, but it
has not provided a firm recommendation. It is possible
to say that Treatment 1 is better than Treatment 3, but
to compare Treatment 1 with Treatments 2 and 4
further analysis will have to be done. For that analysis,
a net benefit curve is useful.

Figure 4.1 is the net benefit curve for the weed control
by seeding rate experiment. In a net benefit curve
each of the treatments is plotted according to its
net benefits and total costs that vary. The
alternatives that are not dominated are connected
with lines. The dominated alternative (Treatment 3)
has been graphed as well, to show that it falls below the
net benefit curve. Because only nondominated
treatments are included in the net benefit curve, its
slope will always be positive.


Marginal Rate of Return
The net benefit curve in Figure 4.1 shows the relation
between the costs that vary and net benefits for the
three nondominated treatments. The slope of the line
connecting Treatment 1 to Treatment 2 is steeper than
the slope of the line connecting Treatment 2 to
Treatment 4.











Figure 4.1. Net benefit curve, weed control by seeding
rate experiment


Net benefits
($/ha)
12.000




11.500




11,000




10,500




10.000-


3,000


3,500


4,000 4,500


Total costs that vary ($/ha)

The purpose of marginal analysis is to reveal just how
the net benefits from an investment increase as the
amount invested increases. That is, if farmers invest
$1,475 in herbicide and its application, they will recover
the $1,475 (remember, the costs that vary have already
been subtracted from the gross field benefits), plus an
additional $1,405.

An easier way of expressing this relationship is by
calculating the marginal rate of return, which is
the marginal net benefit (i.e., the change in net
benefits) divided by the marginal cost (i.e., the
change in costs), expressed as a percentage. In this
case, the marginal rate of return for changing from
Treatment 1 to Treatment 2 is:

$11,765 $10,360 = $1,405 = 0.95 = 95%
$ 3,875 $ 2,400 $1,475
This means that for every $1.00 invested in herbicide
and its application, farmers can expect to recover the
$1.00, and obtain an additional $0.95.


Marginal rate of return


~ ,


2.500


W










The next step is to calculate the marginal rate of return
for going from Treatment 2 (not 1) to Treatment 4.

$11,965 $11,765 $200 25 = 25%
=-- = 0.25 = 25%
$ 4,675 $ 3,875 $800

Thus for farmers who use herbicide and plant at a rate
of 120 kg seed/ha, investing in the higher seed rate
would give a marginal rate of return of 25%; for every
$1.00 invested in the higher seed rate, they will recover
the $1.00 and an additional $0.25.

The two marginal rates of return confirm the visual
evidence of the net benefit curve; the second rate of
return is lower than the first. It is possible to do a
marginal analysis without reference to the net benefit
curve itself (Table 4.2). Note that the marginal rates of
return appear in between the two treatments. It makes
no sense to speak of the marginal rate of return of a
particular treatment; rather, the marginal rate of return
is a characteristic of the change from one treatment to
another. Because dominated treatments are not included
in the marginal analysis, the marginal rate of return will
always be positive.


Table 4.2. Marginal analysis, weed control by seeding rate experiment

Costs Marginal Net Marginal
that vary costs benefits net benefits Marginal rate
Treatment ($/he) 14/hal ($/ha) ($/ha) of return

1 2,400 1,475 10,360i 1,405 95%
2 3,875 800 11,765200 25%
4 4,675 11,965-


The marginal rate of return indicates what farmers can
expect to gain, on the average, in return for their
investment when they decide to change from one
practice (or set of practices) to another. In the present
example, adopting herbicide implies a 95% rate of
return, and then increasing seed rate implies a further
25%. As the analysis in this example is based on only
five experiments in one year, it is likely that the
conclusions will be used to select promising treatments
for further testing, rather than for immediate farmer
recommendations. Nevertheless, a decision cannot be
taken regarding these treatments without knowing what
rate of return is acceptable to the farmers. Is 95% high
enough? What about 25%? The next chapter explains
how to estimate a minimum rate of return.











Chapter Five
The Minimum
Acceptable
Rate of Return



Working capital



Cost of capital


In order to make farmer recommendations from a
marginal analysis, it is necessary to estimate the
minimum rate of return acceptable to farmers in the
recommendation domain. If farmers are asked to make
an additional investment in their farming operations,
they are going to consider the cost of the money
invested. This is a cost that has not been considered in
previous chapters. Because of the critical importance of
capital availability it is treated separately. Working
capital is the value of inputs (purchased or owned)
allocated to an enterprise with the expectation of a
return at a later point in time. The cost of working
capital (which in this manual will simply be
referred to as the cost of capital) is the benefit
given up by the farmer by tying up the working
capital in the enterprise for a period of time. This
may be a direct cost, as in the case of a person who
borrows money to buy fertilizer and must pay an
interest charge on it. Or it may be an opportunity cost,
the earnings of which are given up by not putting
money, or an input already owned, to its best
alternative use.

It is also necessary to estimate the level of additional
returns, beyond the cost of capital, that will satisfy
farmers that their investment is worthwhile. After all,
farmers are not going to borrow money at 20% interest
to invest in a technology that returns only 20% and
leaves them with nothing to show for their investment.
In estimating a minimum acceptable rate of return,
something must be added to the cost of capital to repay
the farmers for the time and effort spent in learning to
manage a new technology.

There are several ways of estimating a minimum
acceptable rate of return (or, more simply, a minimum
rate of return).


A First Approximation of
the Minimum Rate of Return
Experience and empirical evidence have shown that for
the majority of situations the minimum rate of return
acceptable to farmers will be between 50 and 100%. If
the technology is new to the farmers (e.g., chemical
weed control where farmers currently practice hand
weeding) and requires that they learn some new skills, a
100% minimum rate of return is a reasonable estimate.
If a change in technologies offers a rate of return above



























































5/ In cases where the
opportunity field price is
used to calculate gross field
benefits, the estimation of
the minimum rate of return
should be based on the
period from planting to the
time when the household
makes its principal
purchase of the commodity.
This is generally much later
than harvest and thus the
minimum rate of return in
these cases will be higher
than when the field price is
used to calculate gross field
benefits.


100% (which is equivalent to a "2 to 1" return, of
which farmers often speak), it would seem safe to
recommend it in most cases.

If the technology simply represents an adjustment in
current farmer practice (such as a different fertilizer rate
for farmers that are already using fertilizer), then a
minimum rate of return as low as 50% may be
acceptable. Unless capital is very easily available and
learning costs are very low, it is unlikely that a rate of
return below 50% will be accepted.

This range of 50 to 100% is rather crude but it should
always be remembered that the other agronomic and
economic data used in the analysis will be estimates or
approximations as well. This range should serve as a
useful guide in most cases for the minimum rate of
return acceptable to farmers. It is important to note that
this range represents an estimate for crop cycles of four
to five months. If the crop cycle is longer, the minimum
rate of return will be correspondingly higher5. In areas
where the inflation rate is very high. this range should
be adjusted upward by the rate of inflation over the
period of the crop cycle as well. (For more information
on inflation, see pp.71-72.)


The Informal Capital Market

An alternative way cf estimating the minimum rate of
return is through an examination of the informal capital
market. In many areas, farmers do not have access to
institutional credit. They must either use their own
capital, or take advantage of the informal capital
market, such as village moneylenders. The interest rates
charged in this informal sector provide a way of
beginning to estimate a minimum rate of return.
Informal conversations with several farmers who are
part of the recommendation domain should give
researchers a good idea of the local rates of interest. "If
you need cash to purchase something for the farm, to
whom do you go?" and "How much does this person
charge for the loan of the money?" are examples of
relevant questions.

If it turns out that local moneylenders charge 10% per
month, for instance, then a cost of capital for five
months would be 50%. To estimate the minimum rate
of return in this case, an additional amount would have
to be added to represent what farmers expect will repay
their effort in learning about and using the new









technology. This extra amount may be approximated by
doubling the cost of capital (unless the technology
represents a very simple adjustment in practices). Thus
in this example, the minimum rate of return would be
estimated to be 100%.Again, it should be emphasized
that this is simply a way of deriving a rough estimate of
the level of returns that farmers will require.


The Formal Capital Market
It is also possible to estimate a minimum rate of return
using information from the formal capital market. If
farmers have access to loans through private or
government banks, cooperatives, or other agencies
serving the agricultural sector, then the rates of interest
charged by these institutions can be used to estimate a
cost of capital. But this calculation is relevant only if the
majority of the farmers in fact have access to
institutional credit. If they do not, then they will
probably face a cost of capital different from that offered
through relatively cheap institutional credit. In some
cases, it may be that farmers with otherwise similar
circumstances must be divided into two groups
according to their access to one or the other type of
credit. These two groups of farmers would face different
minimum rates of return and may well represent two
separate recommendation domains.

In other cases, institutional credit may be available to
farmers, but only for certain crops or in the form of
rigidly defined credit packages. If institutional credit is
not likely to be available for the recommendations being
considered, then the cost of capital in these credit
programs is not relevant to the estimation of a
minimum rate of return. This is another example of how
on-farm research can provide information to
policymakers, in this case by interacting with credit
institutions to assure that their services are directed to
farmers in as efficient a manner as possible.

If farmers do have access to institutional credit, the cost
of capital can be estimated by using the rate of interest
charged over the agricultural cycle. That is, the rate of
interest should cover the period from when the farmers
receive credit (cash or inputs) to when they sell their
harvest and repay the loan. In addition, it is necessary
to include all charges connected with the loan. There
are often service charges, insurance fees, or even









farmers' personal expenses for things like transport to
town to arrange the loan, that must be included in the
estimate of the cost of capital.

Once the cost of capital on the formal market has been
calculated, an estimate of the minimum rate of return
can be obtained by doubling this rate. This will provide
a rough idea of the rate of return that farmers will find
acceptable if they are to take a loan to invest in a
new technology.


Summary
It is necessary to estimate a minimum rate of return
acceptable to the farmers of a recommendation domain.
In most cases it will not be possible to provide an exact
figure, but experience has shown that the figure will
rarely be below 50%, even for technologies that
represent only simple adjustments in farmer practice,
and is often in the neighborhood of 100%, especially
when the proposed practice is new to farmers. If the
crop cycle is longer than four to five months, these
minimum rates will be correspondingly higher. Where
farmers have access to credit, either through the
informal or formal capital markets, it is possible to
estimate a cost of capital (or an opportunity cost of
capital) and use it to estimate a minimum rate of return.
But even in these cases, it must be remembered that the
figure will be approximate. The next chapter explains
how to use the estimates of the minimum rate of return
to judge which changes in technology will be acceptable
to farmers.











Chapter Six
Using Marginal
Analysis to Make
Recommendations



Marginal analysis


Chapter 4 demonstrated how to develop a net benefit
curve and calculate the marginal rate of return between
adjacent pairs of treatments. Chapter 5 discussed
methods for estimating the minimum rate of return
acceptable to farmers. The purpose of this chapter is to
describe marginal analysis, which is the process of
calculating marginal rates of return between
treatments, proceeding in steps from a lower cost
treatment to that of next higher cost, and
comparing those rates of return to the minimum
rate of return acceptable to farmers. It should be
emphasized again that this type of analysis is useful
both for making recommendations to farmers, where
there is sufficient experimental evidence, and for helping
select treatments for further experimentation. Three
examples of marginal analysis follow.


Weed Control by Seeding Rate Experiment
It might be best to start by returning to the example of
the weed control by seeding rate experiment
summarized in Figure 4.1. After the dominance analysis
there were only three treatments left for consideration,
and the marginal rates of return were calculated. If
Treatment 1 represents the farmers' practice,
will farmers be willing to adopt Treatment 2 or
Treatment 4?

Farmers should be willing to change from one
treatment to another if the marginal rate of return
of that change is greater than the minimum rate of
return. In this case, if the minimum rate of return were
100%, the farmers would probably not be willing to
change from their practice of no weed control,
represented by Treatment 1, to the use of herbicide,
represented by Treatment 2, because the marginal rate
of return (95%)s below the minimum. If the minimum
rate of return were 50%, then farmers would be willing
to change to Treatment 2. Only if the minimum rate of
return were below 25% (which is unlikely) would the
farmers be willing to change from Treatment 2 to
Treatment 4. As long as the marginal rate of return
between two treatments exceeds the minimum
acceptable rate of return, the change from one treatment
to the next should be attractive to farmers. If the
marginal rate of return falls below the minimum, on the
other hand, the change from one treatment to another
will not be acceptable.










Fertilizer Experiment
Figure 6.1 shows the results of a nitrogen experiment in
maize. Table 6.1 gives details on the experimental
design and costs that vary. The yield data are the
average of 20 locations from three years of
experimentation. Table 6.2 is a partial budget for the
experiment. Figure 6.2 shows the net benefit curve and
Table 6.3 shows the marginal analysis (one of the
treatments is dominated).

For the recommendation domain where these
experiments were planted, researchers estimated that
the minimum rate of return for the crop cycle was
100%. With 20 experiments over three years,
researchers felt that they were ready to make a nitrogen
recommendation to farmers, who are currently not using
nitrogen fertilizer on their crop. What should be the
recommendation? Or, in other words, if farmers are
considering investing in nitrogen fertilizer and the labor
to apply it, what should be the recommended level
of investment?


Figure 6.1. Yields from nitrogen experiment


3.500


0 40* 80** 120** 160**
Kg N/ha
* = single application, ** = split application










Table 6.1. Nitrogen experiment data


Average yield Ikg/ha)
Nitrogen Number of for 20 locations
Treatment (kg/ha) applications of N over 3 years
la/ 0 0 2,222
2 40 1 2,867
3 80 2 3,256
4 120 2 3,444
5 160 2 3,544
/ Farmers' practice

Data
Field price of N = $0.625/kg
Field price of maize = $0.20/kg
Cost of one fertilizer application = $5.00/ha
Yield adjustment = 10%
Minimum rate of return = 100%



Table 6.2. Partial budget, nitrogen experiment

Treatment

1 2 3 4 5
0 kg 40 kg 80 kg 120 kg 160 kg
N/ha N/ha N/ha N/ha N/ha

Average yield (kg/ha) 2,222 2,867 3,256 3,444 3,544
Adjusted yield (kg/ha) 2,000 2,580 2,930 3,100 3,190
Gross field benefits (S/ha) 400 516 586 620 638
Cost of nitrogen (S/ha) 0 25 50 75 100
Cost of labor (S/ha) 0 5 10 10 10
Total costs that vary (S/ha) 0 30 60 85 110
Net benefits (S/ha) 400 486 526 535 528


This analysis should always be done in a stepwise
manner, passing from the treatment with the lowest
costs that vary to the next. If the marginal rate of return
of the change from the first to the second treatment is
equal to or above the minimum rate of return, then the










Figure 6.2. Net benefit curve, nitrogen experiment
Net benefits
($/ha)







540 5360/
-^ 120 N *
520 80 N 160 N

500

480 40 N

460 8

440

420

400, 0 N


0 20 40 60 80 100
Total costs that vary ($/ha)


Table 6.3. Marginal analysis, nitrogen experiment

Total costs Net
that vary benefits Marginal rate
Treatment ($/ha) ($/ha) of return
1 0 kg N/ha 0 $400
287%
2 40 kg N/ha $ 30 $486 287%
133%
2 80 kg N/ha $ 60 $526
36%
4 120 kg N/ha $ 85 $535
5 160 kg N/ha $110 $528 Da/

a/ Treatment 5 is dominated

next comparison can be made, between the second and
third treatments (not between the first and third). These
comparisons continue (i.e., increasing the level of
investment) until the marginal rate of return falls below
the minimum rate of return. If the slope of the net
benefit curve continues to fall, then the analysis can be










stopped at the last treatment that has an acceptable rate
of return compared to the treatment of next lowest cost.
If the net benefit curve is irregular, then further analysis
must be done. (Seethe next example, p.43).

In the nitrogen experiment, the marginal rate of return
of the change from 0 kg N/ha to 40 kg Nlha is 287%.
well above the 100% minimum. The marginal rate of
return from 40 kg Nlha to 80 kg Nlha is 133%, also
above 100%. But the marginal rate of return between 80
kg Nlha and 120 kg Nlha is only 36%. So of the
treatments in the experiment, 80 kg Nlha would be the
best recommendation for farmers.

There are a couple of things to notice about this
conclusion. First, the recommendation is not
(necessarily) based on the highest marginal rate of
return. For farmers who use no nitrogen, investing in 40
kg Nlha gives a very high rate of return, but if farmers
stopped there, they would miss the opportunity for
further earnings, at an attractive rate of return, by
investing in an additional 40 kg of nitrogen. Farmers
will continue to invest as long as the returns to each
extra unit invested (measured by the marginal rate of
return) are higher than the cost of the extra unit
invested (measured by the minimum acceptable rate
of return).

The second thing to notice is that the recommendation
is not (necessarily) the treatment with highest net
benefits (120 kg Nlha). If instead of a step-by-step
marginal analysis, an average analysis is carried out,
comparing 0 kg Nlha with 120 kg N/ha, the rate of
return looks attractive (i.e., (535-400)/(85-0) = 159%).
but this is misleading. The average rate of return of
159% hides the fact that most of the benefits were
already earned from lower levels of investment. This
average rate of return lumps together the profitable and
the unprofitable segments of the net benefit curve. The
marginal analysis indicates acceptable rates of return up
to 80 kg Nlha. If the farmers are to apply 120 kg Nlha,
the analysis shows they would only get a marginal rate
of return of 36% on their investment of the last $25. It
is likely that they would be willing to invest their money
in nitrogen up to 80 kg Nlha, and then ask if there is
not some other way of investing that final $25 (a little
extra weeding, fencing for animals, etc.) that would give
a better rate of return than 36%.










In summary, the recommendation is not necessarily the
treatment with the highest marginal rate of return
compared to that of next lowest cost, nor the treatment
with the highest net benefit, nor the treatment with the
highest yield. The identification of a recommendation
requires a careful marginal analysis using an
appropriate minimum rate of return.


Tilage Experiment
This example illustrates some additional aspects of
marginal analysis and the selection of recommendations.
Figure 6.3 presents yield data from a tillage experiment
in wheat. Table 6.4 gives details of the design and the
costs that vary. The yield data are the average of six
locations from one year of experiments. Table 6.5 shows
the partial budget. Figure 6.4 shows the net benefit
curve and Table 6.6 shows the marginal analysis.


Figure 6.3. Yields from tllage experiment
Yield
(kg/ha)

4,400


4,200



4,000


3.800


3,6001


3,400


1 2


3
Treatment











Table 6.4. Tillaga experiment data


Treatment
la/
2
3


Type of
ptow
None
None
Chisel


4 Mold board
a/ Farmers' practice


Number of
cuttivaltona
2
0
2
2


Seeding method
By hand
Zero-till planter
By hand
By hand


Average yleld (kg/hal
for 6 looations
3,800
4,080
4,300
4,470


Data
Tillage costs:


Cultivator
Chisel plow
Mold board plow
Zero-till planter


$7/ha
$16/ha
$22/ha
$20/ha


Cost of seeding by hand
Field price of wheat
Yield adjustment
Minimum rate of return


Table 6.5. Partial budget, tillage experiment


Treatment


Average yield (kg/ha)
Adjusted yield (kg/ha)
Gross field benefits (S/ha)
Cost of plowing ($/ha)
Cost of cultivation (S/ha)
Cost of seeding (S/ha)
Cost of zero-till seeding (S/ha)
Total costs that vary (S/ha)
Net benefits (S/ha)


3,800
3,040
243
0
14
2
0
16
227


2

4,080
3,264
261
0
0
0
20
20
241


3

4,300
3,440
275
16
14
2
0
32
243


$2/ha
$0.08/kg
20%
80%


4

4,470
3,576
286
22
14
2
0
38
248












Net benefits
($/ha)



245.




240.




235,




230


0


Figure 6.4. Net benefit curve, tillage experiment


w _


25 30 35
Total costs that vary (S/ha)


Table ..6, Marginal analysis, tillage experiment


Total costs that
Treatment vary ($/ha)


Net benefits
(S/ha)


Marginal rate of
return


227
241
243
248


350%
17 9%
83%J39%


First, it should be noted that this tillage experiment is
different from the nitrogen experiment in that it tests
four distinct treatments, rather than the continuous
increase of one factor. It is impossible to use 80 kg of
nitrogen without using 40 kg of nitrogen, but using one
tillage method does not require first using a lower cost
method. There are four different options, arranged on
the net benefit curve in order of increasing costs. The
marginal analysis is simply a way of examining various










alternatives for tillage (in this case). The comparisons
are made, as always, in a stepwise manner between one
alternative and the next, in order of increasing costs,
until an acceptable recommendation is identified.

Second. the situation is a bit different from the previous
example in that only six locations from one year are
available for analysis. Thus the analysis will be used to
help plan further experiments, rather than to make
farmer recommendations.

Finally, the shape of the net benefit curve is different
from the previous example. The marginal rate of return
in going from Treatment 1 to Treatment 2 is 350%, well
above the minimum. Therefore Treatment 2 is certainly
a worthwhile alternative to the farmers' practice. Next,
the marginal rate of return in going from Treatment 2 to
Treatment 3 is 17%. and below the minimum.
Treatment 3 can therefore be eliminated from
consideration. But the marginal rate of return between
Treatments 3 and 4 is 83%.and above the minimum
rate of return of 80%. In such cases as this. where the
marginal rate of return between two treatments falls
below the minimum, but the following marginal rate cf
return is above the minimum, it is necessary to
eliminate the treatments) that are unacceptable and
recalculate a new marginal rate cf return. In this
example, it is necessary to calculate a marginal rate of
return between Treatment 2 and Treatment 4. The
result is 39%/ 248-241 = 39%\ which is below the
\ 38-20 I
minimum rate of return. Thus Treatment 4 is also
rejected. If this last marginal rate of return had been
above 80%.however, Treatment 4 would have been the
best treatment.

In this case researchers should continue to experiment
with Treatment 2 (the zero-till planter), which seems to
be a promising alternative to the farmers' practice of
two cultivations before seeding. Treatments 3 and 4 give
higher yields, but their costs are such that they do not
provide an acceptable rate of return. Researchers must
decide if there is sufficient evidence to eliminate these
treatments from future experimentation, or if another
year of testing is worthwhile.











Analysis Using Residuals

The conclusions of a marginal analysis can be checked
by using the concept of "residuals."6/ Residuals (as the
term is used here) are calculated by subtracting the
return that farmers require (the minimum rate of return
multiplied by the total costs that vary) from the net
benefits. Table 6.7 illustrates this method, using the
data from the nitrogen experiment (Table 6.3).


Table 6.7. Analysis of nitrogen experiment using residuals


Treatment


(1)

Total costs
that vary
($/hal


1 0 kg N/ha C
2 40 kg N/ha 3C
3 80 kg N/ha 6C
4 120 kg N/ha 8E

a/ Maximum residual


(2)

Net
benefits
1$/ha)


131
Return
required
[100%x111i
IS/ha)


400
486
526
535


(4)

Residual
1(2)- 13)1
(S/ha)


400
456
466a/
450


The treatments are listed,
costs that vary. Column 1


For the purposes of this
manual the term "residual"
is used in a special way, to
indicate the difference
between the net benefits
and the cost of the
Investment. The reader
should note that the term
has other meanings, both in
economics and in other
fields.


as usual, in order of total
gives the total costs that vary


and column 2 gives the net benefits. Column 3 is the
minimum acceptable rate of return multiplied by the
costs that vary, and represents the return that farmers
would require from their investment in order to change
their practice. For instance, if 40 kg N/ha has costs that
vary of $30/ha, and if the minimum rate of return is
100%, this means that farmers would ask for returns of
at least an additional $30/ha before investing in 40 kg
N/ha. Finally, the residual (column 4) is the difference
between net benefits (column 2) and the return that
farmers require (column 3). Of course this residual is not
the profit, and it is the comparison between the
residuals, rather than their absolute value, that is
of interest.

Farmers will be interested in the treatment with the
highest residual. In this case, the treatment with the
highest residual is 80 kg N/ha, which is the same
conclusion that was reached in the previous analysis.
Stopping at 40 kg N/ha denies the farmers the
possibility to earn more money per hectare. Going on to
120 kg N/ha implies a loss, after accounting for the
return that farmers require.










Residuals can also be used to check the conclusions of
the marginal analysis of the tillage experiment (Table
6.6). Table 6.8 shows the results; Treatment 2 is the one
with the highest residual.


Table 6.8. Analysis of tillage experiment using residuals

11) (2) (3) (4)
Return
Total costs Net required Residual
that very benefits [80% x (1) [(2) (3)]
Treatment (0/hal ($/ha) (S/ha) ($/ha)

1 16 227 13 214
2 20 241 16 225a/
3 32 243 26 217
4 38 248 30 218

a/ Maximum residual

This method of calculating and comparing residuals will
always give the same conclusion as the graphical
method of marginal analysis shown earlier. The method
of using residuals, however, requires an exact figure for
the minimum rate of return, whereas the graphical
method allows comparison of the marginal rates of
return with various assumptions about the minimum
rate of return. Thus it is advisable to use the graphical
method first and then, if necessary, check the
conclusions with respect to a particular minimum rate
of return by calculating residuals.









SOME QUESTIONS ABOUT MARGINAL ANALYSIS

1 Is marginal analysis the "last word" for making
a recommendation?
Marginal analysis is an important step in assessing the
results of on-farm experiments before making
recommendations. But agronomic interpretation and
statistical analysis are also part of the assessment, as
well as farmer evaluation. As researchers conduct on-
farm experiments, they must constantly solicit farmers'
opinions and reactions. Alternatives that seem to be
promising both agronomically and economically may
have other drawbacks that only farmers can identify. To
the extent possible, screening treatments for
compatibility with the farming system should take place
before experiments are planted. But farmer assessment
of the experiments is also essential. It is the farmers
who have the last word.

2 How precise is the marginal rate of return
as a criterion?
It is important to bear in mind that the calculation of
the marginal rate of return is based on yield estimates
derived from agronomic experiments and on estimates
of various costs, often opportunity costs. Furthermore,
the marginal rate of return is compared to a minimum
rate of return which is only an approximation of the
investment goals of the farmers. Discretion and good
judgment must always play an important part in
interpreting these rates and in making
recommendations. If the marginal rate of return is
comfortably above the minimum, the chances are good
that the change will be accepted. If it is close to the
minimum rate of return then caution must be exercised.
In no case can one apply a mechanical rule to
recommend a change that is a few percentage points
above the minimum rate, or reject it if it is a few points
below. Making farmer recommendations requires a
thorough knowledge of the research area and the
problems that farmers face, a dedication to good
agronomic research. and the ability to learn from
previous experience. Marginal analysis is a powerful tool
in this process, but it must be seen as only a part of the
research strategy.

3 Can the marginal rate of return be interpreted if
the change in costs that vary is small?
Certain experiments, such as those that look at different
varieties or perhaps modest changes in seeding rate,










involve changes in costs that may be quite small. If the
yield differences are at all substantial, the resulting
marginal rate of return can be very large, sometimes in
the thousands of percent. In these cases the marginal
rate of return is of little use in comparing treatments.
Thus it is usually not worthwhile calculating marginal
rates of return for variety experiments, unless there are
significant differences in cost between varieties (e.g.,
local maize variety versus a hybrid), or in the market
value of the varieties (e.g., because of consumer
preference).

4 Is it really possible to make recommendations,
using marginal analysis, without considering all the
costs of production?
Remember that the starting point in on-farm research is
the assumption that it is much better to consider
relatively small improvements in farmers' practices,
rather than propose large-scale changes. The idea is
thus to ask what changes can be made in the present
system, and to compare the change in benefits with the
change in costs. Because the focus is on the differences
between two treatments, rather than their absolute
values, costs that do not vary between treatments will
not affect the calculation of the marginal rate of return.
Table 6.9 shows two cases, both using the same yields
and costs that vary. For the partial budget, the marginal
rate of return is calculated in the usual way. The
complete budget includes all of the costs of production;
they are of course constant ($300/ha) for each
treatment. When the marginal rate of return is


Table 6.9. Marginal analysis using a partial budget and a complete budget

Partial budget 1 2 Complete budget 1 2
Gross field benefits Gross field benefits
(S/ha) 500 650 (S/ha) 500 650
Total costs that vary Total costs that vary
($/ha) 100 200 (S/ha) 100 200
Net benefits (S/ha) 400 450 Total of costs that do not
vary (S/ha) 300 300
Total costs (S/ha) 400 500
Net benefits ($/ha) 100 150


Marginal rate 450 400 = 50% Marginal rate 150 100 50%
of return 200 100 of return 500 400










calculated using benefits and total costs, the result is
the same.

5 Is the correct strategy always to consider
small changes in farmers' practices?
Experience has shown that farmers are much more
likely to adopt new practices in small steps rather than
in complete packages. But in following this strategy it
should be realized that farmers can (and do) eventually
adopt a new set of practices over a period of several
years of testing. The complexity of the individual steps
depends on the nature of the agronomic interactions
among the elements being tested and on the resources
available to farmers.

It is often possible to take advantage of this sequential
adoption pattern in making recommendations. Initial
steps may be intermediate between farmers' practice
and the recommendation that would be selected by
marginal analysis. Figure 6.5 is the net benefit curve for


Figure 6.5. Net benefit curve, weed control by fertilizer
experiment
Net
benefits
($/ha)


Improved
weed control
only


Improved
fertilization
only


Farmers'
practice


Total costs that vary (S/ha)










a weed control by fertilizer experiment. The curve
shows that a combination of improved weed control and
fertilization should be the recommendation.

Nevertheless, it is possible to first promote an
intermediate recommendation of improved weed control
only and then add fertilization later. The curve allows
researchers to trace out an efficient set of technologies
for recommendation as farmers increase expenditure
levels. In this case, further analysis would indicate that
adopting fertilizer first, without improved weed control,
would not be a worthwhile option.

More complex changes, such as the introduction of new
crops or cropping patterns, are of course possible as
well. But such changes require extremely careful
planning and analysis which are beyond the scope of
this manual.

6 What is the difference between a marginal
analysis and a continuous analysis of data?
Agronomists often estimate response functions for
factors such as nutrients, and economists use similar
continuous functions to select economic optima. Yet the
methodology of this manual uses a marginal analysis for
sets of discrete alternatives. There are three reasons for
emphasizing the latter method. First, marginal analysis,
using discrete points, can be used for any type of
experimentation, whereas continuous analysis is only
applicable to factors that vary continuously, such as
fertilizer rates or seed rates. Second, the computational
skills and facilities necessary for estimating response
functions are not always available. Finally, great
precision is not required for farmer recommendations
(e.g., for fertilizer levels) because farmers will adjust
them to their individual conditions.

A continuous economic analysis may be very useful in
certain situations, however. But if it is done, it requires
the same degree of care in estimating the benefits and
costs that farmers face that has been emphasized in this
manual for constructing a partial budget and conducting
marginal analysis. The sophisticated analyses that are
often done with unrealistic assumptions about farmers'
yields, field prices, or minimum rate of return do not
give useful conclusions.










7 Does the marginal analysis assume
that capital is the only scarce factor for farmers?
In the marginal analysis, all factors are expressed in
monetary units. This does not necessarily mean that
farmers think of all costs and benefits in monetary
terms, or that cash is necessarily the limiting factor.
Marginal analysis may be used, for instance, in an
experiment that compares treatments which differ only
in the amount of (unpaid)family labor utilized on a crop
which is not sold. To decide whether extra amounts of
labor would be effectively invested to produce extra
amounts of the crop, opportunity costs and prices can
be assigned and the comparison made.

Nevertheless, in cases where family labor is the
predominant source of labor, and experimental
treatments involve significant changes in labor use, care
must be taken in valuing labor. If, for instance, a
change from one treatment to another implies a
reduction in family labor and an increase in cash
expenditure, a modest increase in total costs that vary
may in fact represent a significant increase in cash
outlay (balanced to some extent by a reduction in labor
"costs"). In cases where family labor is a particularly
important factor in farmer decision making regarding
new technologies, a careful analysis must be
undertaken. This is complicated by the fact that the
opportunity cost of labor is sometimes difficult to
estimate. Different members of the household (men,
women, children) will likely have different opportunity
costs of labor, and the time of the year (slack season.
peak season) will also affect the estimate.

One possibility is to do a sensitivity analysis (Chapter 9),
which involves doing several marginal analyses using
different estimates of the opportunity cost cf labor.
Another technique involves estimating the returns to
labor for the treatments and comparing the marginal
returns to labor between two treatments with various
estimates of the opportunity cost of labor. This is a
reminder that there are often alternative analytical
techniques, beyond the scope of this manual, which
may be useful in making decisions about the
appropriateness of a particular technology.

8 Can the concept of marginal
analysis be used for planning experiments?
It is common to consider a change in farmers' practice
by doing a quick calculation of how much additional
yield would be needed to pay for the extra costs of the










new practice. If an extra 100 kg of fertilizer costs
$1,000, and wheat is selling for $5/kg, then the estimate
might be that the farmers would need an extra 200 kg
of wheat ($1,000/$5) in order to "repay the fertilizer."
However, there are three errors in this kind of
calculation.

The first error is in using market prices for fertilizer and
wheat, rather than field prices. The second is not
including the labor or machinery costs associated with
the use of fertilizer. The third is in not including the
minimum rate of return. The following formula corrects
those errors, and provides a useful way for helping to
consider practices that are proposed for
experimentation.

ATCV (1 + M)
P

where AY = minimum change in yield required
ATCV = change in total costs that vary
P = field price of product
M = minimum rate of return (expressed
as a decimal fraction)

In the example just mentioned, if the additional fertilizer
plus the labor to apply it is worth $1,200, the field price
of wheat is $4/kg, and the minimum rate of return is
50%, then:

AY = $1,200 (1 + 0.5)
$4
= 450 kg of wheat

Thus, given current prices, the minimum yield increase
required by farmers from the addition of an extra
100 kg of fertilizer is 450 kg of wheat, not the 200 kg in
the original calculation. The use of this type of
calculation before designing an experiment helps ensure
that the treatments include an economically realistic
range of levels.

SCan marginal analysis be used
when yields are variable or prices change?
Yields in agronomic experiments are usually quite
variable, and prices often change. Methods for
accommodating this kind of variability to marginal
analysis are discussed in Chapters 7, 8, and 9.





Par Fu Vl* Cii


Chapter Seven
Preparing
Experimental Results
for Economic Analysis:
Recommendation
Domains and
Statistical Analysis


Marginal analysis for a particular experiment should be
done on the pooled results from at least several locations
over one or more years. To prepare the experimental
results for this type of analysis, several steps must be
taken. First, researchers must review the purpose of the
experiment in order to decide whether the results of the
analysis are to be used for making recommendations for
farmers or for guiding further research. Second, a review
of results from the different locations will indicate
whether all of the locations belong to the same
recommendation domain and can therefore be analyzed
together. Finally, a combination of agronomic judgment
and statistical analysis will lead to a decision regarding
the yield differences among treatments in the
experiment. If researchers have little confidence that
there are real differences in yields, then the total costs
that vary of each treatment can be compared; the
treatment with the lowest costs will generally be
preferred. If, on the other hand, researchers believe that
the differences observed represent real differences
among treatments, then a marginal analysis should
be done.


Reviewing the Purpose of the Experiment
Each experimental variable in an experiment has a
purpose, and researchers should review the objectives of
the experiment before thinking about an economic
analysis. Some experimental variables are of an
exploratory nature; they are meant to provide answers
regarding response (e.g., is there a response to
phosphorus?) or to elucidate particular production
constraints that have been observed (e.g., is the low
tillering observed in the wheat crop due to a nutrient
deficiency or to the variety?). These variables are meant
to provide information that can be used in specifying
production problems and designing solutions for them.
The treatments in these exploratory experiments are
chosen to detect the possibility of responses, and thus
need not be designed to represent economically viable
solutions to a particular problem. Researchers must bear
this in mind when considering the economic analysis of
experiments with this type of exploratory variable. If the
experimental results provide clear evidence that a
particular production problem exists, the economic
analysis may help to select possible solutions for testing.
If a high level of an insecticide in an exploratory
experiment provided evidence of a response, but if the





Par Fu Vl* Cii


Chapter Seven
Preparing
Experimental Results
for Economic Analysis:
Recommendation
Domains and
Statistical Analysis


Marginal analysis for a particular experiment should be
done on the pooled results from at least several locations
over one or more years. To prepare the experimental
results for this type of analysis, several steps must be
taken. First, researchers must review the purpose of the
experiment in order to decide whether the results of the
analysis are to be used for making recommendations for
farmers or for guiding further research. Second, a review
of results from the different locations will indicate
whether all of the locations belong to the same
recommendation domain and can therefore be analyzed
together. Finally, a combination of agronomic judgment
and statistical analysis will lead to a decision regarding
the yield differences among treatments in the
experiment. If researchers have little confidence that
there are real differences in yields, then the total costs
that vary of each treatment can be compared; the
treatment with the lowest costs will generally be
preferred. If, on the other hand, researchers believe that
the differences observed represent real differences
among treatments, then a marginal analysis should
be done.


Reviewing the Purpose of the Experiment
Each experimental variable in an experiment has a
purpose, and researchers should review the objectives of
the experiment before thinking about an economic
analysis. Some experimental variables are of an
exploratory nature; they are meant to provide answers
regarding response (e.g., is there a response to
phosphorus?) or to elucidate particular production
constraints that have been observed (e.g., is the low
tillering observed in the wheat crop due to a nutrient
deficiency or to the variety?). These variables are meant
to provide information that can be used in specifying
production problems and designing solutions for them.
The treatments in these exploratory experiments are
chosen to detect the possibility of responses, and thus
need not be designed to represent economically viable
solutions to a particular problem. Researchers must bear
this in mind when considering the economic analysis of
experiments with this type of exploratory variable. If the
experimental results provide clear evidence that a
particular production problem exists, the economic
analysis may help to select possible solutions for testing.
If a high level of an insecticide in an exploratory
experiment provided evidence of a response, but if the










marginal analysis then showed an unacceptable rate of
return, researchers would want to examine lower levels
of insecticide or less expensive insect control methods in
subsequent experimentation.

Other experimental treatments test possible solutions to
well-defined production problems. The solutions will
have been selected for testing not only because they
promise economically acceptable returns, but because
they are compatible with the farming system and do not
represent special risks to farmers. When there are yield
differences among treatments in these cases, the
marginal analysis should be more rigorous, because a
recommendation may be made to farmers.

The marginal analysis should be done on the pooled
results of a number of locations, usually over more than
one year. No strict rules can be given here, but the
number of locations should be sufficient to give
researchers confidence that the results fairly represent
the conditions faced by farmers in the recommendation
domain. A very rough rule of thumb might be to include
at least 20 experimental locations (in relatively
homogeneous environments) over two years for each
recommendation domain. The exact number of test sites
required will depend on the variability (across sites and
across years) in the recommendation domain and on the
technology being tested. For instance, fertilizer
recommendations usually require a fairly large number
of locations to adequately sample the range of response
by soil type, rotation, and so forth. Insect control
recommendations may require several years of evidence
to sample year-to-year variability in insect populations,
especially in the case of routine preventive treatments.

Once recommendations are derived they are often
presented to farmers through demonstrations, which
may involve one or more large plots showing various
alternatives next to a similar plot with the farmers'
practice. As a way of following up on the
recommendation the results of these demonstration
plots should also be subjected to an economic analysis.
preferably as part of the demonstration.










Tentative Recommendation Domains
Whether the experiments are of an exploratory nature or
are testing possible solutions, they should be planted in
locations that represent the tentative definition of the
recommendation domain. Recall that a recommendation
domain is a group cf farmers whose circumstances are
similar enough that members of the group are eligible
for the same recommendation.

An example may help. In a particular research area
there is experimental evidence of a response to nitrogen
in maize. Farmers currently use no fertilizer, and an
experiment is designed to test various levels of nitrogen.
Most of the farmers plant maize under rainfed
conditions, although a few have access to irrigation.
Because the response to nitrogen may differ under
rainfed and irrigated conditions, and because of the
small number of farmers with irrigation, only farmers
with rainfed fields are considered. (If there were more
farmers with irrigation, experiments might be planted
with them as well, but they would almost certainly be a
separate recommendation domain.) Most of the farmers
with rainfed fields have land with sandy to sandy-loam
soils. Locations are chosen to represent this range of soil
types, and careful note is taken in the field book of the
soil type at each location. The tentative definition of the
recommendation domain includes the range cf soil
types, but the experimental results may distinguish
separate domains. Nonexperimental variables, such as
variety, planting date, and weed control are left in the
hands of the farmers. A certain range in these practices
is present in the recommendation domain, and the
actual practices at each location are noted in the field
book. The researchers do their best to reject locations
that represent very unusual practices or conditions
(such as a few farmers who plant a special maize variety
to sell as green maize.)

The tentative definition of the recommendation domain
for the fertilizer experiment is thus: "All farmers in the
area who plant maize under rainfed conditions on sandy
to sandy-loam soils." This definition allows for some
variability in conditions and practices, and the selection
of experimental sites tries to represent this range, but
avoids obvious extremes.










Notice that the recommendation domain is defined for
the particular experimental variable. A different
experimental variable (say, a disease-resistant variety)
might be tested in a domain of a different definition. In
this case, the variety might be tested on both irrigated
and rainfed fields, if no difference in its disease
resistance capacity were expected.


Reviewing Experimental Results
The results of each experiment at each location in the
tentative recommendation domain must be reviewed.
Inconsistencies in results between locations can be due
to one of three causes:

Redefinition of the recommendation domain. In the
above example. soil type was being considered as a
possible means of subdividing the recommendation
domain. If the responses are very different at locations
with sandy soils and those with sandy-loam soils, then
there may be two separate recommendation domains
(and two separate economic analyses). Or it may be that
an unexpected characteristic is of importance. Suppose,
in this same example, that some farmers plant a maize-
maize rotation, while others rotate their maize with
fallow. If the responses to nitrogen are different on these
two types of fields, the original recommendation domain
may be refined (by eliminating the rotation that
represents a minority of the farmers) or divided (by
rotation, if both rotations are of importance in the area).

The important point is that researchers must have a
clear and consistent definition of the recommendation
domain whose experiments will be submitted to
economic analysis. Domain definitions are reviewed and
refined during the experimental process. As the number
of possible defining characteristics for domains is greater
than the number of locations to be planted, careful
selection of experimental locations is important. The
routine collection of information adequate to describe
each location (e.g. elevation, soil, cropping history,
management practices) is a most important activity,
without which across-location interpretation is
impossible.










2 Improper experimental management. At times the
experimental results at a location may differ from the
others because of problems in experimental
management. This may include errors by the
researchers (such as applying the wrong dosage of a
chemical), or factors related to the farmer (such as a
cow destroying part of the experiment, or the farmer
failing to weed because of a misunderstanding). In such
cases the location can be eliminated from the analysis
and the researchers will gain a bit more experience-in
the management of chemicals, in locating experiments
where there is little chance of animal damage, or in
carefully discussing with farmers their responsibilities in
the management of an experiment. Part of experimental
management includes the selection of locations. If
locations have to be eliminated because they have
characteristics well outside the normal range of the
recommendation domain (such as very late planting
dates) this too is an indication of the necessity to
improve experimental management.

Unexplained or unpredictable sources of variation.
After eliminating locations from the analysis because
they do not represent the recommendation domain, and
eliminating sites where the management of the
experiment is responsible for unrepresentative results,
there may still be considerable variation in the results
from the remaining locations. This may be due to
factors that are not understood (and may be the focus of
further agronomic investigation and/or discussion with
farmers). Or it may be due to factors that are understood
but not predictable, and hence not eligible for defining a
recommendation domain, like drought or frost. These
sites must be included in the economic analysis, unless
researchers are able to identify particular areas where
the factor is more likely to occur. It may be, for
instance, that the research area can be divided into
more and less drought-prone domains. But if drought (or
frost or insect attack) cannot be associated with
particular areas, then the results of the affected
locations must enter the analysis. More will be said
about treating these risk factors in Chapter 8, but it is
important to emphasize that locations that have been
affected, or even abandoned, because of these factors
must be included in the marginal analysis.










Statistical Analysis
In Chapter 3 it was pointed out that the economic
analysis of an experiment should be done only after
reviewing the agronomic assessment and statistical
analysis. If after reviewing the statistical analysis
researchers do not have confidence that there are real
differences among treatments. then they need to take
another look at the experiment. If the average
differences among treatments are large relative to the
yields obtained by farmers (e.g., 5-10%or more of
average farmer yields), but there is insufficient evidence
that these differences are real, then researchers may
want to review the design or management of the
experiment and perhaps repeat it the next cycle. If the
differences among treatments are small in relation to
farmers' yields, and researchers have no confidence that
the differences are real, then they need consider only
the differences in costs among treatments and choose
the one with lowest costs.

Cases where no significant yield differences exist and no
marginal analysis is required are not necessarily trivial.
If experimentation leads to recommendation of a
practice that lowers the costs of production while
maintaining yields. the gains in productivity of farmer
resources are as legitimate as those from a higher
yielding (and higher cost) treatment. One common
example is that of substituting some form of reduced
tillage for mechanical tillage. This often results in
considerable cost savings, although yields may not
be affected.

In experiments with factorial designs, an examination of
the statistical and agronomic analyses will help point
the way to the most appropriate type of economic
analysis. For example, in an experiment with two
factors, one factor may be responsible for yield
differences although the second factor is not (and there
is no interaction between them). In that case, the yields
for levels of the first factor should be the average for
each level over all levels of the second factor. Such a
case occurs in a nitrogen by tillage experiment in which
there is a response to nitrogen, but not to tillage (Table
7.1). The tillage method to be chosen for further
experimentation is the one that costs the least. The
partial budget for such an experiment will then have











Table 7.1. Yield data for a nitrogoe by tillage experiment

Nitrogen Tillage Average yield
Treatment (kg/ha) method (kg/ha)

1 50 "A" 2,560
2 50 "B" 2,300
3 100 "A" 3,120
4 100 "B" 3.200
Average yield: 50 kg N/ha 2,430 kg/ha
100 kg N/ha 3,160 kg/ha

Average yield: tillage method "A" 2,840 kg/ha
tillage method "B" 2,750 kg/ha


only two columns, corresponding to the two nitrogen
levels (50 kg/ha and 100 kg/ha). The yields for the two
nitrogen levels will be the average yields across tillage
treatments (to take advantage of all the data available,
which should give a better estimate of real differences in
yields between nitrogen levels). The first line of the
partial budget ("Average yield") will thus have 2,430
and 3,160 kg/ha. The costs that vary will include those
associated with the change in nitrogen level (fertilizer,
application costs), but not those associated with tillage.
The marginal analysis of the partial budget will examine
the marginal rate of return of changing from one
nitrogen level to another.

The economic analysis of factorial experiment is
concerned only with factors that exhibit responses or
are involved in interactions. Therefore the interpretation
of experiments including several factors is often
simplified because some factors may be dropped from
the analysis. In the example above, for instance, tillage
was not included in the analysis. But if there had been
an interaction between tillage and nitrogen, the partial
budget would have had four columns (with all possible
combinations of tillage and nitrogen) and the costs that
vary would have reflected both factors.










In the early stages of on-farm experimentation there are
often experiments with a large number of treatments (12
to 15 or more) examining several variables. The
statistical analysis of such experiments may be quite
complex, and its relation to an economic analysis at first
sight may be unclear. The point to remember is that the
purpose of those experiments is to characterize as
quickly as possible the responses and the interactions of
several factors. Once that is accomplished, a small
number of possible solutions can be tested. If the results
of such an exploratory experiment are agronomically
clear (and the statistical analysis can only help in
making this decision), then the next year's experiments
will certainly be simpler, and a marginal analysis will
help to select a reasonable range of treatments for those
experiments. If the results are not clear agronomically,
then further exploratory work is needed, and there is
less that a marginal analysis can contribute to the
selection of treatments for future experiments.











Chapter Eight
Variability in Yields
Minimum Returns
Analysis


Assigning experimental locations to different
recommendation domains and reviewing the
management of the experiments (Chapter 7) help
account for some of the variability in experimental
yields. After doing this, however, some variability will
certainly remain, and farmers and researchers will take
this into account when making decisions about
alternative practices. Some variability in the
performance of particular treatments will be
unexplained, whereas some may be due to identifiable
factors such as drought, frost, or flooding. In either case,
farmers will want to know how this variability might
affect their welfare, and what undesirable outcomes are
possible if they adopt a recommendation. One method
for analyzing experimental data in this way is known as
minimum returns analysis.


Dealing with Risk in On-Farm Research
Recall that the objective of an on-farm research program
is to improve the productivity of farmers' resources.
Besides improving the production of target crops or
animals, this may also include lowering the costs of
production or increasing the stability of production. The
latter is an important factor for many farmers, whose
practices often reflect attempts to reduce the risks of
failure. Common examples of such practices include
staggering planting dates to minimize the risk of losing
an entire crop to drought, or investing extra labor to
double over the maize plants before harvest in areas
where there are strong winds.

Risk has three important implications for an on-farm
research program. First, new technologies that are
proposed for testing should be compatible with farmers'
practices to reduce risk. Before proposing a technology
that relies on a uniform planting date, for instance,
researchers should take account of farmers' rationale for
staggered planting dates. Technologies that do not take
account cf farmers' attempts to reduce risk have little
chance of being adopted.

The second implication is that the risks faced by
farmers may suggest opportunities for developing
recommendations to help stabilize farm production.
Drought risk may be reduced with moisture
conservation techniques, and losses from high winds
may be reduced with shorter varieties. Thus in setting
priorities for an experimental program, researchers










should include the possibility of testing alternatives that
may not necessarily increase average benefits, but
instead help to reduce their year-to-year variability.

The third implication is that researchers will want to be
careful in evaluating how new recommendations modify
the risks currently borne by the farmers in a
recommendation domain. The amount that farmers are
willing to give up (in terms of average net benefits) to
reduce the effects of an uncertain environment is a
measure of their degree of risk aversion. The degree of
farmers' risk aversion may depend on several factors,
but in general it can be said that most farmers in
developing countries are moderately averse to risks. It is
not easy to specify the degree of risk aversion, but it is
something that should be considered when proposing
new recommendations.


Risk and Data From On-Farm Experiments
The source of risk is often thought of as being
susceptible to quantification. Thus it is possible to say
that the probability of less than 400 mm of rainfall in
the growing season is 0.2 (i.e., one year in five). If
researchers have information about the probability of
occurrence for a particular event, then those data may
be used in interpreting experimental results. If, for
instance, it is known that there is a drought on the
average of one year in five, causing a certain percentage
of crop loss, that information can be factored into an
analysis of the results of the on-farm experiments,
whether or not they were conducted during a drought
year. But this type of precise data is not usually
encountered, and researchers need a more useful way of
looking at the variability in their own experimental data.
Even if the source of variability is well specified (e.g.,
midseason drought), probabilities may not be available.
Often the variability observed in experimental results
and in farmers' fields is due to several sources. Thus the
minimum returns analysis presented here is not, strictly
speaking, a method of risk analysis, but rather a way of
assessing the variability due to unpredictable and at
times unexplained causes.










The Farmers' Point of View
Before minimum returns analysis is done to look at
variability the way that farmers do, it is useful to
consider how in fact farmers approach this problem.

First, recall that the marginal analysis is based on the
average yields from a number of locations. If a proposed
recommendation gives an average yield of 3,000 kg/ha,
it is certain that it will have yielded more than 3,000
kg/ha in some locations and less in others. If the
farmers' practice yields an average of 2,000 kg/ha, it too
will exhibit some variation. And if the marginal analysis
indicates that the proposed recommendation has an
acceptable marginal rate of return, when compared to
the farmers' practice, it is a rate of return based on
these average yields. Minimum returns analysis will not
look at averages, but rather at the results from
individual sites. Looking at across-location and across-
year variability is one way of estimating the risks for
farmers associated with the proposed recommendation.
The careful definition of recommendation domains
attempts to eliminate across-location variability as much
as possible. Across-year variability, on the other hand, is
estimated here based on the results of only two or three
years. and tends to underestimate the year-to-year
variability that farmers face. Nevertheless, a careful
minimum returns analysis is a useful way of examining
the variability associated with different technological
alternatives.

Second, note that farmers are more interested in
variability in benefits than variability in yields. A
minimum returns analysis looks at variability in
net benefits.

If the results of a set of on-farm experiments show that
two treatments have the same average net benefits, but
one treatment's results are more variable than the
other's, it is likely that farmers will prefer the treatment
that is more consistent, rather than the one that
sometimes gives very high net benefits but at other
times gives very low net benefits.

But variability per se is not the only factor that farmers
will take into account when deciding among treatments.
If one treatment always gives higher net benefits than









another treatment, it may not matter if the first exhibits
higher variability than the second. As long as marginal
analysis shows that it gives an acceptable rate of return,
and farmers are assured that even in the worst cases it
gives higher net benefits than the alternative, then
farmers will be interested in adopting it.

The most difficult decisions must be taken when the
average net benefits for one treatment are higher than
those for another, but in some locations the net benefits
are lower than those of the alternative. The marginal
analysis (on average results) shows the treatment to be
acceptable, but there are some individual cases where
the benefits are lower than those of the alternative
treatment. Should the farmers choose the treatment that
is better on average, or the one that offers less chance of
low net benefits? It is here that a minimum returns
analysis is most helpful.


Prerequisites for a Minimum Returns Analysis
A minimum returns analysis is a way of screening data
from on-farm experiments in order to give farmers (and
researchers) additional information about the variability
in returns implicit in a proposed recommendation in
Minimum returns comparison with the farmers' practice. A minimum
returns analysis compares the average of the
analysis lowest net benefits for each nondominated
treatment. For the analysis to be relevant, several
prerequisites must be met:

The marginal analysis must have been done on all
locations for a given experiment and for all years. It
should include all locations deemed to belong to the
recommendation domain, including locations with poor
results or those that have been abandoned. A marginal
analysis done only on locations with "good" results will
not be of much use to farmers. At times it is tempting to
remove a particularly poor location from the analysis. If
ten locations were planted in the recommendation
domain, and one location had poor results because of
frost damage, the analysis of the remaining nine will
give farmers an idea of what returns they can expect if
there is no frost. This may not be very useful
information. If nine locations were damaged by frost, no
one would propose analyzing only the single good one!
Thus minimum returns analysis assumes that all
locations have been included in the marginal analysis
done previously.










2 A minimum returns analysis should be done only on
experimental treatments that are being considered for
recommendation. That may include not only the
farmers' practice and the treatment that has been
judged acceptable on the average by marginal analysis,
but also other nondominated treatments that may
provide alternatives if the tentative recommendation
proves unsatisfactory.

Minimum returns analysis presumes that researchers
have tried to explain the reasons for the variability they
observe, rather than assuming it is simply bad luck. The
more precise an idea of the sources cf observed
variability, the more useful the information from the
minimum returns analysis will be for farmers.

Minimum returns analysis is most useful when
recommendations are being considered. Although it does
not pretend to be mathematically precise, it does try to
assess the effects of variability, and this is best
estimated from a large number of results. Minimum
returns analysis is most relevant when done on the
results of at least 20 locations from at least two years.
.The results should be from enough locations and years
to fairly represent the variability that farmers in the
recommendation domain are likely to face.


Minimum Returns Analysis
For simplicity, the steps in the minimum returns
analysis will be illustrated for a comparison between
only two treatments. Table 8.1 lists the yield data from
20 locations over three years of the "0 kg nitrogen"
(farmers' practice) and "80 kg nitrogen" treatments in a
fertilizer experiment. The 80 kg N/ha treatment gives,
on the average, higher yields than the 0 kg N/ha,
although there is considerable variability for both
treatments. The marginal analysis of the average yield
data showed 80 kg N/ha gives an acceptable rate of
return (see Table 6.3).











Table 8.1. Yields by location for Treatments 0 kg N and 80 kg N


Location


Ok N
2,450
2,840
2,130
2,170




2,570


2.222


Average of
20 locations


Yild (kglhe)


The first step is to calculate the net benefits at each one
of the locations for each one of the treatments. This is
not as time consuming as it sounds. In the case of the
80 kg N treatment, the necessary calculations are
shown below:

Net benefits = (Y x A x P)-TCV,

where

Y = yield at one location
A = 1-the yield adjustment
P = field price of crop
TCV = total costs that vary for the treatment

If A = 0.90, P = $0.20/kg, TCV = $60/ha

then the net benefits for treatment 80 kg N for each
location will be:

(Y x 0.9 x $0.20) ($60)

or 0.18 Y 60.

Because Treatment 0 kg N has no costs that vary, the
formula for calculating the net benefits is even easier
(0.18 Y). The net benefits for each location are shown in
Table 8.2.

To do the minimum returns analysis, select the
(approximately) 25% lowest net benefits for one
treatment and compare their average with that of the


80 k N


3,970
3,930
1,870
3,720
*



1,780


3,256










25% lowest net benefits for the alternative. The five
Table 8.2. Net benefits by lowest net benefits representing the 25% worst cases for
location for Treatments each treatment are marked in yellow in Table 8.2.
0 kg N and 80 kg N
If the average of the lowest net benefits for the tentative
(S/ha) recommendation is higher than the average of the
Location 0 kg N 80kgN lowest net benefits for the farmers' practice, then the
recommendation should be made, because even in the
1 441 655 worst cases the recommendation does better than the
2 511 647 farmers' practice.
3 383 277
4 391 610 But if the average for the tentative recommendation is
5 250 593 lower than that for the farmers' practice, then a decision
6 322 619 must be made. The average of the five lowest net
7 490 660 benefits for 0 kg N is $252, whereas the average for the
8 458 600 five lowest for 80 kg N is $244. The absolute value of
9 10 12 these net benefits has little meaning but the difference
between the two should be examined. If the difference is
10 250 612
small, then farmers will probably be willing to accept
11 542 562 this risk, knowing that over the long run they will come
12 512 681 out ahead with the recommendation. In this case, the
13 285 291 difference is only $8, and is small in relation to the
14 387 578 average increase in net benefits ($126). So it is likely
15 375 230 that farmers will be willing to accept this risk. But if the
16 494 661 difference is large, representing a sum equivalent to a
17 485 660 significant part of farmer income or a quantity that
18 295 480 would put farmers in serious debt to a bank or a
19 485 683 moneylender, then it would be best to reconsider the
20 463 260 recommendation. Perhaps an alternative could be found
(in this case it would be worth doing the minimum
returns analysis on 40 kg N as well). If no less risky
Average 400 526
Average 40 alternative is available, then the farmers' practice is to
Average be preferred.
of five
lowest 252 244 It is important to emphasize that this type of analysis
assumes that all locations are representative of a single
recommendation domain, and that there is nothing
special about any individual location. The poor results
for one treatment may or may not be in the same
location as the poor results for another treatment. Thus
in Table 8.2 the farmers' practice does much better than
the recommendation in location 3, whereas in location 5
the reverse is true. But it is assumed that these










locations passed through the analysis described in
Chapter 7. The explanation for these peculiar results
may be a specific factor, such as flooding, or it may be
an undetermined cause. But the decision has been taken
that they both fairly represent the recommendation
domain, should be included in the marginal analysis,
and then included in the minimum returns analysis.

Finally, it should be noted that the minimum returns
analysis is done with actual location by location data.
No attempt is made to fit the data to standard frequency
distributions. The rule of thumb of looking at the worst
25% of cases for each treatment is a guideline only.
Experimental results unfortunately do not always give
smooth curves and normal distributions. The key to
minimum returns analysis, as with the other analytical
techniques described in this manual, is a commonsense
examination of the data from the farmers' point of view.










Chapter Nine
Variability in Prices:
Sensitivity Analysis


Experimental yields are not the only element of the
partial budget that is likely to vary. Input and product
prices are subject to change as well. Researchers need
some way of deciding which prices to use in a partial
budget when making recommendations. At times it is
difficult to predict where prices might be a year or
several years in the future, or difficult to estimate the
opportunity cost of a particular input such as labor. In
these cases, researchers need a way of estimating the
range of prices under which a given treatment may be
recommended. A method for doing this is called
sensitivity analysis.


Which Costs and Prices
Should Be Used in the Partial Budget?
Chapters 2 and 3 emphasized that the partial budget
should use the costs and prices that farmers actually
face, rather than those announced in the newspaper
or set by the government. But beyond this rule there
are still a number of questions that may be asked about
how to select the appropriate price. The price of the crop
may vary considerably within one year, or between
years. Both crop and input prices may be subject to
inflation. And both may be affected by government
policies. What prices should be used in these cases?

It is not uncommon for crop prices to vary within a
year, rising just before harvest and then falling after
harvest. Even if all the farmers in a recommendation
domain store their crop after harvest to sell it at a later
date, it is usually most convenient to base the field
price cf the crop on the market price immediately
after harvest.

If crop (or input) prices vary from year to year, it is
possible to use the average price over the past, say,
three to five years as a basis for calculating field prices.
If researchers have access to price data from ten years
or more, a trend price may be estimated. Very often,
however, these "trends" are due to inflation. Although
inflation is a serious problem for any country, it need
not be an impediment to the marginal analysis. If the
calculations of the costs that vary are based on the
input prices that the farmers will face at the beginning
of the cycle, and if the field price of the crop used for
calculating gross field benefits is based on the crop price
the farmers will receive at the end of the cycle, and if
the minimum rate of return includes the rate of inflation










(which it should if it is based on the rate of interest in
the informal capital market, or in the unsubsidized
formal capital market), then the comparison of the
marginal rate of return to the minimum rate of return is
valid. Alternatively, if input prices and product prices
are taken at one point in time, then the inflation rate
does not have to be included in the minimum rate
of return.

In some cases, prices are controlled by the government.
either directly or through certain policies that affect the
operation of market forces. If input prices are
maintained at low levels through subsidies of some kind
(or if crop prices are maintained at high levels), care
must be taken in using these prices in the economic
analysis of experimental results. If the analysis is to be
used for making recommendations to farmers for future
years, a judgment must be made as to whether the
government can maintain such subsidies. If it seems
unlikely, then it will be better to use more realistic
prices in the calculations.

If, on the other hand, farmers are adversely affected by
government policy, if crop prices are controlled (and
farmers have no alternative markets) or inputs are sold
at higher than world market prices, then there are two
possible lines of action. First, over the short term,
recommendations will have to be based on the prices
that farmers face under these policies. But second, if it
is felt that there is something to be gained by providing
policymakers with information about the consequences
of their current policies and the possible advantages of a
change, the same analysis can be done using estimates
of undistorted prices and be presented to policymakers.
Thus the same set of experiments can be analyzed in
two different ways, for two different audiences; using
current prices for short-term farmer recommendations,
and using alternative prices for contributing to the
consideration of policy options.


Sensitivity Analysis
Markets, inflation, and policies are often unpredictable
enough that, short of access to a crystal ball, there is no
way for researchers to predict prices with any certainty
a few years in the future. Recommendations often
involve an investment in extension agents' time, field
days, pamphlets, or radio programs, and researchers
would like to feel that a recommendation will be able to
















Sensitivity analysis


withstand any likely changes in prices of inputs or crops
for at least a few years.

The best way to test a recommendation for its ability to
withstand price changes is through sensitivity analysis.
Sensitivity analysis simply implies redoing a
marginal analysis with alternative prices. If, for
instance, a fertilizer recommendation is made using
current fertilizer prices, but there are indications that
those prices may increase, a reasonable estimate of the
new prices may be substituted in the analysis. Table 9.1
illustrates such a situation. In the original analysis (case
A), a field price for nitrogen of $0.625/kg was used. The
recommendation of 80 kg N was made, assuming a
minimum rate of return of 100%. If the field price of
nitrogen increases to $0.75/kg, would the same
recommendation hold? Redoing the partial budget (case
B) with the higher price of nitrogen shows that the
recommendation of 80 kg N is now in doubt, because
the marginal rate of return of changing from 40 kg N to
80 kg N is just equal to the minimum rate of return.
Any higher nitrogen prices would necessitate lowering
the fertilizer recommendation.


Table 9,1. Sensitivity analysis for rstrogen expeTimenl


Case A
(Current field price
of N = *0-6251kgl


Case B
(Future field price
of N = $0.75/kg)


Adjusted yield (kg/ha)
Gross field benefits (S/ha)
Cost of fertilizer ($/ha)
Cost of labor (S/ha)
Total costs that vary (S/ha)
Net benefits (S/ha)


Marginal rates of return


0 kg N to 40 kg N = 287%
40 kg N to 80 kg N = 133%


o kg N to 40 kg N = 231%
40 kg N to 80 kg N = 100%


If the minimum rate of return does not change, and the
price of labor and the field price of maize remain
constant, how high can the field price of nitrogen go
before even 40 kg N ceases to be a viable
recommendation? Such questions can be answered by


0 kg N
2,000
400
0
0
0
400


40 kg N
2,580
516
25
5
30
486


80 kg N
2,930
586
50
10
60
526


0 kg N
2.000
400
0
0
0
400


40 kg N
2.580
516
30
5
35
481


80 kg N
2.930
586
60
10
70
516










the formula in Table 9.2. (This is the same formula used
in Chapter 6, p. 54, to help in selecting economically
viable treatments for experimentation). The change in
the total costs that vary will depend on the field price of
N (n) and the labor costs of applying 40 kg N/ha ($5).
The calculation shows that the nitrogen field price can
rise to $1.33/kg before 40 kg N ceases to be a profitable
practice for farmers.

Sensitivity analysis can also be used to examine
assumptions about opportunity costs, particularly those
of labor. At times a partial budget is developed which
uses an opportunity cost of labor that is only a rough
estimate. If the treatments involve significant changes in
labor, an inaccurate estimate of the opportunity cost of
labor may lead to erroneous conclusions. Other
opportunity costs of labor can be substituted in the
partial budget to give an idea of the range over which a
given recommendation would be acceptable to farmers.


Table 9.2: Calculation of maximum acceptable field price
of nitrogen
AY = change in adjusted yield
ATCV = change in total costs that vary
M = minimum rate of return
(expressed as a decimal fraction)
P = field price of product
ATCV (1 + M)
AY =
P
O1

ATCV = AY
1+M

Example
Increase in adjusted yield between
0 kg N and 40 kg N = 580 kg/ha
Cost of labor to apply fertilizer = $5/ha
Minimum rate of return = 100%
Field price of maize = $0.20/kg

To calculate the maximum acceptable field price of nitrogen
(n) in order for the application of 40 kg nitrogen
to be economic:

S 0.2 x 580
40 n + 5- ---
2
n = $1.33/kg










Suppose experimental evidence shows that a certain
herbicide gives the same average yield as the farmers'
hand weeding. A comparison of costs that vary is thus
the only economic analysis necessary for making the
recommendation. Table 9.3 shows these calculations. In
case A, the researchers have assumed an opportunity
cost of labor of $1/day. The total costs that vary of using
the herbicide are lower than those of hand weeding, and
therefore the herbicide should be recommended. But if
the opportunity cost of labor is only $0.50/day, then
hand weeding is the preferred alternative. (Calculations
show that as long as the opportunity cost of labor is
above $0.56/day, the herbicide is to be recommended.)
This illustrates the necessity of carefully studying the
availability and utilization of labor before making
recommendations for something like weed control.

The discussion of sensitivity analysis serves as a
reminder that farmer recommendations may change as
prices change. Agronomic data regarding responses to a
factor are valid as long as the biological environment
and farming practices do not change. The economic
interpretation of that data will depend on changes in
prices. There is thus the need to continually review
farmer recommendations, based on past agronomic
experiments, in the light of present (and future)
economic circumstances.


Table 9.3. Sensitivity snalysia for weed control experiment

Case A Case B
lOpportunity cost (Opport*ity cost
of labor = t$.001day) of labor = 0$.50/dayl

Costs that vary Hand weeding Herbicide Hand weeding Herbicide

Herbicide (S/ha) 0 8 0 8
Sprayer (S/ha) 0 1 0 1
Labor cost (S/ha) 20 4 10 2

Total costs that vary ($/ha) 20 13 10 11










This manual has presented a set of procedures for doing
Chapter Ten an economic analysis of on-farm agronomic
Reporting the Results experiments. The careful use of these procedures will
SAna s help in selecting treatments for further experimentation
of Economic AnalYsis and for developing farmer recommendations. When
researchers report the results of on-farm experiments, a
summary of the results of the economic analysis should
be included. The following points are a checklist for
organizing a report of the economic analysis.

1 Review Objectives of Experiment
Before beginning any analysis, review the objectives of
the experiment. Include a review of the previous
diagnostic and experimental evidence that was used in
planning the experiment and a review of the tentative
definition of the recommendation domain. The purpose
of each variable in the experiment should also be
reviewed. Does it represent a possible alternative to the
farmers' practice, or is it meant to provide initial
evidence about the importance, interactions or causality
of particular production constraints? In other words, do
treatments represent possible farmer recommendations,
or are they being used to help design further
experiments which will lead to such recommendations?

2 Review Experimental Design and Management
Review the design and management of the experiment.
The marginal analysis presented in this manual is
useful only when applied to on-farm experiments with
particular characteristics. The nonexperimental
variables must be at levels representative of farmers'
practice in the recommendation domain, and one
treatment must represent the farmers' practice with
respect to the experimental variabless.

3 Calculate Total Costs That Vary
Identify the variable inputs for each treatment in the
experiment. Make sure that all inputs that vary across
treatments are included, paying particular attention to
changes in labor. Calculate the costs that vary for each
treatment, on a per-hectare basis. For purchased inputs,
base the costs on realistic field prices that farmers in the
recommendation domain must face. For nonpurchased
inputs, develop realistic opportunity costs. Sum the total
costs that vary for each treatment. (A preliminary
calculation of these costs should have been done when
the experiment was being planned.)










4 Calculate Average Yields
Review the results of the experiment at each location.
These may be the results of a single year, or of several
years. Decide if all the locations represent a single
recommendation domain. Decide if any locations should
be eliminated because of errors in experimental
management. Report the reasoning behind these
decisions. Use statistical analysis to help decide if there
are any differences in response among the treatments.
Locations with results that were affected by unexplained
or unpredictable factors must be included in the
statistical analysis.

5 Decide If a Partial Budget Should Be Presented
a) If there are no yield differences among treatments,
the one with lowest total costs that vary should be
chosen for further experimentation or. if there is
sufficient evidence, for recommendation.

b) If there are yield differences among treatments, then
a partial budget will have to be developed.

6 Calculate Adjusted Yields
The first line of the partial budget should show the
yields for each treatment averaged over all locations in
the recommendation domain. The second line shows
adjusted yields based on differences between the
experiments and the farmers' fields with respect to trial
management, plot size, or time or method of harvest.

7 Calculate Gross Field Benefits
Calculate the field price of the crop. Remember, an
experiment may involve more than one crop, and/or
may involve crop by-products, such as fodder, which are
of importance to farmers. The field price of a crop is the
price that farmers receive, less all costs of harvesting
and marketing that are proportional to the yield. The
gross field benefits for each treatment are the adjusted
yields times the field price.

SCalculate Net Benefits
List the costs that vary, and the total, for each
treatment. Calculate the net benefits for each treatment.
The partial budget should contain only yield, cost, and
benefit figures. Assumptions about field prices, yield
adjustments, etc. should be presented beneath the
partial budget as footnotes. Details on experimental
treatments should be clearly presented elsewhere in the
report. in the discussion of the experiment.










9 Do a Dominance Analysis
Arrange treatments in order of ascending total costs that
vary, with corresponding net benefits. Eliminate
dominated treatments.

1 0 Estimate a Minimum Acceptable Rate of Return
Estimate a minimum rate of return for a crop cycle. In
most cases the minimum rate of return will probably be
between 50%and 100% for a crop cycle.

1 Do a Marginal Analysis
A marginal analysis presents the nondominated
treatments on a net benefit curve and calculates the
marginal rates of return between pairs of adjacent
treatments. Compare the marginal rates of return to the
minimum rate of return in order to select acceptable
treatments. Present the results of the marginal analysis
in the report.

12 Draw Conclusions From the Marginal Analysis
a) If the results of the experiment are being used to help
plan further experimentation, then the results of the
economic analysis should be discussed in the report in
light of the choice of appropriate treatments for
experiments in the next cycle.

b) If the economic analysis is being done to develop a
recommendation, then the report should contain a
discussion of the evidence that has been used to make
the recommendation.

13 Before Making a Recommendation,
Do a Minimum Returns Analysis
If data from enough locations and years are available, do
a minimum returns analysis on all the experimental
results to examine the implications of the variability in
the results for farmer welfare.

14 Before Making a Recommendation,
Do a Sensitivity Analysis
If variability in prices or costs is expected, carry out the
relevant sensitivity analysis and include the results in
the report.











References to definitions of terms are printed in
Index boldface type.

Adjusted yield. 10, 23- 25
Adoption of recommendations, 5, 51-52
Agronomic assessment. 3. 12. 21, 58. 62
Average yield, 9, 22-23
Continuous analysis, 52
Cost of capital. 34- 37
Costs that vary. 10. 13- 19
Dominance analysis. 30- 31
Experimental variables. 5-6, 55
Farmer assessment. 3, 49
Field cost. 14
Field price (ofan input). 13--16
Field price (of output). 10. 25- 27, 71
Gross field benefits. 10. 27- 28
Inflation, 35, 71-72
Labor, 16-18, 53. 74-75
Management of experiments, 5-7, 23-25, 59
Marginal analysis, 11-12. 38- 46
Marginal rate of return. 12, 32-33, 49
Minimum rate of return. 34-37, 48. 71-72
Minimum returns analysis, 66- 70
Net benefits. 4. 11. 28
Net benefit curve. 31-32, 41, 45
Nonexperimental variables, 6, 23-24. 57
On-farm experiments, 5-7
On-farm research, 1-3
Opportunity cost. 13. 16-17, 34. 53. 74-75
Opportunity field price (ofan input). 15
Opportunity field price (of output), 27, 35 (footnote)
Packages of practices. 5, 51-52
Partial budget, 9, 27-29
Policymakers. 3, 16. 36. 72
Recommendations. 1, 49, 51-52
Recommendation domain. 7-8, 20-21, 57-58
Residuals. 47-48
Risk, 4-5. 59. 63-66
Sensitivity analysis. 53, 73- 75
Statistical analysis. 3. 21-22, 60-62
Total costs that vary. 11, 18-19
Working capital. 34




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