• TABLE OF CONTENTS
HIDE
 Copyright
 Front Cover
 Title Page
 Table of Contents
 Acknowledgement
 Introduction
 Adaptability analysis: An...
 Single-factor trials
 Factorial trials: Interactions...
 Technology packages and compon...
 Systems trials
 Design of on-farm research-extension...
 Reference
 Index






Group Title: Adaptability analysis : a method for the design, analysis, and interpretation of on-farm research-extension
Title: Adaptability analysis
CITATION PAGE IMAGE ZOOMABLE PAGE TEXT
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 Material Information
Title: Adaptability analysis a method for the design, analysis, and interpretation of on-farm research-extension
Physical Description: x, 189 p. : ill. ; 23 cm.
Language: English
Creator: Hildebrand, Peter E
Russell, John T
Publisher: Iowa State University Press
Place of Publication: Ames
Publication Date: 1996
Edition: 1st ed.
 Subjects
Subject: Agriculture -- Research -- On-farm -- Methodology   ( lcsh )
Agriculture -- Research -- Methodology   ( lcsh )
Genre: government publication (state, provincial, terriorial, dependent)   ( marcgt )
bibliography   ( marcgt )
non-fiction   ( marcgt )
 Notes
Bibliography: Includes bibliographical references (p. 181-185) and index.
Statement of Responsibility: Peter E. Hildebrand and John T. Russell.
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: UF00072042
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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 - 34544425
lccn - 96015735
isbn - 0813824524

Table of Contents
    Copyright
        Copyright
    Front Cover
        Page i
        Page ii
    Title Page
        Page iii
        Page iv
    Table of Contents
        Page v
        Page vi
        Page vii
        Page viii
    Acknowledgement
        Page ix
        Page x
    Introduction
        Page 3
        Science and agricultural technology development
            Page 3
            Page 4
        Broadly adaptable vs. location-specific technologies
            Page 5
            Page 6
        Origin of adaptability analysis
            Page 7
        Purpose of on-farm research-extension
            Page 8
            On-farm research-extension and farming systems research-extension
                Page 8
                Page 9
            Recognizing farm and environmental diversity
                Page 10
                Page 11
            Environmental defined
                Page 12
                Page 13
            Incorporating farmers' perspectives and evaluations
                Page 14
            Enhancing adoption through adaptation and learning
                Page 15
            Location specificity and sustainable agriculture
                Page 16
            Problems with combined analysis of variance in analyzing on-farm trials
                Page 17
        History of stability analysis by regression
            Page 18
            Fifty years of use by plant breeders
                Page 18
                Page 19
            Usefulness of stability analysis in assessing GxE interaction
                Page 20
            Traditional breeders' perspective: Broad adaptability
                Page 21
                Page 22
            Several definitions of stability
                Page 23
            Statistical concerns with regression stability analysis
                Page 24
            The modifications to "stability analysis"
                Page 25
        The book
            Page 26
            Page 27
            Page 28
    Adaptability analysis: An overview
        Page 29
        Introduction
            Page 29
        Important steps in adaptability analysis
            Page 29
            Page 30
        Data requirements
            Page 31
            Sources of data
                Page 31
                Page 32
            Quality of data
                Page 33
        An example of AA from Brazil
            Page 34
            Relating treatment response to environment by regression on EI
                Page 35
                Page 36
                Page 37
                Page 38
                Page 39
                Page 40
                Page 41
            Comparison of results from AA regression and from ANOVA
                Page 42
                Page 43
                Page 44
            Relationship between EI and environmental characteristics
                Page 45
                Page 46
                Page 47
                Page 48
                Page 49
                Page 50
            Analysis within tentative recommendation domains (verification)
                Page 51
                Page 52
                Page 53
                Page 54
                Page 55
                Page 56
            Use of alternative evaluation criteria
                Page 57
            Multiple extension recommendations
                Page 58
                Page 59
        Summary
            Page 60
            Page 61
            Page 62
    Single-factor trials
        Page 63
        On-farm maize variety trials (Paraguay)
            Page 63
            Page 64
            Page 65
            Page 66
            Page 67
            Page 68
            Page 69
            Page 70
            Page 71
            Page 72
        Hormone implants in beef and dual-purpose cattle (Panama)
            Page 73
            Page 74
            Page 75
            Page 76
            Page 77
        On-farm sorghum variety trials (Cameroon)
            Page 78
            Page 79
            Page 80
            Page 81
            Page 82
            Page 83
            Page 84
            Page 85
            Page 86
            Page 87
            Page 88
            Page 89
            Page 90
            Page 91
            Page 92
            Page 93
            Page 94
    Factorial trials: Interactions among factors
        Page 95
        Maize variety-by-fertilizer on-far trial (Malawi)
            Page 95
            Page 96
            Page 97
            Page 98
            Page 99
            Page 100
            Page 101
            Page 102
            Page 103
            Page 104
            Page 105
            Page 106
        Maize N and P fertilization trials (CIMMYT, Mexico)
            Page 107
            Page 108
            Agronomic analysis
                Page 109
            Economic analysis
                Page 109
                Page 110
            Recommendations and multiple extension messages
                Page 111
                Page 112
                Page 113
                Page 114
                Page 115
                Page 116
                Page 117
                Page 118
    Technology packages and components
        Page 119
        Bean systems (Costa Rica)
            Page 119
            Agronomic analysis
                Page 119
                Page 120
                Page 121
                Page 122
                Page 123
            Economic analysis
                Page 124
            Risk analysis
                Page 125
                Page 126
                Page 127
                Page 128
                Page 129
        Potatoes (International Potato Center, Peru)
            Page 130
            Technology package trial, 1978
                Page 130
                Page 131
                Page 132
                Page 133
                Page 134
            Technology package trial, 1979
                Page 135
                Page 136
                Page 137
                Page 138
                Page 139
                Page 140
                Page 141
                Page 142
                Page 143
                Page 144
        Factorial trial of components, with interaction
            Page 145
            Page 146
            Page 147
            Page 148
            Page 149
            Page 150
            Page 151
            Page 152
            Was more than one year of data necessary?
                Page 153
                Page 154
    Systems trials
        Page 155
        Change to pasture-intensive dairy system (New York)
            Page 155
            Page 156
            Page 157
            Page 158
            Page 159
            Page 160
            Page 161
            Page 162
    Design of on-farm research-extension trials
        Page 163
        Nature of on-farm trials
            Page 163
            Research questions
                Page 163
                Page 164
            Extension activities and the learning environment
                Page 165
                Page 166
        Designing for efficiency
            Page 167
            Farmer participation
                Page 167
                Page 168
            Choice of treatments
                Page 169
            Choice of control treatments
                Page 169
                Page 170
                Page 171
            Replications vs. environments
                Page 172
            Environments vs. Years
                Page 173
                Page 174
                Page 175
            Data collection
                Page 176
                Page 177
        Location specificity, biodiversity and sustainable agriculture
            Page 178
            Page 179
            Page 180
    Reference
        Page 181
        Page 182
        Page 183
        Page 184
        Page 185
        Page 186
    Index
        Page 187
        Page 188
        Page 189
Full Text
[ IOWA STATE UNIVERSITY PRESS
2121 South State Avenue Ames, IA 50014-8300
(515) 292-0140 Fax (515) 292-3348 www.isupress.com





September 20, 2001



Peter Hildebrand
Food and Resource Economics Department
PO Box 110240
University of Florida
Gainesville, FL 32611-0240

RE: Adaptability Analysis

Dear Professor Hildebrand:

This letter is to notify you that the Iowa State University Press has reassigned the
copyright of Adaptability Analysis: A Method for the Design, Analysis and Interpretation
of On-Farm Research-Extension, form TX 4-334-716 dated 7/11/96, to you.

A check and a copy of this letter have been sent to the Copyright Office for recordation
purposes.

Si erely,



Gretchen Van Houten
Publishing Director
Iowa State University Press
2121 S State Avenue
Ames, IA 50014-8300
(515) 292-0140 ext. 611
(515) 292-3348 (fax)









Adaptability
Analysis
A Method for the Design, Analysis
and Interpretation of
On-Farm Research-Extension
Peter E. Hildebrand and John T. Russell









Adaptability
Analysis
A Method for the Design, Analysis
and Interpretation of
On-Farm Research-Extension
Peter E. Hildebrand and John T. Russell










Peter E. Hildebrand is professor, Food and Resources Economics
Department, Institute of Food and Agricultural Sciences, University
of Florida, Gainesville. He has a B.S. in animal science and an M.S.
in economics from Colorado State University and a Ph.D. in agricul-
tural economics from Michigan State University. Dr. Hildebrand has
worked in on-farm research for 25 years, including 10 years while in
residence in Columbia, El Salvador and Guatemala. He has coordi-
nated the University of Florida Farming Systems Research-
Extension program for 15 years and was the founding president of
the Association for Farming Systems Research-Extension.

John T. Russell, an independent consultant, has a B.A. from
the College of William and Mary, an M.S. from the University
of Maryland, and a Ph.D. from the University of Florida. He spent
more than 10 years in Africa as a trainer, resident technical advisor,
and consultant in sustainable rural and agricultural development,
experimental design and analysis, crop production, cropping systems
research, and farming systems research-extension. He has also had
experience in Zaire, Rwanda, Burkina Faso, Senegal, Cameroon,
Chad, Tanzania, Madagascar and the Ukraine.





1996 Iowa State University Press, Ames, Iowa 50014
All rights reserved.

Authorization to photocopy items for internal or personal use, or the internal or
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been granted a photocopy license by CCC, a separate system of payments has been
arranged. The fee code for users of the Transactional Reporting Service is 0-8138-
2452-4/96 $.10.

0 Printed on acid-free paper in the United States of America

First edition, 1996.

ISBN 0-8138-2452-4


CIP to come

















Contents



Acknowledgements ix

1. Introduction 3
Science and Agricultural Technology Development 3
Broadly Adaptable vs. Location-Specific Technologies 5
Origin of Adaptability Analysis 7
Purpose of On-Farm Research-Extension 8
On-Farm Research-Extension and Farming Systems
Research-Extension 8
Recognizing Farm and Environmental Diversity 10
Environments Defined 12
Incorporating Farmers' Perspectives and Evaluations 14
Enhancing Adoption Through Adaptation and Learning 15
Location Specificity and Sustainable Agriculture 16
Problems with Combined Analysis of Variance in
Analyzing On-Farm Trials 17
History of Stability Analysis by Regression 18
Fifty Years of Use by Plant Breeders 18
Usefulness of Stability Analysis in Assessing GxE
Interaction 20
Traditional Breeders' Perspective: Broad Adaptability 21
Several Definitions of Stability 23
Statistical Concerns with Regression Stability Analysis 24
The Modifications to "Stability Analysis" 25
The Book 26







Adaptability Analysis


2. Adaptability Analysis: An Overview 29
Introduction 29
Important Steps in Adaptability Analysis 29
Data Requirements 31
Sources of Data 31
Quality of Data 33
An Example of AA from Brazil 34
Relating Treatment Response to Environment by
Regression on El 35
Comparison of Results from AA Regression and from
ANOVA 42
Relationship between El and Environmental
Characteristics 45
Analysis within Tentative Recommendation Domains
(Verification) 51
Use of Alternative Evaluation Criteria 57
Multiple Extension Recommendations 58
Summary 60

3. Single-Factor Trials 63
On-Farm Maize Variety Trials (Paraguay) 63
Hormone Implants in Beef and Dual-Purpose Cattle (Panama) 73
On-Farm Sorghum Variety Trials (Cameroon) 78

4. Factorial Trials: Interactions Among Factors 95
Maize Variety-By-Fertilizer On-Farm Trial (Malawi) 95
Maize N and P Fertilization Trials (CIMMYT, Mexico) 107
Agronomic Analysis 109
Economic Analysis 109
Recommendations and Multiple Extension Messages 111

5. Technology Packages and Components 119
Bean Systems (Costa Rica) 119
Agronomic Analysis 119
Economic Analysis 124
Risk Analysis 125
Potatoes (International Potato Center, Peru) 130
Technology Package Trial, 1978 130
Technology Package Trial, 1979 135







Contents


Factorial Trial of Components, with Interaction 145
Was More than one Year of Data Necessary? 153

6. Systems Trials 155
Change to Pasture-Intensive Dairy System (New York) 155

7. Design of On-Farm Research-Extension Trials 163
Nature of On-Farm Trials 163
Research Questions 163
Extension Activities and the Learning Environment 165
Designing for Efficiency 167
Farmer Participation 167
Choice of Treatments 169
Choice of Control Treatments 169
Replications vs. Environments 172
Environments vs. Years 173
Data Collection 176
Location Specificity, Biodiversity and Sustainable Agriculture 178

References 181


Index


187






viii Adaptability Analysis

















Acknowledgements


Data used in the examples in the book are taken from previously
published, public domain sources (citations are in the References).
Nevertheless, we wish to thank the persons and organizations whose data
were used. In order of appearance, this includes:

B. K. Singh, Professor at EARTH (Escuela de Agricultura de la
Region Tropical Hdmeda) in Costa Rica for the Manaus, Brazil cowpea
data. Also involved in this research were EMBRAPA and EMATER,
Brazil; the Soils Management CRSP funded by USAID; and the Soil and
Water Science Department; and the Farming Systems Program of the
Institute of Food and Agricultural Sciences, University of Florida.
Federico Poey, President, AGRIDEC, Miami, Florida, for the maize
variety trial data from Paraguay.
James R. Simpson, Professor of Food and Resource Economics,
University of Florida; Michael Sands, Rodale Institute; and Luis
Hertentains, INIAP, David, Panama, for the cattle data from Panama.
For the Cameroon sorghum variety data, Cameroon's Institut de
Recherche Agronomique (IRA), including the Testing and Liaison Unit
in Maroua, where the trials were conducted under the direction of Jerry
J. Johnson and Martin Fobasso. Also, thanks to the National Cereals
Research and Extension Project (International Institute of Tropical
Agriculture and U.S. Agency for International Development).
Art Hansen, Professor of Anthropology, University of Florida; and
Emmanuel Mwango and Benson Phiri, both of the Department of
Agricultural Research, Ministry of Agriculture, Malawi, for the Malawi
maize and fertilizer data.








Adaptability Analyst


CIMMYT, Mexico, for the maize nitrogen and phosphorus fertilize
data.
Barbara C. Bellows, Research Associate, SANREM CRSP for the bei
systems data from Costa Rica.
Douglas Horton, CIP, Peru, for the potato technological package an
component data.
L. Toomer and D.L. Emmick, USDA Soil Conservation Service
Syracuse, New York for the dairy grazing systems data.
The name given to the methodology and used as the title for the boo
-Adaptability Analysis-was suggested by K.R. Tefertiller, Professor
Food and Resource Economics, University of Florida, after reading earl
versions of Chapters 1 and 2 of the book. We appreciate his insight an
convincing argument, and acknowledge his contribution.
For the facilities, services and time used during the preparation of thi
book, we acknowledge the Food and Resource Economics Department
Institute of Food and Agricultural Sciences, University of Florida.
Finally, a very special acknowledgement goes to RoseMary Vega d
Espaillat, Secretary, Food and Resource Economics Department
University of Florida, for her patience, skill and good will during th
many drafts of the book and for setting up all the tables and crafting al
the graphs included. The excellent quality of the photo-ready copy i
due to her dedication and efforts. Thanks, RoseMary, from both of us













1


Introduction



Agricultural research and extension efforts during the last half of the
twentieth century have met with considerable success. Globally, food
and fiber production has kept pace with a rapidly expanding human
population even as increasingly less favorable agricultural areas are
brought into production. Per hectare yields of the many important crops
in the most favorable areas have increased dramatically during this period
of time. Yet there remain large and significant pockets around the globe
where the effects of international technology generation and diffusion
efforts by means of established research and extension organizations,
whether international, national or non-governmental, have been little felt.


Science and Agricultural Technology Development

Much of the success of this revolution in agricultural technology has
been based on the scientific agricultural research process developed by
such institutions as the United States Department of Agriculture and the
land-grant university system of the United States and their extensive
networks of agricultural experiment stations, as well as by agricultural
research services in western Europe.and by the International Agricultural
Research Centers (IARCs) of the Consultative Group on International
Agricultural Research (CGIAR). The research process at these stations,
which is largely disciplinary in nature, involves a high degree of
experimental control. There are three reasons for this control: to
minimize variation in non-experimental variables for scientific purposes;
to reduce residual variance for statistical purposes; and to achieve













1


Introduction



Agricultural research and extension efforts during the last half of the
twentieth century have met with considerable success. Globally, food
and fiber production has kept pace with a rapidly expanding human
population even as increasingly less favorable agricultural areas are
brought into production. Per hectare yields of the many important crops
in the most favorable areas have increased dramatically during this period
of time. Yet there remain large and significant pockets around the globe
where the effects of international technology generation and diffusion
efforts by means of established research and extension organizations,
whether international, national or non-governmental, have been little felt.


Science and Agricultural Technology Development

Much of the success of this revolution in agricultural technology has
been based on the scientific agricultural research process developed by
such institutions as the United States Department of Agriculture and the
land-grant university system of the United States and their extensive
networks of agricultural experiment stations, as well as by agricultural
research services in western Europe.and by the International Agricultural
Research Centers (IARCs) of the Consultative Group on International
Agricultural Research (CGIAR). The research process at these stations,
which is largely disciplinary in nature, involves a high degree of
experimental control. There are three reasons for this control: to
minimize variation in non-experimental variables for scientific purposes;
to reduce residual variance for statistical purposes; and to achieve







Adaptability Analys'


maximum expression of treatment variables for disciplinary purposes
Resulting technologies were high yielding and indicators of the potenti
available for agricultural production in the many farming systems of th
world.
During and following World War II the agricultural revolution in th
industrialized world, led by the United States, was characterized b
increasing capital investment with a substitution of tractors' for animal
traction, followed by a decrease in draft livestock numbers, then i
amount of pasture on farms. As other livestock became increasing
limited and availability of manure for fertility scarce, chemical fertilize
use began to increase rapidly. Crop intensification was manifested i
more limited rotations and in a resulting need for and use of chemical
pesticides of all types. All of these factors, as well as irrigation, allowed
farmers to modify natural environments to make them more similar t
those created on experiment stations for scientific and statistical
purposes. In industrialized countries, these forces were creating an
agriculture with homogeneous conditions over very broad areas as
"innovators" and "early adopters" began buying up the farms of those
"laggards" and "non-adopters" who for one reason or another were not
adopting the technology. Industry, as well, benefitted from the potential
thus created for "broadly adaptable" technologies made possible by the
ability of capital-intensive farmers to create environments suitable for
high-yielding modern technologies.
In the 1960s there was a great deal of enthusiasm for the potential of
this science-based, industry-supported agriculture to lead a "Green
Revolution" in the developing countries of the Third World. Wheat and
rice led the way, particularly in Asia. By the 1970s enthusiasm was
dampened as it became evident that the greatest yield advances of the
Green Revolution were being made on those farms with the best
environments and greatest resource bases. The technology researchers
and developers who had confidence in the transferability of science-based
and industry-supported agriculture became interested in "yield gap"
(Ghodake and Walker, 1982) or "yield constraint" (De Datta et al.,
1978) studies to ascertain why technologies did not produce the same
results in the field as they did on the stations where they originated.
These researchers correctly surmised that the gaps were at least in part
responsible for the lack of adoption of what to their minds should have
been "broadly adaptable" technologies.
Also during the 1970s, the approach to technology development now
called Farming Systems Research-Extension (FSRE) was beginning to








Introduction


emerge. This methodological process, heavily influenced by social
scientists (including agricultural economists), began to create an
awareness that the problem lay not with "laggard" farmers but with the
fact that small-scale, limited-resource family farmers were unable to
dominate and/or modify their environments in the ways that capital-
intensive, industrialized farmers were. Because of their constraints, these
small-scale farmers had to live and produce within the environmental
conditions caused by the natural endowment and socioeconomic situations
existent on their farms. The diversity of combinations created by these
conditions meant that technologies would have to be more environment
specific than envisioned for the broadly adaptable technologies of
industrialized countries. They would need to fit in both the base
biophysical environmental conditions and the socioeconomic situations of
the producers if they were to be adoptable. That is, the problem of non-
adoption lay not with the potential target farmers but with the nature of
the technology being generated for them.


Broadly Adaptable vs. Location-Specific Technologies

One research tool widely used by plant breeders over the last 30 years
to evaluate genetic material-new lines, varieties and hybrids-is called
"stability analysis." This procedure, to be discussed in more detail later,
provided a means to assess the response of new materials to different
environments and the interaction of different materials to environmental
changes. Because these materials were initially and extensively tested on
experiment stations, yield levels generally reflected highly productive
environments. The highest-yielding materials under these conditions
often exhibited large linear coefficients (> 1) when regressed on an index
of environmental quality, indicating that they tended to be very fragile
and break down in poor conditions. Materials that were more robust and
held up in poor conditions tended to yield relatively poorly in the
experiment station conditions, resulting in small linear regression
coefficients (<1). The higher-yielding materials with linear regression
coefficients approaching one were considered to be the "best," "most
stable" or "most broadly adaptable" (variety B in Figure 1.1).
It has been argued (Hildebrand, 1990a) that rejecting materials that
excelled in the best environments but collapsed in poorer environments
(variety A in Figure 1.la), and those that excelled in poor environments
but did not respond to good environments (variety C) was a negative







Adaptability Analysis


ENVIRONMENTAL INDEX, El


1 2 3 4 5
ENVIRONMENTAL INDEX, El


FIGURE 1.1 A negative interpretation (top) of treatment-by-
environment interaction resulting in the compromise choice of one
broadly adapted treatment, B, and a positive interpretation (bottom) of
the same interaction, resulting in the choice of two treatments, A and C,
specifically adapted to good and poor environments, respectively.







Introduction


interpretation of the results of stability analysis. In essence, it was a
compromise based on the assumption that one "best" variety had to be
selected (variety B), an assumption based on the belief that environments
could not be stratified adequately to allow for specific recommendations
or could be so modified by use of inputs that "poor environments" would
be due only to unpredictable characteristics such as rainfall and pest
incidence. This compromise resulted in the rejection of many materials
that otherwise might have been well adapted to the poor environments
inhabited by most of the world's small-scale farmers.


Origin of Adaptability Analysis

The modifications to traditional stability analysis (described in detail
on page 25) that resulted in Modified Stability Analysis (Hildebrand,
1984), or MSA, included a rethinking of this negative interpretation. To
generate materials for specific environmental situations, those that do
well in "good" environments are accepted for those "good" environments
even if they do not do well in "poor" environments. Conversely, those
that do well in "poor" environments, even if they do not respond to
"good" environments are accepted for those "poor" environments. This
positive interpretation of environmental interaction (Figure 1. 1b) provides
the opportunity to identify not only genetic materials that excel in wide-
ranging conditions, but also any other kind of technology being evaluated
as potentially useful.
The incorporation of the evaluation of other kinds of technology
(fertilizers, rotations, intercropping systems, livestock practices, etc.) by
means of MSA was another modification of stability analysis. Thus,
MSA could serve as a comprehensive method for the design, analysis and
interpretation of on-farm research data for specific adaptability. It can
be useful in the developing countries, where tremendous environmental
variability exists, and in industrialized countries where concerns with a
more sustainable agriculture require more location-specific technologies
than have been the norm over the past half century.
Unfortunately, confusion over the word "stability" in the name MSA
has prevented many practitioners from fully comprehending and
exploiting the potential of the method for identifying technology adapted
to specific conditions. Since the method in fact aims at specific
adaptability and not broad adaptability, i.e., not stability across diverse
environments, we propose the name Adaptability Analysis (AA), rather







Adaptability Analysis


than Modified Stability Analysis (MSA), as a more accurate description
of the method.


Purpose of On-Farm Research-Extension

On-Farm Research-Extension and Fanning Systems
Research-Extension

Farming systems research-extension (FSRE) is a relatively recent but
increasingly common and familiar approach to the generation, evaluation
and diffusion of agricultural technology, particularly for small-scale,
limited-resource farming systems. The Association for Farming Systems
Research-Extension, founded in 1989, describes the objectives of the
approach as the

development and adoption through participation by farm household
members of improved and appropriate technologies and management
strategies to meet the socioeconomic and nutritional needs of farm families;
to foster the efficient and sustainable use of natural resources; and to
contribute toward meeting global requirements for food, feed, and fiber.

More concretely, there are several characteristics of farming systems
research (FSR) common to almost all variants of the approach. As it
was originally conceived, through the concurrent efforts of many people
working in several different parts of the world in the 1970s, the key
concepts that characterize FSR are that

FSR is farmer-oriented, seeking to provide technologies relevant to
farmers' goals, needs and priorities;
FSR is systems-oriented, viewing the farm holistically and focusing
on the interrelationships and interactions among farm sub-systems;
FSR is problem-solving in nature, with the goal of identifying
specific constraints to improved production and developing specific
solutions;
FSR is interdisciplinary, dependent on collaboration between
biological and social scientists;
FSR complements strategic, commodity and disciplinary research,
drawing upon the existent body of knowledge, and adapting the
results of these research efforts to farmers' circumstances;







Adaptability Analysis


than Modified Stability Analysis (MSA), as a more accurate description
of the method.


Purpose of On-Farm Research-Extension

On-Farm Research-Extension and Fanning Systems
Research-Extension

Farming systems research-extension (FSRE) is a relatively recent but
increasingly common and familiar approach to the generation, evaluation
and diffusion of agricultural technology, particularly for small-scale,
limited-resource farming systems. The Association for Farming Systems
Research-Extension, founded in 1989, describes the objectives of the
approach as the

development and adoption through participation by farm household
members of improved and appropriate technologies and management
strategies to meet the socioeconomic and nutritional needs of farm families;
to foster the efficient and sustainable use of natural resources; and to
contribute toward meeting global requirements for food, feed, and fiber.

More concretely, there are several characteristics of farming systems
research (FSR) common to almost all variants of the approach. As it
was originally conceived, through the concurrent efforts of many people
working in several different parts of the world in the 1970s, the key
concepts that characterize FSR are that

FSR is farmer-oriented, seeking to provide technologies relevant to
farmers' goals, needs and priorities;
FSR is systems-oriented, viewing the farm holistically and focusing
on the interrelationships and interactions among farm sub-systems;
FSR is problem-solving in nature, with the goal of identifying
specific constraints to improved production and developing specific
solutions;
FSR is interdisciplinary, dependent on collaboration between
biological and social scientists;
FSR complements strategic, commodity and disciplinary research,
drawing upon the existent body of knowledge, and adapting the
results of these research efforts to farmers' circumstances;








Introduction


On-farm research is central to FSR, ensuring close collaboration
between researchers and farmers and allowing evaluation of
technologies under the environmental and socioeconomic conditions
in which they will be used;
FSR provides feedbackfrom farmers, permitting communication of
farmers' points of view to policy makers and station-based
researchers.
(Adapted from Merrill-Sands, 1986)

The addition of an "E" to FSR reflects a recognition that the adaptive,
problem-solving nature of FSR, particularly its on-farm research
component, serves an extension function in itself but more importantly
is enhanced by active collaboration with field-level extension personnel.
Many practitioners of FSRE have come to realize that the
interdisciplinarity of FSRE should include, by definition, linkages that
extend across the separation of research and extension and also the
linkages across the boundaries of biological and socioeconomic research.
The term on-farm research-extension also reflects the basic tenets that 1)
on-farm trials or other on-farm research activities often serve an
extension as well as a research function, and 2) such activities are
uniformly more efficient and effective when carried out as joint research
and extension collaborations.
Although there is in practice considerable diversity among various
efforts-projects or national programs, called FSR or FSRE-most such
efforts use an approach characterized by a number of identifiable stages,
such as description or diagnosis, design, testing and dissemination.
These stages can be summarized as follows:

Diagnosis, or initial characterization of the farming systemss, through
activities such as review of existing information, and informal surveys,
e.g., sondeos, rapid rural appraisals, etc. This characterization
includes
preliminary identification of problems and constraints;
tentative grouping into homogeneous farming systems.'


M Many farming systems practitioners consider homogeneous faring
systems to be synonymous with recommendation domains, reflecting early FSRE
methodology (Hildebrand, 1986, p. 52). In this present book, recommendation
domains and homogeneous farming systems are quite different.
Recommendation domains as used here can be described as combinations of







Adaptability Analysis


Planning and design of research activities, including
biological research;
continuing physical and agro-socioeconomic characterization.
Development and evaluation of technologies:
on-station commodity and discipline research;
researcher-managed on-farm trials, including exploratory, site-
specific, and regional trials;
farmer-managed on-farm trials, for 1) evaluation of technologies
by farmers themselves, 2) refined partitioning of recommendation
domains, and 3) initiation of technology diffusion.
Promotion of acceptable technology throughout the appropriate
diffusion domain(s).

In all stages the following activities occur:

Collection, analysis and interpretation of data:
experimental and non-experimental data from trials;
economic farm enterprise records;
other socioeconomic data from surveys.
Iterative, regular reexamination of activities and results, for
continual refinement of recommendation domains;
elucidation, in collaboration with extension, of location-specific
recommendations;
incorporation of farmer and field-extension feedback into the
process;
improved planning of future work.
(Adapted from Hildebrand and Poey, 1985)

Recognizing Farm and Environmental Diversity

Inherent in the FSRE approach to adaptive research-extension is the
recognition that the limited-resource farm households that are important
clientele of FSRE, live and work on farms characterized by a high


environmental conditions and evaluation criteria from information obtained
during the on-farm research-extension effort. This is a concept much more
useful than the older classification of homogeneous farming systems based on
information obtained only during the diagnosis. The definition of homogeneous
farming systems in the diagnosis is valuable to help understand the diversity of
systems in the research domain and as a guide to help locate on-farm trials to
sample this diversity.







Introduction


degree of both biophysical and socioeconomic diversity. FSRE was
developed in large part in response to the failure of previous efforts at
technology development and transfer, efforts which assumed that
technologies "proven" superior on experiment stations would as a matter
of course be recognized as superior and adopted by farmers.
Technologies developed solely under the conditions common on experi-
ment stations are, however, rarely transferable directly to small-scale,
limited-resource farmers. First, the biophysical conditions of stations are
usually extremely favorable to crop and animal production.
Traditionally, selection of research station sites has favored fertile soils
with good structure and water-holding capacity. Over time, continued
application of fertilizers and pesticides has raised even further the basal
productivity of these sites. Experiment stations generally, then, are
unrepresentative even of their geographical area in that they usually
produce consistently high yields as a result of their histories of high-
input, almost "unlimited-resource" management.
The problem, however, is not just that conditions on experiment
stations do not allow identification of technologies that will perform well
under limited-resource, low-input conditions. It is also that the high-
input management regimes characteristic of stations, particularly when
these include irrigation, tend to render the stations more homogeneous,
in effect smoothing over both differences among fields within a station
and also differences among stations. This situation tends to favor the
development of overly delicate technologies, i.e., technologies that
require a certain, specific, narrow (usually high) range of growth factors
in order to perform well. Robust technologies, those capable of good,
or at least adequate, results under the broader, more variable, and often
unpredictable range of conditions faced by most farmers, tend to be
systematically disfavored under usual experiment station conditions.
This is not to argue that commodity or discipline (strategic) research
carried out on experiment stations is unnecessary or irrelevant. Such
research is valuable in the assessment of many alternative technologies
under well-controlled conditions, subject to scientifically rigorous
standards. Through factorial or other multifactor experiments, such
research allows examination and explanation of important interactions
both among experimental factors and between experimental and
environmental factors. It also allows estimation of the upper limits of
the production potential of technologies. On-farm research-extension, as
carried out in the FSRE approach can convey knowledge of important
problems to be addressed by on-station, component research.







Adaptability Analysis


Additionally, FSRE can attempt to adapt the results of this necessary
discipline- and commodity-based experiment station research through
joint research-extension-farmer evaluation of promising technologies
under the range of environmental conditions prevalent in the area in
question.
Sometimes researchers attempt to develop technologies under a range
of environmental conditions by conducting multi-station trials. While on-
station multilocational trails are often an essential component of the
technology development process, they have serious limitations. They do
take into account some environmental diversity (the between-location,
bio-physical effect), but they still fall short of evaluating technologies
under the types of conditions farmers face. First, since each station is
usually a collection of good environments, the range of environments
represented will also be higher than the range of environments found on
farms. In addition, the range of environments will probably be much
narrower. This is because in order for an environment to be "good" it
has to supply some minimum amount of each of many factors required
for crop or animal growth and development. All "good" environments
supply the necessary amounts of all these requirements. On the other
hand, any environment can be "poor" by failing adequately to supplyjust
any one of these factors. Good environments, then, tend to be relatively
similar, while each bad environment can be bad in its own way. In any
event, few would dispute that most experiment station environments are
more like other station environments than one farm is like other farms.

Environments Defined

An environment, as used in this book, is the product of the entire set
of factors, both biophysical and socioeconomic, that can materially affect
crop and livestock productivity. These factors (collectively, the
environment) in turn affect performance and eventual acceptability by
farmers of a given technology. Many types of factors vary to cause
differences among environments. These include, among others:

Farm type:
irrigated vs. rainfed;
mechanized vs. animal traction vs. hand tillage;
commercial vs. subsistence (or large vs. small);
diversified vs. single-enterprise.







Introduction


Nature of the farm household:
ethnic or cultural background;
hierarchy of authority (female- vs. male-headed, etc.);
importance of off-farm or nonagricultural on-farm employment;
access to and control over production factors and returns to
production.
Gimate:
rainfall (amount, distribution, reliability, etc.); temperature;
altitude; humidity, etc.;
Soils:
origin, texture, structure, depth, water-holding capacity, drainage;
fertility, mineral nutrient status, organic matter content, cation
exchange capacity, pH, etc.;
slope, position on toposequence;
color, indigenous classification.
Farmer management:
traditional "farmers' practices" vs. "improved practices," e.g., use
of organic or chemical fertilizers and pesticides, extension
packages, etc.;
date of seeding, timing and frequency of cultural operations, etc.;
rotation, previous crop.
Other:
geographic location, or "agro-ecological zone;"
commonly occurring disease and pest pressures.

It is important to realize that environment, as used here, can be but is
by no means always synonymous with "site" or "location" as these terms
are commonly used by researchers. In the case of multilocational on-
station trials, for example, it is recognized that soil or other differences
within a single location require partitioning of this variability by
blocking; in this case each block is in fact a separate environment.
Similarly, on-farm research programs often group trials within research
villages, and comparison between villages results in each village being
considered and called a separate "site" or "location." Yet within each
village there are sometimes several repetitions of a given trial. These are
most often single replicates, i.e., one replicate per farm, of the set of
treatments to be compared. In such a case, each individual set of the
treatments is a separate environment. Note that "environment" is not
even necessarily synonymous with "farm" or even with "field," since in
the case of single-replicate on-farm trials it is possible-if not








Adaptability Analysis


common-for a farm or field to have more than one replicate of the trial,
and thus more than one environment.
In this book, the term environment usually will correspond to the
"block" or replicate in farmer-managed regional or verification
trials-with each farmer testing a single replicate of the treatments
(usually in a completely randomized block). It will be meant to reflect
the entire set of factors that can cause differences in the responses of
crops and livestock to the technology being evaluated.

Incorporating Farmers' Perspectives and Evaluations

Traditionally, two methods have been employed to incorporate
farmers' perspectives and evaluations into the development of agricultural
technology. The first is through farmer feedback about the technology
after viewing "demonstrations," or after some initial adoption during the
extension/diffusion process. The second is through field-day visits by
farmers to experiment station trials. The first of these methods is
necessary in all cases; it tends, however, to happen rather late in the
technology development process. If there are problems with technologies
being developed by research, finding out about them and "going back to
the drawing board" at the extension/diffusion stage results in much lost
time, labor and financial expense.
The second of these traditional methods is also useful and should not
be neglected. But it must be recognized that farmers are not very likely
to give pertinent opinions and evaluations of technologies they see
"demonstrated" under what to them are foreign conditions, conditions
almost always substantially more favorable than their own.
By doing a considerable portion of the development of new
technologies in close collaboration with farmers-through on-farm
research-extension activities-incorporation of farmers' own evaluations
of those technologies will be more timely, thereby reducing wasted time
and effort, and more relevant to their specific circumstances. This can
only help to ensure relevance of the final recommendations eventually
made. To be most effective, farmers' perspectives and judgments should
be elicited continually during their participation in all phases of the
technology development process. Such participation is essential in
identification of priority problems and potential solutions to those
problems (through both formal and informal diagnostic efforts), in design
and implementation of on-farm trials, and in analysis and interpretation
of trial results.


14







Introduction


It is important that the results of on-farm research-extension, socio-
economic as well as biological, be presented and discussed with farmers,
either individually or in groups. Although it is difficult to translate the
often complex results of trials into simple language that can be
understood by farmers who often have little formal education, the
feedback obtained by farmers, and the value of that feedback in
formulating viable extension recommendations, is well worth the effort.
There is one question researchers should always keep in mind: if a
proposed trial is so complex that its results will not be understood by the
farmers in whose fields or herds it is to be done, is it not too complex
to be an on-farm trial in the first place?

Enhancing Adoption Through Adaptation and Learning

Many innovations extended to farmers are rejected by them because
the learning and mastery of the new technologies involved are difficult,
and positive results are not seen by farmers until after several or many
instances of trial and error. The learning process undergone by farmers
collaborating in on-farm trials is similar to that undergone by other
farmers once new technologies reach the diffusion stage of the
technology development and transfer process. Collaborating farmers
learn, through participation in trials, how best to implement new
technological options and how to modify and adapt them to specific local
conditions in order to get the most out of them. On-farm researchers
learn from these adaptation efforts both how better to design future
technologies aimed at similar farmers, and also how better to interpret
and extend the results of on-farm research.
The fine-tuning (or sometimes the complete overhaul) of technologies
tested under farmers' conditions substitutes to a large degree for the same
process that farmers trying later to adopt the same technologies would
otherwise have to go through. Having a good part of this learning and
adaptation take place in the development and testing phase, rather than
the diffusion phase, spares farmers willing and eager to adopt innovations
the confusion, disappointment and frustration of trying to make poorly
adapted technologies work in their particular circumstances. Avoiding
these negative experiences goes a long way in ensuring the rapid and
lasting adoption of externally introduced agricultural innovations. The
concept of learning curves, how they are affected by improved adaptation
of technologies, and how they affect both the results of on-farm trials






Adaptability Analysis


and the efficiency of the technology development process, will be
discussed later in Chapter 7.

Location Spec(ficity and Sustainable Agriculture

Modern agricultural technology is developed for, and is itself
responsible for, developing highly regulated and increasingly
homogeneous ecosystems. Local variation in soil fertility and water-
holding capacity, and local differences in rainfall regime and in pest
incidence, are compensated for and to some extent eliminated by
irrigation, mechanization, fertilizers and pesticides (Nguyen and
Anderson, 1991). It is for just this sort of agro-ecosystem that "broadly
adapted" varieties and other agricultural technologies are generally
developed in the richer countries of the world.
In recent years, however, there has arisen an increased awareness that
the ecological and economic costs of creating and maintaining these
highly controlled, homogeneous agricultural environments will not be
sustainable in the long term. Greater attention is currently devoted to the
development of "alternative" or "sustainable" agricultural systems. It is
certain that these systems will have greater diversity from one location
to another than do those whose indigenous variability is dominated by
heavy use of external agricultural inputs.
As agricultural policy makers begin to change the incentives which
have encouraged the use of technologies broadly adapted to these often
artificially produced, superior environments, new technologies will have
to be developed "to conform with the environments where they will be
used, not dominate them" (Hildebrand, 1990b, p. 286). This change in
emphasis and the concurrent imperative to target specific environmental
conditions will make on-farm research-extension activities central to the
development of technologies conducive to more sustainable agricultural
systems. Adaptability Analysis (AA), using environmental characteriza-
tion and evaluation criteria appropriate to specific farmers and production
circumstances, will help in ensuring the identification of technologies
specifically adapted to the more variable environmental conditions of
these systems.







Introduction


Problems with Combined Analysis of Variance in Analyzing On-Farm
Trials

The most common analytical method for the analysis of agronomic and
other agricultural trials is the analysis of variance (ANOVA). While a
very powerful and useful tool, analysis of variance is somewhat complex
and sensitive to violations of a number of statistical assumptions; these
assumptions are often not met by the sorts of "messy" data produced
under farmers' conditions, characterized by a high degree of variability,
which is not normally accounted for in the sorts of experiments that lend
themselves to analysis by ANOVA.
The major difficulties with ANOVA for analysis and interpretation of
on-farm trials will be dealt with in detail in Chapter 2. They can be
summarized, however, as follows. First, the important sources of
environmental variability, those likely to affect decisions concerning
specific adaptability of experimental treatments to particular
environmental conditions, must be identified and incorporated into a
specific experimental design ex ante, that is, before the trial is put in the
field. This is a problem in that one of the purposes and great benefits of
on-farm trials is precisely that they help identify those environmental
characteristics liable to determine specific adaptability; these
characteristics are rarely known beforehand. Even if they were known,
however, their incorporation into on-farm trials, as will be seen below,
results in trials that are too large, too complex, and too difficult to
analyze.
The second difficulty with ANOVA is that in order to estimate the
importance of the treatment-by-environment interaction critical to the
analysis of adaptability of different treatments to different environments,
ANOVA requires a suitable error term to test the significance of the
interaction. This is a problem because the types of on-farm trials most
likely to give an accurate and complete range of environmental
conditions are farmer-managed ones, either regional or verification trials,
according to the typology of Hildebrand and Poey (1985). These types
of trials, since they are managed by farmers whose own evaluations of
the treatments reflect the most important analytical criteria, are best and
most profitably implemented with one complete replicate of treatments
per environment (in most cases this means one replicate per field).
Unfortunately, for this sort of trial the treatment-by-environment
interaction cannot be tested by simple ANOVA. There is no error term
with which to test it, since this interaction is itself the residual error







Adaptability Analysis


term, i.e., the term in ANOVA which represents that part of variability
unaccounted for by experimental factors.
Doing on-farm trials with two or several replicates per farm allows for
estimating the treatment-by-environment interaction, and combined
analysis of variance is a powerful technique for statistically estimating
that interaction. If, however, the implementation of trials is made so
complicated that it jeopardizes the full involvement of farmers, for the
sake of an analytical method that most extensionists and many
researchers find impossible to do correctly with the computing methods
at their disposal, and the output of which is often not fully or even
adequately interpreted, the potential benefits of on-farm research are lost.
Adaptability Analysis is not proposed as a substitute for ANOVA as
a tool for the design and analysis of on-farm research-extension.
Adaptability Analysis (alone or in combination with ANOVA) does,
however, have significant advantages over ANOVA alone. First, it is
simple and relatively easy to perform and to interpret; second, it allows
evaluation of the critical treatment-by-environment interaction without the
need for the complications inherent in using more than one replication
per on-farm test site. Finally, the output of AA is in a form
understandable and relevant to farmers and extensionists as well as to
researchers.


History of Stability Analysis by Regression

The method of regressing varietal yields at each of many locations on
the mean yield of all varieties at each location was developed to provide
a means of estimating the interaction of plant genotypes and
environments. Before looking at how Adaptability Analysis modifies this
technique, it would be useful to review briefly its history and
development, its strengths and its statistical weaknesses, and the context
in which plant breeders have employed it.

Rfty Years of Use by Plant Breeders

Plant breeders have long recognized that the production of a given
variety will vary greatly from one trial location, i.e., environment, to
another. Whether different environments result from differences in
climate, soils, disease and pest pressures, or management practices, some
varieties will be better suited to some environments than to others.







Adaptability Analysis


term, i.e., the term in ANOVA which represents that part of variability
unaccounted for by experimental factors.
Doing on-farm trials with two or several replicates per farm allows for
estimating the treatment-by-environment interaction, and combined
analysis of variance is a powerful technique for statistically estimating
that interaction. If, however, the implementation of trials is made so
complicated that it jeopardizes the full involvement of farmers, for the
sake of an analytical method that most extensionists and many
researchers find impossible to do correctly with the computing methods
at their disposal, and the output of which is often not fully or even
adequately interpreted, the potential benefits of on-farm research are lost.
Adaptability Analysis is not proposed as a substitute for ANOVA as
a tool for the design and analysis of on-farm research-extension.
Adaptability Analysis (alone or in combination with ANOVA) does,
however, have significant advantages over ANOVA alone. First, it is
simple and relatively easy to perform and to interpret; second, it allows
evaluation of the critical treatment-by-environment interaction without the
need for the complications inherent in using more than one replication
per on-farm test site. Finally, the output of AA is in a form
understandable and relevant to farmers and extensionists as well as to
researchers.


History of Stability Analysis by Regression

The method of regressing varietal yields at each of many locations on
the mean yield of all varieties at each location was developed to provide
a means of estimating the interaction of plant genotypes and
environments. Before looking at how Adaptability Analysis modifies this
technique, it would be useful to review briefly its history and
development, its strengths and its statistical weaknesses, and the context
in which plant breeders have employed it.

Rfty Years of Use by Plant Breeders

Plant breeders have long recognized that the production of a given
variety will vary greatly from one trial location, i.e., environment, to
another. Whether different environments result from differences in
climate, soils, disease and pest pressures, or management practices, some
varieties will be better suited to some environments than to others.







Introduction


Useful comparison of varieties within a crop improvement program,
then, requires an accurate estimate of the differential responses of a
number of varieties over a range of locations, an estimate of what
breeders call the genotype-by-environment (GxE) interaction.
Early statistical attempts to analyze GxE interaction among a group of
varieties involved refining the analysis of variance procedure to partition
the variability in a multilocational varietal trial into those variance
components attributable to differences among varieties, those due to
differences among locations, and those due to the interaction of the
genotype and location (environment). These attempts were fruitful not
just in helping breeders to better understand GxE interaction but also in
advancing the state of the art of ANOVA. They were, however,
characterized by a statistical sophistication that made them difficult to
grasp or apply without a strong background in mathematical statistics.
The use of regression on the mean yields of all varieties at a trial site
dates from the late 1930s when Yates and Cochran (1938) illustrated that
oftentimes a great deal of the variety-by-location interaction in ANOVA
of multilocation trials can be partitioned out and accounted for by
differences in linear regression on mean yield, an artifice frequently of
use in revealing relations between general fertility and varietal
differences (p. 565).
Finlay and Wilkinson (1963) further developed the concept of using
linear regression of varietal yields on site means as a technique for
assessing adaptation in a plant breeding program. They simplified the
technique by using the individual varietal means as the dependent
regression variable (rather than the difference between individual yields
and the overall mean, as Yates and Cochran had done). Oddly, they
used the total of yields at a site, rather than the mean of all yields, as the
independent regression variable; this difference does not, however,
materially change the interpretation of results. More importantly, they
discussed in detail the implications of the technique for determining the
adaptability of varieties to changing environmental conditions, whether
breeding is for specific adaptability, "for closely defined ecological
conditions," or for general adaptability, "for more extensive conditions
that include a considerable range of environments" (p. 743).
The most widely cited publication describing linear regression stability
analysis, and the first one to use the term "environmental index," adapted
the technique for the purpose of identifying a number of "stability
parameters," one of which was the linear regression coefficient, another
the deviations from regression (Eberhart and Russell, 1966). This work,







Adaptability Analysis


perhaps the most cited in the literature, has confused the interpretation
of the results of such regression analyses. The confusion arises first, as
will be seen below, because Eberhart and Russell changed the definition
of "stability," and second because they proposed statistical measures of
varietal stability as indicators of adaptability, resulting in later over-
emphasis on stability itself rather than adaptability-either general or
specific-to environmental conditions. Nevertheless, in one form or
another, the technique of linear regression of varietal yields on site
means has been used by two generations of plant breeders interested in
dealing with the differential response of varieties to a range of
environments.

Usefulness of Stability Analysis in Assessing GxE Interaction

Linear regression of varietal yields on mean site yields is only one of
many techniques plant breeders have developed to estimate the
importance of the interaction of plant genotype and environment. As
discussed above, it was initially developed to better explain GxE
interaction in a plant breeding program. Once overall variability has
been partitioned into that due to genotype, that due to environment, and
that due to the interaction of the two, the variability accounted for by the
interaction can be further partitioned into that part of the GxE interaction
due to linear regression on site mean and some unexplained (residual)
variability, essentially the deviations from the linear regression.
Stability analysis by regression was not the first nor last method used
by breeders to quantify GxE interaction. Many newer and much more
complex and sophisticated methods have been developed, and many
statistics identified, but linear regression remains much used. Its
usefulness is that it allows application of familiar and easily interpreted
statistics (the regression coefficient and deviations from regression) to the
assessment of GxE interaction. Freeman (1973), in a comparison of
many statistical methods for the analysis of GxE interaction, considers
that "Of all the techniques discussed, there can be no doubt that, for
geneticists, the most fruitful has been the regression approach" (p. 350).
One result of the success of linear regression for analysis of GxE
interaction was that the relationship of varietal yield with site mean,
represented by the regression line, led to something of a preoccupation
with assessing the stability of varieties across environments and
identifying "stability parameters." A multitude of stability analyses arose
to rival the multitude of techniques for assessing GxE interaction. In







Introduction


fact, since one of the chief reasons for growing varieties in a range of
environments has always been to estimate their stability, analysis of GxE
interaction has by and large evolved into and been supplanted by
"stability analysis."

Traditional Breeders' Perspective: Broad Adaptability

There are two reasons why linear regression stability analysis, and
other types of stability analysis as well, have traditionally been developed
and used by plant breeders to identify broad adaptability in varieties.
The first is that it has been assumed that modern crop production
management methods, i.e., the use of chemical fertilizers, pesticides,
mechanization, irrigation, etc., could in most cases dominate the
indigenous factors that tend to cause yields to be generally low and
highly variable. The second is that breeders have thought it impossible
or at any rate too difficult to identify ex ante the specific environmental
conditions that exist in any given farmer's field and to relate these to the
environmental conditions of the several sites in which varieties are tested
in multilocational trials.
Finlay and Wilkinson (1963) rightly considered that varieties with bi
= 1 had "average stability," but their chief goal was not stability, in
itself, rather varieties "well adapted to all environments." These
varieties would be those with average stability and high mean yields
(Figure 1.2). While recognizing the advantage of specific adaptation to
"closely defined ecological conditions," Finlay and Wilkinson
considered this to be impractical for most rainfed field crops, given the
variability due to season: "Even in a uniform edaphic environment a
considerable degree of general adaptability will be important, because of
the marked fluctuation of climatic conditions from season to season." (p.
743). This is an important concern; in using Adaptability Analysis for
identification of specifically adapted technologies, particular care must
be given to ensure that the range of environments in a trial be representa-
tive of the range of environments that exist over years. This point is
discussed in detail in Chapter 7.
Similarly, Eberhart and Russell (1966) defined an "adapted variety" as
one with high yield, bi = 1, and small deviations from regression.
Much attention has been devoted to their definition of a slope of unity
(along with small deviations from regression) as indicating stability, but
it is less commonly recognized that stability per se was not, in the view
of Eberhart and Russell, the primary goal of a plant breeding program.






Adaptability Analysis


The primary goal was a broadly adapted variety. Varieties with bi < 1
would yield less than the average of all tested varieties (i.e., be poorly
adapted) in high-yielding environments; those with bi > 1 would yield
less than average (and therefore be poorly adapted) to low-yielding
environments. This, as has been discussed, is a negative interpretation
of the linear regression on environmental mean; it gives emphasis to
where varieties will be poorly adapted rather than looking actively for
where they will be well adapted. It is also explicitly intended for
"conditions such as exist for maize in the United States, [where] the
breeder usually wants a variety that does above average in all
environments" (p. 38).


>1





1.0 8




I


VARIETY MEAN YIELD


FIGURE 1.2 Broad and specific adaptation, determined by mean yield
and linear regression coefficient (source: Finlay and Wilkinson, 1963).







Introduction


For most limited-resource farmers in many parts of the world,
however, risk avoidance and above-average yields in poor environments
(and seasons) are far more important than above-average yields in all
environments. In part, this importance derives from the fact that limited-
resource farmers are less able to modify their poor environments with
high levels of inputs. Note that the increased importance of agricultural
sustainability is leading many to recognize that all farmers, not just those
in developing countries, must farm within the capabilities of their
environments and not modify the environments to suit the technology.

Several Definitions of Stability

Considerable confusion has arisen from the fact that the several
methods of stability analysis have engendered a great many different
measures of stability. In a recent review, Lin et al. (1986) identified
nine stability statistics which they classed into four general groups and
related this classification to three different concepts of stability. Two
groups of statistics are based on sums of squares from ANOVA. The
other two groups of statistics are based on linear regression, the first
group on the regression coefficient, the second on deviation from
regression. The authors give preference, as does Freeman (1973), to use
of the regression coefficient since it gives the shape, or structure, of the
varietal response as well as its variation.
The sheer number of statistics proposed as stability parameters has
caused confusion; even more confounding is that two of the seminal
papers in the development of the regression approach to stability analysis
define stability, with regard to the regression coefficient, differently.
Eberhart and Russell (1966) define "a stable variety" as one with a
regression coefficient (b) equal to one, and with deviations from
regression (s2d) equal to zero. Finlay and Wilkinson (1963), on the other
hand, consider a variety with b, = 1 to be a variety of average stability,
one with bi > 1 to be a variety with less than average stability, and one
with bi < 1 to be a variety with greater than average stability. By
extension, a "stable variety" would be one with bi = 0.
It is somewhat unfortunate that most plant breeders followed the lead
of Eberhart and Russell in defining stability as b, = 1, which is not
intuitively obvious. Even more unfortunate, however, is that many
breeders and other researchers have followed the lead both of Eberhart
and Russell and of Finlay and Wilkinson in using regression-based
stability analysis as a means of identifying varieties or other new







Adaptability Analysis


technologies that exhibit "broad adaptability." As will be discussed in
detail in Chapter 2, Adaptability Analysis uses regression of treatment on
an environmental index not to identify those treatments that are "stable"
or that have "broad adaptability," but rather to identify those treatments
that are best adapted to particular environments.

Statistical Concerns with Regression Stability Analysis

Stability analysis by regression of treatments on site means has been
much criticized from a statistical viewpoint. The chief criticism was that
because the environmental index, the mean at each trial site or
environment, is calculated from all of the individual treatment yields and
is therefore clearly related to each of them, this type of analysis violates
an assumption of least-squares regression that the dependent and
independent regression variables be independent of each other, i.e., that
their error terms not be correlated. The major problem with what
Freeman (1973) calls "the logical difficulties of regressing one set of
variables on another which is not independent of them" (p. 343), is that
estimates of bi and other regression statistics, as well as tests of their
significance or of differences between them, are biased.
Despite these concerns, the authors of several reviews of stability
analysis methods, including Freeman (1973) and Lin et al. (1986), have
ended by maintaining that until multivariate techniques using independent
environmental measures are developed, linear regression on
environmental mean is perhaps the most useful of the currently available
techniques. Its advantages are that it is relatively simple and, more
importantly, that it permits an analysis of the structure, i.e., a graphical
representation, of the treatment-by-environment interaction.
In any event, none of these statistical concerns compromise the
usefulness of Adaptability Analysis. It is of critical importance that
researchers realize that AA is not intended to give statistically exact
estimates, either predictive or descriptive. Altogether too much emphasis
is now placed on the statistical "significance" of the regression statistics
in AA, as will be discussed later. If the goal of on-farm research-
extension were to attach values to new technologies that pretended to
represent their degree of stability or of general adaptability, then AA
would be flawed. For identifying technologies specifically adapted to
given environmental conditions, however, AA, properly understood and
employed, is the simplest and perhaps the most valuable tool currently
available.






Introduction


Those unduly preoccupied with the supposed statistical problems of
AA should keep two thoughts in mind. The first has to do with ultimate
ends:

When any statistical analysis is carried out the basic question always is:
what use is to be made of the results? It is only a secondary question to
enquire: how are the data to be analyzed? Too much concentration on the
second point leads to what has been described as the third kind of statistical
error: the right answer to the wrong question. (Freeman, 1973, p. 350).

The second consideration is concerned with the need to make the best use
of the methods at hand, even if these are not perfect:

Theoretical objections aside, the on-farm researcher often has but two
alternatives: using (an environmental index) El or being unable to make
useful recommendations within a reasonable period of time. And, as
McCullagh and Nelder (1989) point out, "A first, though at first sight, not
a very helpful principle, is that all models are wrong; some, though, are
more useful than others and we should seek those." (Stroup et al., 1993,
p. 169).

Traditional agricultural research and extension methods have long
failed limited-resource farmers in many parts of the world, a failure
reflected by generally poor adoption of technologies supposedly
"improved" but ultimately shown to be poorly adapted to farmers'
conditions. Given this fact, the practical usefulness of AA in identifying
adapted technologies outweighs any statistical imprecisions. Returning
to the analogy of AA as a tool, it can be likened more to a machete than
to a scalpel. While this analogy may dismay some researchers, it should
cause no concern at all to those charged with the rapid development and
dissemination of new technologies to farmers, nor to farmers themselves.

The Modifications to "Stability Analysis"

Several of the modifications to "stability analysis" as used by plant
breeders for many years have been mentioned. The following is a more
complete list of the modifications that were incorporated in MSA and are
now incorporated in Adaptability Analysis, AA.

By making a positive rather than negative interpretation of the
treatment-by-environment interaction, AA encourages the







Adapability Analyst


development of situation-specific technologies critical to sustainable
agriculture as well as to limited-resource farmers.
By emphasizing fuller characterization of on-farm environments fo
use in the interpretation of results, AA increases the efficiency
farmer and extension involvement in the research-extension process
AA stresses the use of multiple evaluation criteria, including those
most relevant to farmers for whom the technology is bein
developed.
The specificity of AA permits the development and use of multiple
extension messages (recommendations) for technology diffusio
purposes.
AA is suited to the evaluation of all kinds of technologies, not jus
germplasm.
Measures of risk meaningful to farmers as well as researchers ar
incorporated in AA.

Taken together, these characteristics of AA help it serve as a basis fo
any on-farm research-extension program for technology development.


The Book

This book is intended to collect, synthesize, and bring to the attention
of a larger audience the most up-to-date thinking in the use of
comprehensive method for the design, analysis, and interpretation of on
farm research results. The method has come to be called, for better o
worse, Modified Stability Analysis (Hildebrand, 1984), a name which
reflects the nature of its origins, but which unfortunately has tended t
mislead many practitioners of agricultural research and extension as to
its purpose and its full promise.
One of the major purposes of this work, then, is to help correct some
of the misconceptions and misuses of the method, which we propose to
call Adaptability Analysis (AA), because it reflects how the method is
best employed. Another main purpose is to illustrate the broad potential
of Adaptability Analysis through a review of the conceptual
underpinnings of the method and through a series of analyses of real on-
farm data.
To judge from results reported in journals and elsewhere, limits to
adequate exploitation of the method until now have been due in part to
an implicit assumption that it is above all an analysis of stability. This







Introduction


is not the case. The full usefulness of Adaptability Analysis is not-or
is only rarely and incidentally-that it allows identification and
comparison of the stability of agricultural technologies such as improved
varieties and breeds, fertilizer use, and other production practices across
a range of production environments. The advantage of AA is rather that
it is a simple and accurate method of identifying and comparing the
performance of agricultural technologies under specific biophysical
conditions and socioeconomic circumstances. In brief, while traditional
stability analysis seeks to identify broad adaptability, i.e., adaptability
to a wide range of environments, AA seeks to help identify specific
adaptability, i.e., adaptability to particular groups of environments.
In Chapter 2 we present an overview of Adaptability Analysis
including a step-by-step analysis of a real-world example from the
Amazon area of Brazil. Chapters 3 through 6 contain a number of
examples of different types of trials for which AA is an appropriate
analytical tool. We hope that these chapters will serve to spur the
imagination of research-extension workers as well as to illustrate the
flexibility and robustness of the method. Finally, Chapter 7 is devoted
to the design of on-farm research amenable to Adaptability Analysis,
making the investment in the research-extension effort more productive
and adapted technologies more rapidly available to farmers.







28 Adaptability Analysis











2


Adaptability Analysis:
An Overview


Introduction

This chapter presents a method of experimental design, analysis and
interpretation previously called Modified Stability Analysis (MSA),
which we have changed to Adaptability Analysis (AA). MSA was
developed to use the linear regression methods of plant breeders to
analyze farmer-managed, usually single-replication, on-farm trials across
a wide range of conditions, and to incorporate an analysis of risk into
those methods (Hildebrand, 1984). Its advantages are particularly
evident when it is used to identify and develop technologies adapted to
specific on-farm environmental conditions and evaluated by farmers' own
evaluation criteria (Hildebrand, 1990b).
The chapter treats the various steps in AA, the types and quality of
data for AA, the relationship between AA and other methods of analysis,
and the use of risk assessment in AA. An example analysis, taken from
research conducted in the humid tropical zone of Brazil, is used to
illustrate key concepts and to present the various steps in the analysis and
interpretation of on-farm research-extension data.

Important Steps in Adaptability Analysis

The following is an outline of the major steps in the use of
Adaptability Analysis of on-farm trials. Later in this chapter we will
discuss these steps in more detail, using an example of cowpea
fertilization trials for illustration. There is nothing sacred about the











2


Adaptability Analysis:
An Overview


Introduction

This chapter presents a method of experimental design, analysis and
interpretation previously called Modified Stability Analysis (MSA),
which we have changed to Adaptability Analysis (AA). MSA was
developed to use the linear regression methods of plant breeders to
analyze farmer-managed, usually single-replication, on-farm trials across
a wide range of conditions, and to incorporate an analysis of risk into
those methods (Hildebrand, 1984). Its advantages are particularly
evident when it is used to identify and develop technologies adapted to
specific on-farm environmental conditions and evaluated by farmers' own
evaluation criteria (Hildebrand, 1990b).
The chapter treats the various steps in AA, the types and quality of
data for AA, the relationship between AA and other methods of analysis,
and the use of risk assessment in AA. An example analysis, taken from
research conducted in the humid tropical zone of Brazil, is used to
illustrate key concepts and to present the various steps in the analysis and
interpretation of on-farm research-extension data.

Important Steps in Adaptability Analysis

The following is an outline of the major steps in the use of
Adaptability Analysis of on-farm trials. Later in this chapter we will
discuss these steps in more detail, using an example of cowpea
fertilization trials for illustration. There is nothing sacred about the











2


Adaptability Analysis:
An Overview


Introduction

This chapter presents a method of experimental design, analysis and
interpretation previously called Modified Stability Analysis (MSA),
which we have changed to Adaptability Analysis (AA). MSA was
developed to use the linear regression methods of plant breeders to
analyze farmer-managed, usually single-replication, on-farm trials across
a wide range of conditions, and to incorporate an analysis of risk into
those methods (Hildebrand, 1984). Its advantages are particularly
evident when it is used to identify and develop technologies adapted to
specific on-farm environmental conditions and evaluated by farmers' own
evaluation criteria (Hildebrand, 1990b).
The chapter treats the various steps in AA, the types and quality of
data for AA, the relationship between AA and other methods of analysis,
and the use of risk assessment in AA. An example analysis, taken from
research conducted in the humid tropical zone of Brazil, is used to
illustrate key concepts and to present the various steps in the analysis and
interpretation of on-farm research-extension data.

Important Steps in Adaptability Analysis

The following is an outline of the major steps in the use of
Adaptability Analysis of on-farm trials. Later in this chapter we will
discuss these steps in more detail, using an example of cowpea
fertilization trials for illustration. There is nothing sacred about the








Adaptability Analys


number of steps; several could have been grouped together as a sing
"step," and several could have been divided even further. In the vie
of the authors, however, these are the procedures that should be done i
order to ensure getting the most out of the AA method for the efficie
design, analysis, and interpretation of on-farm research-extension.

1. Conduct the trial in function of planned methods of analyst
Trials to be analyzed by AA should include collection of data
adequately characterize each environment and to permit calculati
of all relevant evaluation criteria.
2. Calculate the environmental index, El. El is calculated as t
mean of the yields (for crops, usually in kg or tons per unit la
area). It is an index that is an estimate of each environment
capacity to produce the crop or livestock product in question und
the treatments tested.
3. Relate treatment response to environment.
3a. Plot observations (data points) for each treatment against El o
separate graphs.
3b. Estimate treatment response to El, i.e., the relationship
each treatment to environment. This can by done by regressio
or by drawing a line (straight or curved) by hand.
4. Compare the response of treatments to El and estimate th
treatment-by-environment interaction. Again, this can be don
visually or, if computational capacity is available, by statistic
methods such as that described in Stroup et al. (1993). Visuall
interaction will be indicated by crossover or wide divergence
response lines.
5. If TxEI interaction is Indicated, relate El to environments
characterization and divide environments into potential
recommendation domains. If no clear relationship can be show
between El and any of the environmental characteristics on which
data were collected, divide environments based on yields of th
"check," i.e., of farmers' current practices.
6. Interpret results and define recommendation domains.
6a. Within each potential recommendation domain and for eai
evaluation criterion, evaluate the relative performance of th
treatments, both in terms of mean differences and of variabili
in the means. Means can be reported by themselves (wi
standard errors) or compared by ANOVA, if statistical capaci
is sufficient.
4







Adaptability Analysis: An Overview


It is important to analyze risks associated with each
treatment, for each of the evaluation criteria. Many introduced
or new technologies with higher mean yields (or higher mean
returns) present more risk of low yields or returns as well. A
simple way to estimate risk is by graphing the distribution of
lower confidence intervals associated with each treatment mean.
6b. Produce multiple extension messages for different groups of
farmers for whom different evaluation criteria are relevant.
NOTE: Steps 3 through 6 should be done from the perspective of
multiple evaluation criteria, i.e., criteria important to
different groups of farmers.


Data Requirements

Sources of Data

Data for the analysis of on-farm research-extension can and should
come from a number of sources; each of these sources can supply
important information for improved design, implementation, analysis,
and interpretation of trials for which the AA method is appropriate.

Sondeos are similar to other types of informal "rapid rural appraisal"
(RRA) or "rapid reconnaissance" surveys. They are conducted by
interdisciplinary teams of researchers and development/extension
personnel, and are designed to be low cost and of short duration. Their
purpose is to orient the work of on-farm research-extension through
interdisciplinary understanding of the farming systems in a target area,
including preliminary delimitation of research, recommendation, and
diffusion domains, and through identification of key problems and
constraints faced by farmers (Hildebrand, 1981; FSSP, 1987). Sondeos
can give valuable preliminary evidence of the variability of environ-
mental conditions faced by farmers, of the types of innovations needed
by and acceptable to farmers, and of the types of criteria important to
farmers in the evaluation of new technologies.
Directed surveys ("focused" sondeos, "topical" rapid rural
appraisals) are a useful tool for exploiting the on-going, iterative nature
of the FSRE approach. Similar to conventional sondeos in their use of
interdisciplinary teams, they involve less time and expense. They are
intended to target a narrow, specific aspect of the farming systems of







Adaptability Analysis: An Overview


It is important to analyze risks associated with each
treatment, for each of the evaluation criteria. Many introduced
or new technologies with higher mean yields (or higher mean
returns) present more risk of low yields or returns as well. A
simple way to estimate risk is by graphing the distribution of
lower confidence intervals associated with each treatment mean.
6b. Produce multiple extension messages for different groups of
farmers for whom different evaluation criteria are relevant.
NOTE: Steps 3 through 6 should be done from the perspective of
multiple evaluation criteria, i.e., criteria important to
different groups of farmers.


Data Requirements

Sources of Data

Data for the analysis of on-farm research-extension can and should
come from a number of sources; each of these sources can supply
important information for improved design, implementation, analysis,
and interpretation of trials for which the AA method is appropriate.

Sondeos are similar to other types of informal "rapid rural appraisal"
(RRA) or "rapid reconnaissance" surveys. They are conducted by
interdisciplinary teams of researchers and development/extension
personnel, and are designed to be low cost and of short duration. Their
purpose is to orient the work of on-farm research-extension through
interdisciplinary understanding of the farming systems in a target area,
including preliminary delimitation of research, recommendation, and
diffusion domains, and through identification of key problems and
constraints faced by farmers (Hildebrand, 1981; FSSP, 1987). Sondeos
can give valuable preliminary evidence of the variability of environ-
mental conditions faced by farmers, of the types of innovations needed
by and acceptable to farmers, and of the types of criteria important to
farmers in the evaluation of new technologies.
Directed surveys ("focused" sondeos, "topical" rapid rural
appraisals) are a useful tool for exploiting the on-going, iterative nature
of the FSRE approach. Similar to conventional sondeos in their use of
interdisciplinary teams, they involve less time and expense. They are
intended to target a narrow, specific aspect of the farming systems of








Adaptability Analysis


interest, such as a particular crop or other farm enterprise. Focused
sondeos or RRAs have been used, for example, to identify farmers'
evaluation criteria for specific crops in Nicaragua (Betanco et al., 1990),
and to investigate the traditional grain storage practices in North
Cameroon (Wolfson, 1990). They can be of great value in an on-going
technology development effort by providing increasingly detailed and
complete information on farmers' biophysical and socioeconomic
environmental circumstances.
Enterprise records maintained by farmers with technician help provide
data that can be more accurate than data from surveys based on farmer
recall. Simple forms (see, for example, Shaner et al., 1982, pp. 309-
314) can be developed for traditional farm enterprises. This type of data
serves as ex ante information to help design trials and provides the basis
for analyzing modified technology based on current farming practices,
Enterprise records are also useful to help convert plot data to field-size
situations.
On-station trials often furnish the technologies to be tested under
farmers' conditions before they are selected for dissemination to a larger
population of farmers. Socioeconomic analyses of on-station trial results
are often lacking, leading to on-farm trials of technologies which are
clearly inappropriate to farmers' circumstances. Sufficient analysis of
experiment station trials should be done to allow ex ante socioeconomic
analyses of proposed on-farm trial technologies before such trials are put
in farmers' fields.
On-station trials can also furnish data on additional environments and
can be incorporated into on-farm trial data if a "farmers' control"
treatment has been included in the on-station trials and if sufficient data
are available for environmental characterization. To use on-station data,
those treatments that are used in the on-farm trials, and the "farmers
control" are extracted from the on-station data. Each year, and even
each block can be considered as additional environments and added to the
on-farm data.
On-farm trials, particularly single-replicate, farmer-managed on-farm
trials are the usual source of biological and socioeconomic data
susceptible to full exploitation by Adaptability Analysis. For the most
efficient use of trial data, it is imperative that sufficient characterization,
both biophysical and socioeconomic, of each on-farm trial environment
be done. Deciding just how much and what kinds of data need to be
collected is often not immediately evident; making these decisions is
itself an iterative process. Over time, and through trial and error,







Adaptability Analysis: An Overview


researchers and extensionists should establish a minimum data set for on-
farm trials in their research domains.

Quality of Data

Data from the on-farm trials do not require more than one replication
per environment to be amenable to Adaptability Analysis. However,
there are some characteristics of the data which improve its quality and
reliability (Stroup et al., 1993). The number of environments required
for estimation of the significance of treatment-by-environment (TxEI)
interaction should result in at least 20 degrees of freedom in the error
term of ANOVA. Allowing for estimation of both linear and quadratic
responses of the treatments to El, for four treatments, 10 environments
would be required. In a verification trial with only two treatments (for
example, the recommended treatment and the farmer check) about 23
environments is adequate.
These suggestions, of course, are approximate. The appropriate
number of environments is a function of the variance and the required
sensitivity-all case-by-case situations. To allow for adequate verifica-
tion of treatment performance within two or three potential recommenda-
tion domains, a minimum of 15 to 20 environments should be aimed for.
Experience in analyzing numerous data sets has indicated that if three
conditions are met in a single year's trial, the estimates of environment-
by-treatment response will be consistent across years. These conditions
(adapted from Stroup et al., 1993, p. 172) are:

1. The range of the environmental index (El) should be at least as
great as the mean of the index values, i.e., the ratio of range to
mean should be at least one.
2. The distribution of environments (Els) should be reasonably
uniform from good to poor.
3. The range and distribution of the yields of farmers' current
practices should approximate those normally expected over a
period of years.

It may well be that the distribution of environments is more important
than number so long as some minimum number (again, around 15 to 20)
is achieved. Dividing the range of Els by the overall mean El is a useful
measure of the representativeness of the data. On-farm data usually have
greater range than station trials, even if the station trials are reported







Adaptability Analysis


over a number of years. A quick search resulted in locating the
following ratios:

1. An on-farm potato study (reported in Caldwell and Taylor, 1987,
p. 353) on agronomic practices from 20 farms by the International
Potato Center in the Mantaro Valley of Peru: 1.42/1.
2. An on-farm maize variety study (Poey, n.d., p. 58) conducted by
the farming systems program in Paraguay on 24 farms: 1.21/1.
3. An on-farm maize variety and fertilizer use study (Hansen et al.,
1982) conducted on 14 farms by the farming systems team in
Malawi: 1.57/1.
4. The multilocation, on-station, Yates and Cochran study, when
years are combined (as the authors did): 0.55/1; or 0.83/1 when
years and locations are separate.
(Hildebrand, 1990a, p. 177)

A very narrow range, resulting in ratios less than one as in the Yates and
Cochran (1938) study, usually indicates the mean yields of the trials were
very high because they were conducted under controlled conditions. It
can mean that only the best farms were selected, or that the multiple
environments represent experiment stations in different areas. Although
these ratios are taken from data resulting from the trials, prior attention
to designing the trial to span a considerable environmental range pays
dividends.


An Example of AA from Brazil

The results of an on-farm trial of phosphate fertilization of cowpea
near Manaus, in the humid tropical zone of Brazil (Singh, 1990; Stroup
et al., 1993), are used below to illustrate AA. The government of Brazil
was supporting colonization in the area and was concerned about the loss
of rain forest. The problem this research addressed was the rapid loss
of fertility, particularly phosphorus, in land cleared for cropping. The
research domain chosen was a colonization area along the Rio Preto da
Eva, some two hours by boat upstream from the market town of Rio
Preto da Eva, which was on a paved road connecting to Manaus.
Primary information was collected by means of a sondeo involving
persons from the Brazilian research and extension agencies and a local
organization. Farmers' knowledge of indigenous technology, agronomic








Adaptability Analysis: An Overview


practices, and land types being used were recorded. An extensive soil
sampling program was carried out to understand soil physical and
chemical characteristics and relate them to farmers' rationale for
assigning a particular cropping pattern to a given land type. Classified
by land use type, some were from primary forest farmed for one year,
others from primary forest farmed for two or for three years, and still
others from secondary forest farmed for one, two or three years; one
field was even classified as wasteland. Constraints to maintaining
fertility included distance to market and the availability of cash for
purchasing and transporting inputs. Thirteen on-farm trial sites were
selected in the research domain and four fertilization treatments were
applied in a randomized complete block design with only a single
replicate of the treatments in each environment. The four treatments,
based on previous on-station research were selected:

1. the farmers' local practices (FP), which had essentially no or very
low levels of fertilization, i.e., 0-4 kg/ha P;
2. a "full dose" of triple superphosphate (TSP) plus potassium
containing roughly 0-18-60 kg/ha N-P-K;
3. chicken manure (CM) plus a "half dose" of triple superphosphate,
equivalent to the same total amounts of P and K as the TSP
treatment, but with half the P coming from chicken manure;
4. processed city waste (PCW) plus a half dose of triple superphos-
phate, again with the same levels of P and K, but with half the P
coming from the city waste.

Relating Treatment Response to Environment by Regression on El

Once an on-farm trial is conducted within a given research domain,
relevant evaluation criteria identified, and data necessary for adequate
environmental characterization collected (step 1 on page 30), analysis
begins by plotting the yield of each treatment on the environmental index
(El). A best fit response line is found for each treatment across Els,
either by simple visual estimation or by determination of the best
regression model (usually either linear or quadratic).
Based on the relative responses across environments, important
treatment-by-environment interactions may be identified. These would
indicate the possible existence within the research domain, and for yield
as a criterion, of more than one recommendation domain for the tested
technologies. Finally EI, which can only be known after the data are







Adaptability Analysis


collected, should be related to the environmental characterization
variables collected in order to allow grouping of farms into one or
another recommendation domain.
Yield data for this trial (t/ha) are in Table 2.1. The yields of all four
treatments plotted on the environmental index are shown in Figure 2. la.
The data show a considerable amount of variability (Cvs were around
25%), so that it is difficult tojudge the structure of the response of any
one treatment across Els, due to the overlap of observations from several
treatments. For this reason, it is always advisable to plot the observa-
tions of each treatment individually on El. The yields for the PCW
treatment, for example, are plotted on El separately in Figure 2.lb.
From simple visual examination, the response of PCW across
environments seems to be linear.
In Figure 2.2a, the response of TSP appears also to be linear, while
the response of CM, although having a marked linear component (one
with a slope apparently around zero), seems also to have an upward
"bow" to it, hinting at a possible higher order (e.g., quadratic)
component (Figure 2.2b).
The response of the farmers' local practice (FP) seems to have a
similar, but downward, bow to it, again indicating something more than
a purely linear response to environment (Figure 2.3a). Figure 2.3b
illustrates two alternative regression models to describe the yield
response of the farmers' practices across environments (i.e., across El).
Before evaluating which regression better fits the data, a few words on
the use of the R2 statistic are called for here. The R2 statistic is directly
dependent, in single-order linear regression models, on the slope of the
regression line. This can be seen from the formula for calculating R2 in
such a model:
R2 = (b.) (Exy) / Ey2

Thus, for two simple linear regression lines with the same deviations
from regression (which is perhaps a better measure of true "goodness of
fit"), the line with the greater slope will have the greater R2. For this
reason, R3 is not of much intrinsic value for comparing regression
lines of different treatments in an analysis. It is, however, a good
statistic to help decide, for a single treatment, which of two or more
alternative regression models (e.g., linear or quadratic) is the best.
Just as some sort of curved response across El tentatively can be
judged to be better than a simple straight-line response by visually







TABLE 2.1 Sample spreadsheet, Brazil cowpea on-farm trial data, and regression estimates


TONS/HA EST. LINEAR REGRESSION VALUES EST. OUAD. REGR.

LOC FP PCW TSP CM El Ef FP PCW TSP CM FP CM


0.10
0.00
0.15
0.20
0.50


0.20
0.00
0.50
0.40
0.65


0.15 J).50
0.60 1.20
0.70 0.90
1.20 1.50
1.50 1.80
1.45 1.95
2.20 1.90
1.70 1.65


1.30
1.30
1.35
1.20
1.10
2.10
1.60
2.30
2.20
2.10
2.50
2.60
2.65


1.65
2.00
1.35
1.70
1.50
2.05
2.25
1.80
1.90
1.70
1.90
1.40
2.15


0.813
0.825
0.838
0.875
0.938
1.200
1.413
1.425
1.700
1.775
1.950
2.025
2.038


0.660
0.681
0.701
0.766
0.879
1.440
1.995
2.031
2.890
3.151
3.803
4.101
4.151


0.018 0.254 1.259 1.719
0.035 0.271 1.273 1.721
0.053 0.288 1.286 1.723
0.106 0.339 1.327 1.728
0.194 0.424 1.396 1.736
0.564 0.780 1.683 1.773
0.863 1.069 1.916 1.802
0.881 1.086 1.929 1.804
1.269 1.459 2.230 1.842
1.375 1.561 2.312 1.852
1.621 1.799 2.504 1.876
1.727 1.900 2.586 1.887
1.745 1.917 2.600 1.888


0.139 1.627
0.142 1.639
0.147 1.651
0.161 1.686
0.190 1.739
0.395 1.901
0.656 1.960
0.674 1.961
1.145 1.936
1.299 1.910
1.698 1.818
1.886 1.765
1.919 1.756


Note: FP = farmers' practices; PCW =
El = environmental index.


processed city waste; TSP = triple superphosphate; CM = chicken manure;


5-
4
I
11
12
3
10
7












TABLE 2.1 (Continued) o


FP Regression
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom


X Coefficient(s)
Std Err of Coef.


Output:
-1.12808
0.246645
0.894074


1.409967
0.146328


TSP Regression Output:
Constant 0.369682
Std Err of Y Est 0.227601
R Squared 0.856565
No. of Observations 13
Degrees of Freedom 11


X Coefficient(s) 1.094407
Std Err of Coef. 0.13503


PCW Regression
Constant
Std Err ofY Est
R Squared
No. of Observations
Degrees of Freedom


Output:
-0.84858
0.204428
0.919291


X Coefficient(s) 1.35756
Std Err of Coef. 0.121282


CM Regression
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom


Output:
1.606977
0.284149
0.057473


X Coefficient(s) 0.138066
Std Err of Coef. 0.168579


FP QUAD Regression Output:
Constant 0.522392
Std Err of Y Est 0.208385
R Squared 0.931261
No. of Observations 13
Degrees of Freedom 10

X Coefficient(s) -1.24034 0.945128
Std Err of Coef. 1.146142 0.406341


CM QUAD Regression Output:
Constant 0.350798
Std Err of Y Est 0.274238
R Squared 0.201893
No. of Observations 13
Degrees of Freedom 10

X Coefficient(s) 2.155225 -0.71934
Std Err of Coef. 1.508339 0.534751







Adaptabiliy Anarysis: An Overview


S1




0.5

0


3

2.5

S2

9 1


0.5


a to
ime


S A t
AA A


*o
A*



v* *

_ I_ -- I
SS 1 1.5 2
ENVIRONMENTAL INDEX, El


I *r A I


0 03 1 13. 2 2.
ENVIRONMENTAL INDEX, El





FIGURE 2.1 Yields of all treatments (a) and of PCW alone (b), plotted
on EI.


b



*
*





.
g a I S








40 Adaptability Analysis



2.5 w
a. .

23


|".

05

0 I I I *
0 0.5 1 13 2 25
ENVIRONMENTAL INDEX, El





b
2. -

S- A A
h2 A
AA A
1.5 A
A



0.5

0 l I I ,l--l*
0 0. 1 1.5 2 2.
ENVIRONMENTAL INDEX, El

FT-


FIGURE 2.2 Yields of TSP (a) and CM (b), plotted on El.








Adaptability Analysis: An Overview


1i


03

0







3




2oJ






03W

0


a

*


*
*





I p I I I
05 1 13 2
ENVIRONMENTAL INDEX, El

BI


1 1.5
ENVIRONMENTAL INDEX, El
OBSERVED UEAR QUADRAtIC
*U nmi


FIGURE 2.3 Yields of FP plotted on El (a) and comparison fit of linear
and quadratic regression of FP on El (b).







Adaptability Analysis


examining the scatter plot of observations, as for CM and FP (Figure
2.2b and 2.3a), so also can the best regression fit often be identifit
visually. In the case of farmers' practice in the Brazil data, for example,
quadratic regression results in only a 4% improvement in R' over linear
regression (0.93 vs. 0.89). This is all very well to be able to calculate
and know, but it adds little not told by simple visual comparison of the
linear and quadratic regression lines superimposed on the observations
of FP plotted on El (Figure 2.3b). In the same way, the quadratic
regression of CM (R2 = 0.20) is an improvement over the linear (R2 =
0.05).
Note that the R2 for the CM linear treatment is much lower than for
the others; this is due in large part to the fact that the linear slope is so
near zero, as explained above. (See Table 2.1 for regression statistics).
The question of linear versus quadratic equations to describe the
response of each treatment on El is of some intrinsic interest, but is only
crucial if the resulting graphical picture of the TxEI interaction is
materially different (in terms of cross-overs, etc.). Comparing the graph
of linear regression lines for all four treatments (Figure 2.4a) to that o
linear regression lines for TSP and PCW and quadratic lines for FP and
CM (Figure 2.4b), it is apparent that while the "structure" of the
interaction appears quite different, the practical interpretation of the
graphs for the delineation of recommendation domains would be
essentially the same. For both graphs, the cross-over of CM and TSP
occurs at about El = 1.3, and in both graphs TSP is the superior
treatment in environments with El > 1.3 while CM is the best treatment
in environments with El < 1.3. In this case, then, both the linear and
quadratic regressions result in essentially the same interpretation for
deciding upon two potential recommendation domains, assuming that
yield per hectare is the evaluation criterion of interest. Based on the
criteria for data quality, p. 33, although the El range to El mean ratio is
a bit low (0.89), the distribution and overall range of Els is quite good,
so one could expect the relationships shown in Figure 2.4 to be
reasonably consistent over time.

Comparison of Results from AA Regression and from ANOVA

One of the first things to note from the discussion above is how much
more one learns from AA than from a traditional analysis of variance.
The simple analysis of variance for this trial, which has only one
replication per test site (i.e., per environment), is presented in Table 2.2.









Adaptability Anaysis: An Overview


a
2J




1

2 -


a


ENVIRONMENTAL INDEX, E
F PCW TOP CH z
m inmm eseag &


2.

2






0.5

0 -


0
(0)


OJ 1 1S 2
ENVIRONMENTAL INDEX, El
FP PCW TSP Cm
M 1s111me A


FIGURE 2,4 Regressions of on-farm cowpea fertilization treatments on
El: linear for all treatments (a), and linear for TSP and PCW with
quadratic for FP and CM (b).







Adaptability Analysis


TABLE 2.2 Simple ANOVA for yield over all locations, Brazil cowpea
data

Source of Variation df Prob. ofF


Environment 12 0.00001
Treatment 3 0.00001
Residual 37



This analysis tells us that there exist some important differences in the
mean yields among the four treatments and among the environments
(which in this ANOVA serve as "blocks"). However, had this ANOVA
indicated no significant treatment effects, it could have been due to
masking by a treatment-by-environment interaction, which is not shown
in this table and cannot be estimated by simple ANOVA (see p. 17).
Because the main effect of treatment is significant, the next step is to
examine the differences among treatments. Some group of contrasts
might be of interest, but the usual (if less statistically defensible) method
is multiple mean comparisons, using a test such as LSD or Duncan's
Multiple Range Test. Standard mean separations, again assuming only
one recommendation domain, presented in Table 2.3 indicate that in
terms of yield the CM and TSP treatments were essentially equal and
were superior to PCW, which was not significantly different (at a =
0.05) from the farmers' local practices. So far so good, but the fields
on which the trial was conducted were very different (Table 2.4).
Is it likely that cowpea yield in all of these diverse environments,
given the rapid decline in soil fertility known to be a problem in rain
forest land put into cultivation, would respond to fertilization in exactly
the same way from one farm to another? Would one not expect some
sort of treatment-by-environment interaction across these very different
environments? Analysis of variance including the interaction of
treatment and the linear effect of El (Stroup et al., 1993) indicates that
there is in fact a highly significant TxEI interaction that can be explained
by linear regression on El (Table 2.5).







Adaptability Analysis: An Overview


TABLE 2.3 Mean separation of yields' over all locations, Brazil cowpea
data

Treatment* Mean Yield (t/ha)

TSP 1.87 a
CM 1.80 a
PCW 1.01 b
FP 0.80 b

t Means in a column followed by the same letter are not significantly different
at the 5 % level of probability, according to Fisher's Protected Least Squares
Difference test.
t TSP = Full dose of triple superphosphate ; CM = Chicken manure + half
dose of TSP; PCW = Processed city waste + half dose TSP; FP = Farmers'
practices.


Note that the significance of the TxEI interaction term in ANOVA tells
us only that the slope of at least one regression line is different from one
or more of the other lines. To see the structure of the interaction, it is
more useful to examine the graph of the regression lines (Figure 2.4).
Notice also that the ANOVA conducted to this point, used only to
illustrate the added value and verify the "significance" of what linear
regression on El shows us, can be dispensed with if capacity does not
exist to do it.

Relationship between El and Environmental Characteristics

Environments can be characterized using both biophysical and
socioeconomic factors that may be at the same time both quantitative and
qualitative in nature. Data obtained for characterizing the environments
in the Amazon example include soils characteristics and a category called
"land type," Table 2.4. The soils characteristics are self explanatory.
Land type refers to the kind of forest that was cleared (P = primary, S
= secondary) and the number of years it has been cropped (1 = first
year, etc.). The term WL refers to land that had been cleared by
bulldozer at the time of colonization and was, essentially, waste land.
Because the data in Table 2.4 have been sorted by EI, it is easy to
assess the relationship between El and these characteristics. Lower Els









TABLE 2.4 Characterization of environments, Brazil cowpea on-farm trial data


OBSERVATIONS REGRESSION ESTIMATES

FARM El LAND TYPE pH ECEC* Al SAT P' pH ECEC Al SAT P


7 2.04 PF, 5.2 3.24 59.8 8.0 1.818 1.729 1.978 1.598
10 2.03 PF, 5.4 2.21 61.2 12.9 2.050 1.466 1.933 2.075
3 1.95 PF2 5.3 1.25 80.1 5.0 1.934 1.221 1.335 1.306
12 1.78 PF2 4.9 1.91 63.2 7.0 1.470 1.390 1.870 1.501
11 1.70 PF, 5.0 1.72 64.0 10.6 1.586 1.341 1.845 1.851
1 1.43 SF, 4.7 1.35 74.1 7.6 1.239 1.247 1.525 1.559
4 1.41 PF2 5.1 2.31 86.6 4.0 1.702 1.491 1.129 1.209
5 1.20 PF, 4.9 0.99 82.3 2.3 1.470 1.155 1.265 1.044
2 0.94 SF, 4.6 2.34 94.9 3.4 1.123 1.499 0.866 1.151
9 0.88 SF, 4.3 1.20 94.5 2.0 0.775 1.209 0.878 1.014
6 0.84 WL 4.6 1.66 94.2 4.8 1.123 1.326 0.888 1.287
13 0.83 SF2 4.3 1.94 83.4 6.1 0.775 1.397 1.230 1.413
8 0.81 SF3 4.3 1.83 87.5 0.1 0.775 1.369 1.100 0.830

* (cmol+ charge/kg soil) effective cation exchange capacity.
t g/100 g aluminum saturation.
t jig/g Mehlich I extractable phosphorus.








TABLE 2.4 (Continued)


pH Regression Output:
Constant
Std Err ofY Est
R Squared
No. of Observations
Degrees of Freedom


X Coefficient(s)
Std Err of Coef.


ECEC


-4.20945
0.20941
0.83033
13
11


1.159151
0.157987


AL Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom


X Coefficient(s)
Std Err of Coef.


-0.03168
0.005798


3.872471
0.263772
0.730804
13


Regression Output:


Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom

X Coefficient(s)
Std Err of Coef.


0.254845
0.23133


P Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom


X Coefficient(s)
Std Err of Coef.


0.09733
0.028837


0.902806
0.482468
0.099367
13
11


0.819771
0.356324
0.508751
13
11







Adaptability Analysis


TABLE 2.5 ANOVA of yield over all 13 locations (including the
interaction between treatment and the linear effect of El), Brazil cowpea
data

Source of Variation df Prob. ofF

Environment 12 0.00001
Treatment 3 0.00001
Treatment x El (linear) 3 0.00001
Residual 34



(lower-yielding environments) are associated with lower pHs, lower
phosphorus levels, lower ECECs and higher aluminum saturation. If
desired, these relationships can also be graphed and/or estimated by
regression with El being the dependent variable as was done in Figures
2.5 and 2.6 for land types, aluminum saturation, pH and phosphorous.
In this case, the most useful for farmers and extension agents is the land
type characteristic, because farmers in these conditions seldom, if ever,
have detailed soil information on their fields. It can be seen that both the
nature of the forest that was cleared for the field and the number of years
in use are closely associated with El.
For cowpeas in the Manaus trial, it can be seen that the higher-yielding
environments (those with El > 1.3) correspond to fields taken from
primary forest and in first or second year of use, or from secondary
forest and in first year of use. For these situations, if t/ha is the relevant
criterion, the recommendation would be to use TSP, the highest-yielding
treatment for these environments (Figure 2.4). For all other fields, if
cowpea is to be grown and t/ha is the relevant criterion, chicken manure
(CM) would be the amendment recommended. Thus, in this research
domain, two recommendation domains can be tentatively delineated based
on the criterion of t/ha and described in terms usable by both farmers
and extension personnel.
If those environmental characteristics that were collected show no clear
relationship to the Els, the regression of the treatment representing
farmers' current practices (or local varieties, etc.) on El can be used as
a convenient substitute for defining recommendation domains. In Figure
2.4b, for example, the yield of FP corresponding to the cross-over of







Adaptabllty Analysis: An Overview


2.










II
Oj R ; -

I" -! ~i 5 1


LAND TYPE


AA b

A A


1.2


55 60 65 7


0 75 U A
AI SATURATION

OBSERVED ISQ .73
An


5 90 95 100


FIGURE 2.5 El plotted on land type (a) and El regressed on aluminum
saturation (b).


I I I I I I I


-- ---







Adaptability Analysis


ZJA A


1.6





0.8


PH
OBSERVED R SQ -.73


2 4 6 8 10 12
AVAILABLE P
OBSERVED R Q J .51


FIGURE 2.6 Regression of El on available pH (a) and on phosphorous
(b).


10.3







Adaptability Analysis: An Overview


TSP and CM (where El equals approximately 1.3) is approximately 0.7
t/ha. Therefore the recommendation for farmers who would anticipate
a yield less than 0.7 t/ha would be CM. For those anticipating a cowpea
yield above 0.7 t/ha with their current practices, the recommendation
would be TSP.

Analysis within Tentative Recommendation Domains (Verification)

Having delineated and characterized two tentative recommendation
domains for the criterion t/ha, and by regressing treatment responses to
El, further analysis can be undertaken to help assure that the apparent
differences of treatments within recommendation domains are real. One
means is with analysis of variance and mean separation. Another is with
risk analysis based on a distribution of confidence intervals.

ANOVA or Simple Mean Comparisons. The question addressed by
use of ANOVA to "verify" the existence of different recommendation
domains is the level of statistical significance of treatment differences
within each tentative recommendation domain. In the example, in the
domain of higher-yielding environments (PFi, SF,, and PFD), seven
observations were included in the trial, and in the lower-yielding domain
(PF,, SF2, SFU, WL) there were six. The combined ANOVA across the
two domains is shown in Table 2.6. Combined ANOVA across domains
shows that there are overall differences among treatments (which we
knew from the simple ANOVA in Table 2.2). It also shows that there
is not just a significant TxEI interaction (indicated in Table 2.5), but also
a highly significant treatment-by-domain interaction.
The important term in this analysis of variance is the treatment-by-
domain interaction. This interaction term tells us that the treatment-by-
environment interaction shown but not tested statistically by the
regressions on El in Figure 2.4 is "real." That is, there is very little
probability that the differences in slopes of the regression lines in Figure
2.4 are due just to chance. Note that the advantage of ANOVA is that
it allows one to attach a probability to the estimate of treatment-by-
domain (TxD) interaction, which is related to but different from the
treatment-by-environment (TxEI) interaction shown by regression on El.
The major difference between TxEI and TxD is that El cannot be used
ex ante by farmers in making decisions, while the characteristics which
define domains can.







Adaptability Analysi


TABLE 2.6 Combined ANOVA over tentative land typ
recommendation domainst, Brazil cowpea data

Source of Variation 4f Prob. ofF

Domain 1 0.0001
Error a 11 0.0807
Treatment 3 0.0001
Treatment x domain 3 0.0008
Residual error 33

t High-yielding recommendation domain (El > 1.3) PF,, PF2, SF,,
Low-yielding recommendation domain (El < 1.3) = PF,, SF2, SF,, WL.


The nature and importance (not just statistical significance) of the Tx
interaction is shown by analyzing treatment mean differences within eac
domain (Table 2.7). Note that if the traditional 0.05 level of probability
is used for determining "significance" of the mean differences, one migh
be tempted to doubt the superiority of TSP over CM in the high-yieldin
domain and of CM over TSP in the low-yielding domain, relationship
hinted at by the regressions (Figure 2.4). The differences are statistical
different, however, at the 0.10 level of probability. If they had not bee
significant at this level, they might have been at 0.20 or 0.30. Whe
doing ANOVA or means separations as part of AA, one should no
decide whether an effect is "significant" or not at some arbitrary cut-o
point, no matter how supported by convention that cut-off is. Rather,
one should determine at what level the effect is significant, then decid
whether or not that level of significance is important.
Again, although useful in verifying the recommendation domains,
given the difficulties of combined ANOVA, it is not absolutely necessary
as part of AA, if computing facilities are not available. One should
calculate and compare the means themselves. If statistical mean
separation procedures are not available, or if they can only calculate
LSDs or other statistics at the 5% level, reporting the standard errors or
standard deviations should suffice to give an estimate of the confidence
one has in the mean differences within each domain. The means and
standard errors (or standard deviations) are easily calculated, using
whatever tools are available for calculating regressions on El.







Adaptability Analysis: An Overview


TABLE 2.7 Mean separation of yields by tentative land type
recommendation domain, Brazil cowpea data

Mean Yield ft/ha
Treatment PFJ.. PFE.. S PF,. SF2 SF, WL
Mean Std a= a= Mean Std a= a=
Error 0.05 0.10 Error 0.05 0.10

TSP 2.28 0.137 a a 1.39 0.146 a b
CM 1.87 0.107 ab b 1.71 0.112 a a
PCW 1.56 0.147 be be 0.35 0.096 b c
FP 1.34 0.212 c c 0.19 0.069 b c

t Means in a column followed by the same letter are not significantly different
at the 5% or 10% level of probability, by Fisher's Protected LSD test.
$ TSP Full dose of triple superphosphate ; CM = Chicken manure + half
dose of TSP; PCW = Processed city waste + half dose TSP; FP = Farmers'
practices.


Risk Assessment. Reporting of means and their variability by potential
recommendation domain should be done in every case. Then, even if
combined analysis of variance or mean separations cannot be done
(which will often be the case if statistical knowledge or computing
facilities are limited), the relative performance of technologies within
specific environmental circumstances, i.e., within domains, can and
should still be further analyzed.
One of the most important analyses to be done once recommendation
domains are tentatively delineated, is analysis of risk tofarmers. This
is particularly critical in the lower-yielding recommendation domains.
Minimum levels of performance acceptable to the farmers should be
determined, either through diagnostic activities such as sondeos or formal
surveys of farmers' situations, or through interviews with the farmers
collaborating in the on-farm trialss. In many cases, farmers have, and
will express, some minimum level of performance they must have from
a given crop or animal enterprise. In some circumstances a minimum
yield of a staple food is required merely for the household to survive; in
other circumstances, enough production to ensure family survival plus
income sufficient to school some children is the minimum farmers
expect. It is also not uncommon for farmers to consider how well their







Adaptability Analysis


crops and livestock produce as an indication of their worth, competence,
and standing in the community. Poor performance or outright failure can
be a source of shame to farmers, even if their families survive the
situation. While such motivations are sometimes ignored or minimized
by researchers, their influence in determining the adoption decisions of
farmers is often great. Technologies which on average produce much
above this minimum threshold but which, due to great variability of
performance, have a considerable chance of falling below it, will not be
as acceptable to such farmers as an alternative technology which may on
average produce only somewhat more than the minimum acceptable
level, but which has very little chance of falling below it.
A relatively simple evaluation of the comparative risk of alternative
technologies can be done by calculating and graphing a distribution of
lower confidence limits for the mean of each technology. This should be
done by tentative recommendation domain. Because the consequences of
poor performance are greater in the lower recommendation domain(s),
it is particularly critical to assess risk for the conditions found in this
domain. To calculate distribution of confidence intervals, the obser-
vations only from within the tentative recommendation domain are used.
The sample standard deviation of the data from which the domain mean
is calculated is multiplied by the one-tailed t value corresponding to the
level of probability, and the resulting quantity is subtracted from the
mean:

Lower Confidence Limit = s [(t(-., p ) (s) /Vn]
where y = the treatment mean of observations within
the tentative recommendation domain,
sd = the sample standard deviation associated
with the mean,
n = the number of observations that went into
the calculation of the mean (that is, within
the tentative recommendation domain),
and
p = the probability level (from a one-tailed t
table because interest is only in values
lower than the means).

A table is constructed for the lower confidence limits of the mean at a
number of levels of probability, and the series of lower confidence limits







Adaptability Analysis: An Overview


for each mean is graphed. For the Manaus, Brazil cowpea data for
example, the low-yielding (El < 1.3) recommendation domain, using
t/ha as the evaluation criterion, comprised 6 environments; the mean of
each treatment, then, was derived from 6 observations; therefore n = 6.
Table 2.8 presents the mean, sample standard deviation, alpha levels, t
values (and the risk associated with the t probabilities), and the lower
confidence limits for TSP for this lower-yielding domain. A comparison
of the risks associated with TSP and CM in the lower-yielding domain
is shown in Figure 2.7. Based on this evidence it is quite clear that CM
results in less risk of low yields (t/ha) in the lower-yielding domain than
does TSP. Note from Table 2.7 that the reduced risk is due to both high
mean yield in this domain and less variability associated with the mean,
compared to TSP.


TABLE 2.8 Calculation of lower confidence limits (t/ha) for TSP, low-
yielding domain (PF3, SF2, SF3, WL), Manaus, Brazil cowpea
fertilization trial (n = 6, mean = 1.392, Sd = 0.358)

a t5-) Risk(%) Lower Coqfidence Limit

.250 0.727 25 1.392-(0.358*0.727A/6) = 1.285
.200 0.920 20 1.392-(0.358*0.920'6) =1.257
.150 1.156 15 1.392-(0.358*1.156VA6) =1.223
.100 1.476 10 etc. =1.176
.050 0.015 5 etc. =1.097
.025 ;.571 2.5 etc. -1.016
.010 3.365 1 etc. =0.899
.005 4.032 0.5 etc. =0.802
.0005 6.899 0.05 etc. =0.388

Note: The complete table is reproduced in Table 2.9. It should be noted that
the t value should be taken from a one-tailed I table; the values will correspond
to the probability of a larger negative value of t. This probability, expressed as
a percentage, is the column labeled "risk."


In the Manaus study, no estimate of a minimum acceptable level of
cowpea yield was made. For purposes of illustration, we will use an
estimate of 1.2 t/ha as this minimum acceptable level. From Figure 2.7







Adaptability Analysis


2


1.5 -





0.5


0
05 10 15 20 25
RISK (% CHANCE OF A LOWER VALUE)
Sr rs c1


FIGURE 2.7 Risk assessment (lower confidence limits), for t/ha; on-
farm cowpea fertilization treatments in low-yielding recommendation
domain (PF,, SF,, SF,, WL).


it can be seen that if TSP is applied, the risk is approximately 10%
(corresponding roughly to one year in 10) that yield would fall below the
minimum acceptable level of 1.2 t/ha. If CM is applied to the cowpea
in this recommendation domain, there is almost no chance (< 1%, or
less than roughly one year in 100) that yield would fall below the
minimum acceptable level. Hence, there is much less risk to the farmer
if CM is recommended than if TSP is the recommendation in the lower-
yielding environments and for the criterion of t/ha. The risk of low
yields from the farmers' practices is very high (certain). This is
reflective of the common practice of cropping at most two years in these
conditions. Notice that this risk analysis adds appreciably to what was
learned by comparing the means (Table 2.7). It allows formulation of
estimates of risk in terms useful tofarmers and extensionists. The next
section looks at the effect on recommendation domains when alternative
evaluation criteria are used.







Adaptability Analysis: An Overview


TABLE 2.9 Sample spreadsheet for confidence limits (t/ha), low-
yielding domain (PF,, SF2, SF,, WL), Manaus, Brazil data

Data (t/ha)
Location FP PCW TSP CM El

8 0.10 0.20 1.30 1.65 0.813
13 0.00 0.00 1.30 2.00 0.825
6 0.15 0.50 1.35 1.35 0.838
9 0.20 0.40 1.20 1.70 0.875
2 0.50 0.65 1.10 1.50 0.938
5 0.15 0.50 2.10 2.05 1.200

Domain average 0.183 0.375 1.392 1.708
Domain sample st. dev. 0.169 0.236 0.358 0.275

Prob. of Lower Confidence
Lower Value Limit, t/ha
Alpha (Risk) t,.s FM CM TSP

0.2500 25.00 0.727 0.133 1.627 1.285
0.2000 20.00 0.920 0.120 1.605 1.257
0.1500 15.00 1.156 0.103 1.579 1.223
0.1000 10.00 1.476 0.081 1.543 1.176
0.0500 5.00 2.015 0.044 1.482 1.097
0.0250 2.50 2.571 0.006 1.420 1.016
0.0100 1.00 3.365 1.331 0.899
0.0050 0.50 4.032 1.256 0.802
0.0005 0.05 6.859 0.939 0.388


Use of Alternative Evaluation Criteria

The evaluation criterion used to calculate the environmental index, EI,
above is t/ha-the most common criterion used by agronomists in crop
trials and, because it reflects the impact of environment, appropriate in
most cases as the basis for calculating the El. However, few farmers
use this criterion when making production decisions. If either seed,
labor or cash is their scarcest resource, a more appropriate evaluation







Adaptability Analysis


criterion is kg/kg seed, kg/day of labor in a critical period, or kg/$ of
cash cost, respectively. AA easily lends itself to analysis using multiple
criteria. Figure 2.8 is based on analysis of the cowpea data for the
common farmers' criterion of kg/$ cash cost.
Notice that the same El (usually based on kg/ha yields) is used
regardless of the criterion being evaluated. The El values used to form
the X-axis for the regression of treatments on El (Figure 2.8a) do not
change. The criteria used on the Y-axis (Figure 2.8a) do change. The
same procedures were used to obtain these relationships as were used to
obtain the relationships based on the researchers' criterion, t/ha. Cash
costs of the treatments were FP = $12, PCW = $208, TSP = $98 and
CM = $127 per hectare. Notice that very different conclusions can
result when the evaluation criteria change. This is important because it
relates to the ultimate recommendations that will be made.
For the farmers' criterion of kg/$ cash cost, the farmers' practices,
without soil amendments, is the most appropriate technology of the four
tested in the higher-yielding environments (again, El > 1.3). Although
both TSP and CM yield more in terms of t/ha, their cost (purchase and
transport) is so high that the return per dollar invested is too low
compared with their current practices. However, in the lower-yielding
domain, fffarmers want or need to produce cowpea in these conditions,
then CM is an appropriate recommendation if achieving 16 kg per dollar
of cash expense three out of four times is acceptable (Figure 2.8b). This
level of production would be equivalent to a cost of approximately 6.25
cents per kg cowpea at the farm. If 14 kg per dollar of cash expense
(7.14 cents per kg) is acceptable, this can be achieved approximately 49
out of 50 times using CM as an amendment in the lower-yielding
environments (Figure 2.8b).
Had characterization of the environments not shown any relation with
recommendation domains, the regression of the treatment representing
farmers' practices (Figure 2.8a) could again be used instead. In this
case, for farmers anticipating yields greater than 20 kg/$ cash cost, the
recommendation would be their own practice. For those expecting lower
yields per dollar cash cost, the recommendation would be CM.

Multiple Extension Recommendations

The above analyses can be summarized in a series of extension
recommendations that combine environmental characteristics with specific
evaluation criteria, Table 2.10.








Adaptability Analysis: An Overview


12


10


a


2= A n Aft A- -
1 1. 2
ENVIRONMENTAL INDEX, El
PI PcW TSI CMn I
m guam


5 10 15 20
RISK (% CHANCE OF A LOWER VALUE)
| i TO


FIGURE 2.8 Estimated responses of the four treatments to environment
(El) for cowpea in Brazil using a common farmer criterion, kg/$ cash
cost (a), and risk assessment (lower confidence limits) for kg/$ cash cost
in low-yielding recommendation domain (PF3, SF,, SF3 and WL) (b).







Adaptability Analysis


TABLE 2.10 Multiple extension recommendations for four
recommendation domains based on land type and two evaluation criteria,
cowpea near Manaus, Brazil

Land .Tpe
Criterion PF,, PF,, SF, PF,, SF2, SF,, WL

t/ha TSP CM
kg/$ cash cost FP CM



Farmers in the conditions represented by these data from the Amazon
rain forest usually fell the forest from a patch of land, farm it in annual
crops one or two years, then let it return to fallow (perhaps with a
continuing cassava and/or tree crop). These farmers live in isolated
conditions and have little contact with the market. Cash is scarce but
generally land is not. For these farmers, the criterion of kilograms grain
per dollar of cash cost is most relevant. Table 2.10 indicates that these
farmers would not invest in the TSP or CM because of its cost even
though both produce more yield measured in t/ha and so long as they
have land that can be taken out of forest and farmed only one or two
years. Their best alternative is to fell another piece of forest. If,
however, forest land becomes scarce and/or the government prohibits the
felling of more forest, then after the second year following primary
forest, and after the first year following secondary forest, CM would be
the alternative of those tested to recommend to them. Because of its
cost, TSP would only be recommended if t/ha were the criterion and then
only for the first or second year of use.


Summary

On-farm trials are increasingly common. Sadly, given the large
variability associated with these trials, and given the standard analytical
tools available to researchers, the results of such trials are all too often
that "no firm conclusions can be reached," or "further trials are needed."
This overview of Adaptability Analysis has shown that AA, used either
as a stand-alone method of experimental design and analysis, or in






Adaptability Analysis: An Overview


combination with older, more established methods, is an effective and
efficient tool in the effort to identify improved technologies and produce
extension messages relevant to a range of farmer circumstances, both
biophysical and socioeconomic.
As illustrated by the Brazil cowpea trial, use of AA can quickly and
accurately produce technology recommendations for a variety of
recommendation domains in a way that traditional methods alone cannot.
It does this chiefly through recognizing that the farming systems
research-extension concept of recommendation domain is a function of
the specific socioeconomic circumstances that determine what farmers
want from new technologies and the specific biophysical circumstances
of their farm environments, which determine how new technologies
perform. With just a few simple analytical tools requiring only limited
statistical expertise or computing facilities, AA allows researchers and
extensionists to exploit completely adaptive research done under farmers'
conditions. When these conditions are characterized by a full range of
environmental characteristics (usually biophysical) and interpreted in
light of farmers' multiple evaluation criteria (usually socioeconomic),
Adaptability Analysis can result in multiple extension recommendations
appropriate for specific groups of farmers and specific groups of farm
environments (Figure 2.9).






Adaptability Analysis


Extension
Recommendations


FP


pp


FP










CM


On-Farm
Research-Extension
Multiple in
Criteria Multiple Environments
t/ha

S kP/$ Pi
t/ha
S /S SF,
t/ha
PF
k /$ P

d.. t/ha
SFW



PF,



S kg/ WL
k/$


FIGURE 2.9 Multiple extension recommendations, specific to multiple
recommendation domains associated with different biophysical
characteristics and criteria resulting from variable socioeconomic
conditions.


Pil


SFI
* I

~PF


WL













3


Single-Factor Trials



As discussed in Chapter 2, AA developed from a technique used by
plant breeders for evaluating the stability and adaptability of crop
varieties. Not surprisingly, given that variety trials are probably the
most common type of on-farm trials, one of the most frequent uses of
AA is for determining specific adaptation of new varieties to particular
recommendation domains.

On-Farm Maize Variety Trials (Paraguay)

The first example to be discussed is from the results of a trial
conducted in a single year in 24 on-farm environments in Paraguay
(Poey, n.d.), Table 3.1. Four maize varieties were tested, with a single
replicate of the four varieties per farm. The quality of this data set is
quite high. The ratio of the range of the El to the overall mean El is
1.2:1, indicating that a wide range of environments was included in the
trial. -Furthermore, the range of the farmers' local variety, criollo, spans
what might be expected in normal years, and the distribution of the Els
is quite acceptable. The analysis of variance, over all 24 sites, is
presented in Table 3.2.
The test of significance of the variety-by-environmental index
interaction, which will not be treated here, is explained in detail in
Stroup et al. (1993); it is also discussed briefly in the introduction. In
this case it means that the slopes of the regression lines reflecting linear
responses of yield to environment in the figures below are significant at
a = 0.0588. If the probability had been much greater (ct greater than













3


Single-Factor Trials



As discussed in Chapter 2, AA developed from a technique used by
plant breeders for evaluating the stability and adaptability of crop
varieties. Not surprisingly, given that variety trials are probably the
most common type of on-farm trials, one of the most frequent uses of
AA is for determining specific adaptation of new varieties to particular
recommendation domains.

On-Farm Maize Variety Trials (Paraguay)

The first example to be discussed is from the results of a trial
conducted in a single year in 24 on-farm environments in Paraguay
(Poey, n.d.), Table 3.1. Four maize varieties were tested, with a single
replicate of the four varieties per farm. The quality of this data set is
quite high. The ratio of the range of the El to the overall mean El is
1.2:1, indicating that a wide range of environments was included in the
trial. -Furthermore, the range of the farmers' local variety, criollo, spans
what might be expected in normal years, and the distribution of the Els
is quite acceptable. The analysis of variance, over all 24 sites, is
presented in Table 3.2.
The test of significance of the variety-by-environmental index
interaction, which will not be treated here, is explained in detail in
Stroup et al. (1993); it is also discussed briefly in the introduction. In
this case it means that the slopes of the regression lines reflecting linear
responses of yield to environment in the figures below are significant at
a = 0.0588. If the probability had been much greater (ct greater than








TABLE 3.1 Paraguay on-farm maize variety trial, yield (t/ha).
Varieties
Region Location Suwan Guarani Poblaci6n Criollo El


Misiones
Cordillera
Caazapa
Misiones
Caazapa
Misiones
Caazapa
Misiones
Cordillera
Cordillera
Cordillera
Ybycui
Cnel Bogado
Ybycui
Concepcion
Cnel Bogado
Caazapa
Ybycui
Concepcion
Ybycui
Cnel Bogado
Caazapa
Concepcion
Concepcion
Source: Poey, n.d.


Ibanez Rojas
Piribebuy
Iturbe
San Solano
E.A. Garay
San Juan
Maciel
Yacua Sati
Valenzuela
Eusebio Ayala
Isla Pucu
Caacupe
Nacional
Tacuary
Loreto
Ypayere
Yuty
Sapucai
Concepcion
Pereira Que
Syryryca
Caazapa
Horqueta
Yby-yau


1.334
1.904
2.125
2.090
1.933
2.357
2.421
2.551
2.877
2.701
2.516
3.012
3.462
3.098
4.215
3.184
3.735
4.794
4.799
5.360
4.682
4.960
4.939
5.246


1.383
1.714
1.549
1.733
1.922
1.993
2.192
2.103
1.601
2.259
2.383
2.253
2.646
2.670
2.738
2.672
3.682
3.031
4.128
4.342
4.503
5.514
4.931
5.538


1.306
1.602
1.778
1.895
1.775
1.807
1.556
2.589
2.558
2.467
2.696
2.363
3.262
2.582
2.871
3.341
3.194
3.744
4.472
4.583
4.238
3.708
4.011
4.781


1.660
1.595
1.600
1.728
1.827
1.641
1.690
1.914
2.299
2.243
2.261
2.345
2.153
3.182
2.580
3.443
2.790
2.766
4.247
3.597
4.684
4.136
4.467
4.410


1.421
1.704
1.763
1.862
1.864
1.950
1.965
2.289
2.334
2.418
2.464
2.493
2.881
2.883
3.101
3.160
3.350
3.584
4.412
4.471
4.527
4.580
4.587
4.994


- ~- --~ ---







Single-Factor Tials


TABLE 3.2 Analysis of variance, with interaction of varieties and El

Source of Variation df Prob. > F

Environment 23 0.0001
Variety 3 0.0001
VxEI 3 0.0588
Residual 66
C.V. = 12.6%


0.2, for example), it might not be of interest to do the regression
component of AA but rather to assume just one recommendation domain,
and choose the highest-yielding variety for all environments (note the
highly significant effect of variety). The inclusion of the treatment-by-El
interaction in ANOVA is not necessary to perform AA; one should not
worry if means of calculating it are not available. Its use here serves
only to illustrate the statistical significance of the interaction shown in the
linear regressions. As always, however, it is the practical rather than
statistical significance which is of most importance when analyzing the
results of on-farm research-extension.
To arrive at an understanding of the response of the yields of the four
varieties to environment, individual treatment yields are plotted on
environmental index. First, plotting the individual yields of all varieties
shows the overall variability and some first ideas on response to
environment (Figure 3.1a). While there appears to be little difference
between the varieties in low-yielding environments (El < 2 t/ha), in
better environments Criollo, the local variety (A), seems to yield
generally less than the other varieties, while Suwan (*) appears to be
relatively high yielding in all environments, and Guaranf (*) seems to be
high yielding in the very best environments.
Which is the appropriate regression of yield on environment: linear or
quadratic? Figures 3.1b, 3.2, and 3.3 show the yields of each variety
plotted on El. They all seem to be generally linear, but it might be
argued from visual estimation alone that the response of Poblaci6n
(Figure 3. b) and Suwan (Figure 3.2b) curve slightly downward at very
poor and very good environments, while that of Criollo (Figure 3.2a)
and Guaranf (Figure 3.3) curve slightly upward. The regressions are
presented in Figure 3.4a for the linear model and in Figure 3.4b for the






66 Adaptability Analysis


IS


4 I
a8 *
U





*




0 1 2 5 4 5 6
ENVIRONMENTAL INDEX, El
SUWAN GOAANIM POBAODN PUGLH



b



S 4
0 0 D









ENVIRONMENTAL INDEXEI



FIGURE 3.1 Yields of all varieties (a) and yields of Poblacidn alone (b)
plotted on Enviro en Index ).
0 1 2 3 4 5 6
ENVIRONMENTAL INDEX El



FIGURE 3.1 Yields of all varieties (a) and yields of Poblacidn alone (b)
plotted on Environmental Index (ED).







Single-Factor TWals 67


6
a



I A








0 1 2 9 4 $
1 -












0 --i- t l I- I -- -
0 t 2 3 4 5 6
ENVIRONMENTAL INDEX, El

aUWAN

























Environmental Index (0l).
b *
*

0 4 *



B I *

1 -


0 1 2 9 4 5 6
ENVIRONMENTAL INDEX, E




FIGURE 3.2 Yields of Criollo (a) and Suwan (b) plotted on
Environmental Index (EI).







68 Adaptability Analysis

6
0*

4 -

S *
*c*





o I 2 4 5S-
ENVIRONMENTAL INDEX, El



FIGURE 3.3 Yields of Guaranf plotted on El.

quadratic model. The linear model predicts Suwan to be superior across
all environments, while the quadratic model predicts that Suwan is
superior (in terms of grain yield at least) especially in the average
environments, while being no worse than the other varieties in both low-
yielding and high-yielding environments. In practical terms, then, if
grain yield per unit land area were the evaluation criterion of most
importance to farmers, Suwan could be recommended to all farmers.
In this particular case, where there seems to be just one
recommendation domain (given the evaluation criterion of t/ha),
environmental characterization is of less importance than if there had
been more than one recommendation domain. It may be desirable,
however, to verify that Suwan is superior in both the "good" and "poor"
environments. One of the environmental characteristics collected in this
trial was years each test field had been cultivated, ranging from 15 to 90
or more, Table 3.3. Is environment, as estimated by EI, related to
number of years in use? Visual evaluation of El plotted on number of
years in use (Figure 3.5) indicates an inverse relationship, which is what
one might logically expect. Linear regression of this relationship shows
a significant negative effect, with number of years in use accounting for
75% of the variability in El among the trial environments (p=0.0001).







Single-Factor 7THal 69



a












S1 2 3 4 S 6
ENVIRONMENTAL INDEX, El
SUWAN GUAKAM POIlACION CR)IO r E

















O A 4 ,
0 1 2 5 4 5 6
ENVIRONMENTAL INDEX, El
SUWAN OUARAM POBLAMON CWQUX) HK
I u s lells I


FIGURE 3,4 Linear (a) and quadratic (b) regression of Suwan, Guaranf,
Poblacidn and Criollo on El.








TABLE 3.3 Environmental characterization, Paraguay on-farm maize variety trial

El Region Location Years Use Soil Color Soil Texture* Slope


1.421
1.704
1.763
1.862
1.864
1.950
1.965
2.289
2.334
2.418
2.464
2.493
2.881
2.883
3.101
3.160
3.350
3.584
4.412
4.471
4.527
4.580
4.587
4.994


Misiones
Cordillera
Caazapa
Misiones
Caazapa
Misiones
Caazapa
Misiones
Cordillera
Cordillera
Cordillera
Ybycui
Cnel Bogado
Ybycui
Concepcion
Cnel Bogado
Caazapa
Ybycui
Concepcion
Ybycui
Cnel Bogado
Caazapa
Concepcion
Concepcion


lbanez Rojas
Piribebuy
Iturbe
San Solano
E.A. Garay
San Juan
Maciel
Yacua Sati
Valenzuela
Eusebio Ayala
Isla Pucu
Caacupe
Nacional
Tacuary
Loreto
Ypayere
Yuty
Sapucai
Concepcion
Pereira Que
Ca
SCaazapa
Horqueta
Yby--Yau


>50
>50
80-100
>50
80-100
> 50
80-100
>50
>50
>50
>50
50
40
50
50
20-30
30
15-20
20
20
20-30
20
20
20


* cs = clayey sand; sic = sand-loam-clay; sc = sandy clay; Ic = loamy clay.


It. brown
It. brown
brown
It. brown
brown
It. brown
brown
It. brown
red
dk. brown
red
dk. brown
dk. brown
dk. brown
It. brown
red
red
black
dk. red
reddish
red
red
dk. red
dk. red


rolling
steep
rolling
rolling
rolling
rolling
rolling
rolling
steep
steep
steep
level
level
level
rolling
rolling
rolling
steep
rolling
rolling
rolling
level
rolling
rolling






Single-Factor Trials


0 10 20 30 40 50 60
YEARS IN USE


70 80 90 100


FIGURE 3.5 El plotted on years in use.


In the case of these data, analysis of variance, by potential
recommendation domain based on years of use, supports the conclusion
that Suwan is superior in all locations, i.e., that there is just one
recommendation domain if only t/ha is the criterion used (Table 3.4).
Finally, it can be shown from AA that Suwan does not just have
higher average mean yields than the other varieties, it also presents lower
risk to farmers. In the environments with El < 4, for example, there
is negligible risk of yields of Suwan falling below 2.5 t/ha, while the
chance of Guaranf yielding less than this level is around 5%, and for
Poblaci6n, over 10% (Figure 3.6).
It can be seen from these analyses that AA can indicate the existence
of only a single recommendation domain as well as the existence of more
than one. It can happen, however, that there is just one recommendation
domain using a given evaluation criterion (as for yield/ha in this case)
and more than one domain when using alternative evaluation criteria.


A b -0.041
* Ai R2 0.75


-A


4
a







Adaptability Analysis


TABLE 3.4 Mean separations' over all locations and by tentative
recommendation domain based on years in use, Paraguay maize trials


Variety All Locations Years in Use < 51 Years in Use > 50

.------- Grain yield (kg/ha)----
aj= aof= a=
5 a 0.3o 0.05 o.30 0.5 0.30
Suwan 3.35 a a 4.27 a a 2.26 a a
Guaranf 2.90 b b 3.74 b b 1.89 b b
Poblacidn 2.88 b b 3.63 b bc 2.00 b c
Criollo 2.72 b c 3.45 b c 1.86 b c
LSD 0.215 0.112 0.357 0.185 0.204 0.105

t Means in a column followed by the same letter are not significantly different
by Fisher's Protected LSD test at the 0.05 and 0.30 levels of probability.



3.5




0







0I$ to 00 up
0 5 10 15 20 25
RISK (% CHANCE OF A LOWER VALUE)
SUWAN GUANAM POMAaON CZJOLDO
uw mum seamlls f

FIGURE 3.6 Risk assessment for maize varietal yield in low-yielding
environments (more than 50 years in use).







Single-Factor Trials


Hormone Implants in Beef and Dual-Purpose Cattle (Panama)

The difficulty inherent in on-farm livestock trials has limited the
number of such trials and subsequently limited the number analyzed
using Adaptability Analysis. However, many on-farm livestock trials are
amenable to analysis by AA, as the example below illustrates.
This is a fairly straight forward trial of two treatments conducted on
a large number of farms. A simple, two-treatment trial was conducted
in a large number of farm environments in Panama to evaluate the
efficacy of growth-hormone implants in increasing daily gain of cattle
(Simpson et al., 1988). There were great differences among herds in
this study, including nature of the system and herd size. Four kinds of
system were included: 1 = dual purpose calves, 2 = calves from a
cow/calf system, 3 = combined cow/calf and fattening (grass), and 4 =
fattening (grass). Seasons during which the animals were in the trial
included RB = beginning of the rains (3/15-6/15), RE = ending of the
rains (6/15-9/15), DB = beginning of the dry season (9/15-12/15), and
DE = ending of the dry season (12/15-3/15).
Both treatments (Zeranol* implant and a control) were included in each
of 44 separate trials under regular farm conditions with the number of
animals being subjected to each treatment varying from two to 20.
Weights were taken 90 days after the implant. In 25 of the trials only
one 90-day cycle was included, 9 covered a 180-day period and thus had
the original implant and one reimplant, 3 covered 270 days, and 7 had
the original and three reimplants. Altogether there are 80 environments
represented in the data, Table 3.5.
The observations and relationship between the treatments are shown in
Figure 3.7. The ratio of the El range (1.42) to the El mean (0.92) is
1.60. The range of the control seems to be a reasonable range to expect
and the distribution is quite uniform. Therefore, these data meet the
three criteria for data quality, so we can have confidence in the
relationships indicated.
There appears to be an advantage to the implant in all environments,
although it becomes minimal where El < 1.0. An attempt to character-
ize the environments is shown in Table 3.6 where season and system
were combined and the average Els for the resulting sets were used to
sort. The two highest average Els were for the cow/calf systems in the
two wet periods, the next highest were for the combined system in the
wet periods, followed by the cow/calf system in the dry seasons. The
availability of dams' milk apparently provided a favorable environment







Adaptability Analysis


TABLE 3.5 Daily gain (pounds) from hormone implant and control
treatments for four systems and four seasons, cattle in Panama

System* Seasont Control Implant El


RB
RB
RE
RB
DE
DB
DE
RB
RB
RB
RE
RE
DE
RE
RB
RE
DE
DB
RE
RE
DB
RB
RE
RE
DB
DE
DB
DE
DE
RE
DB
DB
RE
DE
RB


1.53
1.62
1.49
1.39
1.33
1.30
1.41
1.15
0.96
1.29
1.21
1.21
1.22
1.26
1.25
1.21
1.01
1.17
1.13
1.17
1.11
1.03
1.25
0.60
1.13
0.98
1.16
1.14
1.06
1.13
1.01
1.10
1.03
0.93
0.99


1.76
1.54
1.52
1.50
1.53
1.51
1.32
1.54
1.70
1.25
1.31
1.29
1.27
1.22
1.23
1.27
1.47
1.30
1.31
1.26
1.31
1.39
1.13
1.72
1.19
1.28
1.09
1.05
1.11
1,00
1.09
0.99
1.02
1.09
1.02


1.64
1.58
1.50
1.44
1.43
1.40
1.36
1.34
1.33
1.27
1.26
1.25
1.24
1.24
1.24
1.24
1.24
1.23
1.22
1.21
1.21
1.21
1.19
1.16
1.16
1.13
1.12
1.09
1.08
1.06
1.05
1.04
1.02
1.01
1.00







Single-Factor Trials


TABLE 3.5 (Continued)


System* Seasont Control Implant El


RE
DE
RB
DB
RB
RE
DE
DE
DE
RB
DB
RE
RE
RE
RB
DE
DE
DB
RE
RB
DB
RE
RB
RB
RB
RE
DB
DB
DE
DB
RB
DE
DE
DE
RE
RB


1.01
0.87
0.80
0.66
0.79
0.90
1.35
0.96
0.85
0.88
0.83
1.02
0.82
0.79
0.86
0.77
0.72
0.86
0.89
0.74
0.60
0.74
0.73
0.74
0.63
0.56
0.60
0.55
0.68
0.51
0.56
0.53
0.43
0.45
0.50
0.60


1.00
0.98
1.01
1.14
1.00
0.87
0.41
0.80
0.87
0.84
0.88
0.68
0.83
0.85
0.78
0.85
0.87
0.73
0.68
0.82
0.94
0.77
0.75
0.71
0.71
0.75
0.64
0.65
0.51
0.66
0.60
0.60
0.67
0.64
0.59
0.46


1.00
0.92
0.90
0.90
0.89
0.88
0.88
0.88
0.86
0,86
0.85
0.85
0.82
0.82
0.82
0.81
0.79
0.79
0.78
0.78
0.77
0.75
0.74
0.72
0.67
0.65
0.62
0.60
0.59
0.58
0.58
0.56
0.55
0.54
0.54
0.53







Adaptability Analysis


TABLE 3.5 (Continued)


System* Seasont Control Implant El

3 DB 0.49 0.52 0.50
1 RE 0.34 0.65 0.49
3 DB 0.52 0.43 0.47
4 RE 0.53 0.19 0.36
3 DE 0.29 0.42 0.35
4 DE 0.26 0.21 0.23
1 DE 0.25 0.19 0.22
1 DE -0.01 0.45 0.22
4 DE 0.08 0.27 0.17
Source: Simpson et al., 1988
* System: 1 = dual, 2 = cow/calf, 3 = combined, 4 = fattening.
t Season: RB = rains begin, RE = rains end, DB = dry begins, DE = dry
ends.


that helped the calves respond to the implants even in the dry seasons.
Beyond these first six combinations, there was no clear division relating
either to season or system, or the combination of the two. For this
reason, a risk analysis was done for the six top categories in Table 3.6
(calves), then the remaining combinations were grouped into 1) wet
seasons and 2) dry seasons, Figure 3.8.
The superiority of the implant treatment in all cases is evident in the
risk analysis as well. For the calves there is little chance of falling
below a daily gain of 1.25 pounds with the implant. Without the
implant, there is a chance one year in 14 of falling below 1.2 pounds
daily gain in these higher environments. Even in all the poorer
environmental situations the impact of the hormone implant is evident,
Figure 3.8.
Because the cost of the implant required only 3.11 pounds of additional
gain over the 90 days or 0.035 per day (Simpson et al., 1988), the
implants clearly paid for themselves in all environmental situations so no
further economic analysis is required. If the farmers are willing to take
the time (for which charges were included in the cost estimates) then the
implants can be recommended for all environments included in the trial.







Single-Factor 2Tals


1.8
1.6
1.4
12
1
0.8
0.6
0.4
02
0
(0.2)


0.4 0. 0. 1 12 1.4
ENVIRONMENTAL INDEX, EI
CONTROL ThflANA CNRDL DUWNTS
r n0 MPEOs Wrrs


FIGURE 3.7 Relationship of the hormone implants to the control across
environments, cattle in Panama.

TABLE 3.6 Relationship of season and system to environment

Season* System Average El No. Cases

RE Cow/calf 1.305 4
RB Cow/calf 1.270 2
RB Combined 1.142 5
RE Combined 1.059 5
DE Cow/calf 1.043 5
DB Cow/calf 1.032 2
RB Dual p. 0.976 8
DE Combined 0.952 5
DB Dual p. 0.948 4
DB Fatten 0.919 5
RB Fatten 0.879 4
RE Dual p. 0.847 10
RE Fatten 0.788 3
DB Combined 0.777 5
DE Dual p. 0.639 8
DE Fatten 0.624 5

* RB = rains begin, RE = rains end, DB = dry begins, DE = dry ends.







78 Adaptability Analysts

1.4

1.3




0.9 -

07,r ,,0*O.W ,, Won a Intel I men1 1o1 1
OJ 0' 6 sosoawI I NI-I I M
0j

0 S 10 15 20 25
RISK (% TIME BELOW VALUE)
CN, CALVES IMP. CALVES CON, WET IP, WET CON. DY IMP, tRY




FIGURE 3.8 Risk analysis, hormone implants, cattle in Panama.


On-Farm Sorghum Variety Trials (Cameroon)

This example, of sorghum varietal testing in northern Cameroon,
illustrates the use of AA for "validation" or "demonstration" on-farm
trials, usually done with large numbers of locations (environments) and
small numbers of treatments. Regression of El on quantitative
environmental characters is used to predict recommendation domains and
compare the results to known patterns of adoption. The usefulness of the
three criteria for data quality (particularly distribution of Els) in deciding
how many years of testing are needed is also illustrated.
The improved short-cycle photoperiod-insensitive variety S35 was
tested during four years of on-farm "pre-extension" trials. These trials
were a collaborative effort between the parastatal cotton development
agency SODECOTON and Cameroon's Institute of Agronomic Research
(IRA). Despite promising results in the on-farm trials and extensive
recommendation of S35 by SODECOTON, widespread adoption was
slow. In some areas S35 was much in demand and well adopted; in
other areas it was poorly accepted by farmers.






Single-Factor T7als


The trials took place from 1984 to 1987 (Testing and Liaison Unit,
1986; Testing and Liaison Unit, 1987; Johnson, 1988). The trials in
1984 were done in two different groups, according to geographical
location, north or south. Varieties were S35, locals, and 38-3 in the
northern regions and S35, locals, and E35-1 in the central regions. Tests
in 1985 were separated into two groups with different target ranges of
seeding dates. Varieties in the early-seeded sites were S35, locals, and
S34; in the late seeded sites, varieties were S35, locals, S36, and S20.
Varieties for all sites in 1986 and 1987 were S35, locals, CS54, and
CS61. In 1986 and 1987 there was just one set of tests each year. The
treatment called "locals" was not a single variety; each collaborating
farmer selected a locally adapted variety as a local check.
Each on-farm test was conducted on a 0.25-hectare field (50 m by 50
m), was farmer-managed and monitored throughout the season by a
SODECOTON agent. Between-row spacing was 0.80 m; within-row
spacing was 0.40 m between hills, plants thinned to two per hill. In
1984, recommended seeding dates were 20 June to 10 July, choice of
preceding crop was left to the farmer, and all sites were fertilized with
61-13-25 kg/ha N-P-K. In 1985, fertilizer was reduced to just 46 kg
N/ha, as urea, and all sites had cotton as the preceding crop. Test
conditions in 1986 and 1987 were as in 1985 except that recommended
date of seeding was 10 to 20 June, in order to avoid favoring the early-
maturing, improved varieties (Johnson, 1988).
Standard analysis of test results, by year and by test group within a
year, are presented in Table 3.7. In 1984 S35 yielded 80% more than
locals in the north regions and 90% more in the central regions. In late-
seeded tests in 1985, S35 yielded over 20% more than locals. In all the
1985-87 early-seed tests, yields of S35 and locals were essentially the
same. The reason for these differences between years is probably that
1984 was a very low-rainfall year. On the basis of this, S35 had been
extended as a "drought tolerant" variety. Tests in 1984, however, were
also characterized by generally late seeding dates (Table 3.7), in part
because of late rains in many areas, but also because of logistical
difficulties in this first year of tests and because collaborating farmers
were given a somewhat late recommended date of seeding. The rainfall
and date-of-seeding factors are confounded in the general "year effect"
in a combined ANOVA of these trials, making analysis of variance of
limited value for identifying potential recommendation domains. On the
basis of the individual year results, however, S35 came to be
recommended generally as good for seeding late.







Adaptability Analysis


TABLE 3.7 Summary of on-farm variety test results with standard t test
results leading to recommendation of S35 for late seeding

Seasonal Mean
Neldt 7 Jkgha) Rainfall Seeding
Year Test Group S35 Local S.E. (mm) Date

1984 North (42 Sites) 1070 598 99 393 3 July
Central (46 Sites) 1573 829 109 359 6 July
1985 Early-Seeded (42 Sites) 1866 1721 NS 118 504 14 June
Late-Seeded (16 Sites) 1416 1156 160 515 28 June
1986 All (38 Sites) 2164 2128 Ns 145 621 22 June
1987 All (35 Sites) 1889 1825 us 123 604 19 June

t Yields of varieties for a year and test group are significantly (*) or not
significantly (NS) different at the 5% level, by t test.
Source: Adapted from: Testing and Liaison Unit, 1986; Testing and Liaison
Unit, 1987; and Johnson, 1988.


The Adaptability Analysis presented here is based on the data set of
166 on-farm varietal trials in 1984 and 1985 (Table 3.App). These
include 87 environments in 1984 and 79 in 1985. Some on-farm tests in
1985, not strictly varietal tests in the same program but including S35
and farmers' locals, are also included here, illustrating the flexibility and
inclusiveness afforded by AA. Adaptability Analysis was applied to
these data in order 1) to examine the structure of the variety-by-
environment interaction across farm environments; 2) to determine if
more than one recommendation domain existed for these varieties; 3) to
identify those variables identifiable ex ante which could serve to predict
in which recommendation domain a given farm environment would
belong; and 4) to determine whether AA might have predicted in just the
first year or two of tests the patterns of S35 adoption actually seen.
Because each year had a different group of varieties, and because El
depends entirely on the specific varieties included, AA in this example
uses only those varieties common to all years (or all groups of test within
a year), i.e., S35 and the farmers' locals. Linear regression of yields of
S35 and locals on El in 1984 are shown in Figure 3.9. The El : mean






Single-Factor Trals


S2,1500 -



5oo # B lon

0
S00 1,000 1,500 2.000 2,0 3.000 3,5
ENVIRONMENTAL INDEX, El
S5 LOCALS 35 LOCALS



FIGURE 3.9 Regressions of S35 and locals on El for 1984.

ratio (3177 / 1037 = 3.06) is quite good, but the distribution is poor,
with a gap of almost 700 in the upper range of Els. Also of note is the
concentration of Els in the low and mid ranges, including a great number
of Els near zero. Comparison of the regressions indicate that in this
drought year with a late onset of rains, S35 is superior in yield to locals
over all environments, and that this superiority is greatest in the high-
yielding environments.
In 1985, Figure 3.10, the ratio of El to mean is also quite good
(3065 / 1614 = 1.90). Here, however, the distribution of Els is more
even than in 1984, with a smaller gap between El f 2700 and El =
3300. In contrast to 1984, there are many fewer Els, and no
concentration of them, in the range below 500. If the 1985 data have a
flaw, it is that perhaps too few Els are less than 900, since 900 kg/ha is
generally accepted as the average of sorghum yields in northern
Cameroon. Regression indicates again a superiority of S35 (though
smaller) in all environments. There is, however, no convergence of
yields at El approaching zero, but rather a slight convergence at high
Els.
Linear regression of yields on El for the combined data set, Figure
3.11, indicates a greater superiority of S35 over locals compared to the









Adaptability Analysis


2,soo a


a



a -,

0
0 Soo 1,000 1,500 2,000 2~500 $.
ENVIRONMENTAL INDEX, El
S35 LOCALS 5lo Lonl o




FIGURE 3.10 Regressions of S35 and locals on El for 1985.


,500oo
.-

2,400
2,000





0I


0


s50 1,000 1,0 2,000 2,00
ENVIRONMENTAL INDEX, EI
S3 LOCALS US3 LOCALS
I- a 1


FIGURE 3.11 Regressions of S35 and locals on El for 1984-85.







Single-Factor Trials


1985 data, but again without the convergence of yields at very low Els
seen in the 1984 data alone. Here the ratio of El to overall mean is
again very good (3324 / 1311.8 = 2.53) and the distribution is better
than that of either 1984 alone or 1985 alone.
It should be clear that the number of tests (or years of tests) needed to
get a good picture of the variety-by-environment interaction will depend
on how representative the sample of test environments is of the entire
population of environments, across years and locations, for which
recommendations are to be made. In this case, in a region where year
effects such as rainfall and date of seeding (often highly dependent on the
onset of rains) are extremely important determinants of overall mean
yields, one year of data may not be sufficiently representative.
Is there more than one recommendation domain for S35 as opposed to
locals, given that in each year and in the combined data set S35 is
superior to locals across all environments? It is often posited that in the
case of varieties, limited-resource farmers usually want a significant
increase in yields over their own varieties before deciding to adopt a
variety with which they are less familiar. Some claim that a 50%
increase in yields is often required. Based on the 1984 data alone, S35
is seen to produce 50% increase over locals' yields across all
environments (Figure 3.12). Using the larger combined data set,
however, predicted yields of S35 are less than 50% greater than locals'
yields when El is larger than around 1000; S35 produces more than 50%
higher yields when El is less than 1000 (Figure 3.13). Note that at El
= 1000, predicted local yields are around 800 kg/ha, i.e., around the
average for the region. Based on comparing the relative performance of
S35 and locals in proportional rather than absolute terms, then, a case
can be made for two potential recommendation domains.
Regressions of El on date of seeding and on rainfall were done to see
how much of the variability in average yields could be accounted for by
these variables. The results of these regressions are presented in Table
3.8. In the 1984, 1985, and the combined 1984-85 data sets, El was
found to be inversely related to number of days planting following 1
January. This relationship accounted for 17% of the variability in yields
for the 1984-85 subset. It accounted for just 13% of the variability of
yields in 1985 alone and for only 3% of the variability of 1984 yields,
no doubt because such a high proportion of tests in 1984 were seeded
late.









Adaptability Analysis


500 1.000 1500 2,000 2.500 ,000
ENVIRONMENTAL INDEX, El


Soo


FIGURE 3.12 Yield of S35 as a proportion of locals' yield, plotted on
EI, 1984.


3

2.8

2.6

2.4 -

2,






K:A


0 s00 1,000 1,soo 2,000 2o00 3,000
ENVIRONMENTAL INDEX, El


3,so0


FIGURE 3.13 Yield of S35 as a proportion of locals' yield, plotted on
EI, 1984-85.


84



2,$

2.6

2.4

2.




1.6


1.2




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