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 Front Cover
 Title Page
 Introduction
 Table of Contents
 Papers presented in symposium
 Recent papers related to on-farm...
 Reference
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Group Title: Extension and education materials for sustainable agriculture ;, v. 3
Title: Alternative approaches to on-farm research and technology exchange
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Permanent Link: http://ufdc.ufl.edu/UF00053944/00001
 Material Information
Title: Alternative approaches to on-farm research and technology exchange a project of the North Central Region Sustainable Agriculture Research and Education and Agriculture in Concert with the Environment
Series Title: Extension and education materials for sustainable agriculture
Physical Description: 174 p. : ill. ; 28 cm.
Language: English
Creator: Francis, Charles A.
University of Nebraska--Lincoln -- Center for Sustainable Agricultural Systems
Agriculture in Concert with the Environment (Program)
North Central Region Sustainable Agriculture Research and Education Program
Publisher: Center for Sustainable Agricultural Systems, University of Nebraska-Lincoln
Place of Publication: Lincoln NE
Publication Date: 1995
 Subjects
Subject: Sustainable agriculture -- Study and teaching -- United States   ( lcsh )
Genre: bibliography   ( marcgt )
non-fiction   ( marcgt )
 Notes
Bibliography: Includes bibliographical references.
Statement of Responsibility: Charles Francis ... et al., editors.
General Note: "This material was prepared with the support of USDA Agreement no. 92-COOP-1-7266."
Funding: Electronic resources created as part of a prototype UF Institutional Repository and Faculty Papers project by the University of Florida.
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Bibliographic ID: UF00053944
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: oclc - 33825900

Table of Contents
    Front Cover
        Front Cover
    Title Page
        Title Page
    Introduction
        Introduction 1
        Introduction 2
    Table of Contents
        Page i
        Page ii
    Papers presented in symposium
        Page 1
        Decision case studies are ideal for on-farm research
            Page 1
            Page 2
            Page 3
            Page 4
            Page 5
            Page 6
        Use of on-farm research by farmers for technology development and transfer
            Page 7
            Page 8
            Page 9
            Page 10
            Page 11
            Page 12
        Best information for choosing crop varieties
            Page 13
            Page 14
            Page 15
            Page 16
            Page 17
            Page 18
        Adaptability analysis for diverse environments
            Page 19
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            Page 23
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        Use of the focus group in designing, implementing, and evaluating cover crop trials in western Washington
            Page 29
            Page 30
            Page 31
            Page 32
        Complementary abilities and objectives in on-farm research
            Page 33
            Page 34
            Page 35
            Page 36
        Credibility of on-farm research in future information networks
            Page 37
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            Page 40
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            Page 50
    Recent papers related to on-farm research
        Page 51
        Participartory research and other sharing of experience
            Page 51
            Page 52
            Page 53
            Page 54
        On-farm research
            Page 55
            Page 56
            Page 57
            Page 58
            Page 59
            Page 60
        Responsive constructivist requirements engineering: A paradigm
            Page 61
            Page 62
            Page 63
            Page 64
            Page 65
            Page 66
            Page 67
            Page 68
        On-farm research in Kansas, 1993: Summarized results of a farmer opinion survey
            Page 69
            Page 70
            Page 71
            Page 72
            Page 73
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        On-farm experiment designs and implications for locating research sites
            Page 81
            Page 82
            Page 83
            Page 84
            Page 85
            Page 86
        Establishing the proper role for on-farm research
            Page 87
            Page 88
            Page 89
            Page 90
            Page 91
            Page 92
        Farmer participation in research and extension: N fertilizer response in crop rotation
            Page 93
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            Page 96
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            Page 103
            Page 104
        Modified stability analysis of farmer managed, on-farm trials
            Page 105
            Page 106
            Page 107
            Page 108
        Farmer initiated on-farm research
            Page 109
            Page 110
            Page 111
            Page 112
        Participatory strategies for information
            Page 113
            Page 114
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        Farmer participation in research: A model for adaptive research and education
            Page 121
            Page 122
            Page 123
            Page 124
        Communicating between farmers and scientists: A story about stories
            Page 125
            Page 126
        On-farm sustainable agriculture research: Lessons from the past, directions for the future
            Page 127
            Page 128
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        Farmers' use of validity cues to evaluate reports of field-scale agricultural research
            Page 151
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    Reference
        Page 163
        Page 164
        Page 165
        Page 166
    Introduction and tables of contents, volumes 1 and 2, January 1994
        Page 167
        Page 168
        Page 169
        Page 170
        Page 171
        Page 172
    Subscription information for the American Journal of Alternative Agriculture and Journal of Sustainable Agriculture
        Page 173
        Page 174
Full Text






EXTENSION AND EDUCATION
MATERIALS FOR
SUSTAINABLE AGRICULTURE:
Volume 3


Alternative Approaches

to On-Farm Research

and Technology Exchange



A Project of the North Central Region
Sustainable Agriculture Research and Education and
Agriculture in Concert with the Environment


Charles Francis, Rhonda Janke, Victoria Mundy, James King
Editors


University of Nebraska Lincoln


Lincoln, Nebraska


For copies of this publication, send a check for ten dollars made to the
University of Nebraska to cover handling and shipping to:
Center for Sustainable Agricultural Systems
University of Nebraska-Lincoln
Lincoln, NE 68583-0949
April 1995

It is the policy of the University of Nebraska-Lincoln not to discriminate on the basis of gender, age,
in disability, race, color, religion, marital status, veteran's status, national or ethnic origin or sexual orientation.


mwRt










INTRODUCTION


What is the latest thinking about on-farm research and education opportunities and
challenges in the U.S.? A symposium on "Alternative Approaches to On-Farm Research
and Technology Exchange" was convened in Seattle on November 1995, sponsored by the
Division A-8 (Integrated Agricultural Systems) of the American Society of Agronomy. The
symposium was chaired by Wanda Collins and Steve Oberle (Chair of Division A-8) and
attended by more than 100 people. Following the symposium, a number of attendees
requested that we bring the papers together for distribution to a wider audience and make
them available as a publication. Gary Peterson, Editor-in-Chief for ASA publications, gave
us permission to print the papers presented in the symposium, and editors of several journals
likewise agreed that key papers could be reproduced here for easy reference.

There is growing interest in the concept and practice of "participatory on-farm
research" since the highly successful conference at University of Illinois in 1992 (Clement,
1992). Although many key research activities continue to be planted on farmers' fields under
the accepted definition of "researcher-designed, researcher-managed" experiments, there is
growing acceptance of the concept of farmer-designed or team-designed participatory
activities. As we become more convinced of the site-specificity of results and
recommendations, it becomes obvious that there is a vital role for individual farmers to
conduct some of their own testing of new components and systems. We have heard farmers
say, "Research does not cost, it actually pays!" The cooperative spirit is further reflected in
a current series of Extension and NRCS training sessions with the theme, "Everyone a
Teacher, Everyone a Learner" (Carter and Francis, 1995).

Seven papers from the symposium represent current ideas and practices of
participatory on-farm research and education. Fourteen other recent papers or discussion
summaries include items that have received major attention in the last several years, or
represent ideas that have not had broad distribution. A report on "Participatory Research and
Other Sharing of Experience" came from an "open space" discussion at the recent Santa Cruz
cluster workshop of the Integrated Farming Systems initiative sponsored by W.K. Kellogg
Foundation. Others are from University of Illinois and Kansas State University. We realize
there are many more people working in this area, and sincerely invite you to send current
reports of experiences and programs to our Center. If there is a critical mass of additional
materials, we will put them together in a similar summary document for distribution.

Charles Francis, Rhonda Janke (Kansas State U.), Victoria Mundy, James King
Editors








Volumes 1, 2, and 3 are available from:

Center for Sustainable Agricultural Systems
University of Nebraska-Lincoln
225 Keim Hall
Lincoln, NE 68583-0949


Phone:
Fax:
Email:


402-472-2056
402-472-4104
csas003@unlvm.unl.edu


Charles Francis
Center for Sustainable Agricultural Systems
University of Nebraska-Lincoln
225 Keim Hall
Lincoln, NE 68583-0949


Phone:
Fax:
Email:


402-472-1581
402-472-4104
csas002@unlvm.unl.edu


Rhonda Janke
Kansas State University
Department of Agronomy
Throckmorton Hall
Manhattan, KS 66502


Victoria Mundy
Nebraska Impact Office
University of Nebraska
Cooperative Extension
Box 736
Hartington, NE 68739
Phone: 402-254-2289
Fax: 402-254-6891
Email: nerc@25.unlvm.unl.edu


James King
Communications and Information Technology
University of Nebraska-Lincoln
104 Agriculture Hall
Lincoln, NE 68583-0918


913-532-5776
913-532-6315
rrjanke@ksu.ksu.edu


Phone:
Fax:
Email:


402-472-3022
402-472-3093
agcm009@unlvm.unl.edu


Editors


Phone:
Fax:
Email:








TABLE OF CONTENTS



Papers Presented in Symposium
Decision Case Studies are Ideal for On-Farm Research
R. Kent Crookston, University of Minnesota ......................... 1

Use of On-Farm Research by Farmers for Technology Development and Transfer
Stewart Wuest, Baird Miller, Stephen Guy, Russ Karow, Rojer Veseth,
and Donald Wysocki, Washington State U., U. of Idaho, Oregon State U. ....... 7

Best Information for Choosing Crop Varieties
Dale Hicks and Robert Stucker, University of Minnesota ................. 13

Adaptability Analysis for Diverse Environments
Peter Hildebrand and John Russell, University of Florida ................. 19

Use of the Focus Group in Designing, Implementing, and Evaluating Cover Crop
Trials in Western Washington
Dyvon Havens, N. L. Liggett, Lora Butler, and W. C. Anderson,
Washington State University, .................................. 29

Complementary Abilities and Objectives in On-Farm Research
Derrick Exner, Iowa State University ............................. 33

Credibility of On-Farm Research in Future Information Networks
Charles Francis, University of Nebraska-Lincoln ...................... 37

Recent Papers Related to On-Farm Research
Participatory Research and Other Sharing of Experience
Committee Report Summarized by Charles Francis, U. Nebraska Lincoln; from
W.K. Kellogg Foundation Cluster Workshop, Integrated Farming Systems,
Santa Cruz, California; February 23, 1995 ........ .................. 51

On-Farm Research
Emerson Nafziger, University of Illinois (Chapter 19 from 1994 book from
Department of Agronomy, U. Illinois ............................. 55

Responsive Constructivist Requirements Engineering: a Paradigm
Michael Mayhew and Samuel Alessi, Iowa State Univ. and USDA/ARS, Morris,
Minnesota (In Systems Engineering: A Competitive Edge in a Changing World,
J. T. Whalen, D. J. Sifferman, and R. Olson, eds. Proc. 4th Ann. Int. Sym. Natl.
Council on Systems Engin., Aug. 10-12, 1994. San Jose, CA) .............. 61

On-Farm Research in Kansas, 1993: Summarized Results of a Farmer Opinion Survey
Stay Freyenberger, Kansas State University (unpublished) ................. 69







On-Farm Experiment Designs and Implications for Locating Research Sites
Phil Rzewnicki, Richard Thompson, Gary Lesoing, Roger Elmore, Charles Francis,
Anne Parkhurst, and Russell Moomaw, U. Nebraska and Practical Farmers of Iowa
(Amer. J. Altern. Agric. 3:168-173. 1988)........................... 81

Establishing the Proper Role for On-Farm Research
William Lockeretz, Tufts University (Amer. J. Altern. Agric. 2:132-136. 1987) 87

Farmer Participation in Research and Extension: N Fertilizer Response in Crop Rotation
Alan Franzleubbers and Charles Francis, University of Nebraska
(J. Sustain. Agric. 2:9-30. 1991) ................................ 93

Modified Stability Analysis of Farmer Managed, On-Farm Trials
Peter Hildebrand, Univ. of Florida (Agron. J. 76:271-274. 1984) ............ 105

Farmer Initiated On-Farm Research
Ron Rosmann, Practical Farmers of Iowa (Amer. J. Altern. Agric. 9:34-37. 1994) 109

Participatory Strategies for Information Exchange
Charles Francis, James King, Jerry DeWitt, James Bushnell, and Leo Lucas, Univ.
of Nebraska and Iowa State Univ. (Amer. J. Altern. Agric. 5:153-160. 1990) .. .. 113

Farmer Participation in Research: A Model for Adaptive Research and Education
John Gerber, Univ. of Massachusetts (Amer. J. Altern. Agric. 7:118-121. 1992) ... 121

Communicating between Farmers and Scientists: A Story about Stories
Connie and Doc Hatfield, Preston and Wanda Boop, and Ray William,
Oregon and Pennsylvania Farmers, and Oregon State Univ.
(Amer. J. Altern. Agric. 9:186-187. 1994). ................... . 125

On-Farm Sustainable Agriculture Reseach: Lessons from the Past, Directions for the Future
Donald Taylor, South Dakota State Univ. (J. Sustain. Agric. 1:43-86. 1990) ..... 127

Farmers' Use of Validity Cues to Evaluate Reports of Field-Scale Agricultural Research
Gerry Walter, Univ. of Illinois (Amer. J. Altern. Agric. 8:107-117. 1993) ...... 151

Key Recent References ....................................... 163

Introduction and Tables of Contents, Volumes 1 and 2, January 1994 ......... 167

Subscription Information for the
Amer. J. Alternative Agric. and J. Sustainable Agric. ................ 173








Decision Case Studies are Ideal for On-Farm Research


R. Kent Crookston
Department of Agronomy & Plant Genetics
411 Borlaug Hall
University of Minnesota
Saint Paul, Minnesota, 55108


Abstract

Decision cases were pioneered by the Harvard Graduate School of Business
Administration over 75 years ago and are now widely used in business schools around the
world. Today, decision cases are receiving considerable attention within agriculture.

A decision case is a documentation of reality. A decision case is built around a
clearly-identified decision maker, usually one who is struggling with a dilemma to which
there is no obvious solution. A good decision case is publishable, based on anonymous peer
review. Decision cases are one of the best ways to research complex systems that cannot be
reduced to limited variables. A decision case can take a farmer's many years of work and
experience (which a scientist cannot duplicate) and put that experience into a format that can
be used professionally. Decision cases are therefore an ideal means of directing research
toward the relevancy of the non-academic world.

When agricultural researchers project themselves into the shoes of non-academic
decision makers, they experience a paradigm shift. The new paradigm reveals the validity of
experience, the power of social values, and the importance of ethics. I propose that decision
cases would be an excellent compliment to agriculture's conventional research programs,
especially on-farm research.

Validating Experience

Scientists know that the experiences of farmers, the results of their trial-by-error
efforts, have been extremely important in the development of agriculture as we know it
today. George Axinn, who spent many years working with agricultural researchers in
developing countries, observed that "the family farm has been doing farming systems work
for a long time... Each generation has studied its alternatives, and made its decisions...
There were no research grants or publications, but rural people have been doing farming
systems research for generations. If it were not for their research, most of modern
agriculture would be unknown." (pers. comm. G. H. Axinn, Michigan State University,
1990).

Yet, the experiences of today's farmers are given minimal attention by scientists and
their publications. Why? The standard explanation is that an individual farmer's experiences
and conclusions are unique to a specific site and situation. These experiences cannot be
tested or verified as to repeatability. In other words, observations made by farmers on their
own farm are usually considered too subjective.








Decision Case Studies are Ideal for On-Farm Research


R. Kent Crookston
Department of Agronomy & Plant Genetics
411 Borlaug Hall
University of Minnesota
Saint Paul, Minnesota, 55108


Abstract

Decision cases were pioneered by the Harvard Graduate School of Business
Administration over 75 years ago and are now widely used in business schools around the
world. Today, decision cases are receiving considerable attention within agriculture.

A decision case is a documentation of reality. A decision case is built around a
clearly-identified decision maker, usually one who is struggling with a dilemma to which
there is no obvious solution. A good decision case is publishable, based on anonymous peer
review. Decision cases are one of the best ways to research complex systems that cannot be
reduced to limited variables. A decision case can take a farmer's many years of work and
experience (which a scientist cannot duplicate) and put that experience into a format that can
be used professionally. Decision cases are therefore an ideal means of directing research
toward the relevancy of the non-academic world.

When agricultural researchers project themselves into the shoes of non-academic
decision makers, they experience a paradigm shift. The new paradigm reveals the validity of
experience, the power of social values, and the importance of ethics. I propose that decision
cases would be an excellent compliment to agriculture's conventional research programs,
especially on-farm research.

Validating Experience

Scientists know that the experiences of farmers, the results of their trial-by-error
efforts, have been extremely important in the development of agriculture as we know it
today. George Axinn, who spent many years working with agricultural researchers in
developing countries, observed that "the family farm has been doing farming systems work
for a long time... Each generation has studied its alternatives, and made its decisions...
There were no research grants or publications, but rural people have been doing farming
systems research for generations. If it were not for their research, most of modern
agriculture would be unknown." (pers. comm. G. H. Axinn, Michigan State University,
1990).

Yet, the experiences of today's farmers are given minimal attention by scientists and
their publications. Why? The standard explanation is that an individual farmer's experiences
and conclusions are unique to a specific site and situation. These experiences cannot be
tested or verified as to repeatability. In other words, observations made by farmers on their
own farm are usually considered too subjective.








By contrast, scientists makes every effort to eliminate bias from the design and
management of their research. Randomized replicated plots help to overcome unplanned
variability, and whatever variability persists can be measured or estimated. Limited-variable
studies allow scientists to assign significance to some variables and to omit others from
further consideration.

Farmers make no structured effort to eliminate subjectivity from their observations,
and find that cold objectivity often does not fit with family or community relationships and
obligations. This results in a dilemma. Every year, thousands of farmers have highly
valuable experiences which receive limited exposure off the farm. Agricultural researchers
have not yet found an effective way to capture those valuable on-farm experiences without
the subjectivity and bias problem.

Decision cases represent a solution to this dilemma. A properly developed decision
case can take a farmer's many years of work and experience (which a scientist cannot
duplicate) and put that experience into a format and context that can be evaluated and used
professionally. Decision cases are one of the best ways to research complex systems that
cannot be reduced to single variables.

What Is a Decision Case?

Decision cases were pioneered more than 75 years ago by the Harvard Graduate
School of Business Administration. Today, decision cases are used in most leading business
schools throughout the world. The University of Minnesota recently began using decision
cases for research and education in agriculture. The approach has been highly successful and
is becoming the subject of considerable interest by agricultural scientists.

It should be noted that case-type exercises are not new to agriculture; simulations and
technically-based problems have been a part of agricultural education for some time.
However, agricultural cases have typically been descriptive in nature and have often been
based on fabricated or hypothetical situations. The term "case study" or "case" has a variety
of meanings. Depending on the profession, a case study can refer to a lega case, a clinical
case, an appraisal case, or a descriptive case. A decision case is similar to, yet different
from, each of these.

A decision case is a documentation of reality, the written product of investigation into
an actual situation. This is one reason I believe decision cases qualify as legitimate
instruments of research. A valid discovery cannot be fabricated or manufactured. If
scientific data have integrity, they will stand up under scrutiny. Similarly, a good decision
case will be based on documentable reality and observation, not on supposition or conjecture.

A decision case is based on a dilemma. This must be a genuine dilemma for which
there is no obvious, rational, or democratic solution. While working to resolve an engaging
dilemma, case users identify relevant facts, analyze them, and draw conclusions about the
cause of the problem as well actions that might be taken. Sharon McDade (McDade, 1988)
notes that "the most interesting and powerful cases are those that allow for several equally
plausible and compelling conclusions, each with different implications for action. 'Real life'








is ambiguous, and cases reflect that reality. A 'right' answer or 'correct solution' is rarely
apparent."

A decision case focuses on a specific decision maker. Case users need to be able to
relate to this decision maker. As they consider the decision maker's objectives and options,
they realize that their own biases are irrelevant. If significant differences of opinion exist
within a group that is working to solve the decision maker's dilemma, the result is often
synergy. Synergy results in creativity and new insights. This often leads to new hypotheses
for deductive research.

A good decision case is publishable, based on anonymous peer review (Simmons et
al., 1992). Reviewers are asked to determine whether the case deals with issues that are
current and of interest to a wide audience, is well written, is based on sound objectives,
contains sufficient information and documentation to meet the stated objectives, and has been
interpreted adequately.

A Tradeoff

Thomas Bonoma (1985) describes two divergent paths of scientific investigation. The
more popular path involves "controlling situational events in order to observe the validity of
empirical deductions." The other, which he describes as less popular but equally valid
consists of reasoning "from individual and naturally occurring but largely uncontrollable
observations toward generalizable inductive principles." Bonoma suggests a major tradeoff
between "precision in measurement and data integrity" versus "currency, contextual richness
or external validity."

In Figure 1, note that Bonoma places case research just above the line which separates
science from non-science. Note also, however, that much of the non-science has very high
currency or contextual relevance across settings and time. Bonoma suggests that it is not
possible to do "good" research that has both strengths. It is my opinion that Bonoma's
suggested tradeoff represents reality, but that this should not inhibit the use of case studies
any more than the use of controlled experiments. The fact that a decision case is based on
an event that cannot be replicated nor repeated should not be considered a weakness. It is,
in fact, this feature that helps make decision cases uniquely valuable. There is much to be
gained from life's rare and singular experiences, many of which cannot be understood if
removed from their social context.

A New Paradigm

Decision cases require agricultural scientists (researchers and educators) to project
themselves into the shoes of non-scientific decision makers (farmers, agricultural agents,
community leaders, etc.), and to evaluate specific decisions or dilemmas facing these people.
When scientists do this, they experience a paradigm shift. The new paradigm reveals the
validity of experience, the power of social values, and the subtle importance of ethics. The
new paradigm may also reveal the futility of fixed replications over years, or limited
variables, or even statistics.








This new paradigm could help us incorporate relevance into agricultural research.
With this new paradigm we would begin to question a professional approach based almost
entirely on statistically-significant, limited-variable, hypothesis-driven, deductive work; work
which does not accommodate holism, nor take into consideration the populist perspective.

A Proposal

I propose that agriculture learn from the business world and incorporate decision
cases into its research and education efforts. I am confident that quality refereed decision
cases would be an excellent complement to agriculture's data-based research programs.

I propose that we not limit decision case research to farmers and farms. We should
also research key industry and policy dilemmas. We should develop some cases on problems
faced by researchers themselves. In other words, we should develop cases that help us relate
to key decision makers at all levels of the agricultural system, both on-farm and off.

We could effectively include many of these decision-makers in our education
programs. Minnesota faculty have built cases around farmers, scientists, business people and
politicians (Crookston and Stanford 1989; Crookston and Stanford 1992; Crookston et al.,
1993; Davis et al., 1991; Noetzel and Stanford 1992). Some of these people have been
invited to participate with groups of students or professionals assembled to work their cases.
Invitees have benefited from debate and discussion of their dilemmas, and from the synergy
that occurred when diverse viewpoints were focused on recommending a solution.

But the real benefit of decision cases is realized by their users (students). Decision
cases are based on the principle of participative learning. Cases are a highly effective means
of providing students with skills in analysis of problems, synthesis of action plans, and
development of maturity, judgment, and wisdom (Dooley and Skinner 1977; Gragg 1954;
Hammond 1976). These are skills that are acutely needed to direct the research efforts of
scientists who otherwise gravitate toward theoretical academic pursuits, and approval (via
technical publications) of intellectual colleagues.

I am confident that if decision cases were included in our on-farm research programs,
better research, better education and better decisions would be the outcome.

References

Bonoma, T. V. 1985. Case research in marketing: opportunities, problems, and a process.
J. Marketing Research. 22:199-208.

Crookston K. and Stanford M. 1989. AgriServe Crop Insurance. College of Agriculture
decision cases #2. Coll. Agric., Univ. Minnesota, St. Paul, MN 55108.

Crookston, R. K. and Stanford M. J. 1992. Dick and Sharon Thompson's "problem child":
a decision case in sustainable agriculture. J. Nat. Resour. Life Sci. Educ. 21:15-19.

Crookston, R. K., Stanford M. J. and Simmons S. R. 1993. The worth of a sparrow. J.
Nat. Resour. Life Sci. Educ. 22 (2)134-138.








Davis D., Groth J. and Stanford M. 1991. The containment of P. Sorghi. College of
Agriculture decision cases #20. Coll. Agric., Univ. Minnesota, St. Paul, MN 55108

Dooley, A. and Skinner W. 1977. Case casemethod methods. Academy of Management
Review. April, 1977.

Gragg, C. I. 1954. Because wisdom can't be told. Harvard Business School Publ. Case
Devel. and use (9-451-005). Publ. Div., Boston, MA 02163.

Hammond, J. S. 1976. Learning by the case method. Harvard Business School publications
on Case Development and Use (9-367-241). Publ. Div., Boston, MA 02163.

McDade, S. 1988. An introduction to the case study method: preparation, analysis, and
participation. Notes on the case method. Inst. Educ. Management, Harvard College,
Boston, MA 02163.

Noetzel, D. and Stanford M. 1992. Minnesota sunflower (B) the honeybee kill. College of
Agriculture Decision Cases #34. Coll. Agric., Univ. Minnesota, St. Paul, MN
55108.

Simmons, S. R., Crookston R. K. and Stanford M. J. 1992. A case for case study. J. Nat.
Resour. Life Sci. Educ. 21:2-3.






















\


LABORATORY
EXPERIMENTS \
MODELS
SIMULATIONS \
v< \
TESTS
FIELD \0O. \
EXPERIMENTS \ > \
FIELD \
STUDIES
CASE RESEARCH \
\% N


HIGH




>-


(.
W

z



LI
1L-





LOW
1w


STORIES


PERSONAL
OPINION


SCIENCE


NONSCIENCE


MYTHS
LEGENDS


LOW


CURRENCY


Figure 1. A knowledge-accrual triangle (from Bonoma)


ARCHIVES


HIGH


__ _


__








Use of On-Farm Research by Farmers for
Technology Development and Transfer

Stewart Wuest, Baird Miller, Stephen Guy, Russ Karow, Roger Veseth, and Donald
Wysocki, Washington State Univ., Univ. of Idaho, Oregon State Univ.

Introduction

In the United States, as in most of the world, farmers are the decision makers and
managers of agriculture enterprise. Farmers are the adopters, the adapters, and often the
innovators of new farming techniques. Farmers, as well as the public, would benefit by
having effective ways to evaluate and adapt innovative production practices. The Solutions
To Economic and Environmental Problems On-Farm Testing Project was developed to teach
farmers improved, scientifically valid methods for conducting their own evaluations, which
will in turn accelerate the adaptation and invention of new farming practices. We are
presently promoting two types of on-farm test, the "On-Farm Test" and the "Single Replicate
On-Farm Test".

Single Replicate On-Farm Tests

In the single replicate on-farm test, four or more farmers establish a single replicate,
that is, one complete set of treatments. This method was initially developed for testing
spring barley varieties (Johnson et al. 1994), so applying uniform treatments was as easy as
supplying seed of each variety to each farmer. The farmers use their own management
practices to grow the crop, but are instructed on shape and placement of the test strips. The
strips are side-by-side and placed so their length crosses sources of field variability. The
strips should be as long as is practical, and four to five feet wider than the combine header.
Four or more farmers are needed to make this single replicate method work in one particular
climatic zone. Over the past five years, 30 to 50 farmers have participated in the spring
barley single replicate on-farm test program in eastern Washington.

The single replicate on-farm test is useful for developing recommendations about a
variety or production practice for a broad production or climate area. The single replicate on-
farm tests are at least as powerful as the university's variety evaluation trials at detecting
treatment differences (Johnson et al., 1994) These tests are very popular with farmers and
are an important technology transfer tool for variety evaluation in eastern Washington. This
past year the on-farm spring barley variety evaluation sites were used to study the differences
in residue production among varieties. This residue production data will be used by the
NRCS to evaluate residue requirements for conservation compliance provisions.

General On-Farm Tests

Single replicate on-farm tests are a special case of on-farm testing in general, which
are intended for use both by individual farmers or for groups of farmers working together.
Farmers are often interested in evaluating practices unique to their own management systems,
such as modified equipment, or they may be making evaluations specific to their own field
conditions. Therefore the test design must be efficient for an individual farmer working
alone in a unique situation. Generalization to other farms or locations is not a primary goal.








For farmers interested in evaluating alternative practices, we recommend a
randomized, complete block design with two or three treatments and four or more blocks.
As in the single replicate on-farm tests, plots should be laid out as long, narrow, side-by-side
strips wide enough to combine harvest down the middle. Strip length should be 1000 feet or
more where possible. (Wuest et al. 1994).

These methodologies have been presented to farmers in workshops, field tours and
one-on-one. Farmers have gained an appreciation for the value of replicated, scientifically
valid on-farm tests, and understand the danger of unreplicated treatment comparisons. In the
past three years more that 108 individual on-farm tests were conducted in Idaho, Oregon,
and Washington (Wuest et al. 1995). Farmers are learning that on-farm tests are the best
way to discover and verify improved farming practices.

On farm testing gives farmers independence and control. They can determine what to
test, how to test it, and whether to continue a test or simply drop an idea after the first year.
Farmers also like being able to take their data to someone for interpretation. When farmers
approach extension personnel or researchers with unreplicated data there is a much greater
problem with validation and interpretation. The lack of replicated data limits the interest and
amount of time scientists and policy makers invest in evaluating a farmer's claims. Data
from properly designed tests provides a much stronger starting point for discussion and
investigation of a farmer's claims. On-farm testing also allows farmers to try practices
without facing a significant risk of income loss or future problems with weeds, disease, etc,
because the test area can be limited to a few acres.

Effects of Farmer Driven Research on Non-Farmers

We are also interested in the effectiveness of on-farm tests in solving societal
problems related to agriculture. The ability to generate scientifically valid data provides
incentive for bringing people together. When we work with groups, it is the potential for
getting real answers that makes the group hopeful that working together will be worthwhile,
and makes all parties interested in how the experiment is conducted. Involving agricultural
scientists in group problem solving is also much easier if valid data is generated.

Statistical Performance of On-Farm Tests

The coefficient of variation (CV) is a measure of experimental variance, and can be
used as an approximate measure of an experiment's precision. The on-farm tests performed
in the last two years with 3 or more replications had an average CV of 6% with a range of 2
to 16% and a median of 6% (Fig. 2). This represents good control of experimental error for
field experiments. For winter wheat, the tests produced LSD's that range from 3 to 27
bu/ac, with a median of 7.5 bu/ac. Two ways to increase the capacity of the tests to detect
small treatment differences are greater plot length and increased replication. Of course, we
often make measurements in addition to yield, but we use yield as an example because it is
frequently the most important criteria and almost always relevant to the farm manager's
decision making.








Concerns About On-Farm Testing


Several criticisms have been raised concerning the promotion of on-farm tests for
farmers. The first criticism is that on-farm tests are too site and manager specific to be able
to generalize the results in order to make recommendations for other farmers, or to share it
in peer reviewed journals. Although this may be true, our primary goal is to foster adoption,
adaptation, and innovation by farmers, not to further the science of agriculture.

A second criticism is that on-farm tests are likely to miss fine points of
understanding. In large scale, low budget experiments the control of variables is poor,
measurements are few, and we often base our conclusions on gross effects without
understanding what may be the cause of observed effects. This criticism, like the first, is
based on a direct comparison of farmer oriented on-farm tests to researcher oriented
research. On-farm tests are not a substitute for more basic, fact finding research. Tests
implemented by farmers are bound to be focused on performance, and that is why we
consider on-farm testing appropriate as a technology transfer tool as well as a tool for
gaining information on farming practices. Nothing prohibits the use of on-farm tests for
more intensive research, however, and we are seeing more and more cases where researchers
are making use of on-farm tests as research sites.

Supporting Farmers in Their On-Farm Tests

Based on our experience with on-farm testing in the Pacific Northwest we believe
there are three keys to supporting on-farm tests by farmers:

1. Training on the rules for replication, randomization and proper test design. It is very
helpful to have someone with on-farm test experience help design and layout a
farmer's first test, or even a county extension agent's or university researcher's first
on-farm test. We try to ensure that a farmer's first exposure to on-farm testing is a
positive one. Often assistance is only needed once. By the second year the farmer
may be able to design experiments without any help. In other cases the farmer needs
a little help for several years before they understand that good data can only come
from good design, and exactly what that design involves.

2. Weighing equipment for harvest measurements must be available to minimize the time
spent measuring yields during the busy harvest season.

3. Farmers appreciate expert help in interpretation of data. We have shown them how to
analyze data using a free, simplified statistics computer program (AGSTATS) or
simple worksheets, but all of the farmers we know currently rely upon data analysis
from the on-farm testing program or from county extension agents. Remember, it is
the production of useful data on a subject important to the farmer that makes it
worthwhile. The farmer needs to learn something, and needs confidence he or she is
doing it right.

Sometimes the farmer, researcher, or extension agent will want some more intensive
data, such as residue levels, or weed counts. Linking farmers with people able to make
these special measurement also helps increase the benefits of the on-farm test.








Conclusions


This article is intended to outline and stimulate discussion on the use of on-farm tests
as a technology transfer tool. That farmers experiment with new farming methods is not
new, nor is use of scientific methods for evaluation of farming practices. It is the idea of
farmers themselves making use of the scientific method that requires us to rethink our view
of research and technology transfer.

Some people embrace on-farm tests and on-farm research as the perfect alternative to
traditional research, others see on-farm tests as being of a much more limited usefulness.
We believe that encouraging farmers to do tests and also become more involved in university
directed research establishes a continuum in research that perhaps has never existed before.
This continuum spans the gap between experiment station research and farmer observation.
Some on-farm tests will be conducted by a farmer working alone or with a group of farmers,
and other tests will be farmers working under the direction of scientists. Most on-farm tests
will be somewhere in between, with the farmers and scientists discussing goals and designs
together.

We should give the range of goals of an on-farm test the widest possible latitude, but
the validity and accuracy of data should be scrutinized every bit as carefully as experiment
station data. If we can accomplish this, on-farm testing promises to have a powerful and
positive influence on the future of agriculture.

References

Johnson, J.J., B.C.Miller, and S.E. Ullrich. 1994. Using single-replicate on-farm tests to
enhance cultivar performance evaluation. J. Prod. Agric., 7:13-14, 76-80.

Wuest, S.B., B.C. Miller, J.R. Alldredge, S.O. Guy, R.S. Karow, R.J. Veseth, and D.J.
Wysocki. 1994. Increasing Plot Length Reduces Experimental Error of On-farm
Tests. J. Prod. Agric. 7:169-170, 211-215.

Wuest, S.B., B.C. Miller, R.J. Veseth, S.O. Guy, D.J. Wysocki and R.S. Karow. 1994.
1994 Pacific Northwest On-farm Test Results. Department of Crop and Soil Sciences
Technical Report 95-1, Washington State University, Pullman, WA.

Additional Resources

AGSTATS. A statistics program for simple field trials written for IBM compatible
computers. Send disk and postage return mailer, or check for $5 made out to Agric.
Research Foundation, addressed to Russ Karow, Crop Science Building 131, Oregon State
University, Corvallis, OR 97331-3002.

On-Farm Testing: A Grower's Guide. B. Miller, E. Adams, P. Peterson and R. Karow.
1992. Washington State University Cooperative Extension EB 1706. a guide to designing
and carrying out OFT. Includes forms for record keeping. 20 pages, $1.00. Order from
WSU Cooperative Extension Bulletin Office (509-335-2857)








Annual Pacific Northwest On-Farm Test Results. Data and conclusions from tests are
compiled at the end of each year. 1992 to present bulletins are available. Call the WSU
Crop and Soil Sciences Extension Office (509-335-2915) for copies.

STEEP II On-Farm Testing Fact Sheets. Information bulletins that provide instructions and
helpful hints for conducting specific types of on-farm tests. Call Stewart Wuest, STEEP II
On-Farm Testing Coordinator, WSU Crop and Soil Sciences (509-335-3491) for information.

Probability as a Basis for Barley Cultivar Selection by Growers. A paper presenting an
alternative method for evaluation of variety performance. Johnson, J.J., S.E. Ullrich, J.R.
Alldredge, and B.C. Miller. 1994. J. Prod. Agric. 7:175, 225-229.









Figure 1. Histogram of the coefficients of variation of wheat yields from 33 on-farm
tests using 3 or more replications.


Coefficient of Variation Frequency

in On-Farm Tests


12

10

S8

S6
0-
u. 4

2

0


2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
Coefficient of Variation (%)


1992 and 1993 Wheat Yields








Best Information for Choosing Crop Varieties


D. R. Hicks and R. E. Stucker
Agronomy and Plant Genetics, University of Minnesota

Variety choice for any crop is an important decision that affects profitability because
of large differences in yield among varieties. In University of Minnesota soybean trials,
yield differences of 15-20 bu/a are common between the high and low varieties in the trial.
In corn tests, 40-45 bu/a consistently occurs between the high and low hybrids within a
maturity group. Assuming seed costs are not greatly different, choosing the higher yielding
variety/hybrid will result in higher gross return and likewise a higher net return after costs.

Determining the best yielding varieties/hybrids is not an easy task because of all the
sources of information that are available. In a Wisconsin survey', corn growers ranked the
following sources of information to choose corn hybrids as the five most useful: results of
yield tests on their farm, corn company tests on their farm, test results close to their farm,
university tests, and information from corn company agronomists. At the top of the list are
tests conducted on their farm and tests conducted close to their farm which supports other
survey results3 that growers put a value on test results from their farm or locations close by.

This notion of performance on my farm or farms close by as best information to use
to choose hybrids for next year has been around for some time and is not often questioned.
In fact agronomists promote the concept by suggesting that on-farm results are specific and
therefore better for an individual grower because the tests were conducted by the grower
with his/her management practices. So how important is "local" or "on my farm"
information for choosing crop varieties? To answer this question we used results from ten
years of soybean variety tests conducted by the University of Minnesota and eight years of
corn hybrid tests from the University of Wisconsin.

Soybean Results

For soybeans, the tests were from Waseca (Southern Experiment Station), Lamberton
(Southwest Experiment Station) and Fairmont (farmer's field). Planting dates and cultural
practices were those considered optimum for each site and soil situation. For each location
and the average of the three locations, yields were ranked from high to low and the highest
three varieties were chosen as the varieties to grow next year. Yields of these varieties in
next year's test were the measure of how well we did in choosing soybean varieties (Hicks et
al., 1992).

These locations could have been three separate on-farm trials that farmers used to
choose varieties. Use of any one location simulates the situation when growers use their own
on-farm tests to make variety or hybrid choices. Likewise, any one location also simulates
the condition of a test close by when results from "on my farm" tests are not available.

The analysis of soybean test results involved seven cases of choosing varieties and
determining their yield performance next year, which simulates a farmer choosing varieties
and growing soybeans based on those decisions for 7 years (Tables 1 through 3). Higher
yields occurred at each of the three locations when the varieties were chosen from the








3-location average rather than any of the single locations. This was true for all groups of 1,
2, and 3 varieties. For example, the highest yielding variety chosen from Lamberton results
yielded 2% above the test mean when grown next year at Lamberton (averaged over
7 cases). However, the highest yielding variety chosen from the 3-location average yielded
5% above the mean when grown next year at Lamberton (Table 1). Likewise the variety
chosen from the 3-location average yielded higher at Fairmont and Waseca than the varieties
chosen from the Fairmont and Waseca results. One can make the same comparisons for
performance of the highest two and highest three soybean varieties in Tables 2 and 3. In all
comparisons, higher yields occurred when the varieties were chosen from the 3-location
average rather than the single location results.


Table 1. Percent increase in average soybean yields in subsequent year at three locations
using the highest yield soybean variety chosen in that location and the average
yield of three locations.

Percent above trial average yield of highest yielding soybean
variety when grown next year at this location
Choosing
Location Lamberton Fairmont Waseca

Lamberton 2 --- ---

Fairmont --- 4 ---

Waseca --- --- -3

3-Location Avg. 5 6 6


Table 2. Percent increase in average soybean yields in subsequent year at three locations
using the two highest yielding soybean varieties chosen in that location and the
average of the three locations.

Percent above trial average of two highest soybean varieties when
grown next year at this location
Choosing
Location Lamberton Fairmont Waseca

Lamberton 4 --- ---

Fairmont --- 0 ---

Waseca --- --- 2

3-Location Avg. 8 4 5








Table 3. Percent increase in average soybean yields in subsequent year at three locations
using the three highest yielding soybean varieties chosen in that location and the
average of the three locations.

Percent above trial average of three highest soybean varieties when
grown next year at this location
Choosing
Location Lamberton Fairmont Waseca

Lamberton 4 --- ---

Fairmont 1 ---

Waseca --- --- 2

3-Location Avg. 7 3 4

Results in Table 4 were calculated from Tables 1, 2, and 3 and show that
performance is higher when varieties are chosen from the 3-location average rather than a
single location. These results simulate the situation where three farmers each have an
on-farm trial and use the results of their individual trials to choose soybean varieties to grow
next year on their farm. If each farmer chose the highest yielding soybean variety from their
own on-farm results and grew that variety on their farm next year, the average of the three
farms would be 1% above the test mean. Farmer 1 (at Lamberton) would have had yields
2% above the test mean, farmer 2 (Fairmont) 4% above the test mean, and farmer 3
(Waseca) would have chosen a variety that yielded 3% below the test mean (Table 1). If
each of the three farmers would have pooled their results and chosen the highest variety from
the three location average, they each would have chosen a variety that produced 6% above
the test mean (average of 7 cases).

Most growers plant more than one variety. Planting two and three varieties chosen
from the 3 location average resulted in yields of 6 and 5% above the test mean compared
with 2% if the two or three varieties were chosen from a single location (or single farm) test
results.

Table 4. Comparison of relative yields in subsequent year of three groups of soybean
varieties chosen from single locations and using the average of three locations.

Percent above trial average of soybean varieties when chosen at
one or three locations and grown next year at the same locations
Varieties Highest High 2 High 3
from: Variety Varieties Varieties
Single location 1 2 2

3-Location Avg. 6 6 5








Corn Results


We analyzed corn test results from the University of Wisconsin Corn Performance
Testing Program conducted at Arlington, Janesville, and Lancaster, Wisconsin. The same
analysis procedure as discussed for soybean was followed for corn. Hybrids were ranked
and the highest yielding three hybrids were chosen from each single location and from the
3-location average and yield determined from the tests next year. There were 6 years for
choosing corn hybrids and monitoring their next year yields. Results are presented in
Tables 5 through 8.

Corn yields next year at Arlington and Lancaster were higher if the hybrids were
chosen from the 3-location average rather than from each single location. Choosing hybrids
from Janesville to grow at Janesville resulted in slightly higher yields than choosing hybrids
from the 3-location average. When next years' yields were averaged across the three
locations, equal or higher yields occurred when growing 1, 2, or 3 hybrids if the hybrids
were chosen from the 3-location average rather than the single locations. Differences were
small, but in favor of the 3-location average (Table 8).


Table 5. Percent increase in average corn yields in subsequent year at three locations using
the highest yielding corn hybrid chosen in that location and the average of the
three locations.

Percent above trial average yield of highest yielding corn hybrid
when grown next year at this location
Choosing
Location Arlington Janesville Lancaster
Arlington 9 --- ---

Janesville --- 10

Lancaster --- --- -3

3-Location Avg. 10 8 8








Table 6. Percent increase in average corn yields in subsequent year at three locations using
the two highest corn hybrids chosen in that location and the average of three
locations.
Percent above trial average yield of highest yielding two corn
hybrids when grown next year at this location
Choosing
Location Arlington Janesville Lancaster

Arlington 6 --- ---

Janesville --- 6 ---

Lancaster -- -- 0

3-Location Avg. 7 5 0


Table 7. Percent increase in average corn yields in subsequent year at three locations using
the three highest yielding corn hybrids in that location and the average yield of
three locations.

Percent above trial average yield of highest yielding three corn
hybrids when grown next year at this location
Choosing
Location Arlington Janesville Lancaster
Arlington 4 --- ---

Janesville --- 6 --

Lancaster --- --- -1

3-Location Avg. 6 6 0


Table 8. Comparison of relative yields in subsequent year of three groups of corn hybrids
chosen from single locations and using the average of the three locations.
Percent above trial average of corn hybrids when chosen at one or
three locations and grown next year at the same locations


Choosing
Location


Highest
Hybrid


Highest
2 Hybrids


Highest
3 Hybrids


Single location 5 4 3

3-Location Avg. 8 4 4








As discussed about soybeans, each of these three corn testing locations could have
been individual farmers own on-farm trials or a trial close to where someone farms. And,
like soybeans, these results of corn yield trials show growers' performance next year is better
if they use the 3-location average to choose corn hybrids rather than any one of the single
locations.

Conclusions and Recommendations

Should growers use their own results from on-farm trials on their farms to choose
soybean varieties and corn hybrids? If on-farm trials are not done, should growers use
results from single locations that are located close to their farms? Results of these extensive
analyses of soybean and corn yield trials indicate the answer is no to both questions. This
analysis simulates the results a grower would have if they had used results from their own
on-farm trials to choose soybean varieties and corn hybrids rather than the results averaged
across several on-farm trials. For both crops, the best data to use to choose cultivars is the
average across locations or farms. Using results from several locations to choose soybean
varieties and corn hybrids results in choosing varieties and hybrids that generally yield higher
next year than do those varieties and hybrids that are chosen from single farms or locations.

Should a grower have on-farm tests? Each individual location is important to
generate the test results averaged across farms. These results show that growers could make
choices that would improve their yields up to 5% by pooling their data with other growers
and from other locations.

References

Carter, P. R. and K. D. Hudelson. 1992. University corn hybrid trials: are results useful
and reliable for growers? p. 22-32. Proceedings of the 47h Annual Corn and
Sorghum Industry Research Conf. Chicago, IL. 9-10 Dec. 1992. American Seed
Trade Assoc., Washington, D.C.

Hicks, D. R., R. E. Stucker, and J. H. Orf. 1992. Choosing soybean varieties from yield
trials. J. Prod. Agric. 5:303-307.

Rzewnicki, P. 1991. Farmers' perceptions of experiment station research, demonstrations,
and on-farm research in agronomy. J. Agron. Educ. 20:31-36.









Adaptability Analysis for Diverse Environments


P.E. Hildebrand and J.T. Russell

The challenge of making small-farm agriculture more efficient is difficult, especially because it
depends on improving production from a large number of farms operating under a wide range
of conditions, constraints and objectives. The task is shared by many people, including
farmers, policy makers and academics, but an important part of the burden falls on agricultural
researchers and extension agents. (Tripp, 1991, p. 3)


The Challenge

Worldwide, agricultural technology development is facing greater challenges. World
concerns with heavy use of inorganic chemicals associated with broadly adaptable
technologies force farmers and other agricultural researchers to look for other means to
improve productivity. Farms and farmers are highly diverse, and whether commercial or
subsistence, farmers are facing ever-increasing economic stresses. Potential alternative
technologies are often quite location- and environment-specific and may be more difficult to
generate. Budgets for agricultural research and technology diffusion are also becoming much
tighter.

For farmers, the technology challenge is to find new, useful, and tested technologies
that work for them under their conditions. For public, private and non-governmental
organizations, the technology challenge is to make recommendations, specific to widely
varying biophysical environments and socioeconomic situations, both efficiently and
economically and for as many conditions as possible. Thus, with an increasingly difficult
challenge and confronted with decreasing funding, researchers, extension workers and
farmers must search for more efficient and effective means of finding new, acceptable
technologies for diverse environments and socioeconomic situations.

Approaches

One approach being used with commercial farmers is to help them improve their own
experimental methods, so research they conduct on their own farms, based on accepted
experimental methods and a number of replications, provides more reliable results
(Rzewnicki et al., 1988; Illinois Sustainable Agriculture Network, 1992; Frantzen, 1992;
Rosmann, 1994). This approach can provide farmers with information on responses to new
technologies that they are especially interested in and under their own specific conditions, but
it must be repeated over a number of years before farmers can have a reasonable assessment
of its performance over varying climatic conditions (Stucker and Hicks, 1992). An
alternative, and potentially much more efficient approach for farmers with similar interests
but with different situations, is to collaborate by selecting a common set of treatments to be
applied on their own farms and under their own management systems, each applying a single
replication, and then pooling the results for analysis and interpretation.

An effective procedure for design, analysis and interpretation of this kind of
collaborative technology development is the use of Adaptability Analysis (Hildebrand and
Russell, 1994). Adaptability Analysis is a new name applied to a procedure that many








already know: Modified Stability Analysis (Hildebrand, 1984). We have chosen to change
the name because of the confusion surrounding the concept of stability embedded in the older
name. The procedure, as we use it, is not related to stability but rather to adaptability of
technologies to different environments and socioeconomic conditions. Adaptability Analysis
has the potential not only to provide reliable results in fewer years for the specific conditions
of each collaborating farmer, but also to provide information that can be extrapolated to a
much wider number of farmers than just those participating in the trial. Thus, it is more
efficient and economic because: participating farmers manage fewer research plots; farmers
contribute resources to collective research efforts; collaborating farmers obtain reliable
results in fewer years; and returns for collaborating extension and research organizations are
enhanced.

We use two examples to illustrate the procedure:

Bean Systems in Costa Rica
The first example comes Costa Rica (Bellows, 1992; Bellows, et al., 1994). As part
of an integrated study, an on-farm trial was conducted in nine environments during the
second growing season of 1990. This trial compared the traditional bean production system
(tapado), in which bean seed was broadcast into standing fallow which was then cut down,
with four introduced systems involving planting in rows (espeque). The four espeque
systems were: 1) land cleared manually (BARE), 2) natural residue mulching (MULCH), 3)
mulching with Gliricidia sepium (G SEP), and 4) land clearing residues placed in horizontal
windows (W ROW). This particular trial was designed by the researcher in consultation
with the farmers, so it does not represent a true collaborative effort of a group of farmers.
Nevertheless, the results provide a useful example of the kinds of information that can be
obtained from collaboration with common treatments.

In Adaptability Analysis individual treatment yields are regressed on the mean
treatment yields (usually kg ha-') at each location. The mean treatment yields provide a
measure of the quality of the environment at that location for the production of the crop (or
other product) being evaluated. This measure becomes an environmental index, EI, shown in
Figure 1. In the higher-yielding environments, land cleared manually (BARE) yields more
than all other treatments. In the lower-yielding environments, G SEP or W ROW yields
more than the other treatments. In all environments, the traditional system, tapado, yielded
less than all espeque systems.

Of critical importance in interpreting these results is the characterization of the
higher-yielding and lower-yielding environments. It is clear from Figure 1 that the higher-
yielding environments correspond to fields which had been fallowed three or more years.
These environments also correspond to yields in the tapado system of more than 500 kg ha-1.
If a farmer has access to such fields, and an appropriate criterion is kg ha1, then the bare
field system should be recommended. If only fields with fewer than three years of fallow are
available, where yields in the tapado system are probably less than 500 kg ha", then either
W ROW or G SEP treatments will provide the greatest yield.

Because the espeque systems use a full complement of fertilizers and pesticides
compared with the tapado system in which only a molluscacide is occasionally used, different
results are obtained when the criterion of kg $-' of total cost is used, Figure 2. This criterion








is more appropriate to the small-scale bean farmers in Costa Rica for whom cash is a scarcer
resource than land. Comparing the three treatments for which costs were available shows
that when farmers have access to land fallowed at least three years, (and for which the
anticipated yield of the tapado system would be > 500 kg ha-) the tapado system will be
preferred and none of the espeque systems should be recommended. In land fallowed less
than three years, the natural mulch espeque system could be recommended. In no cases,
using the criterion of kg $-', would the high-yielding BARE treatment be recommended to
farmers with scarce cash resources.

Table 1. Preliminary recommendations (extension messages) from bean
system on-farm trial, Costa Rica


Previous years in fallow
Criterion <3 3 or more

kg ha-' Gliricidia mulch Manual clearing
agronomicc) or
Windrows


kg $' Natural Mulch Tapado
(small farmer)


Results from this trial should be considered preliminary because 1) there were only
nine environments and only three with three or more years in fallow, and 2) the range of Els
is small relative to the overall mean El (ratio < 1).


Dairy Systems in New York
A second example is from a dairy farm system trial in New York (Toomer and
Emmick, 1989). In 1989, the New York Soil Conservation Service initiated a study to
evaluate the economic impact of changing to intensive pasturing systems on 15 New York
dairy farms. Before and after data were obtained from dairy producers who had recently
developed intensive pasturing systems and were reducing their use of confined feeding with
harvested feeds. The environmental index, EI, is based on fat-corrected (3.5%) milk
production per cow, a common dairy criterion.

Per cow milk production increased on those farms with low per cow production prior
to the change, but remained constant on the highest producing farms, Figure 3 (taken from
Hildebrand and Russell, 1994). Contrary to expectations, the high producing farms were not
using much pasture after the change, and still were relying on harvested feed. Cost per
animal decreased over all environments, Figure 4, and because production increased in most
environments, cost per CWT of milk decreased, Figure 5. Lowest costs per CWT of milk
were in the mid range environments, corresponding to the use of from about one to two acres
of pasture per animal after the change. Thus, heavy dependence on harvested feeds in
confinement with only about one-half acre of pasture per cow results in high production per








cow (Figure 3), but the use of one to two acres of pasture and a corresponding reduction in
harvested feed can lower cost of production per CWT of milk (Figure 5). The choice
depends on the goals of the individual farmers.

Based on our analysis of many data sets, we posit that the ratio of the El range to the
overall mean El should be at least 1:1. Although the number of farms (environments)
included in this trial was more adequate than the previous bean example, the relative
homogeneity of the environments still limited the range which was sampled. The ratio of the
El range (based on CWT of milk/cow/year) to the overall mean El is only slightly over 0.5.
We think that a much more heterogeneous sample of farms should have been incorporated in
this trial.

Summary

To use Adaptability Analysis, a set of common treatments must be installed on each
environment. One of the treatments should be the current practice of each collaborating
farmer so there is a basis of comparison. Individual farmers do not need to replicate the
common set of treatments on their own farms but can if analysis of treatment responses on
their own farms is of interest to them. The number of environments that need to be included
will vary depending on a number of factors, but 15 to 20 should be adequate in most cases.
Environments can be separate fields on a farm as well as separate farms, or they can be
whole farm systems as in the case of the dairy trial in New York. The differences in
management among farmers create differences in environment and do not need to be
controlled. The environments included in the trial should vary as widely as possible.

If the range and distribution of yields of the current practices approximates what
would be expected for the diverse environments over a period of years, the relationships
among the treatments should be stable if the trial is repeated or the results verified in a trial a
second year. The wider the range and the better the distribution of these yields, the more a
set of environments within a single year can substitute for multiple years.

Collaboration among farmers, by deciding on a common set of treatments, can
improve both efficiency and effectiveness of on-farm research by providing farmers useful
and, tested technologies in a relatively short period of time and with fewer of their own
resources than if they were to do the research on their own. Public, private and non-
governmental organizations working in technology development also benefit because they are
able to make recommendations for many more farmers than just those with whom they are
working.

REFERENCES

Bellows, B.C. 1992. Sustainability of bean (Phaseolus Vulgaris L.) farming on steep lands
in Costa Rica: an agronomic and socioeconomic assessment. Ph.D. diss. Univ. of
Florida, Gainesville.

Bellows, B.C., P.E. Hildebrand, and D.H. Hubbell. 1994. Sustainability of bean
production systems on steep lands in Costa Rica. Agricultural Systems (accepted).








Frantzen, T.J. 1992. Farmer-first research methods: a success story from Iowa. p 12-15.
In Participatory on-farm research and education for agricultural sustainability. Proc.
Conference Univ. of Illinois, July 30 August 1, 1992.

Hildebrand, P.E. 1984. Modified stability analysis of farmer managed, on-farm trials.
Agronomy Journal 76:271-274.

Hildebrand, P.E. and J.T. Russell. 1994. Adaptability analysis. Draft.

Illinois Sustainable Agriculture Network. 1992. 1992 on-farm participatory research
program. University of Illinois, Urbana.

Rosmann, R.L. 1994. Farmer initiated on-farm research. Amer. J. Alt. Agri. 9:34-37.

Rzewnicki, P.E., R. Thompson, G.W. Lesoing, R.W. Elmore, C.A. Francis, A.M.
Parkhurst, and R.S. Moomaw. 1988. On-farm experiment designs and implications
for locating research sites. Amer. J. Alt. Agr. 3:168-173.

Stucker, R.E. and D.H. Hicks. 1992. Some aspects of design and interpretation of row-
crop on-farm research. p 129-151. In Participatory on-farm research and education
for agricultural sustainability. Proc. Conference Univ. of Illinois, July 30 August 1,
1992.

Toomer, L. and D.L. Emmick. 1989. The economics of intensive grazing on fifteen dairy
farms in New York state 1989. Soil Conservation Service, Syracuse, N.Y.

Tripp, R. 1991. Planned change in farming systems: progress in on-farm research. John
Wiley and Sons, New York.








Figure 1. Bean yield (kg ha') response of four espeque treatments and tapado to
environment on steep land in Costa Rica (Bellows, 1992).




1,300 4
BARE
1,200 is
MULCH J -3.5
1,100- GSEP O l .

1,000 TAP 0I *-'3 3
900 0w '
90 WR ROW o o
-- 2.5<
U-
< 800 z .- z




500 1 I
400 -
300 0.5


200 0 I I
500 600 700 800 900 1000
ENVIRONMENTAL INDEX, El








Figure 2. Bean response (kg $- total cost) of two espeque treatments and tapado to
environment on steep land in Costa Rica (Bellows, 1992).





4 4

BARE YRS
3.5 3.5
MULCH
3.5 -- -
TAP m 3


3 / 2.5

0 2
S2.5--




2-
0.5


1.5 I I 0
500 600 700 800 900 1000
ENVIRONMENTAL INDEX, El








Figure 3. Response of per cow milk production to environment before and after change
and acres of pasture per animal after the change, New York dairy systems
(Hildebrand and Russell, 1994).


22


20


| 18
is
S16


S14
U

Z12


10


4

3.5

3

2.5

2

1.5

1 0

0.5


8 I I I I I I I I I a I I I I I
10 11 12 13 14 15 16 17 18
ENVIRONMENTAL INDEX, El (thousands)


I' 0
19 20


O ABEFOREAR BEFORE AFrER ACRES
U MME -iI








Figure 4. Cost per animal before and after change, New York dairy systems (Hildebrand
and Russell, 1994).


2,200

2,000

1,800

S1,600

1,400

Z 1,200

1,000

^ 800

600

400

200
1


0


11 12 13 14 15 16
ENVIRONMENTAL INDEX,


17 18 19
El (thousands)


BEFORE AFTER BEFORE AFTER ACRES


-





/i


Ss /
-- t





--I
- ,
: *


4

3.5




2.5

2

1.5 1

1 "


0.5

0
to


2
4f
4








Figure 5. Cost per CWT milk before and after change, New York dairy systems
(Hildebrand and Russell, 1994).


8

7

6

5

4

3
L *


10 11 12 13 14 15 16
ENVIRONMENTAL INDEX,


17 18
El (thousands)


is


BEFORE AFTER BEFORE AFTER ACRES
S m mmmm


S U
- W '



-









1 1 I I I
9 \^ ^^



-^ ^^^^c

^----


4


3.5


3

2.5


-2


1.5

1 ^


0.5

S 0
20






Use of the Focus Group in Designing, Implementing and
Evaluating Cover Crop Trials in Western Washington
by Dyvon M. Havens, N. L. Liggett, Loma Butler, and W. C. Anderson


Nitrates have contaminated ground water in the major farming areas of Whatcom,
Thurston, and Skagit counties in western Washington. The Skagit River contributes
over 4,100 tons of inorganic nitrogen to the Puget Sound each year. There is concern
that nitrogen fertilizer used in current production practices may contribute to the
problem of nitrates in surface and ground water.

In response to these concerns, an interdisciplinary group of Washington State University
extension and research faculty initiated a project to study the fate of nitrogen in
agricultural crop production and determine if nitrate levels in ground water are
increasing as a result of cropping practices. The team is studying the effects of
practices such as crop rotation, cover cropping, fertilization, and soil fumigation on
nitrate leaching in western Washington. The effort was named the Cropping Strategies
and Water Quality Project.

This paper discusses the development and implementation of a focus group process to
address these questions. A focus group is a diverse group of people who come together
to focus on a common issue, problem, or event. In this case, a 15-member group was
formed.

The core members of the focus group were initially selected by the WSU team on the
basis of several criteria. They needed to be community leaders, innovators, willing to
participate during the ensuing two years, respected in the community, politically astute,
representative of different private and public food, agricultural, and environmental
interests associated with the issue of nitrates, and committed to the future of the Skagit
Valley. At the first focus group meeting, core members were asked: Who else should be
part of this group?

The final group complement was then completed, and it consisted of several crop and
dairy producers, agricultural industry representatives, government agency staff, an
environmental organization, and university faculty and staff.

The focus group technique was selected because it offers a group process for generating
insights, ideas and perceptions; a method for understanding and interpreting how
people see a particular situation or idea; and a "mutual learner" approach, which
encourages all participants, including university faculty, to learn from each other, and to
take advantage of the diverse experiences, knowledge and networks represented.

In the beginning, Focus Group functions were to help give direction and set priorities
for research and educational programming and to share knowledge and ideas. The
group met approximately twelve times in two-hour blocks over a two year period. Over
time, however, the purposes and direction of the Focus Group were gradually modified.










They wanted to learn more about the role of cropping agriculture relative to that of
other sources of nitrate contamination, such as septic systems and manure and forestry.
Members of the Focus Group, as well as outside experts, made educational presentations
on those subjects in which the group felt they needed more information.

They became highly interested in reaching out to share their knowledge with the public,
particularly environmental and non-agricultural groups who, in the past, had been
considered by some members as "the enemy." To quote one Focus Group member: "Any
time we have a chance to educate non-growers about farming, we should do it!"

We saw this positive attitude as an opportunity and contacted the Skagit Audubon
Society, who welcomed us with open arms. Two focus group members and I gave a
presentation to, and conducted an open dialog session with, 75 members of the Audubon
Society. That effort turned out to be one of the most successful aspects of the
educational portion of the project, not only because it was very well received but because
it opened a doorway for future communication between the agricultural community and
this highly visible environmental group.

Another role the Focus Group took on was that of selecting, designing, implementing,
and evaluating an on-farm research project to compare different species of cover crops.

The first step was selecting the study topic. This was achieved using a facilitated
brainstorming and prioritizing technique and involved the entire Focus Group. Design
of the study began with a meeting between one of the researchers and two of the farmer
members in which they created three different design ideas. The ideas were presented to
the larger group. The group then selected the design they preferred, modifying it slightly
for increased practicality and relevance.

5 farmers--3 from the Focus Group and 2 others recommended by the group--cooperated
with us on the project. Each farmer donated 20 acres of land and his or her own labor,
seed, and machinery for the study. Three species of cover crops were planted by each
farmer in September of 1992. The farmer/cooperators kept close records as to:

The cropping history of the site
The soil type.
The dates of field operations.
The types of equipment used.
The number of passes over the field
And the actual seeding rate.

In March, just prior to spring incorporation of the cover crops, the entire Focus Group
and the farmer/cooperators participated in field tours of all the plots. They each were
given forms to evaluate the cover crops. After the cropping season, the WSI team met
with the farmer/cooperators to discuss as a group their perceptions of the on-farm











research. Also, in March of that same year, some of the Focus Group members
participated in conducting a tour of the cover crop trials for the Sustainable Agriculture
Research and Education conference that occurred in Mount Vernon that year. That
presented another opportunity for outreach to the public.

In the meantime extensive on-station research was being conducted at WSU Mount
Vernon and WSU Puyallup, and the Focus Group contributed ideas to that phase of the
project as well.

The Focus Group has also been very supportive in identifying and obtaining new sources
of funding to strengthen the ties between the general public and the agricultural
community.

We are now shifting our efforts to emphasize this latter area of strengthening ties
between the public and agriculture. The Focus Group is very cognizant of the fact that
the future of agriculture in western Washington is dependent upon public support:
support for agricultural activities and for preservation of the land on which the farms
sit.
And so, the methodology for using a Focus Group approach to agricultural research and
extension is continuing to evolve. We are learning every step of the way.

Admittedly, the effort has not been problem free. Probably the most major drawback of
this method is:

Time: A tremendous amount of hours were committed to the project by all
those involved. As the Extension Agent component, 40 days of my
year were spent on the project, and I'm sure some of the team
members put in even more than that.

From the Focus Group's perspective, let me read you a couple of
quotes. One is from a farmer: "Every time I walk off that farm, it
costs me money." Another member said: "I have five business
partners who have little regard for meetings. It is difficult to
explain to them the beneficial outcomes of such a process. They
just see that I am gone."

In other words, it's extremely important that Focus Group members
feel the meetings are relevant to them and their line of work.

Also, when you do things as an interdisciplinary "team," travel time
takes a large chunk of your schedule.

We could not have done this project without the skills and time of a
full-time project assistant.











The other main challenge is that of maintaining relevance for all members:

It has been somewhat difficult to keep non-farmer members
involved, because the subject matter affects them only peripherally.
A huge amount of time of spent nurturing, coaxing, and
communicating one-on-one with all the members of the group to try
and keep them committed and involved.


But overall, we are happy with the process.

New partnerships were forged between the agricultural and environmental
communities.

Research and extension endeavors were more relevant, because we had input from
the stakeholders up front.

All members learned a great deal, not only about specific disciplines, but also
about a new way to work together to address issues facing agriculture

Farmers were given an opportunity to have a hand in controlling their own
destiny, with a potential for influencing future regulation.

And because of our efforts to promote the program, the public is getting a
glimpse of some of the ways farmers are trying to act responsibly when it comes
to agriculture and the environment.

And, as almost a side benefit, farmers are seeing improved soil health from the
use of cover crops.








Complementary Abilities and Objectives in On-Farm Research


D.N. Exner,
Iowa State University Extension/
Practical Farmers of Iowa

On-farm research can represent a "common language" shared by producers and scientists.
In agreeing on a methodology to address experimental questions, agricultural scientists and
farmers begin a process in which differences in perspective and experience are an asset rather
than an obstacle.

Practical Farmers of Iowa (PFI) is a nonprofit membership organization that networks
farmers, scientists, and other ag professionals for the purpose of sharing information about
agricultural practices that are both profitable and environmentally sound. On-farm research
has been an important focus of PFI since 1987. Since 1988, the organization has collaborated
closely with Iowa State University Extension and ISU Experiment Station researchers. With
ISU. facilitation, PFI farmers have carried out more than 350 replicated trials. These trials
have been a vehicle for building relationships between scientists and farmers, and they have
advanced several areas of inquiry.

Collaborations like this show that producers and scientists each bring unique gifts to field
research. Agricultural scientists contribute their scientific understanding and a preciseness of
thought, not to mention laboratory facilities. The producer often makes farming equipment
available. Most importantly,
cooperating farmers often
provide unique and specialized Ab
management that may not be Complementary Abilities
available on most experiment
stations.

Additionally, it is
sometimes the case that farmers
have more clearly in mind both .
the systems aspects and the
overall practical implications of
experimental work, and so they Specific
may help the scientist focus Information ~
his/her efforts. When farmers
think "system," they often are Complementary Objectives
thinking specifically of their









farming system. As such, their informational needs are somewhat specific. The agricultural
scientist is usually interested in the information that can be abstracted to a subset, or
"recommendation domain" of farming systems. In designing an on-farm experiment, thought
should be given to what can be learned from a systems approach versus a focus on discreet
variables. Systems comparisons are more "real world," but their results may be of limited
import because of their specificity.

However, the two priorities need not conflict. The producer wants an on-farm trial
designed to provide the best guide to decisions on that farm. This implies, among other
things, enough replications that trial results can "stand on their own." The agricultural
scientist, on the other hand, needs multiple sites and years. He/she may be less concerned
with replication on any one farm, but this kind of "over-design" hardly impairs the scientist's
research.

Individual farmers who carry out their own on-farm trials should limit the number of
treatments and increase the number of replications as far as feasible. This approach will
provide the most reliable results. Interpretation of the data should acknowledge the site- and
year-specificity of these results. Many on-farm experiments simply compare the farmer's
current practice with one alternative practice, with the null hypothesis being no difference.

In such trials, the dependent variable of most interest is typically some form of crop
yield. Producers are especially interested in the economic implications of on-farm trials. For
most trials, production costs are a function of the treatments, not unknown quantities.
Consequently, it is misleading to calculate analysis of variance from the net profit in each
experimental plot, since this essentially transforms the yield data differently for each
treatment. This kind of interpretation can lead to "significant" differences in profitability in
cases where there is not statistical evidence for a yield difference, or the yield difference is
significant in the other direction (see spreadsheet and table below).


The deficiencies of this
approach are less evident in
experiments where there are
many sites. But individual
farmers may be misled by the
misuse of statistical terminology.


Statistical Analysis of Net $ in Experimental Units
Three Field Trial Cases
Case Yield Yield Cost Net Comment
Diff. Sig. Diff. Sig.
A small, N.S. large, Trial not needed to
positive positive verify cost diff.
B negative positive Sig. less yield, sig.
greater net profit I
small, small "Significant" net
positive N.S. itive profit not based on
crop performance.

Cost is a function of treatment, not an unknown like yield.










Yields in this trial by FarmerZ do not suggest a treatment difference greater than chance:


Previous Crop:
6 pairs in this trial.
5 degrees of freedom.
Crop: CORN
Trt. A Trt.
1 142.30 143.5
2 148.60 150.4
3 152.60 151.4
4 153.20 154.7
5 155.10 157.3
6 154.30 152.4


B
0
0
0
0
0
0


>>>>>>>>
Trt. A:
Trt. B:
Units: BUSHELS
Difference
-1.20
-1.80
1.20
-1.50
-2.20
1.90


>>>8>>>>>>>>>>>>>>
$280.00 /ACRE COST
$240.00 /ACRE COST


Squares
0.36
1.44
3.24
0.81
2.56
6.25


$2.00
PER-BU CORN
PRICE


151.02
Avg.


151.62 -0.60 14.66
Avg. Avg. Diff. Sum of Squares


2.93 s2, variance
0.49 S2, variance of the mean
2.571 Tabular t value
0.858 Experimental t value (Diff./S2). 1.80 :LSD
The observed difference is not significant at the .05 test level.


>>>>>>>>>FARMER_Z
6
5

Pair No.
1
2
3
4
5
6


Previous Crop:
pairs in this trial.
degrees of freedom.
Crop: CORN
Trt. A Trt. B
4.60 47.00
17.20 60.80
25.20 62.80
26.40 69.40
30.20 74.60
28.60 64.80


Trt. A:
Trt. B:
Units:
Difference
-42.40
-43.60
-37.60
-43.00
-44.40
-36.20


HIGHER COST
LOWER COST
DOLLARS NET
Squares
1.44
5.76
12.96
3.24
10.24
25.00


22.03 63.23 -41.20 58.64
Avg. Avg. Avg. Diff. Sum of Squares
11.73 s2, variance
1.95 S2, variance of the mean
2.571 Tabular t value
29.469 Experimental t value (Diff./S2). 3.59 :LSD
The observed difference is significant at the .05 test level.


Subtracting treatment costs from the crop value of the experimental units transforms the data,
but differently for different treatments. The difference in profitability of the two treatments is
"significant" by this method. But statistics was not required to know Treatment A is more
expensive than Treatment B. A crop input, for example, need not be effective but only less
expensive than a comparison treatment in
order for instances of "significant" Independent On-farm Trials

profitability frequently to occur. The table at Single Trial: One Year, One Farm
right suggests some prudent guidelines for Minimize Treatments. Maximize
individual, "stand alone" trials. Replications

No Significant Yield Difference: Base
Economics on Inputs Only

Significant Yield Difference: Base
Economics on Inputs and Crop Value

SGeneralize with Caution


FARMER_Z



Pair Nc








Credibility of On-Farm Research in Future Information Networks

Charles A. Francis
University of Nebraska Lincoln

Abstract

On-farm research can be a valid approach to answering location specific questions on
efficient and economically sound resource management in agriculture. Its value can be
enhanced by use of accepted design criteria or by conducting similar comparisons across
multiple locations. Methods are needed for assessing the credibility of experimental
information, for comparing alternative crop and crop/animal systems and input strategies,
and for evaluating the productivity and sustainability of complex farming systems. There is a
need for new approaches to evaluation criteria, for example the multiple bottom lines that
include bushels per acre, energy productivity and use efficiency, farm income stability,
quality of life for the farm operator, and viability of the larger rural community. Research
results from multiple sources will be integrated into one accessible information network in
the future. Usefulness of statistics will increase as we learn to effectively evaluate issues of
community viability, social and economic equity, and quality of life for humans and survival
of other species in long-term, sustainable production systems. Credible information is a vital
component for design of these systems.

Introduction

Research has long been conducted in farmer's fields, often as a convenient site to be
able to achieve objectives not possible on the experiment station. Gomez and Gomez (1984)
provided several distinctive features, both negative and positive, of farmer locations
compared to research stations:

lack of experimental facilities such as water control, pest control, and equipment for
field operations and processing of harvest

large variation among farms and between fields in a farm, creating a range of
microenvironments suitable for multi-site research

poor accessibility that creates problems of supervision by researchers, opening the
way for increased participation by farmer hosts

lack of field histories and information on soil and climate of fields and research sites,
and need for dialog with farmer to recall available past crop and soil data

availability of farmer and familiarity with local practices for experimentation, making
these sites and this approach unique for study of management variables

This text on statistics in research (Gomez and Gomez, 1984) includes a chapter on
"Experiments in Farmers' Fields'" and makes a clear distinction between experiments
designed for technology generation and for technology verification. In technology
generation, deliberate sites are chosen to represent the physical and biological conditions of








greatest interest to the researcher to complement those trials planned for an experiment
station. Characteristics include homogeneity of the test area, availability of information on
field history and climate/soil for the site, and accessibility to provide some level of control
over the experiments. Design and layout of the trials are simplified by keeping number of
treatments and replications low, and by shaping plots to fit irregular shapes of fields and the
farmer's equipment. Data collection and analysis may involve the farmer, but most often
these are "researcher-designed and researcher-managed" trials.

For technology verification trials, the objective is to compare performance of current
practices with new technology. Because it is important for people to see these trials under
real world conditions, it's important for people to consider a current farmer's practice as a
comparison point (check treatment), and to introduce for comparison only those changes that
are most likely to provide some advantage in yield or profit to the farmer. Treatments are
kept to a minimum, and the potential for using multiple farms or sites must be explored.
Often there is a "yield gap" between current practice and improved practice or technology,
and it is useful to show farmer participants the practical comparisons across several sites the
alternatives to prevailing practices.

The importance of on-farm agronomic trials as a component of farming systems
research was described by Hildebrand and Poey (1985). They described a range of purposes
for conducting on-farm research, including providing a linkage between research and
extension, putting component research in real world conditions, and establishing
communication between conventional researchers and farmers. Four different types of trials
were described: exploratory trials (provide qualitative data on several factors), site-specific
trials (designs and objectives similar to on-station trials), regional trials (best treatments from
site-specific trials for broad testing within a recommendation domain), and farmer-managed
trials (chance for farmers to test one or two outstanding alternatives). Hildebrand and Poey
give both methods and practical examples of how agronomic as well as sociological questions
can be asked in this on-farm research process.

The conditions and situations where on-farm research is especially useful were
summarized by Lockeretz (1987) and Lockeretz and Anderson (1993). There are several
reasons why a working farm or its uniqueness of soil or climate are especially valuable for a
particular project:

to obtain soil types or other conditions that are not available or not convenient at
experiment station sites

to study factors that need larger land areas or special situations that are not available
on experiment stations

to analyze systems that involve interactions among enterprises or involve whole-farm
comparisons

to compare alternative systems performance on farms with performance on experiment
stations








to evaluate factors that are sensitive to management skills, and that may react very
differently under supervision of different farmers

to study long-term effects of a factor that has a history of use on a specific farm, and
whose effects could not be studied without long term investment on station

to analyze production practices used by farmers but not known by researchers, or not
easily accommodated for study on the experiment station

The important step is to set research priorities, decide what needs to be measured, and
choose the site most appropriate to meet those goals. There often is less control under farm
conditions, and communication is essential to make the process work. The 1992 conference
at University of Illinois (Clement, 1992) brought together many of the key people and ideas
available at that time on the topic.

There has been a wealth of experience gathered by researchers on questions and designs
that are appropriate for on-farm work, but little agreement on the degree of participation of
farmers in the process. People in the research community have varied opinions about the
credibility of on-farm research, just as farmers provide mixed reviews of the value of
research on station. How do we bridge this gap in credibility?

Who Owns the Research and Results?

There has been a rapid evolution over the past two decades in the concept of on-farm
research. Spurred by the "Farming Systems Research and Extension" efforts, we have
moved toward including farmers as full members of the research and extension team. At one
time this term referred to any research conducted outside the experiment station; it is now
applied more often to that activity conducted by farmers or farmer groups with or without the
participation of research specialists. This is now seen as a cooperative effort to bring people,
resources, and ideas together to solve common problems in the field and to design
educational programs to share the results with a wider audience (Francis et al., 1989).

With the incorporation of replication and randomization of treatments in large plots in the
field, farmers are growing more confident in the results of on-farm, large-scale comparisons.
Likewise, this adherence to the known experiment designs gives experiment station
researchers greater comfort and confidence in the results. With this confidence has come a
series of publications in the technical literature, often with researchers and farmers as joint
authors, and a wider acceptance of the results in both communities.

A relevant question is, who owns the research and the results? There is no question that
the greater the participation by various interested people, the more ownership each will feel
with both the field activity and the results. If each has an investment in the project -
whether this is land, input costs, time spent collecting data, analysis and interpretation of
results there will be great interest in seeing the final results and in using them.

In the proceedings of the Illinois conference (Francis et al., 1992), we presented a series
of models with different levels of ownership by different participants.








Researcher-driven On-Farm Model: A conventional researcher-driven model with the
concepts, treatments, design, and data collection concentrated in the hands of the
researcher and graduate students, the farmer's participation may be limited to providing
land and some of the cultural work in the field. There is some ownership and benefit to
the farmer, due to where the trial is located and some accessibility to results. Most
ownership resides with the research team, although some sharing may occur through
discussion in the field and joint interpretation of results. This is illustrated in the
"ownership model" in Figure 1 (from Francis et al., 1992).

Farmer-initiated Research Model: This is the type of research initiated by the Practical
Farmers of Iowa and the Nebraska Sustainable Agriculture Society, and often includes
variety or hybrid comparisons, fertilizer levels, weed management alternatives, or tillage
options for the region. Farmers determine which treatments are of interest, and often
include one or more treatments in common across sites. Frequently, field tours and later
meetings or newsletter articles provide results to a wider group of farmers. How much
ownership is held by research specialists depends on the degree to which they are
involved. The farmer-initiated model is illustrated in Figure 2 (from Francis et al.,
1992).

Participatory On-Farm Research Model: This activity is jointly organized and
implemented by a team that includes both researchers and farmers. A high degree of
participation by all players on this team will likely result in a strong feeling of ownership
in the results. Different people on the team may collect different types of data, and then
report these in different places. A researcher interested in mechanisms that cause a yield
response may collect data on growth rates, yield components, or detailed response to
specific treatments; a farmer may want final crop yields and grain quality that are
rewarded in the marketplace and reduced erosion that will enhance the potential for future
productivity. A participatory responsibility model is shown in Figure 3 (from Francis et
al., 1992).

Ownership by Many Additional Groups: Bringing in other partners can add new
dimensions both to support for the research and efficient use of results. A commercial
organization that supplies seed, fertilizer, or chemical product and participates in the
design and collection of data will be apt to use the results in the future. If a government
agency such as ARS or SCS is involved in measurement of specific crop responses or soil
parameters it is likely that these results will reach the technical literature or the
recommendations for farm program participants. The best way to get people to accept
the results is to have participation through the entire process from planning through field
implementation to final presentation of results. Multiple ownership and interests of
different groups is illustrated in Figure 4 (from Francis et al., 1992).


Innovative On-Farm Alternatives for the Future

As we review the on-farm research experience that has accumulated over the past two
decades, and add this to the century of demonstrations and observations that have been made
on farmer fields through research and extension, some intriguing alternatives come to mind.
Most of these are being tested by individuals or groups in various parts of the world, and it








is useful to review them in terms of credibility to both researchers and farmers. Interest to
date has focused primarily on specific designs for comparing alternative practices, but there
are broader economic and environmental implications that can be drawn from the results.

Small Plot-Large Plot Correlations: Charles Shapiro and others in Nebraska (Shapiro et
al., 1989) have harvested long strips as well as small plots from the same strips in on-
farm trials of maize. Over years and locations, they have found a high correlation
between the results from the two contrasting harvest methods, not surprising since they
come from the same universe of treatments and maize plants in the field. What is more
difficult to explain is the lower coefficient of variation that results from the larger plots.
This is contrary to conventional wisdom that the small amount of field variation in a
small plot experiment area will help reduce experimental error and allow the researcher
to detect smaller differences among treatments.

Opportunistic Designs for Agronomic Studies: In a comparison of density effects of
maize and beans on the interface between a two-species strip cropping system, Patti
Boehner and others (Boehner and Francis, 1994) have compared carefully thinned plots
with plots that were discovered in the field with similar differences in plant density.
These "opportunistic plots" had different densities due to insect damage, local flooding or
compaction, skips by the planter, or poor seed coverage. The precise causes were not
determined, but visual evaluation of the resulting plants showed no obvious major
differences between these areas and other parts of the field. Plots were identified and
marked that had the same combinations of density as those in the thinned plots. Results
from the thinned plots were analyzed as a randomized complete block, and the
opportunistic plots as a completely randomized design and a one-way analysis of
variance. There were no significant differences in means of nine parameters measured
(eg. plant height, grain weight, stover weight, seed size) and a high correlation between
results from the two designs. This would be a way to identify plots with treatments in
farmers' production fields and take information from those plots through harvest time,
with much lower cost of establishing the treatments.

Farms as Replications: Roger Elmore has analyzed the data from the Clay County,
Nebraska, corn growers demonstration trials over the past decade (see Rzewnicki et al.,
1988). In these demonstrations, farmers planted an unreplicated field with a number of
promising hybrids identified from personal experience and previous years' uniform tests.
The same hybrids were included in irrigated demonstrations in three or four parts of the
county each year. In each year, an analysis of variance that used farms as replications
showed coefficients of variation of 3 to 4 percent; these trials have been continued for ten
years, and the results are consistent from year to year.

Long Test Strips across Farmer Fields: Farmers have become accustomed to using one
long strip across a field for comparison purposes. This strip is often the width of an
implement (for tillage or other land preparation comparison), width of a planter (for
hybrid or starter fertilizer comparisons), or some multiple of these equipment widths.
This allows precise application of a specific treatment in an area that can be marked
measured, and combine harvested for comparison at harvest. Comparisons of contrasting
cropping systems, soybean varieties, planting dates, fertilizer rates weed management
options, tillage systems, and maize hybrids in various trials in Nebraska and Iowa were








reported by Rzewnicki et al. (1988). These trials had consistently low coefficients of
variation (<1% to about 15% in most comparisons). In a current project near Mead,
Nebraska, we have left one strip across each field without compost application, and then
harvested a combine-width strip from that area and from an adjacent strip with compost
for comparison. With these treatments repeated in a number of fields and over years, a
clear picture of the crop yield response to compost should emerge. This is a low-cost
type of experimentation that is available to every farmer using existing equipment.

Field Sized Comparisons across Several Farms: The national association of regional
agricultural farmer research groups in Argentina brings together interested participants
(six to twelve per group) who essentially have organized their own private extension
system. By choosing key questions that are of interest to several groups, farmers put out
their own comparisons of machinery, fertility or pest management, hybrids, and varieties
on a field scale. Although these fields often differ in size, shape, and management, the
farmers are convinced that bringing together enough data from multiple sites allows them
to make valid decisions based on the results. An analysis of results from these fields by
researchers confirms the value of the information, and many practical production
decisions are made from the pooled results from a large number of farms in a region.

Farmer-Back-to-Farmer Models: These models are an integral part of the farming
systems research approach. The "farmer first" models proposed by Chambers (Chambers
and Ghildyal, 1985; Chambers et al., 1989) and others began in the international centers
and key national programs in the tropics. Rhoades and Booth 1982, 1992) summarized
these ideas in a journal article and in the Illinois symposium, and involve starting with
farmer knowledge and problems, working together to define these problems, exploring
potential solutions, and choosing solutions best fitted to farm conditions through testing.
Following this cycle leads to increased knowledge as well as identification of new
limiting factors. The system is an iterative problem identification and solving process
that can be used in a wide range of conditions.

Augmented Designs for On-farm Hybrid/Variety Tests: Stucker and Hicks (1992)
explored the value of on-farm strip tests as an information resource for farmers making
decisions on cultivars for the next season. They point out the positive value of multiple
sites for these tests, and the minimal additional value of replicating these tests at any one
site. They also calculated the value of tester strips at regular intervals through a test strip
demonstration; these testers do not enhance the statistical value of the results of a multi-
location demonstration/test. Much more important is the number of sites and the
conditions under which they are implemented. The augmented design is one approach
that can be used to increase the statistical validity of comparisons among varieties or
hybrids; this is the replication of a subset of the entire group of cultivars that are mixed
among the unreplicated cultivars in the test. The augmented design allows calculation of
an error term specific to that site, and thus a statistical comparison among cultivars in
each given location.

Credibility of Different Information Networks

There is an obvious challenge to credibility of information, depending on the source and
the perceived objectivity of those who provide the information and recommendations.








Different organizations and information collecting procedures also generate different levels of
credibility. Each farmer asks, "Will this work in my fields under my management systems?"
There is an established review procedure that 'certifies the credibility' of technical
information published in journals; likewise the information published in extension bulletins is
known to have passed through a somewhat rigorous screen for credibility. Is there a way to
establish appropriate screening techniques for other sources? How can these different and
often conflicting sources be rationalized and sorted out by the individual producer? Let's
explore a series of potential future information resources and how we can assess their
credibility.

Farmer information networks: When experiments are designed by a group of farmers
who know and respect each other, and especially when these involve a series of
comparisons that are made on a number of farms, the results may be considered highly
credible by the participants. The Argentine model of multiple sites and large field
comparisons qualify as an example of this credibility.

Farmers in the classroom: The Practical Farmers of Iowa (PFI) have used their on-
farm tests as tour sites and educational areas for adults and for high school vocational
agriculture classes. These sites provided a hands-on way for students to experience the
differences among key treatments such as different hybrids, tillage options, weed
management approaches, and soil fertility strategies. The activity has opened the door to
the classroom, and PFI members have been invited into the agricultural classes to share
their experiences and results of the trials. This is a valuable beginning to the creation of
schools and universities without walls, a recognition that much of value is learned outside
the conventional classroom learning environment. Credibility is gained by using people
with experience in our conventional formal educational settings.

Convergence of university classroom education and extension: A model suggested by
King et al. (1989) in Nebraska describes a gradual convergence of the learning
environments created in the classroom on campus and the extension teaching situations in
the field. There is some use of extension information -- NebGuides, scouting training
guides, video presentations -- in current classroom curricula. Likewise, there is some
transfer of material out of the formal classroom into extension training. We anticipate
much more of this type of interchange in the future. As budgets become tighter and
technical people assume a broader set of roles, and as education moves toward more
integrative activities and longer time frames, we will see a greater overlap of materials
and learning plans. Classroom materials will be used in a wider range of applications,
while practical information used in adult education across the state will find its way into
the classroom. Remote interviews and interactive video will bring the field into the
classroom, as well as projecting the classroom to multiple sites across the state and
region. We see an eventual blurring of the lines between these two activities, and the
development of a continuum of lifelong learning that is integrated from one stage to the
next.

Development of Agricultural Information Networks: One potential role for extension
in the future is management of a comprehensive information network, including an
appropriate screening process for each source of data and recommendations. We
currently have in place a review process for the journal articles generated by researchers








on experiment stations. The peer review process involves at least two independent
readings, an opinion by a technical editor, and a decision by a journal editor. This is an
accepted, although at times lengthy and imperfect, system within the academic
establishment. Extension publications likewise go through a rigorous peer review. One
way to evaluate the credibility of information from other sources would be to establish a
process for peers within the same group to review what is submitted: farmers review
farmer results, commercial industry specialists review other commercial results, non-
profit groups that conduct demonstrations review results within their ranks. If this
information from multiple types of sources were entered into a single data base, or if
several sources could be successfully interfaced, the entire set could be accessed through
key words by anyone with knowledge of how the system works. This could include
students in the university library, a researcher in the laboratory or at a remote site, a
farmer at home with a computer or modem, or a range of potential clients through an
information resource center, currently called an extension office. Perhaps these could be
merged with local libraries, so that the joint activities would be considered 'one-stop
information shopping' in the future.

Conclusions

The information environment is rapidly evolving, with cost of hardware coming down rapidly
and new applications emerging from people's experience with new technologies. With the
increased access to new information comes a serious question of how to evaluate the
credibility of each source. On-farm research is expanding as university-based scientists look
for broader applicability and site-specific applications of systems and technologies. Farmers
are increasingly aware of the importance of using appropriate designs and procedures to
make their experiences on the farm more valuable for future decisions. In this information
environment, it is apparent that we need to:

decide on the most logical location for each experimental project, the goals and
applications of the results, and who will carry out the field activities as well as
interpretations of results.

explore the existing models of ownership and management of on-farm research
activities, and look for other approaches that will draw the appropriate people into the
effort.

evaluate the frontier activities of multiple location, new designs, and innovative
approaches to answering questions through research that involves a range of
participants.

design new information screening or evaluation procedures that will establish the
credibility of results from an array of sources.

These activities will all be a part of on-farm research and use of results in future
information networks in agriculture.








References:


Boehner, P., C.A. Francis, and L. Young. 1994. Comparative experiment designs for
intercropping research. Agron. Abstr. p. 170.

Chambers, R., and B.P. Ghildyal. 1985. Agricultural research for resource poor farmers: the
farmer-first-and-last model. Agric. Admin. 20:1-30.

Chambers, R., A. Pacey, and L.A. Thrupp. 1989. Farmer First: Farmer Innovation and
Agricultural Research. Intermed. Technol. Publ., London.

Clement, L.L., editor. 1992. Participatory on-farm research and education for agricultural
sustainability. Proc. Symposium, Univ. Illinois, July 31-Aug. 1.

Francis, C.A., P.E. Rzewnicki, A. Franzluebbers, A.J. Jones, E.C. Dickey, and J.W. King.
1989. Closing the information cycle: participatory methods for on-farm research. Proc.
Conference, Farmer Participation in Research for Sustainable Agriculture, Fayetteville,
Arkansas, October 8.

Francis, C.A., R. Elmore, C. Shapiro, and J. King. 1992. Who owns the results?
Interpretation and adaptation of on-farm research. Proc. Symposium: Participatory on-
farm research and education for agricultural sustainability, L.L. Clements, editor. Univ.
Illinois, July 31-Aug. 1. p. 154-168.

Gomez, K.A., and A.A. Gomez. 1984. Statistical Procedures for Agricultural Research,
Second Edition. J. Wiley and Sons, New York.

Hildebrand, P.E., and F. Poey. 1985. On-farm Agronomic Trials in Farming Systems
Research and Extension. Lynn Rienner Publ., Boulder, Colorado.

King, J.W., C.A. Francis, and J.G. Emal. 1989. Evolution in revolution: new paradigms for
agriculture and communication. Sixth General Assembly, World Future Society,
Washington, DC, July 16-20. 25 p.

Lockeretz, W. 1987. Establishing the proper role for on-farm research. Amer. J. Altern.
Agric. 2:132-136.

Lockeretz, W., and M.D. Anderson. 1993. On-farm research. Ch. 8 in: Agricultural
Research Alternatives. U. Nebraska Press, Lincoln, Nebraska. p. 99-115.

Rhoades, R., and R. Booth. 1982. Farmer-back-to-farmer: a model for generating acceptable
agricultural technology. Agric. Admin. 11:127-137.

Rhoades, R., and R. Booth. 1992. Farmer-back-to-farmer: Ten years later. Proc. Sympos.
Participatory on-farm research and education for agricultural sustainability, L.L.
Clements, editor. Univ. Illinois, July 31-Aug. 1. p. 18-27.








Rzewnicki, P.E., R. Thompson, G.W. Lesoing, R.W. Elmore, C.A. Francis, A.M.
Parkhurst, and R.S. Moomaw. 1988. On-farm experiment designs and implications for
locating research sites. Amer. J. Alter. Agric. 3:168-173.

Shapiro, C.A., W.L. Kranz, and A.M. Parkhurst. 1989. Comparison of harvest techniques
for corn field demonstrations. Amer. J. Altem. Agric. 4:59-64.

Stucker, R.E., and D.H. Hicks. 1992. Some aspects of design and interpretation of row-crop
on-farm research. Proc. Sympos. Participatory on-farm research and education for
agricultural sustainability, L.L. Clements, editor. Univ. Illinois, July 31-Aug. 1. p. 129-
151.








Figure 1. Research-driven on-farm research model (from Francis et al., 1992).


Area 1: University Researcher


Area 2: Farmer



Area 3: Joint Responsibility


3:
w
I

(:
U)
W

3



FARMER


chooses objectives, treatments
plants and manages experiment
collects data, analyzes results
interprets and uses conclusions


land ownership
unreported observations of trial


land agreement discussions
some discussion of results








Figure 2. Farmer-initiated research model (from Francis et al., 1992).


Area 1: Farmer



Area 2: Researcher



Area 3: Joint Responsibility


I\

3 <

cx:

1 v^


FARMER


land, objectives, treatments
management of trial, data collection
evaluation and interpretation of results


advice on design, analysis
extrapolation of results to other farms


some co-design of project
discussion of results, interpretations








Figure 3. Participatory on-farm research model (from Francis et al., 1992).


Area 1: Researcher



Area 2: Farmer



Area 3: Joint Responsibility


w
I
0

3 <







FARMER


journal publication, professional advancement
application of results to larger universe of farms


incorporation of profitable practices
integration of results with whole farm system


local application to farm conditions
educational tours and programs
planning for future research








Figure 4. On-farm research model with ownership by multiple groups
(from Francis et al., 1992).


INDUSTRY

5

7
--1----


0 9

S4 6 3 1
C


FARMER


2, 3: (Same as Figure 4)

Community

Industry

Community/Farmer

Industry/Farmer

Researcher/Farmer/Community

All Four Groups


* treatment impact on city water supply qi

* implications of results for product sales

* local decisions/regulations on input use

* subsidized demonstration plot with farmer

* long-term environmental impact of practice

* community viability related to practice


Areas 1,

Area 4:

Area 5:

Area 6:

Area 7:

Area 8:

Area 9:








Participatory Research and Other Sharing of Experience
(from W.K. Kellogg Foundation Cluster Workshop, Integrated Farming Systems)
Santa Cruz, California; February 23, 1995

Draft Committee Report

(Cliff Carstens, Tom Guthrie, Andrea Tillman, Charles Shapiro, Helene Murray,
Spencer Waller, Nancy Matheson, Eric Rice, Ricardo Salvador, Rick Exner, Aaron Harp,
David Granatstein, Dan McGrath, Freddy Payton; summarized by Charles Francis)

How do farmers and scientists learn from each other? What is the nature of evidence
that supports different ways of knowing? How do people from different parts of the
agricultural sector each communicate what is important to those others who may be
interested?

These valid questions must be addressed as we communicate with each other about
sustainable agriculture. At the pragmatic field level we need to learn about and implement
farming practices that maintain profitability while saving soil, maintaining water quality,
reducing pesticide use, and improving or protecting the environment in which we live. In a
broader conceptual sense, we need to communicate about watersheds and rural communities,
and consider political questions such as the structure of agriculture and the relationship of
agriculture with its broader client community.

Most of us agree that issues along this spectrum of sustainability from field level
practices to bioregions, both across time and space dimensions, are best considered by a
diverse set of players in agriculture, including farmers, academics, non-profit organization
specialists, and those in agribusiness. Serious impediments to effective communication
about critical issues include using different words and meanings, and the multiplicity of ways
of knowing that exist among individuals and groups. Where the challenges in communication
often come to the fore is with on-farm research and demonstration activities.

Importance of On-Farm Research

The last two decades have seen an emergence of interest and energy invested by
university and industry investigators and extension people in on-farm research activities, in
part to increase the relevance of research. They have used multiple sites on farms to test
technologies in many environments, to find conditions not present on the experiment stations,
to study the effects of specific management styles, or to gain information from producers as
part of the research process.

Farmers likewise have become more interested and involved in the more formalized
structure of field trials that are replicated and randomized, a strategy that has increased the
perceived value to results from research or other experiences. In some cases the field trial
strategy has increased the credibility of results in the scientific community or helped groups
to gain access to funds from government or private foundations. These changes in field
procedure have led to closer cooperation between some farmers and some researchers in
addressing practical and relevant questions in both component technologies and agricultural
systems.








Participatory Research and Other Sharing of Experience
(from W.K. Kellogg Foundation Cluster Workshop, Integrated Farming Systems)
Santa Cruz, California; February 23, 1995

Draft Committee Report

(Cliff Carstens, Tom Guthrie, Andrea Tillman, Charles Shapiro, Helene Murray,
Spencer Waller, Nancy Matheson, Eric Rice, Ricardo Salvador, Rick Exner, Aaron Harp,
David Granatstein, Dan McGrath, Freddy Payton; summarized by Charles Francis)

How do farmers and scientists learn from each other? What is the nature of evidence
that supports different ways of knowing? How do people from different parts of the
agricultural sector each communicate what is important to those others who may be
interested?

These valid questions must be addressed as we communicate with each other about
sustainable agriculture. At the pragmatic field level we need to learn about and implement
farming practices that maintain profitability while saving soil, maintaining water quality,
reducing pesticide use, and improving or protecting the environment in which we live. In a
broader conceptual sense, we need to communicate about watersheds and rural communities,
and consider political questions such as the structure of agriculture and the relationship of
agriculture with its broader client community.

Most of us agree that issues along this spectrum of sustainability from field level
practices to bioregions, both across time and space dimensions, are best considered by a
diverse set of players in agriculture, including farmers, academics, non-profit organization
specialists, and those in agribusiness. Serious impediments to effective communication
about critical issues include using different words and meanings, and the multiplicity of ways
of knowing that exist among individuals and groups. Where the challenges in communication
often come to the fore is with on-farm research and demonstration activities.

Importance of On-Farm Research

The last two decades have seen an emergence of interest and energy invested by
university and industry investigators and extension people in on-farm research activities, in
part to increase the relevance of research. They have used multiple sites on farms to test
technologies in many environments, to find conditions not present on the experiment stations,
to study the effects of specific management styles, or to gain information from producers as
part of the research process.

Farmers likewise have become more interested and involved in the more formalized
structure of field trials that are replicated and randomized, a strategy that has increased the
perceived value to results from research or other experiences. In some cases the field trial
strategy has increased the credibility of results in the scientific community or helped groups
to gain access to funds from government or private foundations. These changes in field
procedure have led to closer cooperation between some farmers and some researchers in
addressing practical and relevant questions in both component technologies and agricultural
systems.








From this interaction has come a wider appreciation of what is considered research,
and a growing recognition that differences might exist in what is accepted as evidence of
success among various stakeholders. We have learned that farmers and researchers often ask
different questions, use distinct methods of seeking answers, and accept potentially different
types of evidence as indicators for making decisions. Further, there are differences in what
to believe and how to access information. People use different language to describe what
they see, and how they define cooperation. This language discloses underlying attitude
differences, and the true nature of these attitudes is at the base of effective communications.

There are rich and growing information resources on the mechanics of on-farm,
participatory research. For example, annual results from the Thompson On-Farm Research
activity have been provided to the public for more than a decade. Rodale Research Institute
has published a manual for on-farm research. A National Conference on participatory
research was sponsored by University of Illinois in 1992, and the proceedings are available.
The symposia of the Farming Systems Research and Extension organization have published
results and a journal that gives many examples under a range of conditions. The results of
an on-farm research workshop at the American Society of Agronomy meetings in Seattle in
1994 will soon be available from University of Nebraska (Center for Sustainable Agricultural
Systems).

What has not been adequately addressed is the nature of language that we use to
negotiate, initiate, sustain, and describe participation; how different groups use terms to
report their results; and how they were derived. We also have not talked much about distinct
types of evidence that are used by different groups to substantiate the results of a field
experience. At times, the process is more important than the product. These are topics that
need to be explored.

Language of Participation

To move beyond the current definitions of on-farm research and ways that people
attempt to cooperate and participate in setting up trials, it is useful to examine some of these
terms and what they mean. Given that people learn by doing, we should use the process of
experiential education as a centerpiece of practical learning about sustainable agriculture.
This means getting out in the field and working, putting real data in the hands of learners
and using that to derive answers to questions they consider important. Dealing with data
from the field can be a group process, especially in the interpretation of results of field trials.
This is in direct contrast to how ve typically listen to experts in an Extension meeting
explaining results and providing us with conclusions.









An Example:
Nitrogen Trials in Nebraska

A research issue identified by farmers in Nebraska dealt with nitrogen use. An
experiential research and learning activity addressed the challenge of how to reduce
nitrogen application rates in corn and sorghum grown in rotation. Farmers conducted
trials with different rates of nitrogen, both in continuous cereal cultivation and in rotation
with legumes. A university project technician helped with design and data collection, and
with a preliminary analysis of the results. In farmer meetings organized by a project
technician, the results were presented in figure format, with a brief explanation of where
and how the experiment was conducted. The meeting was thrown open to farmers to draw
their own conclusions from the data and to share those with others. The only intervention
from project technicians was to answer questions from farmers about why certain results
were achieved, or what the underlying biological reason for results might be. Subsequent
visits with some of the participating farmers revealed that they had reassessed their
decisions on nitrogen use, and had actually reduced applications on cereal fields that
followed a legume.

The Nebraska corn/sorghum example demonstrates a different way to report or
interpret results more directly from the field experience. There is a vital need for innovation
in thinking about communication alternatives between farmers themselves, as well as among
different people with different agricultural interests.

Farmers generally test through the process of trial and error. Machinery is modified
to see if a new configuration works or not, and the next change is built on the one that came
before. It is unlikely that a replicated experiment conducted over time would yield more
useful results. "Who cares? We are doing things and testing them to see if they work!"
People learn from each other by seeing planter modifications in the barn or by observing the
planter in action in the field. Much of the communication can be in the oral tradition or
other means rather than written text. Information processing often seems to occur by
individual testing of the idea against one's own experience using heuristics derived from
previous trial and error learning. Much individual testing is essential.

Just as the language of considering findings needs to be reconsidered, so too does the
language through which participatory research is negotiated and implemented. Declaring
goals, needs, and assumptions, as growers and researchers partner to undertake a project,
should become the standard practice rather than the exception. The context of decision
making in the production system ought to be introduced into the research design. Even the
design and conduct of participatory research, undertaken by a group of individuals to serve
multiple objectives, needs to be addressed.

In the language of participation with growers there is space for values and expression
of feelings. There is room for optimism about agriculture, about hope for the future, and the
context these feelings provide for viewing technologies or alternative systems. This new
language of participation is in direct contrast to the current environment in which much
communication takes place in the traditional academic community. We must generate








alternatives and use ingenuity to address the complex issues associated with sustainable
systems.

Defining Evidence and Credibility

There also are large differences in the types of acceptable evidence that are used by
different groups to validate a field experience. Researchers most often believe in replicated
and randomized experiments conducted under controlled conditions, those results are then
reported in refereed technical outlets. This established, accepted academic procedure
validates work done in the field or laboratory by university researchers and extension
specialists. The results are presented in scientific meetings, in journals or books, or in the
classroom or seminar at the university.

Although farmers may accept some of these results and evidence, there are additional
ways of knowing. There are many non-academic ways of defining evidence that also have
validity in the farming community. Hypotheses can be tested in a number of ways, one of
which is seeing what happened last year, suggesting potential changes, and trying these
changes in the field to see if they work. For many, replications and randomization of plots
are not seen as necessary. This may depend on the type of question being asked and the
potential size of expected differences that are meaningful.

There is a wide range of types of environmental experiences, many of which are
found during the regular conduct of farming activities. Those who are close to the land can
be careful observers of the natural world and the impact of farming practices and systems on
that world. These observations can be communicated in different ways. How do we capture
evidence or describe these experiences and make that description meaningful to others? Does
it matter if this is meaningful to the scientific community? Are there ways either to quantify
or to multiply an experience to make it meaningful to more people, without each of those
having to go through the experience personally? How do we provide windows on this
experience that can be shared with others?

Looking Forward

We are becoming more concerned about the importance of sites specificity of systems
and their components, and how to test those ideas and get them out to others. Much of this
will have to be done on each farm, or at least each type of farm, in each agroecological area.
How can we ground our experiences and use different kinds of experiential evidence to
validate and communicate these experiences to others?

For people to work together, it is important to find ways to seek common ground,
learn if there are common goals and what those are, and to define partnerships that can be
win-win for those involved. To achieve this, it will be critical to negotiate protocols and the
ways to achieve the stated goals. These types of collaboration are based on mutual need and
mutual respect. Such shared values can contribute greatly to our future conduct of on-farm
research and demonstration and will carry over to other collaborative activities.











U
k ~q


Nafziger, Emerson. 1995. On-Farm Research. Chapter 19
in: 1995-96 Illinois Agronomy Handbook, Circular 1333,
University of Illinois, Coolege of Agriculture, Cooperative
Extension Service. p. 195-199.


Chapter 19.

On-Farm Research


Many farmers have become actively involved in one
or more on-farm research projects. These farmers have
become involved with such research and the produc-
tion of new knowledge for several reasons, including
(1) the increasing complexity of crop production prac-
tices; (2) the declining support for applied research
conducted by universities; and (3) the proliferation of
products and practices whose benefits are difficult to
demonstrate. Such on-farm research projects have
included hybrid or variety strip trials conducted in
cooperation with seed companies, tillage comparisons,
evaluations of nontraditional additives or other prod-
ucts, and nutrient rate studies, as well as other man-
agement practice comparisons.


Setting goals for on-farm research
The stated purpose of most on-farm research is "to
prove whether a given product or practice "works
(normally meaning that it returns more than its cost)
on my farm." While this seems like a rather obvious
goal, the person conducting or considering conducting
on-farm research should understand several implica-
tions of such a goal:
1. Like it or not, Illinois farmers operate in a variable
environment, with rather large changes in weather
patterns from year to year and with differences in
soils within and among fields. This forces the
operator to modify the above on-farm research
goal, from "proving whether [something] works"
to "finding out under what conditions [something]
works or does not work"' or to "finding out how
often [something] works." Both of these modifica-
tions will require that particular trials be run over
a number of years and in a number of fields. The
key goal of any applied research project on-farm
or not is to be able to predict what will happen
when we use a practice or product in the future.


The variable conditions under which crops are
produced make such predictions difficult.
2. All fields are variable, meaning that a measurement
of anything (such as yield) in a small part of a field
(a plot) does not perfectly represent that field, much
less the whole farm. Such variability can be assessed
using the science of statistics: for example, the
statistician might look at the yields of six strips of
Hybrid A harvested separately and state, "The
average yield of Hybrid A in these strips was 155
bushels per acre. But due to the variability among
the harvested strips, it is only 95 percent certain
that the actual yield of Hybrid A in this field was
between 150 and 160 bushels per acre"' In other
words, variability means that it is not possible to
be completely precise when the effects of a partic-
ular treatment are measured. Replicating (treating
more than one strip with the same treatment) more
times can help narrow the range of unpredictability,
but the range will never be zero. Some uncertainty
will always be present.
If a whole field could be harvested, the exact
yield (for that year) would be known, and we
wouldn't have to give a range. But with on-farm
research, it is necessary to apply treatments to
smaller parts of the field since no comparisons are
possible if the whole field is treated the same.
Suppose the farmer stripped the whole field, with
Hybrid A mentioned above in one side of the
planter and another hybrid (Hybrid B) in the other
side. After harvesting the strips of each hybrid
separately, the statistician might be able to state,
"Based on the strips chosen to represent Hybrid B,
this hybrid yielded 140 bushels per acre, and it is
95 percent certain that the yield of Hybrid B was
between 135 and 145 bushels per acre." In this
case, since the "confidence intervals" (150 to 160
for Hybrid A; 135 to 145 for Hybrid B) of the two
hybrids do not overlap, it is possible to state that






the yields of the two hybrids were significantly
different. But in this realistic example, note that the
yields of the two hybrids differed by 15 bushels
per acre, and still the confidence intervals came
within 5 bushels of overlapping.
3. Because of the uncertainty, it is necessary to accept
that, when measuring yield (or anything else) in
applied field research, it is virtually impossible to
ever "prove" that some practices or products work
or do not work. Even with the most precise field
trials done in the most uniform fields, it takes a
yield difference of at least 2 or 3 bushels per acre
(1 to 2 percent) between treatments to allow the
researcher to state with confidence that the treat-
ments produced different yields. As a rather silly
example, suppose a farmer went out into a corn
field, divided the field into twenty 12-row strips,
and carefully cut one plant out of every 500 plants
in 10 of the strips, but did nothing to the other 10
strips. It would be absolutely certain that the farm-
er's treatment (cutting out 0.2 percent of the plants)
affected the yield of the treated strips, but it would
also be certain that the farmer would not be able
to measure a significant yield difference between
the two treatments, unless perhaps by accident.
The variability between strips in a case like this
would simply overwhelm a very small but real
treatment effect (the physical removal of the plants
by the farmer). Similarly, a crop additive or other
practice may routinely give small yield increases or
decreases, yet never be proven to work or not to
work.

Types of on-farm trials
The following list comprises different categories of
research that have been popular as on-farm projects,
along with some comments about each:
1. Fertilizer rate trials. Fertilizer is an expensive input,
and so rate trials designed to determine a "best"
rate, or the effect of reducing rates, have been
common. Fertilizer rate is what is called a "contin-
uous" variable two rates for comparison could
differ by 50 pounds per acre, 5 pounds per acre,
or 1 pound per acre; the researcher chooses the
rates. Whether or not different rates will produce
significantly different yields depends, of course, on
what rates are selected. This makes the typical "rate
.reduction" trial difficult to interpret: 140 pounds of
nitrogen per acre might or might not produce a
different yield from the "normal" 160 pounds of
nitrogen per acre, but as was discussed above, a
field experiment often will not pick up a small
difference. As a result, many rate reduction studies
are "successful" in that lower rates do not produce
significantly lower yields. But the response to fer-
tilizer rate needs to be generated by using a number
of rates- more than just two. And the results
should be used to produce a curve showing the


response to fertilizer, rather than comparing the
yields produced by each rate. Remember that the
researcher or operator chooses the fertilizer rates,
and the chance of just stumbling on the "best
possible" rate is low.
To illustrate, consider the following corn yields
produced in a nitrogen (N) fertilizer rate trial:


N rate
0
60
120
180
240


Yield
100
142
164
163
140


Many people looking at these numbers would con-
dude that 120 pounds of N must have been the
"best" rate, since it gave the highest yield. Figure
19.01 is another way to look at the same data. The
curve, generated by a computer, fits the data quite
well in this case.
When the data are presented this way, it is easy
to see that the "best" rate was not in fact 120
pounds of nitrogen per acre; the rate that would
have given the highest yield was about 150 pounds
per acre (actually 148 pounds per acre). It was only
by chance that the researcher did not use that (best)
rate, but when there is only one best rate (one
highest point on the curve), the chance of actually
using that best rate is low. (Because N fertilizer has
a cost, the best economic rate that rate producing
the highest income is less than the rate that gives
the top yield. How much less depends on the price
of N and of corn. In this example, if corn is $2.20
per bushel and N costs $0.15 per pound, then the
N rate providing the best return would be about
137 pounds N per acre).
A curve to present data is used for a fertilizer
example here, but the same principle applies for
any input for which rates are chosen. Examples of
such factors include plant population, seed rate,
and row spacing.

180
180 .... .. ...... ..; .. **;* .. ., ..^ -

160

2 140 A -

120 .- -'

100


100 100 100 100 100
N-rate (Ib/acre)
Figure 19.01. A curve fitted to yields from a nitrogen (N)
rate trial on corn.








2. Hybrid or variety comparisons. Such comparisons
are very common and are usually done in coop-
eration with a seed company. Comparisons have
very good demonstration value, and when results
are combined over a number of similar trials, they
can provide reasonable predictions of future per-
formance of hybrids or varieties. Most of these trials
are done as single (unreplicated) strips in a field. It
is dangerous to use the results of a single trial to
predict future performance. For example, a hybrid
that just happens to fall in a wet spot in the field
may yield poorly only because of its location, and
not because of its genetic potential. Seed companies
are increasingly averaging the results of numbers
of such strip trials, thereby providing better pre-
dictions and making the trials more useful. If par-
ticipating in such trials, a farmer should be sure to
ask the company for results from other locations
as well.
Many people who work with hybrid or variety
strip trials are convinced that the effects of varia-
bility can be removed by using "check" strips of a
common hybrid or variety planted at regular inter-
vals among the varieties being tested. The yields
of such check strips are often used to adjust the
yields of nearby hybrids or varieties, on the as-
sumption that the check will measure the relative
quality of each area in the field, thus justifying
inflation of yields in low-yielding parts of the field
and deflation of yields in high-yielding parts. If all
variation in a field occurred smoothly and gradually
across the field, such adjustments would probably
be reasonable. But variation does not occur that
way, and so it is usually unfair to adjust yields of
entries simply because the nearby check yielded
differently than the average of all of the checks.
The use of such checks can provide some measure
of variability in the field, but it also takes additional
time and space to plant the trial when checks are
used. The only way to know for certain whether
or not performance of a variety or hybrid in a strip
trial was "typical" is to look at data from a number
of such trials to see whether performance is con-
sistent.
3. Tillage. Tillage trials are difficult and often frus-
trating, due in large part to the fact that tillage is
really not a very well-defined term. What one
farmer may call "reduced tillage," for example, may
be very different from what another farmer means
when he or she uses the term. The same is true
for "conventional tillage," and even for "no-tillage'"
due to the large number of attachments and other
innovations in equipment. Motivations may also
differ substantially: while no-tillage versus conven-
tional tillage may seem like a straightforward com-
parison, an attitude of "I know I can make no-till
work" as a basis for doing such a comparison might
result in a very different research outcome than if
the attitude is "I really don't think no-till yields are


as good as in conventional tillage, and I can prove
it." This may be an extreme example, but there are
indications that tillage trials often are not conducted
in a strictly "neutral" research environment.
It is possible to make on-farm comparisons of
tillage practices. Treatments for comparison have
to be selected carefully, keeping in mind that "if
you already know what the results will be, there's
very little reason to do research." Because soil type
usually affects tillage responses, it is always useful
to do tillage trials in several different soil types,
either on one farm or among several farms. Rep-
lication (to sample soil variation in each field) is
also necessary.
4. Herbicide trials. Herbicide and herbicide rate trials
are subject to large amounts of variation among
years and fields due to the fact that soil, weather,
crop growth (and sometimes variety), and weed
seed supply and growth all can affect the outcome.
This makes it very difficult to prove conclusively
that a particular herbicide or combination, or a
particular rate of herbicide, will be predictably better
than another. The use of herbicide additives simply
throws another variable into the mix, and makes
choosing a "best treatment" even more difficult.
Trials in which different herbicides and rates need
to be mixed and applied to strips are often very
time-consuming.
5. Management practices. It can be relatively easy to
compare different plant populations or planting
rates, though calibration of equipment knowing
how many seeds per acre or pounds per acre of
seed are produced by a particular planter or drill
setting can be difficult. Changing the rates also
needs to be done during the busy planting season,
but this can be made easier if calibration is done
beforehand. As discussed above with fertilizer rate
trials, two planting rates that differ only slightly
may often produce similar yields, and finding a
"best" planting rate is difficult. By careful replication
of two or three different rates in a number of fields
over several years, however, it might be possible
(with little risk) to tell whether increased planting
rates would increase yields.
6. "Interaction" and "system" trials. It is known that
a lot of crop production factors interact; that is, the
response to one factor (plant population, for ex-
ample) may depend on choices made related to
other factors (hybrid, for example). While this is
known in principle, it is difficult to design research
to help apply this knowledge. The short life of
many hybrids and varieties adds to this dilemma:
once the research is done to determine the best
population for a particular hybrid, that hybrid will
likely no longer be available. An alternative is to
try to identify hybrids that are "typical" for some
characteristic and thereby can represent a lot of
other hybrids, both present and future. From a
practical standpoint, this is virtually impossible to







do, since it is not possible to know for certain that
a hybrid is really typical, and the definition of a
typical hybrid changes over time.
Interaction trials, by definition, also require more
treatments than do one-factor trials. The simplest
interaction trial has four treatments two levels
of one factor times two levels of another. And such
a minimal number of treatments may not always
tell researchers much. What would be learned, for
example, if two plant populations were used with
each of two hybrids? Farmers will learn that the
hybrids react either the same or differently in
relation to plant populations, but a "best" popu-
lation will not be identified for each hybrid. It may
well be more efficient to choose one hybrid as the
better of the two, then use three or four different
populations to try to see how to increase its yield.
In this type of tradeoff, knowledge is limited to
one hybrid, but the knowledge becomes much
better for that hybrid.
Another example of the problem of measuring
the effects of interactions is seen in "systems"
research. In many such studies, several factors are
changed simultaneously, typically ending up with
only two treatments: the "conventional" system
and the "new" system. While the simplicity of such
trials is appealing, it is often impossible to separate
out the effects of any of the changes the farmer
made in going to the new system. In other words,
it may be possible to compare the overall profita-
bility of the two systems, but it is not possible to
optimize choose the best combination of in-
puts for the system. Systems trials can be mod-
ified by including more treatments and leaving out
one component of the new system for each treat-
ment. This will tell how much, if any, each com-
ponent contributes to the whole system, and will
allow the elimination of those changes that are not
necessary.


Possible risk associated with on-farm
research
On-farm research trials should be selected and
designed so that they carry little risk of loss. Many
trials, such as those comparing hybrids or varieties,
usually include only treatments that yield relatively
well and so represent little risk. It is probably best
to avoid entries in such trials that are certain not to
perform very well, unless there is special interest, for
example, in knowing how modem varieties compare
to old varieties.
Some types of trials involve considerable risk of
yield loss, and the farmer should at least be aware of
this before starting such trials. A good example is
nitrogen (N) rate trials designed to include the use of
no N as one of the treatments. This treatment is
necessary to determine if there is any response to N,
but is probably not necessary to find the best rate of


N; some N is usually needed for best yields. Thus
researchers might use 60, 90, 120, 150, and 180 pounds
N per acre in an N rate trial instead of using 0, 50,
100, 150, and 200. This will reduce the loss associated
with N rates that are too low. The closer spacing of
N rates will as long as the range is wide enough
to include the optimum rate often do a better job
of determining a best rate.
Another example in which untreated "checks" can
cause yield losses would be herbicide trials, where the
use of no herbicide might cause visually dramatic
results, but might not be a practical alternative. As
these examples illustrate, it is probably better to restrict
most on-farm research treatments to those necessary
to identify the most practical treatment or rate, rather
than to try to cover the whole range of possibilities,
including treatments that may never be used on a field
scale.


Getting started with on-farm research
While there is a perception that on-farm research
takes a lot of time and effort, the very large numbers
of variety strip trials prove that farmers will take the
necessary time to do such trials if the rewards are
sufficient. Such rewards might be material for ex-
ample, additional seed often is given to variety strip
trial cooperators or intangible, such as cooperation
in a group project that is expected to provide good
information useful to all group members.
No matter what the perceptions about time and
effort required to conduct on-farm research, it is ab-
solutely essential that the work is clearly specified and
assigned before starting the research. To do this, it is
most useful to write down everything that will have
to be done, when each task must be completed, and
who will do the tasks. The important work gets done
this way, and participants are able to see beforehand
what they will need to do throughout the season to
make the project work.
From a practical standpoint, it is best to undertake
on-farm research projects that do not interfere greatly
with ongoing farming operations, particularly at plant-
ing and harvesting times. For example, it may be easier
to apply nitrogen rates after planting than to delay
planting in order to put on different rates. Trials such
as hybrid trials or planting rate trials that must be
done at planting time can be planned for fields that
are usually ready to plant first (or last), or by trying
other ways to work around the main farm operations.
The following steps initiate on-farm research:
1. Decide what type of research is preferred. It is
much better if this decision can be made by a group,
perhaps a "club', operating with similar goals. It
may also be advisable to ask advice from an ex-
perienced researcher at this stage. Such researchers
may help to ask questions that focus the goal, and
they may often know of previous work that might
prevent wasted effort.








2. Formulate specific objectives. For example, rather
than stating, "We want to compare different ways
to plant soybeans;' make the objectives read, "We
want to see how soybeans in 30-inch rows yield
compared to those in 7-inch rows!'
3. Formulate a research plan to answer questions,
including:
*how many locations and years the research will
be conducted;
who will actually conduct the comparisons;
what soil type restrictions (if any) there will be;
*what if any equipment, herbicide, or variety re-
strictions there will be;
what data (for example, yield) will be taken; and
who will summarize the results.
Several meetings field days, progress discussions,
results discussions should be scheduled as part
of the plan. Make sure the plan is practical that
everyone understands his or her role and has the
right equipment to do the work.
4. Pay attention to work underway, thus providing
encouragement and accountability to individuals in
the group. Field days help do this, along with coffee
shop meetings during the season. Set deadlines for
the assembly of results, and telephone those who
are late to keep everyone on schedule as much as
possible.
5. Have an off-season progress meeting, in which
results are summarized. Plans can be modified for
the next season, but remember that changing treat-
ments or objectives partway through a project is
often a fatal blow to the project: the goals become
fuzzy, and participants may feel that their work has
been wasted. It is certainly inadvisable to stop short
of the goal because the first year's results do not
"prove" what people had hoped they would prove.
6. Have a final project meeting to present and discuss
results from the whole study. While members may
choose their own interpretation of the results, such
discussions are often very educational and useful.
New projects often come from discussions of com-
pleted projects.


A word about statistics
While it is almost universally accepted that statistical
analysis is required for the interpretation of research
results, many farmers and others do not understand
how to do this analysis, or why it is necessary. As
explained above, statistical analysis involves assessing
the variability that is always present, and then making
reasonable, mathematics-based assessments as to
whether or not observed effects are due to chance or
to treatments. When it is concluded that a reasonable


chance exists that differences in production outcomes
were in fact due to treatments, then it can be said that
treatments had a significant effect. This conclusion does
not mean that it has been proven that the treatments
caused differences, only that researchers are satisfied
that their best guess or assessment is probably correct.
When researchers are unable to draw the conclusion
that treatments differed, they say that the treatments
were not significantly different. Note that this last state-
ment does not mean that treatment had no effect.
Rather, it simply says that the research trials were not
able to detect such an effect. There are two possibilities
here: either the treatments really did not have an
effect, or they did have an effect, but the experiment
was not adequate to detect it. Note the indication
above that small effects are very difficult to prove.
This is due to the fact that unexplained variation
("background noise") will usually "drown out" small
effects.
What can farmers and researchers do when they
think treatments should have differed, but the research
trials fail to show that they do differ? If this occurs in
one trial in one field in 1 year, then the obvious
conclusion is that the research needs to be done more
often. Due to the nature of statistics, combining the
results of a number of trials, even when each trial
shows no detectable difference between trials, may
well show a significant treatment effect. The more
replications (years, fields, strips within fields), the
better provided that each comparison is done care-
fully and that the conditions of each comparison are
reasonably similar. Such combining of results provides
much more confidence in making a final conclusion,
whether or not it agrees with what research had
previously predicted.
Doing statistical analysis is not always simple, and
it may often be advisable to work with a researcher
to get results analyzed. Remember that statistical anal-
ysis cannot improve on the research; no amount of
analysis will rescue a trial where the research was
done sloppily or with an improper design. Many
projects have been made useless by poor designs which
do not allow proper analysis and thus do not allow
conclusions supported by solid research.
Above all, keep an open mind: Research designed
"to prove what we already know" is not research, but
a rather sterile exercise. At the same time, applied
research almost always represents "work in progress."
Researchers and farmers can benefit a great deal -
from the confidence such research in progress provides
when deciding to adopt new production practices or
to continue more traditional production practices. The
increase in knowledge that can be obtained from
careful observation of a growing crop and its responses
to evolving management practices is a benefit to farm-
ing in general and to society at large.




Mayhew, M.E. and R. Sam Alessi. 1994. Responsive Constructivist Requirements Engineering: A
Paradigm. p.---. In Don Sifferman and Ron Olson (ed.) Systems Engineering: A Competitive Edge
in a Changing World. Proc. Fourth Int. Sym. of The National Council On Systems Engineering
(NCOSE). August 10-12, San Jose, CA.

RESPONSIVE CONSTRUCTIVIST REQUIREMENTS
ENGINEERING: A PARADIGM

Michael E. Mayhew
Human Systems Analyst, Department of Human Development and Family Studies,
1099 Elm, Iowa State University, Ames, IA 50010

R. Samuel Alessi
USDA, Agricultural Research Service, North Central Soil Conservation Research Laboratory
N. Iowa Ave., Morris, MN 56267


Abstract Poor requirements can lead to cost and
schedule overruns and are therefore a source of low
quality products and stressful work environments.
This paper introduces a "responsive constructivist"
paradigm for use by systems engineers to address
these concerns. The paradigm is "responsive" to
stakeholder statements in a nonlinear but
methodological manner. "Constructivist" refers to the
abstract construction of problem space based on a
linguistic understanding of the various stakeholder's
worldview of the problem, not necessarily upon the
"preordinate positivist" beliefs of science. This
paradigm asserts the necessity of approaching
requirements such that the human component is more
formally embraced. This challenges requirements
engineers to evaluate their own stance of curiosity and
neutrality. Additionally, questioning types and
patterns aid to gather different views of the problem.
This responsive constructivist systems engineering
paradigm can improve the quality of interpersonal
communications, thereby resulting in higher quality
requirements and alternate problem abstractions.

INTRODUCTION

Gathering, documenting and managing
requirements are fundamental systems engineering
activities that enable quality in system designs and
deliverables. Studies have shown that errors
discovered during system construction can be traced
'to improper or missing requirements and that up to
200:1 cost ratio exists between detecting errors in the
maintenance versus the requirements phase (SEI,
1993). Therefore, the extreme importance of
"complete, concise and unambiguous" (Wymore, 1993)


requirements is generally recognized among systems
engineers.
Although common knowledge for systems engineers,
the critical importance of requirements is difficult for
nonsystemic domain experts to understand and accept.
This lack of understanding often complicates the
efforts of systems engineering leading to deleterious,
expensive, or even paralyzing implications for the
project. Systems engineers can readily recall many
incidents of managers, scientists and even users
growing impatient with "just" studying the problem.
From this, there emerges a realization that systems
engineers have a different understanding about
requirements which derives from a fundamental
difference in thinking about how to solve problems.
To assist systems engineers in gathering requirements
while simultaneously dealing with impatient
colleagues, we would like to introduce an alternate
paradigm that clarifies and explains many of the
inescapable human issues. This paradigm begins by
modifying our thinking about science and people in
relation to requirements engineering and problem
solving. We will briefly review the theoretical basis
for this approach then move quickly to pragmatic
topics.
The traditional mode of scientific thinking has been
termed "positive" by nineteenth century French
philosopher, Auguste Comte. Positivism attempts to
merely attain the facts, and only the facts. This
positivist paradigm embodies our deep-seated way of
thinking (Leahy, 1987). It is so deeply entrenched in
our Western idioms and culture that it is irresistible
for us to embrace, while criticism of it poses a threat
to many. Though entrenched, Guba and Lincoln
(1989) argue that the positivistic paradigm fails to


All programs and services of the U.S. Department of Agriculture are offered on a nondiscriminatory basis without regard
to race, color, national origin, religion, sex, age, marital status or handicap.









include the "myriad human, political, social, cultural,
and contextual elements" that are always present when
people collaborate, especially in large-scale
multidisciplinary problem solving. These elements are
often difficult to define or fully understand, yet must
be involved to attain the complete and necessary
requirements for product development that meet the
needs of the end user. Woods (1993) recognizes this
problem when he states that "the natural
connectedness of things are largely uncodified...For all
the work that has been done to date on general
systems theory, Western culture has done just fine
without it or has it? Technology has been borne
along by the laws of nature at blinding speed. But
with how much breakage in social systems,
government, the environment, and economics? And
in how many other dependent 'systems?' What has
been the price of technological accomplishments that
ignored compatibility with the greater 'system.'"
Woods further states that technological inadequacies
have been the major contributor to that breakage.
An alternate approach to the positivist paradigm-
which may hold answers to many of Woods' questions-
has been termed "responsive constructivist." The
primary interest is to understand humans' use of
symbols and language and thereby gain insight into
their world view. For systems engineering, when
individuals seek to collaborate with people of other
world views, there is a need to gain an understanding
not only of their position regarding the particular
question of discussion, but also of their ideological
and professional viewpoints. The responsive
constructivist approach results in a linguistic
understanding that assists in maximizing the
cooperative effort to its fullest potential.
A qualitative methodology for attaining this
linguistic understanding has been developed by
cultural anthropology. We resist presenting this tool
as a method or recipe since it more importantly
suggests to the systems engineer a different paradigm
of inquiry; an epistemology, a way of thinking; also
referred to as "a cybernetics of cybernetics" (Becvar
and Becvar, 1988).
The objective of this paper is to present a
"responsive constructivist" approach for gaining insight
into problematic situations. Derived from cultural
anthropology and adapted by family therapy (Bateson,
1972), the deeply interpersonal and systemic nature of
this approach will be useful for requirements
engineering in the gathering and abstraction of the
problem of interest.


THE REQUIREMENTS PHASE

The requirements phase can be divided into
elicitation, specification, analysis and validation (SEI,
1993). Elicitation approaches include the many group
facilitated discussion techniques, Joint Application
Design (JAD), prototyping, "soft" systems and other
approaches. Additionally, approaches are available for
stabilizing, managing, specifying, analyzing and
verifying the massive number of requirements that are
typically generated.
Among the requirements issues is the central task
of quickly and accurately gathering information in
areas that may be unfamiliar to the requirements
engineer. A central activity then becomes that of
information gathering through the use of questions.
The constructivist paradigm encourages a "responsive"
question-asking strategy where nonlinear interpersonal
dialogue becomes the fundamental generator of high
quality requirements. New questions are formulated
in response to statements made by stakeholders.
Additionally, an abstract "construction" of the problem
space, "grounded" (e.g., justified) in stakeholder's
statements, emerges and is linguistically traceable to
perceived realities of the stakeholders. Fundamental
to applying this approach is an understanding that the
internal stance of the interviewer is far more
important than the questions themselves.
Other disciplines (e.g., Education, Library Science,
Law Enforcement, Family Therapy) also use the
responsive constructivist approach. For example,
library scientists need to quickly and accurately
ascertain their patrons' need for information. Rather
than accepting requests for information at face value
they use probing questions to discover the underlying
need. The fundamental change is their view about
users' statements, and therefore a change of stance
occurs toward how to approach satisfying users' needs.
Questioning techniques are also used that include
closed, open and neutral questions; direct and indirect
questions; refraining from preconceived notions,
self-disclosure, active listening and human awareness
(Long, 1989). The approach has proven to illuminate
users' needs more quickly and efficiently.
Questionnaires and interviews are widely accepted
and used in the requirements phase. Unfortunately,
these approaches, when administered from a
positivistic framework, will require some predefinition
of major ordinal values of interest. For example,
managers and project proposals from their
"preordinate" thinking often predefine solutions,
thereby effectively short-circuiting the systems
engineering methodology. In this mode, the
"preordinate positivist" manager or engineer assumes










some information at the outset to enact their methods
of inquiry (e.g., a questionnaire). However, the
frequently overlooked question is whether the
engineer knows if his methods contain the pertinent
questions relevant to the inquiry. Herein lies the
usefulness of the responsive constructivist approach.

RESPONSIVE CONSTRUCTIVIST
SYSTEMS ENGINEERING

This approach has origins in a branch of cultural
anthropology called ethnography. "Ethnography is the
work of describing a culture. The essential core of
this activity aims to understand another way of life
from the native point-of-view" (Spradley, 1979). This
focus is similar to systems engineering's commitment
to involving end-users, customers, clients, and "anyone
who has the right or responsibility to specify
requirements" (Wymore, 1993) in requirements
elicitation. In this way, systems engineering has
already embraced ethnographic fundamentals.
Therefore, if the client's world is likened to a culture,
then studying the shared values, habits, folklore,
symbols, and rituals of that culture will aid the
systems engineer in understanding the problem.
Ethnography has already proven useful in many
domains where people have deemed it necessary to
gain a greater understanding of a group of people
other than themselves. Recently, ethnographic
research has been applied to various "systems" of
people in our corporate industrialized world
(Goodsell, 1981; Deal and Kennedy, 1982;
Maynard-Moody et al., 1986).
It is upon this backdrop that we propose the term,
"responsive constructivist systems engineering" in the
stead of ethnography and to distinguish this form of
systems engineering from the "preordinate positivistic"
form. To summarize the meanings of these terms, the
systems engineering inquiry, in order to clarify the
essential requirements, must first be "responsive" to
the concerns and issues of stakeholders. Furthermore,
the "constructivist" systems engineer seeks to acquire
the abstract "constructed reality" of those involved in
the requirements phase of systems development. This
paradigm shift is crucial because demands, objects
(human and non-human), technology, and the ongoing
interactions between stakeholders experience continual
change. Hence, new demands, objects, technology,
and interactions subsequently emerge. Under this
paradigm, the notion of 'reality' is considered a human
construct that needs to be accommodated for
continually. This deviates from the positivist notion
that an objective reality exists and can be fully
understood through science, independent of human


viewpoints. Science, in fact, can be considered part of
a constructed reality that is also incorporated into the
constructivist paradigm. Summarily, the responsive
constructivist systems engineer needs to maintain a
different paradigm to requirements than that of the
positivist. This different paradigm includes the
imperative ingredients of curiosity and neutrality.

THE NECESSITY OF
CURIOSITY AND NEUTRALITY

Fundamental to the responsive constructivist
approach is a deliberate internal stance of curiosity on
the part of the systems engineer. This stance of
curiosity leads to patterns of question asking and the
enfranchisement of clients. A stance of curiosity,
when maintained by the systems engineer, is exhibited
as a shift from the stance of "expert" who is gathering
requirements, to that of a "student" who is learning
about real need from people.
Curiosity is necessary since people of varying
disciplines speak different languages containing jargon
unique to themselves. A word or phrase may mean
one thing to one person, be meaningless to another,
or explode into a completely different cognitive
schematic for the person of another professional
persuasion. These semantic differences confound true
communication which may lead to low quality
requirements. Hence, there is need for a stance of
curiosity and an ability of question asking if the
systems engineer and client are desirous of
understanding each other, and ultimately, to
inclusively delineate problems and solutions.
That which assists the professionals in maintaining
their curiosity is neutrality. As Cecchin (1987) cites,
"Curiosity leads to exploration and invention of
alternative views and moves (i.e., changes in the
pattern of the dialogue), and different moves and
views breed curiosity. In this recursive fashion,
neutrality and curiosity contextualize one another in a
commitment to evolving differences, with a
concomitant nonattachment to any particular." Here,
curiosity, while exposing differences, works alongside
of neutrality, or "nonattachment to any particular,"
that allows differences to be identified and assimilated
into the problem space. These differences, exposed by
curiosity and neutrality, add new dimensions (Bateson,
1972) to the problem and therefore allow problems to
be more completely understood and described. Figure
[1] depicts this recursive relation between curiosity
and neutrality and the resultant emergence of clearer
understanding of the problem space. The systems
engineer must learn to embrace new viewpoints when
they appear since it is a fundamental systems principle










Engineer's Internal Stance

Curiosity
Neutrality



Results In
Exposed and Documented
Viewpoints and Requirements

Figure 1. The interview process viewed as a
recursive relationship between curiosity and
neutrality.

that different views of the same thing create new
views and dimensions (Bateson, 1972).
Curiosity will also assist the systems engineer to
avoid being satisfied with cause and effect linear
explanations. Although linearity can be quite useful,
it can also have the effect of terminating dialogue and
conversations (Bateson, 1972). Systems professionals
who seek for causal explanations will tend to assume
the explanation is accurate and desist in exploring
other explanations. Here, the systems engineer is
operating as expert and has taken a stance of certainty
(Amundson et al., 1993). As Amundson et al. (1993)
state, "When we do not account for the position of the
client, we fall prey to the temptation of certainty.
When we attempt to impose corrections from such
certainty, we fall victim to the temptation of power.
Colonization (i.e., expert agreement, group think, etc.)
occurs when our commitment to "expert knowledge"
blinds us to the experience in the room." Figure [2]
depicts how embracing different paradigms can affect
the internal stance of the systems engineer. Internal
stance then motivates patterns of questioning that
subsequently generate human artifacts within
stakeholders. Power and certainty tend to cause
passivity and subordination within clients whereas,
curiosity and neutrality cause clients to be empowered
and find ownership in the problem solving effort.
Under either paradigm, the artifacts become
embedded in the interview process and influence the
human relationships that develop and the quality of
requirements gathered.
We suggest that the ability to maintain a stance of
curiosity and neutrality is a candidate critical skill for
anyone gathering requirements and core competency


of a systems engineering group. To assist individuals
and managers in identifying these critical skills, Table
[1] offers a checklist that can be used to discriminate
between a stance of certainty and a stance of curiosity.

QUESTIONING TECHNIQUE

Once the concepts of curiosity and neutrality are
understood and embraced, questioning techniques can
additionally aid the systems engineer in requirements
elicitation and abstraction. Spradley (1979) presents
an ethnographic inquiry called "The Developmental
Research Sequence." Although a thorough
explanation of this work goes beyond the scope of this
paper, we wish to introduce systems engineering to
this highly developed technique of interpersonal
questioning.
Spradley (1979) discusses three main types of
questions: Descriptive, Structural and Contrast.
Descriptive questions simply elicit information from
stakeholders, thus allowing the systems engineer to
systematically gather descriptive information about the



Internal Stance,
Questioning Pattern
and Human Artifacts
PRaponav Proroinat
srg ConrtaMucwt PostMit
Internal Stancs cty, Cranty
N trty PMwr
Ouedonlng Nutra Indiret, Cod. Dirt,
AppoMac: Natv's Languao, Expar, Languag
Circular quesons. Why qusetons
Human Empowerment, PasMiy,
Artlact Enmanchintmt Subordination.

Figure 2. Paradigm influences on engineer's
stance, questioning approach and human artifacts.

problem. Structural questions are used "to test
hypothesized categories (domains) and discover
additional included terms." Contrast questions are
used to delineate interfaces and relationships.
Spradley also discusses principles for the
administration of these questions. Principles such as
asking different types concurrently, explaining or
announcing the beginning of a question, repeating of
the same question in different ways, and others.
These principles, together with knowledge of question
types and supported by a stance of curiosity and
neutrality, form the basis for gathering complete,
unambiguous requirements and culturally grounded












Table 1. Checklist for assessing internal stance within the investigating system engineer. Yes answers
indicate the particular stance. Adapted from Amundson, et aL (1993).


STANCE OF CERTAINTY

Am I uncomfortable with ambiguity? Do I need to have
structure and clarity?

Do I quickly insist on conclusions about stakeholder's
statements based on my own experience?


Do I get so caught up in stakeholder's presentations of
the problem that I become blinded to exceptions?


When I am presenting information to stakeholders (e.g.
administrators, designers, users, etc.) do I feel that
those who don t "get it" are 'resistant" and that this
resistance must be subverted, broken through, etc.?

Am I overly concerned with asking and answering
"why" questions, in response to statements about the
problem?


Do I direct the inquiry such that I tend to close out
alternative avenues of discovery in the problem space
by narrowing observations to my own perspective?

Do I assume that a symptom of the problem is
critically important and is explained by a single cause?


Do I assume that engaging stakeholders In dialog
does not effect the resulting requirements statements
or the definition of the problem?

Am I concerned with teaching, explaining and
disseminating "expert" knowledge?


Do I tend to discount or overlook the resources (e.g..
abilities, knowledge, experience, expertise, etc.) of
stakeholders?


abstractions of the systems problem.

EXAMPLE

The following dialogue offers an awkwardly brief
snapshot of how responsive constructivist requirements
engineering might look in practice. During the
interview, the most important elements of this
approach are a) frequent restatement of the purpose
of the interview, b) offering explanations of the
engineer's need, thus recruiting the user as a teacher
and c) asking descriptive, structural and contrast


STANCE OF CURIOSITY

* Can I tolerate confusion and ambiguity without
forming premature conclusions?

* Do I move more slowly in defining the problem, taking
time to consider what is being experienced throughout
the interaction with stakeholders?

* When all involved In constructing a description of the
problem become overly convinced of its existence, do
I take time to discover exceptions to the construct?

* When it seems that stakeholders don't "get it,' do I
consider the need to ask the kind of questions that will
move the process forward?


* Do I ask circular questions that allow me to examine
effects of the problem, instead of directly examining
the asserted statement of the problem (e.g., by using
"why' questions)?

* Do I open alternative avenues of discovery in the
problem space by considering observations from
many system levels?

* Do I avoid assuming the symptoms of the problem to
be critically Important, rather that they may fit many
possible explanations?

* Do I strive to consider the stakeholder/engineer human
interaction and its effect on requirements and problem
statements?

* Do I ask questions and look for the special indigenous
knowledge of the stakeholder from my perspective as
a student?

* Do I take care to discover what strengths are present,
even seeing potential problems as possible resources?



questions. The enactment is taken from an actual
meeting between two software engineers (Stu and
Ted) and a user (Usr.). Prior to the time of the
meeting, an early software prototype was being tested
in context for the purpose of gaining a greater
understanding of the user's real need. The prototype
was a record keeping system that had both paper and
software components (Alessi et al., 1993). One of the
engineers, Stu, is quite adept at ethnographic
questioning while maintaining a stance of curiosity and
neutrality. Ted, the other engineer, is a novice to this
approach.











Table 2. Stu and Ted "join" with and "recruit" the user's assistance.


NARRATIVE

Usr: H Guys. How are you today?
Stu: Fine, thanks. How about
yourself?
Usr: Pretty good.
Stu: We thought we'd stop by and
see how the new software is
working.
Ted: Yes, have you been able to
figure it out?



Stu: Well, yes, that's a good question,
but before we get to that, I was
wondering what your reactions to
the software are?
Usr: It seems pretty good. I could tell
you sure have put a lot of work
into the thing.
Stu: Yes we have, but it certainly Isn't
a finished product and that's
where you come in. Your
feedback will help us Improve it
to the point that's useful to you.
After all, you're the expert on
what your needs arel


ANALYSIS


Creating and maintaining a friendly and respectful relationship Is Imperative.


it Is important to state and restate explanations throughout the requirements
gathering process.
Ted's question is problematic for two masons. First during the early stages
of requirements gathering, questions should be broad and explorative. Ted's
question is prematurely focused, therefore, risking brevity of response.
Second, the user may feel uncomfortable with the wording of the question
because perhaps he didn't figure It out and he feels foolish admitting It.

Stu relieves the user from Ted's question without dlsempowering Ted.


Although the user's response appears positive, it lacks commitment.


Stu restates the purpose of their collaboration and allows the user's criticisms
to be framed as feedback leading to the Improvement of the software. Stu also
positions himself as a studenf of the user by explaining his need for feedback,
thereby elevating the user to his rightful place as 'expert.


The first example narrative appears in Table [2].
Stu and Ted do not gain any new information from
the user. They do, however, renew their relationship
(i.e., "join") with the user on a human relational level,
in addition to restating the purpose of their meeting
and "recruiting" the user's expertise. Stu also had to
deal with the present human system, replete with
questions by Ted that could restrict the free flow of
information from the user. Stu's complication
exemplifies potential situations from associates,
management and bureaucrats that complicate the
engineer's task.
Table [3] continues the narrative with the user
pointing out one problematic area in response to Stu's
and Ted's questions of description and restatement of
the user's response. The user, rather than stating the
problem directly, has transformed the need into a new
solution of which he is eager to present. Ted takes a
stance of certainty from which Stu must again redirect
the dialogue back to a responsive constructivist
framework.
Table [4] picks up the dialogue after Stu had a
chance to explore the user's design with circular
questions. Stu was attentive to the user's response to


questions and subsequently co-constructed six domains
of importance to the user. The narrative in Table [4]
introduces further circular questioning to delineate
one of the six domains. We end this example with Stu
forming a question about the booklet reorganization
but placing the question in the context of record
keeping. This approach gives the user a choice of
going in a number of directions but "contextualizes"
the response to the domain of record keeping. Stu
will be attentive for words and phrases that alert him
to new structures.
Contrast questions did not appear in the example
since they generally come after the basic construction
of the problem space has been identified. An example
contrast question might be, "Could you compare (i.e.,
contrast) the booklets and the computer as parts of a
record keeping system?" Here, information about the
relationship between the booklets and the computer
would aid in the design of interfaces.

SUMMARY

The responsive constructivist systems engineering
paradigm presents differences in thinking and













Table 3. Ted and Stu explore the user's design, probing for underlying need.


NARRATIVE

Usr: Well. first I have redesigned the paper
booklets. I've set up this numbering
strategy for all the different items I want to
keep track of and then when I enter them
In the computer I just have to type
sequences of numbers. This should be
really fast and anyway I was having trouble
pulling all those menu items down just to
keep track of something.

Ted: You really don't have to do that You
should have used the group function to
speed the data entry. Here let me show
you on the computer.

Stu: Yes, the system Is pretty fast after it is
initially set up, but I am curious about the
new design. Could you show it to me and
could you describe the process you
experienced while arriving at the new
design?


methodology over the "preordinate positivist"
approach to systems engineering. For problems where
major requirements involve human interaction among


ANALYSIS

The comments of the user displays his active involvement and feelings
of partnership In the process.








Ted has lost curiosity and Is operating In a stance of certainty. He Is
quickly insisting on conclusions about the user's statements based on
Ted's experience with the software. The user deviated from Ted's expert
knowledge in the design of the software and that poses a threat to Ted's
control. Consequently, Ted becomes blinded to the user's experience.
Again, Stu gently redirects the focus from Ted's objections to the user's
expertise. Although this represents a threat to the software in its present
state, Stu is not defeated by this, in fact, this is what is desired. Stu did
not ask the user why he redesigned the booklets because that would
question meanings and motives of the user thereby suggesting a hidden
judgmental component. Rather, Stu asks a question that places the
user's problem In its context. This enables Stu to hear the
developmental story of the discovery of the problem and solution of the
user that provides rich and useful Information to Stu.


people of significantly different views, the responsive
constructivist approach has distinct advantages.
Formulated around obtaining an abstract


Table 4. Stu uses curiosity and a technique of question asking to probe deeper into the user's world.


NARRATIVE


ANALYSIS


Stu: So, to this point I have recorded six overall areas
that you feel need further attention (and Stu lists
them). Is this a correct assessment? Can you thinK
of any more?

Usr: I think you have summed it up pretty well. I can't
think of anything else.
Stu: O.K., I'd like to get a little more specific about each
of these. Let's talk a little bit about the new way to
group booklet pages.
Usr: Sure.
Stu: I'm curious, how has this arrangement helped you
with your record keeping?


Stu has discovered the fundamental domains problematic to
the user. These six items amr the basic structural elements
of what Stu wants to leam from the user. Note that these
'constructions' were derived from the user's 'responses.'
Stu first ftsts that all the structures hove bonn covered.
Slu explained he Is Introducing the next line of questioning.
He picks one of the domains and endeavors to further
explore the structure within it.

Rather than asking direct questions, Stu inquires by putting
his question Into the user's context This way, the user can
take the discussion whichever direction necessary to him.
Additionally, forming questions In a context (e.g., record
keeping) tends to glean more information.









"construction" of the problem space, generated by
"responsive" question-asking, the perceived realities of
stakeholders are more easily and accurately seen.
The recursive relationship between curiosity and
neutrality is a necessary principle of the responsive
constructivist paradigm. Curiosity drives probing
questions while neutrality allows new insight to be
seen and become integrated into the newly
constructed problem space. Alongside curiosity and
neutrality, a host of questioning techniques are
available to aid the systems engineer.
Responsive constructivist thinking is, in many ways,
already part of the systems engineering approach.
This new terminology helps identify differences from
other systems approaches and therefore aids the
formation of an underlying systems engineering
theory. Additionally, pragmatic tools such as the
stance checklist (Table 1) and questioning methods
(Spradley, 1979) are of immediate practical use to
anyone who engages in the activity of gathering
requirements from stakeholders.

LITERATURE CITED

Alessi, R. S., Vang, L., Hjelmfelt, E., Mayhew, M. E.,
and Voorhees, W. B. 1993. Systems engineering
case study: A software-driven whole-farm
management information system, p. 845-852. In
J.E. McAuley and W.H. McCumber (ed.) Systems
Engineering in the Workplace. Proc. Third
Annual International Symposium. National
Council on Systems Engineering (NCOSE).
Arlington, VA, July 26-29, 1993. NCOSE.
Washington, DC.
Amundson, J, Stewart, K. and Valentine, L. 1993.
Temptations of power and certainty. Journal of
Marriage and Family Therapy, 19(2):111-123.
Bateson, G. 1972. Steps to an ecology of mind.
Ballantine Books. New York.
Becvar, D. S. and Becvar, R. J. 1988. Family therapy:
A systemic integration. Allyn and Bacon, Inc.,
Boston.
Cecchin, G. 1987. Hypothesizing, circularity, and
neutrality revisited: An invitation to curiosity.
Family Process. 26:405-413.
Deal, T. E. and Kennedy, A. A. 1982. Corporate
cultures. Addison-Wesley. Reading, Mass.
Goodsell, C. T. 1981. The new cooperative
administration: A proposal. InternationalJournal
of Public Administration. 3:143-155.
Guba, E. G. and Lincoln, Y. S. 1989. Fourth
generation evaluation. Sage Publications. Newbury
Park, CA.


Leahy, M. 1987. Introduction. In Cohn-Sherbok, D.,
Irwin, M. (ed.) Exploring reality Allen and
Unwin., London.
Long, Linda J. 1989. Question negotiation in the
archival setting up: The use of interpersonal
communication techniques in the reference
interview. American Archivist. 52(1):40-50.
Maynard-Moody, S., Stull, D. D. and Mitchell, J. 1986.
Reorganization as status drama: Building,
maintaining, and displacing dominant subcultures.
Public Administration Review. 46:301-310.
Spradley, J. P. 1979. The ethnographic interview.
Holt, Rinehart and Winston. New York.
SEI, 1993. Software requirements engineering. Bridge,
2:17-21, Software Engineering Institute (SEI),
Carnegie Mellon Univ., Pittsburgh, PA.
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analysis. p. 41-46. In J.E. McAuley and W.H.
McCumber (ed.) Systems Engineering in the
Workplace. Proc. Third Annual International
Symposium. National Council on Systems
Engineering (NCOSE). Arlington, VA, July 26-29,
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text. CRC press, Boca Raton, FL.

AUTHOR'S BIOGRAPHY

Mr. Michael E. Mayhew. Michael is a doctoral
candidate at Iowa State University. He has worked
with Sam for the past 5 years adapting the techniques
presented in this paper to an agricultural systems
engineering project. Michael is pursuing a career as
a human systems and design consultant.

Dr. R. Samuel Alessi. Sam has been studying
software and systems engineer techniques and is
applying this aerospace technology to agricultural
problem solving and software development.








ON-FARM RESEARCH IN KANSAS, 1993:
SUMMARIZED RESULTS OF A FARMER OPINION SURVEY


BACKGROUND

You are one of the farmers who, in 1993,
kindly agreed to complete a survey sent from
Kansas State University (KSU) asking your
opinions about on-farm research (OFR). We
also asked if you would be interested in a
summary of the results of the survey when they
became available. Here is the summary! We
hope you will be interested in the results. We
are also making the summary available to the
county agricultural extension agents and to the
Kansas Farm Management Association (KFMA)
field staff.

More detailed results are available in a recently
completed MS thesis.1 A Report Of Progress is
being prepared which will be published by the
KSU Agricultural Experiment Station. It also
will contain more details than is possible to
include in this short summary. If you wish to
receive a copy of the Report of Progress when
it is published and/or wish to work with us as
we try to learn more about OFR, please
complete the form at the back of this summary
and mail it back to us. Thank you in advance!


INTRODUCTION

Three groups of Kansas farmers were surveyed.
Samples were drawn from:

* A complete list of Kansas farmers kept by
Kansas Agricultural Statistics (KAS).


1 Stan Freyenberger, September 1994,
"On-Farm Research in Kansas: Farmer Practices
and Perspectives." Manhattan: Department of
Agricultural Economics, Kansas State University,
125 pages.


* A list of farmers who are members of the
Kansas Farm Management Association
(KFMA).

* The mailing list of the Kansas Rural Center
(KRC).

A total of 2,600 surveys were mailed: 1,100 to
KAS farmers, 900 to KFMA farmers and 600 to
KRC farmers. The number of responses that
were complete enough to use, are shown in
Table 1.

You will notice in Table 1 that KRC farmers
were not well represented in the western part of
the state. Therefore, it is not valid to compare
the three samples for the state as a whole.
Because of this we have only compared the
aggregate results for the KAS and KFMA
samples. However, as you will also see in
Table 1, there are five Crop Reporting Districts
(i.e., the three eastern ones, central and south-
central) where there are an adequate number of
farmers in all three samples, and so we did
another comparative analysis for the aggregate
of these districts only.

We wanted to compare the three samples,
because we:

* Assumed that the KAS sample was
representative of all the farmers in the state.

Were not sure how representative the
KFMA farmers would be of all the farmers
in the state.

Believed that the KRC farmers were likely
to be more actively interested in alternative
or "sustainable" agriculture.

Before we present a summary of the results we


Date: November 8, 1994








would like to clarify two points:

* The term "research" in OFR is used
somewhat more loosely than would be
acceptable to most research scientists.
Since the objective of the survey was to
seek farmers' opinions, the term reflects
what they perceived as research. It was
apparent from the survey results that
"research" in OFR as viewed by farmers
could be anything that was designed to
evaluate alternatives, including formal
trials, demonstrations and farmers' own
experimentation.

* In the following summary, there might be
the perception that only the KRC farmers
are interested in "sustainable" agricultural
practices. Obviously this is not the case.
"Conventional" farmers are also interested
in, and do try to adopt, sustainable
agricultural practices that are compatible
with their goals. All we are suggesting is
that the farmers associated with Kansas
Rural Center may have goals that give
greater priority to sustainable agricultural
practices than other goals, such as
maximizing income. Therefore the term
sustainable should simply be interpreted in
terms of relative commitment. To avoid
possible misinterpretration, we will
therefore, whenever possible, use the term
alternative rather than sustainable
agriculture.


CHARACTERISTICS OF FARMERS

In general, the survey results indicated little
difference between the KAS and KFMA farmers
(which we viewed as mainly conventional
farmers) but there were major differences
between the KAS/KFMA samples and the KRC
sample (which we have just indicated are likely
to be more interested in alternative agriculture).
In presenting the results, reference to major
differences will be guided by tests of statistical
significance.


Points to note in Table 2, are that the KFMA
and KRC farmers were on average younger
while the KRC farmers had a higher level of
formal education. On the other hand, partly
perhaps as a result, they had fewer years of
experience in farm management.

The KRC farmers managed significantly smaller
farms and therefore not surprisingly had a
greater number of dependents working, part or
full time, off the farm.


SOURCES OF INFORMATION

When farmers consider adopting new
technologies it is reasonable to assume they will
use different sources of information for different
technologies. Table 3 indicates that this, in fact,
is the case. In the interest of brevity we have
only presented the three most important sources
of information for each technology. More
detailed analysis indicated that KAS and KFMA
farmers, in particular, tended to rely heavily on
agribusiness for information relating to soil
fertility (e.g., fertilizer), seed treatment, weed
control (e.g., herbicides and tillage equipment),
insect and disease control (e.g., insecticides and
fungicides), and crop varieties. Adoption of
these technologies involves purchasing in the
market place. Other sources of information
were considered very important for the
remaining technologies, which often do not
require major reliance on purchased inputs but
rather require managerial or farming system
adjustments. In this regard own experience
(OE) and KSU research and extension (KS) staff
were important sources of information.

After analyzing the preferred informational
sources about individual technologies, we then
analyzed responses to four other questions. In
this summary, we have not presented the results
in table form, but the general conclusions were
as follows:

* Overall sources of information considered
most reliable were county agricultural

Date: November 8, 1994








extension agents for the KAS farmers, KSU
extension staff for the KFMA farmers, and
own experience for the KRC farmers. If
the KSU research and extension staff
figures are aggregated, then these were the
most reliable sources of information for all
three samples of farmers. Because of the
close association of the county agricultural
extension agents with KSU, it could be
argued that they should also be included. If
they are, then the dominance of KSU
related staff would be even greater.

* Overall sources of information considered
least reliable sources were media (i.e.,
radio and TV) for KAS and KFMA
farmers, and commercial firms for KRC
farmers.

* Media sources judged most useful in
making decisions regarding whether or not
to adopt new technologies were KSU
bulletins for KAS and KFMA farmers, and
alternative agriculture publications for KRC
farmers.

* According to the farmers, organizations
whose research information best met their
needs were county agricultural extension
agents for the KAS group, KSU extension
staff for the KFMA group and, alternative
agriculture organizations for the KRC
group.


COLLABORATIVE OFR EFFORTS

Farmers from all three groups know of more
collaborative on-farm research (OFR) activities
on other farms than was taking place on their
own farms. For the KAS and KFMA farmers,
commercial firms and KSU or county
agricultural extension agents were the most
frequent collaborators in OFR. The most
frequent collaborators of the KRC farmers were
the Kansas Rural Center, followed by
commercial firms. For the KRC farmers
frequency of cooperation with KSU dropped to


fourth place. For all farmers, the county
agricultural extension agent was the second or
third most frequent cooperator.

KFMA and KRC farmers collaborated in nearly
twice as many OFR trials per farmer as KAS
farmers (i.e., 0.85 and 0.83 respectively
compared with 0.43 trials per farmer). For all
three groups, most reported trials were
replicated on their farms rather than on other
farms in the area.

In collaborative OFR work, crops and soils were
by far the most dominant issues examined by all
three groups of farmers.

The roles of researchers and farmers in
conducting OFR differed according to the three
groups of farmers. With KAS farmers,
researchers or technicians tended to both manage
(i.e., make decisions as to when operations
should be done) and implement the trial, while
in the case of KFMA farmers, the outside
cooperator tended to manage the trials but it was
left to farmers to implement them. However, a
participatory approach was more evident with
the KRC group where farmers tended to
implement the trials and manage them as well.

KAS and KFMA farmers preferred to do trials
with county agricultural extension agents and
KSU research staff, while KRC farmers
preferred to cooperate with the Kansas Rural
Center and, to almost the same extent, with
county agricultural extension agents.

Ninety five percent of the responding farmers
expressed a willingness to travel more than 10
miles to see OFR. One-third of the farmers
were willing to travel more than 40 miles for
relevant OFR field-days.

About two-thirds of the KAS and KFMA (68
and 69 percent respectively) farmers would like
to see more OFR, wheras the percentage was
almost 90 percent for KRC farmers. Most
farmers expressed a willingness to cooperate in
OFR and indicated they would provide land,

Date: November 8, 1994








labor and equipment. Compensation was not a
condition for such cooperation, although many
farmers did indicate that they would like to be
covered against loss. This may be influenced by
how much they were consulted in the design of
the trial. Related to this, there was a general
feeling among farmers that they would like to be
involved in determining treatments and plot
layout, although this desire was significantly
stronger in the case of the KRC farmers.


INDIVIDUAL FARMER OFR

Close to 75 percent of all the farmers said they
did testing on their own volition in the last three
years. The percent of KRC farmers engaged in
their own OFR was similar to the other two
groups (i.e., 78 percent compared with 75 and
69 percent for the KFMA and KAS samples).
The average number of trials per farmer over
the three year period, was 0.72 for KAS, 1.22
for KFMA, and 1.68 for KRC farmers. This
indicates substantial differences in the intensity
of OFR. Fifty-four percent of all the farmers
said they implemented two-to-five trials, but 23
percent of the KRC respondents claimed that
during the last three years they had implemented
six or more trials compared to only nine percent
and seven percent of the KAS and KFMA
farmers.

In terms of the technologies tested, the greatest
emphasis in farmers' own testing, as in the case
of collaborative OFR, was on crops and soils.
The lack of OFR work with livestock is perhaps
not altogether surprising given the
methodological problems of doing livestock
trials on-farm. However, farmers doing their
own OFR did tend to do relatively more trials
with livestock than was the case in collaborative
OFR.

Extension bulletins and leaflets were the most
popular media sources for information about
new technologies. However, magazines were
also important, particularly with KRC farmers.
We would speculate that magazines of particular


interest to KRC farmers tend to relate to
alternative agriculture.

All groups reported that farmers first visited
with other farmers and county agricultural
extension agents prior to testing, although KRC
farmers placed significantly greater weight on
information from other farmers.

Farmers in their own OFR tended to test on a
small area before full adoption. This is also
done by researchers, as they run preliminary
tests prior to full-scale experimentation.
However, the survey results also showed two
major points of divergence in OFR between
what the researcher and the farmer would do.
These differences perhaps provide the most
important reasons why the challenge of closer
collaboration between on-station research and
OFR, and between researchers and farmers'
OFR, still remains. The two differences are as
follows:

* To apply their analytical techniques
research scientists tend to rely heavily on
replicating treatments and repeating the
trials in different places and/or different
years. In the survey 44 percent of the
farmers felt that a trial only needed to be
implemented twice in order to validate the
results. Indeed, 34 percent felt that it only
needed to be done once. This issue
becomes more of a problem given the fact
that 37 percent of the farmers do not
replicate treatments in their own OFR.

The use of controls or check plots is also
important to researchers in providing
standards against which experimental
treatments can be compared. Once again it
appeared from the survey results that
farmers tended to be less concerned about
controls, perhaps because of familiarity
with their own farm, and the fact that they
only need to convince themselves of the
value of the results. Only 36 percent of the
farmers implementing their own OFR had
controls likely to be acceptable to

Date: November 8, 1994








researchers, with KRC farmers being the
least supportive of this strategy (i.e., only
28 percent). In fact 25 percent of all the
farmers used only a before- and-after
comparison, and in the case of the KRC
farmers, this percentage was 35 percent.

The implication of the above findings is that
obviously there will need to be compromises on
both sides if effective collaborative working
relationships are going to develop between
farmers and researchers, particularly in OFR.
The results of the survey suggest that many
farmers believe it is important to move towards
greater collaboration between farmers and
research scientists. One small but perhaps
significant fact in support of this is an
implication from Table 4, that farmers used
multiple criteria in evaluating trial results.
Research scientists, on the other hand, tend to
use fewer, and possibly different, criteria.
Including the farmer increases the probability
that the different evaluative criteria will be
weighted according to the farmers' preferences.


FARMERS' VIEWS ON STATION AND OFR

In Table 5 we have recorded the responses to a
number of attitudinal questions regarding OFR.
The results, in general, indicated very little
difference between the attitudes of the KAS and
KFMA farmers, but major differences with the
KRC farmers. In general, the KRC farmers are
more skeptical about the value of university
experiment station-based research (Statements 1
and 3), had stronger convictions than the others
about farmer input into the university-based
research system (Statements 7 and 8), and would
like greater attention being paid to small-scale
farming and to diversified agriculture (i.e., two
hallmarks of alternative agriculture) (Statements
10 and 11). The attitudinal results also implied
a desire on the part of all farmers for closer
collaboration with the university-based research
system, and with other farmers (see responses to
the replication issue in Statements 5 and 6).
Finally, there was support for the notion that the


research process does not finish when it leaves
the experiment station but rather research on-
station and on-farm are part of a continuum (see
Statement 4). In connection with this, farmers
did not appear to mind whether field days were
held on-station or on-farm (Statement 9) and
were not opposed to the idea of the small plots
characteristic of on-station research (Statement
2).


SUPPORT FOR OFR

The following points from analysis of the survey
results support greater attention to OFR in
Kansas:

* Farmers placed considerable reliance on
their own experience and other farmers'
experiences as information sources in
deciding what to do. Support of this was
also provided in agreement with Statements
4 and 8 (Table 5). Our analysis also
indicates they were very willing to share
their own information with others including
farmers and institutions therefore
potentially providing useful roles as
unofficial "extension agents."

Issues that were not crop or enterprise
specific, and sometimes were related to
sustainability, were often mentioned when
farmers listed OFR concerns (Table 6).
Many of these issues require a whole farm
or system perspective and may have a
degree of locational specificity in terms of
their resolution.

OFR is currently practiced by most farmers
(i.e., by both KAS/KFMA and KRC
farmers, although to a greater extent by the
latter) either on their own initiative or in
collaboration with outside groups.
Anything that can improve the usefulness
and impact of the effort and results should
be encouraged.

Farmers expressed a desire to cooperate in

Date: November 8, 1994








OFR, through indicating a willingness to
contribute land, labor and equipment in
such collaborative activities a source
which should be tapped in an era of
increasingly limited research resources.

* As we indicated earlier, researchers tend to
use fewer criteria in evaluating proposed
technologies whereas farmers use multiple
evaluation criteria in their evaluation.
Therefore farmers' involvement can be
important in improving the potential
relevance of proposed technologies.

In support of our belief that OFR should be
encouraged in Kansas, and to complete this
section, we would like to quote a few comments
made by farmers in completing the survey:

* "I have been a strong advocate of
more OFR for several years. I would
certainly be willing to cooperate."

* "OFR could multiply the amount
extension could do, and in doing so
would allow them to stay current with
actual farm practices."

* "It would be interesting to see a
questionnaire sent out to farmers each
year asking them what tests they have
done that year and their results, and
have them compiled and mailed out."

"Thanks for doing this. I feel positive
about this initiative on your part. I
have been an extension agent and I
have fanned. The two often diverge
in the field."

"Thanks for getting the farmer
involved."


OFR AND KSU EXTENSION/RESEARCH

According to the survey there is support for
KSU extension being involved in OFR. One


typical remark was:

"I have cooperated with KSU extension on
experiments before and enjoyed working with
them. I felt the information gained was very
worthwhile and so did the local farmers. I
would work with them again, in a flash, on the
right experiment."

Nevertheless, from a few farmers, there was
some frustration with what they perceived as
current priorities of the extension/research
system. Two examples given by farmers, which
may reflect some confusion between research
and extension, were:

* "Experiment (i.e., research) fields try for
maximum yield by planting earlier than
most farmers. We try for a good average.
Experiment fields try for tops. You need to
follow a normal cropping pattern for the
area."

"Increased yields are not as important as
increased profits." "How about more profit
seminars rather than yield seminars?"

Also, more information and quicker
dissemination of information seemed to be an
issue. Typical comments were as follows:

"Rapid, accurate dissemination of
knowledge is an enormous and growing
problem. A computer bulletin board or
similar service where research results could
be put for everyone to access would be a
big help."

"How do I get research information from
K-State experiments? Do I have to belong
to a special club?"

"I would like to see OFR collected and
published."

"Farmers want more information on how to
escape the chemical go-around."


Date: November 8, 1994








SO WHAT NOW?

Well, it appears obvious that further OFR
initiatives should be encouraged in efforts to aid
both conventional and alternative agriculture
oriented farmers. As we have indicated, the
challenges for improving collaboration between
research scientists and farmers are formidable,
but with good will on both sides much can be
done. As the survey results indicated, a number
of OFR initiatives are already being
implemented by public and private agencies in
Kansas, and these should continue to be
encouraged. The issues are:

* How can OFR in Kansas be expanded?

* How can the payoff from existing and
future efforts in OFR in Kansas be
maximized?

We appreciate that you may find this summary
of the survey results too brief. If this is the case
then, as we said at the beginning, please
complete and mail the form at the end of this


summary and we will send you a copy of an
expanded report (i.e., Report of Progress) when
it is published. Also, if you are interested in
being on the mailing list for future papers on
OFR in Kansas, please indicate this on the
enclosed form. Finally, if an opportunity arises
for collaborative OFR in the future, please
indicate whether or not you are interested.

Perhaps one final point is in order. At the end
of the survey, we gave an opportunity for
farmers to write anything they liked. Table 7
attempts to summarize the remarks that, as you
can see, covered a range of topics. Some
comments were survey related, others OFR
related, and others concerned farming related
issues.

Again, those of us involved in the survey want
to thank you for your part in making this study
possible, in spite of the fact that some of you
indicated, quite rightly, that it was too long!
There have been no baseline studies on OFR in
Kansas, so this information will likely be useful
to a number of different groups.

Stan Freyenberger
Leonard Bloomquist
David Norman
David Regehr
Bryan Schurle

November 1994
















Date: November 8, 1994







Table 1: Useable Sample by Crop Reporting Districte

Region Northern Central Southern Total
KAS KFMA KRC KAS KFMA KRC KAS KFMA KRC KAS KFMA KRC

West 12 22 2 11 24 1 23 19 1 46 65 4
Central 16 25 5 16 26 14 20 16 12 52 67 31

Eastern 17 23 37 18 37 14 20 24 13 55 84 64

Total 45 70 44 45 87 29 63 59 26 153 216 99

S Shaded cells indicate the five districts in which the KAS, KFMA and KRC samples were large enough to permit comparisons of all three samples.


Table 2:


Means of Farmer Sample Characteristics'


Characteristic Statewide Five Districts
KAS KFMA KAS KFMA KRC

Farmer Age (Years) 58.5 a 51.1 b 59.6 a 50.9 b 49.2 b
Education Level2 2.8 3.0 2.9 a 3.1 a 3.5 b

Years Managed Farm 34.3 a 27.1 b 35.3 a 26.9 b 18.7 c

Average Number of:
Dependents 3.4 4.1 3.3 4.1 3.4
Family Members Working Off-Farm 0.56 0.59 0.62 a 0.65 a 1.2 b

Acres: Owned 978 a 740 b 842 a 622 ab 383 b
Rented 1172 1199 860 a 1059 a 460 b
Total 2150 1939 1702 a 1681 a 843 b

Means across columns followed by different letters are significantly different (p = 0.05). Two sets of
comparisons are made (i.e., between KAS and KFMA state-wide and between KAS, KFMA and KRC for
the five district level). Absence of letters indicates differences were not significant. The same approach is
followed for all tables where analogous statistical tests are used.
. Educational level: 1 < High School 4 = BS Level
2 = High School 5 > BS Level
3 = Technical School


Date: November 8, 1994







Most Important Sources of Information for Different Types of Technololg


Ine 1st, 2nc, anda rd choices were weigntea -,z,i respectively. hney were men summed up. ine top cnoice
per group over the five districts is listed along with the percent of the weighted response that the choice
received.
2 KSU: KS KSU Research and Extension Profit: PC Commercial Representatives
Government: PV Veterinarian
GE County Agricultural Ext Agent PS Private Consultant
GS SCS/ASCS PI Input Supply Store/Coop
Non-Profit: PM Media (Radio, TV, Magazine)
NA Alternative Agric. Group Other: OE Own Experience
OF Other Farmer

Table 4- Criteria Farmers Use for Evaluating Test Results


1-1-^ I 2


Reported as a percent of weighted totals. Thre
were calculated.


Date: November 8, 1994


Percent of Choices' and Sources2
Type of Technology
KAS KFMA KRC

Crop Varieties 22 KS 21 KS 18 OF

Soil Fertility 20 PI 20 GE 18 PI

Seed Treatment 19 PC 17 PI 20 PI

Weed Control 23 PI 20 PC 22 OE

Insect/Disease Control 19 PI 18 GE 17 OE

Tillage Method 30 OE 39 OE 36 OE

Alternative Crops 22 OE 21 KS 28 NA

Sustainability Issues 26 PM 21 PM 34 OF

Crop Rotations 40 OE 39 OE 45 OE

Animal Health 56 PV 52 PV 45 PV

Animal Breeding 36 OE 27 OE 30 OE

Animal Nutrition 21 OE 24 KS 20 OE

Facilities/Equipment 31 KS 23 KS 27 KS

Erosion Control 40 GS 43 GS 31 GS
i i I [. ] 4 1 .-- -] -


Criteria' Statewide Five Districts

KAS KFMA KAS KFMA KRC

Increased profit 27 24 28 24 19

Increased yield 22 27 24 26 15

Reduced cost 18 11 20 10 18

Ease of management 10 9 7 9 11

Risk reduction 9 16 6 16 12

Environmental effects 4 4 6 5 16

Others 10 9 8 8 9
..__L1.. n:..


rm


I


J


Table 3:


Q







Table 5: Farmer Attitudes About On-Farm Research'


______StateType of Sample
Statement
Statewide Five Districts
KAS KFMA KAS KFMA KRC

1. Recommendations based on university experiment station results are useful to me. 1.77 1.80 1.81 a 1.71 a 2.08 b
2. University experiment station research plots dealing with agricultural are
generally too small to produce useful information to farmers. 3.38 3.48 3.43 3.53 3.40
3. Current agricultural research on university experiment stations is very relevant to
farmers. 1.95 2.07 1.96 a 2.03 a 2.57 b
4. Before agricultural recommendations are made from university experiment station
trials, results should be tested on working farms. 2.14 2.13 2.21 2.08 1.93

5. On-farm trials set up by outside organizations should be replicated on various
area farms. 2.48 2.51 2.49 a 2.43 a 2.11 b

6. Treatments of your own on-farm trials should be replicated on other farmers'
farms rather than replicating on your own farm only. 1.94 2.00 1.91 1.95 2.02
7. It is important to have farmer input in planning university-based agricultural
research on experiment stations. 2.01 2.00 2.13 a 1.91 a 1.58 b
8. It is important to have farmer input in planning university-based agricultural
research on farmer's farms. 1.83 1.89 1.86 a 1.80 a 1.56 b

9. I would rather visit research station field-days than on-farm research field-days. 3.07 a 3.26 b 3.04 a 3.37 b 3.28 ab

10. I would like research (experiment station and on-farm) to give more attention to
small-scale farming. 2.69 a 2.91 b 2.64 a 2.97 a 1.88 b

11. I would like research (experiment station and on-farm) to give more attention to
diversified agriculture. 2.35 2.43 2.42 a 2.39 a 1.58 b


S Values in columns reflect the following:


1= strongly agree,


2=agree,


3=no strong feelings,


4=disagree,


5=stronglydisagree.


Date: November 8, 1994







Specific OFR Interests of Farmers
(Percent of Responses)


Statewide
Desired OFR
KAS KFMA KRC

Tillage 27 14 7

Crops 24 18 6

Soils/Fertility 11 27 11

Weeds 9 11 6

Livestock 9 14 7

Rotations 2 3 14

Sustainable Farming 11

Other' 18 13 38


KAS: Alternate crops, equipment, horticulture.
KFMA: Management, residue, low-input, irrigation,
rodents, horticulture, drying, alternative crops.
KRC: Alternative crops, grazing, biotech, legume,
cover crops, organic gardening, chemical use,
drying, structures, economics, equipment.


Table 7:


KAS:
KFMA:
KRC:


Comments after Responding to the
Survey (Percent of Responses)


Age, crops, government, extension
Environment, time limits, government
Sustainable agriculture, non-traditional, age, government

Date: November 8, 1994


Table 6:


Statewide
Comments
KAS KFMA KRC

Survey too long or difficult 22 23 8

Economics 14 10 8

Positive OFR comments 11 6 24

Positive KSU comments 8 18 14

Information is needed 6 8 14

Others' 39 35 32







PLEASE COMPLETE THIS FORM IF YOU WISH TO MAINTAIN CONTACT


Your Name:

Your Address:








Occupation if not a farmer:

Do you wish to receive a Report of Progress on the survey when it is available?


Yes:


Would you like copies of any other papers that are free and we produce on OFR?
Yes: No:

IF YOU ARE A FARMER:
Would you be interested in collaborating on collaborative OFR activities if such an
opportunity arose in the future?


Yes:


Are you currently collaborating with someone on OFR activities?


If yes with whom?

Would you consider yourself a conventional or alternative agriculture (sustainable) farmer
- that is in terms of the types of responses reported in the summary?




Please return this form to:

S.Freyenberger/D. Norman
Department of Agricultural Economics
Room 311, Waters Hall
Kansas State University
Manhattan, Kansas 66506


Date: November 8, 1994




Reproduced with permission from: American Journal of Alternative
Agriculture, Volume 3, Number 4, Pages 168-173. 1988.


On-farm experiment designs and implications for


locating research sites

Phil E. Rzewnicki, Richard Thompson, Gary W. Lesoing, Roger W. Elmore, Charles A. Francis,
Anne M. Parkhurst and Russell S. Moomaw


Abstract. Research plot that are large enough to accommodate regular farm ma.
chinery are thought to contain too much field variation to allow reliable interpretation
of experimental results This study was conducted to determine whether experimental.
error was controlled on a wide variety of agricultural field trials that used plots larger
than normally used by research The investigation included trials conducted on an
experiment station and trials conducted on actual commercial farms The planning
and management of the experiments ranged from those completely conducted by uni-
versity researchers to those completely done by farmers
The level of experimental error in all the trials was well within the limits normally
accepted by researchers in agronomy. Plots ranging in length from 125 to 1200 feet
and as wide as one or two passes of standard farm machinery gave experimental results
that were statistically sound Statistical requirements for randomization and replication
were all met.
The ability to use large plots and farmer participation enhances the testing of new
technology on farms This leads to new opportunities to test crop production factors in
a systems setting under actual farm conditions The statitica reliability of the on-farm
designs analyzed in this study should increase cooperation among researers extension
workers, and farmers in research activities


Key words: research plot size, experimental error, actual commercial farms, ran-
domization, replication, new technology, statistical reliability


Introduction

The use of working commercial farms
as sites for conducting agricultural re-
search is often not considered when ex-
periments are planned. However, on-
farm research can provide unique op-



Phl E. Rzemuici a Awomne Eirmmio Apncmor-
alist and graduate student m the DepUtm of Agm-
omy. Uniernmy of Nebraska. Uao. Nebrska. 6583:
Rihaurd Thompson is a farmer and consutart. Borne.
Iow. 50036 Gary W. Laoim Adminisam aveAsat
of Univeriy of Nebrask Agicutmanl Rmearc ana OD
velopment Cente and dnta scdent in the Deau-
mem of Agronomy. Univemty of Nebraska Roger W.
Elmure a Assomae Professor of Agpoomy (Cay Can-
ter). Charl A. Frmas (Lincolt ) and Rusel S.
Moomaw (Concord) ae Profel of Agrmomy and al
ae bEr on Crops Specisha. Univeuny of Nebraska
Anne M. Parkhfa a Professor of Biometry, Biomamn
Center. Univemy of Nebrska. Licol.


portunities to answer some questions
augmenting what can be done on ex-
periment stations. Lockeretz (1987)
provides the following reasons for con-
sidering on-farm research as a compo-
nent of a balanced, overall agricultural
research program:
-desired soil types or other physical
conditions are not available on the ex-
periment station but are available on
farms;
-larger land areas are needed than
those available on an experiment station;
-studies are needed of interactions
among several enterprises within a farm
system;
-constraints of a working farm are
needed to compare the performance of
a system there with its experiment sta-


tion counterpart;
-techniques to be evaluated are partic-
ularly sensitive to levels of management
such as integrated pest management
-farm sites are available where a pro-
duction method has been in use for a
long time and the long-term effects of
such a method are being researched.
Other specific reasons for selecting a
research location on-farm include the
need to test new techniques under a
range of conditions or to analyze a prob-
lem found on an individual field. Cur-
rent public concerns about
environmental quality and renewed in-
terest in the economic feasibility of farm
production rmmn-,e anins are
broader reasons. Lastly, there is an in-
creasing concern by university research-
ers and extension personnel about the
need for a systems approach in devel-
oping new information and recommen-
dations. Actual farm sites can provide
some of the systems to test the appli-
cability of new information found at ex-
periment stations or to investigate new
alternatives.
Much of the literature in recent years
regarding on-farm research justification
and methodology has been generated in
the area of Farming Systems Research
and Extension (FSR/E) (Gilbert et aL,
1980). Evaluation of new technology
with respect to profitability and com-
patibility of new input combinations
with farmer systems is the final stage of
agronomic testing in farm trials of major
international research centers (Sanders
and Lynamm 1982). High rates of adop-
tion of recommended practices have
been found when research is conducted
on farmers' fields (Martinez and Arauz.
1984). On-farm research in the inter-
national arena has not only accom-
plished evaluation and transfer of new
technology, but has also generated new


American Journal of Alternative Agriculture







technology as reseerci lears r of the
benefits of practices developed by farm-
er (Horton, 1984). Modes for defying
the functions of researchers, extension
workers and farmers in on-farm research
have been developed (Kirkby, 1984; Hil-
debrand and Poey, 1985). Criteria have
been devised for categoriing new ex-
tension recommendations. by types of
farmers or recommendation domains a
a result of on-farm research (Byeriee et
aL, 1980).

On-farm research In the
U.SA

In the United States researchers con-
duct some on-farm research. These trials
usually use small plots and speci-lied
equipment and/or hand planting and
harvesting. The researcher provides
nearly all the planning and management
of the on-farm experiment using the
same techniques as applied in experi-
ment station trials. However, new re-
search demands for testing within farm
systems or incorporating farmer man-
agement requires large tracts of land,
increased farmer cooperation and an in-
depth look at the objectives of an ex-
periment and treatment numbers. Also
farmers more readily believe results
from plots on which full sized farm ma-
chinery can be used. Some farmers are
skeptical about results which come from
small plots in conventional experiment
station field trials (Francis at aL, 1986;
Thompson, 1986).
If on-farm research involves conven-
tional farm machinery and large plots,
a small number of treatments is rec-
ommended. With farm strip plots, the
optimum number of treatments is 2 to
5 (Hav~n and Elmore, 1984). Replicates
are necessary to provide an estimate of
the experimental error. Using large plots
does not reduce the number of replicates
needed to achieve research require-
ments. If replication cannot be achieved
on a farm site, it can be obtained if a
number of farms are used with the same
treatments applied to all farms.
The objective of this study was to
show that experimental error can be con-
trolled in agronomic field experiments
using research plots that are larger than
conventional experiment station plots.


Good statisial rigor can be achieved
for a number of types of trials which use
large plots or long strip plots.

Measuring experimental
error

The coefficient of variation (CV) in-
dicates the degree of precision with
which treatments are compared and is
used by experimenters to evaluate results
from different experiments involving the
same character, possibly conducted by
different persons (Steel and Torrie,
1980). It expresses the experimental er-
ror as a percentage of the mean:


CV- SD x 100
X


whas SD standud do.
viaimerd
Vied= of

Omangun
ofepu,.-
uhrnsri


The higher the CV value, the lower is
the ability of the experiment to predict
with a given certainty or probability that
treatment effects are real and not due to
chance alone. To know whether or not
a particular CV is unusually large or
small requires past experience with sim-
ilar treatments. Researchers make judg-
ments on the acceptability of an
experiment based on CVs ,from other
experiments in their subject matter area.
For example, research experience with
transplanted rice at the International
Rice Research Institute indicates that
for rice yield data, the maximum ac-
ceptable level of CV is 6% to 8% for
variety trials, 10% to 12% for fertilizer
trials, and 13% to 15% for insecticide
and herbicide trials (Gomez and
Gomez, 1984). The CV for yield usually
differs from that for other plant response
variables. For example, in a field exper-
iment where rice yield CV is about 10%,
that for tiller number would be about
20% and for plant height about 3%.
Coefficients of variation for yield in ir-
rigated corn hybrid trials in south cen-
tral Nebraska on standard experiment
station trials are in the range of 8% to
15% with SD = 15 to 23 bushels per
acre. For irrigated soybean variety trials
at the same experiment station, CVs are
6 to 12% with SD = 3 to 6 bushels per


acre.
Analysis of variance for split-plot ex-
periments will result in two coefficins
of variation Iftwo treaent factors are
labeled A and B with A being the whole
plot factor and B being the split-plot
factor randomized within whole plots of
A, then the analysis of variance table
would appear as follows for a random-
ized complete block experiment using a
split-plot treatment design:


Soo=s of vuuuo.th
PasseA
P806W A
'Eur (A)
PFsom B
AXI
Era (B)


Dognm ofh tm
f-I

b-I
WO) (bW)
a(u) (W-)


Coeffirairtn 0 Vaiabity
1mess aqua.v ro 4A)/b


CV(B) x 100
-
wham r nbr of block.
a bmatC dIleaC dA.
b =- naub eof lavb eso
emr (A) whole pla enar. aad
ame B) =bplo am.
The CV for factor A is the equivalent
of ignoring the split-plot division and
analyzing only whole plot values (Steel
and Torrie, 1980). The value of CV(A)
indicates the degree of precision at-
tached to the whole plot factor A. The
value of CV(B) indicates the degree of
precision of the split-plot factor B and
its interaction with factor A.


Large plots on experiment
stations

Experiments using large plots have
been conducted with rotations, relay
planting and crop planting dates at the
University of Nebraska Agricultural Re-
search and Development Center
(ARDC) in Eastern Nebraska. Char-
acteristics of these experiments are sum-
marized in Tables 1 and 2. The trials
are all designed as randomized complete
blocks using a split-plot treatment de-
sign. Although these trials were con-
ducted on an experiment station, the
plots were large and standard farm ma-
chinery was used. The ARDC trials pro-
vide examples for determining the


Volume 3, Number 4






Table 1. Dryland corn yields and co i of variation r tn years in long-tm o am trial (oats/
clovaer1anMoybea corn) using large pots at ARDC Mead. Nebraska.
Yield and
Yield grand CooMAlea of man Cod'cient of
mea (bu/acre) vauia (bI/re) varitio n


reliability of such plots for precise ex-
perimentation.
A four-year rotation (oats/clover-
corn-soybeans-corn) had three whole


plot treatments (organic i.e, manure
only, fertilizer only, and fertilizer plus
herbicide). The split-plot factor was the
effect of the previous crop (oats/clover


1976 51 CV(A) 16.0 1982 98 CV(A) 4.1
CV(B) 262 CV(B) 6.3
1977 25 CV(A) 29.4 1983 48 CV(A) 34.5
CV(B) 19.7 CV(B) 17.3
1978 135 CV(A) 3.3 1984 62 CV(A) 6.3
CV(B) 5.7 CV(B) 7.6
1980s 74 CV(A) 11.6 1985 113 CV(A) 6.2
CV(B) 12.9 CV() 5.8
1981 111 CV(A) 6.8 1986 108 CV(A) 2.7
CV(B) 13.0 CV(B) 5.3
Plot sie 40 (16 rows) x 125*, randomid complete block of 3 whole plot traments with 2 split-plot
treatments. 4 replications and 24 plots. Whole plot treatments re three dropping system (orgaic or
manue only, ferlizer only. and fetiizer plus herbicde). Split-pl tremtmen i the effet of the previous
crop (oats/clover or soybeans) on corn yield.
MCV(A) = mean square error (A)/2 (100)
> CV(A) : (I00)
gsMand man

V(B) = mean square eror ( (100)
grad man
S1979 data on corn yield as affected by splitplot factor unavailable.




Table 2. Soybean, wheat and corn yields and coefficients of variation relay cropping trials (1986) and
planting date trials (1987) using large plots at ARDC Mead. Nebraska
Yield
Vad
No. of whole No. of split-plot mean Coefficint
Experiment type Plot size plot trmanents treatments Crop (b/lacre) of variation
Relay cropping 20 2 200' 3 soybean van. 3 plating dates Soybeans 29 CV(A) 4.7
soybeasansd cies dryland CV(B) 10.0
wheat
Wheat 30 CV(A) 9.4
CV(B) 7.8
Relay cropping 20' x 200 3 soybean van- 3 planting dates Soybeans 25 CV(A) 7.8
soybeans and eies irrigated CV(B) 15.2
wheat
Wheat 32 CV(A) 4.7
CV(B) 4.5
Soybean plant- 30' x 800 3 planting dates 3 soybean vari- Soybeans 33 CV(A) 9.0
ing dates eties CV(B) 7.5
Corn planting 30' 160' 3 planting dates 3 corn varieties Corn 121 CV(A) 12.7
dates CV(B) 10.2
'Randomized complete blocks with split-plot treatments; 4 replication and 36 plots in relay cropping
trias: 3 replications and 27 plots planting date trials.

LCV(A) = zmean square error (A)/3 (100)
CV(A) = (100)
pond mean

CV(B) = mean square error (100)
grand mean


American Journal of Alternative Agriculture


or soybeans) on corn yield. Plot size was
40' x 125'. The coefficients of variation
for the first two years, 1976-1977, are
high (Table 1). This is attributed to ini-
tial adjustment of the plots to the ro-
tation treatment combination. From
1978 to 1986 coefficients ofvariation are
within the ranges normally experienced
in agronomic research except for 1983
which was a year of very dry conditions
and low, variable yields. Mean yields in
years 1978 to 1986 were consistent with
corn yields in the region.
Four experiments using narrower and
longer plots are summarized in Table 2.
The soybean planting date trial with
three varieties of soybeans used plots
that were more than a half ace each
and 4 to 6 times longer than those in
the other trials, yet experimental error
is still within acceptable limits. Coeffi-
cients of variation in these experiments
ranged from 4.5 to 15.2 percent, with
yields comparable to commercial fields
on the station and nearby farms in 1987.
Although it is not the purpose of this
study to examine treatment differences,
it is noteworthy that significant differ-
ences among treatment means were
found at a 5% level of signiicance in
analyzing the variance of nearly all these
large plot trials. Results from these ex-
periments at ARDC suggest that large
plots and standard designs can provide
credible information on agronomic ques-
tions using full size equipment and other
commercial practices.


Field length on-farm plots

An innovative farmer group called
The Practical Farmers of Iowa (PFI)
has organized a program for on-farm
research with an understanding of the
need for sound experimental design.
Usual PFI plot size is 8 rows wide by
1200 feet long. The number of treat-
ments is usually fixed at 2 with 6 to 8
replications. The experimental design is
a randomized complete block. The long,
narrow strips are randomized side by
side within a block. Blocks are adjacent
to each other in the same field.
Strip plot width, usually eight rows
depending on equipment width, allows
for one round of planting and harvesting
with 4-row equipment. When the field







is not in a ridge-till or pemaent row
system, the PPI group uses border row
with a strip plot width of sixteen ows.
Only the c enter eight rows ae harvesed
for test data.
The permanent rows of a ridge-til
field facilitate test plot layout. There is
no cross tillage that would spread pre
viously applied materials from one treat
meat plot to another. Experimental
treatment factors can be applied pre-
cisely over the permanent rows.
An ACU electronic grain monitor is
used for weighing the ain combined
from each strip plot in each field.' The
Iowa farmers were concerned about the
accuracy of the grain monitor for weigh.
ing only 30 to 35 bushels of soybeans
from each strip plot. PI compared the
ACU readings for 31 plots of soybeans
with the readings of an electronic weigh
scale. The ACU monitor was consist-
ently within 1.6% of the electronic
weigh scale.
The characteristics and coefficients of
variation of 23 trials conducted on 9
farms in 1987 are outlined in Table 3.
The experiments are categorized by
treatments, including N fertilizer levels
or sources, starter fertilizer levels or
source herbicide levels, varieties, tillage
practices, or different planters. With
corn and soybean yields that are typical
for central Iowa, the coefficients of var-
iation are exceptionally low. The level
of experimental error in these trials
should be very acceptable to researchers
in agronomy. The design with narrower
strip plots and more replicates than used
in the large plots reported in Tables 1
and 2 appears to reduce even further the
level of random variation.
Blocking in the analysis of variance
(not shown) reduced the error of nearly
half of the PFI trials (alpha = .05). The
use of randomized complete block de-
sign as opposed to a completely random
design should be considered a standard
recommendation to reduce experimental
error when on-farm research trials are
planned.
All the PI trials were sensitive


Table 3. Yields and canoeie of iamau fom cmorn d soybean Bn-fm trial of the PratiFarmo
of lowa On 9 farms. 1987.'


No.
1
1
I
2
3
4
5
6
8
I
I

7
1
3
9
1
6


EWPndmemd
tramona


2 N ferilizer levels or sources
2 N ferilimr levels or sourm
2 N ferilizer levels or sources
2 N fertiier lvels or sor
2 N feilier levels or soauc
2 N ferdlim levels or soomce
2 N frtiliar levels or sources
2 N fertlier lev r s soarce
2 N fardlia levels or sources
2 ster fre. levels or sources
2 starter fin. levels or sources
2 sam r erfa. levels or saoome
2 lamer fan. levels or Mouarces
*2 starter fea. levels or sources
2 herbicide levels
2 hiid levels
2 herbicide levels
3 varietai
2 tillage systems


No. of
replicra-
6
6
4
4
6
5
6
6
6
6
6
6

4
6
4
6
6
5
6


rYid
and mm
(bao/ce)
137
112
123
179
146
173
172
137
88
120
128
109
127
117
120
135
120
140
135


135


var-tio
1.6
3.5
1.7
0.7
2.7
3.7
1.7
1.9
2.3
5.0
4.6-
3.4-
0.7


3.1
2.2
3.2
5.9


5.9


Cro Soybeam
I 3 poash fer. sources 8 51 2.9
4 2 herbicide levels 6 55 2.0
I 2 planners 6 52 1.6
1 2 places 7 54 2.1
'All plomt 12 lon 03 idts varied from 4 row to 12 tows with majority at 8 ros rowr widh for y
om farm wa 30o, 36*, 37*, or 38'.
*:----


enough or had enough power to detect
significant differences between treat-
ment means at alpha = .05. A more
detailed discussion on power of experi-
mental designs will follow later. Most of
the Iowa experiments tested the effect
of using lower fertilizer or chemical in-
puts or no chemicals whatever. In nearly
all these trials, higher levels of inputs
provided no significant difference in
yield. The experiments as conducted
gave the Iowa farmers confidence in the
results and a willingness to apply the
knowledge gained to their future man-
agement.

Replication by farm

The area needed for each experiment
for the type of on-farm design used by
the PFI ranges from 8 acres without
border rows to 16 acres with border
rows. If farmers involved with on-farm
research do not want to dedicate that
amount of land to an experiment or if
more treatments are included, the nec-


essary replications can be attained by
testing the same treatments on a number
of farms. Using farms as blocks, exper-
imental error is based on the variation
among experimental units within a block
after adjustment for any observed, over-
all treatment effect.
Two types of studies conducted by
University of Nebraska faculty in co-
operation with farmers utilized replica-
tion by farm. Both used large plots and
offered some control of random varia-
tion within each farm.
Four farms in three counties of North-
east Nebraska were used to test narrow
(15 inch) and conventional (38 to 40
inch) row spacings in soybeans
(Moomaw, 1978). The average length of
the on-farm test plots ranged from 250
to 400 feet. Plot width for each row spac-
ing was one round with the planting
equipment (approx. 25 to 30 feet). Some
control of experimental error on each
farm was attained by conducting at least
two or three replications per row spacing
on each farm. Analyzing the experimen-


Volume 3, Number 4


'Modes of. a d a=n or uadmizk donam emm
-due idamems of ths pod bY the Univui1' of
Nebwask ft anthem or the publish.


I






Table 4. Corn yields and coei n of
varian in four yearn of variety
performance Bials sing nreplaa by
fam. Clay Couny, Nebaska.
Yidd
No. of grmd Co-fi
No. of repi* mean cdm of
Year varieties cam (bu/acre) variation
1984 13 3 173.7 4.0
1985 20 4 177.8 3.4
1986 19 4 172.0 3.7
1987 22 3 174.3 3.4


tal data with the four farms treated as
blocks in a randomized complete block
design, the coefficient of variation was
7.8% with SD = 35 bushels. Soybean
yield for the conventional row spacing
was significantly lower (alpha = .05)
than the narrow row spacing (conven-
tional 43.2 bu/acre, narrow 47.1).
In south central Nebraska. the Clay
County Corn Growers Association is co-
operating with extension personnel in
testing the performance of corn varieties
under irrigation. Table 4 is a summary
of the coefficients of variation found by
using three or four farms each year as
the replications. Plots were in the same
size range as used by the Practical Farm-
ers of Iowa. Strip plots 6 or 8 rows wide
(15 to 20 feet) by field length (1200 to
1300 feet) were used. On each farm, the
crop varieties were managed the same
way as the cooperator managed the re-
maining part of the field. Only one plot
of each variety is used on each farm.
The location of each variety on each
farm is randomly selected.
A common check variety was used
after every third variety in these Clay
County trials. The average of the check
variety on each farm was calculated and
a weighted factor based on the check
plots on either side of the test variety
was then used to arrive at the adjusted
yield for each variety on each farm. Us-
ing check varieties removes some of the
yield variability due to location within
a field; therefore, some control of ex-
perimental error is obtained even with
a high number of treatments. The check
variety should be one that has a yield
record similar to the other varieties (El-
more, 1986).
On-farm variety performance trials
with CV's of 3.4 to 4.0 percent were at


least as reliable as the experiment station
variety trials in the same geographic lo-
cation. Coefficients of variation of 8%
to 15% reported earlier for south central
Nebraska were for experiment station
corn performance trials conducted on
120 varieties.

Power of on-farm designs

Agricultural researchers are familiar
with coefficients of variation and exper-
imental error. But for most farmers,
such statistical terminology may be.
meaningless. Producers can appreciate
differences in yield, so it is of interest to
discuss the statistical concept called
power. In its simplest form, power is
defined as the probability that an ex-
periment can detect the true differences
between two treatment means. For ex-
ample, if one level of nitrogen fertilizer
"truly" produces 95 bushel corn and
another level of that fertilizer "truly"
produces 105 bushel corn, power is the
probability that one experiment will de-
tect this "true" 10 bushel difference.
The "true" yields in this case are the
average values one would measure if an
infinite number of trials were conducted
under the same conditions.
Table 5 is an abbreviated look at the
power of using a randomized complete
block design for detecting differences be-
tween treatment means at the 5% sig-
nificance level when the true difference
between two treatments is 5%, 10%,
and 20% of the overall mean. At a fixed
probability level of significance, power
is increased by an increase in sample
size, a reduction in uncontrolled vari-
ance, or an increase in the magnitude of
the treatment effects. Calculations of
power for Table 5 were performed using
Statistical Analysis System (SAS) com-


putersoftware (SAS Institute Inc., 1982;
detailed .information on determining
power with SAS is given in O'Brien
(1984).)
Most agronomy researchers attempt
to find an experimental design that has
a minimum of 80% power. In this study,
we have found that experimental designs
such as those used by the Practical
Farmers of Iowa using long, narrow
strips and six replication had a 79% to
99% probability ofdetecting a difference
of 10%; for example, 45 bushel soybean
versus 50 bushel soybean with a SD of
1.2 to 2.4 bushels or 95 bushel corn ver-
sus 105 bushel corn with a SD of 2.5 to
5.0 bushels. If coefficients of variation
are as high as 10%, the probabilities of
detecting a difference of 5% or 10% are
very low and it takes at least 6 or 7
replications to detect a difference of 20%
with acceptable power.
The power of an experimental design
can lead to economic evaluation of new
technology. During the planning of an
on-farm trial, cooperators should ask
what amount of true differences between
treatment means would influence them
to adopt or reject a particular treatment
factor. An experiment may establish a
difference of 5 bushels of corn between
two treatment means as significant, but
is this difference important? Deciding
what difference is important would pro-
vide a guideline to the number of rep.
locations needed based on previous
experience on other farms with similar
designs, treatments and resulting exper-
imental error.

Discussion and conclusions

On-farm research designs using large
plots that range in length from 125 to
1200 feet can provide reliable agronomic


Table 5. Power (%) of a randomized complete block design for 5% level of significance.
difference' = 20% difference = 10% difference 5%
No. of CV CV CV CV CV CV CV CV CV
replicates 2.5% 5.0% 10.0% 2.5% 5.0% 10.0% 2.5% 5.0% 10.0%
3 99% 70% 29% 71% 29% 11% 29% 12% 7%
4 99 95 49 95 49 17 49 17 8
5 99 99 66 99 66 23 66 23 9
6 99 99 79 99 79 29 79 29 11
7 99 99 87 99 87 35 87 35 12
Difference between two treatment means expressed as percenage of overall mean.

American Journal of Alternative Agriculture







data from research experiments, with ex-
perimental error controlled for a wide
variety of agricultural factor-If only
two or three levels of inputs ae com-
pared. long strip plots 8 rows wide can
be planted, maintain and harvested by
farmers with little or no assis*an by
researchers or local extension personnel
More complex on-farm designs such as
split-plots or factorials would require
more researher input, at least in the
designing phase. However, farmer's
equipment and management skills can
easily and reliably be used. Models for
design and analysis could be generated
that would allow farmers to conduct
such trials and evaluate the results.
If a producer wants to cooperate but
cannot dedicate enough acreage for the
replications needed, then replications of
the same treatments can be performed
on the farms of other cooperators. An-
other reason for replicating by farms is
to verify the application of new tech-
nology over a range of conditions or a
geographic area.
Further research is needed on the con-
tribution of soil variability to the exper-
imental error of the large plots tested.
Our results show that field variation is
well controlled with the use of narrow
strips approximately 8 rows wide. As
plots are widened, more experimental er-
roris encountered; however, in the trials
of this study, cv's of these wider plots
were still within acceptable limits for
agronomic research. It may be possible
to determine the degree to which soil
conditions have to differ to affect the
precision of an experiment. Soil series
and erosion classes could be studied as
treatment factors in an analysis of var-
iance (Olson and Nizeyimana, 1988).
The interactions of these soil conditions
with agronomic treatment factors of in-
terest could also be investigated.
The statistical reliability of the on-
farm designs analyzed in this study
should enhance the development of
models for integrating research activities
of farmers extension personnel and re-
searchers. Approaches can be explored
involving farmers, extension agents and
researchers in a stepwise research proc-
ess, from identification of problems to
field experimentation to analysis and
interpretation of results. This would


make the greatest possible use of ideas
from the entire group (Francis, 1986).
In 1988, the..University of Nebraska
inriated two projects that require the
cooperation of many farmers, agricul-
tural extension agents and researchers.
Both projects will last for three years.
Field plots have been designed on the
farms of nineteen cooperators out of a
targeted total of twenty-four to compare
crop rotation systems to the farmers'
current practices Fourteen cooperators
out of a targeted total of thirty are com-
paring relay cropping and strip crop sys-
tems to their current practices. These
projects and other on-farm research ac-
tivities should begin to provide us with
information for refinig models for
farer-extension-researcher cooper
tion.
As these models are developed for
practical applications in agriculture,
guidelines for each participant can be
defined. Farmers will be better able to
understand the importance of reliable
expeimental design and to participate
in the analysis of data. Extension work-
ers can provide season-long observation
and management assistance to assure
that experimental plots are treated alike
except for the treatment factors of in-
terest. Researchers can learn new ways
of incorporating problems identified by
farmers and extension agents into their
research agenda which will help them
gain respect and credibility from the ul-
timate users of their research efforts. We
propose an expanded involvement of re-
search and extension specialists with
farmers in a cooperative on-farm re-
search venture to provide practical re-
sults for tomorrow's agriculture.


AeboIlmm. The Rodale lond EmmaF .
PenmVyama provide fuancial suppmon for the rsmarch
worek cgmond c the R. Tha1o tfam whiTh i ftrm
na. 1 i Table 3. Cosatribni from Dep. of AMgromy.
Un. of Ndemka. Linon NE. 6853. Published s
Paporan.4.Joural Serie. Nebraska Ari. Exp. Sm.
Ranived Augut Is. 1988.




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vaite near models The SAS system maks
it sy. p. 84752. In Proceedings ofthe Ninth
Annual SUGI (SAS Uses Group Intern-
dmal) Conference. Hollywood Bach. Flor
ida. March 18-21. SAS Institute Inc., Car.,
NC
15. son. K. R. andE. Nizeymana 1988 Effects
of soil erosio on corn yields of seven linois
soils. Prod. Agric. 1:13-19.
16. Sanders. J.H. and J. K. Lynam. 1982. Eval-
uation of new technology on farms: Method-
ology and some results from two crop
programs at CIAT. Agricultural Systems.
9.97-112.
17. SAS Institute. Inc. 1982. SAS User's Guide:
Statistics. SAS Institute Inc.. Cary, NC.
18. Steel R. G. D. and H. Tore. 1980. Prin-
ciples and Procedures of Statistics: A Biom-
etrical Approach. 2nd ed. New York.
McGraw L pp.- 377-388.
19. Thompson. 1986. A farmer's approach to
on-arm research design. Mimeo for discus
sio. Practical Farmes of Iowa. Boone. IA
do


Volume 3, Number 4





Reproduced with Permission from: American Journal of Alternative
SAgriculture, Volume 2, Number 3, Pages 132-136. 1987.








Establishing the proper role for on-farm research

William Lockeretz


The current status of on-
farm research

Most physical and biological agricul-
tural research is done on experiment sta-
tions or other facilities specifically
intended as research sites. Only a small
portion is done on working, commercial
farms.
There are several obvious reasons for
this. A field dedicated to experimenta-
tion can be monitored much more care-
fully and precisely than land that is part
of a commercial operation and belongs
to someone else. Experimental treat-
ments can be selected in accordance with
the research question, without con-
straints imposed by the larger farm en-
terprise. The required equipment,
personnel, and supporting facilities are
already present on the experimental
farm.
Nevertheless, there are powerful rea-
sons for doing some agricultural re-
search on working farms. Experiment
stations and working farms offer inher-
ently different research environments.
Because of the well-known sensitivity of
agricultural research to external factors,
we have less confidence in results ob-
tained under contrived and artificial
conditions compared to the real-world
farm conditions where the results are
ultimately intended to be applied.
Some on-farm research is going on, to


William Lockeretz is Research Associate Professor,
School of Nutrition. Tufts University, Medford. MA
02155.

This paper was prepared with support from the Center
for Rural Affairs, Walthill. NE.


be sure, but it is not as common as it
should be. It no longer should be re-
garded as applying only to certain kinds
of scientific questions (usually highly ap-
plied rather than basic), or particular
production methods (ones that make less
use of purchased inputs or give more
consideration to resource conservation),
or certain kinds of farmers (those who
are less likely to adopt innovations
spread by traditional diffusion mecha-
nisms). Instead of being relegated to a
few otherwise unfilled niches, on-farm
research could occupy a substantial
place in its own right as a full-fledged
component of a balanced, overall agri-
cultural research program.

Relation to alternative
agriculture

On-farm research is often assumed to
be related to alternative agriculture be-
cause many alternative ideas have been
examined on working farms, and often
have originated there. However, this
connection has come about for reasons
that are largely irrelevant here. By def-
inition, "alternative" ideas are outside
the mainstream of current agricultural
thought, and therefore are more likely
to first be of interest to those who are
out of the mainstream of current agri-
cultural research. Such people are less
likely to have access to a conventional
research site to explore these ideas,
which means that initially, the research
is more likely to take place on-farm.
But "alternative" is a time-dependent
concept; yesterday's alternatives may be
today's recommended practices. Many
mainstream research facilities are now


taking an interest in practices once re-
garded as alternative. There is no in-
trinsic reason that "alternative"
agriculture should not be investigated at
an experiment station. Conversely, there
also is no intrinsic reason that questions
reflecting a "conventional" orientation
should not be investigated on-farm. In-
deed, this is commonly done for varietal
tests and fertility level experiments. The
choice of a research site should be dic-
tated only by the logic and the structure
of the research question, and not be cou-
pled to whether the system being inves-
tigated is or is not widely accepted.
However, for the institutional reason
just described there may temporarily be
a correlation between substance and pro-
cedure. That is, where the subject matter
falls on the alternative/conventional
spectrum will influence whether the
work is done on-farm or at an experi-
ment station. But when the optimal site
is chosen for each study, this connection
should disappear.


Demonstration projects,
adaptive research, and
farmer problem-solving

The diverse,activities that are loosely
placed under the single term "research"
have many different purposes. The less
general or "basic" the research, the more
likely it will be done on a working farm.
Much on-farm research aims at answer-
ing for specific circumstances a question
whose answer is known in a general way
(typically from experiment station or
laboratory work). It might not have an-
swering a question as its primary pur-


American Journal of Alternative Agriculture







pose at all, but rather is intended either
to convince other people of the answer,
as with demonstration plots, or to train
them to be able to answer similar ques-
tions themselves.
A type of on-farm activity common
today is designed mainly to inform farm-
ers about a new practice or to persuade
them that it is desirable. There are good
reasons for placing these demonstrations
on working farms. In that way they are
more visible to working farmers and
their results bear a clearer relation to
working farmers' experiences, thereby
enhancing their credibility. Such proj-
ects are sometimes referred to loosely as
"research," but should really be called
demonstration or educational projects.
For research in the customary sense,
that is, work intended to answer a ques-
tion, the decision to locate research on
a working farm should be based on
whether this will better answer the re-
search question, not whether the results
will be seen, or believed, by more farm-
ers. Choosing sites for extension-type ac-
tivities is an entirely different matter, of
course.
Intermediate between demonstration
activities and basic research are on-farm
projects that deal with techniques that
have been developed at an experiment
station and are thought to be suitable
for some area. However, the techniques
have to be tried out under a range of
conditions, and perhaps adapted or fine-
tuned for a farm's particular circum-
stances. These could be called validation
sites.
Even further from the traditional con-
cept of research is the type intended to
help a farmer solve a particular problem
that he has already identified. Here the
Researchers may not be concerned at all
with how many other farmers might do
the very same thing. This type of on-
farm research is primarily an educa-
tional and training process intended to
enable farmers to answer their own ques-
tions and adjust their production meth-
ods to fit their particular circumstances.
On-farm research has achieved its
most thorough-going formal acceptance
in Farming Systems Research and Ex-
tension (FSR/E), a concept that encom-
passes all the elements just discussed.
However, the validity of working on-

Volume II, Number 3 11 V7.


farm extends far beyond this application.
FSR/E has been applied primarily to
less developed countries, and primarily
to development and evaluation of pro-
duction methods that may soon be rec-
ommended for adoption by the local
farmers. It places particular emphasis on
how these methods perform when prac-
ticed by "real" farmers.
I wish to propose a more general role
than this for on-farm research. It can be
suitable for both more and less techno-
logically advanced agricultural systems,
for a broader range of questions then
merely testing or demonstrating the suit-
ability of specific production techniques,
and for questions in which the human
element (farmers' acceptance, evalua-
tion, and ability to handle a method)
may range from critical to totally irrel-
evant. On-farm research can have a role
in the full spectrum of agricultural in-
vestigations, including some concerned
with the basic dynamics of agricultural
processes.

Farmer participation in
research

On-farm research projects have had
differing levels of farmer participation.
Some researchers consider that greater
involvement of farmers in research is de-
sirable as an end in itself. This belief has
been the basis of some on-farm projects
in which the entire process, not just the
site, differs from conventional experi-
ment station work. Indeed, in the farmer
problem-solving type of research men-
tioned above, developing the farmer's
confidence and ability to solve a problem
may be considered more important than
the particular solution. This is much like
a student research project whose point
is the educational process as such, not
the answer the student comes up with,
which usually was already known by the
teacher anyway.
At the other end of the spectrum are
experiments in which the farmer does
little more than permit the researchers
to use the land, with the management
of the experimental area left entirely to
the researchers. Here the research proc-
ess is fully traditional. Intermediate is
the case in which the researchers plan-
the work, but the farmer has a large


responsibility for record keeping and ap-
plying the experimental treatments.
The appropriate role of the farmer in
planning and executing research is a sep-
arate matter from the question I wish to
concentrate on here: whether a working
farm is the best site on which to answer
a given research question, once that
question has been selected. .However,
these two matters sometimes are linked,
especially when the research examines a
system or technique that the farmer was
already using before the researchers even
knew about it, a circumstance I will con-
sider later. It would hardly make sense
to study such a system without discuss-
ing it with the farmer from the very
beginning.

Circumstances under which
on-farm research is
especially advantageous

Obviously, not all agricultural re-
search is best done on working farms.
The following are situations in which a
working farm is a particularly suitable
site. The list begins with the most com-
mon reasons that this choice is already
being made; reasons further down are
encountered only occasionally.
1. To obtain particular soil types or
other physical conditions that are not
available on the experiment station. This
is already common for some kinds of
highly applied work, such as determin-
ing fertilizer yield response. It also is
routine in testing the performance of
new cultivars and hybrids under differ-
ent weather, disease, and insect pest con-
ditions.
2. To study phenomena that must be
examined on a larger tract than:is avail-
able on an experimental station. A fa-
miliar example is the study of harmful
or beneficial insects that move over an
area much larger than typical small
plots. Other examples include runoff,
erosion and nutrient movement on a
whole-field scale, or tillage and culti-
vation using full-size equipment.
3. To analyze systems that involve in-
teractions among several individual en-
terprises or that intrinsically are of a
whole-farm nature. A typical example
would be analysis of nutrient cycling and
nutrient self-sufficiency of a farm in






which the feeds are produced on the
farm and consumed by the farm's live-
stock, with the manure returned to fer-
tilize feed production. Such phenomena
often are studied by the use of computer
models. However, models are not a sub-
stitute for data collected carefully under
realistic conditions, that is, from a work-
ing farm. Nutrient cycles will depend
strongly on the details of the crop pro-
duction system, livestock management,
and manure handling, and hence cannot
be modeled accurately without reliable
calibration using real data.
4. To compare a system's performance
under realistic farm conditions to its
performance under experimental con-
ditions. On an experiment station, con-
ditions regarded as "irrelevant" can be
controlled precisely, at least in principle.
For example, a fertilizer yield response
trial might include hand weeding, care-
ful cultivation, or precisely timed her-
bicide applications so that weeds are not
yield-limiting. On a working farm, a
more relevant question would be "What
is the fertilizer yield response with weeds
at typical levels?" The answer could be
very different. Similarly, on an experi-
ment station the plots can be planted
and harvested on the optimum dates,
with the optimum plant population and
a uniform stand, with excellent control
of insects and other pests, and, if irri-
gated, with the right amount of water
applied at the right time. Working farm-
ers, who have a fixed amount of labor
and equipment and who have to tend to
many different enterprises, cannot hope
to achieve the same control. On the
other hand, conflicts among different
projects on a research station can also
lead to experimental conditions that are
less than ideal, although not in the same
way as on a working farm. But in either
case, researchers often do not take into
account how the results might be af-
fected by the differing conditions found
on experimental and working farms.
5. To evaluate production techniques
that are particularly sensitive to man-
agement skill. Researchers and exten-
sion workers in developing countries
recognize that a production method will
give very different results depending on
whether it is being used by highly trained
professionals or by typical farmers of the


country. Farming Systems Research and
Extension explicitly takes account of
farmers' motivations, values and knowl-
edge. This recognition seems less firmly
established in the United States. Perhaps
because experiment station researchers
and extension workers may deal more
with "top management" or "progres-
sive" farmers, they may not take explicit
account of the human element as an im-
portant limiting factor in successful
transfer of new techniques to "average"
farmers. This limitation is particularly
relevant to production methods like in-
tegrated pest management that substi-
tute information, judgment, and
monitoring for fixed applications of in-
puts according to a predetermined
schedule.
6. To study the long-term effects of a
production method that has already been
in use on a farm for a long time. Some
aspects of agricultural production be-
come manifest over longer periods than
the duration of a typical experiment sta-
tion project. An example is the long-
term depletion or buildup of soil nu-
trients and organic matter content,
which may take decades to reach equi-
librium when the crop production sys-
tem is changed. Even if a field on the
experiment station can be dedicated to
studying such a phenomenon, at best
there will be a long wait before results
are available. Some research projects
have successfully used farms where a
particular system was already followed
for many years. Because of obvious
problems in establishing good controls
and documenting previous management,
this retrospective approach has limita-
tions, but it can provide quick, if incom-
plete, answers that may in turn justify
prospective studies at an experiment sta-
tion.
7. To analyze a production method or
management system that is already prac-
ticed by some farmers but has not re-
ceived attention from researchers.
Traditionally, topics for research origi-
nate at the experimental facility, with
the results eventually extended to work-
ing farmers. However, farmers some-
times come up with intriguing ideas that
they use on their own farms, but which
they cannot test in a way that would
satisfy a researcher. On learning of such


innovations, researchers may wish to
test them on an experimental farm.
However, if the idea is one that the re-
searcher has little previous familiarity
with, it seems prudent first to conduct
at least a preliminary investigation on
the farm on which it is already being
applied. Otherwise, even with a well-in-
tentioned researcher, something may be
"lost in translation" in moving imme-
diately to an experimental setting. Re-
searchers may not be able to capture the
spirit of an unfamiliar system even while
duplicating its objective features on an
experiment station; techniques that in-
volve a high level of experience-based
judgment may be particularly suscepti-
ble to this problem.
In some of the preceding examples
(especially 1, 2, 3, and 6), the working
farm is chosen simply because it offers
certain physical conditions not available
on an experimental farm (desired soil
type, a large amount of land, or a par-
ticular production history or enterprise
mix). In these cases, that the farm is a
working farm is largely irrelevant; the
same land would have served just as well
if it had been acquired by the research
institution and run as an experimental
farm. But for items 4, 5, and 7, it is
essential that the farm be a working
farm, and that it continue as such during
the research. This raises an important
but not easily answered question: At
what point does involvement in research
distort the character of a working farm
so that it no longer offers the realistic
setting that motivated the choice of an
on-farm site in the first place? It is well
known that the process of observation
can alter the phenomenon being ob-
served. The potential for distortion will
be even greater if it is necessary to com-
pensate the farmer substantially for ex-
tra work or risk; a true working farm
by definition must support itself by its
production activities, not by providing
services for researchers.

Limitations of on-farm
research

The limitations of doing research on
working farms are obvious and widely
recognized, and need only be summa-
rized here. Inability to control the ex-


American Journal of Alternative Agriculture







perimental conditions closely may
introduce confounding effects and in-
crease statistical variability (although, as
discussed in Item 4 above, a positive side
of "less control" is "greater realism.")
There also is a greater risk of total loss
of an experiment. This can occur be-
cause of pest infestations, drought or
other physical/biological stresses that
cannot be countered as effectively as on
an experiment station, or because a
farmer is unable or unwilling to perform
agreed-upon experimental manipula-
tions.
Monitoring the progress of the exper-
iment is more difficult if the site is far
from the researchers' home institution.
On the other hand, if monitoring and
data collection are mainly the respon-
sibility of the farmer, there is a risk that
records will be incomplete or inaccurate.
If most experimental operations are to
be performed by the farmer, the research
must be restricted to less complex de-
signs. No matter how dedicated and
competent, a farmer cannot be expected
to undertake experiments as elaborate as
those done by researchers who do not
also have to look after a working farm
and who have access to specialized sup-
port staff and equipment.


Recommendations

Agricultural research is properly con-
ducted in many different settings, from
growth chambers to greenhouses to ex-
perimental farms. Working farms are
another important and valid research
site. Some agricultural research is al-
ready being done on working farms.
However, this choice of site is often
made out of necessity or expediency, not
for more positive reasons. Only some of
the advantages of on-farm research are
generally recognized by the research
community. The logistical problems and
methodological difficulties of on-farm
research have relegated it to a subordi-
nate status that does not reflect its many
advantages. Appropriate techniques for
the other kinds of research sites are so
much more familiar and well-developed
that researchers are likely to turn to
them automatically, even for questions
that would better be investigated on
working farms.


On-farm research should be accepted
as a legitimate component of a balanced
research program, and researchers
should appreciate more fully its special
contribution. Of course, this contribu-
tion will complement, not compete with,
the role of better established sites. I offer
two suggestions on how this may be
achieved.
1. Systematic review of published on-
farm research. By now, enough on-farm
research projects have been done that
we can examine their strengths and lim-
itations and begin to develop standard-
ized protocols. Generally, researchers
choosing working farms have not con-
cerned themselves with methodological
issues as such; their interest has been in
answering the question. Typically, a
standard small-plot design is used as is,
without verifying whether the experi-
ment complies with the underlying as-
sumptions regarding statistical
distributions, homogeneity of variance,
and so forth. (This is not to say that
experiment station work always attends
to such fine points either.)
It is time to move beyond this ad hoc
approach and put on-farm research
methods on a more systematic basis by
critically examining the accumulated
body of published on-farm studies. Such
an examination would categorize the
types of questions asked and the meth-
ods used, and would attempt to deter-
mine the reliability of the results and
assess the problems that were encoun-
tered. It would also analyze the appli-
cability of results from one farm to
another. Finally, it would attempt to
evaluate the differences in the effective-
ness of the actual research and that of
a comparable study as it might have been
done in a more conventional setting. The
goal would be to help researchers decide
whether to locate a contemplated inves-
tigation on farms, and if so, to give them
guidance in designing a study that is
statistically valid. Even better, such a
review could lead to modified experi-
mental procedures that are better suited
to on-farm work than current designs
that are merely taken over uncritically
to the new setting.
2. Working group of on-farm re-
searchers. Some of the most valuable
instruction in how to conduct on-farm


research will never be gleaned from pub-
lished reports. Time-saving short cuts,
practical rules-of-thumb, and useful
hints for dealing with the unforeseen lit-
tle crises that inevitably plague on-farm
research usually do not find their way
into published papers. Also, research ef-
forts that basically fail usually are not
reported at all.
Yet there is much to be learned di-
rectly from people who actually have
experience in this sort of work, not just
from the condensed and somewhat ster-
ilized accounts that constitute the formal
literature. Therefore I propose periodic
meetings that will offer researchers an
informal opportunity tn exchange "on
the ground" experience. Participants
will be encouraged to talk about things
that didn't work, not just those that did.
Besides presenting their own'=experi-
ences, participants will criticize the work
of others (constructively, one would
hope). The idea would be to develop col-
lectively a body of practical expertise
that otherwise could be developed only
at the cost of many false starts and fail-
ures. Eventually, researchers could un-
dertake on-farm work backed by the
same kind of cumulative experience and
well-developed techniques that now sup-
port experiment station research.

A concluding comment

In the past, there may have been a
prejudice in some segments of the re-
search community against research con-
ducted on working farms. On-farm
research never looks quite as "clean" as
experiment station plots; by implication,
it is not as "scientific". But this preju-
dice, if it ever existed, seems to be fading.
Even if a remnant lingers, those who are
convinced of the value of on-farm re-
search need not worry themselves too
much about combating it. Rather, they
should go ahead with the things that
should be done anyway, for the much
more constructive reasons outlined here.
Fulfilling the potential of on-farm re-
search presents three challenges. First,
we need many more positive examples
-a substantial cumulative body of well-
planned, well-executed on-farm experi-
ments that answer worthwhile questions
more convincingly than would have


Volume II, Number 3







been possible on an experiment station.
Second, we need to face explicitly and
systematically the logistical, technical,
and conceptual problems that now limit
the feasibility and validity of on-farm
research. Finally, we need to validate the
designs appropriate to each type of ac-
tivity.
If these challenges are met, research-
ers will not feel obliged to apologize for,
defend, or even explain having chosen a
working farm as a research site, just as
no one feels obliged today to apologize
for, defend, or even explain having cho-
sen an experiment station. so


SSS e e k VIP wt.-


To Feed the Earth: Agro-Ecology for
Sustainable Development. 1987. By
Michael J. Dover and Lee M. Tal-
bot. World Resources Institute,
Washington, DC. 88 pp. $10.
As the Foreword states, the report
"lays out steps stretching from basic
research to the mechanics of interna-
tional assistance that must be taken
if ecologically based agriculture is to
contribute all it can to feeding the
earth." As one might expect of a report
from a policy research center, it is
strongest in its discussion of policy im-
plications. Like previous World Re-
sources Institute publications, this one
is well-written and easily accessible.
The rationale and justification of the
need for an ecological approach to ag-
riculture is argued well in the Intro-
duction. Industrial agriculture
obviously has been quite successful in
increasing. global food production.
However, serious concerns and uncer-
tainties exist about whether its high
yields can be maintained in the face of
decreasing fossil fuel reserves and in-
creasing environmental deterioration.
Furthermore, even if industrial agri-
culture can be made more sustainable,
the majority of the Third World's poor
farmers will continue to have difficulty
in affording its inputs and will not be
able to depend on their timely delivery.


The study notes that "perhaps as much
as 80 percent of agricultural land today
is farmed with little or no use of chem-
icals, machinery or improved seed."
This unreferenced statistic may be a
little high even for SubSaharan Af-
rica, the poorest region of the world
(OTA, 1987) but the message of the
Introduction seems valid. A need exists
"...for a new view of agricultural de-
velopment that builds upon the risk-
reducing, resource-conserving aspects
of traditional farming, and draws on
the advances of modern biology and
technology."
Before elaborating on this "new view
of agricultural development", there is
a chapter on "Environmental Con-
straints and Problems". This section is
useful for showing the inter-relation-
ship between environment and agri-
culture, and familiarizing the reader
with environmental issues in the trop-
ics. The magnitude of the differences
between tropical and temperate zones
is effectively dramatized by illustra-
tions such as the following: "Cut a tem-
perate-zone forest, and 97 percent of
the nutrients available for new growth
will remain in the soil. Cut a tropical
forest, and almost all of these nutrients
will be hauled away in the timber."
The report does not detail the en-
vironmental problems associated with


industrial agriculture, and does not suf-
fer from this omission. It would have
benefitted, however, from more dis-
cussion of the manner in which envi-
ronmental problems in developed and
developing countries are linked, often
being rooted in the failures of conven-
tional agricultural research and prac-
tices. In developed countries, chemical
inputs are sometimes applied incor-
rectly, and more often than not they
are overused. Misuse of chemical in-
puts, particularly insecticides, occurs
also in the developing world, but a
more fundamental problem is environ-
mental deterioration, caused by and
contributing to low productivity. Ag-
ricultural research can more effectively
address these problems in the Third
World by recognizing the constraints
poor farmers face, and focusing on op-
portunities to improve existing systems
rather than trying to replace them with
industrial agricultural practices. A re-
cent Worldwatch publication, Beyond
the Green Revolution: New Approaches
for Third World Agriculture (Wolf,
1986), develops this theme and is
highly recommended for its relevance.
The third chapter, "Ecological Par-
adigms and Principles for Agricul-
ture", is intended to substantiate the
conclusion that "...if the unexpected is
to be avoided, planning based on eco-


American Journal of Alternative Agriculture


Letters to the editor invited

Beginning with the next issue, the American Journal of Alternative Ag-
riculture (AJAA) would like to carry a "Letters to the Editor" page. Almost
all of our readers are actively involved in alternative agricultural production,
research, education, events, and rural community support organizations. So
a "readers forum" of responses to articles we've printed or comments on
other developments in alternative agriculture should be a good way to circulate
ideas.
We welcome letters, short or long, on topics likely to be of interest to other
AJAA readers. Since our space is limited, we do reserve the right not to
publish all letters, or, at times, to publish only excerpts from them. To take
part in this exchange of ideas, write to: Editor, AJAA, 9200 Edmonston
Road, Suite 117, Greenbelt, MD 20770.




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