Proceedings
Lucas Memorial Symposium
On
SYSTEMS CONCEPTS IN AGRICULTURE
Nor~h Carolina State University
North Carolina A&T State University
March, 1980
Principal Speakers:
Proceedings Editor:
Colin R. W. Spedding
Earl
Harvey J. Gold
o. Heady
Steering Committee:
Sponsored By:
Agricultural Research Service, NCSU
School of Agriculture, NC A&T SU
Science and Education Administration/
Cooperative Research, USDA
NCSU
Leonard W. Aur and
Joseph C. Burns
Donald G. Davenport
Charles E. Main
Larry A. Nelson
Robert S. Sowell
Ronald E. Stinner
Organized By:
Biomathematics Division, Department
of Statistics, NCSU
Will Getz
Program and Proceedings Contents
Lucas Memorial Symposium
Page
Preface. • • •
...
1
SESSION I
4
Chairman - Harvey J. Gold
Director, Biomathematics Program
Department of Statistics
North Carolina State University
Tribute to Henry L. Lucas, Jr.
5
- J. Edward Legates
Dean of the School of Agriculture
and Life Sciences
North Carolina State University
Systems Concepts in Agriculture:
An Overview • • • • • • • •
9
Interdisciplinary Modeling to Improve Agricultural Policies,
Food Production and Human Nutrition. • • . • • • • . • • ••
27
- Colin R. W. Spedding
University of Reading
- Earl o. Heady
University of Iowa
Panel Discussion
- Robert L. Rabb
Department of Entomology, NCSU • • • • • • • • • • . ••
48
- Richard A. King
Department of Economics and Business, NCSU • • • • • ••
51
- J. Lawrence Apple
Agricultural Research Service, NCSU. • • • . • • • • ••
53
- Arthur J. Coutu
Department of Economics and Business, NCSU • . • • • ••
56
. ... . ....... .... .
66
SESSION II • • • • • • •
-e
Chairman - Burleigh C. Webb
Dean, School of Agriculture
North Carolina Agricultural and
Technical State University
Page
.......... . ...... .
67
Education and a Systems Approach to Agriculture • • • • • •
92
Small Farm Systems. • •
- Earl O. Heady
University of Iowa
- Colin R. W. Spedding
University of Reading
SESSION III. • • • • • •
.................
113
Informal discussion sessions with Professors Spedding
and Heady
Plant Growth Models • . • • • • • • • • • • • • • • • • ••
-James Reynolds, Department of Botany, NCSU
- Mien Wann, Department of Statistics, Biomathematics
Program, NCSU
115
Animal Growth and Pasture Systems • • • • • • • • • • •
- Joseph C. Burns, Department of Crop Science, NCSU
- Donald G. Davenport, Department of Animal Science,
NCSU
- Will R. Getz, Department of Animal Science, NC £T SU
116
Pest
-
123
Models and Integrated Pest Management. . . . • • •
Kurt J. Leonard, Department of Plant Pathology, NCSU
Ronald E. Stinner, Department of Entomology, NCSU
Turner B. Sutton, Department of Plant Pathology, NCSU
Mechanization in Agricultural Systems • • • • •
- Robert S. Sowell, Department of Biological and
Agricultural Engineering, NCSU
- James H. Young, Depar~ent of Biological and
Agricultural Engineering, NCSU
126
Economic Modeling of the Food Systems . • • • •
- Gerald A. Carlson, Department of Economics and
Business, NCSU
- Richard A. King, Department of Economics and
BuSiness, NCSU
127
e-
DEDICATED TO
Henry L. Lucas, Jr.
In memory of his vision and inspired
leadership in advancing systems concepts
in the study of agricultural science.
1
PREFACE
It has become increasingly well recognized that many important questions
relating to agricultural research transcend the boundaries of individual disciplines.
This is partly due to the complexity of the economic and social
questions that agriculture is involved with, and partly due to the success of
the individual disciplines in providing a basis for addressing increasingly
complex problems.
The purpose of the Symposium was to bring together people
at North Carolina State University and at North Carolina Agricultural and
Technical State University who are interested in systems approaches to agricultural research so as to promote communication, help develop perspective relating
to such research activities, and help to promote collaboration between scientists
in different disciplines.
The occasion was used to pay tribute to the memory
of Henry L. Lucas, Jr., who was known for his zeal in promoting interdisciplinary
holistic approaches to agricultural science.
The Symposium consisted of
OvO
formal sessions (one on the campus of NCSU,
Raleigh and one on the campus of NC pbT SU, Greensboro) and a series of informal
discussion sessions.
As is true for many symposia, much that was interesting took
place in the informal sessions, and these alas, we are not able to reproduce in
the proceedings.
However, some of the discussion coordinators have provided
summaries which try to capture the gist of the general discussion.
When the steering committee began planning for the Symposium, we first
considered what topics needed to be addressed and then asked ourselves who we
would most like to hear address those topics.
It would be difficult indeed to
think of two people more qualified to do so than Professor Spedding and Professor
Heady.
2
Professor Spedding received his B.S., M.S., Ph.D. and D. Sc. at London.
His thesis work was on the effect of parasites on sheep and their control by
grazing management.
This got him simultaneously into animal, plant and para-
site biology - a marvelous platform from which to begin viewing the overall
system and to appreciate how contributing subsystems must fit together.
His
publications since 1952 have reflected his appreciation of the overall agricultural ecosystem as well as his continued concern with the analytic details upon
which the synthesis must be based.
Until 1975, he was deputy director and head
of the Ecology Department at the Grassland Research Institute.
He is currently
Professor of Agricultural Systems and Head of the Department of Agriculture
and Horticulture at the University of Reading.
Professor Heady received his B.S. and M.S. degrees at the University of
Nebraska and his Ph.D. at Iowa State University.
He has received honorary
degrees from the University of Nebraska and the University of Upsala and is an
honorary member of the Hungarian and Royal Swedish Academies of Science.
~
He has
received numerous academic and professional honors - including the Browning
Medal of the American Society of Agronomy in 1977, and the Henry A. Wallace
Award for distinguished service to Agriculture in 1978.
He has been consultant
to the Organization for Economic Cooperation and Development, FAa, and numerous
governmental agencies around the globe.
He is currently Curtis Distinguished
Professor at Iowa State University and Director of the Center for Agricultural
and Rural Development.
We were fortunate indeed to have J. Edward Legates, Dean of the School of
Agriculture and Life Sciences at NCSU introduce Session I and to have Chancellor
Lewis B. Dowdy, of North Carolina Agricultural and Technical State University
introduce Session II.
We sincerely regret that we were unable to include Chancellor
DOWdy's very stimulating remarks in these proceedings.
3
Particular thanks are due to Ken Keller, NCSU (now with the Boll Weevil
Program, USDA) and Howard Robinson, Director for Research Administration, NC
A&T SU for their help and encouragement during the planning of the Symposium,
and to Dr. Will Getz, Department of Animal Science, NC A&T for his role with
the many details of the Wednesday arrangements.
A special thanks are due to
Nancy Evans for her help in the planning and execution of the million and one
practical details which accompany an event such as this, as well as for the
preparation and typing of the proceedings.
Harvey J. Gold
Raleigh, North Carolina
March
1980
5
INTRODUCTORY REMARKS
J. Edward Legates
It is a high privilege and special honor to make introductory remarks
for the Lucas Memorial Symposium on Systems Concepts in Agriculture.
This
is a subject which was very close to "Curly", as within a year of his
death he visited with me concerning how emphasis on this area could be
enhanced.
It is most fitting that the Symposium be dedicated to Dr. Lucas
in memory of his vision and inspired leadership in advancing the systems
concepts in the study of agricultural sciences.
Always a team player, it is also appropriate that the symposium be
sponsored by a team of research agencies:
The North Carolina Research
Service, The School of Agriculture at North Carolina A & T State
University and The Science and Education Administration of the U. S.
Department of Agriculture.
We are indebted to Harvey Gold for his
guidance and boundless energy in organizing this symposium, and to Ken
Keller, Director Emeritus of the North Carolina Research Service and
Howard Robinson, Research Administrator for North Carolina A & T State
University for their strong encouragement and support.
Dr. Henry L. Lucas, Jr., affectionately known as "Curly", was reared
on a fruit and dairy ranch in Southern California.
from the University of California at Davis.
He received his B. S.
I would add that his interest
in quantitative matters had its proper beginning, since he served as a
dairy herd improvement tester for a time when he was in California.
He moved to Cornell University for graduate study under Dr. Jack
Loosli who is now on the faculty of the University of Florida at Gainesville.
6
It was from the publication of his thesis research on the levels of fat in
dairy cattle rations that I first became acquainted professionally with
Dr. Lucas.
I had the assignment of a seminar on the utilization of fat
by the dairy cow which brought me in touch with the publications of Lucas
and Loos1i.
Following the completion of his Ph.D. in animal nutrition at Cornell
University, he Joined the Department of Statistics at North Carolina State
University in 1946.
He was promoted to professor in 1948 and named a
William Neal Reynolds professor in 1957.
Dr. Lucas for a quarter of a
century was key figure in pasture research in North Carolina and the
southeast.
In the 1950s, he was responsible for the analysis and the publi-
cation of the results of a large regional study relating to the nutritional
values of vegetables to soil, weather and cultural condition.
Nationally
he served on the Committee on Feed Composition of the Natural Research
Council from 1948 through 1960.
In that capacity he directed surveys to
ascertain the nutritional value of hybrid corn, which had largely replaced
older open-pollinated varieties.
He also directed the first really compre-
hensive compilation and publication of the content of feeds and foods with
respect to 65 different nutrients.
Dr. Lucas was regularly called upon by
various units of the National Institutes of Health, the Veterans Administration
and other agencies to evaluate research and training proposals and programs.
For five years, the last year as chairman, he was a member of the Biometry
and Epidemiology Training Committee of the National Institute of General
Medical Sciences.
He also served for two years on the Commission on
Undergraduate Education in the Biological Sciences, three years on the Panel
for Mathematics in the Biological, Medical and Social Sciences of the
7
Commission on Undergraduate Programs in Mathematics Committee of the
Commission on Education in Agriculture and Natural Resources.
As a member of special study groups working under the President's
Science Advisory Committee, he coauthored White House reports entitled
"Biomedical Science and Its Administration -- A Study of the National
Institutes of Health" and "Restoring the Quality of Our Environment".
He organized special sessions at many scientific meetings and symposia
in biological, biomedical and biomathematical areas.
speaker, discussant or panelist at many more symposia.
He was an invited
For seven years,
he was on the program committee and served as a panelist for the Annual
Conference on Design of Experiments in Army Research, Testing and
Development.
He has presented many special lectures, and participated in
a large number of workshops in biometry, data analysis and computer use
throughout the United States, plus serving as consultant to numerous
academic, governmental and industrial organizations.
A number of ten professional scientific societies and four honorary
ones, Dr. Lucas was Fellow of the American Statistical Association and
of the American Association for the Advancement of Science.
He also
received the Award of Merit from the North Carolina State University
Chapter of Gamma Sigma Delta.
Although his scientific career began in animal nutrition, Dr. Lucas'
broad interests soon led him into statistics and the development of
quantitative methods in research.
His work on the design of experiments
with large animals, particularly dairy cows, and his resultant papers on
switchback and changeover trials, have proved useful over a wide range of
applications, such as in medical experiments on the effects of drugs on
human subjects.
He also was a major producer of research results in this
8
area.
In recent years, one of Dr. Lucas' main interests was in providing
leadership for a pioneer program at North Carolina State in biomathematics
training and research.
This program was supported by several major
National Institutes of Health and National Science Foundation grants
totaling over four million dollars for a ten-year period.
Although heavily involved in research and administration, Dr. Lucas
never lost touch with teaching.
The success of his students attests to
his outstanding role as advisor and dissertation direction.
Demanding as
his professional obligations were, "Curly" always took great pride and
pleasure in his family, his church work and even his hobby.
Early in his
career at North Carolina State I enjoyed many softball games where he was
the slow ball pitcher on the Statistics team.
He enjoyed his hobby of
working with pigeons and later in life he took on a less vigorous activity
in executing certain of the latest dance fads.
Dr. Lucas' unselfish service to his colleagues, his untiring attempts
to help them solve their problems, his reasoned and calm judgment, and
his dedication to his profession make him one whose influence will live
on.
We as colleagues, his family and friends do sorely miss him; however,
we can honor him by carrying on the work to which he gave so much.
~
9
SYSTEMS CONCEPTS IN AGRICULTURE:
AN OVERVIEW
'C. R. W. Spedding
First, may I say that I regard it as a great honor to be asked to be
here, and to add my small voice to this tribute to Curly Lucas.
I feel greatly
honored in the sense that I only actually met him once, but I suppose that it
is the measure of the man that I remember it very clearly, although it must
be at least 20 years ago; he made a considerable impression on me.
Perhaps,
unfortunately, we are not able to discover what kind of impression I made
upon
him.
Now I have been asked to talk about systems concepts in agriculture.
In
spite of the fact that this symposium is in memory of a systems pioneer, I have
decided against a historical review, but rather to attempt an assesment of the
current position as I see it, with
particular references to its deficiencies,
as I see them, and as a gUide as to where development perhaps should take place
in the future.
A Systems Approach
Systems concepts are usually considered as part of a systems approach which
embraces both the " sys tems" philosophy and a set of characteristic techniques.
The more sophisticated techniques, of mathematical modeling using a computer,
are now fairly well established and they are certainly based on systems concepts.
One reason why they are nonetheless not entirely adequate is that the methodology
as a whole does not yet reflect the systems approach as a whole.
Indeed, it
may be because the philosophy and concepts are not yet sufficiently explicit
that some important methods of application have not yet been developed.
10
All this makes it timely to review the systems philosophy and to attempt
a statement of its main concepts - not so much with a view to informing others
but as a means of further development.
Much of this is quite independent of
agriculture but the need for further development may be related to the difficulties
of applying the approach to such a multidisciplinary subject and one that is
so influenced by uncontrollable (e.g., climatic) factors.
The Systems Approach is chiefly motivated by a desire for relevance in
research and appropriate understanding.
It arose because of a recognition that the world contains
"systems," as
well as "non-systems" (and the ability to distinguish between them is vital),
and that changes in one part of a system had important effects on the system
as a whole.
Thus improvement of a system could not be expected simply by
making changes in one part, even when this part appeared thereby to be improved.
Furthermore, "improvement" of a part turns out to have little meaning if it
does not result in the improvement of the whole.
These ideas offered logical reasons why scientific research had so
frequently produced apparently irrelevant results, particularly in a subject
like agriculture, and led to the need for new methods (and thus, presumably,
to something different from "science").
But the methods must stem from the
approach and it is not always clear what exactly this approach is.
Here, then,
is an attempt to be precise about it.
A systems approach asks first "What is the system to be improved?" and,
secondly, ''What constitutes an improvement?"
These two questions (and their
supplementaries) ensure that aims and objectives are clear at the outset, in
a way that choosing a research or development topic in any other way does not
achieve.
tit
11
Indeed, these questions are so fundamental to a systems approach that
they may be regarded as not merely characteristic but representative of it.
It would be well worthwhile - indeed I believe it is overdue - to embody
these initial questions into the kind of simple methodology that could be
applied immediately to any practical situation.
The questions are insufficiently thought out and it would be necessary
to examine the word "improve", for example, to see whether it could be changed
for the better.
"Change" would serve but presumably always means "for the
better", although the possibility that one would wish stmply to maintain a
system also has to be taken into account.
l'What constitutes an improvement?"
will normally expose the many different purposes that most people have in mind,
many of them incompatible with each other, and the fact that a decision has
to be made about the terms in which the system and its operation are to be
expressed.
If a financial improvement is sought, for example, the outcome
must be expressed in monetary terms.
If improvement means increased energetic
efficiency, on the other hand, the expression must be in energetic terms.
So, a systems approach is a methodical way of defining problems and
examining possible solutions, based on the idea that practical aims always
involve the improvement of a system or the achievement of a degree of understanding that makes this possible.
Not surprisingly, a systems approach cannot be understood without a
definition of a system.
Definition of a System
There is now general agreement about this, and although definitions are
numerous, they all share much in common.
My favouric: version, currently, is as follows:
12
"A system is a set of interacting components, operating together for a
common purpose and capable of reacting as a whole to external stimuli:
it
is unaffected by its own outputs and has a specified boundary based on the
inclusion of all significant feedbacks."
Anything with these properties can be regarded as a system:
without
these properties, it is not possible to do so (a collection of apples, for
example, cannot normally be regarded as a system, though they can be converted
into one in several different ways).
To mistake a system for a non-system,
or vice-versa, is wholly misleading and may lead to disastrous results.
To
pull a substantial hair out of a bul1's face, on the assumption that it was
an isolated unit and not part of a system, is an example of the consequences
of either not recognizing a system or not
rea1i~ing
where its boundary lay.
The content of the system can only be determined with reference to the
purpose of the system and this is inherent in the way it is being viewed: just
~
as the content of a model of the system follows from the purpose for which
the model is being constructured.
Purpose is virtually the sole and sufficient
criterion for content.
The foregoing should make possible a non-elementary summary of the main
systems concepts.
The Main Systems Concepts
These are reasonably clear (see Spedding, 1978), but the order in which
they are listed cannot really have much meaning.
1.
Systems are identifiable entities TNith important properties and attributes
that
2.
are quite distinct from those of non-systems;
Systems can be of any size or complexity, from a molecule to an elephant
or a universe, and any system can be either a component or a sub-system
of another system;
4It
13
3.
When a system is a component of another system it has specific inputs
and outputs resulting from its interactions with the rest of the system:
an independent system is not bound by such interactions;
4.
In practice, no system is completely closed or completely independent,
but the differences between an object as a component and as a system
are usually very large.
(Compare a free buffalo with one harnessed
within a two-buffalo team pulling a plough);
50
All systems can be modeled, but it is not always practicable to construct
a model at any given level of detail;
6.
The level of detail and the structure of the model should be related to
the purpose of construction and should be as simple as will serve the
purpose;
7.
Models should always be capable of validation and, where possible, should
be validated;
8
0
The required precision and accuracy of a model should be specified in
advance:
9.
otherwise validation is not possible;
Where systems are too large or too complex to be studied in their entirety,
sub-systems may be identified that can usefully be studied separately;
10.
Sub-systems have a degree of integrity and independence of the whole system,
such that they can be studied separately and the results of such studies
incorporated into a model of the whole;
11,
The independence of sub-systems depends on the existence of only a few
main interactions with other sub-systems or components of the whole system;
12.
Sub-systems will usually have the same output (or contribute directly to
it) as the main system, but relate to only some of the components and
thus to only some of the inputs.
(Calf-rearing might be a sub-system of
a milk production system, provided that it included heifer rearing up to
14
the point of first lactation.
Calf~rearing
could also be a sub-system
of a milk and beef production system;
13.
Improvement (or indeed any response) of a system, due to changes in one
or more components, cannot be predicated without the use of a model, of
some kind, representing that system.
Many of these concepts imply that the approach differs from that of
orthodox science and none of them would normally be found stated in most
scientific literature, perhaps because they are most relevant to multidisciplinary situations.
The main recent exception is probably Ecology, but
natural systems are not usually considered to be purposive and the need for a
purpose in modeling them tends to be forgotten.
(This would probably be
denied by ecologists on the grounds that the purpose of models is to gain
"understanding".
However, this is, in my view, a quite inadequate statement,
since understanding always has to be sought for a purpose and it is essential
that this should be specified.
I have argued elsewhere (Spedding, 1975) that
the purposes of understanding a system will generally be related to operation,
repair, improvement or the design of a new system.
It is still the case that
the most appropriate model will depend upon which of these purposes is chosen
and exactly how it is stated.)
If the Systems Approach is not different from Science, it can hardly justify
the attention given to it, as if it was new, and, if it is different, it is
clearly essential to know whether it is consistent with it or not o
Science and the Systems Approach
Science may be said to proceed by the statement of testable hypotheses
and their subjection to rigorous experimentation, designed to test, prove or
disprove, depending upon your point of view.
15
I have argued that these hypotheses have always had to be essentially
simple (Spedding, 1979), because they were expressed in words.
Of course,
with no limit on the number of words, simplicity is not inevitable, although
the capacity of linear sequences of words to describe complex structures does
have some limits (since all the information cannot be held in the mind
simultaneously), especially for dynamic systems.
As a result, scientific hypotheses have tended to be simple, in one of
two senses, either representing a very small, even trivial statement or one
of enormous generality (with one major exception, dealt with later).
In the
first type, hypotheses commonly related to test-tube situations, far removed
from actual systems, and gave rise to doubts about the practical relevance of
the results.
In the second type, simplicity was achieved because the hypothesis
applied very generally, the best examples being natural laws,
such~
the law
of gravity.
The main exception to this argument, and it is a big, important one,
concerns living organisms.
It is clear that, once plants and animals had been
classified and described, it became possible to state a simple hypothesis about
a complex system, only referring to it by its name (e.g., a cow).
description of a cow could then be found in the literature:
A complete
more strictly,
any number of such descriptions could be found - of the inside and the outside
of a cow, of its skeleton, musculature, nervous system or alimentary tract (all
sub-systems).
Such descriptions have not been available, however, for multidisciplinary
systems:
furthermore, there is a sense in which they will never be available.
Biological systems, such as plants and animals, have evolved over a long
period of time and now exist in quite specific forms.
Most major variations
(mutations) would result in death of the organism and the vast majority of
16
e-
individuals resemble each other in all the characteristic features of the
species (or breed or variety).
Intermediate forms, having some of the
characteristics of two or more species do not occur, unless they form a separate
species on their own.
So, simple hypotheses can be stated that relate
to species, families,
classes or orders and thus apply to very large number of individuals.
In agriculture, however, systems have not evolved for so long and have
not done so within a consistent economic climate.
Variations in supply and
demand, and in costs and prices, superimposed on annual variations in the
weather, have meant that a system that has proved profitable in one year may
not do so in the following one.
There have thus been no continuing "climatic"
features to shape the evolving agricultural systems, except for quite short
periods of time.
But there are, of
course~
other important differences, the most important
being the limited extent to which "parent" agricultural systems influence the
nature of their "progeny".
Biological organisms can only change very slowly
but, if necessary, an agricultural system can change totally over quite a
short period of time.
Even forestry can rapidly become an arable system:
grass-
land can be concreted over and carry intensive poultry houses - though the
reverse is more difficult (usually meaning more costly).
are economic constraints.
Of course, there
A farmer who has just invested a great deal of
money in a large milking parlour cannot suddenly stop dairying and become a
grower of peas.
But, over quite short periods, enormous changes are possible,
virtually independent of what went before, and many different enterprises can
be combined in a great many different ways.
There is enormous variation
between systems because there is very little reason why most intermediate
forms should not exist between all the major production systems.
Of course,
17
again, there are some constraints and it would not be economic to have any
production enterprise present on too small a scale.
As small holdings often
illustrate, it is not impossible to operate mixtures down to almost individual
animals, especially if there are some co-operative elements, but there are
several influences that operate to encourage a certain minimum scale of
operation in most production systems.
For example, a milking parlour is obviously underused by a single cow
and five sheep either require a ram, that could serve eight times that number
of ewes, or access to a shared one.
Even so, it is quite possible for an agricultural system to be almost
unique in a number of important respects.
It will therefore not already
be described and documented but will have to be so before it can be the subject
of a hypothesis.
Thus it is that the statement of hypotheses about an agri-
cultural system usually requires a new description, and the latter may apply
to very few others and not even to the same farm business a year or so later.
This need to produce a description for each system may help to explain
why models seem to be constructed by every individual analyst for his own
purposes and relatively few are used by others.
Probably none can be said to
be widely used.
It is also perhaps for this reason that attempts have been made to
construct "core" models that can be further elaborated for individual cases:
the "skeletal" models of Blackie (1976) are of this kind.
Others have aimed at producing models of constituent sub-systems or
processes, that could be recombined appropriately for particular systems.
It is not impossible, of course, to construct a model of beef production,
such as Sanders and Cartwright (1979 a and b) have done for Texas and apply it
to other parts of the world, as has been done with this one in Botswana (ILCA,
1978).
18
What does seem likely, however, is that a great many models may have to
be constructed, for a variety of situations and purposes.
If this is so, the
cost and degree of elaboration need to take it into account.
In any event, the modeling activity may then become more important than
the models, as a means of stating relevant hypotheses.
The systems approach can therefore be described as a genuine extension
of the scientific method, allowing the statement of complex hypotheses as models
or of simple hypotheses about them, as representing complex systems, with
special reference to multidisciplinary dynamic systems.
Since much of the
real world consists of such systems, the approach is of considerable importance
and of widespread relevance.
It may be regarded as being essential in
agriculture.
Of course, a systems approach is applicable to single-discipline situations
and the philosophy should encourage relevant research, but modeling is then
~
applied within the scientific method rather than serving as an extension of
it.
There is thus no clash or inconsistency between science and a systems
approach but there are differences.
One of them concerns original or creative
thinking, a basic activity of the scientist and one which
some scientists see
as threatened by any strong development of a systems methodology which seems
to them to emphasize relevance, planning and a systematic methodology.
However, science is not alone in putting a high value on original thinking:
what has characterised it more than other subject areas, is its insistence
on an agreed methodology for the rigorous testing of these ideas.
A systems
approach does not encourage original thinking any more than scientific experimentation does (and no less, either), but it does provide an appropriate framework for the testing of such ideas, particularly in terms of their relevance
~
19
Yet it is difficult to point to many examples of practical benefit derived
directly from a systems approach.
Presumably the samewas true of science for
a considerable period and, in any event, both science and a systems approach
are as important for their contributions to disciplined thinking as for their
characteristic methodologies.
Nevertheless, the contribution of the systems approach does appear somewhat
limited, particularly in relation to the initial thinking about practical problems and both the philosophy and the methodology need to be developed further
in this direction.
The Present Need
Two questions were suggested earlier as fundamental to the systems approach,
concerned with description of the system to be improved and agreement on what
constitutes an improvement.
It is usually assumed that modeling is the chosen method of describing a
system, but would-be modelers often find it difficult to settle on the scope
and purpose of the model they wish to construct.
There is a tendency to rush
into modeling without sufficient initial thought, just as there often is with
experimentation.
The questions could be combined for both modeling and experimentation,
in the form "What exactly is the hypothesis and what is it being stated about?"
Most agricultural hypotheses not only assume a system, they also assume a
context (such as Southern England or North Carolina), and very often neither
are visualized with any precision.
20
The first task in research may be said to be the determination of a
problem area (e.g., the effect of disease on the productivity of cows) and
then the definition of a problem within it (e.g., the prevention of a specific
disease).
This choice of research topic is usually one of the jealously-guarded
freedoms of the scientist, but total freedom can only be afforded for the very
few and is best reserved for the most original minds.
If only the scientist's
curiosity is satisfied, there may be no immediate practical benefit at all
and this is not satisfactory for applied research.
Where a practical objective
exists, the selection and definition of a problem have to be related to its
achievement.
The question is how to make this choice in a systematic fasion.
Problem Definition
Attempts have been made, by the selection of "key" questions (Walker,
Norton, Conway, Comins and Birley, 1978) and by elaborating a matrix of (a)
topics for models and (b) the attributes of the models needed (MOrley and
Spedding, 1978), but there is still no well-established procedure available
to anyone wishing to use it.
In general, it is likely to involve a succession of models of increasing
precision and detail but of decreasing scope, representing a progression from
the problem area to the specific problem.
It is easily visualized the other way around.
When it is finally proposed
to construct' a model, it can always be asked h01:v it is known that the model
is of any importance.
To demonstrate that it is, the process or system modelled
has to be shown to be part of a larger system and to be an important part in
relation to the function of that system.
e'
21
Thus the importance of a model of a skeleton requires a further model
to show the body including the skeleton and may also require a model of the
population of which the body is a part.
Each model embraces the other, but
is at a less-detailed level and may not be quantified at all.
The focussing down on a problem can therefore be a systematic process
for identifying important research topics, but the methodology does not yet
exist in documented form.
A second difficulty arises from the multiplicity of possible solutions
to any problem, when only a few can be explored.
Solution Scanning
For most problems there appears to be an infinite number of possible
solutions and no means of ensuring that even the most important or relevant
ones are considered.
This is not normally because of the sheer number of
possibilities, however, but much more because of the difficulty of even knowing what they are.
Yet is is obviously possible to consider classes of possible
solutions, as one would for considering animals or plants, and to construct
a hierarchy which, in principle, includes all possible solutions (imaginable
at that time).
This approach was devised by Spedding and Wagner (1978) in relation to
a problem concerned with ways of disposing of a waste product, but there is
no reason why it should be not applied quite generally.
The fact is that any problem can be approached in this manner:
although
it cannot be independent of knowledge of the existence of possible solutions,
it allows them to be examined systematically.
For example, irrigation may
depend upon the movement of water and the question may be posed:
it be moved?".
"How should
22
A hierarchy can be constructured immediately, even without much preparatory thought, which provides a framework within which all possible
solutions can be examined (See Figure 1).
METHODS OF MOVING IRRIGATION WATER
~
Not By Air
(Abandoned as too costly)
ave
Across Water
(Unlikely)
land
In Containers
In ditches
Figure 1.
In Pipes
Carried by
People
A Hierarchy of Solution Scanning.
Carried by
Animals
I
Carried by
Hachines
23
It is clear that there are any number of ways of constructing the
diagram and that not every class has to be expanded:
many will not be worth
pursuing for one reason or another (often cost or impracticability).
These
reasons should be documented, so that they can be reconsidered subsequently
by anyone interested.
It is also clear that solutions can be missed.
For
example, projecting water could be included in "Flowing", but it can be
omitted because it only applies to very short distances.
In general, it is
better to include every class of possibility and the reasons for not pursuing
it.
More serious would be the omission of "underground" at the third level,
alongside "overland".
This could be the result of faulty logic and the frame-
work is more likely to bring it to light than simply thinking at random.
As lower levels of subdivision are reached, it becomes possible to consult
specialists for a further breakdown of the possibilities:
for example, a
specialist may be found who has studied the possible ways of carrying water
by machine.
There is thus considerable scope for methodically seeking out and making
use of relevant knowledge possessed by others - an essential for anyone confronting a multidisciplinary problem for which one individuaL cannot personally
have all the knowledge required.
Where knowledge does not exist, however, and has to be sought by direct
observation or by experimentation, there appears to be some confusion about
the role of a systems approach.
Systems Experimentation
The most common confusion is that experimentation should always be
concerned with recognizably agricultural systems.
The fact is, of course,
24
that it is the sheer difficulty of learning directly from practical experience,
confounded as it is with variable weather conditions and interactions with
people, that gives rise to the need for applied research.
Simply to replicate practical farming situations with increased monitoring
does not necessarily make it possible to interpret results.
On the other
hand, the orthodox scientific approach, of isolating parts of systems in
highly controlled experiments, does not necessarily produce relevant results.
In my view, three major kinds of physical experimentation are needed as
part of a systems approach, although this can be expanded to 5 or 6 in a more
detailed analysis (Spedding and Brockington, 1976).
1.
Experiments on whole systems, or on parts of the real world, with the
object of determining what systems exist, what they contain, where their
boundaries are and what their inputs and outputs are, making possible a
selection of defined problems related to defined systems.
2.
Controlled experiments on unit processes within systems (the effect of
A on B for all relevant values of A and for all the main variables that
influence the A ------4) B process), designed to assist in the construction
of a theoretical model and the quantification of its constituent processes.
3.
Experiments on whole systems, designed to validate models that have been
constructed to represent them or to test hypotheses generated by such
models.
Thus new data would come from experiments of a controlled kind, not
dissimilar from those normally carried out, but designed to quantify processes
that were identified in a model as units of a relevant system.
Experiments
on whole systems would be limited to exploring the real world, preparatory to
model-building, or to testing models or their outputs.
e
25
4It
If a systems approach is to be regarded as an extension of scientific
method to the statement of complex hypotheses, then it must accept the same
kind of discipline in relation to the testing of hypotheses as normally obtains
in science.
References
B1ackie, M. J. (1976).
Farm Firm.
ILCA (1978).
Management Information Systems for the Individual
Agric. Syst. l(1):23-36.
Mathematical Modelling of Livestock Production Systems:
Application of the Texas A&M University Beef Cattle Production Model
to Botswanna.
ILCA Systems Study No.1.
Morley, F. H. W. and C. R. W. Spedding (1978).
~
Sanders, J. O. and T. C. Cartwright (1979a).
Systems Model.
I.
A General Cattle Production
Description of the Model.
Sanders, J. O. and T. C. Cartwright (1979b).
Systems Model.
Unpublished Report.
II.
Agric. Syst.
i
(In press).
A General Cattle Production
Simulation of Animal Performance.
Agric. Syst. 4
(In press).
Spedding, C. R. W.
(1975).
The Biology of Agricultural Systems.
Academic
(1978).
The Effectiveness of Animal Production Systems.
Press.
Spedding, C. R. W.
Proc. 4th World Conf. on Animal Prod., Buenos Aires, 1978.
Spedding, C. R. W.
(1979).
Prospects and Limitations of Operations Research
Application in Agriculture - Agrobio1ogica1 Systems.
Int. Conf. ORAGWA,
Jerusalem, November, 1979.
Spedding, C. R. W. and N. R. Brockington
Agricultural Systems.
(1976).
Experimentation in
Agric. Syst. l(1):47-56.
26
Spedding, C. R. W. and M. A. Wagner
(1978).
Unpublished Report.
Walker, B. H., G. A. Norton, G. R. Conway, H. N. Comins, and M. Birley
(1978).
A Procedure for Multidisciplinary Ecosystem Research:
Reference to the South African Savanna Ecosystem Project.
Ecol. 15: 481-502.
With
J. Appl.
27
INTERDISCIPLINARY MODELING TO IMPROVE
AGRICULTURAL POLICIES, FOOD PRODUCTION
AND HUMAN NUTRITION
E. O. Heady
The assignment for my talk, which I have tried to stay within, takes
me down paths quite parallel and somewhat overlapping with Professor Spedding.
It is a great pleasure to be asked to deliver a seminar in honor of Curly
Lucas.
In recent years, he played an important role in directing the Biomathe-
matics Program at North Carolina State University.
However, at much earlier
times, I have became acquainted with him through our mutual interest in, and
felt need for, interdisciplinary research in predicting agricultural response
in a manner consistent with its particular form and which allowed it to be
better used in economic analysis and decision making.
In my early search for
statisticians who were interested in aiding with work on experimental designs and
statistical analysis to estimate crop and livestock response functions, I found
half of them at North Carolina State University.
Lucas, R. L. Anderson and Dave Mason, with
This half included Henry L.
the former being especially inter-
ested and helpful in livestock and the latter in crops and fertilization.
From
my early acquaintance with Curly Lucas, I can readily see why he became a leader
in systems concepts approach being applied in agriculture at his university.
I believe, however, that his contribution to science was even greater than this
through his particular quantitative or mathematical approach.
Other types of
mathematical models also gained from his insight and foresight, and I would
like to broaden my comments accordingly.
The wisdom of this approach is
seconded in the criteria used to assemble abstracts representing the Biosystems
Modeling Program at North Carolina State University.
The criteria seem to be
28
1) to include biological work in which mathematical science plays a central
role, and 2) mathematical work of clear relevance to biology. 1
Of the purposes set forth for organizing this symposium, a major emphasis
was given to research collaboration among different disciplines.
Accordingly,
a major emphasis in my paper will be the role of interdisciplinary research
in improving decisions and policies.
Supposedly, we would expect interdis-
ciplinary research to be more productive and to be more helpful in decisions
than individual research.
Otherwise, the only reasons for doing interdis-
ciplinary research would be the utility of socializing experienced by the
cooperating scientists. I have enjoyed it for these reasons, but the payoffs
do seem to be much larger, both to farmers and to the general public.
Inter-
disciplinary research has been an important part of my program for the past 2S
years because I have been convinced it is an appropriate way to do much more
research and that it does have a large potential payoff.
The opportunity to do interdisciplinary research employing systematic
mathematical models and concepts is now much greater than it was at earlier
times because of the trends in graduate training in recent years.
A great
amount of mathematics and statistics has become common in most of the major
disciplines of agricultural graduate training.
This direction of training
has increased the ability of more persons to think more abstractly and to have
more mathematical logic behind their research designs and to use it more in
the analysis and communication of results.
In my early quest to engage more
of my fellow biological and physical scientists in interdisciplinary research,
1Gold , Harvey J. et al. Biosystems Modeling. Abstracts of Current and Recent
Work at North Carolina State University. Biomathematics Series No.1,
Institute of Statistics Mimeograph Series No. 1202. Raleigh, 1978.
29
I put considerable effort in explaining a response surface and how algebra
and calculus could be used to derive quantities from it for decision-making
purposes.
One of my cooperators said, "Gee, that's thinking in three dimensions,
and we have never done that before".
He then studied some calculus and learned
how to apply it, but as we were ready to go to his professional meetings to
present the results, he also said, "After we present those partial derivatives,
we need to present some practical down-to-earth material, or no one will have
faith in me again".
It is now, of course, easy to find agricultural scientists
who are well tooled in mathematics and readily accept its application in
modeling, analyzing and communicating agricultural phenomena.
One of the major assignments given to me for this paper is to relate
modeling broadly in application to agricultural and nutrition for public policy
and decision purposes - that is a big assignment.
One important basis for
doing so is the prospective growth in world demand for food and the ongoing
hunger situation in \vhich as much as 500 million people suffer malnutrition
over the world.
Over the long-run, it will be necessary to use agricultural
and food producing resources as efficiently as possible to improve the nutritional status of this mass of people -- or even to keep the situation from
getting worse if high birth rates are maintained in so many countries.
Mathe-
matical models of agriculture can help close this gap especially by helping to
increase the supply of food and to provide research results in quantitative
forms which allow food producing resources to be used more efficiently.
is some extent of malnutrition
There
rooted in values and psychology (obesity,
alcoholism, etc.) of individuals and some rooted in lack of knowledge (including
cholesterol).
However, the vast extent of malnutrition has its root in the
supply or availability of food, the prices of food and levels of family incomes.
People at risk in nutrition from this complex set of causes are mainly in the
30
poor countries of the world.
Poor nutrition over the total population of the
United States comes mainly from other causes.
Mathematical modeling of agri-
culture can best help solve those problems of human nutrition that are due to
supplies and prices of food.
It can do so in leading to a more systematic and
fundamental knowledge of agricultural producing systems and of the interaction
of their subsystems.
The quantification of these relationships then can be
completed more rapidly so that those which can economically increase food supplies
by largest amounts are isolated.
And, as mentioned earlier, these relationships
can be quantified in a manner which allow them to be incorporated into mathematical
optimization or related models.
The latter specify how agricultural resources
should be allocated to produce food most efficiently.
The most food will be realized from a given resource endowment when the
interactions among the many variables in the system (that is, the subsystems)
are known, and can be manipulated to take advantage of these interactions.
His-
torically, we have perhaps been too specialized in our agricultural research.
are some reasons for being so and some need to remain as specialists.
Ther~
Still we
must have research which also spans these specializations to quantify the interactions in the subsystems and relevant variables.
These interactions will be
best identified when research extends across disciplines and fields to match
the workings of the real world.
The variables which represent genetic charact-
eristics and nutritional and other processes of plants and animals aren't
isolated from each other in the production processes.
In some early investi-
gations, myself and some colleagues attempted to quantify a model of milk responses to major feed categories.
Considering only the nutritional inputs,
we were able to explain only a rather modest portion of milk output per cow.
~
31
However, we then incorporated, in the same general mathematical model, variables
representing:
(a) the genetic makeup of cows; (b) the cow's personal character-
istics including her age;
and~)
the surrounding environment.
In combining
nutritional, genetic,management and environmental variables, we were able to
explain nearly all the variance in response of milk per cow.
Unfortunately,
in our case, we haven't followed up on this research.
An important reason for the interdisciplinary modeling of agriculture
production is to provide results which are better adapted to formal and informal
decision models applied by or for farmers.
A range of mathematical models has
long existed to define the conditions of optimal decisions.
of models use physical and biological data in various forms.
The varying types
Conventional
optimizing models suppose functions which are continuous in their variables.
Mathematical programming models suppose discrete and fixed coefficients.
of the
biological-physic~world is
Much
best characterized by continuous functions.
If more experiments were designed in this context not only would there be more
information forthcoming from given research investments, but also the results
would be better adapted for application in economic models for use in guiding
real world decisions.
Somewhat in the same context, interdisciplinary modeling of agricultural
production processes can help economize on research in the sense that given
investments can be made to predict more widely.
this approach can allow research
~esults
Or, with nearly the same meaning,
to be more widely transferable to
environments other than the one for which the research is conducted.
Some
people have given this approach the term "generalization of production functions"
so that predictions can be made to soil and climatic conditions other than those
at the sites of the experiments or for animals other than the quality of those
for which particular experiments were conducted.
Some examples are Perrin [3J
J
32
and Heady and Hexem [2J.
Generally, these results will come in the form of
continuous functions in which not only the primeeKperimental variables (e.g.,
fertilizer variables from which a yield response function is estimated) are
incorporated into the design, but also variables representing management
practices (plant population, crop variety, etc.), climate (rainfall for particular periods, water applied, etc.) and soil characteristics (soil depth, clay
content, etc.).
Quite obviously, research of this kind must be done in numerous
locations and with animals and soils of varying qualities or characteristics.
But once its predictions are available, it can be used in estimates for soils,
animals and locations other than those upon which the generalized function is
based.
Too, for example,if fertilizer response is being predicted, knowledge
and help is needed not only by soil fertility specialists, but also by soil
classification, meteorology, plant and management specialists.
We need much
more research organized in this manner, and it will best be attained through
4IJ
interdisciplinary research revolving around appropriate mathematical or statistical models.
The results not only apply to a broader population of producers
but also could provide a better base for investment policies in developing
countries.
The AID-sponsored soil classification projects at the University
of Hawaii and the University of Puerto Rico and the accomplishments along similar
lines at NCSU are a beginning in this direction.
They attempt to predict
yield response to fertlizer levels and other management practices across soils
and environmental conditions.
If this and other projects are successful, then
we should be able to speed the time when better planning of agriculture can
be accomplished to improve nutrition and agriculture
generally.
Experiments
conducted at some locations then can be better translated into expected yield
responses in other countries and locations.
33
Interdisciplinary research also will enlarge because agricultural
scientists including economists, do not look so jealously upon their specialized
fields as in previous times.
I can remember at a time in my own university
when economists, particularly in extension, thought no one else should make
any economic interpretation of agricultural phenomena; soils specialists
thought economists had no basis for talking about optimal fertilization rates,
etc.
As knowledge and departments have grown and greater funds have become
available these barriers around disciplines and specialized fields within them
have tended to melt away.
Perhaps agricultural research should be organized
around a matrix in which the rows represent problems or broad systems of
agriculture while the columns represent disciplines.
would be across rows as well as down the columns.
The line of authority
The problem or systems
leaders then would have a license to guide, coax or attract over disciplines,
as much as would the discipline leaders, and that is a bit what you have here
in your systems program.
I mentioned previously that wider knowledge and use of mathematics in
agriculture can better aid the design, analysis and communication of research
results.
Do I really mean commuication?
Yes, I do!
The mathematical
characteristics of agricultural responses can be used to both refine and broaden
the choices based on given research.
There are many examples.
In one' case,
we estimated a gain function for hogs in relation to carbohydrate and protein
feeds(the forms in which farmers make decisions).
The least-cost feed is
defined by equating the partial derivative of corn with respect to protein,
for a given weight, to the price ratio of these two ingredients.
The marketing
weight which maximizes profit above feed costs per hog is defined by equating
the partial derivatives of gain with respect to both carbohydrate and protein
34
feeds equal to the price of the ingredients divided by the price of gain.
farmers know calculus.
Few
However, we "operated" the calculus and inserted it
in a "wheel" with two edges.
The farmer needed only to turn the wheel edge
with the price of corn to the wheel edge \V'ith the price of soybean meal.
Effectively, he had taken the derivatives and listed in a lot before him were:
the feed mix to minimize feed costs over a weight range, time to marketing,
best market weight and other information.
Opportunities of this nature will expand rapidly over the near future
with the computerized decision systems now being made available through
agricultural extension services, and even by programmable hand-held computers.
While the basic models, especially programming models, have been available for
a number of years, and were earlier used mainly as research tools, only recently
have extension personnel begun making them available to agricultural producers
as computerized systems.
As the data become available, these models can be
used to aid operators to determine optimum plans or desirable plans for their
farms as a whole, or for certain subsectors of it (e.g., the optimum cropping
and fertilization program, or the optimal cattle feeding system).
Leading
farmers are anxious to use and are willing to pay for these services.
The
main restraints on the wider use of these systems is the supply of people
available for organizing them and the generation of the appropriate physical
and biological parameters to go into them.
Although they usually incorporate some concept of economizing, people
from numerous disciplines can operate these computerized models.
Similarly,
input from several disciplines is generally needed to make them operational.
Few if any research institutes or universities have launched sufficiently large
and organized interdisciplinary programs to provide the wide range of data
needed to fully exploit these opportunities.
While the possibilities are
4IJ
35
exciting to
scientis~and
they would otherwise be drawn voluntarily into such
cooperative endeavors, there also are institutional factors which serve as
barriers.
Generally these factors are not located in the college of agriculture
but more generally in graduate colleges and other focal points of the university
where criteria for advancements of rank, tenure and graduate college membership
are decided.
The resulting uniform criteria generally discourage group and
interdisciplinary research.
They inform the young scientist that if he wants
to advanced in rank and tenure rapidly, he best would go hide away by himself
and come up with the solely authored manuscripts which have some prospects
of acceptance by a refereed journal.
They may not need to be of importance
relative to the real world and its functioning as long as they are solely
authored and have been refereed.
Some credit for these purposes may be forth-
coming if it is a joint paper between two persons, but few points will be
granted for part of an endeavor involving 6-10 persons.
In the interests of
interdisciplinary research and better explanations of complex systems, we
do
need to invert these reward systems so that for relevant problems and phenomena,
higher positive emphasis is given to group and interdisciplinary research.
Special Potentials for Agricultural and Nutrition Policy.
I have been asked to comment specifically on some potentials of modeling
for solving problems of food supply and nutrition.
Each modeling of an agri-
cultural system, to the extent that it leads to improved knowledge of how
systems function and allows alterations in it which increases yields or reduces
inputs, contributes to this goal.
The modeling of micro systems has this
potential, especially if they indicate linkages which promise highest payoffs
•
e
to further research.
Generally, however, the models to be used for improving
food production and nutrition on national scales will need to be more macro in
36
character but, of course, to incorporate the results of modeling smaller
systems.
Certainly macro models for improving food supplies and distribution
need multidisciplinary inputs.
having sufficient numbers of
Those developed so far have suffered from not
biological-physical scientists in their teams.
A number of modeling systems have been quantified at the level of national
agricultural sectors to serve as aids in policy decisions and developmental
planning.
One would expect the foremost of these to be in the centrally
planned socialist economies.
This is not true, however, except for a recent
model of some sophistication developed for Hungary.
Other countries with
centrally planned economies have developed some planning models, but they are
quite modest as compared to some of those found in some market economies.
The
International Institute of Applied Systems Analysis in Austria is trying to
catalyze the development of national models, in the hope that they might be
linked in a world analysis of food production and nutritional potentials.
4It
Scientists working on large and complex national and international policy
models must have a relatively long planning horizon to be successful.
In other
words, they can hardly be expected to come up with the "magic five published
journal articles in three years".
I believe this also to be generally the case
of interdisciplinary modeling of complex agricultural systems, even on smaller
scales.
Considerable start-up time is required before the mathematical structure
of the system can be specified, experiments can be generated (or data can be
synthesized) to provide the estimates of the parameters basic to the system,
which then becomes operational.
Models for Planning at National and Local Levels
The number of disciplines central to national modeling systems depends
on the nature and details of the model.
There are a number of national models
e
•
37
in the United States which have been developed and are being kept up-to-date
for continued policy analysis.
They have been developed by private firms,
universities and the United States Department of Agriculture.
Some are based
entirely on statistically or econometrically estimated time-series relationships of markets and market systems and have incorporated the efforts of only
economists and statisticians.
Generally, these are recursive simulation models
based on time-series and related relationships.
The predictions of some relate
somewhat to crop yields and weather and thus need inputs from agronomists and
climatologists.
At least one is a systems simulation, in which the paramters
are synthesized from whatever sources and requires information on biological
response and physical supplies, as well as economic parameters.
We, at our
institution have both a mathematical programming and an econometric recursive
simulation model which have been operational for a good many years.
And
currently, we are in the process of linking them together in an overall model
which allows evaluation of U. S. agricultural potential (a normative approach)
with some prediction of market impacts (a positive approach).
As some encourage-
ment to those whose models grow large in size and complex in data requirements,
I will summarize a few characteristics of the programming model we have.
I do
so partly because its nature indicates the need for multidisciplinary knowledge
and inputs for models which allow national policy analysis but prOVide not only
economic impacts but also physical and biological impacts back at local levels
ani on specific land classes.
The programming model has numerous versions (i.e., is a set of alternative
models with varying specification of endogenous and exogenous commodities, water
and energy sectors, land class aggregation, and point demands or demand relation-
.
ships).
It has been, within the limitations of its nature, specified to allow
evaluation of alternative national policies, such as conventional commodity
38
programs, supply control alternatives, regulation programs such as those
implemented towards chemical use and non-point pollution by the Environmental
Protection Agency, export potentials and alternatives in rural community employment.
But in each case, the impact can be traced back to the local,regional or
state level.
Also, it allows an analysis of the interaction among regions or
localities.
For example, a "clean water program" which is effective over parts
of Mississippi and Georgia would, without compensation, cause income sacrifice
there as land is devoted to less intensive uses.
In contrast, it would bring
gain to the regions of the Great Plains where land is level, rainfall is limited,
irrigation water is available and soil erosion is low.
The latter regions could
intensity agriculture and gain at the expense of the former regions.
This model has a large appetite for data -- that related to agronomy, animal
science and engineering as well as economics.
It includes 223 separate producing
regions, each with at least 9 different land classes.
(The 54 irrigated regions4lt
have 18 land classes, with 9 each for irrigated and dryland crops).
Yields were
estimated for 11 different major field crops on each of the nine land classes
in each of the 223 regions and generally under three tillage methods (conventional,
minimum tillage and no till) and under several conservation practices (contour,
terrace, strip-crop, up-and-down hill).
It also was necessary to be able to
estimate yields under different fertilization levels conforming with different
pricing regimes.
For certain analyses, it was necessary to generate soil loss
per acre per annum for each crop grown on each land class in each producing
region under potential combinations of tillage methods and conservation practices,
another aspect requiring agronomic inputs.
Developing the land base in each
producing region required the use of soil classification experts.
With live-
stock in each region allowed a ration or feed mix which minimized feed costs
from crops produced in the region or imported from other regions, help by anima14lt
.
39
scientists also was needed.
For certain analyses, it was necessary to estimate
(by entomologists and agronomists), yield impacts from reduced use of certain
pesticides.
A transportation model also was incorporated to allow interregional
commodity movements, interregional competition and fulfillment of regional
and export demands.
While rather simple initial models could be developed
without interdisciplinary inputs, the detail and comprehensive nature of modern
versions could not.
Models of this general scale and nature become rather ultimate tools for
measuring the potential of agricultural and food production and improvement
headed towards improving world nutrition.
They do so since they measure quanti-
ties and explore possibilities at more detailed levels than states and nations.
They can suggest "What investments of what size should be made under a full
range of agro-climatic conditions."
To date, human nutrition components have
not been linked into these models.
Particular Nutrition Approaches
For improving food production and human nutrition, the main potential of
such models is in developing countries where agricultural development has
lagged and many people suffer poverty and malnutrition.
The development and
application of policy models to simultaneously improve food production and
nutrition is the long-run hope in solving problems of malnutrition.
The United
States is engaged in numerous nutrition programs over the world which involve
interventions such as supplementing the diets of school children and pregnant
women.
While these programs are highly humanitarian and provide beneficial
short-run effects, they cannot solve long-run problems of human nutrition in
themselves.
They neither increase food supplies, improve family incomes nor
reduce the real price of food for the future.
Solving these problems in total
40
will best be accomplished by planning models which are used effectively in
increasing agricultural productivity and in improving food distribution systems.
Efforts to link human nutrition modules
models are just now beginning.
to agricultural policy or planning
Recent proposals have been made and initiated
to develop such models in Central America,
Thailand and Columbia.
is wide open, since few developing countries have such models.
The field
A few countries
have developed agricultural sector models which now are in operation.
are programming models in Central America
Thailand.
Included
(although perhaps weak), Mexico and
Others were developed, but are not in active operation.
Michigan
State University developed a system simulation model for South Korea which has
been used to examine alternative agricultural and food policies.
We have a
Thailand model which somewhat parallels the one summarized for the United States
in the sense that it provides geographic detail through a number of agro-economic
zones with several land classes in each and extreme detail on crop production
technologies.
It has been used to examine developmental alternatives for agri-
culture through five year plans including (a) a case where maximum national
development is the objective apart from any special concern for the poor of
any region, who might be suffering from low incomes and malnutrition, and (b)
a case where national development is
restrained to provide greater income
increases in the Northeast Thailand where agricultural technology is backward
and incomes and nutrition are at risk.
While quantified models of these general sizes and natures are a great
hope in better planning of agriculture, improving food supplies and lessening
malnutrition, their execution in many countries must await improved agricultural
statistics and research systems.
In Thailand,we partly solved this problem by
helping to create an agricultural statistics center in the Ministry of Agricculture and by two farm surveys of 7,000 and 20,000 farms respectively.
~
41
General Modeling Opportunities
I have commented on Some rather large models which are adapted to improving
food production and distribution.
Models which have this capacity, I believe,
generally must be of the scale and detail outlined.
before,
th~
But also as I mentioned
badly need the output of other modeling efforts.
Too, I see no
reasons why biologists, physical scientists and others should not do quantitative modeling at regional and national levels.
They can borrow the economics
or economists to complete the task, as the economist or professional planner
can borrow the biology or the biologists.
I would think that this development
would be one of the products of the intense efforts being given to agricultural
modeling through the Biomathematics Program, but it would attempt to integrate
all of these disciplines.
Not all of the large-scale modeling which could be accomplished by
biologists, physical scientists or mathematician-statisticians need be of
national planning.
An important amount of malnutrition occurs because agri-
cultural production is a stochastic process and frequent large-scale droughts
either directly caused it through high food prices over the world.
Given the
distribution of food production that does prevail over time, there is still
an urgent need to develop buffer stocks which reduce the instability of food
availability and prices over time.
If the mere experience of carrying large
grain stocks solved this problem, the United States would have provided the
world with the solution at the end of the 1960s. However,
the large stocks
we had on hand at the beginning of this decade had only minor influence on
the greater instability in food supplies and prices experienced in the 1920s,
so it doesn't seem we have all the answers yet.
Of course, the objectives
of our earlier publicly held stocks were not food stability, but rather reduced
42
market supplies of grains to increase domestic farm prices and income. Our
current stock-building efforts are mainly for the same purpose.
And while a
a few persons have been working at it, there is still need to develop and
quantify buffer stock models which can be executed to lessen instability in
food supplies and of course prices.
For some time into the future, the world,
espeCially North America -- will have capacity to produce large quantities
of food in years of normal weather.
Then as long as other countries carry
insufficient buffer stocks, wild increases in export demands will be placed
on surplus grain producing countries.
As experienced in the 1970s, world
regions such as the Common Market, Japan and the U.S.S.R. protected their consumers by various types of grain purchases and trade maneuvers.
Thus what
otherwise would have been instability for their own consumers, was transferred
into instability in grain prices for grain surplus producing nations and in
food supplies for food deficit developing countries.
still before us:
These prospects are
sufficient grain producing capacity to keep prices depressed
in normal years, but exploding prices in years of sizeable crop short falls.
It is possible that biomathematicians have as great a capability as economists
in developing models whichoould greatly lessen the instability in supplies and
the suffering it generates.
Implementation by national and world politicians
is, of course, another matter and integrating such storage systems with market
systems to get the price effect is also another step.
Fair Values for Agriculture
My paper was to emphasize modeling and analysis as they relate to food
production and human nutrition.
Of course, as I have already emphasized, these
are particularly the problems of the developing poor countries.
We are already
exporting the product of more than one of each of our four crop acres nationally4lt
43
We can model a more efficient North American agriculture (since Canada also
is a surplus producer).
However, the resulting increase in grain exports, if
the greater production went into exports rather than idled land or surplus commodities paid for by treasury payments, would generally go to the rich countries
which increasingly use grain (particularly Eastern Europe) to increase meat
consumption for well-off consumers.
Under current world trade patterns and
tendencies, the countries suffering greatest malnutrition would gain mainly
only as the real prices of food grains are held down.
Of course, if their
incomes are extremely low and they live mainly under a system of subsistence
farming, families suffering malnutrition in poor countries will gain little
from improved food systems in developed countries.
This condition leads immediately to the notion that much of the agricultural modeling to improve agriculture and nutrition needs to be done in
the developing and poor countries where agricultural productivity has lagged
or is low and many people are at nutritional risk.
While they are most in
need of agricultural improvement, it is the developing countries which have
mainly undervalued agriculture.
They have done so partly through investments
in agricultural research which are too meager.
They invest only a fourth to
a third as much in agricultural research, per value of farm products produced,
as do the developed countries.
It has generally been shown that the return
from agricultural research investment, either from increased output or resources
displaced from agriculture, has been high in both developed and developing
countries.
The developing countries also have undervalued agriculture in
pricing policies which have caused farm commodity prices to be low and dampened
investment incentives of farmers accordingly.
Some, such as Thailand, have used
export taxes to keep prices low for domestic consumers.
other parts of the world havedone similarly.
Marketing boards over
Over a period of time, India kept
44
producer's price of wheat at about 50 percent of the world level.
There is
need, of course, to keep food prices within the reach of poor families.
How-
ever, this can be done by coupons and other means which do not distort prices
to farmers in a manner which discourages agricultural development.
this undervaluing of agriculture
Finally,
has sometimes been expressed in the meagerness
of capital investment in this sector as compared to others.
With this long spread undervaluation of agriculture in developing
countries, plus the export complexities related to agricultural improvement
mentioned for the developed countries, the long-run payoff in augmenting agricultural production to improve human nutrition just has to be in the developing
countries and much of the needed work should be on an interdisciplinary basis.
As far as it has run so far, the "Green Revolution" has not been l:a.sed on a single
practice or discipline.
Its full payoff
has come from a group of disciplines
and practices incorporating new varieties, fertilization, pest control and
irrigation.
To the extent that we finally reach a new plateau in food production
through what is known as the "Green Revolution", we may then progress to a higher
plateau through more efficient use of the world's water supplies -- particularly
in countries of tropical climates where the opportunity to capitalize on solar
energy is highest through multiple cropping systems.
In a general way, the
developing countries conform with those world regions with tropical climates.
But to get the most out of these water and energy opportunities, further multidisciplinary efforts are posed.
On the one hand, the legal, economic and social
customs controlling the distribution of water are often faulty -- giving too
much water to some farmers, where its marginal productivity would be high.
Im-
proved engineering operations are needed to prepare land sooner for subsequent
crops in multiple-cropping systems.
Plant breeding, fertilization and water
allocation needs to give emphasis to yield levels and growing seasons which
45
provide optimum production levels not for a single crop in the system, but
for over all crops of a multi-cropping system.
Included
in this bundle of
needs to make greatest use of water and energy supplies is work on water conservation and irrigation efficient systems, optimal water impoundment and release systems and potentially even inter cropping systems which might capitalize
on improved biological nitrogen fixation of some crops grown in the mix.
The developing energy situation will require us to focus more sharply on
using energy and rainfall of tropical regions more effectively,
just as it
will emphasize the need for considering new agricultural systems in the developed
temperate countries.
Each of these settings is an excellent opportunity for
researchers of various disciplines to come together in cooperating modeling
and research efforts.
We are already far behind where we should be, given the
early warning signals we had as far back as 1973.
Needs at Research Centers
Donor agencies in developed countries are attempting to offset the underinvestment in research by the developing countries.
the regional research centers including:
They are doing so through
IRRI for rice-related technologies
in the Philippines, ICRISAT for semi-arid regions in India, IITA for tropical
crops in Nigeria, CYMMT for maize and wheat in Mexico and others.
Directors
of these centers have noted the need to encourage interdisciplinary efforts
in solving the food and nutrition problems of developing countries by adding
an economist or two to their staffs.
Through these additions, considerable
attention has been given to research in forms which is more consistent with
decision environments of cultivators.
These staffing patterns have likely
had beneficial effects in encouraging more interdisciplinary research which is
most applicable on small farms in developing countries and their bundle of
resources.
I am not sure, however, that the need is fully met simply by
46
adding an economist or two to a research center's staff.
Equally needed may be
some more general modeling experts who can transcend all of the disciplines
involved.
Priorities in Research
As mentioned previously, numerous studies have shown that the returns to
society are high for agricultural research in both developed and developing
countries.
However, these studies so far only indicate that the return on
research is high; they don't show where it is highest.
Since all investment
funds are scarce in developing countries, it is important to place priorities
on research and invest not just in agricultural research generally -- but to
invest where the return is highest to the relevant recipients of these
benefits.
It would appear that systems modeling efforts could especially be
fruitful in isolating those areas where research can have the greatest break-
4IJ
through and highest return relative to current food and income problems of
farmers and consumers.
Here is one of the more urgent needs for some expanded
interdisciplinary modeling efforts for the purposes cited.
Until these large opportunities in interdisciplinary modeling to improve
food supplies and human nutrition in developing countries can be fully exploited,
we can extend our abilities at home in advancing world food supplies.
North
Carolina State University has taken a lead to do so in the organization of the
Biomathematics Program which emphasizes interdisciplinary modeling of agricutural and food producing systems.
approach to agricultural research.
Others of us need to follow this broader
The influence of Curly Lucas stands to be
expressed on this broad front of the future.
•
47
References
1.
Evenson, Robert E.
"The Organization of Research," in Schultz, T. W.
(Editor), Distortions of Agricultural Incentives, University of
Indiana Press, Bloomington.
2.
Heady, Earl
o.
and Hexem, Roger W.
Irrigated Agriculture.
1978.
Pp. 223-245.
Water Production Functions for
Iowa State University Press.
Ames, 1978.
Chapter 10.
3.
Perrin, Richard.
"The Value of Information and the Value of Theoretical
Models in Crop Response Research, II American Journal of .A..gricu1tura1
Economics 58(1):54-61.
4.
Schultz, T. W.
February, 1976.
"On Economics and Politics of Agriculture," In Schultz,
T. W. (Editor), Distortions of Agricultural Incentives.
of Indiana Press.
Bloomington, 1978.
Pp. 3-23.
University
48
Panel Discussion
•
Dr. Robert L. Rabb
I do appreciate the opportunity to participate in this recognition of
Curly.
However, many of the remarks that I have prepared now seem redundant,
after hearing these two excellent presentations.
But perhaps it might be
worthwhile to repeat some points of view.
As Dr. Spedding has so effectively shown, there is no one view or
model of agriculture that is sufficient to serve the many purposes of
researchers, practioners, politicians and philosophers. Perhaps we do
need, as he implied, an album of models or views.
And perhaps we should
occasionally flip through this album together just to see how our own
view or model relates to others.
With due deference to the concept of man as a part of nature, it
seems useful to comprehend agriculture as involving two main interlocking
systems: (1) natural systems driven by solar energy and (2) management
systems, imposed on natural systems, driven by man-directed energy
subsidies.
Management systems obviously are constrained by natural laws which
man understands poorly.
Thus, it is fundamental to have continuing research
for the purpose of expanding understanding of the structure and function
of natural systems.
The importance of such basic research is too often
given inadequate attention by decision-makers in agriculture.
In this
vein, I am not quite ready to accept Dr. Spedding's idea that all research
on natural as well as management systems should be directly relevant to
purposes of agriculture.
I am extremely cautious in accepting criteria
of relevance, for many of the most useful scientific advances have been
made by scientists who were merely seeking to satisfy their curiosity.
fear that a more forceful effort to invoke criteria of relevance in
I
49
dispensing research support would inhibit the search for more comprehensive
understanding of nature and be detrimental to progress in agriculture.
And
too, there seems little likelihood that basic research will attract a disproportionate share of the agricultural research dollar.
Obviously, a
strong applied research effort designed to use understanding for agricultural purposes (e.g., maintenance, repair, improvement, or redesign)
is essential for a productive and stable agriculture.
Management systems are arbitrarily imposed on natural systems with
little regard for the dimensions of the latter.
This is at the heart of
differences among agriculturists and other scientists in their concepts of
agroecosystems.
Management systems are constrained to production units, fields, crops,
farms, etc., whereas the species populations of flora and fauna are not.
For example; pest species are not constrained to management units as
generally conceived in agricultural systems - most of them also reproduce
and/or take refuge in uncultivated areas, and many of them are highly
mobile, some moving hundreds of miles to new resources.
To understand the
population performance of such organisms one must study a system much
larger than the management units of the individual farmer or agribusiness
concern.
I view an agroecosystem as an area in which crops are grown, defined
large enough to include the uncultivated as well as cultivated units
between which interchanges of organisms occur.
Obviously, this is not a
neatly defined closed system, but it is realistic.
This view leads to a
better appreciation of the interactions within a farm, among the cultivated
and uncultivated units, and within farming communities, among farms and
farming regions.
•
50
Unfortunately, the research on functional agro-ecosystem units as wideranging as some of our present species has been hampered by the adoption
by research administrations of more restricted definitions of agricultural
systems targeted for research.
as far as I was concerned.
This is the reason the red flag waved,
Obviously a pest species deme may ignore and
encompass many institutional and political unit territories.
So my comments
are made not to be incompatible with those of our speakers, but to encourage
the stretching of this album of views or models to include population
systems as dimensioned by nature rather than arbitrarily defined by man.
51
Dr. Richard A. King
There is a great deal to be gained by opening up cooperation and
interchange among the various disciplines in the School of Agricultural and
Life Sciences and in Physical and Mathematical Sciences, not to mention
other disciplines on the campus.
The question is how to bring this about.
I think that systems research is one promising method.
can be viewed as a process as well as an objective.
Systems analysis
The way one looks at
things may be as important as the particular shape they take.
Systems analysis emphasizes the interrelationships among entities in
a system.
How do we deal with these interrelationships?
Are we ignoring
them or taking them into account?
I liked Professor Spedding's emphasis on
the point "What is the question?"
If you don't address that issue directly
the selection of models that deal with interrelationships seems less important.
With regard to nutritional problems, which were touched on by Professor
Heady, the present stage of many human nutrition models is quite unsatisfactory.
On the Today show this morning at 7:30, a guest was describing his view of
the president dropping out of a foot-race the other day.
He pointed out that
food intakes alone has relatively little to do with physical well-being.
is necessary also to worry about such things as exercise and smoking.
It
In
trying to beat a heart attack, anyone of the pieces is important, but no one
can give the whole answer.
So, if you think of a human being as a system,
you recognize that measuring food input makes as little sense as measuring
the amount of food you give a cow, while failing to measure the amount of
milk she produces.
Food is an input not a product.
The problem is how to
pose the question -- are you interested in measuring inputs or do you wish
to measure the ability of humans to perform work.
52
There is a need to take the philosophy that lies back of the systems
approach into a much wider variety of areas.
point, which is where do we go from here?
That brings me to the last
What are the conditions for success
in attempting to build more comprehensive models?
There has been a good bit
of emphasis on the importance of mathematics and statistics today.
that both are very helpful.
I agree
The size of models that the research people at
Iowa State are able to deal with is very impressive indeed.
not wish to compete in building models of that scope.
We at NCSU may
However, there is a
place for smaller models and for a systems view of the question that we deal
with.
In the next day or so we may develop suggestions on how to extend
that approach in our agricultural research programs.
53
Dr. J. Lawrence Apple
Thank you very much.
Now that the Dean is gone, I must take exception
with one item in his presentation.
pitch softball pitcher.
perspective.
He indicated that Curly was a slow-
I never viewed it that way, from a batter's
Curly was a great pitcher.
And we in Plant Pathology used
to have some great contests with the team from Statistics.
I have enjoyed these presentations and I hope that all of you have.
I think few of us question at this point in time, the applicability of the
systems concept to many of the complex production problems in agriculture,
whether they be animal or plant production.
Some of the very difficult
integration that we now pursue demands a systems approach, if we are to
cope with these problems successfully.
But we have had many growing
pains in applying the systems concept in a general way, and broadly, to
agricultural problem solving processes.
bit ahead of his time.
I think Curly Lucas was a man a
Curly had a vision of the application of systems
science to agricultural and biological systems before his contemporaries
were really ready to accept them.
Curly was responsible for bringing the
Centers of Excellence Biomathematics Program Grant to North Carolina State
University.
We did not take full advantage of the opportunities afforded
by that program in those early years.
But there were many reasons for
that, among which was the lack of adequate quantitative expertise among
biologists to interact effectively with the Biomathematicians.
The
improved quantitative skills on the part of many of our scientists now
enable us to move into a new realm of disciplinary integration.
I see the systems approach as a tool of science.
Dr. Spedding's paper
questions whether the systems approach is consistent with the scientific
method and with science as we know it.
I personally see no inconsistency.
54
James Conant, in his book Science and Common Sense, defined science as "an
interconnected series of concepts and conceptual schemes that have developed
as a result of experimentation and observations and are fruitful of further
experimentation and observation."
I see nothing in that which is incon-
sistent with the systems analysis techniques.
In fact, they are applicable
to the interconnection of these concepts and conceptual schemes.
Professor Heady indicated that supposedly interdisciplinary research
is more productive and thus more efficient than strictly disciplinary
research.
We can challenge that statement from many perspectives in
certain situations.
Many of us see disciplinary integration as necessary
in researching the "fringes" of the disciplines.
Consequently if we
classify the main effects in a system as disciplinary and the interactions
as interdisciplinary, interdisciplinary research may focus on effects that
have less impact upon the output of the system than some of the main effects
researched by the disciplinarians.
Further, I don't think any of us see a
world in which we do not need continued specialization within the
disciplines.
plinary input.
Interdisciplinary research must be built upon strong disciAnd the fact that we have interdisciplinary teams in no
way detracts from the need for each one of those persons to be a highly
skilled disciplinarian -- and to perhaps be an exceptional one -- to be
able to bridge the gap between his or her science and that of associated
sciences.
I would not argue that with the fact that good interdisciplinary
science might have more transferability, but it may take longer for a
given increment of output, and it will probably cost more because the
validation component of that system must involve a wider range of variables
than is generally necessary for disciplinary research.
55
Dr. Heady, I could not pass without saying a thing or two about economists, but your paper implies that we achieve disciplinary integration by
adding an economist to a research team.
broadly.
Many of us see it much more
There are many other essential components, obviously depending
upon the definition of the problem that we choose to address with that
interdisciplinary team.
Bob Rabb gave you perspectives that are pest management oriented, and
since I happen to come from that same general area of "persuasion
both obviously think somewhat alike.
we
We have a program on our campus that
we call IPOMS (Integrated Pest and Orchard Management Systems).
The 13
scientists on that research team represent most of the essential
disciplir~ry
components.
I am pleased that one of them is an economist.
One of my
major concerns, being involved with research management, is the structure
and function of these interdisciplinary teams and how to overcome those
deterrents to interdisciplinary integration at the scientist's level that
Dr. Heady has so aptly recognized in his paper.
We have been struggling
with these questions at the School level, and I know that you have struggled
with it from your individual and departmental perspectives.
It is a situ-
ation that we have to learn to cope with more effectively in the future
than we have in the past, because obviously there are many complex
problem areas that will demand this appraoch in the 1980's and beyond.
56
Dr. Arthur J. Coutu
~rofessor
Art Coutu was originally scheduled as one of the discussants.
He unfortunately became ill and was unable to participate, but has submitted
these remarks.)
My remarks would have focused on some points of concurrence and concern.
Concurrence:
1.
That systems analysis is essentially mathematical modeling.
2.
That systems analysis enhances the search for more systematic and
fundamental knowledge of producing systems towards the objective of
increased output levels by substituting knowledge for limiting resources.
3.
That systems analysis brings a focus on quantifying interactions of
sub-systems.
4.
That systems analysis enhances predictability.
5.
That systems analysis enhances the transferability of research results.
6.
That systems analysis is an aid to the design, analysis and extension
of research results.
Concerns:
1.
That the issue of limited acceptance of systems methodologies is related
to:
(a) The limited flexibility of agricultural research administrators
associated with the long history of the departmental-schooluniversity structure,
(b) The lack of institutionalized processes relating to rewards
and promotions.
e
2.
However, more basic to the acceptance of systems methodologies are concerns
relating to:
57
(a) The position of the individual researchers loss of independence
if he chooses to accept the role of a synthesizer of research
results rather than solely a contributor to a flow of research
results.
(b) The position of the individual researchers loss of objectivity
if one chooses to be a researcher and an active advisory to
private or public choice makers.
The advantange of a system
approach to quantify complex sub-systems may be off-set by the
disadvantage associated with this researcher - decision maker
linkage.
3.
Another concern is the likely change in agricultural research administration if systems approaches are to be more widely applied.
As Dr. Heady
pointed out, what we may need is a few researchers even more specialized
than presently, but with the majority, at any institution, focusing on
quantifying interactions.
What changes in research administration are
required to permit such allocation decisions of researchers?
4.
A generally perceived disadvantage of systems approaches relates to
increased intervention and/or control by technocrats.
of systems concepts counter this perception?
How can advantages
58
C. R. W. Spedding
If I may, I would like to take up Professor Gold's point, which he managed
to slip in early on, about my belief that there are systems and non-systems and
the fact that I don't accept that any collection of items constitutes a system.
Just to make it clear, my belief is that a collection of items does not
constitute a system by any definition that I know.
If we take an example, there
are four glasses independently sitting on the tray there.
I consider those to
be a collection of glasses and they do not constitute a system.
One of the
values of a systems concept is the recognition that if you touch one bit of it,
the whole thing may react because of the interactions.
But I can remove one
of those glasses without any effect on the rest, and I can add some more to that
same collection.
Now that's not to say that the glasses could not become a
system by joining them up in some way so that they are a part
of'~
system.
For example, if you take the whole bench plus the tray plus the glasses and the
force of gravity, you can in fact work out a system of which those four glasses
are a part.
But it is the recognition of just where the system is that seems to
me to be of such great importance.
All I am saying is that a collection of items
as they stand does not to me constitute a system.
Very likely that will come up
again.
Now there were a half dozen other points that I would like to comment
briefly on - in no particular order.
I also believe that natural systems require study as well as managed systems.
I suppose the difference I may have implied is that while it is quite obvious
that managed systems are purposive, it may seem that natural systems are not.
The point I would like to add there is that there may not be a purpose in a
natural system, depending on how religious you happen to be, but that is largely
59
irrelevant.
But you have to have a purpose in studying it; and the model you
construct of a natural system will have to have a purpose.
Any definition of a
model will tell you that its an abstraction, it's not going to have in it
everything that is in the real world.
The only criterion I know for deciding
which bit of the real world must be represented in the model and which not is
the purpose for which we are building the model.
If the purpose says one thing,
then you must have it in, if it says another, you can leave it out.
So, the
building of models to my mind is always purposive whether they are of natural
systems or not.
I also accept, of course, the need to extend the idea well
outside agriculture and in many overseas systems including the nomadic ones I
referred to, it is hard to distinguish where the agriculture finishes and where
the rest of the man's life begins.
They are much more closely integrated than
that, and they require not merely the addition of an economist, which has
already been commented on (although I took it that Professor Heady waS orginally
~ying
that it wasn't a sufficient way of going about it), but they also require
anthropologists and sociologists, and so on.
But my attitude as to what dis-
cipline must be in there is simply what is required to understand the system
for your particular purpose.
well.
If it's got law in it, you must have a lawyer as
If it hasn't got law in it, you don't need a lawyer.
I talked a bit about the distinction between systems and non-systems.
I
didn't mean to imply that there isn't a perfectly respectable kind of research
which is not concerned with systems.
There is a great deal of research which
follows from curiosity, defining interesting questions.
Science depends upon
this kind of exploratory thinking and experiments to back up the thinking and
advance it, and that doesn't require necessarily the recognition of systems.
certainly doesn't require attention limited to systems.
But I think it would
still benefit from a recognition that there are such things and that it is
worthwhile recognizing them when they are there.
It
~
60
This brings me to the point of the fellow who didn't find it necessary to
recognize an elephant when he met one.
He was quite happy if it were a zebra,
and all I can say is he has been lucky so far, but I wouldn't bank on it.
One
of the interesting things about recognizing a system is that you are then
able to harness all you h?Ve learned about it, and anybody has ever learned
about it, which should be available to you through education.
It is no use going
around with a great deal of knowledge about what an elephant does or is capable
of doing, and not being able to apply it because you don't know whether you are
facing an elephant or a zebra.
And I can tell you an elephant crossing is
quite a different matter from a zebra crossing.
There was also a point by Dr. Apple about the study of a species for nonsystems research and, of course, I accept this entirely.
A man trained in one
discipline can very well study a particular species and it may not be even
necessary for him to recognize when he sees a cow that it is, in fact, a system,
although I think he would benefit by so doing.
The point I want to make is this:
that if you are in applied research, then it's as well to recognize it and recognize
the properties of that research.
If you are in pure research, following your
curiosity, then that's fine too.
What I think is most unfortunate is if some-
body is doing the one kind and thinking he doing the other.
And there are a
great many people who are studying, let us say the cow, unrelated to any milk
production system, and firmly convinced that they are doing applied research
because they believe that almost anything they learn about this cow will actually
lead to an improvement of applied systems which contain cows.
Now, of course,
it may do so eventually, but there is no guarantee of this at all.
I can think
of any number of bits of information you can learn about a cow which will benefit
no milk production systems whatever.
What I am saying is that if you are in
the business of applied research, wanting to benefit an applied system, then you
61
will have to recognize that if you are going to study a cow, it ought to be
in the context of systems which contain cows.
Then what you actually do experi-
mentally with your cow will be different, but will be related to the systems
in which you envision using the information that you then collect.
I thinkcne of the contributor's thought I'd maybe overdone the contrast
or comparison between systems work and science.
The reason I laid some stress
on this is that there is a grave danger that systems people only talk to each
other.
And, of course, we are all happy about it, we all have our hearts in roughly
the same sort of place, and we don't misunderstand each other to the same extent.
But there is a very important sense in which systems operators actually want to
use scientists - they want to harness them.
Now, there are grave dangers if you
then don't take into account the scientist's point of view.
There are a great
many scientists who have been saying for the last few years, "What is all this
fuss about?
Why are you making such a fuss about this systems approach?
Either
~
it's the same thing as science, which we have been doing all along or it's different.
Now if it's different, what is the difference?
it at odds with it?"
And is it consistent, or is
And it seems to me that from the scientist's point of view,
that it is an extremely important matter to resolve.
It's also true, of course,
that the vast majority of systems workers will have to retain their roots in their
disciplines.
at it.
Indeed, they have to be, as was implied by Dr. Apple, pretty good
They have to obtain a standing in their own discipline which allows them
to communicate with the best operators in that discipline, and still make available to a multidisciplinary approach the contribution they have to make from their
discipline.
This is a tough job, and need to be taken into account in such things
as career structures and the formation of multidisciplinary teams.
But, in addit-
ion to those (and they would be the majority), there is some requirements for
generalists, or more accurately, I think, generals, because it is a leadership
role that is needed.
Somebody has to form the picture to which all these
62
disciplinarians contribute their bits.
~
These may not be required in great
numbers, but they are absolutely essential.
And, I like the bit about Curly
Lucas being a man ahead of his time, provided by that, one doesn't suggest that
this is in any sense a waste.
I sometimes think that men ahead of their times
are the only kind worth having, but perhaps that is going too far.
I think
that what is normally meant is that a mean ahead of his time is a man ahead
of the majority.
And this is the definition of the kind of leadership I think
we require.
E. O. Heady
I am not sure if I am at all certain there is a mysterious difference
between systems analysis and science and that they aren't the same thing at
some point.
I can think of some person who wants to improve crops, say yield
per acre, as if he were an explorer who would travel around the world.
~
ought to be guided by some notion of where he is going and why:
some kind of model, but he could be just an explorer that
them home and tries them.
crops.
with a
has a model also.
on.
fin~things,
takes
tve try to develop a new variety, equipped
that we use as input into it.
background of the plant.
He might have
In the early days, that's what we tried to do with
Now we have other methods.
theo~J
He
We know a bit about the genetic
I think the geneticist working in the latter method
The model provides a hypothesis, with new variables to work
A notion of a difference between science and systems analysis probably
disappears in the sense that they both require some kind of a model.
certainly a big opportunity opening up on the system
side.
There is"
You can see that
this is true on my own campus, as \vell as on other campuses.
But when some people are talking about system analysis, they are also
talking about economic analysis.
In my own experience, they want to get the
economic answer and the question is, how do you tie up a systems concept, which
63
gives you the economic answer as well as the physical and biological answer.
In this sense any conventional optimizing or explanatory economic model tied
to ahLological model will be useful in the process.
This has been one of my
own experiences.
With respect to Dr. Apple's discussion, I think I did say freely that I
felt we do need specialists, that we do need individual scientists.
need these because we have to have knowledge which they pursue.
certainly do need much more interdisciplinary research.
We do
But we
Think of all the
work we did in the past decades when we had the soil fertility expert carrying
on his experiments, the plant variety man carrying on his experiments, a tillage
man carrying on his experiments.
Each one of these variables or sets of variables
representing these practices interacted with each other, but still we did find
each one following his own specialization, when we could have found out much
more about the interaction on the full potential of yield by combining these
sets of variables in somewhat more complex, but more efficient experiments.
I say that we really need both.
disciplinary research.
So,
We need some specialists, we need some inter-
Also, I didn't really say that the economist would solve
the research centers' problems, and that he would become the only modeler.
I
think the directors of international research centers have thought this sometimes,
in adding an economist to the staff.
I think what the economist has done has
more nearly helped guide them into the kinds of research which are relative to
small scale, poor subsistence farmers around the international research centers,
but has not served necessarily as a central modeller.
What I am suggesting is
that maybe these stations really need a general modeller who isn't necessarily
an economist.
I think we need economists and modellers both.
As far as trying to incorporate more disciplines into the research pattern,
I think this is a long run sort of thing.
I think it is less expensive in the
e
64
long run.
In the state of Iowa, we started out with fertilizer experiments on
every soil type and subsoil type that we had, and the crop varieties changed
by the time we got the new information.
the old information was obsolete.
By the time you got some information,
In my view, this would be less costly if we
could incorporate the soil and environmental variables into, say, fertilizer
response research - and its been attempted in a place or two.
this would be less costly rather than more costly.
for interdisciplinary research of this kind.
In the long run
There are many opportunities
I don't see how this can be very
well accomplished unless they either become interdisciplinary or the individual
research worker takes on the knowledge of all disciplines.
I am not quite ready
to say that I can do this sort of thing yet, because I still need the help of
other people from other disciplines.
Harvey J. Gold (NCSU)
I would like to address a question to Professor Heady.
You spoke about
inverting the reward system, as necessary in fostering interdisciplinary and
multidisciplinary research.
Do you have any suggestions on how to implement
this?
E. O. Heady
I used the word "invert".
I suppose we did invert the system; we had Some
kind of a combined weighing system which gave the number of people that worked
on a project plus their outputs each an appropriate weight.
I don't suppose
that this is a very easy task, but probably what we at our instituion have to do
is get to the graduate dean, or whoever it is, and tell him that some of these
other things are important, and not just "these five refereed articles."
have a little different system from other institutions.
review aren't going to be from a related subject at all.
We
People who make this
They are going to look
65
at the journals and see who reviewed them and so on.
This is all they look
for -- refereed journal articles, written by the individual.
I don't think
it is very hard to change this criterion, but you might get some other people
together who know something about the problem set at which the research is
directed, its importance in solving this problem set and the sense in which
this team of people have gone forward in solving the problem.
I believe
that criteria could be developed, but we have to break down a lot of traditions
which now exist.
I am sure that if I wanted to do research just as fast as
I can and produce more journal articles, the way to do it is to hide away and
do it on my own - not go through all the fuss and bother of working with each
people.
But if I wanted to get some relevant result, which has meaning to the
world, it does mean interacting with other people and in the short run at least
this will take more of my time.
In the long run, I though I will have produced
a greater product, so I think it is just a matter of breaking down the
These traditions can be attacked.
I attack them a bit myself.
traditions.~
I am sure some
of our administrators know that there are other things that are important, and
that they are going to have some kind of weight in making decisions in the future.
I think that the times at our institution are past, when if you brought results
from one young staff member, and he was only a co-author with four or five
other staff members on six journal articles, that he would not be in a position
to be advanced.
67
S11ALL
FA~1
SYSTEMS
Earl O. Heady
Ladies and gentlemen, Chancellor Dowdy, Professor Gold, Professor
Spedding, fellow academicians.
It is a pleasure to be on the University
campus of North Carolina A & T State.
here.
You have very nice facilities
I want to find out more about them, talk to some of my former
graduate students, including Sid Evans, and find out more about your
program.
Policies With Respect to Farm Size
Initially, I am going to speak generally on the philosophical side
of farm size, and then more nearly address the problem of small farms,
and how we might analyze them to understand the opportunities that they
should and might have.
In the first place, I believe that we in the land grant university
system have generally given inadequate attention to small farms.
Only
a few states, including North Carolina, Tennessee and Missouri, have paid
ample or relatively ample attention to small farms.
Other states of the
nation are entirely lax in the amount of attention given to small farms.
In fact, I propose the possibility that United States policy has never
fully and effectively been for small farms.
We sort of pat
ourselves
on the back, and talk about the family farm and how well we have promoted
it in the United States.
But when you go back and look at it, our national
68
policy has generally been for larger farms, and not for smaller farms.
This is not in the sense that for the nation as a whole we facilitated
large-scale plantation farms, as were initiated in parts of the South.
In
the rest of the country, we didn't do so and thought we had policies in
other directions.
But certainly these policies basically have always
been, and still are, except for a few detours, for larger farms.
}~ybe
we shouldn't say that we have a large farm policy, but our policies have
always been towards larger farms -- and more for large farms than for
small farms.
With very restricted exceptions our national agricultural
policy has encouraged farms to become larger.
Perhaps it is impossible
to have a developing agriculture without encouraging farms to become
larger.
If, in the less developed countries, we want to encourage farms
to progress in the sense of using new technologies which involve the use
of new and more inputs to have a greater output, then we have to encourage
each one of these farms to be larger in the amounts of inputs uses and in
the output produced.
Developmental Policies
We went through a long period of positive economic development of
agriculture in the United States.
policy ever derived.
Perhaps we had the best developmental
American students in the last several decades have
traveled the world over trying to understand the process of agricultural
development.
wnat makes agriculture develop?
Japan develop so rapidly?
country Y7
m~y
did agriculture in
Why is it developing in country X, but not in
Mostly these experts should have stayed at home to
~~amine
69
the policies that we used in the States, to understand the process of
agricultural development.
We didn't have any specialists in economic
development 200 years ago, but we had some thinking people who unwittingly
came up with a program with about all the right ingredients to positively
encourage and hurry the economic development of agriculture.
of this economic development program were:
The elements
to keep the supply of re-
sources large, to keep the price of resources small, to keep the cost
structure of farms consistent with factor prices and production functions
(favorable tenure), and to keep the prices of commodities high and stable.
These are the instruments that encourage agricultural development.
In
general this was the essence of our development policy, the most successful of the world.
We had no experts to tell the government what steps
it should take for development, but the laws that the government legislated, were the very ones needed to get a rapid development of agriculture,
and also to cause farms to become larger over time.
We didn't give attention to farm size policy up to the year 1776.
It was up to other institutions, people and governments to determine the
sizes of farms to this point in time.
our own hands.
After 1776, we took policies into
One of the more specific steps, in terms of what the
size farms should be, was the Homestead Act, effective from the mid-19th
century from the Midwest on West..
It was a "good-sized" farm policy.
But it was not a small farm policy.
Families migrated to farms, and
homesteaded from the Northeast, from Europe and other places, and settled
on land units, which they couldn't cultivate all in one year.
improve it in two to three years, but they usually had a bit of
They could
70
uncultivated land left, until their sons grew up to help farm it.
The
Homestead Act wasn't a small farm policy, it was a bigger farm size policy,
as compared to actual crop operating farms in the East and Northeast.
But this Act was part of several successful steps in the economic development of agriculture, to keep the supply of a resource (in this case land)
large, and its price low to farmers.
as they should.
And farmers acted theoretically,
They spread out over the landscape, organized farms and
started producing commodities and putting them on the market.
Soon the national public domain was exhausted, and we no longer had
cheap land for people.
Sometimes we were more imaginative than perhaps we
are today in acquiring land which could be used for this purpose.
began to follow this same procedure for other resources.
supply was nearly exhausted, we needed new knowledge.
We
After the land
Instead of waiting
for the private sector to develop the new knowledge, we were one of the
earliest countries to develop a vast socialized research facility for
agriculture.
We socialized the production, and communication of agricultural
research by creating what became known as our "land-grant universities".
So we then increased the supply of knowledge resource and, as with land,
we kept the supply of this resource large and kept its price or cost to
farmers low.
Historically though, I believe it is appropriate to say
that aside from the few detours, our emphasis in the land grant universities has been with large farms.
The largest farms came to us
first, and in my part of the country, the largest farms still come to us
and we still help the largest farms most.
Our computerized systems are more
geared to the large farm than they are to the small farms.
They are most
advanced educationally and they are the ones that can best understand and
use this kind of information.
71
In addition to land and knowledge resources, which have been very
important, of course, labor was important to agriculture in early days.
But we had initially, at early times, and for a long time later, kept
the price of labor low, not only black labor, but also white labor.
We
brought labor under a flexible immigration policy favored to settle the
United States.
States.
This immigrant labor was not produced in the United
Most of it was produced in Europe at that time.
Hence, we
didn't have to spend a large share of wage goods or our national income
in producing labor.
Labor came to us on boats in large amounts, already
produced at a low real price.
The process was perhaps the most mammoth
international development program that ever existed as people migrated
particularly from Europe to the United States.
have to pay the cost of their upbringing.
settle farms.
The United States didn't
They came as labor, ready to
This program might be about the equivalent, I suppose, as
if we today offered to take all of the Indian babies born over the next
several years and feed and raise them to working age, and then send
them back to India to add to the work force.
So, through this work
force opportunity and slavery, we encouraged low-priced labor which
also encouraged the economic development of agriculture.
With respect to other resources, we used similar policies.
We
didn't wait for the private sector to provide all the capital needed for
agriculture.
We created a farm credit system which acted as an inter-
mediary, and gathered in funds which were then loaned to agriculture,
to get capital into the hands of farmers at lower prices.
Keep the price
of capital or credit for farmers lower, and you expect farmers to use
72
more of it.
And they did use more of it.
But we provided it to farms
during those times, as we still do, in a manner which encouraged larger
farms to use more of it than smaller farms.
We supplied it to farmers
in a manner such that the more you had capital, the more you could get.
In other words, it was loaned on the basis of the equity the farmer
already had.
If he had a larger farm with a greater capital valuation,
he could borrow more money from the Federal Land Bank, than if he had
a small farm.
And similarly down through the production credit admin-
istration, all farmers were eligible for public credit, but larger farms
were "more eligible". The more assets the farmer had, the more he could
borrow.
So our credit policy also has been one that small farms could
use, but was more beneficial to large farms.
Compensation Policies
After these early developmental policies to enlarge the supply of
resources and keep their prices low to farmers, we next adopted a series
of steps which I would call "developmental policies".
favored larger farms.
And all of them
Perhaps we should not say that they were large
farm policies, but they were all in the direction of larger farms.
I
have been saying this for a few years, and I now see that Secretary
Bergland has finally gotten concerned over this same general problem.
What are our policies doing with respect to the structure of agriculture?
What'kind of structure do we want and what are our farm policies doing
about it.
By the 1920's or 1930's we were rich enough in the United States
that the demand for food had become inelastic price-wise and otherwise.
73
It became inelastic with respect to income, so that as people's income
grew, the per capita demand for food didn't grow, only slightly.
It
became inelastic with respect to price, so that if prices fell, additional
quantities of food didn't make up for the price fall.
Or, conversely if
output increased, price fell by a greater percentage than did price of
food.
Hence, the revenue of farmers would be less with more output, as
long as we didn't have growing exports market.
initiated several compensation policies.
Under these conditions we
We compensated farmers for the
fact that we were making the supply of food greater, thus lowering revenue
to them.
We paid farmers directly for not producing.
they were, the more they were paid for not producing.
paid more than smaller farmers.
But the larger
Larger farmers were
We also set up a series of loan rates and
price supports whereby farmers could take out loans on commodities if the
market price was less than support or target prices.
recourse loans.
These were non-
Farmers could turn the commodity over to the government
and take the higher price.
But the more commodity for ,Yhich he could
get a loan (and turn the commodity over to the government), the more he
gained.
Larger farmers were better off than smaller farmers.
is true with our present target prices.
The same
You get paid more if you are a
larger farmer, than if you are a smaller farmer.
We have been going through a long-term process in which a complex set
of public policies has invited larger farms, not smaller farms.
been somewhat of an unwitting policy.
It has
It has resulted in a very efficient
74
agriculture, but where are we going with it?
carry larger farms?
How far are we going to
In my own state we have around 130,000 farms.
I
estimate that with the technology now available, we can operate the state
of Iowa with 14,000 farms.
We are now at a point where about 20% of the farms in the United
States produce about 80% of the output.
fewer and bigger farms.
We are moving continuously to
This is still the prospect ahead of us.
We do have pockets of small farms, in some locations.
these farms stay small?
How long can
Does society wish them to stay small?
means do we have for keeping them small?
them attain satisfactory income levels?
What
What means do we have to help
Largely these farms are small
because some people who were once in agriculture full time and are now
old.
They are in semi-retirement; they don't want to do anything else.
They want to stay on a small farm until they die.
of their housing and subsistance.
The farm is the source
We have other people who would like to
get into farming but have few resources.
To get into successful farming
in most locations, there is only one way; you have to have a father, a
father-in-law, or another relative, who can start you in farming.
Other-
TNise, chances are slim.
We do find people who have worked and accumulated a few funds, or
have inherited small funds and hope to start out on a small unit and grow
into a bigger one.
They are small now, and their incomes are low.
Too, we have some people who have found a place in the country to
rent, maybe an abandoned farmstead
~vith &L
acreage around it.
They
carry on more or less a subsistence kind of farming; producing for the
household, selling a little, haVing some small livestock herds, etc.
•
7S
Then we have a fourth category.
This is a unit where a person
inherits a small farm, or has enough money to buy a small farm.
He
then finds this small unit will not provide a level of living to satisfy
his family.
the farm.
So he and his wife or other members of his family work off
He is a part-time farmer.
hours, and on weekends.
A good many part-time farmers operate 160 acres,
and some even farm 240 acres.
technology.
He can farm efficiently after work
They can do so rather readily with today's
Prospects are that the small part-time crop farm of the
future will include as many as 240 acres.
This development hasn't been caused by a bunch of "nasty foreigners"
who have come into the United States and bought up the land.
It isn't
caused mainly. by a bunch of "cruel corporations" from outside of agriculture, coming in and buying up the land and putting together large
units.
It's mostly a result of large family farms buying some more land
and putting together a larger unit.
They seem assured that it is the
foreigners and the outside corporations that are hurrying a structurer of
larger and fewer farms.
But it is they that are competing among them-
selves and who are hurrying the ongoing trend of high land values and
larger farms.
\-Te are rapidly on the road.
be produced vnth fewer than
~OO,OOO
farms.
The U. S. food supply can now
We could already get along
with that few now, only 7-8,000 farms per state.
Farms and Connnunity Structure
This structure might give us an efficient food system, but what does
it do to the country structure
that of rural connnunities and of the
76
people who live there?
Are we just going to have 8,000 farms per state,
and empty space otherwise?
Or are we going to have other things going on
in the countryside, other community business activity and social
organizations?
How does farm structure impact on the rural community?
We made a nationwide study, setting up three scenarios on a large
programming model.
In one of these, we put only large farms, supposing
the United States to be made up of only large farms.
We asked, "what
then would be the outcome with respect to agriculture and the rural
community?"
Then we set up the model again, supposing there were only
small farms in the United States and asked, Hwhat would be the results?"
There are a lot of tradeoffs involved.
If the structure of agriculture
were that of entirely small farms, the total revenue of agriculture would
be greater than if it were made up of all large farms.
be less efficient, in some sense.
Small farms would
With a given set of resources, the
output would be smaller with smaller farms than larger farms.
Because
food demand is inelastic, the less produced, the higher is the revenue.
Hence the gross income of farming in aggregate would be greatest under small
farms.
The total net income of farming also would be greatest under
small farms.
But the income per farm would be low under small farms,
because there would be so many to "divide up the pie. ll
If the country
were composed only of small farm systems, income would be inadequate,
about $7,700 per family, which would not allow an acceptable living
standard.
Prices for food would be somewhat higher under all small farms
than under all large farms because the output would be smaller under the
former.
On the other hand, the amount of inputs purchased in the rural
•
77
community would be greater, and the amount of labor used on farms in rural
communities would be greater under a structure of small farms, than a structure of large farms.
And the non-farm employment and income generated in
rural communities would be greater under small farms than under large
farms.
Exactly the opposite holds true under large farms, of course.
Output would be greater, food prices would be less, total income to agriculture would be smaller, income per farm would be greater -because there are fewer farms, the price of food to consumers would be
less, and the non-farm employment of people in the rural communities and
income generation would be smaller under large farms.
farms, we would have an in-between situation.
farms would be at a "liveable level".
would be reasonable.
For medium sized
Incomes for the medium-size
Food prices (the farm gate portion)
So we do have these choices which can be made, by
society.
I don't know the present goals of society with respect to farm size
and structure.
Is it for family farms?
Family farms can now be 3,000
acres in Iowa; they can be 10,000 acres in Western Kansas.
Also, we can
hardly have a meaningful policy for farms which says we are going to have
many farms in the future, but only if they are family farms.
of Iowa is already being farmed by 120,000 family farms.
policy do we
~vant.
The state
So what kind of
Sometime it has to be put in terms oth.er than those
of "family farms", because family farms can be very large farms.
decision is up to society in the next 10-20 years.
This
After Secretary
78
Bergland's hearings this winter, maybe we can shake something out of
the public's desires.
Some people say:
"well, we really aren't interested
in agriculture anymore, we are interested in the food system".
But is
it true that we can be interested in the food system, and not farm
structure and numbers?
At one time,
then the small family farms.
we were interested in what was
But if we are no longer interested
in farms, per se, only the food system, do we just forget about farm
size and structure?
These are very important questions that agriculturists
must ask themselves in the decade or perhaps should have asked in the
last decade.
Policies to Maintain Farm Size
There are policy means by which restraints could be placed on farm
size.
As mentioned previously, the main policy means we have used so far
have encouraged farms to get larger.
restraints on farm size.
Ife have never used effectively
We did do so slightly in the Farmers Home
Administration of the 1930's which was mainly for small farmers.
goals are quite different.
It, also is for big farmers.
We
Now its
have
had these small detours towards small farms, but very few of them.
There are means by which we could help encourage smaller farms.
One
would be simply to have limits on the amount of benefits that farmers can
gain from all commodity programs.
In a way it appears that we did put
limits on the gains to commodity programs.
We had a period in which
cotton farms in the Southeast and California were getting direct payments
of $250,000 - $300,000 a farm just for not planting crops.
Subsequently,
79
we developed payment limits, but have put them back up to $55,000 per
farm, which is still quite large.
up to the $55,000 limit.
Some farms have been enlarged to grow
But we never put a limit on the amount a
farmer could gain from price supports and non-recourse loans.
have said in the Cornbelt that,
If
We could
you can get a target price support, or
the advantage of a non-recourse loan up to 10,'000 bushels of corn, but
not for 200,000 bushels of corn lf ,
Do we want these kinds of steps in
the future, or do we want something else?
We could also have restrained
size under programs of the past by saying that they apply to a family
farmer only if he stays on this farm, at this size, but not if he
expands.
In other words, we have
farm~,
farm!.
If farmer A and
farmer! stay on their units and farm, they have access to all of the
public benefits which are paid out of the treasury for the benefit of
family farms.
He would tell th.em:
these benefits
~ ~
your farm lf ,
farm lf •
But i f B sells to
"Farm~,
you can continue to have
And!, you can have th.ese benefits on
~,~
no longer has the benefits on B
farm, only on the farm that he held previous-ly,
These procedures \vould
have tended to give people who stay on th.eir own farm, a greater value
for staying than to th.ose who migh.t buy it.
But as programs have
evolved, if farmer A buys farm!, he gains the public program benefits
to both.
Hence, there 'N'ere procedures whereby we could have re.verted
the process, so that it favored small farms as much as it favored
large farms.
Or, ,ole could have simply told the large farms,
If
you are
big and commerical, you are bigger than other small country industry,
Therefore, you go into the money market for your loan.
Go to the banks,
80
not the federal land bank or the production credit association.
We are
going to save that for what we now define as "medium-sized farms".
Of course, if we wanted to be drastic or really serious about family farms
(I am not sure that we are}, then we could have put on a progressive tax
relative to the amount of horsepower per tractor, relative to the amount
of acres per farm, or relative to the number of animals per herd.
Opportunities and Models for Small Farms
If we were really serious about favoring small farms, we could readily
do so.
We have some work underway examining possibilities for small
farms·.
There are some problems in dealing
~vith
small farm systems.
One
is not to make opportunities for them which are just the "best of the
worse:!, but instead to provide them \vith at least the "worst of the best".
That is, we do not want to create farms that are too small, define
situations for them which give them
L~comes
which are entirely impos-
sible, and give them no hope for the future.
of analysis we might use.
I will outline one method
First, we need to decide about how large farms
should be in terms of income; what is the level of income, considering
inflation ahead, that can provide an acceptable standard of living for
this day and age.
We probably don't want to shoot for a size of farm
which is smaller than this.
But how can we determine the farm size that
will provide an acceptable income level?
would be a relatively simply approach.
programming model.
To determine this, my choice
I would select a mathematical or
I would take some farms of typical size as they exist
at the present time, say 80 acres or whatever.
Using survey data, I
would find out what kinds of technologies are now used on these farms.
I
81
would then evaluate a set of activities which define present technologies
on these farms.
Next, I would put together a programming model with
equations to represent the amount of each type of soil on a typical farm.
Equations also would represent the labor by months, or months in which
labor is scarce on this farm.
I would include equations for capital
supplies available in different seasons on this farm, if the farm internally has to generate part of its own capital.
I also would use equations
to represent the building space that might accommodate livestock.
Then
I would have columns in a matrix, variables in the system, for example,
for corn which could be grown by different technologies or systems.
Each
one of these corn technologies would be a different variable in this system
of present technologies found on small farms and which are candidates
for study.
I would do similarly for other crops and livestock which are
relevant on this farm.
I would include a borrowing variable for this farm,
and have an equation which represents as upper restraint on its borrowing
capacities.
I would include two or three capital equations if there are
different kinds of credit wnich can be extended somewhat separately to
this farm.
I might include some objectives other than profit maximization
in the objective function for this farm.
If this farm were partly a
subsistence farm, it needs to produce a certain minimum of certain commodities for the household, and I would specify these quantities as righchand-sides in the equations.
Then, aside from these kinds of minimum
restraints on quantities to be produced, I would start out with a conventional objective function vnth prices multiplied times quantities.
I would repeat this version once as an owner operated farm, and then as
82
a rented farm.
This approach would suggest the best that this farm could
accomplish under present technology as an owner operated farm and as a
rented farm.
I would repeat solutions under several levels of prices.
Under price inflation, what kind of income expectations can we have for
this farm in the future?
I would try several levels of price expectation
so that people would understand that income will vary with connnodity prices
and costs of the future.
I would do similarly for different levels of capital.
The capital
restraint could be parameterized to show the level of income expected with
different initial amounts of capital, under present and new technology,
under various price levels and under rental or ownership.
Then having
completed these steps, I would organize an interdisciplinary team, with
all of the persons who had technical knowledge that might go into this
farm.
We then could formulate an entirely new set of variables repre-
senting all the alternative technologies which could be used on the farm.
Every version of every new technology could be a different variable for
this farm.
Then \Ve would again solve the model, say first for an owned
farm, where the capital was at the same level as initially, and determine
the level of income expected.
I would do so similarly with a rented
farm, again under the various price levels and under alternative capital
levels, to estimate how much income could be generated under each of these
situations.
We would thus determine whether we had isolated an organi-
zation-:or set of technologies for this small farming system which promises
a reasonable level of living in the future.
If the system still says,
"noll, then I would parameterize the land area.
I would run up the land area
83
and see how far size has to increase, in terms of acres of land, before
a level of living could be attained which would provide satisfactory
income to a farm family in the future.
After I had evaluated several of these alternatives, and had come
to an income level which appears satisfactory for the future, I would
select this as my target.
in the future.
It is a fairly static target, as one point out
Perhaps, this is the organization, the farming system,
that can prevail in six years.
Next, I would develop a dynamic model,
starting where the farmer is at the present, and build the model yearby-year.
A set of diagonal matrices would include resource restraints
for respective years, with an overlap of variables for the relevant years.
I would use the dynamic model to determine the best path to be followed
in getting from the present to the future organization.
(Perhaps the
income and resources of the static plan would serve as l'tail end" restraints to be attained in the dynamic model).
For each year, we should
be able to track when new enterprises should be added, and at each point
that new capital or other resources should be used.
This would be a
relatively simple approach in setting up a model for studying the
adequacy of small farms and would indicate the policy measures and the
steps that could be used to improve income.
Fro~
my initial philosophical
statements, in terms of policy and
where we are taking ourselves with respect to farm size, we should come
to understand where we want to go.
left, because
policy-~nse
If we are to have some small farms
we decide that we should have small farms, then
we should know how to tackle the small farm problem and guarantee that
they will have income enough to bring to their families opportunities
that exist for other families in our society.
84
Harvey J. Gold (NCSU)
I would like to ask a question on the balance between the small farms
and the large farms.
If you could gaze into the future, in what way do
you see things like energy limitations and other kinds of natural resources constraints altering the balance between small farming and large
farming systems?
E.G. Heady
I think that if the scales are in the direction which tilt to favor
one more than the other in the long run, it is towards the large farm,
given the set of public policies that we still have, unless we create
policies more specifically for small farms.
I don't see anything in the
cost structure which disadvantage the large farm.
I don't think the
energy situation will, as nearly as I can see it.
Maybe some of the
agricultural engineers here can provide other information.
As nearly as
we have been able to figure out, the ratio of energy to scale and work
performed is about the same for large equipment as for small equipment.
You can isolate any scale economies in this sort of thing.
But to the
extent that a large machinery complement can cultivate a larger acreage
more efficiently than a small one, there will be some slight energy
advantages in this direction.
You don't have to haul the machine to the
fields so many times as you do a small one, for example.
It isn't that there are extremely large gains for larger farms.
We
estimate with present technology, there is not much gain between 1,000
acres and 2,000 acres for a farm in the Corn Belt.
The cost curve
flattens; the cost of production begins to approach the mathematical
85
limit of variable cost per acre.
So there is no great advantage after
1,000 acres per in the farm machine complement.
advantage either.
But there is no dis-
So as the farm becomes bigger, it can at least operate
as efficiently.
Then I believe we have a different set of values among young farm
operators in this day and age.
The young farm operators I see that are going
back to farm, don't plan to operate their father's 480 acres and stay
with it.
They want to have incomes like a medium sized manufacturing
firm, and they are going to operate 1,500 acres, 2,000 acres, 2,500
acres.
This is the general leaning of the young, well-educated manager
of this day and age.
It's not the mom-and-pop farm of the past.
The
farmer doesn't plan to go back and get a farm of that size, take out a
mort age and work the rest of his life and payoff that mortage.
going to operate it on a large scale basis now.
He isn't going to worry
about paying off his farm like mom and pop and grand-dad did.
gets his equity built up, he is going to build further.
borrow another
$300,000.
He is
~Vhen
he
He is going to
The small banks get about all the $300,000
units that they can take care of, and they are now having to go to the
large city banks to accomodate large farms.
are going to the large city banks.
More and more of our farmers
I think the pressure, the momentun,
is much more in thi.s direction than it is in the opposite direction.
Don W. Hayne (NCSU)
I am not sure ffarvey was speaking to this point, but ecologically the
energy balance is highly negative in modern farming.
We are consumimg
86
fossil energy on any modern civilized farm, although I saw an article
about energy production based on sugar cane, which calculated that you
might have a plus there.
Does that change the picture at all?
That is,
with the increasing cost of energy, you feel that this will simply be
passed on to the consumer indefinitely?
E.G. Heady
There is not much of a chance. to pass it on to the consumer in agriculture yet, because we can still produce so abundantly in agriculture,
that prices tend to be burdened rather than lifted.
With all the farmers
we have, there is no way that they can get together and say, "We are
going to charge the consumer more".
They are price takers, and they are
going to take a price at the level of the market.
So what happens to food price will more nearly depend on what we
export than on the cost of energy for sometime to come.
A deeper question is, in the long run would energy prices ever go so
high that they would cause us to go back to the farming of l890s.
We
could get along alright in the United States, if we had farms, say back
to 1900 in terms of mechanical power, as
~ve
have our present biology.
We might even shift our biology a little bit to go half way back to the
1940s - some hybrid of present biology and 1940s biologies when we were
using more crop rotations to control insects, and using more crop rotations for fertility.
We could, as I said, go back to 1890 in terms of
power for agriculture, and have some kind of combination of 1890 power,
1940s technologies and 1970 technologies.
We wouldn't have trouble
producing our own food, we just wouldn't worry much about the rest of
87
the world.
The food would be higher priced, and we would probably
support it on that basis.
From this standpoint, our outlook isn't quite
as blurry as that of a lot of other nations.
That is, while our outlook
can be fairly good, that of other countries wouldn't be.
In fact, we
just completed a study of "What i f we now used the 1940s technology?"
Well, the main thing it means for the United States is that our exports
would be less.
We would have to cut exports back to 35% of what they are,
and could still consume the same amount of food that we do.
The 1940s
had a much less energy intensive agriculture than the agriculture that
we have at the present time.
We have a lot of flexibility.
Much depends
on how much food we are going to give the rest of the world.
This is
the most burning problem in the United States with its current food
producing capacity.
How much food can we furnish the rest of the world?
How many other people in the world have had to go hungry?
How drastically
are we going to have to pull down populations in the rest of the world,
and hOiv well can we get along?
Robin G. Henning eNC
"~T
3U)
Hith increasing costs of transportation and distribution, it appears
there could be substantial benefits from production and direct marketing
(farmers' markets or roadside stands) of directly consumed farm products.
vfuat potential do you see for small farm production of such
products, especially fresh fruits and vegetables, near areas of high urban
population?
88
E.O. Heady
There are a few small steps in this direction.
Almost any large town
in the midwest now has a farmer's market, something you didn't find 10
years ago.
I think it might be partly the outgrowth of the fact that
there are a lot of people who are working 40 hours a week who like to have
something to do, and this is not only an occupation, it is also a hobby,
a recreational activity for them.
But if every citizen of the United
States went back to the victory garden, we could get a lot of food produced with a lot less energy.
Maybe this is what we ought to encourage.
W.T. Ellis (NC A&T SU)
Dr. Heady, do you have any knowledge from research which has been done
which would point out to a farmer when he should stop farming, maybe if
he has 50, 100, 150 acres.
Now let me give you a case in point.
Guilford Country, farm land is valued at about $1,000 an acre.
In
When
should the farmer cut off, when he knows he is not realizing a profit?
At what price of farm land should one stop farming that land?
Have you
done any research on this question?
E.O. Heady
No, I haven't.
That is an interesting piece of research, and probably
very relevant for a lot of people.
There would be one group that would
fall one \vay, and another group that would fall another way.
highly inflated land values, you have to do two things.
With these
First, you have
to project with some kind of model, increasing land values, and the
income that relates to these.
Most farm incomes won't support the land
~
89
values that currently surround them.
So if you are going to hold on
to the land, you are going to hold on to it for purposes other than
the income it will generate.
We could set up a model in which we would
calculate at what level of land price you take the capital gain out of
your land and invest it elsewhere and have a higher income, which would
also inflate at the level of land.
It might be a little hard to find some
of these other alternatives.
There would be one group of farmers which
would fall in this category.
At some point others would be better off to
take the funds, invest elsewhere and have greater levels of income.
is another group for whom this would be never true.
There
That is, your income
is substantial now, you are still accumulating savings, land values are
inflating, and it is about as good a hedge against inflation as we have,
you had better hang on to it.
In fact, in some countries in Europe at the
present time, they are a little bit worried, with the traditional size
family farm in Europe (which is much smaller than our family farms), but
which has been purely commercial family operations, say 80 acres in West
Germany.
Some of these countries have long had a pension system for
farmers.
Now there is quite a little concern that they won't stop
farming.
Normally they would stop and let somebody else take over for an
opportunity in agriculture.
But since land values are inflating so fast,
these people are staying in agriculture, to capture further capital gains
forthcoming to them.
So I don't know what the size would be, I think we
would have to work it out.
Ive could develop a model for this analysis.
90
Peter Hartman (Lincoln University)
I think we have a problem with an
only in terms of income.
object~ve funct~on
when expressed
How do you handle more complicated objective
functions?
E.O. Heady
I was putting some other things in a little different way, as equations
and restraints.
That is, for subsistence foods I said that the farmer
had to produce n amounts of this,
~
amounts of this, x amounts of this.
I wasn't putting those into the objective function, but I said he had to
produce these.
There could be other approaches with this model.
One
would be a multiobjective function - not to attach dollars to commodities,
but also to put in other quantities.
For example, another quantity we
could have would be days labor off the farm.
A day off the farm brings
in some income, and we would enter this into the objective function.
it also brings the farmer some disutility.
But
Every day on the farm has
positive utility, every day off the farm has negative utility.
We would
have some psychologists and others to help us measure this utility, as
has been tested.
So we can begin to put these things such into the ob-
jective function, and put weights on them.
Certainly there are a lot of
people on small farms who are there because of values that attach to small
farms.
Maybe we should use an even more simple approach.
approach that I mentioned first.
We might use the
We would run off for five years all the
possible income accommodations that exist,
We get an answer of the level
91
of income consistent with operating this farm under new technology, as a
rented farm and as an owned farm, with different amounts of capital and
under different price levels.
We go talk to the farmer and get a group
of experts to project and place their own subjective probabilities and
quantitative probabilities on prices in the future.
We tell the farmer
that this would be price expected at this level of capital, but he says
that I can only get to this
of capital.
level of capital, so we only plot his level
We then say, "This will be the expected income".
Now the
farmer may decide that it is about 15% more than he is getting at the
present time, and it is 25% less than he gets working off the. farm.
It
is up to him, with his value system and the way he subjectively weighs
things, to decide whether he wants to stay on the farm and take 30% less
income or whether he would rather move to town.
approach we should take.
Perhaps this is the
92
EDUCATION AND A SYSTEMS APPROACH TO AGRICULTURE
C. R. W. Spedding
As Dean Webb says, I am supposed to provide the last of the chores for
this session and talk about education and a systems approach to agriculture.
I do this with some reluctance because in fact I would much rather have followed up the topic of small scale farming which Professor Heady introduced,
since I think it is of very great importance. So, if every now and again you
find that I am sliding in a bit about small-scale farming, you'll understand
the reasons why.
My general habit, asyou may have noticed yesterday, is to define the
terms I use, just in case we are using them differently.
bother to define agriculture in this case.
sufficiently the same thing.
However, I won't
I'll assume that
~ve
all mean
That might be a dangerous assumption, actually,
and I just want to emphasize that agriculture to me is not the same as farming.
It is much more important than that, and is of concern to people who are not
farmers.
By agriculture I mean world agriculture.
Incidentally, although
the majority of the food - certainly the food that enters into trade - is of
course produced on large farms, the majority of the farmers in the world are
small farmers, and I entirely support what I think Professor Heady
arguing; that the small farmer has received very little attention in the past
and practically none until very recent times from research workers.
Indeed, it
has been thought of as one of the solutions to being a small farmer to become
a big one.
farmers.
This is, of course, a feasible solution for about 10% of the small
The other 90% who lose
their land to the chap who becomes
farmer become either no farmers at all or employed by the big farmer.
a big
Very
little attention has been given to actually considering what could be done
for the small farmer who is going to remain a small farmer at least in terms
93
of the acreage he farms.
So~
agriculture to me is a world subject of enormous
no means just concerned with food production.
importance~
and by
A couple of weeks ago, I was on
a farm in Ethopia where 40% of the income comes from the sale of cow dung, which
is used for fuel.
In many of the small farms across the world, it is by no
means a question of whether you produce food; it is a question of whether you
produce fuel to cook your food, or whether you produce something to thatch your
hut with or even build your hut.
So the products of agriculture are much wider
than just food production if you look at it on a world scale.
In the United Kingdom, it is often questioned whether agriculture is a fit
subject for a university at all.
That
is~
I think, partly due to the image in
many of the non-agriculturalist minds that agriculture is synonomous with farming,
an occupation in the U.K. which concerns only about 3% of the population directly.
The other 97% eat of
the agriculturalists.
course~
quite markedly, and in total very much more than
e
But they have not historically regarded agriculture as
a particularly important subject.
My own view is that agriculture is certainly
a fit subject for university education, and if I were forced to make comparisons,
I would claim that it is the single most important subject that I can think of.
Taking that view, I am not particularly concerned whether the product of an
agricultural education - the agricultural student - necessarily engages in
agriculture when he leaves the university.
As far as I am concerned, it is
a very healthy situation if people enter many other walks in life including
politics and,
sClf~
ural education.
posts like the minister of agriculture, having had an agricultThis I think would be of enormous benefit.
As I take it, the function of a university education is to produce an
educated citizen.
If it doesn't do that, then I think you have a problem in
94
justifying it at a university.
Of course, I believe that a student who emerges
with agricultural qualifications from a university should be fit to play some
active role within an agricultural industry, because very often that's what
he wants to do.
But I don't think that sort of vocational training is enough.
I can think of many other more efficient ways of training farmers than by going
through the university education process.
I used the word "citizen," so I'd better define that.
A citizen in my
view is somebody who has both the rights and responsibilities to enter into
those debates which influence and eventually govern the way his society behaves,
both internally and externally.
And I think the main business of a university
education is to produce people who are capable of doing that.
Now, to enter
into the most important and topical debates requires a certain amount of information, a certain amount of understanding, and many of the current debates do
concern agriculture.
Debates that ought to be the business of the citizen are
concerned with agriculture.
They are concerned with the questions of to what
extent a country should be self-sufficient; to what extent we ought to help
feed hungry people in other parts of the world; to what extent that can be
done by producing more food, when what the hungry people are short of, of course,
is not just food, but money.
If they had money, they wouldn't be hungry.
And
there is no more sense in trying to feed hungry people by producing more food,
than there is in meeting their need for shoes by setting up a shoe factor; or even
if you had recognized that they're short of money, establishing a bank in the
locality.
None of these things actually make the slightest difference to hungry
people, who are poor.
has money is hungry.
If they are not poor, they are not hungry.
Nobody who
Now these are subjects which are of major concern, to any
citizen, and ought to form the debates which citizens are concerned with.
I
95
don't believe that the average citizen in my country, who is a non-agriculturalist, is any way equipped to take part in these kinds of debates.
The
topics are extremely complex - it is very dangerous to oversimply them - and
require vastly more information and understanding of agriculture than the
average citizen has.
So, I would like to see agriculture as a major educational subject at
universities and more widespread than directed just to people who eventually
want to find their living in agriculture.
One of the attractions of agriculture as a university subject, is its
remarkable combinations of interests.
It is a multi-disciplinary subject, of
course, by definition; but it is a remarkable combination of science subjects and
non-science subjects.
It is concerned with money, management and economics, with
biology, human beings, and with combining all these different facets into ways
of life.
This, I think, makes it of enormous value from an educational point
of view - vastly more so than the single discipline subjects.
As an education
for the kind of life that people are going to lead, it seems to me highly appropriate, because most parts of life are in fact of this kind.
confined to one discipline or to one small subject.
They are very rarely
It is also concerned as a
subject with problem solving, and this again is something which seems to me to
be of the utmost importance educationally.
It is what most people, or the most
important people, are going to be concerned with.
The people who are not problem
solvers are likely to be problems.
Now, given that a systems approach is useful, the simple question needs to
be posed, "Should agricultural students not at least be aware of it, or perhaps
competent in some of the techniques?"
That was my starting point some years
ago, when I became concerned with agricultural education.
It seemed to me that
e
96
those questions ought to be posed, and my answer was, "Yes, they should know
something about it, but not necessarily have great competence at the end of an
ordinary agriculture degree:
there wasn't time for that .• But at least enough
to know if they were fitted or interested or in any way adapted to the sort of
techniques that a systems approach involved."
When I first went to the University
of Reading, I thought that any greater expertise in systems technology would have
to be left to a higher degree level; what you call graduate studies.
extent I still think that is true.
that level.
To some
There is a great need for development at
But the methods of a systems approach now seem to me to be extra-
ordinarily relevant to undergraduate studies, not only for multi-disciplinary
subjects, but also within disciplines.
However, it ism agriculture as a whole
that the approach seems to me to be most relevant, and I don't imply by this
any particularly sophisticated need.
When I use the term model for example, I
certainly include sophisticated mathematical models, but I also include the
simplest kind you can imagine.
As I was trying to convey yesterday, I don't
have any preconceived notions as to what kind of model is appropriate until
somebody has told me what the purpose of building it is.
Now I would like to say a word about the teaching of agriculture.
I will
refer chiefly to what goes on at Reading, or what went on at Reading.
The problems with agriculture as an educational subject seem to me to be
largely two:
one, how do you learn it, and the other one, who do you teach it?
An enormous number of students are trying to learn agriculture.
ThiS, indeed,
seems tobe an expectation - not an unreasonable one of an agricultural student at
a university.
But I haven't yet discovered anybody who is actually teaching
agriculture, and this seems to me to be a gross deficiency.
In my own depart-
ment of agriculture and horticulture, we have an agricultural degree, and it
seemed to me that if we were arguing that, if there is a degree in the subject,
97
there ought to be a subject.
And, if there is a subject, we ought to know
what is is, and we ought to be teaching it.
So I looked around, and I
my
Saw
colleagues teaching either crop production, animal production, or agricultural
engineering, or farm management, or some other component of agriculture, but
I didn't actually see anybody teaching agriculture.
All of these chaps, of
course, earnestly believed that's what the students were studying.
But, it is
left with the student, largely unaided, to put all this together and try to make
of it some picture of agriculture.
Now, if it is possible that the student
unaided should do this - conjure up a picture of agriculture - why haven't some
of the staff already got it, and if they've got it, why aren't they passing it on.
Well, it turns out that hardly anybody has it.
And, this is partly because
being reared in single disciplines, they don't feel comfortable outside them;
they feel they are in somebody's else's preserve, and they don't feel competent
4It
and so they tend to leave it alone.
Now, whenever we discuss
a systems approach in agriculture, we keep on
emphasizing the interactions between all these bits and pieces.
how important these interactions are.
We stress
But who is dealing with them, if all
people are teaching one or another of the separate bits?
So I came to the
conclusion that for the student of agriculture, some kind of framework is required
which deals with agriculture as a whole, into which he could slot the bits and
pieces he learned from all the people who taught him.
exist so we had to try and devise one.
Now the framework didn't
And, as a matter of fact, at the Univ-
ersity of Reading a student arrives highly motivated towards agriculture.
One
characteristic of the student of agriculture, at least in the United Kingdom,
is that he knows why he is at the University; he knows precisely what he want to
study, and what he is going to do with it eventually.
proposition from students of some other subjects.
This is a very different
But, in the past, what he
98
found was that in his first year, hardly anybody mentioned agriculture.
They
taught him soil science, chemistry, cell biology and dreadful things like
statistics, and he began to say to himself where is this agriculture that I
came to study.
Of course, he couldn't really find it.
about it, everybody said to him, "Well, stick around.
arrive there."
And, if he complained
Eventually we will
What happened at Reading was that if he stuck around for a
couple of years, he then entered his final honors year, which he was told would
be quite different.
And so it was.
When he started it, he was asked what he
would like to specialize in, crop production or animal production, or agricultural
engineering, and so on.
So, he never really arrived at this whole agriculture
that he thought he had come to study.
And I think it perhaps not too great a
distortion to say that in most countries, agriculture which is thought of as a
multi-disciplinary subject, has by and large tended to break itself dow'U into
parts which, if not single discipline studies, are somewhat close to that.
What we now do at Reading University is to give a course
agricultural systems in the first year.
The
we
call
responsibility of that course
is to provide this kind of framework within which the study of agriculture can
take place.
Don't mistake me; I am not supposing that you can study agriculture
as a whole all the time without paying considerable attention to fairly detailed
studies of component parts.
This would be true of every subject.
If you wanted
to teach people about buildings, you would have to make a choice as to whether
you talked about buildings as a whole or spent one day looking at bricks, the
next day looking at concrete, and the next day looking at water pipes, and so
on.
And you would be forced to the conclusion that of course you must have a
knowledge of the bits and pieces that are going to be put together to form the
whole, as well as knowledge of the whole.
The question is often posed, which
should come first, and I don't believe there is a
right way
round for this.
99
What I think is necessary, in order to motivate students, is to give an
occasional~
glimpse of whole buildings at an early stage and not just confine yourself to
the bricks and pipes, hoping eventually they will have some notion of what this
is going to add up to.
A Framework for Agricultural Studies
The main function of such a framework can really be summed up in about four
points.
The first one, is to define and describe agriculture in order that the
student should know what it is, what it does, who does what within it and how
many of them there are, where agriculture is carried out and for what sorts of
purposes.
I emphasize that last one because agriculture is in fact carried out
for a great many different purposes; and usually not for anyone of the range of
purposes, but for a blend of them which is rarely appreciated even by the chap
carrying it out.
The second function of the framework is to make it possible for a student
to see the relevance of the disciplinary studies he is going to be engaged in by
seeing where they fit into the total picture, how important they are, and what
he needs to know about them.
The third function of the framework is to provide a representation of agriculture and its main processes so that these models can be clothed with detail
as time, effort,
interest
and opportunity allow.
Now what this means so far, is that the student ought to have, in studying
agriculture, a sufficient picture of what it is all about, in a sufficiently
organized form, so that when somebody comes and talks to him about soil science,
he knows why soil matters.
He knows which bits of soil science are important
to him, and he can ask relevant questions because he knows to some extent what
he needs to get to get out of soil science.
If he is trying to build up a PictuJlt
100
of agriculutre as a whole, it isn't the whole of soil science that he needs.
I would like to put the student in the position of knowing that, because the
chap lecturing to him almost certainly won't.
One of the difficulties in a
subject as large and as diverse as agriculture is that you have to call upon
other people to teach in it.
And the odds are that in the early stage of a
course in agriculture, agriculturalists will call in, let us say, a soil
scientist.
The soil scientist not being an agriculturalist, frequently will not
have the least idea of what part of his science is required in agriculture.
What he will actually try to do is to teach a potted version of the same course
that he teaches to students who are doing a degree in soil science.
Not unnatur-
ally he will be continually complaining that he doesn't have time to do it.
course is grossly overcrowded.
adequately in the short time you
His
How can you expect him to deal with soil science
h~e
allocated to him?
You will get the same
message from all the others who are contributing - the cell biologists, the
botanists, the zoologists, etc.
The fact is, of course, that agriculture ought
not to be just a collection of other people's subjects.
something different.
It ought to add up to
Of course, it requires contributions from those subjects,
but in general those making the contributions will not know what they will be
called upon to provide.
Now, the student will be a great deal better off if he has an idea of what
he wants to get out of it, so he can listen to what the soil scientist says, and
he can latch on to those bits that help him in his study of agriculture.
He
can ask questions or take other opportunities as his time and interest allow to
go and read around the subject in order to clothe this framework you have given
him with bits culled from the different disciplines.
He has no chance really of
doing that if you haven't provided him with the framework in the first place.
101
The fourth and final function of the framework is to illustrate how
complex, multidisciplinary subjects can be treated quantitatively, even when no
one person can possibly possess all the relevant knowledge because nobody is
capable of holding in their minds all the detailed knowledge of all the subjects
which can usefully contribute to agriculture.
This then is rather analogous to understanding buildings.
If you want to
understand whole buildings, it is no use becoming a specialist in bricks or a
specialist in plumbing or electrics.
But, you have to know enough about all
these components to be able to finish up with a worthwhile building.
This is
something which the man who knows about bricks only will not achieve, nor will
the plumber.
One of the interesting things about this argument is that your
understanding of components then changes, and your attitude as to what you need
to know about them changes.
Once you have gotten the hang of buildings you have
a rough idea of what your interest mould be in plumbing; what kind of things
you need to know about plumbing to fit buildings.
~
The better your knowledge
and grasp of the whole subject you are studying, the better placed you are to
know what it is you need to know about all the constituents or components of
the system.
Problem Solving in Education
Now I mentioned the need for a problem-solving approach.
is a natural activity within agriculture.
having to try and solve problems.
I think that this
Everbody in agriculture is continually
There are different levels of detail, of course,
and 'as I tried to hint yesterday, you will find a hierarchy of problems as well;
some at a much finer subdivision of detail than others.
The advantages of a pro-
blem-solving approach are its practical relevance (because agriculture actually
proceeds in the way) and its value as a stimulation to thought and to informati04lt
102
seeking.
That's a very important part of an educational process; that is, not to
consider the process just as a passive transfer of information, as in the wellworn statement of passing information from my notes to yours without passing
through the mind of either of us.
But actually to create in the student a
desire to seek information on his or her own part, to go looking for it.
And
so, a problem solving approach is one which focuses the student's mind on what
he needs to know, and automatically provides an incentive to learning and discovery of knowledge for himself.
It is also a stimulus to the reformulation of
what has been learned - another very imp9rtant educational activity, in my view.
Problem solving, I also believe, is an important activity in its own right,
and should form a part of the educational process whether the vehicle be agricculture or any other subject.
And it is something that can be learned.
Problem
solving doesn't have to be left to a sort of intuitive process, unique to each
individual practicing it.
There are some helpful things you can learn.
There
are different sorts of problems, and the methods of solving them are different.
For example, if you think about a cross-word puzzle, it will soon become apparent
to you that if your problem is to solve one - to fill in all the appropriate
letters, and get the right words - then, the more people you have engaged on it,
within reason, the faster you will proceed, as you will be drawing upon a much
wider range of expertise, knowledge, experience and so on.
But if your problem
is to construct a cross-word puzzle, you had better do it on your own.
It is
virtually impossible to construct a crossword puzzle by a team effort.
This is,
I believe, self-evident, if you think about it, and yet in practical terms, you
will find teams being set up to solve problems for which teams are inappropriate,
and individuals trying to tackle problems where teams would be the fastest way
to proceed.
103
A problem solving approach also focuses attention on the application of
principles, and this I think is an extremely important part of any educational
process.
Principles of Agriculture
You might well ask, "What are agricultural principles?"
In fact, if
you are engaged in agricultural education, I think you should ask, "what
are agricultural principles?" and I think the answer will be that you don't
know any.
If you select a book with the word principles in the title, you
will probably find that it's about principles, but it won't actually state any.
It is quite an interesting exercise to have a go at this, and state the
principles of agriculture.
The first thing you encounter is that most of the
principles that anybody will state are principles of one of the constituent
subjects.
They are not agricultural principles.
the law of gravity.
They'll include for example,
That is not an agricultural principle.
~
It is true that
most agricultural things, when thrown up in the air will also fall to the ground,
so agriculture is not inconsistent with the law of gravity.
make gravity an agriculture principle.
But that doesn't
Many of the principles of physics,
botany, and so on are, of course, relevant to agriculture.
Agricultural oper-
ations will make use of them, but that's quite different from identifying them
as agricultural principles.
Now you may say, "Why all this stress on principles?"
Well, I think that they ought to be true, and usable, and serve as a basis for
action.
If not, then by my definition, they are not principles.
But you try
formulating them for agriculture, and you will find that it is extremely difficult,
and in my experience it has never been done.
Looking back on some other subjects, it seems to me a characteristic of
an undeveloped subject not to have its principles stated, because they have not
•
104
yet emerged.
Yet, the whole business of education is to try and extract out
of a lot of people's painful experience, ideas which you can pass on, and which
you learn from without actually having to fall down
in~ery
pot-hole yourself.
You can only express this summarized knowledge in terms of principles, if it is
going to be useful.
(I may say that in my view one of the purposes of education
is to stretch and expand the mind without actually unhinging it.)
I think that
there is an educational value in actually attempting to state the principles
even if you are not very successful, and I think that this is what the student
ought to be trying to do.
What is the purpose of an agricultural student actually
accumulating great heaps of unrelated facts from all these constituent s ujects
if he can't link them together in some principles which he can use as a basis
for action, so that he emerges with nothing but a great sort of data bank, and
not much of an idea of how to make use of it.
Another value of attempting to state principles of agriculture is that it
very rapidly identifies gaps in what you need to know in order to state them.
This is one of the advantages that most people claim for building models.
Even
if the model is never used and will never be used, mostly they will claim they
identified Some important gaps that need filling.
I don't believe that for a
moment, because you don't know whether it is an important gap until having filled
it, you see whether you have a useful model as a result.
Simply having identified
a gap doesn't seem to me to tell youthat it is also worth filling.
In this case,
the principles of agriculture, I think are worth stating, ought to be stated, and
if you have a got and can't do it, then I think you do identify a gap which is
worth thinking about.
Now you won't be surprised to discover that hierarchies of principles also
exist.
If you think about agricultural principles, they can relate to very
big topics, regional agriculture, whole agricultural systems, or they can relate
105
to how a farmer feeds his cow today - a particular cow.
If there aren't
principles which do that, it is hard to see how agricultural knowledge is
being useful in a practical sense.
•
So every principle. must relate to some specified class of objects,
activities, organisms or, more generally, systems.
If an agricultural system is divided up (or separated out) into its
constituent biological component organisms and processes, it is quite likely
that the principles that are relevant will also be biological, rather than
agricultural.
What would render a principle agricultural in these circumstances would
be the establishment of the relationship of the component to the agricultural
system of which it is a part, and thus to the agricultural objectives and purposes of that system.
Thus, biological principles can be stated about a cow, but an agricultural
~
principle (e.g., concerned with how to feed a cow) would have to take into account
the dairy or beef production system of which the cow is a part.
This will
generally mean that an agricultural principle will have to refer to a specified
agricultural system (to which the principle relates or applies) and this reference can only be to a named system, if systems have been classified and named
in such a way that a description can be found (somewhere) associated with the
name.
If this is not possible, then the system referred to has to be described
and such a description has in any event to be a usable one - it cannot usefully
be a massive, all-embracing document including all that is known.
So in my view, an agricultural principle will almost certainly have to relate
to an agricultural system.
And that means that the system to which it is going
106
to relate has to be recognized and described, and the description of any
agricultural system is bound to be in the form of a model.
Nobody wants to
describe a system in all its trivial detail, but rather to describe it in terms
of essentials, and that is essentially the modeling process.
Thus a systems approach, which I originally thought was relevant to
agriculture and should be added on to the top of a study of agriculture, now
turns out to be, in my mind, absolutely fundamental to the teaching of agriculture,
and I fail to see how an agricultural education can be embarked upon without it.
107
Harvey J. Gold (NCSU)
You made what was to me an excellent point in saying that some education
in agriculture is fundamental to literacy or to the completeness of any
individual's education.
Is there any interest among non-agricultural students
at Reading in taking your course in agricultural systems?
C. R. W. Spedding
Yes, there is.
Not as much as there should be, of course.
But we teach
agriculture to students of both geography and applied zoology now, and this is
regarded as a sensible part of their education.
Of course, in both cases you
can see some connection, and I would guess that is the main reason students are
oriented that way.
A student of applied zoology will thinkm himself that agri-
culture represents one potential area of employment, so possibly he should know
something about it.
That is not my main reason for encouraging it, and if there
was a way of incorporating agriculture as an essential part of everybody's
education at a university, I would think that worth considering.
I would go
further and say that I would like to see it a part of education at schools.
The
reaction of the schools at the U.K. is one of enormous interest, but it is a
virtual impossibility to insert anything else in the crowded timetables.
On the
contrary, by and large they are chucking out subjects.
I've come to the conclusion that there is no way in which agriculture as a
subject could be introduced to school education.
a subject in its own right.
Perhaps it ought not be, as
What we are now doing with schools involves a
substantial program, which is funded by industry purely on the grounds that
they agree with the proposition that the non-agriculturalist ought to know more
about agriculture than he currently does.
They are actually funding an activity
whereby we encourage school visits to our farms, where we tell them something
agriculture.
We provide the teachers with material they can use both before
ab~
108
and after the visit to illustrate their subject.
that goes for any subject, including mathematics.
As far as I am concerned,
I see no reason why agric-
ultural examples wouldn't be just as relevant there, as
like politics or history.
well as in subjects
I think that in a great many countries in the world,
it is hard to understand history if you don't understand agriculture, for example.
E. O. Heady
In most agricultural colleges in the United States, students take
a
course in farm management, which in a way "puts it all together" in an operating
farm, so as to decide what enterprises should be competing against each other.
You have to know something about the technology for this.
Would you call that
studying agriculture?
C. R. W. Spedding
I think it is a step in the right direction, but a small step.
interesting to see who are the integrators.
It is very
I have a farm management section
in my own department, and they certainly think of themselves as integrators.
I don't think it is their business to convey the whole picture of agriculture
which I have in mind, but they are certainly engaged in the integrative process,
and that's a help.
I know that economists very often see agricultural economics
as quite different from farm management.
In some places, agricultural economists
also think of themselves as giving an integrated picture.
My experience with
those pictures is that there's not a lot of biology in there, and if you take
biology out of agriculture, you don't have an awful lot left.
Sidney Evans eNC A&T SU)
Dr. Spedding, what is the length of exposure of students to this course
with respect to weeks or hours, other designation of time spent in the course
during a given academic term?
109
C. R. W. Spedding
The situation at Reading at the moment is that the agricultural systems
course in the first year goes on for the whole of that first year, at the rate
of about two hours a week of lectures and about three hours a week of visits
to practical situations, such as farms, chunks of industry; we see those visits
as part of the agricultural systems course.
I believe that it ought to be more,
but it isn't possible to achieve that at the present time, because we could only
find additional time by cutting something else out, and we can't find anybody
who is prepared to concede that he could manage with less time in the first
year.
If I did persuade, say a soil scientist,cra pest man, actually to reduce
the amount of time he spent contributing, he would grow very suspicious if I
immediately filled it up with one of my courses.
the length of time is the most important thing.
find anybody capable of doing it at all.
I don't necessarily think that
The problem is to first of all
I think it is important that it
include~
the very first lecture the student gets because in the nature of a framework the
sooner you meet it the better and more useful it is.
would like to see it expanded, if that were possible.
that sort of thing in his second year.
is now an option called Agricultural
My conclusion is that I
The student gets no more of
In the third and final year, there
Systems which the student can choose if
he wishes to retain a whole view of agriculture right to the end and have some
familiarity with ways of doing that.
Charles F. Murphy (NCSU)
'Dr. Spedding, this question may only peripherally relate to your topic
this afternoon, but as somebody who doesn't understand the systems approach well,
I am trying to put it together.
tural research.
system.
In our country, we have people managing agricul-
I guess that we can think of agricultural research as a total
~
These people either directly or indirectly manage research by determining
110
where the money inputs are going to go.
At the present time, as I interpret
it, managers have looked at this total system and feel they can have the
greatest positive impact by emphasizing specific topic areas, such as photosynthesis and nitrogen fixation.
It wasn't too many years ago, that these same
people decided quantitative genetics was an area where they could have a big
impact, and that pleased a lot of people in this room.
Sometime before that
they thought atomic energy applications, such as mutation genetics would have
an impact.
I suspect if this conference is really successful, and well sold,
they may decide that putting large amounts of money into systems approaches would
be a good way to impact.
But it seems that the result of this is sort of a
roller-coaster ride for the people who are actually engaged in agricultural research.
.~e
these people making a mistake in the way they look at this system,
or is it just the nature of the beast?
c.
R. W. Spedding
Well, thank you, this is a very important topic, but a very complex one.
I don't want to appear to be splitting hairs but, of course, there is a difference
between the research system and the agricultural systems which research is designed to serve.
Now of course you can if you like, look at the research system
and see how that functions, but I don't really think you are talking about that.
I think you are talking about the question of how do people see the whole of an
agricultural system and decide where to put their priorities for research, what
is going to make a difference and so on.
the past, there have been these phases.
I think it is perfectly true that in
Things came in and went out, and I
don't think that is a very successful way of going about it.
But the last thing
I would want to do is to suggest that it is a simple matter of doingfr one way
only.
I think it is useful to think of about two kinds of research in agriculture
111
as well as in other areas.
practical relevance.
appropriate.
One which is designed for immediate or short-term
Now, that's where I think a systems approach is most
If that is what you want to do, that is, to improve some agri-
cultural system, then the points that I made yesterday apply.
You have to know
what the system you are trying to improve is, and what you believe constitutes
an improvement.
Those are difficult enough questions, but if you don't answer
them, I can't see hav you have a particularly good chance of actually improving
something.
That is to suggest that if I want to switch the lights off, instead
of working out how to get to the light switch most rapidly, I just wander about
and hope that I meet it sometime.
If that's my objective, then I ought to pursue
it systematically anda systems approach is a means of doing that.
But of course,
if I wandered about the room, I might come upon a much better or cheaper light
source that happens to exist and which I don't know about.
So, equally in
agriculture and other research, there is a strong argument in parallel for encouraging
areas.
scientists to engage in their business - and they have to do it
withi~
You can't just say, alright, we recognize that scientists have to be
given this freedom, and they discover something which we never could have planned,
wouldn J t have known about, and would have been vastly more important than all the
planned developments.
I think that has to be taken on board, but you can't just
then say, "Right, we will liberate all these people with infinite money, and no
accountability, and off they go."
The first thing you run into is a shortage
of people who can actually do that sort of thing successfully.
Most estimates
are that it is only about 10% of the research population who are worth supporting
on that basis.
Most of the others would be better employed in a more planned
approach. By and large, I don't think you do require the same number of people
in these freer exercises of exploration of toally new ideas, and it is important
to have the right caliber of people.
So very likely 10% of the people and 1070
112
of the cost would be as good a way of arriving at that sort of proportion as
any other.
Now, if you've got the right people, you are certainly not going
to benefit agriculture by letting them loose, in say, particle physics.
And
within agriculture you probably do have to make guesses like photosynthesis is
worth looking at.
There I do believe the systems approach can also be helpful
to the research manager who thinks to himself, "Now what does agriculture actually
consist of.
What are the major resources going in?"
nitrogen as one of the most important.
Of course, he'll identify
In fact, if a single thing dominates
agricultural output in the world, nitrogen would come out near the top of the
list - not necessarily fertilizer nitrogen, because the vast majority of the
world does not actually have that.
nitrogen fixation of some sort.
strong
possibilit~
The majority of the world is still on
But, if we reckon that there is at least a
that the energy costs of making artificial fertilizer make
it sensible to look at alternatives -- not to prejudge the issue, but in order
that we have some alternatives -- then you have got to recognize that agriculture
operates by tilling a few inches of land, sandwiched between an atmosphere with
8070 nitrogen and usually a tremendous lot in the soil as well.
If one could in-
crease fractionally, the amount of that nitrogen incorporated in the cycle, one
would have solved a problem.
So, I think that if an adequate picture of the
whole agricultural system is created, you can come to some sensible judgement
that it will be worth releasing really high caliber minds with the freedom
to explore new ideas within the general area of say, nitrogen fixation.
113
SESSION III
114
INFORMAL DISCUSSION SESSIONS
The very nature of an informal session makes it impossible to capture
in a written proceedings.
The following remarks were provided by the discussion coordinators.
Their purpose is to convey some of the principal concerns that were voiced,
as well as the main points that were emphasized, and questions that were raised.
The level of detail varies from session to session, depending upon the type of
session and the inclincations of the discussion coordinators.
In each case,
there is an attempt to capture at least the gist of the discussion.
115
Informal Discussion Session
PLANT GROWTH MODELS
Coordinators:
J. Reynolds
M. Wann
This discussion focused on the role of mathematical and computer
modeling in primary productivity research in agricultural systems.
Gen-
erally, emphasis was placed on whole-plant or crop level modeling.
Various
types of modeling approaches were discussed, i.e., climatological-phenology
models, regression models for yield prediction, and physiologically-based
growth models; this lead to a survey of agricultural plants for which such
models have been developed.
Several topics emerged during this session which generated much interest:
(1) the problems of model validation ("validation" vs. "usefulness"); (2) the
role of models in directing agricultural research (e.g., genetic breeding,
yield forecasting); (3) economic considerations in model-building (why highly
empirical models are favored over mechanistically-oriented models); (4) the
application of existing crop models to other commodity groups; and (5) the
relative position of modeling techniques and the data (information) bases for
most agriculture crops.
116
Informal Discussion Session
ANIMAL GROWTH AND PASTURE SYSTEMS
Coordinators:
Burns.
J. Burns
D. Davenport
W. Getz
The simulation of grazing systems in beef-forage production enter-
prises requires models that characterize both plant and animal growth as well
as the interaction between the plant and animal biological systems.
A signi-
ficant component of this interaction is the grazing behavior of animals relative
to pasture composition and availability of forage on offer.
The selectivity
of grazing and frequency and intensity of plant defoliation can alter morphological characterisitcs of the plant, possibly its regrowth and appreciably
alter digestible dry matter intake and subsequently animal performance.
Both Dr. R. D. Mochrie, Department of Animal Science, and I have cooperated
over the past several years with the late Dr. H. L. Lucas in designing and
conducting experiments to provide coefficients for modeling intake of free
grazing animals.
This work has been done using the warm season perennial,
Coastal bermudagrass.
The general model developed by Dr. Lucas has been published
recently (Formulation and role of input-output models in animal production,
Proceedings, First International Symposium Feed Composition, Animal Nutrient
Requirements, and Computerization of Diet, July 11-16, 1976, Utah State University, Utah, USA) for a general situation assuming uniform grazing by animals.
The development of a model that provides good prediction would be advantageous in evaluating many forages, both currently used varieties as well as new
species, relative to their on-farm production potential.
•
117
Consideration will need to be given to the quantity of available forage,
nature of the forage on offer (i.e., species composition, leaf-stem ratio,
and quality of the fractions), plant growth rate, animal preference for plant
parts, dry matter intake and effects of animal trampling and excretion.
Sea-
sonal dry matter yield estimates will allow prediction of animal gain per unit
area.
Such responses are generally determined through large scale grazing trials.
However, the expense and complexity of such trials limit the number of forage
specie-treatment combinations that can be evaluated.
The use of a reliable
model that would allow prediction of relative estimates of animal gain per unit
area from small plot studies would permit the comparison of many forage specietreatment combinations.
This permits intensive large scale evaluation of those
forages that offer the greatest potential.
Further, new forages in the develop-
mental stages can be evaluated early for their potential animal productivity
per unit area.
Future research is anticipated to further develop and test models that
reasonably predict animal response from grazing situations.
Cooperation is
sought with an individual(s) with modeling expertise to formulate expressions
describing the appropriate interrelationships.
Davenport.
Feeding a herd for maximum profit is a goal of dairymen.
Developing
easily executed procedures for selecting maximum profit combinations of feeding
system and ration fonnulation(s) suited for dairy farm businesses differing
with respect to herd size, facilities, equipment, cropland, labor, etc. is a
goal of dairy researchers.
Profit is a function of size and efficiency.
In a dairy farm business,
number of cows, freshened at least once, denotes size and (receipts-expenses)!
118
cow denotes efficiency.
Profit associated with feeding is measured as income
above feed cost where feed cost should include all ingredient and feeding expenses incurred in getting the ration to the point of consumption.
Feeding-
profit maximization results from proper and simultaneous short-run and long-run
manipulation of both the number of cows and individual-cow feeding rate(s)
relative to the acquisition costs, opportunity costs and availabilities of
resources pertinent to feeding including labor, storage facilities, feeding
equipment and facilities and feedstuffs.
Profit, known and/or predicted, is
the criterion upon which herd and individual-cow management decisions are
based.
Incomes from milk sales for individual cows are effectively determined
from Dairy Herd Improvement records, but individual-cow feed inputs and consequently income above feed cost become increasingly difficult to quantify
accurately as
herd size, mechanized feeding, blended ration use and whole-
herd or group feeding increase.
Individually-fed concentrates and group-fed
roughage(s) and whole-herd group feeding of concentrates blended with roughage(s)
to form a complete
ercial farms.
rati~represent
the extremes among feeding systems on comm-
Practical intermediates are systems in which a complete blended
raticnis formulated for and group fed to each of two, three or more productionlevel subgroups of cows.
To employ individual-cow feeding rates and profits as. bases for herd and
cow management decisions each cow's inputs (physical and dollar amounts) of each
ration component must be accurately determined either by actual measurement or
by estimation.
Estimation is the only alternative with group-fed ingredients
whether they are components of a blended ration or not.
Mathematical esimation
of inputs by the application of input-state-output models to quantitative descriptions of physiologic and/or metabolic functions of cows and chemical and
•
119
physical properties of feedstuffs which positively or negatively affect rate
of consumption represents a procedure with merit.
Two interdependent modeling needs of equal importance to dairy cow
husbandry and management exist.
One is the need for complete and precise
quantification of all known and to be known direct and interactive effects
of internal and external physical, chemical, physiological, nutritional,
biochemical, metabolic, genetic, behavioral, etc. phenomena on nutrient utilization and production.
Meeting this need has as its purpose a complete under-
standing of all systems and mechanisms associated with dairy cow performance
and the accumulation of basic information pertinent to meeting a practical
need.
The second is the need for modeling efforts which quantify the direct
and interactive effects of factors which can be manipulated through management
to maximize dairy cow profits.
That basic models be adequately defined before
practical model investigations commence is not appropriate.
Current and critical
economic pressures affecting dairy cow profits, particularly fluctuating and
generally increasing feed costs, dictate that practical model(s) rationally
derived from feeding experiment data and consistent with substantiated basic
modeling results are needed now.
As with any investigative endeavor, problems
ffi1d weaknesses can be expected with early practical models, but refinements and!
or alterations will follow as new or revised evidence from basic modeling or
other research efforts is generated.
Dr. Lucas listed the more important of the factors to be considered in
practical models in his justification for "dynamic" versus "steady-state" models.
He stated, "In general, inputs and side conditions interact with system state
to produce not just
outputs, but also changes in system state.
For example,
feeding level, feed character and environmental factors interact with genetic
makeup, age, size, stage of lactation, stage of gestation and fatness of a dairy
120
cow to produce continually changing patterns of fatness, body size, fetal
size, etc., as well as milk yield, fecal output, etc.
Representations which
take changing system state as well as inputs and outputs explicitly into
account can well be called dynamic models.
They are perhaps
better described
by the term "input-state-output" than by "input-output"."
Dr. Lucas also discussed rational models derived by reasoning from quantitation determinations of biological constants associated with basic systems
and mechanisms as opposed to empirical models derived by curve fitting.
Parameters
in empirical models tend to exhibit considerable lability depending on the
distribution, ranges and combinations of factors affecting performance included
in the models, and as a consequence, when applied to observations consisting
of affecting factors with different distributions, ranges and combinations, result in extremely biased or even nonsensical estimates of outputs .. Whereas,
the parameters of rational models have definite interpretation and apply over
more divergent distributions, ranges and combinations of affecting factors.
The
latter do not reflect the disproportionate effects of observations at the extremes
found in least squares estimates of empirical model parameters.
Of primary
concern are production functions exhibiting negative marginal responses to inputs when only positive, but decreasing marginal responses exist.
The latter
is the case in dairy cow feeding where appetite and/or stomach capacity and not
management control inputs maxima.
The application of old functions to cows with
higher producing abilities than those from which the functions were derived
would be a major problem area.
Theoretically, both production per cow and mean efficiency as milk per
unit of intake decrease across the range of practical feeding systems from
individually-fed concentrates and group-fed roughage(s) through a whole-herd
single blended complete ration system.
General assumptions are that a group-
121
fed complete ration formulated for the mean, median or other mid-range
production level in a group of cows will result in overfeeding of low producers and underfeeding of high producers.
The magnitudes of over- and under-
feeding are not known but are suspected to be quite small because of correlations
between producing ability and appetite.
Most among-systems comparisons result
in small advantages in production and negligible advantage in efficiency due
to separating and feeding cows or groups of cows according to producing ability.
In many cases differences due to system are small enough such that labor and/
or capital expenses for the more intensive systems are more than enough to
negate minor superiorities with respect to production level.
That is, construc-
tion or renovation costs to convert to a different system may be large enough
to more than balance any gains in production from switching systems.
Two problems need resolution.
Choice of system and whether or not to
change depends on the current system and the cost of changing.
The second is
the need for modeling efforts which have application in the formulation of
maximum profit rations for groups of cows varying over some range with respect
to producing ability.
The basic decisions are:
1.
Group or not group.
2.
Selection of ration for each group; whether one, two, three
or more groups of cows.
Getz:
It has been only recently that A&T has had the opportunity to begin the
process of planning and developing an agriculture research program as such .
. That program is still evolving as individual scientists receive funding
for studies planned; more or less within the priority constraints of specific
departments.
122
Some of the research is collaborative in nature and involves components
which give it some breadth of scope; other is more narrow in focus.
There has been no conscious effort, to attempt as yet, to organize from
the outset, projects which, shall we say, attempt to look at whole agricultural systems; although it can be said that in some instances some research
which is undertaken could include, as an object, an effort to examine how
whole systems are working, where they break-down, and how they can be modified.
Because the research program at A&T is young and still expanding some
real opportunities exist for the inception of studies examining systems or
taking a systems approach toward solving problems associated with presently
utilized production, marketing, etc. systems, and further to develop perhaps
more viable systems.
Perhaps useful to mention would be studies in sheep physiology, beef
production, alfalfa breeding, swine genetics, and soil chemistry, currently
underway or planned which have scope for integration into systems studies.
Our own systems research will develop as individual researchers begin
thinking about the viability and appropriateness of such an approach.
This
Symposium is expected to stimulate such thinking.
I do wish to ask Professor Spedding a couple of questions:
1) When
one attempts to investigate agriculture production systems, for example, they
tend to be rather long-term in nature.
Such long-term research isfrequently
more vulnerable to critical changes in funding, administrative policy changes,
etc. which means that in the end the whole effort may bring a few results.
How
does one balance the need for looking at whole systems, with the risks associated
with long term studies?
123
Informal Discussion Session
PEST MODELS AND INTEGRATED PEST MANAGEMENT
Coordinators:
Leonard.
K. Leonard
R. Stinner
T. Sutton
The use of disease resistant varieties is the primary method of
controlling many important diseases of cereal crops and other major food
crops.
Adaptations of plant pathogens to overcome specific types of
resistance result in the periodic breakdown in the protection provided by
resistant varieties.
We are involved in development of mathematical models
to describe the processes of natural selection in pathogen populations that
lead to their adaptation to new host varieties.
The models will be used to
expand the theoretical understanding of the genetic interactions of hosts
and their pathogens and to allow predictions of optimal systems of managing
the use of genes for disease resistance in varietal rotations or mixtures.
These systems can be integrated into disease control programs that employ
crop rotation and use of pesticides.
In fact, the problem of selection of
pathogen genotypes tolerant of specific pesticides is analogous to that of
the breakdown of resistant varieties and can be simulated with the same kinds
of models.
Stinner.
The discussion centered on the conceptual types of models needed
in understanding agricultural systems and for use by agricultural workers.
The immediate need is for short-term, single-field predictors which incorporate
"economic thresholds" and action options in decision making optimization
models.
This approach, however, does not look at the future needs of agriculture,
nor does it account for interactions of control strategies among disciplines,
124
nor can it offer suggestions for altering the basic structure of the system.
The second approach is in the development of regional, longer-term
predictors to look at potential problems and solutions.
Many tactics may not
be of use on an individual farm basis (e.g., post-harvest residue destruction
for control of tobacco cornworm), practices in one crop may influence insect
problems on another in the same season (e.g., use of multi-eared varieties
of corn will increase corn earworm populations which subsequently infest cotton
and soybeans), or in the following season(e.g., non-till practices such as
corn planted in soy strubble could increase soil insect problems).
Unless
this broader approach is taken, we will continue to create new problems (or
resurrect old) due to the complex, interactive nature of these man-dominated
systems.
Sutton.
Many Plant Pathology faculty members are involved in studies which
~
provide critical data for various components of plant disease models or which
lead to the development of disease management tactics and strategies.
The soy-
bean and apple disease management programs are perhaps more closely associated
with overall management programs.
These programs focus on the interrelationships
between disease and other components of the agro-ecosystem and seek disease management tactics and strategies which are compatible with the environment.
Both
projects are a part of the national IPM program (Consortium for Integrated Pest
Management).
The soybean project involves investigators in five departments on campus
and focuses on characterizing the bionomics and population ecology of the various
soybean pests, establishing economic thresholds for them, developing management
strategies for each pest, and implementing a program at the farm level.
Plant
pathologists are specifically involved in characterizing and quantifying damage-~
125
density relationships for major soybean nematodes.
Population dynamics are
being studied under various grower management programs as well as under controlled
field plots.
These data will then be used to develop a model for nematode
dynamics which will be used to predict plant response under various population
levels.
This model will be incorporated as a submodel in a soybean growth model
currently under development at the University of Florida.
The apple project was initiated in 1976 as a North Carolina Agricultural
Research Service Project.
In this project, investigators in eight departments
are working together to develop and implement a comprehensive apple pest
management project for North Carolina.
The initial phases of the study involved
monitoring current grower management practices and evaluating pest models and
delivery systems developed in other states.
The project is currently focusing
on the development of pest and apple tree growt4 models for North Carolina
conditions and implementation of a grower advisory service.
Within the apple project, disease management studies have focused on factors
which influence the onset and development of the "summer diseases" of apple
(those diseases generally more severe in the warmer and most moist growing
conditions in the southern United States.
Quantifying pathogen populations is
a major difficulty in developing management programs for these diseases.
In-
fective propagules are difficult to detect in the arispora and numbers near or
below the detection threshold can often cause serious disease loss.
Studies
are also underway to evaluate disease models developed for other apple growing
areas.
126
Informal Discussion Session
MAN-MACHINE INTERFACE
Coordinators:
R. Sowell
J. Young
This discussion centered around problems inherent in the introduction
of technological innovation in the agricultural production system.
In the
course of the discussion, it was in particular pointed out that research
conducted within the research institutions of technologically developed
countries is geared to large scale capital intensive agriculture.
Results
of such research is often not applicable to limited resource farming systems.
The more general point was made that research in the area of agricultural
technology and engineering must be especially sensitive to the type of system
for which the technology is intended.
At the same time, interactions between
the technology and the biology need to be considered.
As an example, a crop
cultivar which matures uniformly is highly desirable for systems in which
harvesting will be done by machine.
Such a cultivar would be disastrous for
the family type farm in which harvesting must be spread out over a period of
time.
127
Informal Discussion Session
ECONOMIC MODELING OF THE FOOD SYSTEM
Coordinators:
G. Carlson
R. King
The explicit examination of interdependencies lies at the heart of
a systems approach to the food system.
There are a variety of inputs that
may be selected in the production processes that take place at the farm level.
Agricultural production system modeling conducted by economists (who
heavily borrow biological data from other disciplines) might be divided into
two types:
(1) that emphasizing the description of existing technology and
farmer behavior, and (2) that concentrating on a view of the biological system
complete enough to recommend actions for agricultural producers to take.
Often, the descriptive models utilize
econom~tric
analysis of farmer and
farm characteristics to test various ideas on how farmers are adjusting to new
technology, uncertain weather or changes in relative prices of production
inputs.
Crop acreage adjustments, land price estimation, labor supply, and
production function analysis are examples.
Usually, not as much attention
is directed in this type of model to within-year resource adjustments or crop
or livestock growth rates.
When economists wish to model within-year agricultural systems, to make
recommendations on optimum use of variable resources like pesticides, fertilizer
or labor, much more interaction with biologists is required.
Interactions of
weather, added inputs, soil, pests and other factors are critical.
of timely biological information is now more useful.
Modeling
Observations from con-
trolled experiments and simulation of multiple components are often used.
thesis of large amounts of data is needed.
Syn-
128
Next comes the question of how to model the interdependencies that exist
between the decisions at the farm level and those that are made at the processor level.
These decisions, in turn, must be coordinated with wholesaler-
retailer purchase and sale decisions.
Finally, it is important to consider
consumer choices in selecting the desired mix of food and non-food expenditures.
For some questions, it is reasonable to examine one slice of this system.
But
for others, it is necessary to view the several components as parts of an interconnected system with two-way communication between adjacent segments bringing
about a consistent and efficient set of choices at every level.
It may be helpful to use the milk sector to illustrate different degrees
of complexity that might be considered in developing a systems model.
The
simplest model would consist of a demand curve for raw milk at the farm level
and a supply curve representing the response of dairy farmers to alternative
levels of farm prices.
This might be expanded to consider supply and demand
curves for each of several regions, including questions of processing plant
locations, technical changes, product flows and spatial price relationships.
A more complete model would distinguish between fluid grade and manufacturing grade milk production and sales decisions.
Constraints on the system
might be added, such as the effects of the existence of the N. C. Milk Marketing
Commission as a force in establishingfarm level raw milk prices.
Market forces
are modified through the existence of administered pricing decisions in a very
large fraction of the dairy industry throughout the country.
Finally, we
might introduce the Commodity Credit Corporation as a purchaser of manufactured
milk products to provide minimum levels of support for milk producers throughout
the country.
129
One conclusion to be drawn from this dairy system example is that
there may be a variety of alternatives to be considered in modeling an
agricultural system.
investigated.
The one selected will depend upon the question to be
There is no one ideal way to model a system.
done in systems modeling?
What can be
What is worth doing in systems modeling?
What
should be the role of the North Carolina Agricultural Research Service in
encouraging the use of a systems approach to questions facing farmers,
marketing firms, and consumers in the state?
This symposium has provided an
opportunity for scientists in a variety of disciplines to give some thought
to the untapped potential that lies in a more comprehensive view of the food
system.