Citation
Journal of farming systems research-extension

Material Information

Title:
Journal of farming systems research-extension
Running title:
Journal for farming systems research-extension
Abbreviated Title:
J. farming syst. res.-ext.
Creator:
Association of Farming Systems Research-Extension
Place of Publication:
Tucson Ariz. USA
Publisher:
Association of Farming Systems Research-Extension
Publication Date:
Language:
English
Physical Description:
v. : ill. ; 23 cm.

Subjects

Subjects / Keywords:
Agricultural systems -- Periodicals -- Developing countries ( lcsh )
Agricultural extension work -- Research -- Periodicals ( lcsh )
Sustainable agriculture -- Periodicals -- Developing countries ( lcsh )
Genre:
serial ( sobekcm )
periodical ( marcgt )

Notes

Dates or Sequential Designation:
Vol. 1, no. 1-
General Note:
Title varies slightly.
General Note:
Title from cover.
General Note:
Latest issue consulted: Vol. 1, no. 2, published in 1990.
Funding:
Electronic resources created as part of a prototype UF Institutional Repository and Faculty Papers project by the University of Florida.

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University of Florida
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University of Florida
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The University of Florida George A. Smathers Libraries respect the intellectual property rights of others and do not claim any copyright interest in this item. This item may be protected by copyright but is made available here under a claim of fair use (17 U.S.C. §107) for non-profit research and educational purposes. Users of this work have responsibility for determining copyright status prior to reusing, publishing or reproducing this item for purposes other than what is allowed by fair use or other copyright exemptions. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder. The Smathers Libraries would like to learn more about this item and invite individuals or organizations to contact Digital Services (UFDC@uflib.ufl.edu) with any additional information they can provide.
Resource Identifier:
22044949 ( OCLC )
sn 90001812 ( LCCN )
1051-6786 ( ISSN )

Full Text


Volume 5, Number 2
1995







o ur nal


for Farming Systems Research- Extension











Ii
. Me s

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Journal

for Farming Systems Research-Extension


Volume 5, Number 2, 1995


Published by
the Association for Farming Systems Research-Extension

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Journal for Farming Systems Research-Extension ISSN: 1051-6786
Editor
George H. Axinn
Department of Resource Development
Michigan State University
East Lansing, Michigan
U.S.A.

The Journal for Farming Systems Research-Extension is published by the Association for Farming Systems Research-Extension (AFSRE), an international s6ciety organized to promote the development and dissemination of methods and results of participatory on-farm systems research and extension. The objectives of such research are the development and adoption through participation by farm household members of improved and appropriate technologies and management strategies to meet the socioeconomic and nutritional needs of farm families; to foster the efficient and sustainable use of natural resources; and to contribute toward meeting global requirements for food, feed, and fiber.
The purpose of the Journal is to present multidisciplinary reports of onfarm research-extension work completed in the field, and discussions on methodology and other issues of interest to farming systems practitioners, administrators, and trainers. The Journal also serves as a proceedings for the international Farming Systems Symposia from which selected and refereed papers are included. It also welcomes contributed articles from members of AFSRE who are unable to attend the symposia. Contributed articles will be judged by the same review process as invited articles.


The AFSRE President is: Dr. Nimal Ranaweera Addl. Secretary (Project Development) Ministry of Agric., Lands, & Forestry "Samnpathpaya" 82, Rajamalwatta Road Battaramulla SRI LANKA Fax +94-1-868919 or +94-8-88798 e-mail: Nimal@deptag.ac.lk


The AFSRE Secretary/Treasurer is: Dr. Virginia Cardenas
Department of Agricultural
Education and Rural Studies
College of Agriculture
University of the Philippines at
Los Bafios, College, Laguna
PHILIPPINES'
Fax +63-2-813-5697 or +63-2-942-914 e-mail: VRC@mudspring uplb.edu.ph


Correspondence regarding articles for this journal should be addressed to: George H. Axinn, Editor, JFSRE, E-Mail -- axinn@pilot.msu.edu FAX 517-353-8994 Postal: 313 Natural Resources Building, Michigan State University, East Lansing, Michigan 48824-1222, U. S. A.


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Journal for Farming Systems Research-Extension Volume 5, Number 2, 1995

CONTENTS

IV. Message from the President of AFSRE

V. From the Editor's Desk

1 On the Systems Dimension in FSR
Richard Bawden

19 The Conceptual Basis for Targeting Farming Systems: Domains, Zones,
and Typologies Keith M. Moore

39 Adoption of Soybean: A Comparative Analysis of Cultural Practices in
Zaire and Nigeria
Dennis A. Shannon, Mwamba Kalala M., Kubengu Mudilamika and
Mpoy Audiamvita

55 The Impact of Plan Sierra's Outreach Programs on the Sustainability of
Hillside Farming Systems in the Dominican Republic
Scott G. Witter and Michael P. Robotham

67 An Interrogative Approach to System Diagnosis: An Invitation to the
Dance
P.G. Cox, A.D. Shulman, P.E. Ridge, MA. Foale andA.L. Garside

85 Sustainable Peri-Urban Vegetable Production and Natural Resources
Management in Nepal
Hans G.P. Jansen, David J. Midmore, and Durga Dutta Poudel

109 Agricultural Production on the Extensive Margin: The Case of Mexico's
Northern A Itiplano
Paul N. Wilson and Gary D. Thompson

129 Letter from the Secretary/Treasurer of AFSRE


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MESSAGE FROM THE PRESIDENT OF AFSRE

Let me first and foremost wish you welcome to the latest issue of the Journal. It has been a long time since the release of the last issue, and I apologize for the delay in this volume being released.
There was a change in the editorship and the process of selection for a new editor went through a protracted period. This took some time. However, I am now pleased to announce that Dr. George H. Axinn of Michigan State University, and a very old friend of the Association, has taken over the editorship. He will continue for some time, initially handling Volume 5 and Volume 6.
I am aware that a number of members were concerned about the lack of publications, and even questioned the rationale of joining the Association. While recognizing the delay, at no stage was the idea of abandoning the Journal ever considered. Rather, it was the Association's considered view that all attempts must be made to have the Journal continue, and make it sustainable by canvassing for more and more high quality articles.
The rotational process of the Presidency has brought the current Presidency to Asia, which I think is challenging to the Southern countries, and also will provide a new dimension to the Association. The shift had its initial problems of logistics, which I am pleased are now over. The Association's office has now moved to the Philippines. The address of the Secretary/Treasurer, Dr. Virginia Cardenas, is shown inside the front cover, and her statement to the membership is at the end of this issue.
Once again, let me apologize for the delay and at the same time assure you that the professional competence and the quality will continue.

Dr. Nimal Ranaweera
President

..







FROM THE EDITOR'S DESK


This issue of the Journal for Farming Systems Research-Extension has been a long time coming. As mentioned by our President, Dr. Nimal Ranaweera, in his letter at the beginning of this issue, Dr. Timothy R. Frankenberger shifted to a different position over a year ago, and the Association for Farming Systems Research-Extension was without an editor for many months.
As a former agricultural editor who has been working with farming systems and other rural development related activities for almost fifty years in Africa, Asia, Latin America, and the USA, I was facing the change from full-time professor to working Professor Emeritus in 1996 with some trepidation. So when a member of the AFSRE Board asked me if I would consider serving for a while as Editor of JFSRE, I was enthusiastic. Now, after reviewing over seventy manuscripts in the "backlog" for this journal, and assembling those most nearly ready for this issue, my enthusiasm continues.
This issue opens with two conceptual papers which I believe reflect the "state-of-the-art" in farming systems research-extension. Richard Bawden, in the opening paper, draws attention to the crucial dimension which the word systems reflects in the name of our Association, and the name of this journal. His scholarly approach sheds light on the systemic essence of our approach to research and extension about farming. In the second paper, Keith Moore also points to the lack of conceptual precision and transdisciplinary consensus among practitioners, and attempts to address this confusion by clarifying many of the key ideas in the field.
The following two papers move directly to the farm. A group of four authors working in Zaire and Nigeria focus on the soybean, and do a comparative analysis of cultural practices in the two countries. Then, in the next paper, two field workers in the Dominican Republic analyze sustainability of hillside farming systems.
In the next article a group concerned with farming systems work in Australia suggest a fresh new approach to thinking about the decisions which farming people make every day
And this is followed by two more "production-focused papers. One addresses vegetable production in Nepal, and the next describes and analyses agricultural production in one part of Mexico.
All of these papers were in some state of review or revision when I came to this assignment, and I am personally and professionally pleased to present them in this issue. But as a newcomer to this assignment, I must admit that they were selected because they seemed to fit what AFSRE is all about, and they were also the most ready for publication among some seventy papers in various stages of the process. Now I am in the process of working with peer reviewers scattered all over the world, trying to sort out articles for the next two issues of this journal. We shall also be having new papers presented at the 1996


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Symposium in Colombo in November, and I hope additional manuscripts will be written by other members of the Association.
One of the most difficult tasks of the Editor, along with the reviewers, is to determine the criteria for selection of new articles for this journal. After all, there are hundreds of other journals in every aspect of farming, agriculture, forestry, fisheries, livestock, and related fields in every part of the globe. Which might better be published in a more focused disciplinary journal? And which are ideally suited for the Journal for Farming Systems ResearchExtension? A goal for each issue might be papers from as many different parts of the world.
After correspondence with officers of AFSRE, and some members of the Board, the set of tentative criteria on the next page have emerged. They are included here with a request for your input, as a reader of this Journal. Please look at the list, suggest additional items if you wish, and suggest other changes which should be made. Assuming that most articles might not meet all of these suggested criteria, preference could be given to those which meet more than other papers. I hope an Editorial Committee will soon be formed for this Journal, perhaps with members from each continent. Your comments and suggestions will be shared with this editorial committee, and perthaps we can evolve a set of criteria which will be helpful to members of the Association, readers of thie Journal, and future editors. Please send me your ideas.

George H. Axinn
Editor, JFSRE







Important Notice

Please turn to the last page of this Journal for an important message from our Secretary/Treasurer, Dr. Virginia Cardenas. For readers who are not members of the Association for Farming Systems Research-Extension, and for those who may not yet have renewed their membership, you will find impoirtant information at the back of this issue.


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Tentative Criteria for Selection of Future Articles for this Journal

a. Articles will address farms as whole systems, including their production and consumption, and involving the plants, livestock, and humans on the farming system, or the institutional support services for farming systems, including the policy structure.

b. If possible, field research would involve partnerships among professional agricultural/forestry scientists, extension personnel, and farmers (men, women, and children). Ideally, that includes collaboration in deciding what to study, designing the research, collecting the data, analyzing and evaluating the findings, etc.

c. Human data will be disaggregated by gender.

d. Where papers involve several layers of a systems hierarchy -- from soil microenvironments (for example), to whole farms, to communities of farms, to larger agro-ecological zones, to national policy -- they will deal with these phenomena from a systemic perspective.

e. Papers which deal with FSRE as a concept, and its relationship to other approaches to rural development, etc., will assume that FSRE refers to work like that described in (a) through (d) above.

f. Papers should demonstrate the relationship between new material being presented and the current literature in FSRE, as published in earlier issues of this journal, proceedings of AFSRE symposia, etc.

In addition to Regular Articles, the Journal will occasionally include a section of Field Notes. These are more brief, need not have literature citations, but are expected to share relevant field experience which may be useful to other practitioners and scholars.


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ON THE SYSTEMS DIMENSION IN FSR


Richard Bawden'

INTRODUCTION

The trouble with Farming Systems Research (FSR), is the word in the middle. While most of us are pretty clear when it comes to thinking about what 'farming' is, and about what we mean when we talk about 'research', the notion of 'systems' is far more elusive. Farming is what farmers do, and research is what researchers do. And farming research, is research into farming. So where does the systems bit fit in?
This is not, by any means, a trivial question. The demands on agricultural researchers are getting more and more complex, and it is therefore essential that the theories that they hold, and the research and development methods that they practice, are relevant to the situations being faced. The challenge of agriculture of course, is to continue to produce more and better quality food to feed an ever-growing global population, while trying, simultaneously, to improve the welfare of farmers and the rural communities in which they live, and the integrity of the global environment which all of us share. As there are some disturbing signs, that none of these three goals are currently being achieved, it is vital that research approaches to agriculture and rural development, are subjected to critical review with respect to their relevance.
And this brings us back to this issue of the word 'systems' at the heart of FSR, and what it is supposed to be signifying with respect to the nature of the research and development which is conducted in its name. These matters are of significance not just to the researchers themselves, but to policy makers and donors, who have to take decisions with respect to the allocation of scarce R&D resources, to educators, who have to be clear about the nature of the specific research competencies that their students need to acquire, and above all, to farmers everywhere.
The purpose of this paper is to explore this matter of the systems dimension of FSR, in a manner which, hopefully, will add to the clarifications which have been sought about the 'true' nature of FSR, almost since the time of its emergence as a major theme in international agricultural research. The essential argument that I want to put, is that by paying more attention to this



1 Professor of Systemic Development, University of Western Sdydney Hawkesbury, Richmond NSW Australia.


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issue of 'systems', and what it might mean to both their theory and practice, FSR practitioners could significantly improve the quality and relevance of their approach to research. The systems sciences, which have evolved as the sciences of complexity, have much to offer to agriculture and rural development, and there is a lot to be gained from FSR practitioners learning more about what it means to be systemic.
Attention to the systems dimension, would bring farming systems researchers into closer affinities with other major systemic approaches to agricultural and rural development including agroecosystems analysis and development, systems analysis and simulation, and the emerging critical learning systems approach to development.

THE SEARCH FOR CLARITY

It is generally accepted that the term Farming Systems Research emerged in the mid- 1970s as applied to the development of technologies for small-scale farmers of limited resources (Hildebrand 1982). In response to what was being seen as inappropriate approaches to the technological needs of resource-poor farmers in the Third World, the arguments presented in favour of FSR supported two fundamental propositions: (i) "that development of relevant and viable technology for small farmers must be grounded in a full knowledge of the existing farm system, and (ii) that technology should be evaluated not solely in terms of its technical performance, but in terms of its conformity to the goals, needs and socio-economic circumstances of the targeted small farm system, as well" (Merrill Sands 1986). These two new imperatives for research revealed the farm management and agronomy 'pedigree' of the movement, while also setting it clearly apart from the types of commodity and/or discipline focussed research which characterised the agendas of the international agricultural research centres of the day.
Given that the initiatives for this new, more encompassing, research approach to agricultural development came essentially from the 'ground-up', and concurrently in various locations across the world, FSR evolved into more of an umbrella term for a class of research approaches than a descriptor for a particular research methodology. This was in clear contrast to other approaches to agricultural development which also promoted the merits of approaching whole farms as systems, such as agro-ecosystems analysis (Conway 1985) or the earlier initiatives in agricultural systems analysis (Dent and Anderson 1971). Under these diverse circumstances, it was not surprising that a wide variety of different concepts, approaches and research methods would be grouped under the FSR heading, leading to considerable confusion about what the approach really meant.


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Calls for greater clarity in the terminology of concepts and methods of FSR, first emerged from major reviews of the enterprise, nearly two decades ago (Dillon et al 1978; Gilbert et al 1980). Despite these appeals however, by the mid-eighties, not only did confusion still exist, but the problem seemed to have got more acute, as the range of activities encompassed by the term FSR, had broadened considerably. This realisation triggered a flurry of responses, and a number of writers sought to reduce some of the confusion by attempting to clarify terms and concepts, and creating classifications of the approach. Fresco (1984) for example, distinguished between Francophone and Anglophone traditions, describing the former as a "more formal, long-term and large-scale research undertaking aimed at developing the potential of a geographical region" while the latter "does not aim at a profound change in traditional agriculture, but rather at incremental changes". Others have suggested more complex schemes. Simmonds (1985), for instance, suggested that one could distinguish between (a) FSR in the strictest sense, (b) on-farm research, and (c) new farming systems developments. Merrill Sands (1986), went further in suggesting that six types were more appropriate, identifying these as (a) farming systems analysis (FSA), (b) farming systems adaptive research (FSAR), (c) farming systems component research (FSCR), (d) farming systems base-line data analysis (FSBDA), (e) new farming systems development (NFSD) and (f) farming systems research and agricultural development (FSRAD).
The variations among these different FSR types of research activity are associated with matters like the intentions of the researcher, the extent to which farmers themselves are involved, the level of innovativeness, and the extent to which researchers from disciplines beyond agriculture, are involved. In spite of their variations, all of the approaches have in common, the fact (a) that they are complementary to conventional commodity and disciplinary research, and (b) that they are, to a greater or lesser degree, what Merrill Sands (1986) refers to as "systems oriented": There are two aspects of this orientation, because of the fact that both the object of FSR "is regarded as a system", and "because of the interdisciplinary way in which it strives after problem solutions" (Brouwer and Jansen 1989).
In other words FSR is 'systems oriented' because both the object to be researched and the methods by which it is researched, can be regarded as 'systems' the farming system as an 'objective thing' on the one hand, and the inquiry as a 'systems process'on the other. In practice I want to suggest that rather than characterising most FSR endeavours, these two aspects, of research object and research process, actually represent two different traditions: The first could be seen as research into farming systems (which I shall refer to as first generation FSR) and the second, systemsresearch into farming (or second generation FSR). This distinction is very important, or it emphasises issues concerned with the 'nature of nature' and 'the nature of knowledge' which sit


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at the very centre of what we can call the 'systems movement'. They also provide a focus for discussions about how the application of systems thinking and practices can be improved to create a third generation FSR, which combines both first and second, while adding new attributes of its own. This is therefore a useful point at which to jump into the systems dimension of FSR, with an elaboration of what currently seems to characterise it.

THE CURRENT SYSTEMS DIMENSION

A typical definition of a farming system from the 'systems orientation' perspective above, is that offered by Shaner et al (1982): "a unique and reasonably stable arrangement of farming enterprises that the household manages according to well-defined practices in response to physical, biological and socio-economic environments and in accordance with the household's goals, preferences and resources. These factors combine and influence output and production methods."
This definition provides us with an important insight into the matter of the 'systems dimension' of FSR, for it represents a prime example of what Checkland (1988) refers to as the confusion in the use of the word 'system' between "everyday language" and the "language of professional discourse". There is little doubt that the statement above is much more reflective of "everyday language" than of "professional discourse", with very little evidence of any careful formulation of the system as an abstract coherent entity with special properties. FSR practitioners typically talk of farming systems just as they might talk casually about a nation's research system, or its health system, or their institute's pension system. Thus, as emphasised elsewhere (Bawden 1991), the systems model in FSR rarely extends much beyond fairly loose descriptions of sets of relationships between farming enterprises, the household, and the environmental influences under which they both operate. In other words, they really do not express the formality of systems conventions in terms of 'systems organisation', 'structure', 'properties', 'boundary conditions', 'cybernetics' or 'behaviour', in any meaningful manner. Thus even the statement that "the farming system is part of larger systems e.g. the local community and can be divided into subsystems e.g. cropping systems" (Shaner et al 1982), gives little evidence of the conceptual significance of such a hierarchy, let alone the characteristics of the systems at each of the different 'levels'. Indeed the use of words like "larger" and "divided into", while quite appropriate for "everyday language", are actually quite misleading in terms of conveying the sense of the concept of the hierarchical relationships between "subsystems", "systems" and "suprasystems", as they would be expressed in "professional discourse". Similar comments can be made about the language used by Fresco (1984) in the following statement about the concept of hierarchy


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in a cropping system: "at the lowest level, one finds the cell and the plant organs, followed by the plant itself. Plants combine into crops, crops into fields that may carry crop populations of various species and variety, weeds and pathogens. The farm is situated at the next higher level. Groups of farms combine into villages or land-use units. These in turn combine in regions, which may cover a part of a country, an entire country or even a group of countries"
In many ways this lack of conceptual rigour with regard to the perceived nature of farming systems, is quite predictable and defensible, given the claim that the foundations of FSR were practical rather than theoretical or philosophical (Norman and Collinson 1985). And indeed there are exceptions: Dillon (1984) for instance, presented a relatively formal model of a farm as a "purposeful system" which included the following subsystems: (a) technical, (b) formal structural, (c) psychological or informal structural, (d) goals and values, and (e) managerial. There is little evidence however that this model of a socio-technical system, which clearly reveals a farm management economics perspective of a farming system, has been embraced by FSR practitioners, and "every day language" continues to persist with regard to the systems dimension of this, the first generation FSR movement research into farming systems. This is not to state that other attempts have not been made to formalise the systems dimension or the 'systemicity' of the approach as we might refer to it.
Some workers have developed interesting perspectives on farming systems by integrating ecological principles with farm management ones in their attempts to express the systems nature of their endeavours. Approaches such as these, which reflect our second generation FSR through their emphasis on systems research into farming, have allowed the incorporation of quantitative mathematical tools such as computer simulations and optimisation techniques (Hart 1982; Norman and Collinson 1985; Penning de Vries et al 1993). And as has been illustrated by recent conferences on the application of systems approaches to agricultural development (Jones and Street 1990; Penning de Vries et al 1993) and by the emphasis in so many editions of the international journal Agricultural Systems, there is certainly no shortage of base data for use in both biological and socio-economic systems simulations. The sophistication of so-called expert systems for agricultural application, are providing considerable evidence in support of the contention that the second generation of FSR is becoming increasingly robust, and a useful set of five categories has been proposed to differentiate between the increasing variety of approaches in this domain (Jones 1989). These range from "heuristic expert systems" which come closest to approximating the sort of "seat of the pants" decision-making strategies used by recognised experts, to "problem specific skills" which utilise expert data bases.


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For all these aspects of the systems dimension of FSR, both 'first' and 'second' generation however, we are still left with the sense that the matter of the application of formal systems concepts, theories and philosophies, remains unaddressed within the approach. There are those who argue that farming systems approaches to research are more systematic than systemic (Holt and Schoorl 1985; Bawden 1990) more concerned with the rigour and linear logic of the process, than with the systemic interconnections of either the object of the research or the process used. It has even been suggested that "a critical approach to study and to developing farming systems has hardly been developed" with FSR "anything but a lucid concept", (Brouwer and Jansen (1989).
With these provocative statements as our motivation, and the sections above as the context, I must'now turn to a critical elaboration of what I mean when I talk of "being systemic" and the conceptual and methodological challenges that that presents to FSR practitioners. In arguing the case for the systemic transformation of FSR, I draw attention to the claim at the start of this paper about the threefold complex challenges that we all face as researchers concerned with sustainable agricultural and rural development. I want to suggest that future improvements at any level of agricultural 'systems' must be evaluated as much for its ethical defensibility as for its social desirability, as much for its ecological responsibility as for its economic viability, and as much for its aesthetic acceptability as for its technical feasibility. And I want to further submit, that all of these aspects of 'development' must be regarded as fundamentally achievable through the research and development methodologies that we use. In this I am calling for a 'third generation FSR'; one which we might call Critical Farming Systems Research, or perhaps more correctly, Critical Systems Farming Research!

BEING SYSTEMIC

Whenever we approach issues in the name of something or another, we are bringing to bear certain ways of thinking and acting, as well as certain values and assumptions which characterise that way of being. When we are being scientific, for instance, we approach the world through deductive or inductive ways of thinking, and we test our thoughts by creating and testing hypotheses about the world about us, in a very systematic, value-neutral and methodical manner. In being scientific, we are also holding on to certain assumptions about the nature of the world, including the humans within it, and about how knowledge about both can be acquired. What we do in this world when we are being scientific then, strongly reflects particular ways of thinking and particular views of the world which we have learned, somehow and somewhere along the line of our education and training as scientists. We rarely, if ever, think about the composition of these worldviews, nor about the way they 'frame' the way


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we think about, or act in, the world. If we refer to these characteristics of thinking and acting and assuming, as paradigms, we can submit that for most of us most of the time, our paradigms remain tacit. I use the plural here, because we do not necessarily think or act in the same way, or hold to the same assumptions of beliefs, when we are being something other than being scientific, like being superstitious, for instance, or being mystical, or being parental! Thus each one of us can use different paradigms for different occasions. There are even differences between particular paradigms within science, as was first argued strongly by Thomas Kuhn (1970).
Following Burrell and Morgan (1979), and using now the language of professional discourse, we can refer to four particular sets of assumptions in order to discriminate between different paradigms: assumptions about the nature of reality (ontology) assumptions about the nature of knowing about reality
(epistemology)
" assumptions about ways of inqyiry into the nature of reality
(methodology)
" assumptions about the way human beings are (human nature),
To rewrite our descriptions on being scientific in these terms, and choosing the paradigm of science that a research agronomist would probably hold, we can argue that when such a researcher approaches a technical problem he or she assumes (believes) that:
" there is a reality 'out there' which exists independently of anyone
observing it (a realist ontology);
" objective, knowledge about that reality can be acquired as
scientific, value-neutral truth (an objectivist epistemology); explanations (hypotheses) about the nature of reality and
generated in response to objective observations in it, can be
validated through experimentation (an empirical methodology);
and
" humans are rational, objective, interest-seeking, goal-setting
beings (a rationalist interpretation of human nature).
It does not take much imagination to realise that a farmer brought up in a culture in which magic imbues all of nature with a soul or 'animus' (animism), with myth and legend the source of knowledge about it, holds very different assumptions about the world and how we can come to know about it, to our agronomist. But the differences do not have to be so extreme as this for there to be very significant differences in paradigms, with a systemic paradigm for instance, being very different from a non-systemic one.


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It is now appropriate to turn our attention to a discussion of the way we are when we are being systemic, and explore what this might mean through reference to the four sets of assumptions above. As might be expected by now, the situation is by no means straightforward, even if again we confine our descriptions to the language of professional discourse; for the trouble is that there are two essentially different schools of systemics which are often differentiated by being termed 'hard' and soft' respectively. Let's start with the 'hard' case and explore the assumptions held by a 'hard' systems researcher, for in most respects, he or she holds the same beliefs as the agronomist described above, with one very notable exception.
It hasn't been mentioned yet, but there can be very important differences within the same category of assumptions above. It is one such 'within-category' difference that allows us to highlight the essential difference between a conventional agronomist and a systems researcher. Thus, within a realist ontology, there are two very different belief positions which we can refer to as holism on the one hand, and reductionism on the other, with our systems researcher holding to the former set of beliefs and our agronomist, the latter.
For our immediate purposes, holism refers to the belief that the world about us is structured in the .form of whole entities (which we will call systems) with each system having properties different from the sum of its own parts, as well as from other systems around it. All systems are part of other systems, just as they themselves are composed of (sub)systems. Reductionism, on the other hand presents the absolute opposite belief position: the world is not structured into whole entities with special properties, but consists instead of a mass of elements and events which may or not be causally related to each other. Any whole entity that does seem to exist, will be equal to nothing more than the sum of its parts, and if it should appear otherwise, that is merely a reflection of the ignorance of the observer rather than any innate property of the entity itself.
Simple though this distinction between holism and reductionism might sound, it represents very powerful differences in terms of paradigms, and this means that the arguments in this paper for more attention to be paid to the systems dimension of FSR, is actually a call for a profound shift in paradigms. Let me add emphasis to this statement, by further exploring the nature of systems and relating this to the notion of holism and to the concept of being systemic.

THE NATURE OF SYSTEMS

The two notions that are central to holistic beliefs, and which therefore shape the 'systems movement' are: that systems are coherent whole entities, and that as coherent entities, they possess properties distinct from either their component sub-systems or the suprasystems of which they themselves are part. These unique characteristics are referred to as emergent properties as they


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emerge at each level of any systems hierarchy as a result of what we might refer to as 'recursive interrelationships' mutual relationships between subsystems and systems and suprasystems in which each level of system influences the others. From a holist perspective, surprise is anticipated, but never predictable. No matter how comprehensive the studies of the relationships between component subsystems, or between the system and its suprasystem are, they can never reveal emergent properties.
As a whole entity, a system must have some way of staying whole. It must therefore have a boundary which not only keeps it as a coherent whole, but separates it from other systems and from its environment (which is itself of course, a (supra)system). One way by which the bounded system retains its coherence, is through the inter-relationships between its component subsystems, which because they are necessarily different from each other, are often in tension with each other. It is the tension of these differences which gives rise to the oneness and to the uniqueness of the entity. Thus as one of the pioneers of systems thinking put it, "there is a glorious unity of difference" (Bertalanffy 1981).
The tension of difference that occurs within systems, is replicated between them too; and especially between systems at different levels within systems hierarchies. Systems need to have sufficient (requisite) variety and redundancy to be able to deal with these inter-system tensions, just as they must have sensitive mechanisms for communication and feedback (cybernetics) within themselves, and between themselves and their environments. Another way of putting this, is that systems must be flexible enough to deal with the changes in their environment to which they are coupled through cybernetic processes. As is being increasingly recognised with respect to the 'natural' environment (Hollings 1995), these changes are often discontinuous and non-linear, and therefore non-predictable. As chaos theory and systemic studies of complexity are revealing, a small change in one system can often result in a very significant (and most surprising) change in other systems far removed from it, in either space or time, and occurring through processes of amplification that probably can never be understood. This has enormous implications with respect to the 'globalisation' of 'local' effects such as pollution, or biocide resistance, or micro-climatic change, or market dynamics. The opposite situation can also occur, with similar comments to be made about the processes of attenuation, in this case.
As this latter discussion reveals, systems, in addition to their wholeness, and to their emergent properties, also have dynamics, and assumptions about these are also of prime importance to researchers of systems. Matters of systems dynamics are central to notions about their capacities for stability and sustainability, and these in turn, are obviously of central importance to farming systems and the nature of their relationships with other systems with which they inter-relate.


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There are many other characteristics of systems that are fundamental to their nature, but the three we have just elaborated the coherence of wholeness, the emergence of properties, and their dynamics are sufficient to indicate the significance of renewing the focus on the systems dimension of farming systems research.
So far we have been concentrating on concepts from the 'hard' systems school, with its realist assumptions about the nature of reality. In other words we have been talking about systems as they are presumed to exist in nature. As has been pointed out elsewhere (Bawden et al 1985), inasmuch as FSR practitioners are systemic in their research approaches to the development of farming systems, they largely follow the traditions of the 'hard' systems school. In other words, they approach cropping enterprises, whole farms, groups of farms, or even entire rural communities, with a sense of their respective wholeness' as if they believed that each level of organisation actually exists as a 'real' system within a hierarchy of systems, ranging from the cropping enterprise right up to the community. Typically however, their interest in what we might call formal systemics does not proceed much beyond this point. There are very few studies indeed of the emergent properties of farming systems for instance, or of their cybernetics, or of their 'tensions of difference', or their requisite varieties or redundancies, or of systemic insights into the inter-relationships of farming systems with their environments as suprasystems. Principles of systems dynamics are rarely if ever invoked in debates about the crucial issues of stability or sustainability of agricultural systems in first generation FSR circumstances, although this is one of the potential transformations that the second generation can bring and where of course, the tools of systems simulation hold such promise.
Much could also be learnt from those involved in agroecological studies, where formal systems concepts have been adopted, and used in the measurement of flows of energy and material cycling through agricultural ecosystems (Hart 1982). Of particular significance is the work of
agroecosystem analysis and its connection with development. Here agroecosystems have been conceptualised as "well-defined systems of cybernetic nature" (Conway 1987) and their properties explored specifically from perspectives of their productivity, sustainablity and sustainability as well as equitability and autonomy (Marten 1988). Conway (1990) has used these concepts to make the point that agricultural development represents trade-offs between systems properties, and it is to such matters that second generation FSR practitioners are increasingly turning. Yet trying to model matters of equitability and autonomy of ethics and social justice and symmetry of power relationships as if they were objective and quantifiabe aspects of human nature, is clearly not possible.
So it is important to now turn our attention to forms of being systemic which move us beyond objectivism and allows us to explore situations using


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systemic concepts, but from a notion of 'human activity systems', and the 'soft' school.


SOFT SYSTEMS PRAXIS

So far our systems focus has been on what we identified earlier as a perspective which differed from conventional (reductionist) agricultural research only in its holist assumptions, significant enough though these are. We now need to take two more steps into the systemic world, which will move us very significantly from this position into radically new territory yet territory demanded by our need to focus on the sustainability of farming systems in ways which include matters of ethics, aesthetics, and social justice as much as productivity, economic viability and social desirability.
The first of these steps is to move from objectivist assumptions about how things are known (epistemologies) to what we will refer to as relativist. Drawing on Berstein (1983), we make this distinction in the following manner: Objectivism means that there is some unchanging standard which we can use as a framework to determine the nature of truth, and reality, and goodness or rightness. Thus in our 'hard' farming systems above, nature is taken as an objective reality about which scientific 'truth' can be discovered, and it is 'right' and 'good' to seek more productivity through the application of such truths, in the name of progress. In contrast, relativism is the basic belief that all of these concepts can only be as relative to some context or another such as societal or cultural norms, or a particular set of theories.
The notion of 'systems' now takes on quite a different meaning from that which we have been using so far: When we assume a relativist position on holism, we are shifting from beliefs about how the world is in reality, to plausible descriptions of it from different perspectives from system as 'objectively knowable thing', to system as 'an abstract concept'. In this manner we can now talk about a system of inquiry as if it were a 'real' system with all the characteristics of systems that we have described, while in fact, we know that it is nothing but an abstract idea. Thus this is not the same as slipping back into 'everyday language' for this time we hold on to the rigours of the characteristics of systems as coherent wholes, which display emergence through recursions, and which are dynamic in the face of environmental change.
Conversations between people can now be considered to be inquiry systems, if they focus on how those people go about improving their own situations by explicitly thinking and acting in systemic ways. In other words, sets of ideas and communications can shaped in such ways that they become systems of inquiry through the systemic actions of these involved in them! The concept of wholeness through 'tension of difference' now takes on a very human face, as


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the FSR practitioner faces up to the 'new reality' that different people can have very different views indeed about the same situation. When stakeholders are invited to participate in the development process and contribute to discussions about what constitutes improvements, differences of opinion and conflict are the norm rather than the exception.
To exemplify what all this means in FSR practice, let us turn to the vital matter of citizen participation in the process of sustainable agricultural and rural development, and about who decides what it is that constitutes improvements to agricultural and rural situations, and what perspectives guide them in their choice. Cornwall et al (1994), while remarking on the enormous surge of interest in participation in the process of agricultural research and extension, have also claimed that "most FSR/E scientists continue to investigate for or sometimes on their farmer 'clients' rather than with them". In other words, the farm continues to be regarded as a researched system rather than a researching one; a system which is able to 'learn its way forward into better futures' as a result of those who are participants in it, learning themselves to think and act in systemic ways.
'Soft' systems thinking can liberate this situation in a systemic manner with FSR scientists construing participation as essential to the way they think and act systemically their systemic praxis, if you will for the process of participation is a fundamental systemic issue. As Skowlimowski (1985) has it, "wholeness means that that all parts belong together, and that means they partake in each other. Thus from the central idea that all is connected, that each is part of the whole, comes the idea that each participate in the whole. Thus participation is an implicit aspect of wholeness".
There are of course many different degrees of participation, ranging from mere tokenism to the true emancipation of citizens (Arnstein 1969), and this matter will be an important one when we finally turn to the issue of criticality. For now we can simply recognise that the higher the extent to which participation occurs in the development process, the greater the range of interpretations of what it is that constitutes improvements to specific situations. Even within farming itself, it has long been recognised that there are a multitude of purposes with which any farm can be endowed, with farmers holding to complex sets of reasons for doing what it is that they do, and involving instrumental, social, expressive, and intrinsic goals (Gasson 1973). This purposeful nature of farming, presents such a complex mosaic for discussions about what it is that constitutes improvements precisely because people have such different values and worldviews from each other, and even within themselves at different stages of their lives. Thus, as there will be different notions of what constitutes truth, and justice, and fairness, and goodness, and rightness, under such relativistic circumstances, it is vital that the methods of systemic research include ways by which these differences can


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be made explicit and accommodated in the search for agreement about improvements.
The major insight of Peter Checkland, who is regarded as the 'father' of the soft systems school, is that the exploration of any messy or complex situation will reveal a number of different perspectives on that situation which can be modelled as 'human activity systems', each of which reflects a specific worldview, which therefore need to be made explicit through the process of systemic inquiry. This need to qualify purposeful human activities by the worldviews that support them in the search for improvements to situations by participants in that situation, is very different from the ability to name a single pursuit like productivity growth by an observer who is presumed to be independent of that system. Herein then lies the essential distinction between the 'soft' and the 'hard'. As Checkland (1995) has recently put it "the crucial difference is between on the one hand an approach which assumes the world to be a complex of systems, some of which may be malfunctioning, and on the other an approach which makes no assumptions about the nature of the world, beyond assuming it to be complex, but assumes that the process of enquiry can be organised as a system of learning".
The methodology which Checkland developed from his systemic thinking about messy, complex and purposeful situations, is thus itself approached as if it were a learning system (Checkland 1981) with each of its stages being a subsystem of the system of inquiry itself. In its use, it will reveal a host of issues of significance to a range of stakeholders who are committed to coherent debates about accommodating conflicting interests within political and social contexts, and which enable action to be taken to improve problematic situations. As Jackson (1995) has recently described this process, "the purpose is to generate a systemic learning process in which the various participants in a learning situation come to appreciate more fully, each other's world views and the possibilities for change, and a consensus or at least accommodation (however temporary) becomes possible between those who started with and may still hold divergent views".
There are a number of key issues of importance to FSR practitioners, from this 'soft' systemic perspective. The most obvious of these is that it provides a systemic framework for the vital first step in FSR, of including the farmers themselves in the process of decisions about what constitutes improvements. It allows differences of opinion between researcher and farmer, and between different farmers who might be participating together in a development project, to be identified, and most crucially, linked to different worldviews, which are made explicit. It also provides a vehicle for including other stakeholders iii the development process, and exploring the posible social, cultural and economic impacts both positive and negative that changes at the level of individual farms might have on the region as a whole. In this manner, some of the basic


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concepts of systemic thinking, in terms of interrelationships, coherence, hierarchy, etc are revealed and highlighted.
It follows from this that one of the key actions to be taken to improve the problematic situation of FSR itself as a participative research approach, is the improvement of the capacity of its practitioners to think and act systemically. An important thesis in this regard, has been developed by Marcia Salner who submits that individuals are only able to develop an effective systemic capability once they have reached, what she refers to as a particular "epistemic competence" associated with the acceptance of an epistemology of "contextual relativism" (Salner 1986). In other words, what she is calling for, is equivalent to a basic shift in one's pesonal assumptions from 'objectivism' to 'relativism', as we have defined these above. This is no light matter, for what she is demanding is that we not only learn to question some of the most fundamental beliefs that we hold, but that we are prepared, under certain circumstances, to suspend and even change them to accommodate different circumstances. Knowing what epistemological (and ontological) assumptions one is making under any circumstance, is a critical first step in knowing the advantages of also embracing other worldviews, and this means, in the present context that FSR practitioners need to learn how to learn about being systemic (Bawden and Packham 1993). Yet there is.a fundamental paradox here: FSR practitioners need to reach a particular level of 'epistemic' development before they are able to really see the merits of thinking and acting in systemic ways. However they are only likely to reach this stage of development if they are self critical of their present approaches, and the assumptions upon which they are based They need to be able to think in systemic ways in order to appreciate the advantages of thinking in systemic ways!
And this brings us to the last of our areas of exploration; criticality.

THE ESSENCE OF CRITICALITY

Perhaps the easiest way of introducing the essence of being critical, is to focus on the issue of assumptions about human nature that were introduced earlier. In the discussion about being scientific, it was proposed that human beings were rational in an objective way. Experience tells us however, that that is often not the case, nor is it confined only to situations referring to an objective world for there are other worlds too; thus Jurgen Habermas (1985) refers to "the social world of legitimately regulated interpersonal relations", and each person's "own subjective world of experience", in addition to an objective world. The search for agreement about what needs to be done in the name of improvements must therefore take account of all three of these worlds. For the FSR practitioner, this means that the objective world of the 'hard farming system' is set within the 'soft' relativist world of social difference, which itself is set within interpretations posed by personal subjective experience.


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According to Habermas, action oriented to reach shared understanding what he terms communicative action occurs through a common endeavour to achieve consensus in situations where participants are free to state their views and have an equal chance to do so (Habermas 1984). Unequal distributions of power, lead to distortions in such communication (Habermas 1984) and thus the ultimate goal of communicative action is one of emancipation.
A number of workers have taken these key ideas and incorporated them into what they call 'critical systems' approaches, where the practitioners assume positions of criticality of everything they think and do: from the epistemological / ontological / methodological / ethical assumptions that they and others in the situation hold, as well as of the contexts of power and communication that prevail (eg Flood 1990; Jackson and Jackson 1991: Ulrich 1983). None of these endeavours have directly involved matters of agriculture or rural development, although recent proposals by Ulrich (1996) to extend critical systems thinking and actions to "citizens", is particularly pertinent to the situation that we face in agriculture, given the fact that our constituency for sustainable food production is essentially everyone in every community across the globe!
There is no doubt that critical systemic approaches would be entirely appropriate to agricultural and rural development situations, and represent systems dimensions that need to be urgently explored in the next phase of development of FSR as it moves into its third generation.

A SYSTEM OF FSR SYSTEMS

Following the logic established during the discussion about the 'soft' school, it will now be apparent that what has been presented here as critical systemics, soft systemics and hard systemics, itself represents a system of inquiry systems which would be entirely appropriate for FSR. Each of these three 'levels' of inquiry would reveal quite different emergent properties about situations in which improvements were sought in the name of the sustainable agricultural and rural development. And there will frequently be different interpretations and conflicts both within each domain as well as between them. What seems relatively straightforward as an improvement in the technical performance of a 'farming' system, is clearly much less so when considered from the point of view of who might benefit from changes made, and who might be penalised, and who decides which is which. And this in turn is further complicated by the fact that discussions with participants about the 'best' strategies for development, give a highly biased picture as a result of the distortions of communication that are occurring within the community as a function of the asymmetry of power relationships within it. Critical inquiry aims to clarify the third of these matters, soft systemics are appropriate for exploring human activities, and hard systems methodologies are needed to explore strategies for


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change at the technical level. Clearly, each level of inquiy is critically
dependent on the other two.
As has been argued, the multi-facets of responsible development dictate the need for FSR practitioners to consider the ethical, aesthetic, social, and ecological aspects of their activities, in addition to those economic and technical ones which are conventionally included. There is little hope of this being achieved in the absence of a systemic perspective which portrays the sense of wholeness in all of this and which allows for the participation of relevant stakeholders in conversations which are as free from communicative distortions as possible.
The case has been made elsewhere (Bawden and Packhan 1993; Bawden 1995) that this in turn, demands particular forms of education and training which allow epistemic capabilities relevant to systemic competence, to be developed.
To those who would be dismayed by the theoretical / philosophical focus of this article, I conclude with two observations: firstly that there is nothing so practical as a good theory, and secondly, that any approach which claims to embrace 'systems' in its title, needs to be able to defend that position.
In the face of the daunting challenges of the need for continuing growth of global food production without compromise to environment or community, we have little choice but to continually seek ways of improving our praxis as responsible, ethically defensible and hopefully systemic practitioners.

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Bertalamffy, L. von 1981, Systems View of Man. (Paul Violette ed.) Westview Press. Boulder. Bemstein, R.J. 1983 Beyond Objectivism and Relativism. Basil Blackwell Ltd. Oxford. Brouwer, R. and Jansen, K. 1989, -Critical Introductory Notes on Farming Systems Research in
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Burrell, W.G. and Morgan, G. 1979, Sociological Paradigms and Organisational Analysis.
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Checkland, P.B. 1981, Systems Thinking: Systems Practice. John Wiley. Chichester. Chedkland, P.B. 1988, The Case for 'Holon'. Systems Practice 1:235-238. Checkland, P.B. 1995, Model Validationin Soft Systems Practice. Systems Research 12: 47-55. Conway, G.R. 1985, Agroecosystem Analysis. AgriculturalAdministration 20: 31-55. Conway, G.R. 1990, Agroecosystems. In Systems Theory Applied to Agriculture and the Food Chain.
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Cornwall, A., Guijt, I., and Welboume, A- 1994, Acknowledging Process:Challenges for Agricultural
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Intermediate Technology Publications. London.
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of New England. Armidale.
Dillon, J.L., Plucknett, D.L., and Vallaeys, G.J. 1978, Farming Systems Research in the
International Agricultural Research Centres Rome Italy. Technical Advisory Committee to the
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Flood, R.L. 1990, Liberating Systems Theory. Human Relations. 42:216-22 Flood, R.L. and Jackson, M.C. 1991, Creative Problem Solving: Total Systems Intervention. Wiley.
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Gasson, R. 1973, Goals and Values of Farmers. Journal ofAgricutural Economics 14: 521-538. Habermas, J. 1984, The Theory of Communicative Action vol 1 Reason and the Rationalisation of
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Habermas, J. 1985, Questions and Counterquestions. in R.J. Bemstein (ed) Habermas and Modernity.
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Hast, R.D. 1982, The Ecological Conceptual Framework for Agricultural Research and Development.
in W. Shaner, P. Philipp, and W. Schmhl (eds) Readings in Farming Systems Research and
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Hildebrand, P. 1982, Motivating Farmers to Accept Change. in W. Shaer, P. Philipp, and W. Simdil
(eds) Readings in Farming Systems Research and Development. Westview Press. Boulder.
Hollings, C.S. 1994 An Ecologist View of the Malthusian Conflict. in Population,
EconomicDevelopment, and the Environment (K. Lindam-Kiessling and H.Landberg, eds)
Oxford University Press. New York. Ch. 4 pp 79-103.
Holt, J.E. and D. Schoorl 1985, Technological Change in Agriculture the Systems Movement and
Power. Agric. Systems 28:69.
Jackson, M.C. 1995 Beyond the Fads: Systems Thinking for Managers. Systems Research 12: 2542. Jones, P. 1989, Agricultural Application of Expert Systems Concepts. Agri Systems 31:3. Jones, J.G.W. and P.R. Street (eds) 1990, Systems Theory Applied to Agriculture and the Food
Chain. Elsevier Appl. Sci. Publ. New York.
Kuhn, T.S. 1970, The Structure of Scientific Revolutions. The University Press. Chicago. Marten, G. 1986, Productivity, stability, sustainability, equitability and Autonomy as Properties of
Agroecosystem Analysis. Agric. Systems: 291- 316.
Norman, D. and Collinson, M. 1985, Farming Systems Research in Theory and Practice. in
Agricultural Systems Research for Developing Countries J.V. Remenyi (ed)ACIAR Canberra. Penning de Vries, F., Teng, P. and Metselaar, K. 1993 (eds), Systems Approaches for Agricultural
Development. Kluwer Publishers. Dordrecht.
Salner, M. 1986, Adult Cognitive and Epistemological Development Systems Research 3: 225-232. Shaner, W.W., Philipp, P.F. and Schmehl, W.R. 1982, Farming Systems Research and Development:
A Guideline for Developing Countries. Westview Press. Boulder.
Skowlianowski, H. 1985, The Co-operative Mind as a Partner of the Creative Evolution. Proceedings oi
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Ulrich, W. 1993, Some Difficulties of Ecological Thinking, Considered from a Critical Systems
Perspective: A Plea for critical Holism. Systems Practice 6:583-611.
Ulrich, W. 1996 Critical Systems Thinking for Citizeas: A Research Proposal. Research Memorandum
# 10. The Centre for Systems Studies. The University of Hull.


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THE CONCEPTUAL BASIS FOR TARGETING
FARMING SYSTEMS: DOMAINS, ZONES AND TYPOLOGIES

Keith M. Moore1



ABSTRACT


Farming system concepts have increased the capacity to understand and provide assistance to farm households throughout the world. Unfortunately, identifying and
resolving problems specific to particular types of farming systems often suffers from a lack of conceptual precision and transdisciplinary consensus among practitioners. This paper addresses this conceptual confusion by clarifying farming system concepts and their application in the targeting of specific farming systems. It begins at the generic level of concept formation by locating farming systems within the context of an agricultural systems hierarchy, highlighting the significance of the choices and limits inherent in the systems approach. A second level of conceptual tools (domains, zones and typologies) are then discussed. These concepts are necessary for the targeting of specific groups of farm households with common problems and for which common solutions can be identified. At this level, systems logic must also be applied as more precise specification of particular research domains leads to the identification of homogeneous types of farming systems. Such specification requires more in-depth preliminary investigation than normally conducted and greater emphasis on the socio-economic conditions
differentiating farm households.


1
Rural Sociologist, INRA/MIAC Aridoculture Center, Setat, Morocco. This paper was written is
part of the MIAC/INRA Morocco Dryland Agriculture Applied Research Project under USAID Project No. 608-0136.


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INTRODUCTION

Targeting agricultural research and development for a farming clientele requires a clear set of conceptual tools. Farming systems practitioners have provided such tools. However, considerable confusion exists in the literature and in practice about the application of farming system targeting concepts. This confusion stems from two sources: (1) the haste to demonstrate impact; and (2) the lack of a transdisciplinary theory of farming systems.
The applied nature of farming systems research and development
encourages a problem-oriented focus for practitioners. Farmer problems have priority over reflection on conceptual issues. In practice, this orientation relies heavily, if not consciously, on the disciplinary training of the practitioners. Conceptual consensus is difficult to attain. It is easier to move on to the next practical "problem". This reliance often violates the assumptions of the farming systems approach, leading to mis-specification of both problems and solutions.
Diagnosis of farmer problems functions on two distinct conceptual levels and necessitates more preliminary investigative work than is usually envisioned. The concept of farming system provides a means to understand the dynamic totality of agricultural development. However, this way of thinking cannot be applied directly to the solving of farmer problems. Empirically-existing farming systems are too heterogeneous. A second level of concepts is needed to focus efforts in the specification of different types of farming systems for which specific problems can be identified and solutions developed.
This paper clarifies the targeting concepts of farming system research and development. Concepts are defined and their epistomological and practical interrelationships specified. It then provides a checklist of criteria for differentiating types of farming systems.


THE CONCEPT OF SYSTEM


Central to the farming systems research and development approach is the concept of system (Shaner et al., 1982; Marcotte and Swanson, 1987; FAO, 1989). However, its familiarity as a concept within different disciplinary traditions and applied contexts has lead to confusion within the literature and between colleagues (Gibbs, 1985; Brossier, 1987). In order to clarify the role that system plays, it is useful to recall the generic properties of systems: (1) systems are holistic integrations of interrelated elements; and (2) although set


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within given boundaries, they are open to interaction with their environments (Shaner, 1982; Conway, 1986; FAO, 1989).


The popularity of the systems concept among agricultural scientists stems, pragmatically, from the desire to effectively grasp the dynamic totality of agricultural development. The interrelationships' between the factors which combine to generate agricultural products and sustain the lives of those producing them are complex. They cannot be simply reduced to a few causal relations between variables or a function of one of its components (Baker and Norman, 1990). The whole is greater than the sum of its parts.
Agricultural production depends on the interaction of all physical, biological and socio-economic factors (Shaner, 1982; Brossier, 1987). A change in one element leads to changes in others, and the combined effect reverberates throughout the whole. The concept of system provides a general framework in which to account for this dynamism and complexity.
Agricultural systems, however, have been conceived at many levels. These systems can be understood as hierarchically-nested in one another: (1) from the physical and biological systems at the field level; (2) nested in crop and livestock systems to a farm household system; (3) nested in community and infrastructural systems composing the regional and national agricultural systems. In fact, a multiplicity of agricultural system hierarchies have been specified (e.g., Hart, 1982; Gibbs, 1985; and Conway, 1986). This multiplicity has led to confusion between different approaches (Gibbs, 1985). The system level (and, consequently, the hierarchy in which it is nested) that becomes the object of study and intervention is often dependent on the disciplinary background of the practitioner (Gibbs, 1985) and the technological biases inherent in the agricultural development approach (Oasa and Swanson, 1986). The open relationship between system levels and their environments is the conceptual root of this problem.
In their review of farming systems literature, Brush and Turner (1987) suggest two caveats to systems analysis that provide guidance in the clarification of the systems approach.

First, systems must be artificially limited if analysis is
to proceed, and second, described systems are, therefore, heuristic and artificial analytical devices rather than natural phenomena. Although the systems approach tends to push the investigator toward descriptive holism, effective limits on the scope of system modeling are imposed by problems of scale. The larger the descriptive model the weaker it becomes for describing specific behavior.
Finer-grained models, on the other hand, may lack the


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explanatory power of more general ones. It needs to be emphasized that the systems approach is descriptive rather than explanatory. It helps identify what processes exist and how sets of interrelated components function together.
(Brush and Turner, 1987:27; emphasis in original).


The point is that systems are useful, albeit artificial, constructs. As such, choices must be made to limit the scope of the work for heuristic purposes. The consequent product is descriptive of targeted farming systems, rather than construction of causal models or policy prescriptions (Whyte, 1983). It is expected that policy decisions and causal modeling will be shaped by farming system descriptions. Following sections describe the choices and conceptual limits that are imposed in such a task.


THE FARMING SYSTEM AND ITS ENVIRONMENT

Since the core of confusion in farming systems research involves the level at which the farming system is defined, it is useful to introduce a hierarchy of agricultural systems. Hierarchical presentations are founded on the idea that systems are nested in one another, each higher level often assumed to encompass a larger geographical space or range of phenomena. This
simplifying assumption allows for greater facility in presenting relationships between agricultural systems, but involves an explicit choice in how real world conditions are to be conceived and described.
Two issues are involved. First, a progressive centralization of system functions occurs in ascending a systems hierarchy. This results in
reductionist analyses (Marcotte and Swanson, 1987) or, simply, less capacity to describe specific behavior (Brush and Turner, 1987). It also has important implications for analysis since system functioning at one level (the farm household) may be seriously affected by system components at another level (national agricultural policy), yet the interrelationships have been obscured by the nested hierarchy.
Second, there is no single hierarchy. Various agricultural system hierarchies have been designed to focus on specific problem areas. The traditional farming systems approach is conceived with reference to productive units or specific enterprises within those units. Commodity system approaches (e.g., Friedland et al., 1981) are conceived with reference to the production, circulation and distribution of specific commodities. Agro-ecological zones (e.g., Conway, 1986) and land-use patterns (e.g., ICARDA, 1989) have also provided the basis for hierarchical structures. These latter are conceived with reference to the bio-physical resource base in


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which agricultural production is conducted. Each of these highlights certain interdependencies while others are left unaccounted.
The agricultural systems hierarchy presented in Figure 1 does not resolve these issues. Rather, it provides an overarching frame of reference for the farming systems approach presented here, as well as a means to, integrate analyses across system levels. The farm household is the chosen focal point because the objective is to characterize the systemic conditions which influence the adoption, adaptation or rejection of new agricultural technologies. This focus assumes household units of production and consumption, where all decisions on allocating human and material resources must ultimately be reconciled. Norman and Gilbert (1982) provide a concise working definition of a farming system.


The specific fanning system adopted by a given farming household results from its members, with their managerial know-how, allocating the three factors of production (land,
labor, and capital) to three processes (crops, livestock, and
off-farm enterprises) in a manner which, with the knowledge
they possess, will maximize the attainment of their goal(s).
(Norman and Gilbert, 1982:17)

This conception of a farming system is open to both the socio-economic environment, usually considered in terms of higher system levels, and the bio-physical environment. The bio-physical environment can be broadly-defined in terms of agro-ecological zones at higher system levels and more narrowly-defined in terms of biological and physical interactions at lower levels. Lower system levels involving the management of crops, livestock, and non-farm activities are developed as nested in the farming system and referred to for purposes of clarity as sub-systems (see Figure 2). Household resources of land, labor and capital, although allocated across sub-systems as the wheels of production are set in motion, are kept analytically distinct for conceptual clarity.
Conceptual models (Shaner et.al., 1982) or flow diagrams (Garrett, 1984) are often presented with more detailed specification of sub-system components and their linkages. This is an important way of suggesting interdisciplinary data needs, and ultimately leads to the quantification of material flows between components. Although many flow diagrams have been referred to in the development of criteria for typology construction (below), such specification must remain open until a particular research domain and type of farming system are identified and described.


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Figure 1: Hierarchy of Agricultural Systems


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Figure 2: The Farming System


TARGETING DOMAINS
Applying system concepts in agricultural development requires an intermediary set of conceptual tools to provide a bridge from the theoretical understanding of farming systems to the applied targeting of technology research and development.
As with the system concepts, considerable confusion exists concerning the specification of targeting-concepts (Wotowiec et al., 1988). Common practice within the fanning systems research and extension tradition has been to quickly survey a region and establish recommendation domains on the basis of easily identifiable characteristics (Sinmmonds, 1985). Recommendation domains are also specified at other stages of the technology' research and development process. Consequently, Wotowiec et al. (1988:77) note, the term "recommendation domain' has been stretched to cover too many situations and too many different purposes." Such hastily conceived recommendation domains are founded on the assumption of homogeneity among both farmers and farming conditions. They further note that homogeneous groups should not be identified until "researchers have an adequate understanding


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of the variability inherent in local farming systems, usually not accomplished early in the work in an area" (Wotowiec et al., 1988:74).
The following specification of targeting domain concepts is based on Wotowiec et al. (1988). This refinement of the domain concepts allows for clear differentiation between targeting applications, but does not lose sight of the diversity of farm households and farming systems in homogenously targeted groups. Wotowiec et al. stress three points: (1) domain definition should recognize a problem focus; (2) the concept of domain should be linked to the farming system research and development sequence; and (3) socio-economic considerations should be included. Three types of domains are offered in order of their application in the farming system research and development sequence: research domains; recommendation domains; and diffusion domains.


Research Domains
Research domains refer to environmental (agro-ecological and socio-economic) ranges that are defined in terms of a particular problem to be addressed. They set the initial context for research and development activities, often specified in terms of bio-physical factors. Broadly defined, a research domain is an area in which agricultural development efforts are focused to improve production and incomes of farm households. For pragmatic reasons, initial specification of the research domain is often geo-politically limited to a province or other administrative unit.

Within this geo-political area, further refinement of the research domain must be specified in terms of geo-social location, soil types and cropping potentials that distinguish different agro-ecological zones. It should be noted, that while geographic boundaries facilitate conceptualization, they are not absolute. There is always a degree of interspersion among research sub-domains. The problem focus for each sub-domain should be further narrowed because particular bio-physical and socio-economic constraints in each agro-ecological zone limit the range of possible solutions.
Research domains correspond to the diagnosis phase of farming systems research and development, that is, the planning of agricultural research and extension activities. The object of the diagnostic phase is to provide an adequate understanding of the variability in regional farming systems in order to facilitate the targeting of homogeneous farming systems. As more is learned about farming systems in the region, research domains can be more narrowly defined for purposes of targeted sondeos and on-farm diagnostic trials leading to the identification of recommendation domains.


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Recommendation Domains
A recommendation domain is a group of farm households or farming systems with a common specific problem for whom a tested solution may be adapted to farm household needs and potentials. It assumes a homogeneity of the group, both socio-economically and agro-ecologically, with reference to the problem and to the solution. A recommendation domain cannot be established prior to problem diagnosis and solution development. Recommendation domains are both tentative and variable in nature, depending on problem definition and the evolution in the adaptive research process that defines the solution. They can be modified over time as new information is gathered and improved understanding of the problems achieved. This usually evolves as researchers and farmers develop shared perceptions of problems and their potential solutions. Since a recommendation domain is limited to a specific problem and its solution, a farm household may be involved in several recommendation domains, depending on the number of problems and solutions addressed.
There is no recommendation domain if no solution is proposed for a specific problem. The specification of recommendation domains cannot occur until: 1) a specific farming system has been identified; and, 2) a solution that fits within the particular constraints of that farming system has been found. Often early diagnostic work (surveys, sondeos, and on-farm trials) in the research domain will only narrow the range of the research domain in which more focused diagnostic investigation with farmers can be pursued.

Diffusion Domains
A diffusion domain is a network of interpersonal communication that circulates information and knowledge about solutions to problems faced by farm households and their farming systems. A diffusion domain is not equivalent to a recommendation domain. Sharing similar constraints does not imply sharing of interpersonal relations. Although a diffusion domain is logically most active at the end of the farming systems research and development sequence, early identification of such networks can facilitate targeting of these information flows. Who -- men, women or children -- is involved in which communication network is important.


FARMING SYSTEM TYPOLOGIES

The use of typologies is gaining credence among farming system practioners. Increasing recognition of important differences between farm operations within the same area (Garrett, 1984, Wotowiec et al., 1988; Bagchee, 1990) has led practitioners to develop methods reducing the heterogeneity of farming systems to relatively homogeneous types (Capillon, 1986, Low, 1986a;


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Marz, 1990). Farming system typologies have been most frequently developed on a disciplinary root incorporating an interdisciplinary perspective. Early typological frameworks building on Ruthenberg (1980) were essentially agronomic, closely linked to the technical aspects of agricultural technologies. These typologies tend to remain at the cropping system level with socio-economic or animal husbandry characteristics as exogenous, when considered. More recent typologies, such as those of Capillon (1986), Low (1986a), and Marz (1990) have had a more economic focus. However, most typologies developed within the farming systems tradition exhibit little conceptual development beyond the original disciplinary considerations.
Typically, when practitioners incorporate a socio-economic element they simply take a single indicator (usually a size referent) and designate two or three groups (Lopez-Pereira et al., 1990; Kabay and Zepeda, 1991; Langworthy, 1991; Franzel, 1992). Although large and small provide a basis for obvious differences between farms, no systemic logic determines the break point between groups. Issues of risk (Cancian, 1980), technical efficiency (Azzam et al., 1993), the quality of resources (Park, 1993), and the interdependence of activities conducted (Norman and Gilbert, 1982) must be taken into account.

Assessing the farming systems research and development approach, Bagchee (1990:129) noted that "there is very little guidance in the literature on what exactly one can take as the factors or dimensions that differentiate one farming system from another". Although typologies can be derived theoretically or empirically, their use in farming systems research and development has evolved because of initially poor pre-conceptions of existing farming systems. Consequently, farming system typology construction has been inductive, involving a progressive refinement of concepts for the targeting of research and development activities. A fully transdisciplinary theory of farming systems has yet to be developed.

The Concept of Typology
A typology is a multi-dimensional classification based on relations of contiguity or similarity (Bailey, 1973). Like a system, it is an artificially constructed conceptual tool facilitating comprehension and analysis. As such, it has similar limitations. However, these tools have different, although interrelated, purposes. Systems provide a way of thinking about the empirical world. Typologies aid in applying those modes of thinking to the diversity of observable conditions.
The objective of farming system typologies is to provide a categorization and description of farming systems to facilitate recommendation domain development. To achieve this task, a typology must identify homogeneous farming system conditions. The characteristics described for each type of


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farming system enable precise specification of more narrowly-defined research domains and help focus the dialog between development agents and farmers concerning problem diagnosis and solution development.
Several questions are posed in moving from the systems theoretic framework to the identification of empirical farming systems: (1) How many types are there? (2) What are the differentiating criteria? (3) How many criteria are necessary? (4) How will farm households be assigned to different groups? These questions cannot be answered at this stage. The answers are ultimately a product of the dialog with existing conditions. However, previous farming systems research and literature on typology construction involving farm households can be combined to provide sufficient guidance to elaborate a conceptual checklist to initiate that dialog.
Although the following set of criteria that form the basis for conceptual development of a farming systems typology constitute tentative working hypotheses, they are not formulated for explicit testing. Rather, they suggest data collection and analyses needs to ensure the development of a useful typology. Farming system typologies provide a context for hypothesis testing (i.e., clearly specified research domains); working hypotheses simply guide the process of typology development (research domain specification).
Typology development requires specifying: (1) the scope of the phenomena to be classified; (2) the criteria used in classification; and (3) a method to apply the criteria identifying different types. The scope of the phenomena has been defined above. Farming systems by their nature are composed of complex interdependencies centering on the farm household. The range of potential diversity in farming systems requires that classifying criteria focus on multiple dimensions of the components involved in those interdependencies. Although a single criterion may serve to distinguish different farming systems in a specific place at a specific time, it is not sufficient to discern the complex interdependencies that define those different farming systems. Some means of multiple classification should be utilized (e.g. Moore et al., 1993). At the outset, however, a listing of potentially relevant criteria should be specified.
Before developing the list of conceptual criteria, it should be noted that the most common criteria used to distinguish between farming systems has been that of environmental or agro-ecological zones (Winkelmann and Moscardi, 1982; Hart, 1982; Wotowiec et al., 1988; Jamtgaard, 1989; Gillard-Byers and Blackie, 1990; Bagchee, 1990). At the heart of most bio-physical typologies is a conflation of zone and system concepts. These typologies assume that there is an equivalence between zone and system; and that bio-physical factors of the environment reflect homogeneity in socio-economic factors at the farm household level. Agro-ecological zones have a place in the hierarchy of agricultural systems (see above), however, they are not a substitute for the farm household level. In fact, Low (1986a) found that there was


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greater variation between types within homogeneous agro-ecological regions than between regions. In the approach suggested here, agro-ecological zones provide an environmental context in which farming systems at the farm household level are defined.

Typology Construction Criteria
The following consideration of criteria for differentiating farming systems at the farm household level introduces social scientific literature outside of the bio-physically-driven farming systems tradition. Although a vast literature exists on the processes of agricultural development and differentiation of peasant households (Garrett, 1984), this review is limited to recent literature focusing on farming systems and farm household typologies. Realizing the non-observability and historical nature of causal mechanisms differentiating farming systems (Whatmore et al., 1987a; White, 1989), the focus is on farm household and related sub-system indicators frequently linked to those processes of development and differentiation.

Labor is the most frequently mentioned type of criteria. Social scientists have focused on this key component of peasant farming since Lenin (1960) and Chayanov (1966) described the dynamics of agricultural development and differentiation early in this century. Indeed, Chayanov called these farm households "family labor farms". The single most identified distinguishing factor influencing farm household behavior has been off-farm wage labor activity (Moore, 1984; Low, 1986b- Shand, 1986; Whatmore et al., 1987b; Eboli and Turri, 1988; Tully, 1990). Consequently, the analysis of farm household member opportunity costs is gaining wide acceptance (Low, 1986b; Tully, 1990). In addition, labor criteria also include:
(1) a specification of the gender and age division of labor; (2) the types of tasks assigned to these different categories; (3) the duration and seasonality of labor allocations; (4) the intensity of the labor process; and (5) the amount and timing of non-household labor hired or exchanged (see also: Coughenour and Swanson, 19&3; Pascon, 1986; Moock, 1986; Poats et al., 1988; Singh, 1988; Hebinck, 1990; Marz, 1990).

Wealth often is suggested as an important indicator of differentiation. The level of productive and consumptive durable goods indicates the cumulation of advantages and disadvantages which the dynamics of the agricultural system elicits over time within farm households (White, 1989). Capital now predominates over land ownership as the key dimension of rural social stratification. The advantage of these factors as indicators is that they provide a measure of the potential for capitalization from non-farm (i.e., from wage labor and non-agricultural activities) as well as farm sources. These criteria include: (1) the possession of farm equipment; (2) debt or credit


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levels; (3) non-agricultural investments in businesses or housing; and (4) the possession of consumption durables (see also: DeWalt and DeWalt, 1980; Garrett, 1984; Low, 1986b, 1989; Hebinck, 1990; Tully, 1990).

Land has been a standard criterion for distinguishing between farm households. As the fundamental environmental resource involved in all agricultural systems, it is often included in the design of typologies with such designations as small, medium and large. Due to this overly simplistic usage, it is coming into increasing disrepute (Whatmore et al., 1987a; White, 1989; Bagchee, 1990). Size categories on their own do not account for land quality in terms of soil type, depth, water availability or slope. Tenure status is also an important dimension indicating the social relations governing the use and output of accessable land. However, different forms of tenure status, in themselves, do not necessarily indicate the value of the land to each of the partners in the relationship (Whatmore et al., 1987a). In addition. intra-household relations concerning the ownership and use of land should also be taken into account (Moock, 1986). Criteria indicating the land resources available to each household include: (1) the amount of land operated; (2) the quality of that land; and (3) the social relations governing the use of the land (see also: Garrett, 1984; Pascon, 1986; Hebinck, 1990).

Household demographic composition has gained increasing attention as a key criterion distinguishing farm households and their modes of functioning. It has long been recognized that stage in the family life cycle and extended family structures have an important impact on how the farm is run (Chayanov, 1966). The number, age and gender of household members has a direct impact on both labor resources and consumption needs (Pascon, 1986). Since the farm household is the primary unit of analysis, it is important to specify what constitutes a household. Household studies continue to point out not only that family may not be an adequate concept, but that household is also an ambiguous concept (Netting et al., 1984; Moock, 1986; White, 1989). Here we define the household as composed of all
persons residing in the producing and consuming unit. This may include non-family-related persons. Non-resident family members also may be significant contributors/consumers. These need to be accounted for separately. Demographic criteria should include: (1) the number, age and gender of all household and non-resident family members; and (2) their family (or non-family) relationships (see also: Crummett, 1987; Eboli and Turri, 1988; Poats et al., 1988; Wilk, 1989).

Crop composition and production techniques are the most frequent criteria used to differentiate farming systems. Land use patterns, whether perennial, annual or seasonal provide important information on how a farming system is


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managed (Ruthenberg, 1980). Types of crops grown further indicate the objectives -of production whether for home consumption, animal feed or sale. Crop production techniques and the use of purchased inputs are often distinctive of different farming systems. Crop yields, although essentially a performance indicator, are sometimes used to differentiate types. These criteria include: (1) the area committed to each crop produced; (2) the rotation patterns of these crops; (3) land preparation techniques; (4) the use of purchased inputs, (5) cultivation practices, (6) harvesting techniques; (7) crop yields; and (8) the end use of each crop (see also: Hart, 1982).

iUvestock composition and production techniques are less often mentioned, but equally important criteria (McDowell and Hildebrand, 1980). Herd size and stocking rate indicate the scale and intensity of livestock activities. Animal purchases and sales allow for distinguishing between market and consumption orientations. The combination of types of animals may suggest multiple purposes. The distinction between local and improved breeds, the use of purchased feeds, and the use of veterinary services indicate production techniques. An important additional criteria often missed is the extent and form of herd ownership. Livestock criteria should include: (1) the number and type of livestock; (2) stocking rate; (3) purchases and sales of livestock, (4) the feeding schedule- (5) the use of purchased feeds and veterinary services; and (6) the social relations concerning the ownership and management of livestock.

Farm and family goals are frequently mentioned as key determinants of farming system dynamics. These goals provide the ultimate motivation for engaging in agricultural production. Although manifested individually, they involve culturally-defined values of the community, as well as of particular productive activities (Long, 1984). The real potential for choice in these matters may be mitigated by the need for physical survival within culturally-defined norms. Like other causal mechanisms, such values are difficult to observe directly. Although the frequently hypothesized dichotomy between self-sufficient peasants and market-oriented producers is rarely 'found, the uses of produced outputs often serve as valuable indicators of household goals. Criteria to indicate these goals include: (1) the types of production pursued; (2) the level and form of self-sufficiency; and (3) expectations for children (see also: Capillon, 1986; Eboli and Turi, 1988; Long and van der Ploeg, n.d.).
Closely linked with farm and family goals is the historical traiectorv of the farm operation. Although seldom used in typology development because of the retrospective data collection and complex analyses involved, it is important to place the farming system on an evolutionary path to map the dynamics of system development (Capillon, 1986, Diaz et al., 1990). Criteria


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for indicating such trajectories could include all criteria mentioned above for previous developmental stages of the farm household systems.


SUMMARY AND CONCLUSIONS

The concepts and criteria discussed in this paper provide tools for the targeting and implementation of agricultural research and development. Two levels of conceptual tools are involved. The first provides a way of thinking about the dynamic totality of agricultural development. The second channels this way of thinking toward the applied targeting of specific farming systems. Both aid in the diagnosis of farmer problems and how those problems may be resolved.
The concept of system is central to applied agricultural research and development. Farming operations must be conceptualized and examined as systemic wholes involving farm household members allocating their time and resources to three interdependent processes (crops, livestock, and off-farm activities) in order to attain their culturally and materially defined goals. These processes have been defined as sub-systems nested in and composing, with household resources, the farming system.
Furthermore, farming systems exist within both bio-physical and socio-economic environments. Systematically interlinked, these
environments compose a hierarchy of agricultural systems. Within this hierarchy, the ultimate decision to adopt, adapt or reject technological innovations is made at the farm household level. Nevertheless, important characteristics of the agricultural system as a whole need to be taken into account; in particular, the agro-ecological and socio-economic conditions in which farming systems exist.
This farming systems conceptual framework provides a way o thinking about the systematic interdependence of both the internal and external components of farming operations. However, considerable diversity exists in the real world of farming. Additional conceptual tools are necessary to bridge the gap between this way of thinking and real world applications. Research domains need to be clearly specified and ultimately narrowed to specific farmer problems and their solutions.
Bio-physical features conditioning production potentials provide the initial basis for research domain specification. Agro-ecological zonage of geo-politically defined research domains should be developed to account for these sources of differentiation. However, not all farm households are equally endowed with resources; nor do they combine their resources in the same manner. The appropriate targeting of farming systems for technological innovations also requires the identification of these differences and the limits which they impose.


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Within the context of agro-ecologically defined research domains, the interdependence of resources and productive activities at the level of the farm household provide the systemic logics distinguishing one type of farming system from another. The potential for a technology to be a solution to a "farmer's problem depends on: its applicability to the specific
agro-ecological conditions in which the farming system exists; and its
compatibility with the socio-economic capacities and objectives of the individual farm household managing that farming system. Narrowing research domains to a farming systems typology is the final span in the bridge from conceptual understanding of farming systems to applied problem diagnosis and solution development.
The specification of a typology of farming systems does not complete the targeting cycle. The bridge must be crossed. The role of the typology is to focus investigation and dialog with farmers within clearly-defined and homogeneous research domains for problem diagnosis, testing and solution development. Only then, can targeted recommendation domains be established and messages circulated through diffusion domain networks.



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ADOPTION OF SOYBEAN: A COMPARATIVE ANALYSIS OF CULTURAL PRACTICES IN ZAIRE AND NIGERIA


Dennis A. Shannon, Mwamba Kalala M., Kubengu Mudilamika and Mpoy Mudiamvita C.1


ABSTRACT

The suitability of soybean for peasant agriculture in subSaharan Africa has been in dispute for many years. This controversy has affected the allocation of resources for research in soybean. In order to justify research in soybean, a survey was conducted to document soybean production and utilization in Gandajika, Zaire. Soybean cultivation was widespread in the area. Cropping practices were described and compared with cropping practices in areas of Nigeria where soybean is grown. The cropping practices in the two countries reflected similarities and differences in the physical environment, major crops and economic conditions.
Similarly, farmers' perceptions and perceived constraints relating to soybean production reflected differences in the physical and economic environments. Soybean culture was integrated into traditional farming systems in both Zaire and Nigeria. These observations suggest that soybean was relevant to farmers in these regions, and that research to improve soybean production was needed. This survey served to justify continued research in soybean, thus demonstrating the utility of multi-disciplinary crop surveys for orienting
research programs to the needs of farmers.


Assistant Professor, Department of Agronomy and Soils, 202 Funchess Hall, Auburn University, Alabama 36849-5412, U.S.A., formerly Agronomist, International Institute of Tropical Agriculture, Ibadan, Nigeria; formerly, Institut National pour l'Etude et la Recherche Agronomique, B.P. 2037, Kinshasa I, Zaire; and Research Assistant and Former Research Assistant, respectively, Programme National Lgumineuses, B.P. 22, Mbuji Mayi, Kasai Oriental, Zaire.


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INTRODUCTION


Until recently, many agriculturalists familiar with Africa believed that soybean (Glycine max) was not suited to peasant agriculture in sub-Saharan Africa. This belief was predicated on the assumptions (1) that African farmers would not eat soybean (Vanneste, 1986); and (2) that agronomic limitations rendered the crop unsuited to low-resource farmers in the lowland humid and sub-humid tropics. Unlike most other legume crops, soybean varieties developed in temperate regions were not able to nodulate and fix nitrogen with Rhizobium bacteria indigenous to African soils (Dashiell et al., 1987). Secondly, when seed of these varieties were stored under conditions of high humidity and temperature, they rapidly lost their viability. These two constraints severely restricted the crops' utility to small-scale farmers who could not obtain inoculant and proper seed storage facilities. Whingwiri (1987) reported that soybean production was "not very attractive" to peasant farmers in Zimbabwe owing to a number of constraints including poor nodulation, lack of knowledge about inoculants, shattering and low yields. However, soybean varieties are now available that combine high yield potential with improved seed longevity, a capacity to nodulate with a wide range of rhizobium strains common to tropical African soils and resistance to shattering and common diseases (Dashiell et al., 1987; IITA, 1992). This has greatly improved the chances of soybean being accepted under a wide range of tropical conditions.
The assumption that farmers will not eat soybean has been based upon attitudes and beliefs regarding soybean foods. Common objections to soybean related to cooking time, taste, unfamiliarity with acceptable uses, and belief by some that the crop was poisonous (Weingartner et al., 1987). Others felt that due to perceived problems limiting home consumption, as well as technical constraints in growing the crop, soybean was mainly suited to large-scale commercial production destined for industrial processing. Doubts concerning the potential adoption of soybean were so prevalent that by 1986, the International Institute of Tropical Agriculture (IITA), where much innovative research had been undertaken to address problems affecting soybean in the tropics, relegated soybean to a secondary position among mandate crops (p. 44 in IITA, 1987). Two years later, the Institute ranked the projected importance of soybean and the productivity of research in soybean as low (IITA, 1988). Although attitudes toward soybean may be changing, misgivings persist regarding the crop's appropriateness to small-scale farming in sub-Saharan Africa.
Sub-Saharan Africa is vast, with a range of agro-ecological environments and a multitude of ethnic cultures with their own traditions,


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foods, crops, cropping systems and economic conditions. It is therefore risky to generalize from experiences at one location to other locations within the region. Yet, it is necessary to anticipate the likelihood of adoption of technological interventions in order to make rational choices in the prioritization of research activities. For research activities to be cost-effective, the technologies developed must be adopted by many people over large geographic areas. Thus, evidence of the adoption of soybean as a crop at a single location, although positive, is not adequate to predict adoption in other areas, or to justify a major research program. However, evidence of adoption in areas differing in ethnic cultures, foods, crops, cropping systems and economic environment may be seen as strong evidence that the crop is likely to be adopted in other areas within the region where it is introduced.
Shannon and Mwamba (1994) presented evidence that soybean has been integrated into the diet of farmers in Zaire, Nigeria and other sub-Saharan countries. Commonly cited problems with soybean preparations were based upon preconceived notions of how the crop would be utilized. Those who predicted that the crop would not be adopted failed to anticipate the development of indigenous preparation methods to which these problems did not apply. In Gandajika, Zaire, soybean flour is used in traditional maize porridge, in the maize-cassava staple, nshima, or added to vegetables. The grain is also roasted and eaten whole. In Nigeria, soybean is used in a number of traditional foods, the most important being as a substitute for locust bean (Parkia spp.) in daddawa, for melon seed (Citrullus lanatus) in egussi stew, and as a supplement to cowpea (Vigna unguiculata) in moin moin and akara (Pfeiffer, forthcoming; Smith et al., 1993). In Gandajika, soybean ranked second to cowpea in terms of area planted to grain legumes (Shannon and Mwamba, 1994). Virtually all production is used for human food, with one third being consumed directly within producing households. In the Tivspeaking area of Benue State, Nigeria, the average farmer interviewed produced over 400 kg in 1989 (Smith et aL, 1993), a substantial production for smallscale farmers.
Documentation on existing cropping systems involving soybean in sub-Saharan Africa is rare, and mostly limited to socio-economic rather than agronomic information. However, the existence of established cropping practices adapted to specific agro-ecological environments and farming systems is evidence of adoption of the crop. In this paper, examples of the cultivation of soybean by low-resource farmers in Zaire and Nigeria are discussed in relation to the larger issue of soybean adoption in sub-Saharan Africa. Established cropping practices involving soybean grown by low-resource farmers at one location in Zaire are described and compared to cropping practices reported from Nigeria. Similarities and differences in soybean cropping practices in the two countries are analyzed according to crop and environmental characteristics,


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existing cropping systems, economic considerations and farmers perceptions in the two countries. The usefulness of simple crop surveys is also discussed.


GANDAJIKA SURVEY

The history of research and extension on soybean in the Kasai Regions of south central Zaire was summarized by Shannon and Mwamba (1994). Soybean adoption occurred largely as a result of efforts in Western Kasai which began about 1960 and included active promotion of soybean cultivation, research into appropriate ways to prepare soybean foods, village-level education in preparation of soybean foods, and emphasis by medical institutions on using soybean to combat protein deficiency in children. Mills were established by foreign missionaries, where the grain was purchased, roasted and ground into flour for resale. Seed of adapted soybean varieties were supplied by the experiment station at Gandajika in Eastern Kasai. In 1985, a national grain legume research program (Programme National L~gumineuses) was established with headquarters at Gandajika. Questions were raised about how much research effort should be allocated to soybean relative to other grain legume crops. At issue was whether priority should be given to widely-grown traditional crops such as cowpea, with major production problems (insect pests) for which low-cost solutions were not readily available, or to the relatively new crop, soybean, which had not yet been widely adopted, but which was being widely promoted within the country. Rapid gains in the productive potential of soybean were possible through the introduction of improved varieties developed in a similar ecology in Nigeria by IITA. In 1986, an external evaluation team questioned the emphasis placed by the national program on soybean, citing both the relatively small area in soybean production compared to more established grain legume crops, and doubts concerning the potential adoption of soybean as a food. Only limited anecdotal information was available on soybean production in Zaire, and the extent of adoption was unknown. Documentation of soybean adoption was needed to justify a major research effort in soybean. In Gandajika, soybean was seen planted during the 1985/86 and 1986/87 growing seasons. A survey was conducted to document the importance of soybean in the area and to learn about soybean production practices.
Gandajika may be considered an area of secondary adoption, since it had benefited mostly indirectly from the extension efforts in Western Kasai. The services available in Western Kasai for milling the grain were not accessible to Gandajika farmers, who had to rely on their own resources to process and market the crop. Thus Gandajika was a good area for assessing the potential for adoption of soybean, because external incentives were limited.


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Gandajika is a zone (equivalent to a county in North America) in the Eastern Kasai Region of Zaire. It is located in Guinea savanna (Vanhamme et al., 1955), with a mean annual rainfall of approximately 1450 mm distributed between late August and mid-May. Annual crops are grown in two seasons per year, September to January being referred to as Season A, and February to May as Season B. Maize (Zea mays) and cassava (Manihot esculenta) are the principal crops grown. Soils are mildly acid, predominantly Alfisols, with texture ranging from sand to sandy clay loam.


Methods
A total of .115. farmers in 9 groupements or village groups within the Gandajika Zone were interviewed in April 1987 to obtain information on the production and utilization of soybean and farmer knowledge of, and attitudes toward, soybean. The survey was conducted in villages along several roads radiating from the city of Gandajika. The population consisted of three tribal groups, the Kaniok, Luba and Nsonge, and reflect variations in the cropping practices observed in the area. The survey was carried out by technicians trained in agronomy and rural sociology at the secondary and university level. A questionnaire was drawn up in French, but the interviews were open-ended and conducted in the Tshiluba language. Because reliable census data were not available, interviewers circulated in the fields and interviewed farmers as they were encountered. Although the initial intent was to obtain equal numbers of farmers who had or had not grown soybean, it was discovered that all the farmers interviewed had grown the crop.
Plant spacings were measured by the interviewer. Production was reported by farmers in units of meca (approximately 3 kg) and this, together with information on the area planted, was used to estimate yields. Additional details of the survey methods were presented in Shannon and Mwamba (1994).
Observations and farmer responses regarding growing of the soybean crop by small-scale farmers are presented in this paper. Information regarding its importance and use were presented by Shannon and Mwamba (1994).


Results and Discussion
Almost none of the farmers interviewed could name the variety they planted, but most grew a variety requiring three months from planting to harvest. The growing period and field observation suggested that the majority planted SJ 127, seed of which was produced at the experiment station at Gandajika. This variety is short-statured, has yellow seed, abundant gray pubescence and matures in 85-95 days. Jupiter, mentioned by name by three individuals, has green seed at Gandajika, tawny pubescence and matures in 120-125 days.


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Soybean was usually cultivated by the men (64.4 %) or by the entire family (30.4 %). Individual women (widowed or divorced) accounted for 4.3 % of fields surveyed. Most farmers planted soybean in small plots near the household, indicating its place as a minor crop. However, .23.5 % planted soybean in the larger fields away from the household, which suggests that the crop has some commercial importance to these farmers.
Agronomic practices: Soybean was most often planted sole (79.1 % of respondents), but 15.7 % of farmers intercropped with maize. Another 5.2 % of farmers sometimes planted soybean sole and sometimes intercropped. Soybean most often followed maize in the rotation (64.4 %). Other crops included cowpea (6.9 %), cassava (4.3 %), cotton (3.5 %), peanut (Arachis hypogaea, 2.6 %) or fallow (3.5 %).
Not all farmers selected the soil on which they planted soybean (Figure 1). Of the approximately one third who did select soils, their choices suggest a knowledge that soybean did not tolerate low fertility soils as well as some other crops, such as cassava and cowpea.
Fertilizer was scarce and was applied almost exclusively on maize. Only 10.4 % reported having ever applied fertilizer on soybean and none had done so in the season when interviewed.
Farmers planted soybean on flat seedbeds (55.7 %), ridges (37.4 %) or beds (6.1 %). Flat seedbeds were chosen to permit a high plant density and to save time (Figure 2). Ridges were chosen to facilitate weeding and to improve fertility and moisture conditions for the crop. Beds were chosen to facilitate intercropping with maize.
Soybean was planted in two seasons per year. Higher yields may be expected in the Season A (September to January) because of more abundant rainfall. However, better drying conditions at harvest ensure that better quality seed will be harvested in Season B (January to May), provided sufficient rain is obtained during grain filling for the crop to produce grain. When asked to indicate which was the better season in which to grow soybean, farmers were almost equally divided in their choice. Given the problems of seed longevity in soybean (Dashiell et al., 1987), it is easy to understand their apparent ambivalence regarding the two seasons.
Soybean was planted in September (65.2 %) and October (24.3 %) in Season A and harvested in December (54.7 %) and January (32.2 %). It was planted again in January (46.9 %) and February (51.3 %), in Season B, and harvested in April (35.6 %) and May (61.7 %). Planting of soybean coincided, in Season A, with the period when the farmer completed seeding and began weeding his primary crop, maize (Figure 3). Harvest of Season A soybean took place when the farmer was beginning his maize harvest, preparing land and planting Season B maize and cotton (Gossypium hirsutum). Planting of Season B soybean coincided with planting of Season B maize, but soybean harvest


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% of 115 Farmers


Rich 19.5 %


NO 64.3 %


YES 35.7 %


V Clay-Loam",
. .1. .5 %


% of 41 Farmers


Clayey
\32.0%



Sandy 4.8 % Sandy-Loam 4.8%
~~Previously Black Cropped
9.7 % 9.7 %


Soil Type Chosen for Soybean


Choose Soil?

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D.A. SHANNON, K.M. MWAMBA, M. KUBENGU, M.C.MPoY


Figure 2

% of Farmers
70

60

50

40
High Density
30 -W Easier to Weed
SLess Weeds
20 Moisture/Fertility
M Saves Time
10 11 IM Intercropping
1 = Less Lodging

Seedbed: FLAT RIDGES BEDS
Farmers: 64 43 7


preceded that of maize. From the standpoint of labor use, it may have been more convenient to plant in Season B.
Only 2.6 % of our sample saved their own seed. The remainder either purchased grain for planting in the local market (54.0 %), or obtained seed from a neighbor (24.3 %), the research station (13.0 %) or other family members (6.1 %). This situation was typical of other grain legumes grown in the region, such as cowpea and peanut. Harvested grain was either consumed or sold to pay off debts or in response to financial emergencies. At planting time, farmers purchased seed if they had cash. If not, no grain legumes were planted on their farm.
Between row spacings of sole cropped soybean ranged from 20 cm to 100 cm, within row spacings ranged from 10 cm to 50 cm. The most popular spacings were 30-40 cm between rows (54.8 %) and 20-30 cm within the row (77.9 %). These spacings applied generally to flat seedbeds and were consistent with recommended practices (Vanneste, 1986). Between-row spacings of 50-80 cm (22.0 %) were generally used when planting on ridges, while the narrow spacings were used on flat seedbeds. Seeds per hill ranged from two to seven, with 3 (37.3 %), 4 (28.6 %) and 2 (24.2 %) seeds per hill being most popular.
These seeding rates gave densities often exceeding 300,000 plants/ha. Results of a plant density trial conducted on-station indicate that 200,000


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% of 115 Farmers
250 z
'0 (A}

200


150i


100 "



50
0 . . -_ _Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul

LAND CLEARING TILLAGE PLANTING
WEEDING HARVEST

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D.A. SHANNON, K.M. MWAMBA, M. KUBENGU, M.C.Mpoy


plants/ha are adequate to obtain satisfactory yields ( Shannon et al., 1988). The short stature of the popular variety, SJ 127, especially on the degraded soils near many households, may have influenced some farmers to adopt high densities.
With intercropping, between-row spacings ranged from 20 to 80 cm, with 80 cm being the most frequently recorded (22.2 %) followed by 60 cm (16.7 %) and 40 cm (16.7 %). Within-row spacing ranged from 20 cm to 100 cm, but was usually between 30 cm and 20 cm (77.7 %). Two (50.0 %) to three (38.8 %) seeds were normally planted per hill. Densities were consequently highly variable.
Yields: Average yields were estimated at 412 kg/ha in the previous season, with a range in yield of 90 to 2400 kg/ha. The latter was remarkable since no fertilizers were used.
Area Planted: Field size varied from 20 m2 to 1 ha, but averaged 0.18 ha is Season A and 0.11 ha in Season B. Not all farmers planted soybean in Season A and of those who did, not all were able to estimate the area they cultivated in Season A. Consequently, it must not be inferred that farmers planted more area in Season A than in Season B.
Farmer evaluation of soybean: Most farmers reported no problems growing soybean (62.6 %). Insects were considered a problem by a quarter of farmers (24.3 %). Poor germination, an unproductive variety and shattering were also mentioned. To counter insect pests, some farmers applied ashes or insecticides to the plants. Timely harvest was necessary for their shattering-prone variety and rogueing was practiced to minimize the spread of the shimbu mealybug (Crypticerya spp.).
Most farmers cited marketing as a major constraint limiting the amount of soybean grown (82.6 %). Half stated that they had little time to grow soybean. The problem of drying the soybean following Season A was cited by 38.3 % of respondents. Lack of a good variety was cited by a quarter of the farmers (27.8 %), as was over-abundant rainfall (27.0 %). Other
constraints mentioned by a minority of farmers were shattering (10.4 %), ignorance (7.8 %), difficulty in eating soybean (6.1 %), insect pests (5.2 %), low soil fertility, not enough rain, insufficient land, weeds and diseases (<5 %).

COMPARISON WITH SURVEY RESULTS FROM NIGERIA

In order to make inferences regarding the adoption of soybean in other parts of sub-Saharan Africa, it is useful to compare adoption trends in other culturally distinct areas of sub-Saharan Africa where soybean has also been introduced. Soybean production by small-scale farmers in sub-Saharan Africa has been largely undocumented. Only in the case of Nigeria, was sufficient data found to permit a comparison with production practices in Zaire.


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Background
Results similar to those obtained in Gandajika were obtained in surveys conducted in Nigeria by Woodworth (Woodworth et al., 1992; Smith et al., 1993), Knipscheer et al. (1985), J. Smith (unpublished manuscript) and Pfeiffer (forthcoming). Woodworth conducted surveys in Benue State, located in Southern Guinea savanna (Keay, 1959) in the eastern "Middle Belt" region of Nigeria. Knipscheer et al. conducted their survey in Benue and neighboring Kaduna State. Smith and Pfeiffer conducted surveys in the forest/savanna transition zone in Oyo State in western Nigeria. Both areas were located at approximately 70 N latitude in environments similar to that in Gandajika. Rainfall was approximately 1300 mm, distributed bimodally (IITA, 1985; Shannon, 1983) and soils were predominantly coarse-textured, moderately acid Alfisols with low exchange capacities.
In Benue State, the Tiv tribe has been growing soybean since it was introduced in 1937 (Root et al., 1987). In Ayepe and Igangan, Oyo State, Nigeria, soybean was first introduced in 1986 and 1987, respectively (J. Smith, unpublished manuscript2; Pfeiffer, forthcoming). Although some reference will be made to all the locations, most of the information available on cropping systems was obtained in Benue State, the area with the longest history of soybean cultivation in Nigeria and where soybean is most extensively grown.

Yield
In Benue State, grain yield samples measured by Woodworth (1990) in 40 farmers' fields in 1989 averaged 819 kg/ha and ranged from 144 kg/ha to 1609 kg/ha. The same farmers obtained average farm-level yields of 621 kg/ha (Woodworth et al., 1992). The difference between farm level and sample yields was attributed to shattering losses, but may also reflect field heterogeneity. Based on interviews of 70 farmers from villages selected at random from the eastern (Tiv) part of the state, Smith et al. (1993) estimated average soybean yield in 1989 at 700 kg/ha.
These average yields were higher than that obtained by Gandajika farmers. This may be partly because Benue farmers applied fertilizer to soybean whenever possible, whereas Gandajika farmers did not. Also, many Gandajika farmers planted on degraded soils near the homestead, thus lowering the average yield.




2 Improvement of maize-based cropping systems. Unpublished Annual Report for 1989. Resource and Crop Management Program. International Institute of Tropical Agriculture, Ibadan, Nigeria.


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Area Cultivated
Woodworth (1990) collected data from 20 farmers within 40 km of Gboko, a major center for soybean cultivation in Benue State, and 20 farmers living more than 40 km from Gboko. Near Gboko, average area per farmer was 1.13 ha. Fifty percent of the farmers cultivated more than 1 ha, while 40 % cultivated between 0.4 and 1 ha. Away from Gboko, average area was 0.53 ha. Only 15 % of those sampled cultivated more than 1 ha while 45 % cultivated less than 0.4 ha.
The average areas planted to soybean were considerably more than in Gandajika. However, many Gandajika farmers produced two crops of soybean each year, while Benue farmers produced only one crop.
At Ayepe, the crop was grown uniquely for local consumption (Pfeiffer, forthcoming). Plot sizes averaged only 0.03 ha (J. Smith, unpublished document2). At Igangan, Oyo State, the crop was sold to a commercial buyer (Pfeiffer, forthcoming). Plot sizes were larger, some reaching 0.5 2 ha.

Rotations and Associations
In Benue, soybean followed fallow, peanut, soybean or yam (Dioscorea spp.) in the rotation (Woodworth, unpublished manuscript3), in southern Kaduna State, soybean followed sorghum (Sorghum bicolor) (Knipscheer et al., 1985), while soybean generally followed maize in Gandajika (Figure 3). Contrary to Gandajika, only 13 % of Benue farmers grew soybean as a sole crop. The majority intercropped with sorghum (75 %), while the remainder intercropped with cassava (Manihot esculenta) or strip planted in citrus (Citrus spp.) orchards. In the sorghum/soybean intercrop, sorghum grew much slower than did soybean and reached its full height only after soybean reached maturity. Consequently, there was little competition to the soybean crop. In orchards, soybean was planted to suppress weeds, especially Imperata cylindrica (A. Chichi, personal communication), and to increase land use efficiency. Because of the open canopy of widely-spaced citrus, it may be assumed that the soybean crop suffered only minimal shading.
In Gandajika, sorghum was rarely seen in farmers' fields, having been almost totally replaced by maize. Some Gandajika farmers intercropped soybean with maize, which grew faster and taller than soybean and, unless very widely spaced, significantly shaded the soybean. Gandajika farmers therefore


3Paper presented at the National Meeting of the Nigerian Soyabean Scientists. March 1990.


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did not benefit greatly from intercropping their soybean, while Benue farmers did.

Farmer Assessment
Soil improvement was listed by 63 % of the 40 Benue farmers as a benefit derived from growing soybean (Woodworth, unpublished manuscript3). Advantages of intercropping were cited by 13 %. The variety grown by Gandajika farmers, SJ 127, provided little foliage, and thus could not be expected to contribute much to soil fertility. The advantage of this variety was its short growing season (90-95 days) allowing two crops per year. However, Malayan, the traditional variety grown in Benue State, was late maturing (125130 days), produced excessive vegetation under good growing conditions and could be expected to' suppress weeds and provide N-rich biomass.
Many of the problems listed by Benue farmers shattering losses, labor requirements for threshing, difficulty in uprooting plants at harvest, lack of fertilizer, and damage by insects and birds (Woodworth, unpublished manuscript3) differed from those mentioned by Gandajika farmers. The problems cited above may reflect the greater demand placed on labor because of the greater areas planted and greater production in Benue than in Gandajika. The rapid drying conditions caused by the Harmattan (dry air masses from the Sahara desert) in Nigeria also made timely harvest more critical in Nigeria than in Gandajika. The concern about fertilizer and insect control suggested that Benue farmers were willing to invest to increase soybean yields.
In an earlier survey of soybean production by Knipscheer et al. (1985), farmers in Benue State reported average seed losses of 12 % due to poor germination, while farmers in Kaduna State reported over 50 % losses. Onethird of farmers in the two states had to replant some of their soybean crop due to birds, seed deterioration, and in Benue State, to drought.
Poor germination was not mentioned at Gandajika. This may be attributed in part to the fact that Gandajika farmers often planted twice each year. As a result, the seed used for planting was stored for shorter periods, at most 5 months in Gandajika, compared to 7-9 months in Benue State. Longer storage under ambient conditions of high humidity and temperature are conducive to seed deterioration and loss of viability (Dashiell et al., 1987).

CONCLUSIONS

Adoption of Soybean
The results of the surveys in Gandajika and in Nigeria disprove the commonly-encountered belief that soybean is not suitable for production by small-scale peasant farmers in sub-Saharan Africa. Soybean was already integrated into cropping systems of small-scale farmers in Gandajika, and in


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Benue and Kaduna States, Nigeria. It was also being integrated into cropping systems in Ayepe and Igangan, Oyo State, Nigeria.
Small-scale or peasant agriculture in Africa should not be viewed as static. African farmers, like their counterparts in more developed parts of the world, integrate new crops into existing cropping systems when it is shown to be beneficial to them. The history of cassava, maize and peanut attest to this fact. All were introduced into Africa around the sixteenth century (Jones, 1959; Miracle, 1966; Purseglove, 1968) and spread at different rates to become major crops in traditional cropping systems throughout much of sub-Saharan Africa. It is reasonable to conclude that soybean may be successfully introduced into cropping systems in other areas of sub-Saharan Africa where the soil and climatic conditions are suitable for growing the crop.

Integration of Soybean into Cropping Systems
The cropping practices developed by farmers in the two countries reflect logical adaptations to local conditions. For example, the very dry conditions during the first months of the dry season in Benue State, enable Benue farmers to store soybean seed much longer than would be possible at Gandajika, where relative humidity during the dry season remains high. As a result, in Benue, a single crop of soybean is planted each year, while in Gandajika, two crops are planted a year, despite the unfavorable harvest conditions and labor constraints of Season A.
The popularity of intercropping in Benue and sole cropping in Zaire reflect the difference in primary crops and their growing cycles. The fact that Benue farmers used soybean for soil fertility improvement, while Gandajika farmers did not, reflects the difference in growth characteristics of the soybean varieties used.
Differences in constraints listed by farmers reflect differences in the economic status of the crop. In Gandajika, where little commercialization of soybean existed, farmers were concerned about sale of excess production. In Benue, where the local marketing system was well developed, farmers were concerned with labor constraints and lack of inputs which restricted increases in soybean production.
Response to Markets: Plot size appeared to be influenced by whether or not the crop was grown only for domestic consumption or for sale. Production areas were small where soybean was grown primarily for home consumption, as in the case of Ayepe and three quarters of Gandajika farmers. The relatively large areas planted by farmers in Igangan and the Tiv areas of Benue State, as well as by a minority of farmers in Gandajika, demonstrate that farmers will expand their production of soybean when markets are available for excess production. In later surveys conducted in Gandajika, it was learned that some of the farmers who produced in greater quantities had obtained contracts to sell soybean to buyers outside the local area Thus rational management choices


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were made by farmers in Zaire and Nigeria, which took into account local environmental conditions, crop varietal characteristics, the existing cropping systems and the economic environment when determining the place of soybean within farmers' overall cropping system.

Crop Surveys
The surveys reported here played an important role in justifying research agendas. The survey at Gandajika showed that soybean was widely grown and was integrated into cropping systems of the community, thus confirming its relevance to farmers and providing justification for continued research into the crop at a time when such research was being questioned. The surveys in Nigeria undoubtedly played a similar role in justifying continued research in soybean there also. In addition, the insights into farmer practices, constraints and perspectives gained from these simple multi-disciplinary, cropspecific surveys served as a basis for planning future research that would be relevant to local conditions and address real constraints to production. Much useful information was gained in a very short time and at limited cost.

Implications for Future Research
Research into new crops or cropping practices always involves a risk that the new technology will not be appropriate or accepted by farmers. Although results from a single location would be inconclusive, evidence of adoption and integration into existing farming systems in areas with different bio-physical and socio-economic conditions demonstrates that the new technology can be widely accepted and extended into new areas. In this paper, we have shown that soybean has been successfully integrated into farming systems in areas of Zaire and Nigeria differing in bio-physical and socioeconomic conditions and existing cropping systems. Continued research to improve soybean production may be justified based on the expectation that the crop will be integrated into farming systems in other parts of sub-Saharan Africa.

ACKNOWLEDGEMENTS

This research was financed by the United States Agency for International Development, Project 660-0091, Applied Agronomic Research and Extension (RAV), with technical assistance from the International Institute of Tropical Agriculture, Ibadan, Nigeria. The authors are grateful to Director Kilumba Ndayi of the Programme National Ligumineuses for providing staff for the survey and to Dr. Joyotee Smith and Dr. L. Upton Hatch for useful suggestions.


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D.A. SHANNON, K.M. MwAMBA, M. KUBENGU, M.C.Mpoy


REFERENCES

Dashiell, K.E., L.L. Bello and W.R. Rod. 1987. Breeding soybeans for the tropics. Pages 316 in S.R.
Singh, K.O. Radile and K.E. Dashiell, eds., Soybeans for the Tropics: Research, Production and
Utilization. New York: Wiley & Sons.
IUTA, 1985. Annual Reportfor 1984. International Institute of Tropical Agriculture, Ibadan, Nigeria. IITA, 1987. IITA Annual Report and Research Highlights 1986. Interational Institute of Tropical
Agriculture, badan, Nigeria.
UTA, 1988. IITA Strategic Plan 1989-2000. International Institute of Tropical Agriculture, Ibadan,
Nigeria.
ITA. 1992. Sustainable Food Production in Sub-Saharan Africa: 1. ITA's Cointributims.
International Institute of Tropical Agriculture, Ibadan, Nigeria.
Jones, W.O. 1959. Maniac in Africa. Stanford: Stanford University Press. Keay, R.J.W. 1959. An outline of Nigerian vegetation. 2nd edition. Lagos, Nigeria: Federal
Government Printer.
Knipscheer, H.C., K.M. Menz and P. Ay. 1885. The production and market potential of soybeans in
Nigeria. Quarterly Journal of International Agriculture 24 (2): 171-184.
Miracle, M.P. 1966. Maize in Tropical Africa. Madison, WI: University of Wisconsin Press. Pfeiffer, J.M. Forthcoming. Factors affecting adoption of soybeans into cropping systems and diets by
small farmers, rural households and petty traders. Paper presented at the Symposium on Sustainable Agriculture in Africa: Socio-Cultural, Political, and Economic Considerations. Ohio
State University Center for African Studies. 25-26 May 1990.
Purseglove, J.W. 1968. Tropical Crops:Dicotyledons. London: Lcngmans. Root, W.R., P.O. Oyckan and K.E. Dashiell. 1987. West and Central Africa: Nigeria sets example for
expansion of soybeans. Pages 81-85 in S.R. Singh, K.O. Rachie and K.E. Dashiell, eds., Soybeans
for the tropics: research, production and utilization. New York, NY: John Wiley & Sons
Shannon, D.A. 1983. A study of factors responsible for variable growth of soybean in the Southern
Guinea savanna of Nigeria. Unpublished Ph.D. Thesis. Comell University, Ithaca, New York.
Shannco, D.A and Mwamba, K.Mv 1994. Adoption of soybean in sub-Saharan Africa: a comparative
analysis of production and utilization in Zaire and Nigeria. Agricultural Systems 46: 369-384.
Shannon, D.A., MI Ngoyi and N. Kilumba. 1988. On-staticn agronomic trials with soybeans and
groundnuts. Pages 163-165 in International Institute of Tropical Agriculture. Resource and Crop
Management Program Annual Report for 1986. Ibadan, Nigeria.
Smith, J., J.B. Woodworth and K.E. Dashiell. 1993. Government policy and farm level tedmnologies:
the expansion of soybean production in Benue State, Nigeria. Agricultural Systems in Africa 3
(1): in press.
Vanhamme, J., J. Clamant, and A. Collin. 1955. Aperqu sur l'Ficonomie agricole de la Province de
Kasai. Direction de lAgriculture des Forets et de ltlevage, Minist&e des Colonies, Royaume de
Belgique, Bruxelles.
Vanneste, G. 1986. Document IV La culture du soya. Pages 171-172b in Seminar National sur le
Soja. Proceedings of a conference held at Kananga, Zaire, 3-10 May 1972. Comit6 de Coordination pour le Developpement en Ripublique du Zaire and Comit6 Diocesain de
Developpement Integral de Kananga. R-edition.
Weingartner, K.E., K.E. Dashiell and A-I. Nelson. 1787. Soybean utilization in Africa: making a place
for a new food. Food and Nutrition 13: 21-28.
Whingwiri, E.E. 1987. Soyabean production in communal and small-scale sectors. pp. 13-17 in
Soyabeans in Southern Africa. Proceedings of a workshop for the Southern Africa region n the
basics of soyabean cultivation and utilization. University of Zimbabwe, Harare, Zimbabwe.
Woodworth, J., J. Smith and K. Dashiell. 1992. Survey and crop season study of soybean in Benue
State Nigeria. Tropical Oilseeds Journal. 1: 75-76.


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THE IMPACT OF PLAN SIERRA'S OUTREACH PROGRAMS ON THE SUSTAINABILITY OF HILLSIDE FARMING IN THE DOMINICAN REPUBLIC

Scott G. Witter' andMichael P. Robotham2

ABSTRACT

This paper focuses on the iihpact that Plan Sierra's agricultural outreach programs have had on the sustainability of small Dominican hillside farms in a 1,700-squarekilometer region. Comparisons are made between farms adopting controlled innovations and those who have not had any contact with Plan Sierra and represent the state of
farming systems in the region if Plan Sierra did not exist.


INTRODUCTION


The analysis presented in this paper is based on Harrington's (1992) article on alternative approaches to measuring sustainability as an objective of research. Harrington describes three major approaches to sustainability: agroecology, equity, and sustainable growth. He also provides a classification of sustainability problems based on internal vs. external, reversible vs. irreversible, and agricultural productivity vs. public health variables. And Harrington concludes that such measurements are in their early stages and much remains to be done by FSRE practitioners in partnership with disciplinary specialists.
The objective of this research is to measure the impact of Plan Sierra's outreach innovations on the sustainability of small hillside farming systems in the Dominican Republic. To measure their impact, comparisons are made between two kinds of farming systems within similar agroecological zones-(1) farms that have been modified based on external contact with Plan Sierra and
(2) those that have evolved based on internal inputs from family and friends.


1
Associate Professor, Departmet of Resource Development, Michigan State University.
2
Dept. of Agriculture and Soil Science, University of Hawaii-Manoa.


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Farming systems that have been modified as a result of a series of innovations delivered by Plan Sierra are considered controlled systems. Those farms that have not had contact with Plan Sierra are called state because they represent the state of Dominican hillside farmers if Plan Sierra did not exist, Harrington (1992) uses the example of the effect of tillage practices (a control innovation) on the state of soil depth. A cause and effect relationship exists between the use of control innovations and the condition of the existing farming system.
From an agroecology perspective the state system represents the status of the farming system without directed innovations (outreach and training programs by Plan Sierra), and any changes or alterations in practice were introduced by an internal mechanism, such as friends or family. In the controlled system, the inputs and practices were initially external to the community and were introduced by Plan Sierra and not by an internal mechanism.
When a farming system reaches its production goals and still maintains the quality of its natural resource base, it is said to be sustainable. Such a system extracts only as many resources as the environmental system can renew, produces fewer wastes than can be absorbed by the environment, and optimizes the efficiency of renewable resource use (Conway, 1994; Harrington, 1992; Barbier and McCracken, 1988).
This paper focuses on the internal questions related to decision making by the farmers to work with Plan Sierra, adopt conservation practices, use chemical inputs, maintain a given crop and animal diversity, produce crops and animals for sale, hire off-farm labor. The paper also details the migration and family characteristics of the farmers questioned. Directional measurements are made using Cramer's V and frequency statistics to illustrate the degree of difference between the control and state systems (Craft, 1991). To be statistically significant the CV values have to be significant at the p=.05 level. Cramer's V Strength of associations are measured as follows: <.10 weak,. 11.25 weak to moderate, .26-.40 moderate, .41-.50 moderate to strong, and > .50 strong (Craft, 1991).


PLAN SIERRA

Plan Sierra is located on the north central slope of the Cordillera Central overlooking the agriculturally rich Cibao Valley. The region has a range of micro climates allowing for a wide variety of crops to be grown (i.e., onions to coffee). The project area covers 1,700 square kilometers.


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The most current population estimate comes from the 1980 census, which indicated that 110,000 people lived in the region. Ninety percent, virtually all farming families, lived in the countryside in a series of small villages; 10 percent (nonfarmers) were reported as living in three small cities. The 90 percent living in the country represent Plan Sierra's primary clientele.
Plan Sierra was created by a grant from the government of the Dominican Republic in 1979 to address the needs of the rural poor. Its objectives were and are to (1) create a demonstration project for managing upland and mountainous agriculture; (2) develop a coordinating mechanism to link existing management institutions together to address the problems of the sierra; and (3) respond in a timely manner to small farmer needs rather than to pursue predetermined targets. To this end, the core of all programs at Plan Sierra are the small farmers and their rural communities (Santos and Quezada, c1979).
Plan Sierra was officially removed from all government control in 1983, although it was still receiving almost 70 percent of its operating capital from the government (de Janvry and Hecht, 1984). By 1991 this figure had
decreased to 18 percent and is currently under 10 percent of the total yearly operating budget. The rest comes from U.S. foundations, the German Development Assistance Program (GTZ), income-generation projects, grants from Dominican foundations, and private donations.
Over the course of its operations, Plan Sierra has conducted activities in the areas of infrastructure development, health, education, and agriculture (de Janvry and Hecht, 1984). The reduction of soil erosion through better farm management and the proper use of conservation practices has been a central focus of the agricultural program (Jimenez and Gutierrez, 1993). Extension practitioners-the outreach workers of Plan Sierra-have used information pamphlets, radio programs, community meetings, site visits, and intensive training at the Kellogg Foundation-sponsored Los Montones training facility.


FARMING SYSTEMS ANALYSIS

For this study, a farming system was defined as "a unique and reasonably stable arrangement of farming enterprises that the farm household manages according to well-defined practices in response to the physical, biological, and socioeconomic environments and in accordance with the household's goals, preferences, and resources" (Shaner et al., 1982). This definition stresses that a farming system exists within an agroecological zone and responds to the physical, biological, and socioeconomic environments. The importance of this context is also stressed by a number of other authors (Brush and Turner, 1987; FAO, 1989; Friedreich, 1992).


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DATA COLLECTION

A questionnaire was developed in concert with the Instituto Superior de Agricultura (ISA), the Center for Urban and Rural Development (CEUR) at Universidad Catholic Madre y Maestra (UCMM), and Plan Sierra staff in Santiago, Dominican Republic. The questionnaire was designed to collect a wide array of information regarding the farmstead: agroforestry, soil and water conservation management practices, farm inputs and outputs, credit, use and availability of water, demographics, inheritance, and coffee production. The survey was field tested twice during the fall and winter of 1992-93 by faculty from ISA. During March 1993, a group of three trained ISA enumerators, who had not had previous contact with Plan Sierra, and a research assistant (Michael Robotham) from Michigan State University conducted the survey. Interviews where held in Spanish by the Dominican enumerators.
Participants in the study were selected by the interviewers using a stratified random sampling procedure. Enumerators began at the center of a village and proceeded out in three different transects following existing roads and trails leading to the farms. Each was provided with a list of farmers living in the community who had participated in Plan Sierra's outreach programs. Upon locating a controlled farm, the enumerator asked to speak to the head of the household (in all cases a man) and interviewed that person. The enumerator then would proceed to the nearest neighbor who had not taken part in any of Plan Sierra's outreach programs and interview that individual. This approach was taken to guarantee a direct comparison of control and state farms within same community and agroecological zone.


MEASURING THE DIFFERENCES IN CONTROLLED AND STATE SYSTEMS

Seven groups of questions were selected to measure if there were statistically significant differences between the controlled and state farms. The questions dealt with: (1) farmer and farmstead characteristics; (2) adoption and use of soil and water conservation practices; (3) migration and family characteristics;
(4) hiring of off-farm labor; (5) crop and livestock diversity, (6) commercial sale of crops and animals; and (7) use of chemical inputs.

Farmer and Farmstead Characteristics
Comparisons were made between farmers and farmsteads of the two groups to discover whether there were significant differences between the type of farmers (or farm) that Plan Sierra worked with and those they did not. To determine significant differences (p >_ .05), the controlled (n=96) farms data were separated from the state (n=65). The size of farms ranged from 1 tarea (16


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tareas=l hectare) to 500. Ninety-two percent of the controlled group's farms averaged 1.92 hectares and 98.5 percent of the state farms averaged 2.06 hectares. Cramer's V (CV) tests of all farms indicated that there were no statistically significant (CV=.571, p=.573) differences between the two groups using size of the farm as a variable.
Further comparisons of the number of years farming, number of land holdings, number of farmers who owned their farms, and the age of the farmers yielded no statistically significant difference between the groups. A significant difference was recorded for income; however, 61 percent of the control and 78 percent of the state groups chose not to respond to questions regarding their income.
Farmers where asked to rank the level of importance of obtaining farming inputs such as seeds, fertilizer, herbicides, pesticides, and credit. Again, no statistically significant differences were found. Farmers were also asked to rank the importance of environmental problems to their farm, specifically soil erosion, flooding, drought, insects, and weeds. A moderately significant difference was established between the two groups with respect to soil erosion ( CV=.266, p=.010). Eighty percent of the control group ranked soil erosion as very important or important compared to 64 percent of the state group. No other significant differences were recorded.

Adoption and Use of Soil and Water Conservation Practices
The adoption of conservation practices was seen as a good indicator of the farmer's willingness to adopt an important innovation. Use and 1dng-term maintenance of conservation practices were seen as measures of the farmer's understanding of the importance of long-term stability of the farming unit.
The strongest and most significant differences between the control and state groups were recorded for questions related to the adoption and maintenance of soil and water conservation practices (SWCP). Questions about SWCPs were divided into three groups related to the length of adoption and types of practices used, where the farmer first learned about SWCPs, and who provided the most useful information on SWCPs.
Ninety-five percent of the control group and 25 percent of the state. group had adopted SWCPs. Cramer's V tests indicated a very strong and highly significant difference (CV=.761, p=.000). Thirty-seven percent of the control and none of the state participants used SWCPs on all of their farm land (CV=.836, p=.000). Comparisons of the length of time SWCPs have been practice were also strongly and significantly different (CV=.781, p=.000) with the control participants adopting and maintaining longer than state. Only one farmer who had adopted SWCPs reported ceasing to use them, and then only because he was 80 years old.


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Only 5 percent of the control farmers indicated family and friends as sources of SWCP information. By contrast, all of the state group who practiced SWCP listed family and friends as their source of SWCP information. When asked which source of information was most useful, 88 percent of the control group indicated the external input from Plan Sierra. When the state farmers were asked for their most useful sources of SWCP information, only 15 percent of the farmers interviewed felt that they had received useful information from family and friends.

Migration and Family Size Characteristics
The size of the family and the number of individuals who had to migrate from the farm were seen as good indicators of the stability of the farming unit to meet the needs of the family unit. Analysis regarding the demographics and migration of the two groups showed little significant difference, except for the number of adults living at each farm. The control farms averaged 3.8 adults, while the state averaged 3.1 (weak-moderate difference, CV=.208, p=.003). The control group average 2.2 children per farm and the state 2.4, and there were no significant differences between the two groups with respect to the ages of the farmers and their children.
There were weak to moderate differences in the number of children per farm who had migrated to the United States and to other parts of the Dominican Republic (CV=.166, p=.107; and CV=.190, p=.055); slightly more (2 and 10 percent) came from the controlled farms. And no significant difference existed in the number of adults who migrated from the farm.

Hiring Off-farm Labor
The ability to hire and pay off-farm labor was deemed to be a good indicator of a prosperous farming unit. When asked if their farm required outside labor, 73 percent of the control and 66 percent of the state farmers answered yes. Significant differences were recorded in the amount of labor needed for weeding (CV=.275, p=.002; control 56 percent, state 29 percent) and harvesting (CV=.194, p=.047; control 53 percent, state 58 percent). No significant differences were recorded for soil preparation or planting, but in both instances a slightly higher percentage of labor was needed on control farms.

Crop and Livestock Diversity
Crop diversity and livestock diversity were measured as the number of different types and species found on each farm, respectively. The assumption was made that the more diverse the farm, the more stable the farm would be to environmental and market fluctuations. A moderate to strong difference at a significance level of. 148 was recorded for crops.


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Commercial Sale of Crops and Animals The demonstrated ability to produce enough crops and/or animals to permit a portion to be sold was assumed to be a good measure of the stability and prosperity of the farming system type. Once again, a moderate to strong difference was recorded at the .126 significance level below the .05 level deemed acceptable for this study.

Use of Chemical Inputs
The ability to purchase and use insecticides, herbicides, and commercial fertilizers was assumed to be a relevant measure both of the farmer's willingness to accept new innovations and of a farmstead that could produce enough to allow the farmer to purchase these inputs. Comparisons indicated a highly significant moderate difference (CV=.3 98, p= .000) between the control and state. Sixty-eight percent of the control farmers and 28 percent of the state group used commercial fertilizer.
A highly significant weak-to-moderate difference (CV=.191, p=.015) was recorded for use of insecticides; 41 percent of the control farmers and 22 percent of the state farmers used insecticides. There was no significant difference between the two groups for the use of herbicides with only 3 percent of the control and 5 percent of the state farmers using this input.


CONCLUSIONS

The objective of this research was to measure the impact of Plan Sierra's outreach innovations on the sustainability of the small hillside farming systems found in a 1,700-square-kilometer area of the Dominican Republic. To do this farmers were divided into two groups: the control group consisted of farmers who were affected by the external influences of outreach programs sponsored by Plan Sierra; the state group consisted of farmers who relied on internal influences from family and friends. Comparisons of seven variables were made between the groups of farmers living in the same agroecological zone: (1) farmer and farmstead characteristics, (2) adoption and use of soil and water conservation practices, (3) migration and family characteristics, (4) need for off-farm labor, (5) crop and livestock diversity, (6) commercial sale of crops and animals, and (7) use of chemical inputs.

Farmer and Farmstead Characteristics
Comparisons were made between the control and state groups to see if there were significant differences in farm size, ownership, use of credit, number years farming, and income. No significant differences were determined


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between the groups for farm size, ownership, and number of years farming. There were also no significant differences in farmers' perception of the difficulties in obtaining a variety of farming inputs, environmental problems, and pests. These combined results indicated that there was no specific type of farmer or farm that Plan Sierra was targeting for its programs and, thus, the two groups could be logically compared.
The only significant differences recorded in this category were income, the use of credit, and the perception that drought, soil erosion, selling the harvest, and weeds were problems; more control than state farmers answered yes in each instance. Further investigation of the control group showed that many of the farmers reporting higher incomes and using credit were involved in coffee production. Plan Sierra encourages coffee production for erosion control and increased income. Because of Plan Sierra's programs control farmers also put a higher emphasis on soil erosion and weeding.
A higher percentage of state farmers found it more difficult to obtain seeds and sell their harvest, and felt that drought was a problem. The lack of an organized training program-other than Plan Sierra-helping farmers to raise seeds and find a market for their products indicates that for state farmers there are no programs that are adequate substitutes for those implemented by Plan Sierra.

Adoption and Use of Soil and Water Conservation Practices
Plan Sierra has had a dramatic impact on the adoption and maintenance of soil and water conservation practices. This has been accomplished through the use of demonstration farms and by onsite extension practitioners who stress long-term thinking and use a number of self-help innovations rather than cash payments to gain farmer adoption. Plan Sierra is having a positive impact on the local environment, as well as on downstream ecosystems, by reducing sediment loads in the rivers and streams that flow through the region.

Migration and Family Size Characteristics
Significantly more adults were found to be living in the control families than state ones. In an attempt to explain this, comparisons were made between the groups to see if more control farmers were inheriting (staying on the family farm) or buying new farm land. Fifty-five percent of the control farmers indicated that they inherited their primary farm land compared to 43 percent of the state group. This would appear to be an indicator that more control farmers are taking over the family farm, which, in turn, is an indicator that the farming system itself is more environmentally sustainable. Additional field research is required to confirm this assumption.


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Only weak to moderate differences were recorded for migration, where people migrated, and the number of family living at home-slightly more children from the control farms were recorded as leaving the region. Openended questions relating to migration found the most often-given reasons for leaving were related to school, employment, and moving to live with relatives. This information indicated that there was no clear difference between the two groups and that Plan Sierra's external outreach programs were not clearly influencing the migration of children from the farm.

Hiring Off-farm Labor
Control farms were more likely to need off-farm labor for soil preparation, planting, and weeding than state farms. Plan Sierra emphasis on SWCPs means that more time is required in the field for contour plowing and plowing between live or dead barriers and on terraces. Weeding is considered an essential part of water conservation and is stressed during training sessions. The analysis indicates that there is little difference in the amount of labor needed for harvesting.

Crop and Livestock Diversity
While not significant at the .05 level, there was a moderate-to-strong difference between the groups in the number of crops grown. Plan Sierra trains farmers to raise their own seeds, often providing the first season's seeds to those who participate in its programs. The farmers are then required to pay Plan Sierra back in kind during the next year. This provides stability to the system and allows Plan Sierra to maintain a seed stock for future participants. A similar system is used for animals. The strong significant difference for animal diversity appears to be more reflective of agroecological factors than Plan Sierra's programs.

Crops and Animals for Sale
Though not significant at the .05 level, the same pattern exists'for the farmers who have crops and animals for sale as for those who have a higher degree of diversity in their farms. Plan Sierra provides information and help to the farmers for marketing their crops, especially for coffee growers. This appears to be beginning to have an impact, but will require further study.

Commercial Chemicals Used
The strongest, most significant difference between the groups was in the use of commercial fertilizers. Plan Sierra has a program that provides its participants with more ready access to fertilizer and encourages its use. Currently, there are no specific data available to support the contention that this increased fertilizer use is having a significant impact on the yield of the crops produced. Nevertheless, it is a reasonably safe assumption that increased


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use of fertilizers does increase crop yield when all other inputs are held constant.
The research presented in this paper indicates that Plan Sierra has had a positive long-term impact on the region and farmers living within boundaries of the project. This is especially true of the adoption and maintenance of soil and water conservation practices. The long-term maintenance of these
conservation practices exceeds any of those previously documented programs in the Dominican Republic (Carrasco and Witter, 1993). Plan Sierra has
demonstrated that long-term sustainability of physical resources can be accomplished through well developed and presented outreach programs.
Because of the success of these conservation programs and their community organizational programs, Plan Sierra's outreach programs are being
incorporated into the current Rio Yaque del Norte Watershed Project (Witter and Carrasco, 1996). Plan Sierra is located in the headwaters of the Rio Yaque watershed. Plan Sierra's outreach providers and participating community leaders have been sought out to become involved in designing new programs for the Rio Yaque project. There is still much that we can learn about the organization of women's groups, participant-paid youth environmental programs, and community health programs used by Plan Sierra.



REFERENCES



Barbier, E., and J. McCracken. 1988. Glossary of selected terms in sustainable economic
development. IED Gatekeeper Series SA&. International Institute for Environment and
Development.
Brush, S.B., and B.L. Turner Jr. 1987. The nature of fanning systems and views of their change. In.
B.L. Turner Jr. and S.B. Brush, eds., Comparative farming systems. New York: The Guilford
Press.
Carrasco, D.A. and S.G. Witter. 1993. Constraints to sustainable soil and water conservation: A
Dominican Republic Example. Journal of the Human Environment 22(6): 347-350.
Conway, G.R. 1994. Sustainability in agricultural development: Trade-offs between productivity,
stability, and equitability. Journal of Farming Systems Research-Extension 4(2): 1-14. Craft, J.L. 1991. Statistics and analysis for social workers. Itasca, IL: F.E. Peacock Publishers. de Janvry, A., and S. Hecit. 1984. Reporte de la evaluacion de los primeros cinco anos del Plan
Sierra. Unpublished evaluation, Plan Sierra, San Jose de Las Matas, Dominican Republic.
Food and Agriculture Organization (FAO). 1989. Farming systems development: Concepts, methods,
and applications. Rome: FAO.
Friedreich, K.H. 1992. A farming systems approach to agricultural development. In Readings in
farming systems. Rome: FAO, pp. 107-114.
Harrington, R.R. 1992. Measuring sustainability: Issues and alternatives. Journal of Farming
Systems Research-Extension 3(1): 1:22.
Jimenez. A., and L. Gutierrez 1993. Notes from a presentation and discussion with Jimenez and
Gutierrez at Plan Sierra on 10 March 1993.
Santos, B., and N. Quezada. c1 979. Report on Plan Sierra. Instituto Superior de Agricultura, La Herra,
Dominican Republic.


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Shaner, W.W., P.F. Philipp, and W.R. Schmehl. 1982. Farming systems research and development:
Guidelinesfor developing countries. Boulder, CO: Westview Press.
Witter, S.G. and D. A. Carrasco. 1996. Water quality: a development bomb waiting to explode:- a
Dominican example and possible solution. Journal of the Human Environment 25(3): 199-204.


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AN INTERROGATIVE APPROACH TO SYSTEM DIAGNOSIS: AN INVITATION TO THE DANCE



P.G. Cox, A.D. Shulman, P.E. Ridge, MA. Foale, and A.L. Garside'


ABSTRACT

We collected the rules that farmers assert they use in their decisions to implement various technological components in broadacre dryland farming systems in Central Queensland.
Coherent rule sets were obtained for the use of: fertiliser, opportunity cropping; surface management; and crop
sequence. But these rule sets did not have a linear tree-like structure. In some situations, rules are not organised in sequences of actions but are triggered by external events.
There were interconnections between the different sets of rules. The rule sets captured differences between farming systems, and between regions with a different history of agricultural development. They were stable in response to perturbation (e.g. severe drought), and adaptive to long-term trends in system status (e.g. fertility decline). The rule sets are an effective and efficient way for farmers to manage the use
of technological components in a variable environment.

Both exploratory Rapid Rural Appraisal (RRA) and ethnographic decision tree modelling are inadequate qualitative approaches for system diagnosis in face of the sequential, inter-dependent and contingent nature of decisions about the use of technological and other options. The rule sets do provide useful models of farmer decision-making. They help to specify: criteria for the design of revised models; a baseline by which to judge the claims of new models; a source of insights; entry points for change; and the basis for substantial dialogue. We have adopted a dance metaphor, in which researchers are invited to learn the rules of complex,

'The first, third, and fourth listed authors are with the Agricultural Production Systems Research Unit, PO Box 102, Toowoomba, Queensland 4350, Australia; the second author is with the Communication Research Institute of Australia, PO Box 8, Hackett, ACT 2602, Australia; and the fifth author is with the Queensland Department of Primary Industries, PO Box 6014, Rockhampton, Queensland, 4702, Austxalia.


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dynamic and inter-linked dances, to help both explore and act on these issues. The dance is a more appropriate metaphor than a tree for articulating change in the way in which
researchers and farmers communicate.

INTRODUCTION

Rapid appraisal methods (Doorman, 1991; Kumar, 1993; van Nieuwkoop et al., 1994) have been a feature of farming systems research (FSR), since the early 1980s. Collinson (1981) saw this as a low cost approach for understanding the issues faced by small farmers. Hildebrand (1981) emphasised the value of bringing multiple perspectives to bear on the identification of problems, constraints and opportunities defining the performance of agricultural production systems i.e. for system diagnosis. By the mid-1980s, a rapid rural appraisal (RRA) was seen by many as "the essential first step" in FSR (Beebe, 1985). The late 1980s saw the development of a wide range of rapid appraisal methods for various purposes, including the instigation and management of' change in agricultural systems (participatory rural appraisal or PRA) e.g. McCracken et al. (1988), Feldstein and Jiggins (1994). RRAs are increasingly used as an active intervention to promote change in directions owned by certain (usually inadequately empowered) constituents. They are best for indicating problems or opportunities to be explored further, and for initiating a process of exploration and change, rather than for making generalisations.
Rapid appraisal methods have been used in Australia by researchers including Ison and Ampt (1992), Dunn and Gray (1992), and Hamilton (1991). In Central Queensland, we also used the concept of a participatory rural appraisal to help identify issues requiring attention in crop production, and to initiate a research programme to address them (Cox et al., 1993a). There was sufficient agreement between the farmers involved, and between the farmers and the researchers, about the issues (largely to do with the management of nitrogen in a region only recently beginning to experience noticeable decline in native nitrogen fertility) to form the basis for a research programme.
Gladwin (1976, 1983, 1989) presented her ethnographic decision tree methodology in part to counter some perceived inadequacies of these rapid methods for system diagnosis. In general, descriptive/exploratory RRA has been successful at identifying broad themes requiring the attention of development workers; a PRA provides a framework for beginning to address those issues. However, we are given little guidance about how to proceed in order to pinpoint opportunities for intervention once issues have been identified although, ultimately, these are matters for negotiation between stakeholders. There is insufficient depth to the understanding provided by an exploratory RRA adequately to specify a starting point for negotiations; nor for the detailed design of an intervention to address specific issues; nor for assessing the


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adequacy of any intervention in satisfying the design criteria established during the process. Ethnographic decision tree modelling potentially provides a formalism to do this. Hierarchical (ethnographic) decision models of farmer behaviour have been used in Australia by Murray-Prior (1994).
Our own interest was in the possibility of specifying opportunities for decision support for broadacre dryland crop production in southern and central Queensland, Australia, through better understanding of decision-making processes and the decision-making environment of farmers (Cox et al., 1993b). The Agricultural Production Systems Research Unit is a simulation modelling group established in 1991 jointly by the Queensland Department of Primary Industries and the CSIRO Division of Tropical Crops and Pastures in the belief that decision support technologies, based on the simulation of biophysical processes, can contribute to improved farm-level decision-making.
We began by attempting to represent the content of certain production decisions by specifying the relationships between physical components (such as weather, soil water and nitrogen supply) and actions. We proposed to use Gladwin's decision tree methodology to help uncover the basis for decisions about the use of individual technological components such as nitrogen fertiliser. Decisions are patterned into event nodes and decision nodes which are explored in a logical sequence. This is a behavioral decision tree, comparable in representation (but not in normative content) with the representations of decision analysis (e.g. Goodwin and Wright, 1991). The models can be validated against external data sets, and used predictively.
The attractiveness of Gladwin's framework lay partly in its apparent ability to expose an underlying structure (of linkages between events, and weaknesses in the response of farmers to them) that we might usefully exploit to specify better the design of interventions to support decision making about the use of various technological components. Also, the superficial isomorphism between an ethnographic decision tree model and a rule-based expert system (Alty and Coombs, 1984) suggested that this might be a representation that is adequate not only to identify entry points for change but also to effect change. However, we found this approach inadequate for our purpose. It became clear that an alternative metaphor was needed. This paper explains why and presents an alternative and, we believe, more productive metaphor for system diagnosis.

METHODS

During 1992/93, we interviewed ten farmers in Central Queensland (four in Banana near Moura in the Dawson-Callide region, six in Capella in the Central Highlands) about the way in which they use, or do not use, several technological components believed by researchers to contribute to a more sustainable farming system: opportunity cropping (growing a crop whenever a planting opportunity occurs following rainfall); use of inorganic fertiliser (as


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the natural fertility of the land declines to a point where fertiliser application becomes profitable); purposeful sequences of crops (e.g. in rotations including legumes and a pasture phase); and reduced tillage (to improve water infiltration, reduce run-off and erosion, and increase cropping intensity). The preoccupation of researchers with these particular technological components reflected their appreciation of the importance of water and nitrogen as constraints on the performance of local farming systems. The study period was characterised by a prolonged drought.
The farmers had already begun working with us on a pilot programme of on-farm research (Cox et al., 1993a) and relationships between researchers and farmers had been established before the interviews. The Banana group was a natural group that was operating effectively before our intervention in the onfarm programme; the Capella group was artificial. They were recognised locally as amongst the best farmers in the two areas. Their approach to farming captures many aspects of best current practices. The original interviews were conducted by an agronomist and an economist in the way described by Collinson (1981). Conversations were directed towards discussion of the technological components of interest to researchers, but ranged widely within that constraint. A questionnaire was not used; nor were the interviews tape recorded. Emphasis was on how farmers describe their current practices, and interrogatively exploring opportunities for improved agricultural production. The interviews lasted between two and six hours.
Following the initial round of interviews, we patterned what we had been told as a series of production rules (i.e. IF. .THEN). We made no attempt to distinguish what people said they do and what they do do (Argyris et al., 1985). The rules were grouped into sets corresponding with the technological components we were interested in. The responses were checked and adjusted in a second round of interviews with individual farmers, and in a third round of group discussions at the two main locations. We used copies of the edited notes to feed back to the farmers on both occasions. We also interviewed four other farmers in the Banana area whom our original respondents suggested we should contact to get further insight into local decision-making behavior. Some information about other technological components was also documented. Later, we tried to represent these rules as decision trees as suggested by Gladwin. A preliminary report of this activity has been presented previously (Cox et al., 1993b).
RESULTS

We were only partly successful in terms of establishing a coherent tree-like set of logically related rules that govern behavior. For instance, we were able to impose some structure in the form of a partial decision tree describing the rules for winter crop planting and fertiliser use by farmers in Central Queensland, Figure 1. In this case we could identify more depth, in terms of the number of


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explicitly-stated event and decision nodes and the relationships between them, than for most of the decisions we looked at, although this depth was not apparent in our initial patterning of the rule sets elicited from farmers.

Differences Between Farmers/Farming System
Differences between farmers/farming systems were most pronounced in the Banana area where the rules ior all four components were clearly different for a producer who was primarily interested in cattle (and grew crops for feed), through mixed cattle/cropping enterprises, to a cropping enterprise where cattle are agisted following crop harvest until it rains. For example, the farmers with a cattle with crops system used a different pattern of crop sequence (typically sorghum-sorghum-sorghum-sorghum-oats) than those with crops with cattle and crops systems. The latter plant anything suitable depending on the time of year when an opportunity arose, provided that they have sufficient reserves of soil water and a planting opportunity occurs ("opportunity cropping"). The cattle with crops system manager was not concerned about soil compaction from cattle; the crops system manager was clearly concerned about this and took measures to avoid it happening. The resolution of the rule sets was thus sufficient to distinguish different farming systems, Table 1.
If the farmer is mainly interested in cattle, the crop sequence is dominated by grain sorghum. This can be grown more than three years in a row including ratoon crops. No paddocks are exclusively reserved for crops. If the main interest is in cropping, sorghum does not exceed 50% of the cropping programme. No more than three successive sorghum crops are grown in the same paddock. Wheat and sunflower are also grown regularly. Mung bean is introduced into the crop sequence where there are few surface stones that interfere with harvesting. Green manure crops or lucerne may be used for restoration of fertility.

Differences Between the Two Regions
The area around Capella has a longer history of cropping than Banana. It was opened up for cropping shortly after World War II; the Banana area began wide-scale cropping only 20 years ago, after dairying was abandoned. This is apparent in some of the rules to do with fertiliser use e.g. Capella has a much longer history of fertiliser use and the farmers there have more complex rule sets abotit the best way to use fertiliser.
In Banana, the farmers interviewed use several indicators of the need for nitrogen (N) fertiliser. These include: soil fertility; the history of wheat protein content from that paddock; and cropping intensity. Indicators of the need for phosphorus (P) fertiliser include: soil tests; soil type (together with knowledge of previous soil tests); and plant symptoms ("yield OK").The farmers in


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Figure I: Example of a partial decision tree: winter crop planting and N fertiliser in Capella.


(USE NITROGEN; DON'T USE NITROGEN)


IS SOIL WATER > 70%? YES -





IS THE FERTILITY HIGH? NI'TP'LANT]





IS SOIL NITRATE
NHIGH?NONEFRN




CAN I APPLY
-N AT PLANTING? (ED
( 1PPLYN ATI
-h. APPLYNAT THERE APLANTING OPPORTUNITY? P

APPLY N BEFOREI PLANTING

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Table 1: The crop sequence rule sets of Bruce and Sheila, both farmers in Banana.


Bruce (a cropping system):
I just crop. I have cattle on agistment with the proviso that when we get 2-3 inches or more rain, they've got to go. It depends on what season it is. I can't say what I am going to plant when; it doesn't work. I like rotating my crops. Wheat will not grow on top of sorghum unless you bung a heap of fertiliser on it. I tried Caluna peas one year; they worked well (like a season's fertiliser). You've got to plan for these things a bit so you are ready when it happens. There's possibilities for doing anything but the seasons they're the problem.


Sheila (a cattle with cropping system):
I grow sorghum and cattle. I grow wheat occasionally if I don't get the sorghum in on time (maybe every second or third year). I grow sorghum mainly for cattle. In some paddocks, I grow oats for fodder. I move the fodder crop around; its never in the same place twice. Now I'm trying to get some legume into it. I've thrown in some snail medic when planting wheat/oats; that works well. I have used lucerne (with pigeon grass once); it lasted 4-5 years. I have used wheat, lucerne and pigeon grass at the same time (once). Ratoon sorghum means that I can get more acres of forage. This means that more acres can be planted to grain [sorghum].



Capella have a more comprehensive rule set for fertiliser use than those in Banana. Additional rules for N use relate to: routine nitrate N soil tests; the amount of soil water; seasonal climate forecasts; tax; and equipment (the last two governing timing rather than need). Additional features for P fertiliser use include: the level of VAM (mycorrhiza incidence influenced by crop history); and the nature of the crop (broadleaved).

Representation as Decision Trees
We tried to represent the patterned data as decision trees. We were unable to do this satisfactorily. We went looking for inter-connecting branches, linked to other event and decision nodes, but did not find them. We encountered certain specific problems for a tree model:
linear structure not apparent Although the rules clearly defined a bifurcation or decision point (and a way of handling that decision), the logical sequence of decisions did not emerge from our interview data. It appeared that we were able to see the outer twigs of a decision "bush", but the main limbs


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were obscured. It may be that a linear sequential representation did underlie these rule sets, but this was largely tacit and unconscious. The coherence is provided externally by the way in which the rule sets interact with the sequence of events to which the grower is exposed and which progressively unfolds through time. Coherence does not belong to the rule sets in isolation but only in the context of the situation in which they are applied; it is an emergent property. Linearity is not, and need not be, a feature recognised by farmers in their decision-making behaviour.
interconnections between rule sets The rule sets outlining the way in which individual technological components were managed appeared to intersect at various points. Decisions about cattle determine what crops are grown (both the sequence and the use of opportunity cropping e.g. sorghum versus sunflower, and planting on a reduced soil moisture profile since the crop can be fed off to the cattle if it fails) and tillage practice (e.g. less concern about soil compaction if cattle are kept). The rule sets for tillage practice and opportunity cropping are inter-linked at several points e.g. adoption of zero tillage can generate more planting opportunities and, perhaps, longer planting windows; requirements for stubble cover may dictate crop choice.
links to additional rule sets Other technological components were referred to by farmers in the course of the interviews. In particular, it became apparent that marketing and seasonal climate prediction were topics engaging their interest at the moment. Marketing opportunities are increasingly important following the progressive deregulation of grain marketing in Australia. The interest in seasonal climate prediction was heightened because of the length and severity of the drought in Central Queensland that has lasted since 1991.
simultaneous management of multiple indicators of system performance The criteria by which the performance of the system is judged is not captured in a single numeraire (cf. Scott et al., 1976), nor in simple biophysical measurements such as the status of water and nitrogen. Rather, there are many variables which suggest how the system is performing in both the short- and long-term, which are monitored to a greater or lesser extent. These include revenue, bank balance, water and nitrogen (available N in the short-term; total N in the long-term), but also the status of weeds and pests, and opportunities for experimenting and learning.
open systems The systems described by farmers, and captured in the rule sets, emphasise the open nature of agriculture production systems to the transfer of materials, information and power. One farmer timed his harvesting operations to coincide with the school holidays with its attendant increase in the availability of family labour; opportunity cropping is a recognition of, and a response to, the replenishment of soil water. Outside information impinges on the system and is progressively incorporated, however patchily. In a situation of severe and prolonged drought, for the farmer whose equity falls sharply, the management of the farming system can be dictated by the bank manager.


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stability of the rule sets The rule sets appear to be stable over the period of our interaction with the farmers (about two years). This is a bit surprising in view of the severity of the drought they were experiencing which might have been expected to precipitate more erratic behaviour. We found little evidence of this. There was a greater tendency to consider spring sorghum if a planting opportunity arose, and to plant on a less-than-full profile of soil water, but these minor modifications were well within the resolution of the current rule sets. We suspect that the relative stability of the rule sets is not due to conservative behaviour, nor to a limited ability to modify rule sets, but rather reflects the contingent nature of the rule sets within the context of a variable environment.
rule sets adaptive to changing circumstances All the farmers we spoke to were actively experimenting with their system. Some were starting to think through the best *ay to use inorganic fertiliser in face of a changing perception of the importance of fertility decline in their soil. Some were adopting moisture-seeking deep planting with modified equipment to increase the frequency of planting opportunities. Some were conducting experiments with legumes and green manure crops in face of concern about increasing dependence on nitrogen fertiliser. And some were responding to new information by incorporating it into their rule sets.
importance of interpretation Because the rule sets are contingent on other things happening, either within the boundaries of the system or impinging from the outside world, the ways in which these events are interpreted become of major significance. Some of the discrepancies between the behavior of farmers defined by the rule sets and the recommendations of professional science have to do with differences in the interpretation of the implications of different behaviors. One farmer in Capella regularly tills the soil following a rainfall event to keep it "soft and fluffy" because his interpretation of his own observations (model) suggests that fluffy soil absorbs water faster, even when the advantages of stubble retention for water infiltration have been demonstrated by professional advisers using an iconic model viz. a rainfall simulator. From several incidents, it became clear that farmers and researchers had a different understanding of the way in which plant roots grow and of their ability to extract soil water deep in the profile.

DISCUSSION

System diagnosis is an important step to take prior to considering an intervention. It helps to specify the design characteristics of the intervention and enables us to develop the relationships needed to carry it through. It may be a mistake, however, to spend too much time in this phase of an investigation at the expense of beginning to experiment with novel technologies. It is a first cut at the design process which will be progressively refined as the investigation proceeds. However, it is an important step where social science can make a


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significant contribution (Dewalt, 1985; Tripp, 1985; Jones and Wallace, 1985; Maxwell, 1986; Goodell et al., 1990). Sutherland (1987) discusses the contribution of sociology to FSR. Qualitative research (e.g. Denzin and Lincoln, 1994) helps to provide the basis for a sympathetic understanding of what farmers are trying to do in the way they manage their systems.
Before groups such as the Agricultural Production Systems Research Unit in Australia can develop relevant and useful decision support, they must develop and maintain regular activities for system diagnosis. Otherwise there is a real danger that interventions will be only marginally effective because they fail to address the most important needs of farmers for information and decision support. But it may also be that performance could be lifted most effectively and efficiently by more widespread application of rule sets already used by the most productive farmers.
Our attempt to pattern the decision-making behavior of farmers in Central Queensland demonstrated the extent to which their espoused models are apparently non-linear; non-sequential; relatively straightforward (small number of rules); coherent (exhibiting few contradictions); inter-linked (across technological components); contingent (on the open nature of agricultural production systems); stable (in the face of perturbation such as drought); and dynamic and adaptive to changing conditions (such as long-term fertility decline) and the generation of new knowledge (whether from their own resources or from outside). Different rule sets can be distinguished corresponding with differences in farmer/farming system, region and technological components.
These characteristics are not captured within a decision tree framework without imposing a structure that was not explicit in our discussions with farmers about their rule sets. Decision rules are invoked as needed, rather than in an ordered sequence defined by a tree. This limitation applied whether we examined the rule sets asserted by individuals, or (like Gladwin) the aggregated rule sets of a group. This difference between our experience and Gladwin's may be due to the contingent (marginal, responsive) nature of Central Queensland agriculture, but we suspect that it is a general feature of broadacre dryland farming in our region.
Phillips and Gray (1994) are similarly critical of reductionist decision/rational action models of farming practice because of their lack of both cultural consistency and social/spatial specificity. Alternative forms of knowledge representation and decision rules (e.g. networks, blackboard systems) may provide a more fruitful approach than linear or tree structures. Other approaches to this problem include the participatory development and use of pictorial representations such as resource maps (e.g. Lardon and Albaladejo, 1990; Lightfoot and Minnick, 1991), and soft systems methodology (e.g. Macadam et al., 1990; Mills-Packo et al., 1991). The values underlying both these methodologies also influenced the way in which we approached this


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activity: our concern for gaining understanding of the points of view of the various participants in a problem situation (the value of multiple perspectives); an a priori rejection of the notion that any single representation would prove adequate by itself for any substantive intervention in a human activity system, even though this may be essential to initiate negotiation about change (the use of multiple models); and acceptance of the fundamental importance of communication in bringing about change (through the successive re-negotiation of meaning).
But the argument is not that we were insufficiently skilled to impose a treelike structure on our data. Rather, that a tree is an inadequate metaphor for our purpose, which is to bring about directed change in the management of agricultural production systems. The rules that farmers assert about the way in which they manage various technological components, and our behavior in engaging with farmers about their rule sets, can be represented as a dance. The rule sets can be learned (perhaps by treading on toes, even if inadvertently!). We engage in the dance in order to learn the rules. As we proceed to engage new partners, we propose rules that we have learnt from previous partners. These new rules may or may not be the ones used by our new partner and s/he will tell us if we propose an inappropriate step. We learn the rules by breaking them within the notional dance. This accords with Garfinkel's ethnomethodological approach to learning the rules governing the bounds of acceptable behavior (Garfinkel, 1967). The dancing lessons that we were involved in are extractive in that we were seeking to learn someone else's rules for their own dances. But the dances are also inherently participative: as we become more skilled in the steps, we may begin to influence the conduct of farmers' own dances.
Another way of learning the steps is deliberately to seek out people whose behavior, in their own mind, departs from accepted norms. Some farmers are even more adept than us at breaking the rules in the notional dance. We can learn about the old steps from their experience in developing new ones. New steps developed by practitioners are likely to be particularly powerful because they incorporate local constraints and opportunities which outsiders may miss (e.g. Loevinsohn et al., 1994).
There are many dances going on at once, each with its own rules. Thus, farmers have different dances governing the use of different technological components; and different farmers employ different dance steps for each component. Most interestingly, when farmers were presented with other farmers' dances, they could name them ('That's what Joe does."). Farmers are often aware of the rules used in other people's dances: there may be some steps that can be incorporated into their own routines. They are certainly aware of how their rule sets depart from the norm for farmers in their area, and will offer explanations for such discrepancies often in a self-deprecating way. They are


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keen to learn about the rules of other farmers. All this provides additional evidence that farmers recognise multiple farming systems.
Researchers are involved in co-producing/choreographing/rehearsing/ performing multiple dances with different farmers. The rules can be, and are, modified as the dance proceeds: it can evolve and adapt to changing circumstances. In order to change the rules, change agents have to engage their partners in the dance and discover its rules. We need to demonstrate the new steps, and what advantage they have over the old steps. The new steps may require dancing faster or jumping higher, perhaps beyond the dancing skills of our partners or requiring special apparatus, in which case the new steps may not be adopted. Sometimes, the new steps will work. They will fit with the existing repertoire to provide a more elegant and polished performance. Often, new steps will be adapted before being incorporated by the farmers into other dances.
In Central Queensland, we did begin to introduce some new steps: use of moisture seeking planting equipment when a farmer was faced with a marginal planting opportunity; introduction of a farmer from the Darling Downs who knew different dances to do with zero tillage; use of deep soil coring. But, because of the severe drought experienced throughout the study period, we had limited opportunities to perform emergent steps.
The rule sets, made explicit by the dance, do provide useful models of farmer decision-making. They help to specify criteria for the design of revised models through the specification of:
* a template change can be achieved either by presenting the existing rule
set with a new situation (e.g. additional information about soil nitrogen), or by incrementally modifying the rule set (e.g. so people interpret additional
information differently);
* a baseline by which to judge the marginal improvement claimed for
professional models which need to address more effectively and efficiently the situation currently managed using a particular rule set e.g. use of a
decision support system;
" a source of insights through demonstration of the possibilities associated
with novel steps through the comparison of different rule sets, including those of professional researchers e.g. the use of moisture-seeking planting
equipment;
* entry points for change where the current steps fall down, or where the
superiority of alternative steps is evident e.g. the justification for improved soil monitoring in a situation where soil fertility has declined to a point
where yield and quality may now be responsive to fertiliser use;
" and the basis for substantial dialogue between people who know different
dances e.g. participation in a joint programme of on-farm research.
A knowledge representation based on production rules has several shortcomings. For other than trivial problems, its behavior is difficult to predict


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because this depends on the conflict resolution strategy used (e.g. the ordering of the rule set), and it is not particularly convenient for the representation of randomness (Suppes et al., 1994). Indeed, the logical structures that we have loosely called "trees" (e.g. Figure 1) are not trees at all as this term is more rigorously defined in graph theory (Wilson, 1972): because a tree has exactly n1 edges where n is the number of vertices (nodes); and because a tree cannot contain any circuits. Thus, in Figure 1, there are several ways of determining whether nitrogenous fertiliser should be applied which provide parallel connections between adjacent vertices (i.e. there are more than n-I edges); and, because the decision to plant is repeated in successive seasons, this tree-like segment of the rule set is called recursively within a larger decision framework (i.e. there are circuits).
Dorward (1991), in an example from Malawi, points out that integrated decision rules in small-holder farm management provide an analytical approach which may provide more effective communication in farmerextensionist-researcher dialogue and facilitate the development of more relevant extension messages. He distinguishes two major types of decision: 'task decisions', to do with the allocation of resources to particular tasks; and 'checking decisions', which can interrupt tasks before their completion in face of changing circumstances. This also implies a structure that is not a tree: because there are multiple links between vertices (to maintain both task and checking functions); and because the possibility of revising a decision implies recursion and hence the formation of circuits.
Winograd and Flores (1986) and Wood and Wood-Harper (1993) stress the importance of interpretation as the sticking point in system design not decision-making as a choice between pre-defined alternatives. We have found several examples from our work of a difference in interpretation of a farm situation e.g. how surface management affects the way in which water enters the soil, and how the roots of crops grow in relation to spatially distributed soil resources such as water and nitrogen. These differences in interpretation, if they can be successfully negotiated, potentially lead to improvements in system performance. They do not yet depend on increasing complexity in model specification either to identify them, or to exploit them. Rather they depend on rendering some model components more transparent, more open to discussion, and more available for transformation to fit, or dance with, local circumstances.
In system diagnosis, we are attempting to put ourselves in farmers' shoes, and to appreciate what they are trying to do, so we can report back to professional colleagues about what the world is like and how we might best intervene to change it or at least to facilitate change. But this is very difficult. At best, one can learn to move, or dance, with others. That requires establishing a relationship with them; it cannot be done vicariously. The establishment of relationships directly between participants is needed for the development of a system of action. We are not advocating that a small number


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of professional researchers should form regional dance teams in order to learn to dance with everybody else, this would be cumbersome and costly. But it is important that we learn to dance, and to dance with a range of different partners, if our intention is to influence the decision-making behavior of others in any particular direction and to any great extent.
Janesick (1994) talks about the dance of qualitative research design: "The qualitative researcher is like the dancer, then, in seeking to describe, explain, and make understandable the familiar in a contextual, personal, and passionate way."
The dance metaphor for communication is functional in that it captures the combination of stability and fluidity that we found in the rule sets. It emphasises the requirements for interaction and engagement in order to effect change. The dance satisfies the three criteria that Casti (1989) proposes as characteristic of a good model: its simplicity (the dance metaphor is readily understandable both by researchers and researched); it agrees to a reasonable degree of accuracy with most of the observed data (it captures in an elegant way both what farmers do, and how farmers and researchers communicate about what they do), and explanatory power (it describes what we need to do in order to effect change in the actions of both partners).
The dance metaphor is consistent with the actor-oriented sociology of Long and Long (1992). Unlike systems models (e.g. R61ing, 1988) that emphasise information flow and linkage between different parties in an agricultural knowledge and information system (or AKIS), the guiding notions of the dance are discontinuity between actors in their knowledge of the steps (engagement and dis-engagement in different dances, not linkage), and transformation of meaning (not the transfer of information). Discontinuity because of the way the dance metaphor distinguishes and values different ways of doing things, transformation of meaning through the progress of the dance between partners with different levels of skill, and preference for different dance styles.
It is a counter to the "central source" perspective of many FSR practitioners and planners that Biggs and Farrington (1993) argue has been the main constraint to sustained implementation of FSR. and is compatible with their political economy perspective. Knowledge emerges and develops, and behavior changes, as a product of the interaction conceptualised as dance.

CONCLUDING REMARKS

Gladwin's ethnographic decision tree modelling helped to explore the richness underlying individual decision-making, not captured by an exploratory RRA, and provided a readily-understandable representation. However, excessive preoccupation with the tree representation (and particularly its use in a predictive manner) can impede understanding about the behavior of managed systems, and restrict the access of change agents to them. Our approach is


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consistent with postmodern ethnography (Glesne and Peshkin, 1992; Sarup, 1993) in our concern with how researcher and researched affect each other, how the research act is socially constructed, and the conscious choice of metaphor.
The dance metaphor provides a model of communication that captures in an intriguing way the activities of both data collection and intervention. Indeed, system diagnosis and subsequent attempts to effect change cannot easily be separated: diagnosis, treatment and prognosis go hand in hand. It emphasises the primacy of action, and of action as a source of understanding (Penman, 1994). It captures the need for engagement in order to effect change. It is simultaneously participative and extractive. Qualitative research will have an increasing role in system diagnosis, particularly as system diagnosis and the management of intervention converge within a problem-based methodology (Robinson, 1993).
The proposed change in metaphor (from a tree to a dance) is an important step in changing the behavior of farming systems researchers: the way in which they view the world; the Way in which the potential for change in the management of agricultural production systems is negotiated with other stakeholders; and how we act together to effect change in the probability that certain outcomes will be realised.

ACKNOWLEDGMENTS
The Land and Water Resources Research and Development Corporation, the Grains Research and Development Corporation and the Rural Indusries Research and Development Corporation of Australia all supported financially the work described here. We are grateful to Lisette Ackhurst for technical support. The paper has benefited from the valuable suggestions and comments of Warwick Easdown, Duncan Lowes, Neil Macleod, Peter Carberry and Bob McCown.

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SUSTAINABLE PERI-URBAN VEGETABLE PRODUCTION AND NATURAL RESOURCES MANAGEMENT IN NEPAL'




Hans G.P. Janser, David J. Midmore,- and Durga Dutta Poudel"


ABSTRACT


A diagnostic survey was conducted by a multidisciplinary team in Bhaktapur, Dhading, and Bara districts in Nepal to gain a better qualitative and quantitative understanding of vegetable cultivation in districts which are important for Kathmandu's daily vegetable supplies; and to elicit farmers' perceptions about biotic and abiotic growing constraints. The survey featured informal, but structured farmer interviews, and covered farmers' practices and knowledge, levels of inputs and yields, field observations, and problem identification. Vegetables in the three survey district are grown in a large number of highly different cropping patterns, varying from year-round vegetable cultivation in Bhaktapur, rice or maize-based systems in Dhading, and rice-wheat systems in Bara. The use of inputs in vegetable cultivation is high and at times excessive, particularly for chemical fertilizers and insecticides. However, returns to vegetable growing exceed those to rice and wheat manifold, but risk and capital investment are also considerably higher.
While diseases and (particularly) pests seriously reduce yield, most vegetable growers suffer from a lack of information

1 All three authors were at the Asian Vegetable Research and Development Center (AVRDC), Taiwan, R.O.C. at the time this paper was prepared.
2 Research Program on Sustainability in Agriculture (REPOSAUAW/CATIE/MAG), Guipiles, Costa Rica.
3 Department of Biology, Central Queensland University, Rockhampton Qld 4702, Australia.
4 University of Georgia, Athens, Georgia, U.S.A.


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about pest and disease management, with integrated pest management (IPM) practices virtually unknown. Similarly, more and better information is needed regarding appropriate use of chemical fertilizers. The performance of extension services in providing the necessary information is inadequate.
Both input delivery and output marketing systems are inefficient, resulting in scarcities or even lack of inputs at critical times and high costs for farmers. Particularly, quality and availability of seed represent a major difficulty for many vegetable growers. Suggestions are offered for follow-up research activities as well as government investments and
policy measures.


Background and Justification
Vegetable production in Nepal during 1991/92 is reported as just over 1 million tons (t) (ASD 1992; VDD 1992; Table 1). The 1992 production is obtained from an area of 140,500 hectares (ha) (DOH 1992). The latter represents just over 5% of total arable land in Nepal (Jansen 1992). At approximately 2% of domestic production (unpublished data from the Agricultural Marketing Development Division), international trade in vegetables is relatively minor and mostly with India, partially because of the overvalued Nepali rupee. However, imports of vegetables exceed exports manifold, with unrecorded imports from India adding considerably to a negative trade balance in vegetables of about 3 million US dollars in 1991 (unpublished data from the Department of Customs). The government has interest in closing the vegetable trade deficit through increasing commercial production of vegetables near major foci of demand (so-called peri-urban vegetable production). Improvements in the management of the natural resources base, better crop management, identification and introduction of appropriate production technologies, and better handling and marketing of the products all could contribute to achieving this goal. Government efforts in these areas center around the so-called Special Program which focuses on peri-urban vegetable production. The government tries to make improved seeds, credit, training, and extension support available to participating farmers.
Average post-harvest weight-based losses in vegetables in Nepal have been estimated at around 25% (Sharma 1988, Shrestha et al. 1992). Losses for vegetables produced in the terai and sold in the Kathmandu area are in the order of 23% for tomatoes, 16% for cabbage, and 12% for cauliflower (Werner and Subedi 1991). Physical losses occur mainly due to insufficient market information, poor transportation facilities, and inappropriate handling procedures.


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Table 1. Area, production and yield of vegetables in Nepal, 1991/92

Location Area (103 ha) Production (103 t) Yield (t ha-')


Bhaktapur 4.21 30.00 7.12
district

Dhading 2.02 19.42 9.59
district

Bara district 16.40 119.51 7.27

Nepal 140.49 1009.63 7.18


Source: unpublished data from the Vegetable Development Division, Department of Agriculture, Ministry of Agriculture.


Kathmandu, the major focus of urban demand, has an estimated population of 1.2 million, which has doubled during the decade of the 1980s (Rekhi et al. 1990), and is now growing at an even higher annual rate of 7.3%, dramatically exceeding the national population growth of 2.1% per year (national census 1991 and World Bank 1994). Current interest in vegetable production is based upon the fact that vegetable consumption, in a country where animal forms of protein and nutrients are minimal, is low -- less than 50 kg per capita per year. Moreover, vegetable production in Kathmandu Valley is decreasing rapidly due to increasing urbanization and industrialization. The total volume of vegetables supplied to markets in Kathmandu Valley has been estimated at only 27,000 mt (HMG/FAO 1993) which (ignoring seasonality in supplies) translates into an average yearly per capita supply of less than 25 kg, or less than one-third of the recommended amount (Ali et al., forthcoming). To ensure vegetable supply which maintains the acceptable level of around 75 kg per capita per year, while keeping abreast with predicted population growth, production of vegetables must be intensified on existing land, especially if new marginal land tracts are not to be exploited. Such an intensification has been experienced in a number of Southeast Asian countries in which a demand-led expansion of vegetable production has taken place.
The high urban demand for vegetables has already stimulated the emergence of peri-urban vegetable production as a high-input industry. Often


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high doses of pesticides and fertilizers are applied that may cause severe environmental degradation and human health hazards. Commercial vegetable production areas (Special Program areas) such as the Kathmandu peri-urban vegetable production are likely to be responsible for a disproportionately large part of total chemicals used in vegetable production.
Kathmandu receives its supply of vegetables from diverse geographic regions. During the hot summer months, vegetables are supplied predominantly from the highlands. (i.e., temperate crops, e.g., cabbage), with some supplementary leafy vegetables from the peri-urban context (including the "true" peri-urban zone of Thimi/Bhaktapur in Kathmandu Valley where yearround vegetable production is practiced, and Dhading and Kavrepalanchowk districts). In addition, some cucurbitaceous vegetables are supplied from the terai, during the summer. Supplies of vegetables during the winter (October-February) are dominated by the terai (Nepal and India) with less from the Valley, when cool temperatures in the highlands preclude production of most crops.
For the future it is imperative to plan expansion of production in accord with available national and purchased inputs (especially fertilizer and seed); with marketing facilities with concomitant inroads into reduction of postharvest losses; and with development of technology which will ensure equitable increases in production while ensuring environmental protection (in the widest sense to include humans too).


METHODOLOGY

Diagnostic methods, combined with RRA-type surveys, have proven to be extremely useful for eliciting farmers' problems, constraints and opportunities; identifying potential areas for intervention; and prioritizing preliminary key researchable issues (Fujisaka 1989, 1991; Tripp and Wooley 1989). A diagnostic survey lasting one week was carried out in November 1993 by a multidisciplinary team comprised of personnel from the Asian Vegetable Research and Development Center (AVRDC) and a number of collaborating agencies, including the Vegetable Development Division (VDD) and various other offices of the Department of Agriculture, among vegetable growers in three major vegetable supply areas for Kathmandu. Members of the team included an agricultural economist, a geographical information systems (GIS) specialist, a plant pathologist, an entomologist, a cropping systems specialist, two horticulturists, as well as one or two local extension specialists in each survey site. Members of the survey team discussed objectives and methods prior to beginning the field survey. While working in pairs using informal, open-ended interviews but guided by a checklist, farmers were interviewed to understand farmer practices and technical knowledge. The team characterized


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and described the climate, cropping systems, topography, soil, vegetable marketing channels, agronomic practices, major land-use systems, and transportation systems of vegetables in the study areas. In addition, the AVRDC team members visited various government organizations, NGOs, wholesalers, retailers, as well as national and international research organizations. Except for the Thimi area in Bhaktapur district where nearly exclusively year-round vegetable cultivation is practiced, many farmers were busy harvesting their main rice crop and preparing land for winter vegetables during the period of the survey. Farmers were mostly interviewed on their farms or in the villages, and members of the survey teams made sure to visit farmers' fields. The information gathered was synthesized with some the (limited) secondary information available.
The survey was conducted among a total of 50 vegetable growers in the following three districts in Nepal, all of which are important suppliers of vegetables to Kathmandu city (Rekhi et al. 1990): Bhaktapur, Dhading, and Bara (Fig. 1). A total of 17 sites were visited based on the importance of vegetables in prevailing cropping patterns: 5 sites in Bhaktapur (Sano Thimi, Dhadikot, Shiphadole, Jhoukhel, and Bhakha Bazaar); 7 sites in Dhading (Simle, Dharke, Khanikhola, Gobre Tar, Gajuri Tar, Baireni, and Benighat); and 5 sites in Bara (Bishambarpur, Prastoka, Raghunathpur, Amritgunj, and Hariharpur). At each site, three randomly chosen farmers were interviewed.


OBJECTIVES

The specific objectives of the survey, the first of its kind for vegetables in Nepal, included the following:
a. To gain an understanding of vegetable cultivation systems in districts which are important for Kathmandu's daily vegetable supplies. b. To provide a qualitative and (to a lesser extent) quantitative description of vegetable growing practices in the three survey districts in terms of cultivation methods, production systems, yields, input use, and profitability. c. To elicit farmers' perceptions of major constraints, both those of a biophysical and of a socioeconomic and institutional nature. d. To arrive at suggestions for research and government policies aimed at improving vegetable production.


DESCRIPTION OF THE STUDY AREAS Location
Bhaktapur district in Kathmandu Valley was chosen primarily because of its geographical proximity to Kathmandu and because most of it is representative of the "true" peri-urban vegetable zone. The survey area is a plateau with an


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elevation ranging from 1,300 to 1,450 m asl. The road distance from Kathmandu is less than 15 km for all survey sites in Bhaktapur district. Vegetable areas in Bhaktapur district do not usually exceed 0.3 ha and may be as small as 0.05 ha. Average farm size in Bhaktapur is just under 0.5 ha (HMG 1991).
Dhading district is located just outside the valley but with all survey sites within 1-4 hours' drive (between 25 and 90 kin) from Kathmandu. The survey area is quite hilly, with distinct lowlands and uplands, where elevation ranges from 450 to 1,000 m asl, i.e., considerably lower than the Kathmandu Valley plateau. Dhading district is an important supplier of vegetables to Kathmandu Valley, particularly during the period February-June for tomato, eggplant, beans, and capsicum (HMG/FAO 1993). At 1.3 ha, average farm size in Dhading is nearly three times that of Bhaktapur (HMG 1991). Similarly, vegetable areas (up io 0.5 ha) generally are also considerably larger than in Kathmandu Valley.
Bara district is located in the terai area, bordering Bihar state in India. The area is flat, with an elevation of about 100 m asl. Distances from Kathmandu are substantial at about 300 km, taking 10 hours by road. Bara district is also an important supplier of vegetables to Kathmandu Valley, particularly during the period October-February for tomato, eggplant, onion, radish, cauliflower, cole crops, and okra (HMG/FAO 1993). Farm sizes are larger than in the other survey districts, with an average of 1.6 ha (HMG 1991). However, due to relatively high capital requirements for vegetable cultivation and local extensionists' bias, survey farms were all somewhat larger than the average, with farm sizes up to 10 ha.

Climate
Temperatures show an inverse relationship to altitude, with mean maximum temperatures in Dhading significantly exceeding those in Kathmandu Valley, while in the terai the climate is truly tropical. Summer maximum temperatures exceeding 300C and dropping only marginally below 26oC at night during that period, combined with lots of rain and high relative humidity (>80%), make for difficulties in vegetable cultivation during this period. However, the terai has a definite advantage over higher altitude locations elsewhere in Nepal during some of the coldest months. For example, Kathmandu is heavily dependent on the terai for winter season fresh tomato (Bhattarai 1992). The climate in Dhading district is classified as subtropical, with generally lower night temperatures than in Bara district. Night temperatures in Kathmandu Valley drop well below 20'C virtually throughout the year, creating larger possibilities for year-round vegetable cultivation in Bhaktapur district. On the other hand, night temperatures during the winter months are too low for some fruiting vegetables (e.g., tomato), creating a dependency during those months on the terai.


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Even though vegetables are mostly grown under irrigated conditions, rainfall is important, particularly where the irrigation water is mainly supplied by surface sources with rain water from hills, rivers, streams, and springs diverted (either directly or through canals) to the vegetable fields. Average yearly rainfall does not differ dramatically between the three survey districts (varying from 1,420 mm in Bhaktapur to 1,585 mm in Dhading), although
rainfall variability in Bhaktapur is only about half that in the other two survey districts. If the number of rainy days gives any indication about rainfall intensity as well as spread, then rainfall is considerably more evenly spread in Bhaktapur district as compared to Dhading and (particularly) Bara district. Relative humidity is somewhat lower in Bara as compared to the other two survey districts.

Cropping Patterns
The features of the different land types in each of the survey sites in the three districts are given in Table 2. Besides visual observations, information regarding soils was obtained from land capability maps generated by a Canadian-funded land resource mapping project (KESL 1986). More detailed information can be found in Jansen et al. (1994).


Table 2. Land features in survey sites

District/ Elevation Landscap Soils
survey site m)

Bhaktapur diti

Sano Thimi 1,300 traditional year-round vegetable growing area, dark loamy
flat riverside terraces, borehole irrigation fertile, with river sand
Dhadikot 1,310 relatively new vegetable area, rice-based systems, dark loamy,
gentle slopes, flat riverside terraces, surface fertile, well irrigation drained

Siiphadole 1,430 year-round vegetable cultivation, slightly sloping clayey, well
lands, leveled terraces, borehole irrigation drained

Jhoukhel 1,344 rice-based vegetable growing systems, lowlands, dark and
mainly surface irrigated with few borcholes fertile Bhakha Bazaar 1,330 vegetables mostly grown in rice-based systems, heavy wet
sloping, terraced lands soils
Dhading district

Simla 700 vegetables mostly grown in rice-based systems on alluvial, well
flat riverside leveled terraces, some year-round drained vegetable cultivation, surface irrigation


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Dharke 750 vegetable cultivation in both uplands and in alluvial, well
lowlands along riversides, sloping lands, terraces, drained surface irrigation but not year-round

Khanikhola 1,000 vegetables cultivated in combined upland maize- clayey,
based and lowland rice-based farming systems, infertile, well terraced, irrigation not year-round in erosion uplands prone

Gobre Tar 460 rolling landscape without terracing, vegetables reddish, low
grown in both wheat-maize and rice-based organic
systems, surface irrigation but not year-round matter

Gajuri Tar 450 lowland vegetable cultivation after rice on reddish, low
sloping, well-terraced land with surface irrigation organic matter

Baireni 450 combined upland and lowland farming systems, reddish, low
vegetables grown in both rice-wheat and maize- organic based systems, surface irrigation but not year- matter round in uplands
Baighat 500 vegetables (only winter) cultivated on hill clayey, deep,
plateaus with some leveled flat terraces as well, well drained in rice-wheat, rice-maize and rice-potato based systems, good surface irrigation facilities

Bara district

Bishambarpur 91 traditional rice-based vegetable growing area, flat alluvial,
with borehole irrigation fertile, not
well drained

Prastoka 99 flat, close to river with farmer-built temporary alluvial,
dam, some tubewell irrigation as well, vegetables fertile, not grown in rice-based systems well drained

Raghunathpur/ 90 flat, traditional rice-based vegetable growing alluvial,
Puraia/Inaruwa area, borehole irrigation fertile, deep,
not Well
drained

Amritgunj/ 81 flat area with vegetable cultivation mostly in alluvial,
Salempur rice-based systems but also some year-round fertile, deep,
canal irrigation for low-lying lands, boreholes for not well lands higher than canal drained

Hariharpur 82 flat area with vegetable cultivation mostly in alluvial,
rice-based systems but also some year-round fertile, deep cultivation, canal irrigation for low-lying lands, bordholes for higher lands


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