MULTI-SCALE INTEGRATED ANALYSIS OF AGROECOSYSTEMS © 2004 by CRC Press LLC Advances in Agroecology Series Editor: Clive A.Edwards Agroecosystem Sustainability: Developing Practical Strategies Stephen R.Gliessman Agroforestry in Sustainable Agricultural Systems Louise E.Buck, James P.Lassoie, and Erick C.M.Fernandes Biodiversity in Agroecosystems Wanda Williams Collins and Calvin O.Qualset Interactions Between Agroecosystems and Rural Communities Cornelia Flora Landscape Ecology in Agroecosystems Management Lech Ryszkowski Soil Ecology in Sustainable Agricultural Systems Lijbert Brussaard and Ronald Ferrera-Cerrato Soil Tillage in Agroecosystems Adel El Titi Structure and Function in Agroecosystem Design and Management Masae Shiyomi and Hiroshi Koizumi Tropical Agroecosystems John H.Vandermeer Advisory Board Editor-in-Chief Clive A.Edwards The Ohio State University, Columbus, OH Editorial Board Miguel Altieri University of California, Berkeley, CA Lijbert Brussaard Agricultural University, Wageningen, The Netherlands David Coleman University of Georgia, Athens, GA D.A.Crossley, Jr. University of Georgia, Athens, GA Adel El-Titi Stuttgart, Germany Charles A.Francis University of Nebraska, Lincoln, NE Stephen R.Gliessman University of California, Santa Cruz Thurman Grove North Carolina State University, Raleigh, NC Maurizio Paoletti University of Padova, Padova, Italy David Pimentel Cornell University, Ithaca, NY Masae Shiyomi Ibaraki University, Mito, Japan Sir Colin R.W.Spedding Berkshire, England Moham K.Wali The Ohio State University, Columbus, OH © 2004 by CRC Press LLC MULTI-SCALE INTEGRATED ANALYSIS OF AGROECOSYSTEMS MARIO GIAMPIETRO CRC PRESS Boca Raton London New York Washington, D.C. © 2004 by CRC Press LLC Library of Congress Cataloging-in-Publication Data Giampietro, M. (Mario) Multi-scale integrated analysis of agroecosystems/Mario Giampietro. p. cm. (Advances in agroecology) Includes bibliographical references and index. ISBN 0-8493-1067-9 (alk. paper) 1. Agricultural ecology. 2. Agricultural systems. I. Title II. Series. S589.7.G43 2003 338.1–dc22 2003059613 This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. A wide variety of references are listed. Reasonable efforts have been made to publish reliable data and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the consequences of their use. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming, and recording, or by any information storage or retrieval system, without prior permission in writing from the publisher. The consent of CRC Press LLC does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific permission must be obtained in writing from CRC Press LLC for such copying. Direct all inquiries to CRC Press LLC, 2000 N.W. Corporate Blvd., Boca Raton, Florida 33431. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation, without intent to infringe. Visit the CRC Press Web site at www.crcpress.com © 2004 by CRC Press LLC No claim to original U.S. Government works International Standard Book Number 0-8493-1067-9 Library of Congress Card Number 2003059613 Printed in United States of America 1 2 3 4567890 Printed on acid-free paper © 2004 by CRC Press LLC If a student is not eager, I won’t teach him; If he is not struggling with the truth, I won’t reveal it to him. If I lift up one corner and he can’t come back with the other three, I won’t do it again. —The Analects, Confucius © 2004 by CRC Press LLC Preface Warning to the Potential Reader of This Book Discussing the implications of a paradigm change in science, Allen et al. (2001) said: “A paradigm change modifies protocols, vocabulary or tacit agreements not to ask certain questions” (p. 480). If we agree with this brilliant definition, and therefore if we accept that a scientific paradigm is “a tacit agreement not to ask certain questions,” the next step is to find out why certain questions are forbidden. In general, the questions that cannot be asked from within a scientific paradigm are those challenging the basic assumptions adopted in the foundations of the relative disciplinary scientific knowledge. The enforcement of this tacit agreement is a must for two reasons. First, it is required to preserve the credibility of the established set of protocols proposed by the relative disciplinary field (what the students learn in university classes). Second, it makes it possible for the practitioners of a disciplinary field to focus all their attention and efforts only on how to properly run the established set of protocols, while forgetting about theoretical issues and controversies. In fact, the acceptance of a scientific paradigm prevents any questioning of the usefulness of the established set of protocols developed within a disciplinary field for dealing with the task faced by the analyst. When dealing with a situation of crisis of an existing scientific paradigm—and many seem to believe that in relation to the issue of sustainability of human progress we are facing one—we should expect that such a tacit agreement will get us into trouble. Whenever the established set of protocols (e.g., analytical tool kits) available for making analysis within disciplinary fields is no longer useful, the number of people willing to ask forbidden questions reaches a critical size that overcomes the defenses provided by academic filters. After reaching that point, criticizing the obsolete paradigm is no longer a taboo. In fact, nowadays, several revolutionary statements that carry huge theoretical implications about the invalidity of the foundations of leading scientific disciplines are freely used in the scientific debate. For example, expressions like “the myth of the perpetual growth is no longer acceptable (why?),” “it is not possible to find an optimal solution when dealing with contrasting goals defined on different dimensions and scales (why?)” and “we cannot handle uncertainty and ignorance just by using bigger and better computers (why?)” in the 1970s and 1980s have been sanguinary battlefields between opposite academic disciplines defending the purity of their theoretical foundations. These expressions are now no longer contested. Actually, we can even find softened versions of these statements included in the presentation of innovative academic programs and in documents generated by United Nations agencies. This situation of transition, however, generates a paradox. In spite of this growing deluge of unpleasant forbidden questions about the validity of the foundations of established disciplinary scientific fields, nothing is really happening to the teaching of protocols within the academic fields under pressure for change. In fact, at this point, the lock-in that is protecting obsolete academic fields no longer works against posing forbidden questions. Rather, it works by preventing the generation of answers to these © 2004 by CRC Press LLC forbidden questions. The mechanism generating this lock-in is simple and conspiracy-free. Academic filters associated with obsolescent disciplinary knowledge do their ordinary work by attacking every deviance (those who try to find new perspectives). This applies to both those who develop nontraditional empirical analyses (e.g., putting together data in a different way, especially when they obtain interesting results) and those who develop nontraditional theories (e.g., putting together ideas in a nonconventional way, especially when they obtain interesting results). The standard criticism in these cases is that “this is just empirical work without any sound theory supporting it” or that “this is just theoretical speculation without any empirical work supporting it.” When innovative theories are developed to explain empirical results, the academic filter challenges every single assumption adopted in the new theory (even though it is totally neglecting to challenge even the most doubtful assumptions of its own discipline). Finally, whenever the academic filter is facing the unlikely event that (1) a new coherent theory is put forward, (2) this theory can be defended step by step starting from the foundations, (3) experimental data are used to validate such a theory and (4) this theory is useful for dealing with the tasks faced by the analysts, the unavoidable reaction is always the same: “This is not what our disciplinary field is about. Practitioners of our field would never be interested in going through all of this.” Obviously, the analysis of this mechanism of lock-in—very effective in preventing the discussion of possible answers to forbidden questions—has a lot to do with the story that led to the writing of this book. This is why I decided to begin with this preface warning potential buyers and readers. This book represents an honest effort to do something innovative in the field of the integrated analysis of sustainability of agricultural systems, that is, an honest effort to answer a few of the forbidden questions emerging in the debate about sustainability. This book reflects a lot of work and traveling to visit the most interesting groups that are doing innovative things related to this subject in various disciplinary and interdisciplinary fields. I wrote this book for those who are not happy with the analytical tools actually used to study and make models about the performance of farming systems, food systems and agroecosystems, and especially for those interested in considering various dimensions of sustainability (e.g., economic, ecological, social) simultaneously and willing to reflect in their models the nonequivalent perspectives of different agents operating at different scales. The mechanism that generated the writing of this book is also simple. There is an old Chinese saying (quoted by Röling, 1996, p. 36) that puts it very plainly: “If you don’t want to arrive where you are going, you need to change direction.” What does this mean for a scientist or practitioner changing direction? In my interpretation of the Chinese saying, this means going back to the foundations of the disciplinary knowledge that has been used to develop the analytical tools that are available and in use at the moment and trying to see whether it is possible to do things in an alternative way. When I started my journey many years ago, as a scientist willing to deal with the sustainability of agriculture, I had to swim in a sea of complaints about the inadequacy of reductionism, the lack of holism and the need of a paradigm shift in science. This ocean of complaints was linked to the acknowledgment of a never-ending list of failures of the applications of the conventional approach in relation to the sustainability of agriculture in both developed and developing countries. However, in spite of all of these complaints, when looking at scientific papers dealing with the sustainability of agriculture, in the vast majority of cases I found models that were based on the same old set of tools (e.g., statistical tests and differential equations). These models were applied to an incredible diversity of situations, always looking for the optimization of a function assumed to represent a valid (substantive) formal definition of performance for the system under investigation. Since I was then and still am convinced that I am not smarter than the average researchers of this field, I was forced to realize that if I wanted to arrive in a different place, I had to change the path I was on. Otherwise, I would have joined the party of optimizers already jammed at the end of it. When you take a wrong path and want to get on another one, you must go back to the bifurcation where you made the bad turn. This is why I decided to go back to the theoretical foundations of the analytical tools I was using, to try to see if it were possible to develop an alternative set of tools useful to analyze in a different way the complex nature of agroecosystems. Then I found out that the new field of complex systems theory implied the rediscovery of old epistemological issues and new ways of addressing the challenge implied by modeling. © 2004 by CRC Press LLC This book is an attempt to share with the reader what I learned during this long journey. The text is organized in thr ee parts: Reality. After acknowledging that there is a problem with reductionism when dealing with the sustainability of agroecosystems (in Chapter 1), the remaining four chapters provide new vocabulary, narratives and explanations for the epistemological predicament entailed by complexity. Chapter 2 starts by looking at the roots of that predicament, focusing on the neglected distinction between the perception and representation of reality. Additional concepts required to develop an alternative narrative are introduced and illustrated with practical examples in Chapter 3. The resulting challenge for science when used for governance in the face of uncertainty and legitimate contrasting values is debated in general terms in Chapter 4. Finally, an overview of the problems associated with the development of scientific procedures for participatory integrated assessment is discussed in Chapter 5. This part introduces a set of innovative concepts derived from various applications of complex systems thinking. These concepts can be used to develop a tool kit useful for handling multi- scale integrated analysis of agroecosystems. In particular, three key concepts are introduced and elaborated on in the three chapters making up this second part: 1. Chapter 6—Multi-scale mosaic effect 2. Chapter 7—Impredicative loop analysis 3. Chapter 8—Unavoidable necessity of developing useful narratives to surf complex time ecosystems. This part presents a tool kit based on the combined use of the previous three concepts to obtain a multi-scale integrated analysis of agroecosystems. This third part is organized into three chapters: 1. Chapter 9—Bridging disciplinary gaps across hierarchical levels 2. Chapter 10—Bridging changes in societal metabolism to the impact generated on the ecological context of agriculture 3. Chapter 11—Benchmarking and tailoring multi-objective integrated analysis across levels After having put the cards on the table with this outline, I can now move to the warning for potential readers and buyers: Who would be interested in reading such a book? Why? This is not a book for those concerned with being politically correct, at least according to the definitions adopted by existing academic filters. This book is weird according to any of the conventional standards adopted by reputable practitioners. This is scientific research in agriculture that is not aimed at producing more and better. Rather, this is research aimed at learning how to define what better means for a given group of interacting social actors within a given socioeconomic and ecological context. Within this frame, the real issue for scientists is that of looking for the most useful scientific problem structuring. It should be noted that hard scientists who use models to individuate the best solution (a solution that produces more and better than the actual one) are operating under the bold assumption that it is always possible to have available: (1) a win-win solution, that is, that more does not imply any negative side effects and (2) a substantive formal definition of better that is agreed to by all social actors and that can be used without contestation as an input to the optimizing models. According to this bold assumption, the only problem for hard scientists is that of finding an output generated by the model that determines a maximum in improvement for the system. If we were not experiencing the tragic situation we are living in (malnutrition, poverty and environmental collapse in many developing countries associated with bad nutrition, poverty and environmental collapse in many developed countries), this blind confidence in the validity of such a bold assumption would be laughable. After having worked for more than 20 years in the field of ecological economics, sustainable development and sustainable agriculture in both developed and developing countries, I no longer, unfortunately, find the blind confidence in the validity of this bold assumption amusing. © 2004 by CRC Press LLC • Part 1: Science for Governance:The Clash of Reductionism against the Complexity of • Part 2: Complex Systems Thinking: Daring to Violate Basic Taboos of Reductionism. • Part 3: Complex Systems Thinking in Action: Multi-Scale Integrated Analysis of Agro- Sustainability, when dealing with humans, means the ability to deal in terms of action with the unavoidable existence of legitimate contrasting views about what should be considered an improvement. Winners are always coupled to losers. To make things more difficult, nobody can guess all the implications of a change. If this is the case, then how can this army of optimizers know that their definition of what is an improvement (the one they include in formal terms in their models as the function to be optimized) is the right one? How can it be decided by an algorithm that the perspectives and values of the winners should be considered more relevant than the perspectives and values of the losers? Sustainability means dealing with the process of “becoming.” If we want to avoid the accusation of working with an oxymoron (sustainable development), we should be able to explain what in our models remains the same when the system becomes something else (in a sustainable way). That is, we should be able to individuate in our models what remains the same when different variables, different boundaries and emerging relevant qualities will have to be considered to represent the issue of sustainability in the future. Optimizing models either maximize or minimize something within a formal (given and not changing in time) information space. When dealing with a feasible trajectory of evolution, the challenge of sustainability is related to the ability to keep harmony among relevant paces of change for parts (that are becoming in time), which are making up a system (that is becoming in time), which is coevolving with its environment (that is becoming in time). This requires the simultaneous perception and representation of events over a variety of space- time scales. The various paces of becoming of parts, the system and the environment are quite different from each other. Can this cascade of processes of becoming and cross-relations be studied using reducible sets of differential equations and traditional statistical tests? A lot of people working in hierarchy theory and complex systems theory doubt it. This book discusses why this is not possible. These fundamental questions should be taken seriously, especially by those who want to deal with sustainability in terms of hard scientific models (by searching for a local maximum of a mathematical function and for significance at the 0.01 level). It is well known that when dealing with life, hard science often tends to confuse formal rigor with rigor mortis. In this regard, the reductionist agenda is well known. To study living systems, we first have to kill them to prevent adjustment and changes during the process of measurement. The rigorous way, for the moment, provides only protocols that require reducing wholes into parts and then measuring the parts to characterize the whole. Is it possible to look at the relation of wholes and parts in a new way? Can we deal with chicken-egg paradoxes, when the identity of the parts determines the identity of the whole and the identity of the whole determines the identity of the parts? Obviously, this is possible. This is how life, languages and knowledge work. This book discusses why and how this can be done in multi-scale integrated analysis of agroecosystems. Finally, there is another very interesting point to be made. Are these forbidden questions about science new questions? The obvious answer is not at all. These are among the oldest and most debated issues in human culture. Humans can represent in their scientific analyses only a shared perception about reality, not the actual reality. Models are simplified representations of a shared perception of reality. Therefore, by definition, they are all wrong, even though they can be very useful (Box, 1979). But to take advantage of their potential usefulness—in terms of a richer understanding of the reality— it is necessary to be aware of basic epistemological issues related to the building of models. The real tragedy is that activities aimed at developing this awareness are considered not interesting or even not “real science” by many practitioners in hard sciences. On the contrary, this is an issue that is considered very seriously in this book. From this perspective, complex systems theory has merit to have put back on the agenda of hard scientists a set of key epistemological issues debated in disciplines such as natural philosophy, logic and semiotics, which, until recently, were not viewed as hard enough. It is time to reassure those potential readers who got scared by the outline and the ensuing discussion. What does all of this have to do with a multi-scale integrated analysis of agroecosystems? Well, the point I have been trying to make so far is that it has a lot to do with multi-scale integrated analysis of agroecosystems. In the last 20 years, I have been generating a lot of numbers about the sustainability of agricultural systems by studying this problem from different perspectives (technical coefficients, farming systems, global biophysical constraints, ecological compatibility) and using various sets of variables (energy, money, water, demographics, sociality). In the beginning, this was done by following intuitions about how to do © 2004 by CRC Press LLC things in a different way. Later on, after learning about hierarchy theory, postnormal science and complex systems theory (especially because of the gigantic contributions of Robert Rosen), I realized that it was possible to back up these intuitions with a robust theory. This made possible the organization of the various pieces of the mosaic into an organic whole. This is what is presented in Part 2 of this book. Part 2 provides new approaches for organizing data and examples of applications of multi-scale integrated analysis of agroecosystems to real cases. The results presented in Part 3, in my view, justify the length and heterogeneity of issues presented in Parts 1 and 2. In spite of this, I understand that cruising Parts 1 and 2 is not easy, especially for someone not familiar with the various issues discussed in the first eight chapters. On the other hand, this can be an occasion for those not familiar with these topics to have a general overview of the state of the art and reference to the literature. There is a standard predicament associated with scientific work that wants to be truly interdisciplinary. Experts of a particular scientific field will find the parts of the text dealing with their own field too simplistic and inaccurate (an uncomfortable feeling when reading about familiar subjects), whereas they will find the parts of the text dealing with less familiar topics obscure and too loaded with useless and irrelevant details (an uncomfortable feeling when reading about unfamiliar subjects). This explains why genuine transdisciplinary work is difficult to sell. As readers we are all bothered when forced to handle different types of narratives and disciplinary knowledge. Nobody can be a reputable scholar in many fields. To this end, however, I can recycle the apology written by Schrödinger (1944) about the unavoidable need of facing this predicament: A scientist is supposed to have a complete and thorough knowledge, at first hand, of some subjects and, therefore, is usually expected not to write on any topic of which he is not a life master. This is regarded as a matter of noblesse oblige. For the present purpose I beg to renounce the noblesse, if any, and to be the freed of the ensuing obligation. My excuse is as follows: We have inherited from our forefathers the keen longing for unified, all-embracing knowledge. The very name given to the highest institutions of learning reminds us that from antiquity to and throughout many centuries the universal aspect has been the only one to be given full credit. But the spread, both in width and depth, of the multifarious branches of knowledge during the last hundred odd years has confronted us with a queer dilemma. We feel clearly that we are only now beginning to acquire reliable material for welding together the sum total of all that is known into a whole; but, on the other hand, it has become next to impossible for a single mind fully to command more than a small specialized portion of it. I can see no other escape from this dilemma (lest our true who aim be lost for ever) than that some of us should venture to embark on a synthesis of facts and theories, albeit with second-hand and incomplete knowledge of some of them—and at the risk of making fools of ourselves. To make the life of the reader easier, the text of the first eight chapters has been organized into two categories of sections: 1. General sections that introduce main concepts, new vocabulary and narratives using practical examples and metaphors taken from normal life situations 2. Technical sections that get into a more detailed explanation of concepts, using technical jargon and providing references to existing literature The sections marked “technical” can be glanced through by those readers not interested in exploring details. In any case, the reader will always have the option to go back to the text of these sections later. In fact, when dealing with a proposal for moving to a new set of protocols, vocabulary and tacit agreements not to ask certain questions, one cannot expect to get everything in one cursory reading of a book. Actually, the goal of the first eight chapters is to familiarize the reader with new terms, new concepts and new narratives that will be used later on to propose innovative analytical tools. This means that the structure of this book implies a lot of redundancy. The same concepts are first introduced © 2004 by CRC Press LLC [...]... across Levels 11 .1 Farming System Analysis 11 .1. 1 Defining Farming System Analysis and Its Goals 11 .1. 2 Farming System Analysis Implies a Search for Useful Metaphors 11 .1. 3 A Holarchic View of Farming Systems (Using Throughputs for Benchmarking) 11 .1. 4 Benchmarking to Define Farming System Typologies 11 .2 Individuating Useful Types across Levels 11 .2 .1 The Land-Time Budget of a Farming System 11 .2.2 Looking... Requirements for Inputs 10 0 5.2.2.2 Household’s Perspective 10 1 5.2.2.3 Country’s Perspective 10 1 5.2.2.4 Ecological Perspective 10 1 5.3 Basic Concepts Referring to Integrated Analysis and Multi- Criteria Evaluation 10 2 5.3 .1 Definition of Terms and Basic Concepts 10 2 5.3 .1. 1 Problem Structuring Required for Multi- Criteria Evaluation 10 2 5.3 .1. 2 Multi- Scale Integrated Analysis (Multiple Set of Meaningful Perceptions/Representations)... Linking Alfa-Numerical Assessments to Spatial Analysis of Land Uses across Levels 11 .3 .1. 1 Definition of Lower-Level Characteristics and Household Types 11 .3 .1. 2 Definition of Household Types 11 .3 .1. 3 Moving to Higher Hierarchical Levels © 2004 by CRC Press LLC 369 369 369 373 377 380 392 392 398 402 403 408 410 412 417 417 417 418 420 11 .3 .1. 4 An Overview of the Organizational Structure of the Information... Domains 11 .2.3 An Example of the Selection of Useful Typologies 11 .2.3 .1 The Frame Used to Compare Household and Village Types 11 .2.3.2 Characterization of Useful Household Typologies 11 .2.3.3 Analysis of the Strategies behind Household Typologies 11 .2.3.4 Characterization of Useful Village Typologies 11 .3 An Overview of the MSIA Tool Kit and the Impossibility of Multi- Agent Simulations 11 .3 .1 Linking... to Multi- Criteria Analysis 10 8 5.4.3 Conclusion 11 1 5.5 Soft Systems Methodology: Developing Procedures for an Iterative Process of Generation of Discussion Support Systems (Multi- Scale Integrated Analysis) and Decision Support Systems (Societal Multi- Criteria Evaluation) 11 2 5.5 .1 Soft Systems Methodology 11 2 5.5.2 The Procedural Approach Proposed by Checkland with His Soft System Methodology 11 5... Extending the Multi- Scale Integrated Analysis to Land Use Patterns 14 2 6.3 Using Mosaic Effects in the Integrated Analysis of Socioeconomic Processes 14 6 6.3 .1 Introduction: The Integrated Analysis of Socioeconomic Processes 14 6 6.3.2 Redundancy to Bridge Nonequivalent Descriptive Domains: Multi- Scale Integrated Analysis 14 7 6.4 Applying the Metaphor of Redundant Maps to the Integrated Assessment of Human... Looking for Multi- Scale Mosaic Effects 12 9 6 .1 Complexity and Mosaic Effects 12 9 6 .1. 1 Example 1 129 6 .1. 2 Example 2 13 1 6 .1. 3 Mosaic Effect 13 2 6.2 Self-Entailments of Identities across Levels Associated with Holarchic Organization 13 4 6.2 .1 Looking for Mosaic Effects across Identities of Holarchies 13 4 6.2.2 Bridging Nonequivalent Representations through Equations of Congruence across Levels 13 7 6.2.3 Extending... Pressure 319 References 322 10 Multi- Scale Integrated Analysis of Agroecosystems: Technological Changes and Ecological Compatibility 325 10 .1 Studying the Interface Socioeconomic Systems-Farming Systems: The Relation between Throughput Intensities 325 10 .1. 1 Introduction 325 © 2004 by CRC Press LLC 10 .1. 2 Technical Progress in Agriculture and Changes in the Use of Technical Inputs 330 10 .1. 2 .1 The Biophysical... Systems 15 0 6.4 .1 Multi- Scale Analysis of Societal Metabolism: Same Variable (Megajoules), Different Levels 15 0 6.4 .1. 1 Linking Nonequivalent Assessments across Hierarchical Levels 15 1 6.4 .1. 2 Looking for Additional External Referents: Endosomatic Flow —the Physiological View 15 5 6.4 .1. 3 Looking for Additional External Referents: Exosomatic Flows —the Technological View 15 5 6.4.2 Multi- Scale Integrated Analysis. .. Introducing the Concept of Impredicative Loop © 2004 by CRC Press LLC 17 1 17 1 7.2 Examples of Impredicative Loop Analysis of Self-Organizing Dissipative Systems 17 2 7.2 .1 Introduction 17 2 7.2.2 Example 1: Endosomatic Societal Metabolism of an Isolated Society on a Remote Island 17 4 7.2.2 .1 Goals of the Example 17 4 7.2.2.2 The Example 17 5 7.2.2.3 Assumptions and Numerical Data for This Example 17 7 7.2.2.4 Changing . interacting in this field for many years now. • Multi- Scale Integrated Analysis of Agroecosystems This 11 st begins with Tiziano Gomiero (co-author of Chapter 11 ), who a few years ago decided to do his. all of this have to do with a multi- scale integrated analysis of agroecosystems? Well, the point I have been trying to make so far is that it has a lot to do with multi- scale integrated analysis. (Mario) Multi- scale integrated analysis of agroecosystems/ Mario Giampietro. p. cm. (Advances in agroecology) Includes bibliographical references and index. ISBN 0-8 49 3 -1 06 7-9 (alk. paper) 1. Agricultural