Multi-Scale Integrated Analysis of Agroecosystems

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ISBN: 9780849310676 | 0849310679
Cover: Hardcover
Copyright: 11/24/2003

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Ecologists, agronomists, and others who may question the validity of current models for determining sustainable growth of agroecosystems, need a new set of analytical tools that more effectively address the complex nature of related processes. Those who challenge traditional assumptions of optimization and static factors in agricultural modeling demand new methods beyond differential equations and traditional statistical tests.Multi-Scale Integrated Analysis of Agroecosystems explores alternative ways to study agricultural sustainability, presenting new approaches to organizing data and applying complex systems theory to actual cases. This innovative text recognizes the changing dynamics of the multiple processes and cross-relations within an environment, proposing a clearer analysis of agroecosystems than that which can be provided by rigid, reductionist methods.Main concepts, new vocabulary and narratives, and practical examples open the book, followed by technical chapters that provide a more detailed explanation of concepts. The final section of the book presents a tool kit based on these concepts, resulting in strong support of empirical observations that challenge traditional notions regarding the sustainability of farming systems, food systems, and agroecosystems.

Part 1 Science for Governance: The Clash of Reductionism Against the Complexity of Reality

1 The Crash of Reductionism against the Complexity of Reality

(12)

1.1 Example 1: In a Complex Reality It Is Unavoidable to Find Multiple Legitimate Views of the Same Problems

(4)

1.1.1 Contrasting but Legitimate Policy Suggestions for Sustainability

(2)

1.1.2 Looking at Nonequivalent Useful Pictures of a Person, Which One Is Right?

(2)

1.2 Example 2: For Adaptive Systems "Ceteris" Are Never "Paribus" - Jevons' Paradox

(5)

1.2.1 Systemic Errors in Policy Suggestions for Sustainability

(1)

1.2.2 Systemic Errors in the Development of Strategies: The Blinding Paradigm

(2)

1.2.3 Systemic Errors in the Representation of Evolution: The Myth of Dematerialization of Developed Economies (Are Elephants Dematerialized Versions of Mice?)

(2)

References

(3)

2 The Epistemological Predicament Entailed by Complexity

(28)

2.1 Back to Basics: Can Science Obtain an Objective Knowledge of Reality?

(11)

2.1.1 The Preanalytical Interference of the Observer

(7)

2.1.2 The Take of Complex Systems Thinking on Science and Reality

(1)

2.1.3 Conclusion

(2)

2.2 Introducing Key Concepts: Equivalence Class, Epistemic Category and Identity (Technical Section)

(5)

2.3 Key Concepts from Hierarchy Theory: Holovs and Holarchies

(8)

2.3.1 Self-Organizing Systems Are Organized in Nested Hierarchies and Therefore Entail Nonequivalent Descriptive Domains

(1)

2.3.2 Holovs and Holarchies

(2)

2.3.3 Near Decomposability of the Hierarchical System: Triadic Reading

(4)

2.3.4 Types Are out of Scale and out of Time, Realizations Are Scaled and Getting Old

(1)

2.4 Conclusion: The Ambiguous Identity of Holarchies 39

2.4.1 Models of Adaptive Holovs and Holarchies, No Matter How Validated in the Past, Will Become Obsolete and Wrong

(1)

References

(3)

3 Complex Systems Thinking: New Concepts and Narratives

(28)

3.1 Nonequivalent Descriptive Domains and Nonreducible Models Are Entailed by the Unavoidable Existence of Multiple Identities

(2)

3.1.1 Defining a Descriptive Domain

3.1.2 Nonequivalent Descriptive Domains Imply Nonreducible Assessments

(1)

3.2 The Unavoidable Insurgence of Errors in a Modeling Relation

(3)

3.2.1 Bifurcation in a Modeling Relation and Emergence

(3)

3.3 The Necessary Semantic Check Always Required by Mathematics

(2)

3.3.1 The True Age of Dinosaurs and the Weak Sustainability Indicator

(2)

3.4 Bifurcations and Emergence

(1)

3.5 The Crucial Difference between Risk, Uncertainty and Ignorance

(3)

3.6 Multiple Causality and the Impossible Formalization of Sustainability Trade-Offs across Hierarchical Levels

(6)

3.6.1 Multiple Causality for the Same Event

(2)

3.6.2 The Impossible Trade-Off Analysis over Perceptions: Weighing Short-Term vs. Long-Term Goals

(2)

3.6.3 The Impossible Trade-Off Analysis over Representations: The Dilemma of Efficiency vs. Adaptability

(2)

3.7 Perception and Representation of Holarchic Systems (Technical Section)

(7)

3.7.1 The Fuzzy Relation between Ontology and Epistemology in Holarchic Systems

(6)

3.7.2 Dealing with the Special Status of Holovs in Science

(1)

3.8 Conclusions

(1)

References

(3)

4 The New Terms of Reference for Science for Governance: Postnormal Science

(22)

4.1 Introduction

(2)

4.2 The Postnormal Science Rationale

(10)

4.2.1 The Basic Idea

(1)

4.2.2 PNS Requires Moving from a Substantial to a Procedural Definition of Sustainability

(1)

4.2.3 Introducing the Peircean Semiotic Triad

(1)

4.2.4 A Semiotic Reading of the PNS Diagram

(9)

4.2.4.1 The Horizontal Axis

(1)

4.2.4.2 The Vertical Axis

(1)

4.2.4.3 Area within the Two Axes

(3)

4.3 Quality Replacing Truth: Science, Sustainability and Decision Making

(2)

4.4 Example: What Has All This to Do with the Sustainability of Agriculture? The Challenge of Operationalizing the Precautionary Principle

(5)

4.4.1 The Precautionary Principle

(1)

4.4.2 Ecological Principles and Hazards of Large-Scale Adoption of Genetically Modified Organisms

(1)

4.4.3 Reduction of Evolutionary Adaptability and Increased Fragility

(1)

4.4.4 Precautionary Principle and the Regulation of Genetically Modified Organisms

(2)

4.5 Conclusions

(1)

References

(3)

5 Integrated Assessment of Agroecosystems and Multi-Criteria Analysis: Basic Definitions and Challenges

(36)

5.1 Sustainability of Agriculture and the Inherent Ambiguity of the Term Agroecology

(5)

5.2 Dealing with Multiple Perspectives and Nonequivalent Observers

(4)

5.2.1 The Unavoidable Occurrence of Nonequivalent Observers

(1)

5.2.2 Nonreducible Indicators and Nonequivalent Perspectives in Agriculture

(3)

5.2.2.1 Land Requirements for Inputs

100

(1)

5.2.2.2 Household's Perspective

101

(1)

5.2.2.3 Country's Perspective

101

(1)

5.2.2.4 Ecological Perspective

101

(1)

5.3 Basic Concepts Referring to Integrated Analysis and Multi-Criteria Evaluation

102

(5)

5.3.1 Definition of Terms and Basic Concepts

102

(2)

5.3.1.1 Problem Structuring Required for Multi-Criteria Evaluation

102

(1)

5.3.1.2 Multi-Scale Integrated Analysis (Multiple Set of Meaningful Perceptions/ Representations)

102

(1)

5.3.1.3 Challenge Associated with the Descriptive Side (How to Do a MSIA)

102

(1)

5.3.1.4 Challenge Associated with the Normative Side (How to Compare Different Indicators, How to Weight Different Values, How to Aggregate Different Perspectives - Social Multi-Criteria Evaluation)

103

(1)

5.3.1.5 The Rationale for Societal Multi-criteria Evaluation

103

(1)

5.3.2 Tools Available to Face the Challenge

104

(3)

5.3.2.1 Formalization of a Problem Structuring through a Multi-Criteria Impact Matrix

105

(1)

5.3.2.2 A Graphical View of The Impact Matrix: Multi-Objective Integrated Representation

106

(1)

5.4 The Deep Epistemological Problems Faced When Using These Tools

107

(5)

5.4.1 The Impossible Compression of Infinite into Finite Required to Generate the Right Problem Structuring

107

(1)

5.4.2 The Bad Turn Taken by Algorithmic Approaches to Multi-Criteria Analysis

108

(3)

5.4.3 Conclusion

111

(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)

112

(11)

5.5.1 Soft Systems Methodology

112

(3)

5.5.2 The Procedural Approach Proposed by Checkland with His Soft System Methodology

115

(4)

5.5.2.1 Step 1: Feeling the Disequilibrium, Recognizing That There Is a Problem Even if It Is Not Clearly Expressed

115

(1)

5.5.2.2 Step 2: Generate Actively as Many Points of View for the System as Possible

115

(1)

5.5.2.3 Step 3: Explicit Development of Abstractions, Finding the Root Definitions

115

(3)

5.5.2.4 Step 4: Building the Models

118

(1)

5.5.2.5 Step 5: Returning to Observations of the World and Checking the Model against What Happens

118

(1)

5.5.2.6 Step 6: Exploration of Feasible and Desirable Changes

118

(1)

5.5.2.7 Step 7: Identification of Desirable and Feasible Changes for the System

119

(1)

5.5.2.8 Step 8: Evaluation, Widening the View of the Whole Process

119

(1)

5.5.3 Looking at This Procedure in Terms of an Iteration between Discussion Support System and Decision Support System

119

(4)

5.6 What Has All This to Do with the Sustainability of Agriculture? Example: The Making of Farm Bills (Institutionalizing a Discussion of the Social Contract about Agriculture)

123

(2)

5.6.1 What Should Be the Role of Colleges of Agriculture in the New Millennium?

123

(1)

5.6.2 The Case of the U.S. Farm Bill 2002

124

(1)

References

125

(4)

Part 2 Complex Systems Thinking: Daring to Violate Basic Taboos of Reductionism

6 Forget about the Occam Razor: Looking for Multi-Scale Mosaic Effects

129

(42)

6.1 Complexity and Mosaic Effects

129

(5)

6.1.1 Example 1

129

(2)

6.1.2 Example 2

131

(1)

6.1.3 Mosaic Effect

132

(2)

6.2 Self-Entailments of Identities across Levels Associated with Holarchic Organization

134

(12)

6.2.1 Looking for Mosaic Effects across Identities of Holarchies

134

(3)

6.2.2 Bridging Nonequivalent Representations through Equations of Congruence across Levels

137

(5)

6.2.3 Extending the Multi-Scale Integrated Analysis to Land Use Patterns

142

(4)

6.3 Using Mosaic Effects in the Integrated Analysis of Socioeconomic Processes

146

(4)

6.3.1 Introduction: The Integrated Analysis of Socioeconomic Processes

146

(1)

6.3.2 Redundancy to Bridge Nonequivalent Descriptive Domains: Multi-Scale Integrated Analysis

147

(3)

6.4 Applying the Metaphor of Redundant Maps to the Integrated Assessment of Human Systems

150

(12)

6.4.1 Multi-Scale Analysis of Societal Metabolism: Same Variable (Megajoules), Different Levels

150

(8)

6.4.1.1 Linking Nonequivalent Assessments across Hierarchical Levels

151

(4)

6.4.1.2 Looking for Additional External Referents: Endosomatic Flow - the Physiological View

155

(1)

6.4.1.3 Looking for Additional External Referents: Exosomatic Flows - the Technological View

155

(3)

6.4.2 Multi-Scale Integrated Analysis of Societal Metabolism: Two Variables (Megajoules and Dollars) and Different Levels (Technical Section)

158

(4)

6.5 Holarchic Complexity and Mosaic Effects: The Example of the Calendar

162

(5)

6.6 Holarchic Complexity and Robustness of Organization Overview of Literature (Technical Section)

167

(2)

References

169

(2)

7 Impredicative Loop Analysis: Dealing with the Representation of Chicken-Egg Processes

171

(60)

7.1 Introducing the Concept of Impredicative Loop

171

(1)

7.2 Examples of Impredicative Loop Analysis of Self-Organizing Dissipative Systems

172

(19)

7.2.1 Introduction

172

(2)

7.2.2 Example 1: Endosomatic Societal Metabolism of an Isolated Society on a Remote Island

174

(9)

7.2.2.1 Goals of the Example

174

(1)

7.2.2.2 The Example

175

(2)

7.2.2.3 Assumptions and Numerical Data for This Example

177

(2)

7.2.2.4 Changing the Value of Variables within Formal Identities within a Given Impredicative Loop

179

(1)

7.2.2.5 Lessons from This Simple Example

180

(3)

7.2.3 Example 2: Modern Societies Based on Exosomatic Energy

183

(2)

7.2.4 Example 3: The Net Primary Productivity of Terrestrial Ecosystems

185

(5)

7.2.4.1 The Crucial Role of Water Flow in Shaping the Identity of Terrestrial Ecosystems

185

(3)

7.2.4.2 An ILA of the Autocatalytic Loop of Energy Forms Shaping Terrestrial Ecosystems

188

(2)

7.2.5 Parallel Consideration of Several Impredicative Loop Analyses

190

(1)

7.3 Basic Concepts Related to Impredicative Loop Analysis and Applications

191

(18)

7.3.1 Linking the Representation of the Identities of Parts to the Whole and Vice Versa

191

(3)

7.3.2 An ILA Implies Handling in Parallel Data Referring to Nonequivalent Descriptive Domains

194

(2)

7.3.3 The Coupling of the Mosaic Effect to ILA

196

(2)

7.3.4 The Multiple Choices about How to Reduce and How to Classify

198

(1)

7.3.5 Examples of Applications of ILA

199

(10)

7.3.5.1 The Bridging of Types across Different Levels

199

(3)

7.3.5.2 Mosaic Effect across Levels: Looking for Biophysical Constraints and for Useful Benchmarking

202

(5)

7.3.5.3 Using ILA for Scenario Analysis: Exploring the Assumption Ceteris Paribus

207

(2)

7.4 Theoretical Foundations 1: Why Impredicative Loop Analysis? Learning from the Failure of Conventional Energy Analysis (Technical Section)

209

(9)

7.4.1 Case Study: An Epistemological Analysis of the Failure of Conventional Energy Analysis

210

(3)

7.4.2 The Impasse Is More General: The Problematic Definitions of Energy, Work and Power in Physics

213

(5)

7.4.2.1 Energy

213

(1)

7.4.2.2 Work

213

(2)

7.4.2.3 Power

215

(3)

7.5 Theoretical Foundations 2: What Is Predicated by an Impredicative Loop? Getting Back to the Basic Fuzzy Definition of Holovs Using Thermodynamic Reasoning (Technical Section)

218

(7)

7.5.1 A Short History of the Concept of Entropy

219

(2)

7.5.2 Schroedinger's and Prigogine's Metaphor of Negative Entropy

221

(10)

7.5.2.1 Applying This Rationale to Autocatalytic Loop across Hierarchical Levels

222

(2)

7.5.2.2 Interpreting the Scheme Proposed by Prigogine in Metaphorical Terms

224

(1)

7.6 Conclusion: The Peculiar Characteristics of Adaptive Metabolic Systems

225

(2)

References

227

(4)

8 Sustainability Requires the Ability to Generate Useful Narratives Capable of Surfing Complex Time

231

(48)

8.1 What Remains the Same in a Process of Sustainable Development?

231

(20)

8.1.1 Dissipative Systems Must Be Becoming Systems

231

(2)

8.1.2 The Perception and Representation of Becoming Systems Require the Parallel Use of the Concepts of Identity and Individuality

233

(6)

8.1.3 The Impossible Use of Dynamical Systems Analysis to Catch the Process of Becoming

239

(1)

8.1.4 The Nature of the Observer-Observed Complex and the Existence of Multiple Identities

240

(3)

8.1.5 How to Interpret and Handle the Existence of Multiple Identities

243

(3)

8.1.6 The Metaphor of Adaptive Cycle Proposed by Holline about Evolution

246

(5)

8.2 Using the Concepts of Essence, Type and Equivalence Class When Making the Distinction between Identity and Individuality (Technical Section)

251

(17)

8.2.1 The Unimportance of DNA in the Definition of Essences in Biological Systems: The Blunder of Genetic Engineering

251

(8)

8.2.1.1 Phenotype: The Realization of a Type Associated with an Essence

254

(1)

8.2.1.2 Genotype: The Role Played by the Characteristics of the Template Shared by a Class of Realizations

255

(4)

8.2.2 How to Obtain an Image of an Essence within the Frame of Network Analysis

259

(5)

8.2.3 The Difference between Self-Organization (Emergence of Essences and New Relevant Attributes) and Design (Increase in Fitness within a Given Essence = Set of Attributes/Associative Context)

264

(3)

8.2.4 Conclusions: So What? How Should We Deal with the Representation of Becoming Systems?

267

(1)

8.3 The Additional Predicament of Reflexivity: The Challenge of Keeping Useful Narratives within the Observed-Observer Complex

268

(7)

8.3.1 The Surfing of Narratives on Complex Time

268

(1)

8.3.2 The Moving in Time of Narrative

269

(2)

8.3.3 In a Moment of Fast Transition, Moving Narratives in Space Can Imply Moving Them in Time

271

(4)

References

275

(4)

Part 3 Complex Systems Thinking in Action: Multi-Scale Integrated Analysis of Agroecosystems

Introduction to Part 3

279

(4)

What Is the Beef That Has Been Served in the First Two Parts of This Book?

279

(2)

What Is the Beef That Is Served in Part 3?

281

(1)

Reference

282

(1)

9 Multi-Scale Integrated Analysis of Agroecosystems: Bridging Disciplinary Gaps and Hierarchical Levels

283

(42)

9.1 Applying ILA to the Study of the Feasibility of Societal Metabolism at Different Levels and in Relation to Different Dimensions of Sustainability

283

(16)

9.1.1 The Application of the Basic Rationale of ILA to Societal Metabolism

283

(9)

9.1.1.1 Step 1: Discussing Typologies

284

(2)

9.1.1.2 Step 2: Defining the Critical Elements of the Dynamic Budget

286

(6)

9.1.2 Establishing Horizontal Bridges across Biophysical and Economic Readings

292

(3)

9.1.3 Establishing Vertical Bridges, Looking for Mosaic Effects across Scales (Technical Section)

295

(4)

9.2 Validation of This Approach: Does It Work?

299

(7)

9.2.1 The Database Used for Validation

299

(2)

9.2.2 The Representation of Development According to Economic Variables Can Be Linked to Structural Changes in Societal Metabolism Represented Using Biophysical Variables: The Correlation of III with the Chosen Set of Indicators of Development

301

(2)

9.2.3 Changes Associated with Economic Development Can Be Represented Using an Integrated Set of Indicators on Different Levels and Descriptive Domains: Assessing Changes Related to Development on More Hierarchical Levels

303

(3)

9.2.3.1 Physiological/Nutrition: Individual Hierarchical Level

304

(1)

9.2.3.2 Economic Development: Societal Hierarchical Level (Steady-State View)

304

(1)

9.2.3.3 Socioeconomic Level: Societal Hierarchical Level (Evolutionary View)

304

(2)

9.3 Applications to the Analysis of the Role of Agricultural Systems

306

(8)

9.3.1 The Particular Identity of the Metabolism of a Socioeconomic System Implies Minimum Thresholds on the Pace of Throughputs in the Various Components

306

(2)

9.3.2 Determination of Minimum Thresholds for Congruence over the Loop

308

(6)

9.3.2.1 Minimum Throughput of Food per Hour of Labor in the Agricultural Sector

308

(1)

9.3.2.2 Minimum Throughput of Food per Hectare of Land in the Agricultural Sector

309

(1)

9.3.2.3 Minimum Throughput of Flows of Added Value per Hour of Labor in the Agricultural Sector

310

(3)

9.3.2.4 Minimum Throughput Dollars per Hectare of Land in the Agricultural Sector

313

(1)

9.4 Demographic Pressure and Bioeconomic Pressure

314

(8)

9.4.1 Introducing These Two Basic Concepts

314

(3)

9.4.1.1 Demographic Pressure

314

(1)

9.4.1.2 Bioeconomic Pressure

315

(2)

9.4.2 The Effect of the Quality of the Diet, Trade and Market

317

(1)

9.4.3 How Useful Are These Two Concepts When Looking at Data?

318

(7)

9.4.3.1 Technological Development, GDP vs. Fraction of GDP from the Agricultural Sector, and Employment in Agriculture

318

(1)

9.4.3.2 The Effect of Demographic and Bioeconomic Pressure

319

(3)

References

322

(3)

10 Multi-Scale Integrated Analysis of Agroecosystems: Technological Changes and Ecological Compatibility

325

(44)

10.1 Studying the Interface Socioeconomic Systems-Farming Systems: The Relation between Throughput Intensities

325

(11)

10.1.1 Introduction

325

(5)

10.1.2 Technical Progress in Agriculture and Changes in the Use of Technical Inputs

330

(6)

10.1.2.1 The Biophysical Cost of an Increasing Demographic and Bioeconomic Pressure: The Output/Input Energy Ratio of Agricultural Production

333

(3)

10.2 The Effect of the Internal Bioeconomic Pressure of Society on Terrestrial Ecosystems

336

(16)

10.2.1 Agriculture and the Alteration of Terrestrial Ecosystems

336

(3)

10.2.2 The Food System Cycle: Combining the Two Interfaces of Agriculture

339

(3)

10.2.3 Dealing with the Informalizable Concept of Integrity of Terrestrial Ecosystems

342

(3)

10.2.4 Looking at Human Interference on Terrestrial Ecosystems Using ILA

345

(7)

10.2.4.1 Can We Use This Approach to Represent Different Degrees of Alteration?

347

(5)

10.3 Animal Products in the Diet and the Use of Technical Inputs

352

(14)

10.3.1 Introduction

352

(1)

10.3.2 Technical Coefficients: Material and Methods Used to Generate the Database

352

(7)

10.3.2.1 Overall Assessment of Fossil Energy Used in Animal Production

359

(1)

10.3.3 Looking Ahead: What Can We say about the Future of Agricultural Production?

359

(6)

10.3.4 The Sustainability Issue: What Are the Future Biophysical Constraints in Agriculture?

365

(4)

10.3.4.1 Can We Forecast an Inversion in the Medium Term of Existing Negative Trends?

365

(1)

References

366

(3)

11 Multi-Scale Integrated Analysis of Farming Systems: Benchmarking and Tailoring Representations across Levels

369

(64)

11.1 Farming System Analysis

369

(23)

11.1.1 Defining Farming System Analysis and Its Goals

369

(4)

11.1.2 Farming System Analysis Implies a Search for Useful Metaphors

373

(4)

11.1.3 A Holarchic View of Farming Systems (Using Throughputs for Benchmarking)

377

(3)

11.1.4 Benchmarking to Define Farming System Typologies

380

(12)

11.2 Individuating Useful Types across Levels

392

(25)

11.2.1 The Land-Time Budget of a Farming System

392

(6)

11.2.2 Looking for the Mosaic Effect across Descriptive Domains

398

(4)

11.2.3 An Example of the Selection of Useful Typologies

402

(15)

11.2.3.1 The Frame Used to Compare Household and Village Types

403

(5)

11.2.3.2 Characterization of Useful Household Typologies

408

(2)

11.2.3.3 Analysis of the Strategies behind Household Typologies

410

(2)

11.2.3.4 Characterization of Useful Village Typologies

412

(5)

11.3 An Overview of the MSIR Tool Kit and the Impossibility of Multi-Agent Simulations

417

(14)

11.3.1 Linking Alfa-Numerical Assessments to Spatial Analysis of Land Uses across Levels

417

(9)

11.3.1.1 Definition of Lower-Level Characteristics and Household Types

417

(1)

11.3.1.2 Definition of Household Types

418

(2)

11.3.1.3 Moving to Higher Hierarchical Levels

420

(1)

11.3.1.4 An Overview of the Organizational Structure of the Information Space

421

(5)

11.3.2 Multi-Objective Characterization of Performance for Different Agents

426

(2)

11.3.3 The Impossible Simulation of the Interaction of Agents across Levels

428

(3)

References

431

(2)

Index

433

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Multi-Scale Integrated Analysis of Agroecosystems

Summary

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