Advances in Modeling Agricultural Systems (eBook)

Preface 7
Contents 9
Contributors 12
The Model Driven Architecture Approach: A Framework for Developing Complex Agricultural Systems 18
1 Introduction 19
2 MDA and Unified Modeling Language 20
3 Modeling Behavior 23
3.1 The Object Constraint Language 23
3.2 The Action Language 24
4 Modeling a Crop Simulation 25
4.1 The Conceptual Model, or PIM 25
4.2 Providing Objects with Behavior 27
4.3 Data Requirements 30
4.4 Code Generation 31
4.5 Results 31
5 Conclusions 32
References 34
A New Methodology to Automate the Transformation of GIS Models in an Iterative Development Process 36
1 Introduction 37
2 The Software DevelopmentProcess 38
3 The Model Driven Architecture 41
4 The New Interactive Development Method 42
4.1 The Principle of the Continuous Integration Unified Process Method 42
4.2 The Software Development Process Approach: A Generalization of the MDA Approach 45
4.3 The Software Development Process Model: A Modeling Artifact for Knowledge Capitalization 45
4.4 The Complete Set of Transformations Enabling a Full MDA Process for Databases 46
4.4.1 Diffusion Transformation and Management of the Software Development Process Model 47
4.4.2 The GIS Transformations 47
The GIS Design Pattern Generation Transformation 47
The Pictogram Translation Transformation 48
4.4.3 The SQL Transformation 50
5 Conclusions 51
References 52
Application of a Model Transformation Paradigm in Agriculture: A Simple Environmental System Case Study 54
1 Introduction 54
2 The Continuous Integration Unified Process 56
3 Transformations of the Continuous Integration Unified Process in Action 57
3.1 Construction of the Software Development Process Model 59
3.2 First Iteration 60
3.3 Second Iteration 65
4 Conclusions 69
References 70
Constraints Modeling in Agricultural Databases 72
1 Introduction 72
2 The Object Constraint Language 73
3 Example of a Tool Supporting OCL: The Dresden OCL Toolkit 77
4 Extending OCL for Spatial Objects 79
5 Conclusions 81
References 81
Design of a Model-Driven Web Decision Support System in Agriculture: From Scientific Models to the Final Software 83
1 Introduction 83
1.2 General Points 83
1.2 Generic Design of Decision Support Systems 85
1.2 Development of DSS Software for Phytosanitary Plant Protection 86
2 Design of the Scientific Model 88
2.1 Description of the ‘‘Plant-Parasite-Phytosanitary Protection’’ System 88
2.2 The Plant Model 90
2.3 Parasite Model 93
2.4 The Phytosanitary Protection Model 96
3 The Scientific Model ’s Set Up and Validation 97
3.1 Principle 97
3.2 Methods Used for Sensitivity Analysis, Calibration, and Validation 98
3.3 The Choice of Modeling and Validation Tools 99
4 Software Architecture of the Scientific Model 100
4.1 Class Diagram of the Plant-Parasite-Phytosanitary Protection System 101
4.2 The Plant Model 104
4.3 The Parasite Model 109
5 The Application’s Architecture 110
5.1 The Three-Tier Architecture and the Design Pattern ‘‘Strategy’’ 110
5.2 The Three-Tier Architecture Layers and the Technologies Used 112
5.2.1 The Presentation Layer and Client-Server Communication 112
5.2.2 The Business Layer and the Dependency Injection Design Pattern 113
5.2.3 The DAO Layer and Hibernate 114
6 Conclusions 115
References 116
How2QnD: Design and Construction of a Game-Style, Environmental Simulation Engine and Interface Using UML, XML, and Java 119
1 Introduction 120
1.4 Conceptual Background: Learning Through Games 120
1.4 QnD: A Game-Style Simulation for Adaptive Learning and Decision Making 121
2 QnD Design Overview: Designing from Ideas to a Playable Game 122
2.1 GameView Design 122
2.2 Simulation Engine Design 123
2.3 QnD Use-Case Designs: Three Actors, Many Roles 127
3 Questions and Decisions About Elephant-Vegetation Dynamics in the Kruger National Park, South Africa 129
3.1 KNP Elephant Model Development Strategies 130
3.2 Design2Game: Translating Systems Designs and Previous Modeling Efforts into QnD SimulationEngine and GameView Implementations 131
3.2.1 QnDEleSim SimulationEngine: Setting Spatial and Temporal Execution 132
3.2.2 QnDEleSim SimulationEngine: Setting Input Drivers and Scenarios 132
3.2.3 QnDEleSim SimulationEngine: Setting CLocalComponents, DData, and PProcesses 132
Climatic Inputs 132
Simulating Woody Plant Layer Growth 133
Wet and Dry Season Dynamics 134
Simulating Grass Layer Area and Biomass 137
Simulating Elephant Populations 138
Simulating Fire 138
3.2.4 QnDEleSim GameView: Setting the User Interface 138
3.3 Ongoing QnD:EleSim Calibration and Validation Activities 139
3.4 Serious Play: Playing Games for Systematic Analysis 139
4 Conclusions 141
1 Technical Appendix 142
References 144
The Use of UML as a Tool for the Formalisation of Standards and the Design of Ontologies in Agriculture 146
1 What Is an Ontology? 146
2 UML as an Ontology Language 148
3 Similarity and Differences Between UML and Traditional Languages Used to Describe Ontologies 150
3.1 Mappings Between UML and Ontology Languages 150
3.1.1 Class and Subclass 150
3.1.2 Object/Individual 151
3.1.3 Attribute, Association/Property 152
3.1.4 Multiplicity/Cardinality 153
3.2 Differences Between UML and Ontology Languages 154
3.2.1 In OWL but Not in UML 154
3.2.2 In UML but Not in OWL 154
4 Farm Information Management Project 155
4.1 Exchange of Agricultural Data: A Need That Is Partially Met 155
4.2 Ontology Definition in Agriculture: A Means of Communication 156
4.3 Use of UML 157
5 Conclusions 160
References 161
Modeling External Information Needs of Food Business Networks 163
1 Introduction 163
2 Guideline for Modeling External Information Needs in Networks 165
2.1 Analysis and Differentiation of the External Information Needs in Supply Networks 166
2.1.1 Analysis Information Needs 166
2.1.2 Information Needs Differentiation 169
2.2 Categorization Scheme and Personalization Filters 174
2.2.1 Categorization Scheme 174
2.2.2 Personalization Filters 177
3 Evaluation of the Modeling Guideline 177
4 Conclusions 178
References 179
Enterprise Business Modelling Languages Applied to Farm Enterprise: A Case Study for IDEF0, GRAI Grid, and AMS Languages 181
1 Introduction 181
2 Modelling Languages 183
2.1 Modelling Language Diversity 183
2.2 One or Several Modelling Languages? 183
2.3 Enterprise Modelling Languages 184
2.4 Case Study of Three Enterprise Modelling Languages 185
3 IDEF0 Language and Business Functional Models 186
3.1 IDEF0 Language Presentation 186
3.2 IDEF0 Business Functional Models 188
4 GRAI Grid Language and Decisional Models 193
4.1 GRAI Grid Language Presentation 193
4.2 GRAI Grid Decisional Models 195
5 AMS Language and Organizational Models 197
5.1 AMS Language Presentation 197
5.2 AMS Organizational Models 198
6 Discussion 201
6.1 Interest of Enterprise Modelling Languages 201
6.2 Need of Complementary Modelling Languages 202
6.3 Necessary Adaptation to Farm Characteristics 203
7 Conclusions 203
References 204
A UML-Based Plug& Play Version of RothC
1 Introduction 206
2 The RothC Model 207
2.1 RothC Data Requirements 208
2.2 Decomposition of an Active Compartment 209
2.3 State Variables and Outputs 209
3 RothC Stand-alone Model 209
3.1 Weather 210
3.2 Soil 211
3.3 Management 211
3.4 Plant 212
3.5 Decomposable Plant Material 212
4 RothC Plug& Play Component
4.1 Dependency Injection Design Pattern 215
4.2 Communication with Other Components 216
5 Conclusions 220
References 220
Ontology-Based Simulation Applied to Soil, Water, and Nutrient Management 222
1 Introduction 222
2 Ways in Which Ontologies Can Be Applied to Modeling Agricultural and Natural Resource Systems 225
2.1 What Is an Ontology? 225
2.2 Literature Review 227
2.3 System Structure 230
2.4 Representing Symbols and Equations 230
2.5 Connecting to External Databases 232
2.6 Integration with Other Information 233
2.7 Ontology Reasoning 234
2.8 Model Base 234
3 Example: A Soil, Water, and Nutrient Management Model 235
3.1 Lyra Ontology Management System 235
3.1.1 Lyra Database Management Facilities 236
3.1.2 Authoring Tools 236
The EquationEditor 238
The Symbol Editor 238
The Mathematical Expression Editor 240
The Unit Editor 240
The SimulationEditor 242
The Structure Editor 242
The Simulation Controller 242
Additional Model Publishing Tools 244
3.2 Citrus Water and Nutrient Management System 245
3.2.1 Model Structure 245
3.2.2 Model Functions 248
3.2.3 Defining System Symbols 249
3.2.4 CWMS Application Implementation 250
4 Conclusions 253
References 254
Precision Farming, Myth or Reality: Selected Case Studies from Mississippi Cotton Fields 256
1 Introduction 257
2 Multidisciplinary Teams 258
3 Precision Agriculture and Information 260
3.1 Case 1: Simulation and Variable-Rate Nitrogen with Mississippi Delta Cotton, 1998 260
3.1.1 Case 1A: Update of Cotton Simulation Model Efforts in Precision Agriculture 265
3.2 Case 2: Statistical Analyses of Field-Level Precision Agriculture Experiments 266
4 Collecting and Managing Information 273
5 Precision Farming Equipment 275
5.1 Case 3: Development of Geo-referenced Site-Specific Prescriptions 276
5.2 Case 4: The Promise of Wireless Interconnectivity 279
5.3 Dollars and Sense 280
6 Conclusions 281
References 282
Rural Development Through Input-Output Modeling 286
1 Input-Output Models and Applications in Rural Development and Agriculture 287
2 Case Application 288
2.1 Methodology and Data 289
2.2 Regionalization Technique 290
3 The Computational Procedure 291
4 Input-Output Multipliers and Impact Analysis Results 296
5 Conclusions 299
1 Appendix: The Code of the GAUSS Computer Package 300
References 306
Modeling in Nutrient Sensing for Agricultural and Environmental Applications 309
1 Introduction 310
2 Statistical Modeling 311
2.1 Partial Least Squares Regression Analysis 311
2.2 Stepwise Multiple Linear Regression 313
2.3 Prediction Models 314
3 Modeling Application - Example 1: Phosphorus Sensing for Soil 314
3.1 Soil Sampling and Reflectance Measurement 314
3.2 Data Analysis 315
3.3 Results and Discussion 316
4 Modeling Application - Example 2: Nitrogen Sensing for Citrus Production 320
4.1 Citrus Leaf Sampling and Reflectance Measurement 320
4.2 Data Analysis 321
4.3 Results and Discussion 322
5 Conclusions 325
References 325
Estimation of Land Surface Parameters Through Modeling Inversion of Earth Observation Optical Data 328
1 Introduction 328
2 Statement of the Problem: EO Data and CR Modeling 330
3 The PROSPECT-SAILH Canopy Reflectance Model 332
4 Experimental Data Acquisition 334
4.1 Test-Site Description and Ground Measurements 334
4.2 Earth Observation Data: CHRIS/PROBA Imagery 336
5 Canopy Reflectance Model Inversion 336
5.1 An Inverse Ill-Posed Problem 336
5.2 Optimization and Analysis of the Inversion Procedure 339
5.3 Inverting PSH Model with Real CHRIS/PROBA Data 344
6 Conclusions 346
References 347
A Stochastic Dynamic Programming Model for Valuing a Eucalyptus Investment 350
1 Introduction 350
2 Literature Review 352
3 Problem Description 353
3.1 The Investment Decisions 354
4 Methodology 355
4.1 The Binomial Lattice 356
4.2 Decision and State Variables 357
4.3 Dynamic Programming Model 358
5 Case Study 359
5.1 Brief Characterization of the Portuguese Forest Sector 360
5.2 Data and Parameters 360
5.2.1 The Initial Investment: Plantation and Maintenance Costs 360
5.2.2 Wood and White Paper Pulpwood Prices 361
5.2.3 Wood and White Paper Pulpwood Quantities 361
5.2.4 The Exercise Price for the Cutting Option $K$ 361
5.2.5 The Risk-Free Interest Rate rf 362
5.2.6 The Abandonment and Conversion to Another Land Use Value R 362
6 Results 362
6.1 Results for the Base and Extended Problems 363
6.2 Applying the Optimal Strategies 365
7 Conclusions 367
1 Appendix A 368
2 Appendix B 369
References 369
Modelling Water Flow and Solute Transport in Heterogeneous Unsaturated Porous Media 371
1 Introduction 372
2 The General Framework 373
2.1 Derivation of the Flux Statistics 373
2.2 Results 377
3 Macrodispersion Modelling 378
4 Discussion 380
4.1 Velocity Analysis 380
4.2 Spreading Analysis 384
5 Conclusions 390
References 391
Genome Analysis of Species of Agricultural Interest 394
1 Introduction 395
2 Genome Analysis and Applications in Agriculture 396
3 Biological Data Banks and Data Integration 399
4 Analysis of Biological Sequences: Sequence Comparison and Gene Discovery 400
5 Transcriptome Analysis 402
6 Systems Biology: The Major Challenge 403
7 An Italian Resource for Solanaceae Genomics 405
8 Conclusions 408
References 408
Modeling and Solving Real-Life Global Optimization Problems with Meta-heuristic Methods 412
1 Introduction 412
2 Modeling Real-Life Problems 414
3 Meta-heuristic Methods 415
3.1 Simulated Annealing Algorithm 415
3.2 Genetic Algorithms 416
3.3 Differential Evolution 416
3.4 Harmony Search 417
3.5 Tabu Search 417
3.6 Methods Inspired by Animal Behavior 417
3.7 Monkey Search 418
4 Applications 419
4.1 Forest Inventories 420
4.2 Lennard-Jones Clusters 421
4.3 Simulating Protein Conformations 423
5 Conclusions 425
References 426
Modeling and Device Development for Chlorophyll Estimation in Vegetation 429
1 Introduction 429
2 Methodological Approaches to Estimating Phytocenosis Parameters 431
2.1 Method Based on Use of the First Derivative 431
2.2 Principal Components Analysis 432
3 Support Vector Regression 433
4 Algorithms and Software 435
5 Device for Remote Measurement of Vegetation Reflectance Spectra Under Field Conditions 435
6 Results 437
7 Conclusions 437
References 438
Clustering and Classification Algorithms in Food and Agricultural Applications: A Survey 440
1 Introduction 440
2 Data Mining Algorithms 441
2.1 k-Means Algorithm 441
2.1.1 Algorithm k-Means 442
2.2 Fuzzy c-Means Clustering 443
2.3 k-Nearest Neighbor Classification 445
2.3.1 Training Phase 445
2.3.2 Testing Phase 445
2.4 Artificial Neural Networks 446
3 Applications 448
3.1 Grading Methods of Fruits and Vegetables 449
3.1.1 Image Interpretation by k-Means Algorithm 449
3.1.2 Image Interpretation by Neural Networks 450
3.2 Machine Vision and Robotic Harvesting 452
3.3 Classification of Wines 453
3.4 Classification of Forest Data with Remotely Sensed Images 455
4 Conclusions 457
References 457
Mathematical Modelling of Modified Atmosphere Package: An Engineering Approach to Design Packaging Systems for Fresh-Cut Produce 462
1 Fresh-Cut Produce 462
2 Modified Atmosphere Packaging 464
3 An Engineering Approach to the Package Design 466
3.1 Modelling the Gas Transport Through a Polymeric Film 467
3.2 Modelling the Respiration Process 470
3.3 Material Balance in MAP 473
3.4 Packaging Design Procedure 474
3.4.1 Selection of Packaging Material 476
3.4.2 Selection of Product Weight or Film Area 477
3.4.3 Optimization of the Volume 477
3.5 Package Simulation 478
3.6 Variability in Product/Package on Equilibrium Modified Atmosphere 479
4 Case Study 480
4.1 Product Characteristics 481
4.1.1 Type of Product and Storage Conditions Required 481
4.1.2 Mathematical Model for Respiration Rate 481
4.2 Package Characteristics 482
4.3 Variable Optimization: Product Weight 483
4.4 Package Simulation and Validation 483
Nomenclature 486
References 487