Papajorgji / Pardalos Advances in Modeling Agricultural Systems

1. Auflage 2009
ISBN: 978-0-387-75181-8
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 25, 522 Seiten

Reihe: Springer Optimization and Its Applications

Agriculture has experienced a dramatic change during the past decades. The change has been structural and technological. Structural changes can be seen in the size of current farms; not long ago, agricultural production was organized around small farms, whereas nowadays the agricultural landscape is dominated by large farms. Large farms have better means of applying new technologies, and therefore technological advances have been a driving force in changing the farming structure. New technologies continue to emerge, and their mastery and use in requires that farmers gather more information and make more complex technological choices. In particular, the advent of the Internet has opened vast opportunities for communication and business opportunities within the agricultural com- nity. But at the same time, it has created another class of complex issues that need to be addressed sooner rather than later. Farmers and agricultural researchers are faced with an overwhelming amount of information they need to analyze and synthesize to successfully manage all the facets of agricultural production. This daunting challenge requires new and complex approaches to farm management. A new type of agricultural management system requires active cooperation among multidisciplinary and multi-institutional teams and ref- ing of existing and creation of new analytical theories with potential use in agriculture. Therefore, new management agricultural systems must combine the newest achievements in many scientific domains such as agronomy, economics, mathematics, and computer science, to name a few.

Autoren/Hrsg.

Papajorgji, Petraq

Pardalos, Panos M.

Inhaltsverzeichnis

1;Preface;7
2;Contents;9
3;Contributors;12
4;The Model Driven Architecture Approach: A Framework for Developing Complex Agricultural Systems;18
4.1;1 Introduction;19
4.2;2 MDA and Unified Modeling Language;20
4.3;3 Modeling Behavior;23
4.3.1;3.1 The Object Constraint Language;23
4.3.2;3.2 The Action Language;24
4.4;4 Modeling a Crop Simulation;25
4.4.1;4.1 The Conceptual Model, or PIM;25
4.4.2;4.2 Providing Objects with Behavior;27
4.4.3;4.3 Data Requirements;30
4.4.4;4.4 Code Generation;31
4.4.5;4.5 Results;31
4.5;5 Conclusions;32
4.6;References;34
5;A New Methodology to Automate the Transformation of GIS Models in an Iterative Development Process;36
5.1;1 Introduction;37
5.2;2 The Software DevelopmentProcess;38
5.3;3 The Model Driven Architecture;41
5.4;4 The New Interactive Development Method;42
5.4.1;4.1 The Principle of the Continuous Integration Unified Process Method;42
5.4.2;4.2 The Software Development Process Approach: A Generalization of the MDA Approach;45
5.4.3;4.3 The Software Development Process Model: A Modeling Artifact for Knowledge Capitalization;45
5.4.4;4.4 The Complete Set of Transformations Enabling a Full MDA Process for Databases;46
5.4.4.1;4.4.1 Diffusion Transformation and Management of the Software Development Process Model;47
5.4.4.2;4.4.2 The GIS Transformations;47
5.4.4.2.1;The GIS Design Pattern Generation Transformation;47
5.4.4.2.2;The Pictogram Translation Transformation;48
5.4.4.3;4.4.3 The SQL Transformation;50
5.5;5 Conclusions;51
5.6;References;52
6;Application of a Model Transformation Paradigm in Agriculture: A Simple Environmental System Case Study;54
6.1;1 Introduction;54
6.2;2 The Continuous Integration Unified Process;56
6.3;3 Transformations of the Continuous Integration Unified Process in Action;57
6.3.1;3.1 Construction of the Software Development Process Model;59
6.3.2;3.2 First Iteration;60
6.3.3;3.3 Second Iteration;65
6.4;4 Conclusions;69
6.5;References;70
7;Constraints Modeling in Agricultural Databases;72
7.1;1 Introduction;72
7.2;2 The Object Constraint Language;73
7.3;3 Example of a Tool Supporting OCL: The Dresden OCL Toolkit;77
7.4;4 Extending OCL for Spatial Objects;79
7.5;5 Conclusions;81
7.6;References;81
8;Design of a Model-Driven Web Decision Support System in Agriculture: From Scientific Models to the Final Software;83
8.1;1 Introduction;83
8.1.1;1.2 General Points;83
8.1.2;1.2 Generic Design of Decision Support Systems;85
8.1.3;1.2 Development of DSS Software for Phytosanitary Plant Protection;86
8.2;2 Design of the Scientific Model ;88
8.2.1;2.1 Description of the ‘‘Plant-Parasite-Phytosanitary Protection’’ System;88
8.2.2;2.2 The Plant Model ;90
8.2.3;2.3 Parasite Model ;93
8.2.4;2.4 The Phytosanitary Protection Model ;96
8.3;3 The Scientific Model ’s Set Up and Validation;97
8.3.1;3.1 Principle;97
8.3.2;3.2 Methods Used for Sensitivity Analysis, Calibration, and Validation;98
8.3.3;3.3 The Choice of Modeling and Validation Tools;99
8.4;4 Software Architecture of the Scientific Model ;100
8.4.1;4.1 Class Diagram of the Plant-Parasite-Phytosanitary Protection System;101
8.4.2;4.2 The Plant Model ;104
8.4.3;4.3 The Parasite Model ;109
8.5;5 The Application’s Architecture;110
8.5.1;5.1 The Three-Tier Architecture and the Design Pattern ‘‘Strategy’’;110
8.5.2;5.2 The Three-Tier Architecture Layers and the Technologies Used;112
8.5.2.1;5.2.1 The Presentation Layer and Client-Server Communication;112
8.5.2.2;5.2.2 The Business Layer and the Dependency Injection Design Pattern ;113
8.5.2.3;5.2.3 The DAO Layer and Hibernate;114
8.6;6 Conclusions;115
8.7;References;116
9;How2QnD: Design and Construction of a Game-Style, Environmental Simulation Engine and Interface Using UML, XML, and Java;119
9.1;1 Introduction;120
9.1.1;1.4 Conceptual Background: Learning Through Games;120
9.1.2;1.4 QnD: A Game-Style Simulation for Adaptive Learning and Decision Making;121
9.2;2 QnD Design Overview: Designing from Ideas to a Playable Game;122
9.2.1;2.1 GameView Design;122
9.2.2;2.2 Simulation Engine Design;123
9.2.3;2.3 QnD Use-Case Designs: Three Actors, Many Roles;127
9.3;3 Questions and Decisions About Elephant-Vegetation Dynamics in the Kruger National Park, South Africa;129
9.3.1;3.1 KNP Elephant Model Development Strategies;130
9.3.2;3.2 Design2Game: Translating Systems Designs and Previous Modeling Efforts into QnD SimulationEngine and GameView Implementations;131
9.3.2.1;3.2.1 QnDEleSim SimulationEngine: Setting Spatial and Temporal Execution;132
9.3.2.2;3.2.2 QnDEleSim SimulationEngine: Setting Input Drivers and Scenarios;132
9.3.2.3;3.2.3 QnDEleSim SimulationEngine: Setting CLocalComponents, DData, and PProcesses;132
9.3.2.3.1;Climatic Inputs;132
9.3.2.3.2;Simulating Woody Plant Layer Growth;133
9.3.2.3.3;Wet and Dry Season Dynamics;134
9.3.2.3.4;Simulating Grass Layer Area and Biomass;137
9.3.2.3.5;Simulating Elephant Populations;138
9.3.2.3.6;Simulating Fire;138
9.3.2.4;3.2.4 QnDEleSim GameView: Setting the User Interface;138
9.3.3;3.3 Ongoing QnD:EleSim Calibration and Validation Activities;139
9.3.4;3.4 Serious Play: Playing Games for Systematic Analysis;139
9.4;4 Conclusions;141
9.5;1 Technical Appendix;142
9.6;References;144
10;The Use of UML as a Tool for the Formalisation of Standards and the Design of Ontologies in Agriculture;146
10.1;1 What Is an Ontology?;146
10.2;2 UML as an Ontology Language;148
10.3;3 Similarity and Differences Between UML and Traditional Languages Used to Describe Ontologies;150
10.3.1;3.1 Mappings Between UML and Ontology Languages;150
10.3.1.1;3.1.1 Class and Subclass;150
10.3.1.2;3.1.2 Object/Individual;151
10.3.1.3;3.1.3 Attribute, Association/Property;152
10.3.1.4;3.1.4 Multiplicity/Cardinality;153
10.3.2;3.2 Differences Between UML and Ontology Languages;154
10.3.2.1;3.2.1 In OWL but Not in UML;154
10.3.2.2;3.2.2 In UML but Not in OWL;154
10.4;4 Farm Information Management Project;155
10.4.1;4.1 Exchange of Agricultural Data: A Need That Is Partially Met;155
10.4.2;4.2 Ontology Definition in Agriculture: A Means of Communication;156
10.4.3;4.3 Use of UML;157
10.5;5 Conclusions;160
10.6;References;161
11;Modeling External Information Needs of Food Business Networks;163
11.1;1 Introduction;163
11.2;2 Guideline for Modeling External Information Needs in Networks;165
11.2.1;2.1 Analysis and Differentiation of the External Information Needs in Supply Networks;166
11.2.1.1;2.1.1 Analysis Information Needs;166
11.2.1.2;2.1.2 Information Needs Differentiation;169
11.2.2;2.2 Categorization Scheme and Personalization Filters;174
11.2.2.1;2.2.1 Categorization Scheme;174
11.2.2.2;2.2.2 Personalization Filters;177
11.3;3 Evaluation of the Modeling Guideline;177
11.4;4 Conclusions;178
11.5;References;179
12;Enterprise Business Modelling Languages Applied to Farm Enterprise: A Case Study for IDEF0, GRAI Grid, and AMS Languages;181
12.1;1 Introduction;181
12.2;2 Modelling Languages;183
12.2.1;2.1 Modelling Language Diversity;183
12.2.2;2.2 One or Several Modelling Languages?;183
12.2.3;2.3 Enterprise Modelling Languages;184
12.2.4;2.4 Case Study of Three Enterprise Modelling Languages;185
12.3;3 IDEF0 Language and Business Functional Models;186
12.3.1;3.1 IDEF0 Language Presentation;186
12.3.2;3.2 IDEF0 Business Functional Models;188
12.4;4 GRAI Grid Language and Decisional Models;193
12.4.1;4.1 GRAI Grid Language Presentation;193
12.4.2;4.2 GRAI Grid Decisional Models;195
12.5;5 AMS Language and Organizational Models;197
12.5.1;5.1 AMS Language Presentation;197
12.5.2;5.2 AMS Organizational Models;198
12.6;6 Discussion;201
12.6.1;6.1 Interest of Enterprise Modelling Languages;201
12.6.2;6.2 Need of Complementary Modelling Languages;202
12.6.3;6.3 Necessary Adaptation to Farm Characteristics;203
12.7;7 Conclusions;203
12.8;References;204
13;A UML-Based Plug&Play Version of RothC;206
13.1;1 Introduction;206
13.2;2 The RothC Model;207
13.2.1;2.1 RothC Data Requirements;208
13.2.2;2.2 Decomposition of an Active Compartment;209
13.2.3;2.3 State Variables and Outputs;209
13.3;3 RothC Stand-alone Model;209
13.3.1;3.1 Weather;210
13.3.2;3.2 Soil;211
13.3.3;3.3 Management;211
13.3.4;3.4 Plant;212
13.3.5;3.5 Decomposable Plant Material;212
13.4;4 RothC Plug&Play Component;212
13.4.1;4.1 Dependency Injection Design Pattern;215
13.4.2;4.2 Communication with Other Components;216
13.5;5 Conclusions;220
13.6;References;220
14;Ontology-Based Simulation Applied to Soil, Water, and Nutrient Management;222
14.1;1 Introduction;222
14.2;2 Ways in Which Ontologies Can Be Applied to Modeling Agricultural and Natural Resource Systems;225
14.2.1;2.1 What Is an Ontology?;225
14.2.2;2.2 Literature Review;227
14.2.3;2.3 System Structure;230
14.2.4;2.4 Representing Symbols and Equations;230
14.2.5;2.5 Connecting to External Databases;232
14.2.6;2.6 Integration with Other Information;233
14.2.7;2.7 Ontology Reasoning;234
14.2.8;2.8 Model Base;234
14.3;3 Example: A Soil, Water, and Nutrient Management Model;235
14.3.1;3.1 Lyra Ontology Management System;235
14.3.1.1;3.1.1 Lyra Database Management Facilities;236
14.3.1.2;3.1.2 Authoring Tools;236
14.3.1.2.1;The EquationEditor;238
14.3.1.2.1.1;The Symbol Editor;238
14.3.1.2.1.2;The Mathematical Expression Editor;240
14.3.1.2.1.3;The Unit Editor;240
14.3.1.2.2;The SimulationEditor;242
14.3.1.2.2.1;The Structure Editor;242
14.3.1.2.2.2;The Simulation Controller;242
14.3.1.2.3;Additional Model Publishing Tools;244
14.3.2;3.2 Citrus Water and Nutrient Management System;245
14.3.2.1;3.2.1 Model Structure;245
14.3.2.2;3.2.2 Model Functions;248
14.3.2.3;3.2.3 Defining System Symbols;249
14.3.2.4;3.2.4 CWMS Application Implementation;250
14.4;4 Conclusions;253
14.5;References;254
15;Precision Farming, Myth or Reality: Selected Case Studies from Mississippi Cotton Fields;256
15.1;1 Introduction;257
15.2;2 Multidisciplinary Teams;258
15.3;3 Precision Agriculture and Information;260
15.3.1;3.1 Case 1: Simulation and Variable-Rate Nitrogen with Mississippi Delta Cotton, 1998;260
15.3.1.1;3.1.1 Case 1A: Update of Cotton Simulation Model Efforts in Precision Agriculture;265
15.3.2;3.2 Case 2: Statistical Analyses of Field-Level Precision Agriculture Experiments;266
15.4;4 Collecting and Managing Information;273
15.5;5 Precision Farming Equipment;275
15.5.1;5.1 Case 3: Development of Geo-referenced Site-Specific Prescriptions;276
15.5.2;5.2 Case 4: The Promise of Wireless Interconnectivity;279
15.5.3;5.3 Dollars and Sense;280
15.6;6 Conclusions;281
15.7;References;282
16;Rural Development Through Input-Output Modeling;286
16.1;1 Input-Output Models and Applications in Rural Development and Agriculture;287
16.2;2 Case Application;288
16.2.1;2.1 Methodology and Data;289
16.2.2;2.2 Regionalization Technique;290
16.3;3 The Computational Procedure;291
16.4;4 Input-Output Multipliers and Impact Analysis Results;296
16.5;5 Conclusions;299
16.6;1 Appendix: The Code of the GAUSS Computer Package;300
16.7;References;306
17;Modeling in Nutrient Sensing for Agricultural and Environmental Applications;309
17.1;1 Introduction;310
17.2;2 Statistical Modeling;311
17.2.1;2.1 Partial Least Squares Regression Analysis;311
17.2.2;2.2 Stepwise Multiple Linear Regression;313
17.2.3;2.3 Prediction Models;314
17.3;3 Modeling Application - Example 1: Phosphorus Sensing for Soil;314
17.3.1;3.1 Soil Sampling and Reflectance Measurement;314
17.3.2;3.2 Data Analysis;315
17.3.3;3.3 Results and Discussion;316
17.4;4 Modeling Application - Example 2: Nitrogen Sensing for Citrus Production;320
17.4.1;4.1 Citrus Leaf Sampling and Reflectance Measurement;320
17.4.2;4.2 Data Analysis;321
17.4.3;4.3 Results and Discussion;322
17.5;5 Conclusions;325
17.6;References;325
18;Estimation of Land Surface Parameters Through Modeling Inversion of Earth Observation Optical Data;328
18.1;1 Introduction;328
18.2;2 Statement of the Problem: EO Data and CR Modeling;330
18.3;3 The PROSPECT-SAILH Canopy Reflectance Model;332
18.4;4 Experimental Data Acquisition;334
18.4.1;4.1 Test-Site Description and Ground Measurements;334
18.4.2;4.2 Earth Observation Data: CHRIS/PROBA Imagery;336
18.5;5 Canopy Reflectance Model Inversion;336
18.5.1;5.1 An Inverse Ill-Posed Problem;336
18.5.2;5.2 Optimization and Analysis of the Inversion Procedure;339
18.5.3;5.3 Inverting PSH Model with Real CHRIS/PROBA Data;344
18.6;6 Conclusions;346
18.7;References;347
19;A Stochastic Dynamic Programming Model for Valuing a Eucalyptus Investment;350
19.1;1 Introduction;350
19.2;2 Literature Review;352
19.3;3 Problem Description;353
19.3.1;3.1 The Investment Decisions;354
19.4;4 Methodology;355
19.4.1;4.1 The Binomial Lattice;356
19.4.2;4.2 Decision and State Variables;357
19.4.3;4.3 Dynamic Programming Model;358
19.5;5 Case Study;359
19.5.1;5.1 Brief Characterization of the Portuguese Forest Sector;360
19.5.2;5.2 Data and Parameters;360
19.5.2.1;5.2.1 The Initial Investment: Plantation and Maintenance Costs;360
19.5.2.2;5.2.2 Wood and White Paper Pulpwood Prices;361
19.5.2.3;5.2.3 Wood and White Paper Pulpwood Quantities;361
19.5.2.4;5.2.4 The Exercise Price for the Cutting Option $K$;361
19.5.2.5;5.2.5 The Risk-Free Interest Rate rf;362
19.5.2.6;5.2.6 The Abandonment and Conversion to Another Land Use Value R;362
19.6;6 Results;362
19.6.1;6.1 Results for the Base and Extended Problems;363
19.6.2;6.2 Applying the Optimal Strategies;365
19.7;7 Conclusions;367
19.8;1 Appendix A;368
19.9;2 Appendix B;369
19.10;References;369
20;Modelling Water Flow and Solute Transport in Heterogeneous Unsaturated Porous Media;371
20.1;1 Introduction;372
20.2;2 The General Framework;373
20.2.1;2.1 Derivation of the Flux Statistics;373
20.2.2;2.2 Results;377
20.3;3 Macrodispersion Modelling;378
20.4;4 Discussion;380
20.4.1;4.1 Velocity Analysis;380
20.4.2;4.2 Spreading Analysis;384
20.5;5 Conclusions;390
20.6;References;391
21;Genome Analysis of Species of Agricultural Interest;394
21.1;1 Introduction;395
21.2;2 Genome Analysis and Applications in Agriculture;396
21.3;3 Biological Data Banks and Data Integration;399
21.4;4 Analysis of Biological Sequences: Sequence Comparison and Gene Discovery;400
21.5;5 Transcriptome Analysis;402
21.6;6 Systems Biology: The Major Challenge;403
21.7;7 An Italian Resource for Solanaceae Genomics;405
21.8;8 Conclusions;408
21.9;References;408
22;Modeling and Solving Real-Life Global Optimization Problems with Meta-heuristic Methods;412
22.1;1 Introduction;412
22.2;2 Modeling Real-Life Problems;414
22.3;3 Meta-heuristic Methods;415
22.3.1;3.1 Simulated Annealing Algorithm;415
22.3.2;3.2 Genetic Algorithms;416
22.3.3;3.3 Differential Evolution;416
22.3.4;3.4 Harmony Search;417
22.3.5;3.5 Tabu Search;417
22.3.6;3.6 Methods Inspired by Animal Behavior;417
22.3.7;3.7 Monkey Search;418
22.4;4 Applications;419
22.4.1;4.1 Forest Inventories;420
22.4.2;4.2 Lennard-Jones Clusters;421
22.4.3;4.3 Simulating Protein Conformations;423
22.5;5 Conclusions;425
22.6;References;426
23;Modeling and Device Development for Chlorophyll Estimation in Vegetation;429
23.1;1 Introduction;429
23.2;2 Methodological Approaches to Estimating Phytocenosis Parameters;431
23.2.1;2.1 Method Based on Use of the First Derivative;431
23.2.2;2.2 Principal Components Analysis;432
23.3;3 Support Vector Regression;433
23.4;4 Algorithms and Software;435
23.5;5 Device for Remote Measurement of Vegetation Reflectance Spectra Under Field Conditions;435
23.6;6 Results;437
23.7;7 Conclusions;437
23.8;References;438
24;Clustering and Classification Algorithms in Food and Agricultural Applications: A Survey;440
24.1;1 Introduction;440
24.2;2 Data Mining Algorithms;441
24.2.1;2.1 k-Means Algorithm;441
24.2.1.1;2.1.1 Algorithm k-Means;442
24.2.2;2.2 Fuzzy c-Means Clustering;443
24.2.3;2.3 k-Nearest Neighbor Classification;445
24.2.3.1;2.3.1 Training Phase;445
24.2.3.2;2.3.2 Testing Phase;445
24.2.4;2.4 Artificial Neural Networks;446
24.3;3 Applications;448
24.3.1;3.1 Grading Methods of Fruits and Vegetables;449
24.3.1.1;3.1.1 Image Interpretation by k-Means Algorithm;449
24.3.1.2;3.1.2 Image Interpretation by Neural Networks;450
24.3.2;3.2 Machine Vision and Robotic Harvesting;452
24.3.3;3.3 Classification of Wines;453
24.3.4;3.4 Classification of Forest Data with Remotely Sensed Images;455
24.4;4 Conclusions;457
24.5;References;457
25;Mathematical Modelling of Modified Atmosphere Package: An Engineering Approach to Design Packaging Systems for Fresh-Cut Produce;462
25.1;1 Fresh-Cut Produce;462
25.2;2 Modified Atmosphere Packaging;464
25.3;3 An Engineering Approach to the Package Design;466
25.3.1;3.1 Modelling the Gas Transport Through a Polymeric Film;467
25.3.2;3.2 Modelling the Respiration Process;470
25.3.3;3.3 Material Balance in MAP;473
25.3.4;3.4 Packaging Design Procedure;474
25.3.4.1;3.4.1 Selection of Packaging Material;476
25.3.4.2;3.4.2 Selection of Product Weight or Film Area;477
25.3.4.3;3.4.3 Optimization of the Volume;477
25.3.5;3.5 Package Simulation;478
25.3.6;3.6 Variability in Product/Package on Equilibrium Modified Atmosphere;479
25.4;4 Case Study;480
25.4.1;4.1 Product Characteristics;481
25.4.1.1;4.1.1 Type of Product and Storage Conditions Required;481
25.4.1.2;4.1.2 Mathematical Model for Respiration Rate;481
25.4.2;4.2 Package Characteristics;482
25.4.3;4.3 Variable Optimization: Product Weight;483
25.4.4;4.4 Package Simulation and Validation;483
25.5;Nomenclature;486
25.6;References;487