Advances in Modeling Agricultural Systems

1. Front Matter

   Pages i-xviii

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2. The Model Driven Architecture Approach: A Framework for Developing Complex Agricultural Systems

   • Petraq Papajorgji, Ryan Clark, Eric Jallas

   Pages 1-18

3. A New Methodology to Automate the Transformation of GIS Models in an Iterative Development Process

   • André Miralles, Thérèse Libourel

   Pages 19-36

4. Application of a Model Transformation Paradigm in Agriculture: A Simple Environmental System Case Study

   • André Miralles, Thérèse Libourel

   Pages 37-54

5. Constraints Modeling in Agricultural Databases

   • François Pinet, Magali Duboisset, Birgit Demuth, Michel Schneider, Vincent Soulignac, François Barnabé

   Pages 55-65

6. Design of a Model -Driven Web Decision Support System in Agriculture : From Scientific Models to the Final Software

   • Loudovic Tambour, Vianney Houlès, Laurence Cohen-Jonathan, Valerie Auffray, Pierre Escande, Eric Jallas

   Pages 67-102

7. How2QnD: Design and Construction of a Game-Style, Environmental Simulation Engine and Interface Using UML, XML, and Java

   • Gregory A. Kiker, Rohit Thummalapalli

   Pages 103-129

8. The Use of UML as a Tool for the Formalisation of Standards and the Design of Ontologies in Agriculture

   • François Pinet, Catherine Roussey, Thomas Brun, Frédéric Vigier

   Pages 131-147

9. Modeling External Information Needs of Food Business Networks

   • Melanie Fritz

   Pages 149-166

10. Enterprise Business Modelling Languages Applied to Farm Enterprise: A Case Study for IDEF0, GRAI Grid, and AMS Languages

    • Vincent Abt, Frédéric Vigier, Michel Schneider

    Pages 167-191

11. A UML-Based Plug&Play Version of RothC

    • Petraq Papajorgji, Osvaldo Gargiulo, James W. Jones, Sibiri Traore

    Pages 193-208

12. Ontology-Based Simulation Applied to Soil, Water, and Nutrient Management

    • Howard Beck, Kelly Morgan, Yunchul Jung, Jin Wu, Sabine Grunwald, Ho-young Kwon

    Pages 209-242

13. Precision Farming, Myth or Reality: Selected Case Studies from Mississippi Cotton Fields

    • Jeffrey L. Willers, Eric Jallas, James M. McKinion, Michael R. Seal, Sam Turner

    Pages 243-272

14. Rural Development Through Input–Output Modeling

    • Konstadinos Mattas, Efstratios Loizou, Vangelis Tzouvelekas

    Pages 273-295

15. Modeling in Nutrient Sensing for Agricultural and Environmental Applications

    • Won Suk Lee, Ismail Bogrekci, Min Min

    Pages 297-315

16. Estimation of Land Surface Parameters Through Modeling Inversion of Earth Observation Optical Data

    • Guido D’Urso, Susana Gomez, Francesco Vuolo, Luigi Dini

    Pages 317-338

17. A Stochastic Dynamic Programming Model for Valuing a Eucalyptus Investment

    • M. Ricardo Cunha, Dalila B. M. M. Fontes

    Pages 339-359

18. Modelling Water Flow and Solute Transport in Heterogeneous Unsaturated Porous Media

    • Gerardo Severino, Alessandro Santini, Valeria Marina Monetti

    Pages 361-383

19. Genome Analysis of Species of Agricultural Interest

    • Maria Luisa Chiusano, Nunzio D’Agostino, Amalia Barone, Domenico Carputo, Luigi Frusciante

    Pages 385-402

20. Modeling and Solving Real-Life Global Optimization Problems with Meta-heuristic Methods

    • Antonio Mucherino, Onur Seref

    Pages 403-419

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.

• Java
• algorithms
• classification
• communication
• databases
• design
• digital elevation model
• mathematical modeling
• model
• modeling
• networks
• optimization
• programming
• simulation
• software

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