New Technologies for Constructing Complex Agricultural and Environmental Systems

New Technologies for Constructing Complex Agricultural and Environmental Systems

Petraq Papajorgji (Canadian Institute of Technology, Albania) and François Pinet (Irstea/Cemagref - Clermont Ferrand, France)
Release Date: March, 2012|Copyright: © 2012 |Pages: 417
ISBN13: 9781466603332|ISBN10: 146660333X|EISBN13: 9781466603349|DOI: 10.4018/978-1-4666-0333-2

Description

The worldwide increase in population has led to numerous innovative approaches to the difficulties posed by food production and agriculture in general.

New Technologies for Constructing Complex Agricultural and Environmental Systems presents high-quality research on the design and implementation of information systems in the fields of agronomics, mathematics, economics, computer science, and the environment. This book gives holistic approaches to the design, development, and implementation of complex agricultural and environmental information systems, addressing the integration of several scientific domains such as agronomy, mathematics, economics, and computer science. This book will only become more important as the world searches for the best ways to manage food production.

Topics Covered

The many academic areas covered in this publication include, but are not limited to:

  • Agricultural Production
  • Decision Support Systems
  • Eco-technologies
  • Embedded sensor and mobile database
  • Geographic Information Systems
  • Global climate change
  • Precision farming
  • Simulation and optimization of agricultural systems
  • Soil, air and water quality models
  • Water Management

Table of Contents and List of Contributors

Search this Book:
Reset

Preface

Agricultural and environmental related sciences face the daunting challenge of dealing with problems of an increasing number of factors of different nature that requires to be approached with a multidisciplinary and multi-institutional team. Addressing these complex scientific issues demands the refinement of existing, as well as the creation of new, analytical theories with potential uses in agriculture and the environment. Therefore, the scientific approach needs to combine the newest achievements in many scientific domains such as agronomy, mathematics, economics, and computer science to name a few.

The objective of this book is to present the novel work on the design and implementation of information systems in the field of agriculture and environment. The main goal is to present how the new technologies can improve the construction of these complex systems. The work presented in this book is selected from the International Journal of Agricultural and Environmental Information Systems (IJAEIS) vol.1 & 2. In this preface, we introduce the topics of these articles.

Data warehouses

Recently, we have seen a large increase in sources of geo-referenced agricultural and environmental data:
-certain data are obtained through sensors and remote detection systems – e.g. through systems integrated within spreaders to monitor work carried out, using networks of sensors and satellite images, 
-data can be produced from environmental simulation models,
-other data are entered using specialized computer programs (e.g. using a computer application which records practices). 

All this information requires effective storage methods, as well as effective integration and analysis methods. Data warehouses (DW) (Berson & Smith, 1997; Martin R., 2008; Rizzi, Abello, Lechtenborger, & Trujillo) are the most appropriate modern systems for applying such methods. This new type of database is used to integrate, accumulate and analyze data from various sources (Calì, Lembo, Lenzerini, & Rosati, 2003). Various information from different databases are integrated into a data warehouse for combined analysis (List, Bruckner, Machaczek, & Schiefer, 2002). Depending on requirements, this can be loaded every week, every month, every year or even less frequently. These data are usually organized in a form referred to as being multidimensional in order to facilitate the calculation of indicators by combining different criteria. The indicators are made up of aggregated information obtained by functions such as sum, average, variance etc. Data from a data warehouse can be combined to provide hitherto unknown causal links. For this purpose, users can view data from the data warehouse using OLAP (On-line Analytical Processing) type tools (Thomsen, 1997). Causal links can also be obtained automatically by using data-mining algorithms (Breault J., Goodall C., & P., 2002). 

As indicated in (Pinet, 2010), the use of data warehouses is therefore important within a decision-making context. For example, a data warehouse containing economic, urban and environmental information can be used to help find the best place for establishing a new infrastructure. Data warehouses can supply data about algorithms for optimization or simulation (e.g. for optimizing the way agricultural work is organized). The concept of data warehouses is relatively recent and has great potential for assessing the impact of actions, practices, scenarios and programs both from a socio-economic as well as an environmental point of view (Nilakanta, Scheibe, & Rai, 2008; Pinet, et al., 2010; Schneider, 2008; Schulze C., Spilke J., & W., 2007)(Burmann & Gómez, 2007).

The papers of IJAEIS (Bimonte, 2010; Mahboubi, Bimonte, Faure, & Pinet, 2010; Sboui, Salehi, & Bédard, 2010) presented in this book deal with DW technologies.

The authors of (Mahboubi, et al., 2010) show that DW provides interesting technologies for managing and visualizing simulation results. This technology allows modelers to analyze and compare these results and their corresponding simulation models. (Mahboubi, et al., 2010) proposes a generic schema and an OLAP tool to analyze the results. The proposed schema can guide modelers in designing specific data warehouses and analyzing data. In the paper, the authors implement a data warehouse for the analysis of the savanna evolution. These simulations have been performed using SimExplorer, a tool also presented in IJAEIS (Chuffart, Dumoulin, Faure, & Deffuant, 2010). This tool is dedicated to facilitate the design of computer experiments on any simulation model. The joint use of DW technologies and SimExplorer allow modelers to obtain a traceability of simulations.

In (Bimonte, 2010), the author presents the implementation of a spatial DW-based tool called GeWOlap. GeWOlap allows users to model and visualize of complex and geographic indicators in a very flexible way. The author describes the use of the tool on simulated environmental data concerning the pollution of the Venice lagoon. He presents how the tool could be used to analyze water pollution phenomena in the Venice Lagoon in terms of several factors, such as time, pollutant, and location.

(Sboui, et al., 2010) deals with the issue related to spatial DW interoperability. The interoperability between spatial DW aims at facilitating the reuse of their content. In many situations, such as a simultaneous and rapid intervention in environmental emergencies using different spatial DW, decision-makers need to interoperate several heterogeneous data to support environmental applications.

Ontologies

As mentioned  in (Papajorgji, Pinet, Miralles, Jallas, & Pardalos, 2010; Pinet, Roussey, Brun, & Vigier, 2009), Gruber defined an ontology as “a formal explicit specification of a shared conceptualization” (Gruber, 1993a). According to Gruber, conceptualization refers to a model of phenomena in the world after identifying the relevant concepts of these phenomena. Explicit means that the type of concepts used, and the constraints on their use are explicitly defined. Formal refers to the fact that the ontology should be machine readable. Shared reflects the idea that ontology should capture consensual knowledge accepted by the communities (Gruber, 1993b). Hendler defines also ontology as “a set of knowledge terms, including the vocabulary, the semantic interconnections, and some simple rules of inference and logic for some particular topic” (Hendler, 2001). Thus, an ontology specifies the semantics of relations between the modeled concepts and should enable some sort of reasoning.

Usually, one considers that an ontology represents a domain of knowledge. It can be used to formalize complex knowledge of various domains. Ontologies can be represented in various forms (diagrams, formal models, thesauruses, etc.). They should contain: 
-a vocabulary of terms,
-a set of term definitions which identify concepts and fix the term interpretation (in the domain of knowledge considered),
-a representation of  relationships between concepts,
-an agreement of a community of ontology users about term definitions and the ontology structure.

The paper of IJAEIS (Hunter, Becker, Alabri, Ingen, & Abal, 2011) focuses on the use of ontologies to relate resource management actions to environmental monitoring data in South East Queensland. The authors propose ontologies and tools to integrate different datasets and relate management actions to water quality indicators. This paper describes the Health-e-Waterways Project, which is a cross-disciplinary collaboration between information scientists, water resource managers and stakeholders. It provides an overview of the ontologies that have been developed, the system architecture and the query, reporting and visualization interfaces - that are enabling scientists and policy makers to identify trends in ecosystem health indicators both geographically and temporally and to understand the impact that water resource management actions are having on water quality. This data integration approach enables scientists and resource managers to identify which actions are having an impact on which parameters and to adapt the management strategies accordingly. 

In the paper “A conceptual model of grassland-based beef systems” (Martin R., 2008), authors address the challenging issue of designing management strategies with regard to cattle demand. The article describes the application of an ontology of agricultural production systems to the generic conceptual model SEDIVER that supports the representation and dynamic farm-scale simulation of specific grassland-based beef systems. The most salient and novel aspects of SEDIVER concern the explicit modeling of (a) the diversity in plant, grassland, animal and farmland, (b) management strategies that deal with the planning and coordination of activities whereby the farmer controls the biophysical processes. By using the SEDIVER conceptual framework, one can expect to capture part of the subjective and context-specific knowledge used in farm management and, in this way, enable scientific investigation of management practices. 

(Karetsos, Haralampopoulos, & Kotis, 2011) presents an ontology-based method for the production of learning designs. These works are applied on the domain of sustainable energy education. The proposed framework includes an ontology of the sustainable energy development domain and an educational model designed in compliance with widespread standards. The authors envisage this framework both as a means to support the authoring of learning scenarios and as a provisioning of a field for conversation about which should be the appropriate form of an authoring tool in this area. 

GIS-based systems and spatial analyzes

Nowadays, numerous environmental and agricultural data are geo-referenced (Miralles, Pinet, & Bedard, 2010). They can be generated by remote sensing systems or other computer applications. In order to take benefits of these valuable data, some new solutions propose to integrate them into Geographical Information Systems (GIS) (Goodchild, et al., 1996; Laurini & Thompson, 1992). These systems allow taking into account characteristics of these complex data and phenomena (such as semantics, spatial components, etc.).

The papers published in IJAEIS (Bimonte, 2010) and (Sboui, et al., 2010), cited above, provide two examples of GIS-based technologies and spatial analyzes applied to environment.
The paper “Application of Support Vector Machines to Melissopalynological Data for Honey Classification” (Aronne, De Micco, & Guarracino, 2010) address the problem of the discrimination of geographical origin and the selection of marker species of honeys using Support Vector Machines and z-scores (Louveaux et al. 1978). The methodology is based on the elaboration of palynological data with statistical learning methodologies. This innovative solution provides a simple yet powerful tool to detect the origin of honey samples. In case of honeys from Sorrento Peninsula, the discrimination from other Italian honeys is obtained with high accuracy.

The authors of the IJAEIS paper "Urban versus Rural: the decrease of agricultural areas and the development of urban zones analyzed with spatial statistics" (Murgante & Danese, 2011) use spatial statistics and point pattern analysis to characterize the "periurban areas". They apply their method to the Potenza Municipality in Italy.

The paper entitled "Coupling geographic information system (GIS) and multi-criteria analysis (MCA) for modelling the ecological continuum in participative territorial planning" (Batton Hubert, Bonnevialle, Joliveau, Mazagol, & Paran, 2011) concerns participative territorial planning at a metropolitan scale taking into account ecological stakes. GIS and MCA techniques are proposed to take into account participative issues and to test new methodological proposals. 
The paper “Spatial pattern mining for soil erosion characterization” (Selmaoui-Folcher, Flouvat, Gay, & Rouet, 2011) describes the protection and the maintenance of the environment of New Caledonia. Authors state that among environmental problems, erosion has a strong impact on terrestrial and coastal ecosystems. However, due to the volume of data and their complexity, assessment of hazard at a regional scale is time-consuming, costly and rarely updated. Therefore, understanding and predicting environmental phenomenons need advanced techniques of analysis and modelization. In order to improve the understanding of the erosion phenomenon, they propose in this paper a spatial approach based on co-location mining and GIS.

Entity-Relationship (ER) (Chen, 1976) and Object-Oriented (OO) formalisms (Din & Idris, 2009) are widely used to describe environmental information of databases or computer programs. Numerous recent examples are available - see for example: (Almeida, Martins Ferreira, Eiras, Obermayr, & Geier, 2010; Anselme, et al., 2010; Asseng, Dray, Perez, & Su, 2010; Bimonte, 2010; Campo, Bousquet, & Villanueva, 2010; Farolfi, Müller, & Bonté, 2010; Goodall, Fay, & Bollinger Jr, 2010; Kraft, Vaché, Frede, & Breuer, 2011; Lagabrielle, et al., 2010; Lenz-Wiedemann, Klar, & Schneider, 2010; Merot & Bergez, 2010; Miralles, Pinet, & Bedard, 2010; Moglia, Perez, & Burn, 2010; Papajorgji & Pardalos, 2006; Papajorgji, Pinet, Miralles, Jallas, & Pardalos, 2010; C.  Parent, S.  Spaccapietra, & E.  Zimanyi, 2006; C. Parent, S. Spaccapietra, & E. Zimányi, 2006; Perez & Dragicevic, 2010; Pinet, 2010; Pinet, et al., 2009; Pinet, Duboisset, & Soulignac, 2007; Pinet, et al., 2010; Raffaetà, et al., 2008; Saqalli, Gérard, Bielders, & Defourny, 2010; Simon & Etienne, 2010; Spaccapietra, Parent, & Zimányi, 2007; Stempliuc, Lisboa Filho, Andrade, & Borges, 2009). For 20 years, several research teams adapted ER and OO to facilitate the modeling of spatio-temporal information (Bédard, Larrivee, Proulx, & Nadeau, 2004; Bédard & Paquette, 1989; Miralles, Libourel, Papajorgji, & Pardalos, 2009a, 2009b; Pinet, Kang, & Vigier, 2005). Because this type of information is complex, one goal of researchers is to propose specific notations to clarify its representation in models. According to the experiments presented in (Parent, et al., 1998), using a formalism specifically dedicated to spatio-temporal information allows for a 22% reduction in the number of entities and relationships in an ER diagram (without losing semantics), compared to a traditional ER model. The UML-based formalism called Plug-in for Visual Languages (PVL) is a well-known method to model spatio-temporal information (Bédard, 1999, 2009). The IJAEIS paper (Miralles, et al., 2010) provides a PVL tutorial. The authors illustrate PVL on different environmental examples.

Decision support systems

The paper “Using Soclab for a Rigorous Assessment of the Social Feasibility of Agricultural Policies” (Adreit, Roggero, Sibertin-Blanc, & Vautier, 2011) presents a theoretical and methodological framework to take into consideration the social dimension in a sustainable development project. To do this, the authors have developed the SocLab software environment, which implements a formalization of a well-established sociological theory, and enables to model social organizations, to analyze their properties and to simulate social actors’ behaviors. The authors used SocLab to assess the social acceptability of new agricultural practices more in line with the preservation of water resources and natural environments, in a well-defined context. The paper shows how they used it and presents the main results.

The paper "Pyroxene: a Territorial Decision Support System Based on Spatial Simulators Integration for Forest Fire Risk Management" (Maillé & Espinasse, 2011) introduces an architecture, called Pyroxene, dedicated to the integration, the execution and the synchronization of simulation models. The final goal of Pyroxene is to help decision makers to manage forest fire risks. Pyroxene is a multi-agents system. It takes into account models for the ecosystems dynamics, the urban dynamics and the forest fire risks.

In the paper “On the use of abduction as an alternative to decision trees in environmental decision support systems “ (Wotawa, 2011), the author discusses the use of rule-based systems and decision trees for representing the knowledge used in decision support systems. According to the author, neither decision trees nor rule-based systems are very well suited for knowledge representation for decision support systems and in particular diagnostic systems. As an alternative he proposes the use of abductive diagnosis, which allows for deriving root causes from effects where the underlying knowledge base represents cause effect relationships directly. Such models are usually available in the natural sciences. In order to show the use of abductive diagnosis in the environmental field, the author uses a model that was used for diagnosing a wastewater treatment plant.


Survey on new computer-based techniques applied to agriculture and environment

Three papers present surveys on computer-based technologies related to object-oriented and UML-based modeling, data mining and crop systems simulation.

The paper “MODELING: a central activity for flexible information systems 
development in agriculture and environment” (Papajorgji, et al., 2010) aims to show author’s vision on using model-based approaches to design complex and flexible agricultural and environmental information systems. At the center of this modeling approach is the Unified Modeling Language that facilitates expressing visually concepts of a problem domain and their relationships (Papajorgji, 2005; Papajorgji, 2009; Papajorgji & Clark, 2005; Papajorgji & Pardalos, 2006; Papajorgji & Shatar, 2004; Pinet, et al., 2007). UML has a core of notations that are generic and that can be used to model problems in any domain (OMG, 2009). Furthermore, UML can be extended to create profiles in order to take into consideration modeling concerns in a particular problem domain. UML profiles are created to use UML in designing spatial systems, ontologies and model driven architecture-based systems. A UML profile is created to design web-based systems and a recent profile makes it possible to use UML for business modeling purposes. The paper presents the state of the art in modeling agricultural and environmental systems and provides discussions for future directions.

In the paper “Data Mining Techniques in Agricultural and Environmental Sciences” (Chinchuluun, Xanthopoulos, Tomaino, & Pardalos, 2011) authors show their vision on the importance of knowing and efficiently using data mining and machine learning-related techniques for knowledge discovery in the field of agriculture and environment.  Data mining is the process of extracting important and useful information from large sets of data. This information can be converted into useful knowledge that could help to better understand the problem in study and to better predict future developments.

The paper, “The Role of Crop Systems Simulation in Agriculture and Environment” (Boote, Jones, Hoogenboom, & White, 2011) describes the evolution of the simulation of crop systems in the last 30-40 years. The goal of this paper is to give author’s vision on how crop systems simulation can serve important future roles in agriculture and environment and to prioritize research to better meet support these roles. The most important roles and uses of crop systems simulation are seen in five primary areas: 1) Basic research synthesis and integration, where simulation is used to integrate and synthesize our understanding of physiology, genetics, soil characteristics, management, and weather effects, 2) Strategic tools for research planning/policy to evaluate strategies and consequences of genetic improvement or management of resources or decisions to produce biofuel crops rather than food crops, 3) Applications for management purposes, where crop systems simulations are used to evaluate impacts of climate variability on production, consequences of weather and nutrient management on water use and nutrient use, consequences on economics, water use, and nutrient leaching, 4) Real time decision support to assist in management decisions (irrigation, N fertilization, sowing date, projected harvest, yield forecast, pest management), and 5) Education both in class rooms and extension contexts, to explain how crop systems function and are managed. Authors see a good potential to link crop models to molecular genetics, in effect, modeling from knowledge of genes of different cultivars to phenotypic performance in different environments. They see a continuing need to improve the crop models for simulating root growth and nutrient uptake, coupling of diseases and pests, fully-coupled energy balance, and response to climate change. 

References

Adreit, F., Roggero, P., Sibertin-Blanc, C., & Vautier, C. (2011). Using Soclab for a Rigorous Assessment of the Social Feasibility of Agricultural Policies. International Journal of Agricultural and Environmental Information Systems, 2(2).
Almeida, S. J. d., Martins Ferreira, R. P., Eiras, Á. E., Obermayr, R. P., & Geier, M. (2010). Multi-agent modeling and simulation of an Aedes aegypti mosquito population. [doi: DOI: 10.1016/j.envsoft.2010.04.021]. Environmental modelling & software, 25(12), 1490-1507.
Anselme, B., Bousquet, F., Lyet, A., Etienne, M., Fady, B., & Le Page, C. (2010). Modelling of spatial dynamics and biodiversity conservation on Lure mountain (France). [doi: DOI: 10.1016/j.envsoft.2009.09.001]. Environmental modelling & software, 25(11), 1385-1398.
Aronne, G., De Micco, V., & Guarracino, M. (2010). Application of Support Vector Machines to Melissopalynological Data for Honey Classification. International Journal of Agricultural and Environmental Information Systems, 1(2), 85-94.
Asseng, S., Dray, A., Perez, P., & Su, X. (2010). Rainfall-human-spatial interactions in a salinity-prone agricultural region of the Western Australian wheat-belt. [doi: DOI: 10.1016/j.ecolmodel.2009.12.001]. Ecological Modelling, 221(5), 812-824.
Batton Hubert, M., Bonnevialle, M., Joliveau, T., Mazagol, P.-O., & Paran, F. (2011). Coupling Geographic Information System (GIS) and Multi-Criteria Analysis (MCA) for Modelling the Ecological Continuum in Participative Territorial Planning International Journal of Agricultural and Environmental Information Systems, 2(2), 29-51.
Bédard, Y. (1999). Visual Modelling of Spatial Database towards Spatial PVL and UML. Geomatica, 53(2), 169-185.
Bédard, Y. (2009). Perceptory Web site.
Bédard, Y., & Paquette, F. (1989). Extending entity/relationship formalism for spatial information systems. Paper presented at the AUTO-CARTO 9, Baltimore.
Bédard, Y., Larrivee, S., Proulx, M. J., & Nadeau, M. (2004). Modeling geospatial databases with plug-ins for visual languages: A pragmatic approach and the impacts of 16 years of research and experimentations on perceptory. Lecture Notes in Computer Science, 3289, 17-30.
Berson, A., & Smith, S. (1997). Data Warehousing, Data Mining, and OLAP (Data Warehousing/Data Management): Computing Mcgraw-Hill.
Bimonte, S. (2010). A Web-Based Tool for Spatio-Multidimensional Analysis of Geographic and Complex Data. International Journal of Agricultural and Environmental Information Systems, 1(2), 42-67.
Bimonte, S. (2010). A Web-Based Tool for Spatio-Multidimensional Analysis of Geographic and Complex Data. International Journal of Agricultural and Environmental Information Systems, 1(2), 42-67.
Boote, K. J., Jones, J. W., Hoogenboom, G., & White, J. W. (2011). The Role of Crop Systems Simulation in Agriculture and Environment. International Journal of Agricultural and Environmental Information Systems, 1(1), 41-54.
Breault J., Goodall C., & P., F. (2002). Data mining a diabetic data warehouse. Artificial Intelligence in Medicine, 26(1), 37-54.
Burmann, A., & Gómez, J. (2007). Data Warehousing with Environmental Data Information Technologies in Environmental Engineering (pp. 153-160).
Calì, A., Lembo, D., Lenzerini, M., & Rosati, R. (2003). Source Integration for Data Warehousing Multidimensional Databases: Problems and Solutions (pp. 361-392).
Campo, P. C., Bousquet, F., & Villanueva, T. R. (2010). Modelling with stakeholders within a development project. [doi: DOI: 10.1016/j.envsoft.2010.01.005]. Environmental modelling & software, 25(11), 1302-1321.
Chen, P. (1976). The Entity-Relationship Model: Toward a Unified View of Data. ACM Transactions on Database Systems, 1(1), 9-35.
Chinchuluun, A., Xanthopoulos, P., Tomaino, V., & Pardalos, P. M. (2011). Data Mining Techniques in Agricultural and Environmental Sciences. International Journal of Agricultural and Environmental Information Systems, 1(1), 26-40.
Chuffart, F., Dumoulin, N., Faure, T., & Deffuant, G. (2010). SimExplorer: Programming Experimental Designs on Models and Managing Quality of Modelling Process. International Journal of Agricultural and Environmental Information Systems, 1(1), 55-68.
Din, J., & Idris, S. (2009). Object-Oriented Design Process Model International Journal of Computer Science and Network Security, 9(10), 71-79.
Farolfi, S., Müller, J.-P., & Bonté, B. (2010). An iterative construction of multi-agent models to represent water supply and demand dynamics at the catchment level. [doi: DOI: 10.1016/j.envsoft.2010.03.018]. Environmental modelling & software, 25(10), 1130-1148.
Goodall, J. L., Fay, J. P., & Bollinger Jr, D. L. (2010). A software library for quantifying regional-scale nitrogen transport within river basin systems. [doi: DOI: 10.1016/j.envsoft.2010.04.007]. Environmental modelling & software, 25(12), 1713-1721.
Goodchild, M. F., Steyaert, L. T., Parks, B. O., Johnston, C., Maidment, D., Crane, M., et al. (1996). GIS and environmental modeling: progress and research issues.
Gruber, R. (1993a). Towards Principles for the Design of Ontologies Used for Knowledge Sharing. In International Workshop on Formal Ontology, Padova, Italy, 1993. Available as technical report KSL-93-04.
Gruber, R. (1993b). A Translation Approach to Portable Ontology Specifications. Knowledge Acquisition, 5(2), 199-220.
Hendler, J. (2001). Agents and the semantic web. IEEE Intelligent Systems and Their Applications, 16(2), 30-37.
Hunter, J., Becker, P., Alabri, A., Ingen, C., & Abal, E. (2011). Using Ontologies to Relate Resource Management Actions to Environmental Monitoring Data in South East Queensland. International Journal of Agricultural and Environmental Information Systems, 2(1), 1-19.
Karetsos, S., Haralampopoulos, D., & Kotis, K. (2011). An Ontology-Based Framework for Authoring Tools in the Domain of Sustainable Energy Education International Journal of Agricultural and Environmental Information Systems, 2(1), 40-62.
Kraft, P., Vaché, K. B., Frede, H.-G., & Breuer, L. (2011). CMF: A Hydrological Programming Language Extension For Integrated Catchment Models. [doi: DOI: 10.1016/j.envsoft.2010.12.009]. Environmental modelling & software, 26(6), 828-830.
Lagabrielle, E., Botta, A., Daré, W., David, D., Aubert, S., & Fabricius, C. (2010). Modelling with stakeholders to integrate biodiversity into land-use planning - Lessons learned in Réunion Island (Western Indian Ocean). [doi: DOI: 10.1016/j.envsoft.2010.01.011]. Environmental modelling & software, 25(11), 1413-1427.
Laurini, R., & Thompson, D. (1992). Fundamentals of spatial information systems. Fundamentals of spatial information systems.
Lenz-Wiedemann, V. I. S., Klar, C. W., & Schneider, K. (2010). Development and test of a crop growth model for application within a Global Change decision support system. [doi: DOI: 10.1016/j.ecolmodel.2009.10.014]. Ecological Modelling, 221(2), 314-329.
List, B., Bruckner, R. M., Machaczek, K., & Schiefer, J. (2002). A Comparison of Data Warehouse Development Methodologies Case Study of the Process Warehouse. Lecture Notes in Computer Science, 2453, 203-215.
Louveaux J., Maurizio A., Vorwohl G. (1978) Methods of Melissopalynology, Bee World 59, 139–157.
Mahboubi, H., Bimonte, S., Faure, T., & Pinet, F. (2010). Data warehouse and OLAP for Environmental Simulation Data. International Journal of Agricultural and Environmental Systems, 1(2), 1-19.
Maillé, E., & Espinasse, B. (2011). Pyroxene: a Territorial Decision Support System Based on Spatial Simulators Integration for Forest Fire Risk Management. International Journal of Agricultural and Environmental Information Systems, 2(2), 73-92.
Martin R. (2008). Data Warehouse 100 Success Secrets - 100 most Asked questions on Data Warehouse Design, Projects, Business Intelligence, Architecture, Software and Models: Emereo Pty Ltd.
Merot, A., & Bergez, J. E. (2010). IRRIGATE: A dynamic integrated model combining a knowledge-based model and mechanistic biophysical models for border irrigation management. [doi: DOI: 10.1016/j.envsoft.2009.11.003]. Environmental modelling & software, 25(4), 421-432.
Miralles, A., Libourel, T., Papajorgji, P. J., & Pardalos, P. M. (2009a). Application of a Model Transformation Paradigm in Agriculture: A simple environmental system case study Advances in Modelling Agricultural Systems (pp. 37-54): Springer.
Miralles, A., Libourel, T., Papajorgji, P. J., & Pardalos, P. M. (2009b). A new methodology to automate the transformation of GIS models in an iterative development process Advances in Modelling Agricultural Systems (Vol. 25, pp. 19-36): Springer.
Miralles, A., Pinet, F., & Bedard, Y. (2010). Describing Spatio-Temporal Phenomena for Environmental System Development: An Overview of Today’s Needs and Solutions. International Journal of Agricultural and Environmental Systems, 1(2), 68-84.
Miralles, A., Pinet, F., & Bedard, Y. (2010). Describing Spatio-Temporal Phenomena for Environmental System Development: An Overview of Today’s Needs and Solutions. International Journal of Agricultural and Environmental Systems, 1(2), 68-84.
Moglia, M., Perez, P., & Burn, S. (2010). Modelling an urban water system on the edge of chaos. [doi: DOI: 10.1016/j.envsoft.2010.05.002]. Environmental modelling & software, 25(12), 1528-1538.
Murgante, B., & Danese, M. (2011). Urban versus Rural: the decrease of agricultural areas and the development of urban zones analyzed with spatial statistics. International Journal of Agricultural and Environmental Information Systems, 2(2), 16-28.
Nilakanta, S., Scheibe, K., & Rai, A. (2008). Dimensional issues in agricultural data warehouse designs. Computers and Electronics in Agriculture, 60(2), 263-278.
OMG. (2009). Unified Modeling Language (OMG UML), Infrastructure Version 2.2.
Papajorgji, P. (2005). A plug and play approach for developing environmental models. Environmental Modelling and Software, 20(10), 1353-1357.
Papajorgji, P. (2009). State of the Art in Modeling Agricultural Systems Encyclopedia of Optimization (pp. 3693-3704).
Papajorgji, P., & Clark, R. (2005). A Model Driven Approach to Agricultural Systems (slides), EFITA 2005, Portugal.
Papajorgji, P., & Pardalos, P. M. (2006). Software Engineering Techniques Applied to Agricultural Systems An Object-Oriented and UML Approach: Springer.
Papajorgji, P., & Shatar, T. M. (2004). Using the Unified Modeling Language to develop soil water-balance and irrigation-scheduling models. Environmental modelling & software, 19(5), 451-459.
Papajorgji, P., Pinet, F., Miralles, A., Jallas, E., & Pardalos, P. M. (2010). Modeling: a central activity for flexible information systems development in agriculture and environment. International Journal of Agricultural and Environmental Information Systems, 1(1), 1-25.
Papajorgji, P., Pinet, F., Miralles, A., Jallas, E., & Pardalos, P. M. (2010). Modeling: a central activity for flexible information systems development in agriculture and environment. International Journal of Agricultural and Environmental Information Systems, 1(1), 1-25.
Parent, C., Spaccapietra, S., & Zimanyi, E. (2006). Conceptual Modeling for Traditional and Spatio-temporal Applications: Springer.
Parent, C., Spaccapietra, S., & Zimányi, E. (2006). The MurMur project: Modeling and querying multi-representation spatio-temporal databases. Information Systems, 31(8), 733-769.
Parent, C., Spaccapietra, S., Zimanyi, E., Donini, P., Plazanet, C., & Vangenot, C. (1998). Modeling Spatial Data in the MADS Conceptual Model. Paper presented at the the International Symposium on Spatial Data Handling, SDH 98, Vancouver, Canada, July 11-15,1998.
Perez, L., & Dragicevic, S. (2010). Modeling mountain pine beetle infestation with an agent-based approach at two spatial scales. [doi: DOI: 10.1016/j.envsoft.2009.08.004]. Environmental modelling & software, 25(2), 223-236.
Pinet, F. (2010). Precise Design of Environmental Data Warehouses. Operational Research, 10(3), 349-369.
Pinet, F., Duboisset, M., & Soulignac, V. (2007). Using UML and OCL to maintain the consistency of spatial data in environmental information systems. Environmental modelling & software, 22(8), 1217-1220.
Pinet, F., Duboisset, M., Demuth, B., Schneider, M., Soulignac, V., & Barnabe, F. (2009). Constraints modeling in Agricultural Databases Advances in Modeling Agricultural Systems: Springer.
Pinet, F., Kang, M. A., & Vigier, F. (2005). Spatial constraint modelling with a GIS extension of UML and OCL: application to agricultural information systems. Lecture Notes in Computer Science, 3511, 160-178.
Pinet, F., Miralles, A., Bimonte, S., Vernier, F., Carluer, N., Gouy, V., et al. (2010). The use of UML to design agricultural data warehouses. Paper presented at the International Conference on Agricultural Engineering, AGENG 2010.
Pinet, F., Miralles, A., Bimonte, S., Vernier, F., Carluer, N., Gouy, V., et al. (2010). The use of UML to design agricultural data warehouses. Paper presented at the International Conference on Agricultural Engineering, AGENG 2010.
Pinet, F., Roussey, C., Brun, T., & Vigier, F. (2009). The Use of UML as a Tool for the Formalisation of Standards and the Design of Ontologies in Agriculture Advances in Modeling Agricultural Systems: Springer.
Raffaetà, A., Ceccarelli, T., Centeno, D., Giannotti, F., Massolo, A., Parent, C., et al. (2008). An application of advanced spatio-temporal formalisms to behavioural ecology. GeoInformatica, 12(1), 37-72.
Rizzi, S., Abello, A., Lechtenborger, J., & Trujillo, J. Research in data warehouse modeling and design: dead or alive? . Paper presented at the Proceedings of the 9th ACM international workshop on Data warehousing and OLAP.
Saqalli, M., Gérard, B., Bielders, C., & Defourny, P. (2010). Testing the impact of social forces on the evolution of Sahelian farming systems: A combined agent-based modeling and anthropological approach. [doi: DOI: 10.1016/j.ecolmodel.2010.08.004]. Ecological Modelling, 221(22), 2714-2727.
Sboui, T., Salehi, M., & Bédard, Y. (2010). A systematic approach for managing the risk related to semantic interoperability between geospatial datacubes. International Journal of Agricultural and Environmental Information Systems, 1(2), 20-41.
Schneider, M. (2008). A general model for the design of data warehouses. International Journal of Production Economics, 112(1), 309-325.
Schulze C., Spilke J., & W., L. (2007). Data modeling for Precision Dairy Farming within the competitive field of operational and analytical tasks. Computers and Electronics in Agriculture, 59(1), 39-55.
Selmaoui-Folcher, N., Flouvat, F., Gay, D., & Rouet, I. (2011). Spatial Pattern Mining for Soil Erosion Characterization. International Journal of Agricultural and Environmental Information Systems, 2(2), 73-92.
Simon, C., & Etienne, M. (2010). A companion modelling approach applied to forest management planning. [doi: DOI: 10.1016/j.envsoft.2009.09.004]. Environmental modelling & software, 25(11), 1371-1384.
Spaccapietra, S., Parent, C., & Zimányi, E. (2007). Spatio-temporal and Multi-representation Modeling: A Contribution to Active Conceptual Modeling Active Conceptual Modeling of Learning (pp. 194-205).
Stempliuc, S. M., Lisboa Filho, J., Andrade, V., & Borges, K. (2009). Extending the UML-GeoFrame data model for conceptual modeling of network applications. Paper presented at the International Conference on Enterprise Information Systems, Milão, Italy.
Thomsen, E. (1997). OLAP Solutions: Building Multidimensional Information Systems: John Wiley & Sons.
Wotawa, F. (2011). On the use of abduction as an alternative to decision trees in environmental decision support systems. International Journal of Agricultural and Environmental Information Systems, 2(1), 63-82.

Author(s)/Editor(s) Biography

Petraq Papajorgji is a professor and dean of engineering at the Canadian Institute of Technology, Albania. His field of reseach is modelling of complex information system. He is author of a number of books in modelling and has a number of publications in his research area.
François Pinet is a research director at the French Research Institute for Agricultural and Environmental Engineering (Clermont Ferrand, France). His field of research is in environmental information systems and geomatics. He belongs to several scientific committees of different conferences and journals in these fields.

Editorial Board

Editorial Advisory Board

James W. Jones, University of Florida, USA
Panos M. Pardalos, Center for Applied Optimization - University of Florida, USA
Gerhard Shiefer, University of Bonn, Germany

Associate Editors

Yvan Bedard, Laval University, Canada
Sylvie Daniel, Laval University, Canada
Guillaume Deffuant, Cemagref - Clermont Ferrand, France
Eric Jallas, ITK/CIRAD, France
James W. Jones, University of Florida, USA
Sotiris Karetsos, Agricultural University of Athens, Greece
Robert Laurini, INSA Lyon, France
Ki Joune Li, University of Pusan, South Korea
Andre Miralles, Cemagref - Montpellier, France
Pierre-Alain Muller, University of Haute-Alsace, France
Panos M. Pardalos, Center for Applied Optimization - University of Florida, USA
Nikolla P. Qafoku, Pacific Northwest National Laboratory, USA
Mario Rosario Guarracino, ICAR-CNR, Italy
Michel Schneider, Blaise Pascal University, France
Gerhard Shiefer, University of Bonn, Germany

List of Reviewers

Luiz Henrique Antunes Rodrigues, University of Campinas, Brazil
Thierry Badard, Laval University, Canada
Lotfi Bejaoui, Laval University, Canada
Jose Luis Braga, Universidade Federal de Vicosa, Brazil
Jean-Pierre Chanet, Cemagref – Clermont Ferrand, France
Christophe Claramunt, Naval Academy Research Institute, France
Birgit Demuth, Dresden University of Technology, Germany
Jean-Paul Donnay, University of Liege, Belgium
Veronique Gouy, Cemagref - Lyon, France
Alaine Guimares, Ponta Grossa State University of Parana, Brazil
Vianney Houles, ITK, France
Mariane Huchard, University of Montpellier, France
Spiros Kaloudis, Agricultural University of Athens, Greece
Nikos Manouselis, Agro-Know Technologies, Greece
Jason Papathanasiou, Aristotle University of Thessaloniki, Greece
Sabri Pllana, University of Vienna, Austria
Carlo Previl, UQAM, Canada
Stanley Robson de Oliveira, Embrapa, Brazil
Evan Rroco, Tirana University of Agriculture, Albania
Kleber Xavier Sampaio de Souza, Embrapa, Brazil
Antonio M. Saraiva, Universidade de São Paulo, Brazil
Miguel-Angel Sicilia, University of Alcalá, Spain
Jerome Steffe, ENITA Bordeaux, France
Ludovic Tambour, ITK, France
Sonia Ternes, Embrapa, Brazil
Manuel Valiente-Gomez, University of Castiglia la Mancha, Spain
Alain Viau, Laboratory of Geomatics Applied in Agriculture and Environment, Canada
Jan Erik Wien, Wageningen University, The Netherlands
Petros Xanthopoulos, Center for Applied Optimization - University of Florida, USA
Fatos Xhafa, Polytechnic University of Catalonia, Spain
Pandi Zdruli, CIHEAM-Mediterranean Agronomic Institute of Bari, Italy