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Special issue on Information and Decision Support Systems for Agriculture and Environment

Abstract : Tasks such as monitoring and managing the ecological status of rivers, sharing environmental information, and helping farmers in their decision making are challenges for which information and decision support systems (DSS) represent effective solutions. New theoretical and technical challenges emerge from the integration of several scientific domains such as agronomy, ecology, mathematics, information technology and computer science. The objective of this special issue is to show how the latest advances in research in information and decision support systems can be applied to environmental and/or agricultural systems. This special issue has been organized in conjunction with the international workshop on Agricultural and Environmental Information and Decision Support Systems (AEIDSS 2013) held by the International Conference on “Computational Science and its Applications” (ICCSA 2013), June 24–27, 2013 (Ho Chi Minh City-Vietnam). The authors of the papers presented at this workshop have been invited to submit an extended version of their manuscript to this special issue, but the special issue call for papers was also open to external contributions. Twenty papers have been submitted to this special issue, and ten have been accepted for publication after revisions; each submission has been evaluated by three reviewers. The papers in this special issue address various current topics in informatics, such as data interoperability, linked open data, management of very large datasets, analysis of spatial and temporal information, and advanced formal methods for developing DSS. Here, we describe the main contributions found in the articles. The paper “Semantic Annotation of the CEREALAB database by the AGROVOC Linked Dataset” addresses linked open data. As indicated by the authors, the number of open data government initiatives is increasing worldwide, and new research is needed to help resource providers to publish and link their datasets. The authors have developed an experimental methodology to publish, link and enrich open data by performing automatic semantic annotation of schema elements. This approach is applied to the CEREALAB database, a public relational database designed for storing genotypic and phenotypic data on plant breeding. The authors of the paper entitled “A Knowledge Discovery Process for Spatiotemporal Data: Application to River Water Quality Monitoring” combine different methods to extract knowledge from water-quality data collected along rivers. The results can then be used to determine spatial indicators to assist experts in their interpretation of land use impacts on water quality. The paper “User Centered Ontology for Sri Lankan Farmers” proposes an ontological approach to provide context-specific information and knowledge to farmers based on their own context. As stated by the authors, a knowledge repository of agricultural information is proposed to respond to user queries, considering the context in which information is needed by farmers at different stages of the farming life cycle. The approach has been tested among farmers in Sri Lanka. The article “Evaluation of Urban Sprawl from space using open source technologies” presents a case study of analyzing urban sprawl using satellite data. The authors mention that these data are related to a coastal zone with the presence of sand and rocks, characterized by a fragmented ecosystem and small towns with an increasing rate of urbanization and soil consumption. Support vector machine algorithms are applied for satellite image classification, and open-source software is used. The paper “An Infrastructure-oriented Approach for supporting Biodiversity Research” presents D4Science, a data infrastructure that facilitates accessing and reusing biodiversity data. According to the authors, D4Science is an integrated and flexible environment based on existing databases and information systems. The authors highlight that the infrastructure facilitates the discovery, selection, access and processing of data. The paper “Model-Driven Engineering Applied to Crop Modelling” presents a framework to formalize mechanistic models of plant growth. This approach is based on a new domain-specific language dedicated to crop modeling and is used to facilitate the development of software applications for decision support systems. This framework allows modelers to generate software code automatically. The contribution entitled “Decision Support for Agri-Food Chains: A Reverse Engineering Argumentation-Based Approach” defines an objectives-driven decision support system defined for the end products of an agri-food chain. A reverse engineering technique and a logic-based formalization is proposed in the paper. The article “Increasing dependence on foreign water resources? An assessment of trends in global virtual water flows using a self-organizing time map” highlights that self-organizing time maps can be used to model, visualize and analyze virtual water fluxes. The virtual water is analyzed through the food trade in different countries. The final two papers show how data warehouse technology can be adapted, used and applied for environmental and ecological applications. The first of these papers proposes a method for the enrichment of phenotypic and genotypic data warehouse analysis, using question-answering systems. The main goal is to facilitate the decision making process in cereal breeding programs. The authors of the last paper propose to use hierarchical agglomerative clustering with the Gower similarity index to generate analysis dimensions in a data warehouse. The data set used is related to birds along the Loire River in France. As shown in these short des]criptions of the papers, this special issue presents new research advances in techniques related to information and DSS technologies, such as ontology-based systems, data warehouses, data mining and model-driven engineering. The application examples provided by the authors clearly show the role of these new technologies in the fields of agriculture, environment and ecology and their potential for the next few years
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Déposant : Migration Irstea Publications <>
Soumis le : samedi 16 mai 2020 - 07:40:28
Dernière modification le : jeudi 2 juillet 2020 - 13:57:31


  • HAL Id : hal-02602313, version 1
  • IRSTEA : PUB00046225



François Pinet, S. Bimonte, A. Miralles, F. Flouvat. Special issue on Information and Decision Support Systems for Agriculture and Environment. 26 (2), Elsevier, 2015, Ecological Informatics. ⟨hal-02602313⟩



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