Integrated environmental modelling is gaining momentum for addressing grand scientific challenges such as monitoring the environment for change detection and forecasting environmental conditions along with the consequences for society. Such challenges can only be addressed by a multi-disciplinary approach, in which socio-economic, geospatial, and environmental information becomes inter-connected. However, existing solutions cannot be seamlessly integrated and current interaction paradigms prevent mainstream usage of the existing technology. In particular, it is still difficult to access and join harmonized data and processing algorithms that are provided by different environmental information infrastructures. In this paper we take a novel approach for integrated environmental modelling based on the notion of inter-linked resources on the Web. We present design practices for creating resource-oriented interfaces, driven by an interaction protocol built on the combination of valid linkages to enhance resource integration, accompanied by associated recommendations for implementation. The suggested resource-oriented approach provides a solution to the problems identified above, but still requires intense prototyping and experimentation. We discuss the central open issues and present a roadmap for future research.
Tixier, P., Malezieux, E., Dorel, M., Bockstaller, C., and Girardin, P.
European journal of agronomy, 2007 Feb., v. 26, issue 2, p. 71-81.
Rpest indicator, environmental factors, environmental impact, crop management, environmental indicators, cropping systems, simulation models, crop models, Musa, bananas, risk assessment, model validation, pesticides, leaching, and water pollution
Includes references Like many intensive monocultures, some features of banana-based cropping systems may be detrimental to the environment. Pesticide use is a major cause of surface water and groundwater pollution. The risk of water pollution due to pesticides is very high in the insular areas of the French West Indies (FWI). In order to assess these risks and help design more sustainable cropping systems, we propose using an indicator to assess the risk of pesticide pollution over time (Rpest). Rpest provides dynamic assessments through a linkage with a cropping system model that simulates environmental factors and agricultural practices. An expert validation check was conducted and demonstrated that Rpest can rank cropping systems by risk as well as experts. A sensitivity analysis highlighted that the indicator can take the properties of active ingredients and pedoclimatic data into account in assessments. Rpest helps to pinpoint high pollution risk periods. It can also be used to test alternatives and compare systems. This tool can be integrated into a model-based, global, prototyping methodology used for more sustainable cropping systems.
Includes references West African cotton production has increased rapidly in recent years. Cotton is being cropped under new ecological conditions by new cotton-producing farmers, but the cropping techniques recommended by developers have essentially remained the same. Methodologies are needed to generate a broad scope of recommendations on cropping techniques to deal with the increasing diversity concerning farmers and cropping conditions. A conceptual model of a cotton field was developed that approaches a crop field as a biophysical system under the influence of a “technical system” (i.e. the combination of farmers' practices implemented in the field). The system outputs were restricted to yield and the main yield components. A theoretical model was first designed on the basis of published data and expert knowledge on cotton physiology, local soil-climate conditions and farmers' practices. It was based on five specific hypotheses on links between technical and biophysical systems. The hypotheses were tested in a local farmers' network. Thirty “cropping situations” (soil-crop-technique combinations) were selected in farmers' fields around Katogo village (Mali), a village that had been previously selected for a cotton crop management prototyping program. Homogeneous groups of situations were drawn up on the basis of the dynamics of crop aerial biomass accumulation. They were compared for their management and environment features. The initial conceptual model was then simplified, while taking the measured variability in its components and the sensitivity of the outputs to these components into account. This conceptual model is being evaluated in other villages, where we have partnerships with farmers, in order to develop a version adapted to a broad range of situations.