Geographic Information System Based and Visual Modeling Interfaces

With rapid improvements in computer technology and computational power, there are an increasing number of agricultural system models supported by Windows interfaces containing geographic information system (GIS) functionality to perform high-resolution simulation (e.g., multiple spatial land units within a field or watershed) of various biological, physical, and chemical processes in the soil-plant-atmosphere continuum. Most linkages between GIS and various models have been performed for models characteristically applied at larger scales (e.g., SWAT, WEPP-Watershed, and AGNPS), although field-scale models have been linked to GIS as well (e.g., GLEAMS and EPIC). Two important factors are commonly considered when developing high-resolution agricultural modeling tools: (1) agricultural system models require a large number of input data sets representing the initial conditions of the field or watershed; and (2) agricultural system models typically incorporate a large number of model parameters to quantify the underlying physical and biogeochemical processes. Thus, these tools should be capable of managing large and complex data sets while maintaining an acceptable level of user-friendliness. GIS technology has been used extensively for agricultural modeling including data preparation, model parameter extraction, and model results visualization. Research efforts have generally used one of the following approaches for linking GIS and simulation models: (1) embedding GIS functionalities into the model; (2) embedding a model into a GIS; (3) loose coupling - that is, GIS is used to generate model input files and display model results; and (4) full coupling of GIS and a model. The most common approach for developing GIS-based agricultural modeling interfaces is loose coupling - examples of this include GIS linkages of the AGNPS, SWAT, and AGWA models. In general, the database (including multiple GIS layers) is used as a data-collection source, and GIS tools are used for extraction (in many cases automated) of necessary data from the GIS data layers including land-use areas, reach networks, soil information, and elevation. The extracted data are then passed to the model interface, read and processed, and model input files created for simulation. Many of the GIS-based interfaces for agricultural system models are based on the ESRI ArcView 3.x (Avenue) programming language. Ongoing research efforts for many agricultural system models are tied to new interface development under the ArcGIS 9.x (Visual Basic, VB.NET, Python, JavaScript) framework, and to system (software) updating to enhance Internet accessibility.

Traditionally, agricultural system models have been developed and implemented in a code-based modeling approach, that is, constructing a model as a sequence of lines of code in a common programming language such as Fortran, Visual Basic, C, C++, or Java. However, it is becoming increasingly more common to construct and link model components in a visual or 'icon-based' modeling environment. Proprietary visual modeling environments (e.g., Stella (isee systems, Inc.), Vensim (Ventana Systems, Inc.), POWERSIM (Powersim Software AS), and Model Builder (ESRI, Inc.)) are systems where models are constructed by assembling and linking icon-based model components using a sophisticated button- and menu-driven Graphical User Interface. There are three advantages in using these systems for agricultural model development: (1) they typically contain a large number of built-in functions (e.g., mathematical, logical, and statistical including risk and Monte Carlo analysis) that greatly simplify model development and evaluation; (2) they are easy to learn, intuitive to use, and familiar to users of Windows-based software; and (3) they can readily import and export data, generate graphs and tables, and work well together with other commercial applications through dynamic linking with spreadsheets and other software programs. In some respects, the icon-based modeling systems have many of the same capabilities of the more comprehensive modular frameworks described above; however, they are somewhat constrained in their flexibility and effectiveness in producing complex and mul-tifaceted models. These limitations have been somewhat alleviated through a hybrid linkage of code- and icon-based modeling approaches. This concept has been successfully demonstrated by using Vensim to develop a new seed bank module and linking the module (through dynamic linked library support) to the APSIM farming systems model.

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