Gage, Stuart (Michigan State University, 207 Manly Miles Bldg., 1405 S. Harrison Rd., East Lansing, MI, 48824; Phone: 517-355-2135; Fax: 517-432-9415; Email: gages@msu.edu)

A Modeling Application Integrative Framework for Regional Simulation of Crop Productivity, Carbon Sequestration and Greenhouse Gas Emissions

S.H. Gage *, M. Colunga-Garcia, P.R. Grace, H. Yang, G.R. Safir, G.P. Robertson,

A. Shortridge, A. Prasla, A. Ali, S. DelGrosso, P. Wilkins

A Modeling Applications Integrative Framework (MASIF) has been developed to support the analysis of regional simulation models. A framework is necessary because of the scale of the inputs to such models and the results derived from experiments with model simulations. The essence of the framework is to enable management and analysis of data inputs to models and resulting output from models. Our approach is to utilize commercial applications that have flexible and scaleable capacities to support analysis in three basic arenas: data management, mapping and statistics. Oracle was selected to support the database management function. ESRI ArcGIS, was selected to perform geographic manipulations. SPlus was selected as the preferred statistical analysis utility to conduct analysis of model input and output. Each of these software environments can operate across computational platforms. The Microsoft OS was selected as our platform choice and the VisualNet programming environment is utilized to knit database, mapping and statistical analysis into the MASIF framework. During the CASMGS enterprise we have held five workshops at Michigan State University designed to support model developers from Colorado, Nebraska, and Alabama to integrate regional crop and carbon simulation models into MASIF. The following models have been linked to MASIF: Muchow-Sinclair maize, Sinclair soybean, DSSAT, DAYCENT, and SOCRATES. The crop simulation models require daily meteorological input and can output growth and productivity estimates, from daily to seasonal time scales, based on analytical requirements. We have successfully linked crop models to a daily meteorological database for the North Central Region (NCR). This database comprises 32 years (1970-2001) of daily maximum temperature, minimum temperature, precipitation and solar radiation from 1053 point location encompassing the NCR contains over 11 million records. Model results from a single experimental simulation can produce a similar or larger output data stream. The DAYCENT or SOCRATES carbon models do not require the same high temporal scale input as daily crop models but require historical and current land use, climate and soil patterns and simulation may require large runs at annual time steps for long time series over large geographic regions. To accommodate carbon sequestration simulations, the National Land Cover Data has been integrated to estimate the percentage land use/land cover at the county level for the NCR. We have used this information along with the NPP determinations to run the SOCRATES model to estimate the soil carbon dynamics on a county-wide basis for the NCR region in the past, present and future (assuming several global warming scenarios). This comparative framework will also be used in the analysis of other models. We are in the process of enhancing accuracy of regional simulations of carbon sequestration through the integration of remote sensing derived variables into crop simulation models. Remote sensing provides the capability for observing large areas, at varying spatial and temporal scales, for attributes that can be used to estimate aboveground biomass and the proportion of exposed soils. We have developed a capacity to utilize MODIS data and are in the process of integrating this data into simulation models via MASIF. Remote sensing estimates will be used as drivers to replace simulated biomass or LAI outputs from crop physiological growth and development models. In summary, the MASIF environment enables a model analyst to conduct regional runs and experiments using any of the models integrated into MASIF.