Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract only
Publication Acceptance Date: 7/5/2009
Publication Date: 11/5/2009
Citation: Hatfield, J.L. 2009. Methods to Enhance the Parameterization of Soil, Crop, Meteorological, and Management Inputs for Models [abstract]. ASA-CSSA-SSSA Annual Meetings, November 1-5, 2009, Pittsburg, PA. 2009 CD-ROM. Interpretive Summary:
Technical Abstract: Simulation models require a variety of inputs of soil, crop, meteorological, and management variables. These variables represent a range of spatial and temporal scales depending upon the model. Within each variable class there is a wide range of different inputs that are often required for effective use of a model; unfortunately, not all of the variables are easily obtained or readily available. Future development of agricultural simulation models will require more extensive use of input variables because as models become more sophisticated the input requirements will also increase. This will present a challenge to both experimentalists and modelers in terms of how these variables are parameterized and even measured. One of the major challenges is that many of the variables are expensive to measure on a fine spatial scale and there will have to be techniques developed that allow for spatial interpolation to create input variables at a fine resolution scale. For example, rainfall data is often recorded with rain gauges that provide a daily total amount, but with Doppler radar systems, we are able to create a more detailed spatial resolution map of rainfall that could be used as input into a model. In a similar fashion, many crop models require an estimate of soil residue cover and this variable could be obtained via remote sensing indices that would provide both a spatial map of these data and a temporal assessment of the changes during the fall or spring. There are direct measurements that can come from the variables themselves and often we may have to approach this problem through the use of surrogates that are derived from a combination of observed parameters. Improvements in model input will require that we examine a number of different methods that can be used to measure or derive the variables needed to enhance the performance of simulation models.