Submitted to: BARC Poster Day
Publication Type: Abstract only
Publication Acceptance Date: 4/19/2012
Publication Date: N/A
Citation: Interpretive Summary:
Technical Abstract: Explanatory, or process-based, crop models are computational tools that have been developed for a wide range of applications, such as optimizing crop production and simulating the effects of climate change. Crop models rely on a diverse set of input variables for predicting outcomes such as crop yield, water use, and nitrogen demand. These input variables can be characterized into three basic categories: soil parameters (such as texture and hydraulic properties), weather parameters (such as daily observations of temperature, precipitation, and radiation), and management parameters (such as planting date, density, depth, irrigation, fertilization, and cultivar variety). Process-based crop models are generally developed for field-scale analyses but with the use of geospatial data and a geographic interface these models can be applied to more regional-scale applications. However, the sensitivity of crop model outputs to each of the input variables over a regional extent has yet to be fully explored. Two explanatory crop models developed by the USDA-ARS were evaluated: SPUDSIM for potatoes and MAIZSIM for corn. The results show the relative influence of each of the input categories on simulating crop yield and will provide valuable information for quantifying the uncertainty in regional-scale crop production analyses.