THE FUTURE OF SPATIAL MODELING AT GPSRU

Authors: Ascough Ii, James; Green, Timothy; Ahuja, Lajpat; McMaster, Gregory; Ma, Liwang

Submitted to: Workshop Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 4/2/2004
Publication Date: 4/20/2004
Citation: Ascough Ii, J.C., Green, T.R., Ahuja, L.R., Mcmaster, G.S., Ma, L. 2004. The future of spatial modeling at GPSRU. Workshop Proceedings. Intl. Workshop on Applications, Enhancements, and Collaboration of ARS RZWQM and GPSFARM Models, April 20 - 22, 2004, Fort Collins, CO.

Interpretive Summary: Measurement of agricultural processes across multiple scales has become more feasible in recent years due to the improvement of automated data collection technology, and the tremendous amount of site-specific data being collected. Unfortunately, scientific analysis capability has lagged behind the technological advances driving data collection efforts. Models for predicting runoff, chemical transport, and crop yield in agricultural landscapes are simultaneously improving, making it possible to include physical, chemical and biological outcomes in agricultural system modeling. However, nearly all of these models are scale-dependent and designed only for homogeneous areas, or simplistically represent soil-plant-water processes at larger scales. Given these limitations, the objectives of spatial agricultural system modeling at GPSRU are threefold: 1) develop new statistical and physical scaling concepts and methods to improve quantification of landscape processes causing space-time variability in water, chemicals, soils and plants; 2) develop software tools for spatially distributed, process-based simulation of agricultural systems that apply across multiple scales, respond to management and climate effects on crop production, and address both on-site and off-site environmental impacts; and 3) develop new methods and protocols for scale-appropriate model parameterization and model evaluation. This paper details general methodology for accomplishing the spatial modeling objectives, briefly describes research results to date, and lists expected benefits from future research.

Technical Abstract: Measurement of agricultural processes across multiple scales has become more feasible in recent years due to the improvement of automated data collection technology, and the tremendous amount of site-specific data being collected. Unfortunately, scientific analysis capability has lagged behind the technological advances driving data collection efforts. Models for predicting runoff, chemical transport, and crop yield in agricultural landscapes are simultaneously improving, making it possible to include physical, chemical and biological outcomes in agricultural system modeling. However, nearly all of these models are scale-dependent and designed only for homogeneous areas, or simplistically represent soil-plant-water processes at larger scales. Given these limitations, the objectives of spatial agricultural system modeling at GPSRU are threefold: 1) develop new statistical and physical scaling concepts and methods to improve quantification of landscape processes causing space-time variability in water, chemicals, soils and plants; 2) develop software tools for spatially distributed, process-based simulation of agricultural systems that apply across multiple scales, respond to management and climate effects on crop production, and address both on-site and off-site environmental impacts; and 3) develop new methods and protocols for scale-appropriate model parameterization and model evaluation. This paper details general methodology for accomplishing the spatial modeling objectives, briefly describes research results to date, and lists expected benefits from future research.