Skip to main content
ARS Home » Research » Publications at this Location » Publication #170458
Title: MODEL PARAMETERS FOR AGRICULTURAL SYSTEMS WITH NONLINEAR PROCESS INTERATIONS AND SPATIAL VARIABILITY

Author
item Green, Timothy
item Ahuja, Lajpat
item Ascough Ii, James
item Ma, Liwang
item Erskine, Robert - Rob
item McMaster, Gregory

Submitted to: Agronomy Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 7/23/2004
Publication Date: 7/23/2004
Citation: Green, T.R., Ahuja, L.R., Ascough II, J.C., Ma, L., Erskine, R.H., McMaster, G.S. 2004. Model parameters for agricultural systems with nonlinear process interations and spatial variability. Agronomy Abstracts a04-5627. (CD-ROM). 2004.

Interpretive Summary: Models and model spatial units are applied at different scales, often without explicitly adjusting parameter values. Scale-dependence in model parameters usually results from heterogeneity in the underlying properties below the scale of application. Thus, it is important to recognize process and dimensionality changes with scale; different processes may become dominant at different scales. The estimation problem is further compounded by interactions in space and time between various system components. We are finding that fractal scaling behavior is appropriate for characterizing observed spatial patterns. When fractal behavior prevails, a 'nested' sample design is advantageous, where sample locations are clustered from larger to smaller scales. Surrogate measures, such as topographic attributes, may be used to estimate model parameters, or at least provide guidance for sampling. Examples from recent field campaigns and spatial model parameterization will be presented using these concepts, and opportunities for future research will be identified.

Technical Abstract: Models and model spatial units are applied at different scales, often without explicitly adjusting parameter values. Scale-dependence in model parameters usually results from heterogeneity in the underlying properties below the scale of application. Thus, it is important to recognize process and dimensionality changes with scale; different processes may become dominant at different scales. Parameter estimation depends on the model structure, and detailed knowledge of the active processes and sub-scale patterns of heterogeneity may be required to address the issues of parameterization and scale effects rigorously. The estimation problem is further compounded by interactions in space and time between various system components. At the field scales of interest, we are finding that fractal or multifractal scaling behavior is appropriate for characterizing observed spatial patterns. When fractal behavior prevails, a 'nested' sample design is advantageous, where sample locations are clustered from larger to smaller scales. Surrogate measures, such as topographic attributes, may be used to estimate model parameters, or at least provide guidance for sampling. Examples from recent field campaigns and spatial model parameterization will be presented using these concepts, and opportunities for future research will be identified.