Identifying, understanding, and describing spatial processes in agricultural landscapes - four case studies

To evaluate the quality of the ecosystem and for making resources and land management decisions landscapes have to be assessed quantitatively. For a b etter understanding of landscape processes and their characterization, the analysis of the inherent variability is a major factor. Four case studies i n which problems associated with landscape analysis are discussed. Spatial processes remain a main focus, as their analysis provides information on th e relation between relevant state variables in agricultural landscapes. Var iogram analysis showed that mineral soil nitrogen (N-min) sampled in a fiel d at different scales, domains, and times is an instationary spatial proces s. Spatial association of grain yield, soil index and remotely sensed veget ation index may not be identifiable from kriged contour maps as local coinc idence may be obscured behind classified areas. Crop yield in subsequent ye ars and remotely sensed information are not related if a unique response is assumed. An alternative data stratification procedure is described here fo r the identification of different response functions in agricultural ecosys tems. Processes of crop yield and underlying variables are described in aut oregressive state-space models. This technique incorporates both determinis tic and stochastic relations between different variables and is based on re lative changes in space. (C) 2001 Elsevier Science B.V. All rights reserved .