Identifying, understanding, and describing spatial processes in agricultural landscapes Ð 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 better understanding of landscape processes and their characterization, the analysis of the inherent variability is a major factor. Four case studies in which problems associated with landscape analysis are discussed. Spatial processes remain a main focus, as their analysis provides information on the relation between relevant state variables in agricultural landscapes. Variogram analysis showed that mineral soil nitrogen (Nmin) sampled in a ®eld at different scales, domains, and times is an instationary spatial process. Spatial association of grain yield, soil index and remotely sensed vegetation index may not be identi®able from kriged contour maps as local coincidence may be obscured behind classi®ed areas. Crop yield in subsequent years and remotely sensed information are not related if a unique response is assumed. An alternative data strati®cation procedure is described here for the identi®cation of different response functions in agricultural ecosystems. Processes of crop yield and underlying variables are described in autoregressive state-space models. This technique incorporates both deterministic and stochastic relations between different variables and is based on relative changes in space. # 2001 Elsevier Science B.V. All rights reserved.