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 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 field 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 identifiable from kriged contour maps as local coincidence may be obscured behind classified areas. Crop yield in subsequent years and remotely sensed information are not related if a unique response is assumed. An alternative data stratification procedure is described here for the identification 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.