Agroecozones are geographic areas that share similar biophysical characteristics for crop production, such as soil, landscape, and climate, which define the potentials for agricultural productivity. Delineation and characterization of agroecozones would greatly enhance agricultural decision-making and management, as well as the extrapolation of experiment station research and field trials to forms and landscapes of similar agronomic behavior. Currently, agroecozones are often represented with static and rigid boundaries derived by using data obtained by averaging observations over a period of time. The boundaries of these regions, however, are fuzzy and reflect change over time and space. Furthermore, the measurements used as the basis of the-delineation are themselves uncertain. Agroecozones may be obtained by treating the input data as well as the resultant regions as fuzzy. Clustering is one of the most common approaches to derive agroccozones delineation. In this paper, we explore the suitability of some fuzzy clustering approaches for this problem. Experimental results show that fuzzy algorithms generate more accurate delineations as measured by their closeness to the Major Land Resources Areas (MLRA) map. However, they both require greater computational resources. Some additional advantages of using a fuzzy approach are also illustrated. This approach should be viewed as an additional tool available for modeling and analysis of important processes in spatial environmental decision support systems. (C) 2002 Elsevier Science B.V. All rights reserved.
