Spatially-based model of land suitability analysis using Block Kriging

In recent years, methods of fuzzy reasoning have been successfully developed for land evaluation. The accuracy of such land evaluation depends on the quality of weighing land characteristics with respect to their effects on crop production. This paper presents a spatially-based model of land suitability analysis. The main purposes were to (1) establish land suitability indices for irrigated wheat yield and (2) use of geostatistics technique for mapping of fuzzy land suitability index using kriging method. The fuzzy set methodology was employed in the modeling procedure, and block kriging method was used to spatial interpolation approach. The study area was divided into 14 land units and 9 land characteristics considered to be relevant to irrigated wheat. Due to higher weight, gravel volume percentile in the soil was the most significant characteristic (criteria) and the soil depth was the least significant criteria among all effective criteria in irrigated wheat yield. The correlation coefficient between land index and observed yield in the study area was 0.77 (r = 0.77) for the fuzzy method. The best model for fitting on experimental variogram was selected based on less RSS values and the gaussian model was selected for estimation of fuzzy land indices. Use of the kriging technique that exploits spatial variability of data is useful in producing continuous land suitability maps and in estimating uncertainties associated with predicted land suitability indices.