Agriculture has a significant role in the development of this world trade economy in historical perspective. Now, for the betterment of foodstuff security and foreign debt strategies, observing the assumptions behind the production of national agriculture in countries like Pakistan seek attention. For those countries whose economy directly depends on agriculture it is essential for them to get precision in this field. Information System like GIS provides the set of tools to handle the upcoming issues related in this world. Fuzzy GIS is a newly emerging field of computational intelligence. It combines fuzzy logic with spatial context. Most of the natural phenomena are fuzzy in nature. They show a degree of uncertainty or vagueness in their extent and attribute, which cannot be expressed by a crisp value. Agriculture is one of the fields of spatial domain which needs to be described in fuzzy terms. Fertilizer is a key input for the agriculture sector. In this research, the spatial surfaces of fertilizers are developed for the wheat crop using a fuzzy decision support system. The algorithm of our system takes soil nutrients and cropping time as input, applies fuzzy logic on the input values, defuzzifies the fuzzy output to crisp value and generates a fertilizer surface. We have also shown the use of type-II fuzzy sets for developing a fuzzy system for the generation of a fertilizer surface within a specified spatial extent, given nutrients of soil and cropping time. The type-II fuzzy system provides the capability of handling a higher level of uncertainty in defining the linguistic classes of nutrients in soil. This research has presented a basic platform for the development of spatial surfaces using a fuzzy and type-II fuzzy inference engine based on human linguistic values. The resultant output surface of fertilizer using fuzzy or type-II fuzzy describes the amount of fertilizer needed to cultivate a specific crop in a specified area. The complexity of our algorithm is ( ), where is the height of the raster, is the width of the raster, and is the number of expert rules. Though the analysis presented in this research are only for irrigated wheat crop, but they clearly depict that the use of a fuzzy inference system in a GIS can help identify the spatial patterns of the requirement of fertilizers for any crop.