During the last few years, yield maps have become economically feasible for farmers due to technological advances in precision agriculture. However, evidence of yield profitability is still uncertain, and variability in yield has seldom been correlated to variability in profits. Differently from yield maps, profit maps can supply additional information about the economic return for each particular area of a field. The objective of the present work was to study how management decisions can be facilitated by transforming yield-map datasets into profit maps and the importance of the selection of interpolator type. Yield and profit maps were generated for each data set (three soybean fields and one corn field) using the inverse of the distance (ID), the inverse of the square of the distance (IDS) and kriging (KRG) as interpolation methods. It can be concluded that profit maps are an important tool for the diagnosis of the spatial variability of economic return because they can assist farmers with management decision-making. The impact of the interpolator type was less than 200 kg ha-1 for the yield and US$ 30 ha-1 for the profit, indicating that the choice of interpolator type is of secondary importance. In addition, the profit maps showed large variability that would not be easily found if only yield maps were analyzed.