Recent development in precision agriculture technologies have generated massive amount of geospatial data of farming, such as yield mapping, seeding rates, input applications, and so on. However, producers are still struggling to convert those precision data into farm management decisions to improve productivity and profitability of farming.  Indeed, deriving accurate decisions at each site of the field requires complex and comprehensive modeling of crop yield responses to various inputs (fertilizer, water, seeds, etc.) that are very complicated and varying across growing conditions (soil, weather, slope, etc.).  Even the most state-of-the-art crop growth simulation models still have difficulty to reach that accuracy level, and can easily generate large margin of errors in some parts of the field.  While the accurate modeling of crop growth is still an ongoing research effort in plant and soil sciences, this study explores an alternative decision-making method from the economic perspective.  The main idea is a simple profit mapping approach that constructs high resolution spatially explicit profit maps for the crop fields and stops planting the unprofitable areas within the fields.  As a case study, 21 corn-soybeans fields’ geospatial production data were collected from a farm in the Mississippi Delta from 2012 to 2016.  Profitability maps are calculated at resolution of 10-meter grids by computing the crop sale revenue and direct costs of farming operation for each grid. Based on the assumed price scenario that is similar to the current market (corn $3.5/bushel, and soybean $10/bushel), about 4% of the total 88,023 grids are unprofitable over the period 2012 to 2016.  The total amount of profit loss from those grids is $6,896 annually, which can be avoided by retiring those grids from production.  When crop prices decline, the profit gain from retiring unprofitable grids will further increase.  This study provides an illustration of a simple yet useful approach to convert digital farming data into decision making, and quantifies the profit improvement that can be achieved at whole farm level.