Extreme temperatures around flowering of wheat have the potential to reduce grain yield and at farm scale their impact can be spatially variable depending on topography. Twenty-five data loggers were installed at 0.8-m height across a 164-ha farm in the southern Mallee of Victoria, Australia to spatially record the daily course of temperatures around the average date of flowering of wheat in the region. The experiment was conducted during 2-years period. In 1 year, the farm had no crop cover and in another year the farm had a wheat crop. Multiple linear regression analysis techniques were used to fit models relating daily extreme temperatures to the farm topographic features of elevation, aspect and slope, and the average maximum and minimum temperatures of each day at the farm in order to identify areas of high risk of extreme temperatures around the time of the flowering of wheat. The fitted regression models explained 90% and 97% of the variability in maximum and minimum temperatures, respectively, when the farm had no crop cover and 80% and 94% of the variability in maximum and minimum temperatures, respectively, when the farm had a wheat crop cover. When the farm had no crop, only minimum temperature was partially explained by the topography however, both maximum and minimum temperatures were partially explained by the topography when the farm had a wheat crop. From this study it was concluded that, (1) high temperature variations were found across the farm (2) temperature variations were only partially explained from the developed model presumably due to the flatter topography of the farm and (3) the relationships obtained from this study could be used in a crop model which can explain variation in grain yield based on the topography of a field.