A major barrier to the adoption of precision agriculture in dryland cropping systems is our current inability to reliably predict spatial patterns of grain yield for future crops for a specific paddock. An experiment was undertaken to develop a better understanding of how edaphic and climatic factors interact to influence the spatial variation in the growth, water use, and grain yield of different crops in a single paddock so as to improve predictions of the likely spatial pattern of grain yields in future crops. Changes in a range of crop and soil properties were monitored over 3 consecutive seasons (barley in 2005 and 2007 and lentils in 2006) in the southern section of a 167-ha paddock in the Mallee region of Victoria, which had been classified into 3 different yield (low, moderate, and high) and seasonal variability (stable and variable) zones using normalised difference vegetation index (NDVI) and historic yield maps. The different management zones reflected marked differences in a range of soil properties including both texture in the topsoil and potential chemical-physical constraints in the subsoil (SSCs) to root growth and water use. Dry matter production, grain yield, and quality differed significantly between the yield zones but the relative difference between zones was reduced when supplementary irrigation was applied to barley in 2005, suggesting that some other factor, e.g. nitrogen (N), may have become limiting in that year. There was a strong relationship between crop growth and the use of soil water and nitrate across the management zones, with most water use by the crop occurring in the pre-anthesis/flowering period, but the nature of this relationship appeared to vary with year and/or crop type. In 2006, lentil yield was strongly related to crop establishment, which varied with soil texture and differences in plant-available water. In 2007 the presence of soil water following a good break to the season permitted root growth into the subsoil where there was evidence that SSCs may have adversely affected crop growth. Because of potential residual effects of one crop on another, e.g. through differential N supply and use, we conclude that the utility of the NDVI methodology for developing zone management maps could be improved by using historical records and data for a range of crop types rather than pooling data from a range of seasons.