This study examines the effectiveness of publicly-available data to identify farm-scale regions of similarity, and classifies the distribution of physical (available water capacity (AWC)) and chemical (pH and electrical conductivity (ECe)) soil constraints in a northern NSW dryland cotton system. The incorporated open-source information could identify spatial variability in key soil attributes, though further data accuracy improvements will be increasingly valuable. ECe (salinity) levels were low, and there was no relationship between AWC and cotton or wheat yield. However, a strongly alkaline pH within a crops capable rooting depth across 70% of the property was identified, with an easily interpretable depth to pH constraint map produced. While depth to pH constraint had a major impact on wheat yield, no impact on cotton yield was observed. Such a discrepancy highlights the importance of analysing multiple years of crop yield monitor data as the most limiting soil constraint may vary based on seasonal conditions. The accurate characterisation of depth to pH constraint across both the property and region will assist in classifying its influence on other crop types and growing seasons in an overall bid to overcome its possible negative implications on productivity and profitability via the adoption of best management practices. Further work will focus on using the developed approach to map the depth to other important soil constraints, such as sodicity.