Plant available water capacity (PAWC) of soils predicted from crop yields better reflects within-field soil physicochemical variations

•Plant available water capacity (PAWC) can be predicted through single/mixed crop yields.•Predicted PAWC maps matched well with within-field spatial variation of soil types.•Predicted PAWC better reflects the chemical-physical constraints to crop growth.•APSIM simulated yield maps using predicted PAWC maps are useful for yield zoning. Within field variations of plant available water capacity (PAWC) of soil is one of the major causes of spatial yield variability in dryland agriculture systems, as PAWC interacts with pre-season and in-season rainfall and other climatic variables to determine crop growth and final yield. Quantification of such variations helps to better understand the changes in soil texture and subsoil constraints to inform spatially explicit management practice. An inverse modelling approach to estimate PAWC from crop yields was developed as a more cost-effective alternative to traditional soil sampling methods. In this study, we further extend this approach to predict and map in-field variations of PAWC from yield maps of single and multiple crops. Soil PAWC maps were produced based on inversely predicted PAWC using crop yield maps together with in-field management information, and compared with: 1) available water capacity derived using laboratory-measured soil properties, and 2) soil types derived from proximally sensed soil spectra and ground geophysics for four representative farms in Australia. The results show that the predicted PAWC maps matched well with within-field spatial variation of soil types, and well reflected the impact of soil constraints (e.g. salinity), and soil classifications from soil survey and local experience. This demonstrates that the predicted PAWC from crop yield using inverse modelling can reflect the soil physicochemical variations within-field. The generated PAWC maps can be combined with process-based modelling to predict crop yield and yield zones and to inform spatial field management and soil sampling.