1 CSIRO Agriculture and Food, GPO Box 1700, Canberra ACT 2601, ACT, Australia, firstname.lastname@example.org
Soil water holding capacity is a key soil property affecting dryland crop yield, and is therefore important for crop management in semi-arid climates like Australia. This paper explores two approaches: one developed using process-based modelling to inversely predict plant available water capacity (PAWC) of soils from crop yield, another one built with machine learning to predict soil available water capacity (AWC) spatially based on bio-climate variables. Our results indicate that soil PAWC can be skilfully predicted with water-limited crop yield (R2 of 0.84~0.98 and RMSE of 14.5mm~30.2mm across 10 sites) and that the bio-climate variables together with a machine learning approach could explain up to 50% of the variance in soil AWC across sites. These results demonstrate the potential to use climate and crop yield data to predict soil water holding capacity.