High-resolution prediction of soil available water content within the crop root zone

- Pseudo continuous PTF accurately predicted soil water content.
- ECa was a useful proximal attribute for spatial prediction of soil AWC.
- GWR, regression kriging and co-kriging had highest performance.

SummaryA detailed understanding of soil hydraulic properties, particularly soil available water content (AWC) within the effective root zone, is needed to optimally schedule irrigation in fields with substantial spatial heterogeneity. However, it is difficult and time consuming to directly measure soil hydraulic properties. Therefore, easily collected and measured soil properties, such as soil texture and/or bulk density, that are well correlated with hydraulic properties are used as proxies to develop pedotransfer functions (PTF). In this study, multiple modeling scenarios were developed and evaluated to indirectly predict high resolution AWC maps within the effective root zone. The modeling techniques included kriging, co-kriging, regression kriging, artificial neural networks (NN) and geographically weighted regression (GWR). The efficiency of soil apparent electrical conductivity (ECa) as proximal data in the modeling process was assessed. There was a good agreement (root mean square error (RMSE) = 0.052 cm3 cm−3 and r = 0.88) between observed and point prediction of water contents using pseudo continuous PTFs. We found that both GWR (mean RMSE = 0.062 cm3 cm−3) and regression kriging (mean RMSE = 0.063 cm3 cm−3) produced the best water content maps with these accuracies improved up to 19% when ECa was used as an ancillary soil attribute in the interpolation process. The maps indicated fourfold differences in AWC between coarse- and fine-textured soils across the study site. This provided a template for future investigations for evaluating the efficiency of variable rate irrigation management scenarios in accounting for the spatial heterogeneity of soil hydraulic attributes.