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

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