Prediction of Plant Available Water at Sowing for Winter Wheat in the Southern Great Plains

Sowing plant available water (PAW(s)) can impact wheat (Triticum aestivum L.) stand establishment, early crop development, and yield. Consequently, PAWs is an essential input in crop simulation models and its estimation can improve agronomic decisions. Our objective was to identify effective methods to predict PAWs in continuous winter wheat by exploring empirical and mechanistic models based on the preceding 4-mo summer fallow. The mechanistic soil water balance models dual crop coefficient (dual K-c) and simple simulation model (SSM) were calibrated, validated, and tested using soil moisture datasets collected from 2009 to 2013 in Oklahoma totaling 29 site-years. Additionally, PAWs was predicted using empirical nonlinear models based on cumulative fallow precipitation and the soil's plant available water capacity (PAWC). Both the dual K-c and SSM models resulted in normalized root mean squared error (RMSEn) below 12% (20 mm) for the calibration and validation datasets. Modeled PAWs for the prediction dataset was within +/-30% of field observations in 67% of the site-years for both dual K-c and SSM models, with RMSEn of 27 and 32%. An inverse-exponential and a logarithmic model of PAWs using cumulative fallow precipitation and PAWC both resulted in RMSEn = 23 and 29% in the calibration and validation datasets. The dual K-c model was slightly superior to empirical models based on nonlinear regression analysis, and was superior to the SSM model. Initializing the dual K-c at the start of the preceding fallow or using empirical relationships allow for acceptable predictions of PAWs, eliminating the need for subjective PAWs values.