296-6 Empirical and Mechanistic Prediction of Plant Available Water at Sowing for Wheat in the Southern Great Plains.

Poster Number 314

See more from this Division: ASA Section: Climatology & Modeling
See more from this Session: Model Applications in Field Research: II

Tuesday, November 17, 2015
Minneapolis Convention Center, Exhibit Hall BC

Romulo Lollato1, Andres Patrignani2, Jeffrey T. Edwards3 and Tyson E. Ochsner3, (1)Agronomy, Kansas State University, Manhattan, KS
(2)Plant and Soil Sciences, Kansas State University, Manhattan, KS
(3)Plant and Soil Sciences, Oklahoma State University, Stillwater, OK
Abstract:
Plant available water at sowing (PAWs) can impact wheat 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 predict PAWs in continuous winter wheat (Triticum aestivum L.) by modeling the soil moisture dynamics of the preceding 4-mo summer fallow. The mechanistic soil water balance models dual crop coefficient (dual Kc) and Simple Simulation Modeling (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 non-linear models based on cumulative fallow precipitation and the soil’s plant available water capacity (PAWC). Both the dual Kc 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 Kc and SSM models, with RMSEn of 27 and 32%, respectively. An exponential and a logarithmic model of PAWs using cumulative fallow precipitation and PAWC both resulted in RMSEn = 23 and 29% in the calibration and validationdatasets, respectively. The dual Kc model was slightly superior to empirical models based on non-linear regression analysis, and was superior to the SSM model. Initializing the dual Kc at the start of the preceding fallow or using empirical relationships allow for acceptable predictions of PAWs, eliminating the need for subjective PAWs values.

See more from this Division: ASA Section: Climatology & Modeling
See more from this Session: Model Applications in Field Research: II