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Prediction of Plant Available Water at Sowing for Winter Wheat in the Southern Great Plains
Author(s) -
Lollato Romulo P.,
Patrignani Andres,
Ochsner Tyson E.,
Edwards Jeffrey T.
Publication year - 2016
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2015.0433
Subject(s) - mean squared error , sowing , empirical modelling , calibration , coefficient of determination , mathematics , water content , environmental science , crop yield , precipitation , crop simulation model , soil science , agronomy , statistics , meteorology , computer science , biology , geography , engineering , geotechnical engineering , programming language
Sowing plant available water (PAW s ) can impact wheat ( Triticum aestivum L.) stand establishment, early crop development, and yield. Consequently, PAW s is an essential input in crop simulation models and its estimation can improve agronomic decisions. Our objective was to identify effective methods to predict PAW s 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, PAW s 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 (RMSE n ) below 12% (20 mm) for the calibration and validation datasets. Modeled PAW s 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 RMSE n of 27 and 32%. An inverse‐exponential and a logarithmic model of PAW s using cumulative fallow precipitation and PAWC both resulted in RMSE n = 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 PAW s , eliminating the need for subjective PAW s values. Core Ideas Wheat is among the most important crops cultivated in the southern Great Plains. Plant‐available water at sowing is a critical input in dynamic crop simulation models. Mechanistic water balance models or empirical models can predict wheat PAW at sowing. Models here shown decrease the need for subjective initial PAW values in crop models.