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

Abstract

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 eff ective methods to predict PAW s in continuous winter wheat by exploring empirical and mechanistic models based on the preceding 4-mo summer fallow. Th e mechanistic soil water balance models dual crop coeffi cient (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 fi eld 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. Th e 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.

Genotype‐specific nitrogen uptake dynamics and fertilizer management explain contrasting wheat protein concentration

“…Daily maximum and minimum temperatures, precipitation, incident solar radiation, and reference evapotranspiration (ET o ) were recorded near the field experiments in a weather station pertaining to the Kansas Mesonet (Patrignani, Knapp, Redmond, & Santos, 2020). Plant available water at sowing (PAWS) was estimated according to Lollato, Patrignani, Ochsner, and Edwards (2016). Seasonal water supply (WS) accounted for in-season precipitation plus PAWS, and the seasonal water demand (WD) was seasonal ET o .…”

Plant Traits to Increase Winter Wheat Yield in Semiarid and Subhumid Environments

“…Daily weather data required to run SSM-Wheat were precipitation (P), solar radiation (S rad ), maximum air temperature (T max ), and minimum air temperature (T min ) (Lollato et al, 2017). Plant available water at sowing was calculated for each location-year using an empirical model developed based on precipitation during the preceding summer period and the soil's available water-holding capacity (Lollato et al, 2016). While simplistic, this empirical model showed greater accuracy to predict available soil water at sowing for continuous winter wheat systems than more complex, mechanistic models (Lollato et al, 2016).…”

“…Plant available water at sowing was calculated for each location-year using an empirical model developed based on precipitation during the preceding summer period and the soil's available water-holding capacity (Lollato et al, 2016). While simplistic, this empirical model showed greater accuracy to predict available soil water at sowing for continuous winter wheat systems than more complex, mechanistic models (Lollato et al, 2016). Typical sowing date and plant density were obtained using USDA-NASS (2010) reports, winter wheat variety trial networks (Edwards et al, 2013;Lingenfelser et al (2013) and Neely et al, 2013), and crop sowing guides (Krenzer, 2000;Shroyer et al, 1996).…”

MesoSoil v2.0: An updated soil physical property database for the Oklahoma Mesonet

“…Vadose Zone Journal indices (Krueger et al, 2019), improving wildfire prediction (Krueger et al, 2015(Krueger et al, , 2016, relating soil moisture to crop and forage yields (Krueger et al, 2021;Lollato et al, 2016), estimating potential groundwater recharge (Wyatt et al, 2017), and estimating soil organic carbon content (Kerr & Ochsner, 2020).…”

“…(2013) using the original Rosetta pedotransfer function (Schaap et al., 2001), or Rosetta1, has been used to convert soil matric potentials measured by heat dissipation sensors into soil volumetric water content estimates, commonly referred to as soil moisture. Prior research has used these volumetric water content data for a number of applications, including developing soil moisture‐based drought indices (Krueger et al., 2019), improving wildfire prediction (Krueger et al., 2015, 2016), relating soil moisture to crop and forage yields (Krueger et al., 2021; Lollato et al., 2016), estimating potential groundwater recharge (Wyatt et al., 2017), and estimating soil organic carbon content (Kerr & Ochsner, 2020).…”