An integrated crop and hydrologic modeling system to estimate hydrologic impacts of crop irrigation demands

The Impact of Spatial Soil Variability on Simulation of Regional Maize Yield

“…The model was calibrated using six years (2004)(2005)(2006)(2007)(2008)(2009)) of field trial data for Dyna-gro 58K02 grown at TVREC and validated for the years 2000-2011. Statistically, the model performed well in simulating the measured maize yields (for a detailed description of the calibration procedure, refer to McNider et al, 2015). Calibrated values of the CERES-Maize model are given in table 4.…”

“…Because the application of DSSAT was at a regional scale in this study, a general cultivar that characterizes the cultivars grown in Alabama was desired. A methodology was developed by McNider et al (2015) to calibrate a maize cultivar for regional application of DSSAT in the southeastern U.S. Generalized likelihood uncertainty estimation (Beven and Binley, 1992) was used to modify the genetic coefficients to reduce the difference between simulated and observed yields and anthesis dates. Statistical parameters, including the coefficient of determination and root mean square error (RMSE), were used to calibrate McCurdy 84aa, a DSSAT default medium-to full-season maize cultivar, so that it would mimic the target cultivar.…”

“…Over the past few decades, maize yields in Alabama have been fluctuating, partly due to extreme weather events (NASS, 2015) and partly due to competition from the irrigated agriculture in the western part of the U.S. (McNider et al, 2015). Various studies have revealed how maize growth and yields have been impacted by climate variability, especially the El Niño Southern Oscillation (ENSO) (Sharda et al, 2012) in the southeastern U.S. (Hansen et al, 1998;Legler et al, 1999).…”

“…DSSAT version 4.5 comprises models for more than 28 crops that simulate crop growth, development, and yield along with management strategies that involve irrigation, fertilizer application, crop rotations, and others. DSSAT has been widely used to simulate crop water use and yield along with the development of management strategies under different soil and climate conditions (Liu et al, 2011;Soler et al, 2011;McNider et al, 2015). DSSAT has also been employed at various scales to simulate the impact of climate change on crop production (Carbone et al, 2003;Tubiello et al, 2002), to study the impact of climate variability on agriculture (Persson et al, 2009;Andresen et al, 2001), and to forecast yield (Soler et al, 2007;Bannayan et al, 2003).…”

Development of Soil Moisture Profiles Through Coupled Microwave-Thermal Infrared Observations in the Southeastern United States

“…The mean annual precipitation is 1250-1500 mm based on the 1981- Management (Miller and White, 1998). These soils are known to have relatively low water holding capacity that can lead to great temporal variation in upper level (1-10 cm) SM conditions and relatively frequent short-term droughts (1-4 week period) during growing seasons in various parts of the region (McNider et al, 2014).The Southeastern U.S. is one of the more data rich regions of the 110 world (climate and soils data) providing ample opportunity for calibration as well as validation of results. 2015).…”