Development and preliminary evaluation of an integrated field scale model for perennial bioenergy grass ecosystems in lowland areas. (October 2016)

• Record Type: Journal Article
• Authors: Tian, S.; Youssef, M.A.; Chescheir, G.M.; Skaggs, R.W.; Cacho, J.; Nettles, J.
• Is Part Of: Environmental modelling & software. Volume 84(2016:Oct.)
• Page Start: 226
• Page End: 239
• Publication Date: 2016-10
• Subjects: DRAINMOD-GRASS -- Hydrology -- Biogeochemistry -- Grass growth -- Switchgrass; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.06.029
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Computer models are useful tools for evaluating environmental and economic sustainability of proposed dedicated cellulosic grass ecosystems for biofuel production. This study developed an integrated, field scale, and process-based ecosystem model (DRAINMOD-GRASS) for simulating hydrological processes, soil carbon and nitrogen cycling, and plant growth in cropping systems for producing bioenergy grasses in lowland areas. We tested the model using measurements from three replicated switchgrass (Panicum virgatum) plots located in eastern North Carolina, USA. Results showed that the model accurately predicted 5-year (2009–2013) biomass yield. Predicted daily water table depth closely matched field measurements with Nash-Sutcliffe coefficient of 0.86. The model also accurately predicted temporal dynamics of daily soil moisture and temperature with Nash-Sutcliffe coefficients of 0.7 and 0.9, respectively. Predicted seasonal changes in net N mineralization and nitrification rates were comparable to field measurements in 2011 and 2012. Highlights: Developed an integrated field scale model for perennial bioenergy grass ecosystem. DRAINMOD-GRASS was tested in a switchgrass field with 5-year measurements. The model performed well in predicting grass yield, hydrology, and N cycling. DRAINMOD-GRASS can be useful tool for bioenergy grass ecosystem in lowland areas.