Modeling spatial and dynamic variation in growth, yield, and yield stability of the bioenergy crops Miscanthus × giganteus and Panicum virgatum across the conterminous United States

C4 perennial grasses are being considered as environmentally and economically sustainable high yielding bioenergy feedstocks. Temporal and spatial variation in yield across the conterminious United States is uncertain due to the limited number of field trials. Here, we use a semi‐mechanistic dynamic crop growth and production model to explore the potential of Miscanthus × giganteus (Greef et. Deu.) and Panicum virgatum L. across the conterminous United States. By running the model for 32 years (1979–2010), we were able to estimate dry biomass production and stability. The maximum rainfed simulated end‐of‐growth‐season harvestable biomass for M. × giganteus was ca. 40 Mg ha−1 and ca. 20 Mg ha−1 for P. virgatum. In addition, regions of the southeastern United States were identified as promising due to their high potential production and stability and their relative advantage when compared with county‐level maize biomass production. Regional and temporal variation was most strongly influenced by precipitation and soil water holding capacity. Miscanthus × giganteus was on average 2.2 times more productive than P. virgatum for locations where yields were ≥10 Mg ha−1. The predictive ability of the model for P. virgatum was tested with 30 previously published studies covering the eastern half of the United States and resulted in an index of agreement of 0.71 and a mean bias of only −0.62 Mg ha−1 showing that, on average, the model tended to only slightly overestimate productivity. This study provides with potential production and variability which can be used for regional assessment of the suitability of dedicated bioenergy crops.

[1]  Stephen P. Long,et al.  Meeting US biofuel goals with less land: the potential of Miscanthus , 2008 .

[2]  Mark E. Borsuk,et al.  Biomass Production in Switchgrass across the United States: Database Description and Determinants of Yield , 2010 .

[3]  Stephen P. Long,et al.  Seasonal dynamics of nutrient accumulation and partitioning in the perennial C4-grasses Miscanthus × giganteus and Spartina cynosuroides , 1997 .

[4]  Scott A. Staggenborg,et al.  Performance of Annual and Perennial Biofuel Crops: Yield during the First Two Years , 2010 .

[5]  G. McIsaac,et al.  Miscanthus and switchgrass production in central Illinois: impacts on hydrology and inorganic nitrogen leaching. , 2010, Journal of environmental quality.

[6]  Fernando E. Miguez,et al.  A semimechanistic model predicting the growth and production of the bioenergy crop Miscanthus×giganteus: description, parameterization and validation , 2009 .

[7]  Atul K. Jain,et al.  An integrated biogeochemical and economic analysis of bioenergy crops in the Midwestern United States , 2010 .

[8]  Thomas B. Voigt,et al.  A quantitative review comparing the yields of two candidate C4 perennial biomass crops in relation to nitrogen, temperature and water , 2004 .

[9]  M. Katz Validation of models , 2006 .

[10]  C. Gautier Surface Radiation Budget , 1984 .

[11]  Darren T. Drewry,et al.  Implications for the hydrologic cycle under climate change due to the expansion of bioenergy crops in the Midwestern United States , 2011, Proceedings of the National Academy of Sciences.

[12]  M. Tollenaar,et al.  Yield potential, yield stability and stress tolerance in maize , 2002 .

[13]  E. Fereres,et al.  Water stress, growth, and osmotic adjustment , 1976 .

[14]  A. Hastings,et al.  The development of MISCANFOR, a new Miscanthus crop growth model: towards more robust yield predictions under different climatic and soil conditions , 2009 .

[15]  S. Anthony,et al.  Identifying the yield potential of Miscanthus x giganteus: an assessment of the spatial and temporal variability of M. x giganteus biomass productivity across England and Wales , 2004 .

[16]  Carl J. Bernacchi,et al.  The impacts of Miscanthus×giganteus production on the Midwest US hydrologic cycle , 2010 .

[17]  Stephen P. Long,et al.  More Productive Than Maize in the Midwest: How Does Miscanthus Do It?1[W][OA] , 2009, Plant Physiology.

[18]  James W. Jones,et al.  POTENTIAL USES AND LIMITATIONS OF CROP MODELS , 1996 .

[19]  Salvador A. Gezan,et al.  Is UK biofuel supply from Miscanthus water‐limited? , 2008 .

[20]  Albert Weiss,et al.  Simulating switchgrass growth and development under potential and water-limiting conditions. , 2009 .

[21]  J. Boyer,et al.  Leaf enlargement and metabolic rates in corn, soybean, and sunflower at various leaf water potentials. , 1970, Plant physiology.

[22]  Thomas B. Voigt,et al.  Miscanthus: A Promising Biomass Crop , 2010 .

[23]  Bjarne Stroustrup,et al.  C++ Programming Language , 1986, IEEE Softw..

[24]  G. Collatz,et al.  Coupled Photosynthesis-Stomatal Conductance Model for Leaves of C4 Plants , 1992 .

[25]  J. Clifton-Brown,et al.  Miscanthus biomass production for energy in Europe and its potential contribution to decreasing fossil fuel carbon emissions , 2004 .

[26]  Chris Somerville,et al.  Feedstocks for Lignocellulosic Biofuels , 2010, Science.

[27]  J. Scurlock,et al.  Miscanthus : European experience with a novel energy crop , 2000 .

[28]  P. Derfler,et al.  The United States Department of Agriculture , 1872, Nature.

[29]  John Clifton-Brown,et al.  The modelled productivity of Miscanthus×giganteus (GREEF et DEU) in Ireland. , 2000 .

[30]  John Clifton-Brown,et al.  Carbon mitigation by the energy crop, Miscanthus , 2007 .

[31]  M. Maughan Evaluation of switchgrass, M. x giganteus, and sorghum as biomass crops: Effects of environment and field management practices , 2011 .

[32]  Fernando E. Miguez,et al.  Modeling Miscanthus in the soil and water assessment tool (SWAT) to simulate its water quality effects as a bioenergy crop. , 2010, Environmental science & technology.

[33]  J. D. Tarpley,et al.  Surface radiation budgets in support of the GEWEX Continental‐Scale International Project (GCIP) and the GEWEX Americas Prediction Project (GAPP), including the North American Land Data Assimilation System (NLDAS) project , 2003 .

[34]  Jimmy R. Williams,et al.  An integrative modeling framework to evaluate the productivity and sustainability of biofuel crop production systems , 2010 .

[35]  F. Dohleman,et al.  Does greater leaf-level photosynthesis explain the larger solar energy conversion efficiency of Miscanthus relative to switchgrass? , 2009, Plant, cell & environment.

[36]  Fernando E. Miguez,et al.  Meta-analysis of the effects of management factors on Miscanthus × giganteus growth and biomass production , 2008 .

[37]  Douglas L. Karlen,et al.  Corn stover feedstock trials to support predictive modeling , 2010 .

[38]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[39]  S. W. Humphries,et al.  WIMOVAC: a software package for modelling the dynamics of plant leaf and canopy photosynthesis , 1995, Comput. Appl. Biosci..