Spatial forecasting of switchgrass productivity under current and future climate change scenarios

Abstract

Evaluating the potential of alternative energy crops across large geographic regions, as well as over time, is a necessary component to determining if biofuel production is feasible and sustainable in the face of growing production demands and climatic change. Switchgrass (Panicum virgatum L.), a native perennial herbaceous grass, is a promising candidate for cellulosic feedstock production. In this study, current and future (from 2080 to 2090) productivity is estimated across the central and eastern United States using ALMANAC, a mechanistic model that simulates plant growth over time. The ALMANAC model was parameterized for representative ecotypes of switchgrass. Our results indicate substantial variation in switchgrass productivity both within regions and over time. States along the Gulf Coast, southern Atlantic Coast, and in the East North Central Midwest have the highest current biomass potential. However, these areas also contain critical wetland habitat necessary for the maintenance of biodiversity and agricultural lands necessary for food production. The southern United States is predicted to have the largest decrease in future biomass production. The Great Plains are expected to experience large increases in productivity by 2080-2090 due to climate change. In general, regions where future temperature and precipitation are predicted to increase are also where larger future biomass production is expected. In contrast, regions that show a future decrease in precipitation are associated with smaller future biomass production. Switchgrass appears to be a promising biofuel crop for the central and eastern United States, with local biomass predicted to be high (>10 Mg/ha) for approximately 50% of the area studied for each climate scenario. In order to minimize land conversion and loss of biodiversity, areas that currently have and maintain high productivity under climate change should be targeted for their long-term growth potential.

Dedicated Energy Crops and Crop Residues for Bioenergy Feedstocks in the Central and Eastern USA

“…Warmer areas of Florida and the Texas and Louisiana Gulf Coasts have the highest long-term productivity potential. Some of the lower productive agricultural lands in the Northern Great Plains would be expected to experience large increases in productivity with climate change from the model [116]. Yield was predicted to increase in regions with predicted higher temperatures and precipitation.…”

Using satellite vegetation and compound topographic indices to map highly erodible cropland buffers for cellulosic biofuel crop developments in eastern Nebraska, USA

“…Because switchgrass is highly productive (and more productive when fertilizers and chemicals are applied to encourage its growth as a dense monoculture) and has higher biomass production than most grassland species, the total estimated switchgrass biomass productivity was assumed to be double that of the total estimated grassland biomass productivity based on previous study results ( Anderson-Teixeira et al, 2012;Behrman et al, 2012;Bonin and Lal, 2014;Fike et al, 2006;Jager et al, 2010;Kiniry et al, 2008;McLaughlin et al, 2006;Schmer et al, 2010;Tulbure et al, 2012;Vogel et al, 2002;Wullschleger et al, 2010).…”

Estimates of Biomass Yield for Perennial Bioenergy Grasses in the USA

“…All models, with the exception of Jager et al [9], are process-based models, which simulate carbon assimilation and allocation processes for Miscanthus and/or switchgrasses. Among these models, the EPIC and ALMANAC models use radiation use efficiency to calculate switchgrass yields [12,16], while other models use more detailed biophysical methods to simulate carbon assimilation. The major distinction between ISAM and other models is that ISAM is the only model which accounts for dynamic response of carbon allocation, LAI growth, as well as root growth and distribution among the soil layers to environmental factors, such as precipitation, temperature, and radiation.…”

“…No attempt has been made to evaluate the biomass yield for Miscanthus using an empirical-based approach, mainly because field trials for Miscanthus are sparser than for switchgrass and usually centralized in the Midwest region [3,11]. Several attempts have therefore been made using mechanistic models to estimate the yield and the spatial and temporal variability in yield of bioenergy grasses, including ALMANAC [12], MISCANMOD [13,14], MISCANFOR [15], EPIC [16], WIMOVAC (BIOCRO) [17], Agro-IBIS [18], Agro-BGC [19], and TEM [20]. Nair et al [10] reviewed the differences among these models.…”