The potential implications of bioenergy crop production for water and energy balance and carbon and nitrogen dynamics in the United States

In this dissertation, I systematically investigate the impacts of large-scale growth of bioenergy crops on water and energy balance and coupled carbon and nitrogen dynamics in the US. To achieve this objective, I apply and extend a coupled biogeochemical and biogeophysical model, the Integrated Science Assessment Model (ISAM), to develop a dynamic crop growth model for specific row crops (corn and soybean) and bioenergy grasses (Miscanthus, Cave-in-Rock and Alamo switchgrasses). The dynamic crop growth model in ISAM accounts for crop-specific phenology development, carbon and nitrogen allocation between leaf, stem, and roots/rhizome, vegetation structure development (leaf area, canopy height and root depth and distribution), crop-specific N cycling (such as biological N fixation by Miscanthus, spring and winter N translocation for bioenergy grasses etc.), and agricultural management practices (e.g. N fertilizer, planting and harvest, irrigation etc.) as well as their interactions with environmental factors (temperature, water, light, nutrient variability). This extended ISAM is used to address the following questions: (1) How do biomass yields of bioenergy grasses (Miscanthus, Cave-in-Rock and Alamo switchgrasses) spatially and temporally vary with spatial and temporal variability of environmental conditions (climate, soil water and nutrient availability, etc.) in the US; (2) what is the interplay between bioenergy grass production and water quantity and quality in the US; (3) what is the potential net ecosystem carbon exchange (NEE) and N2O emission with the expansion of bioenergy grasses in the US; and (4) how does application of N fertilizer affect NEE and N2O emission with the expansion of bioenergy grasses in the US. Chapter 1 includes a brief introduction to the overall objectives and content of this dissertation. Chapter 2 introduces the extended version of ISAM, which includes dynamic crop growth processes such as the crop specific phenology scheme, dynamic carbon allocation scheme, and dynamic vegetation structure simulation. The dynamic carbon allocation scheme accounts for light, water, and nutrient stresses while allocating the assimilated carbon to leaf, root, stem, and grain pools. The dynamic vegetation structure simulation captures the seasonal variability in LAI, canopy height, and root depth better than the previous version of ISAM. The model also implements dynamic root distribution processes in soil layers, which simulates the root response of soil water uptake and transpiration more accurately than the previous version of ISAM. Observational data for LAI, above and below ground biomass, and carbon, water, and energy fluxes are compiled from two AmeriFlux sites, Mead, NE and Bondville, IL, to calibrate and evaluate the model’s performance. For the purposes of calibration and evaluation, we use a corn (C4)-soybean (C3) rotation system over the period 2001-2004. The dynamic crop growth simulation better captures the diurnal and seasonal variability in carbon and energy fluxes relative to the static simulation implemented in the previous version of ISAM. Especially, with dynamic carbon allocation and root distribution processes, the model’s simulated GPP and latent heat flux (LH) are in much better agreement with observational data compared to the static root distribution simulation. Modeled latent heat based on dynamic growth processes increases by 12-27% during the growing season at both sites, leading to a 13-61% improvement in modeled GPP compared to the estimates based on the previous version of ISAM. In Chapter 3, I follow the methods from Chapter 2 to integrate the dynamic crop growth processes for Miscanthus and two different cultivars of switchgrass, Cave-in-Rock and Alamo, into ISAM, to estimate the spatial and temporal variations of biomass yields over the period 2001–2012 in the eastern USA. The validation with observed data from sites across diverse environmental conditions suggests the model is able to simulate the dynamic response of bioenergy grass growth to changes in environmental conditions in the central and southern USA. The model is applied to identify four spatial zones characterized by their average yield amplitude and temporal yield variance (or stability) over 2001–2012 in the USA: a high and stable yield zone (HS), a high and unstable yield zone (HU), a low and stable yield zone (LS), and a low and unstable yield zone (LU). The HS zones are mainly distributed in the regions with precipitation larger than 600 mm, and a mean temperature range of 292–294 K during the growing season, which are found in southern Missouri, northwestern Arkansas, southern Illinois, southern Indiana, southern Ohio, western Kentucky, and parts of northern Virginia. The LU yield zones are distributed in the southern parts of the Great Plains with water stress conditions and higher temporal variances in precipitation, such as Oklahoma and Kansas. The three bioenergy grasses may not grow in LS yield zones, including the western parts of the Great Plains due to extreme low precipitation and poor soil texture, and upper part of north central, northeastern, and northern New England due to extreme cold conditions. In Chapter 4, I apply the well-calibrated and validated ISAM to the eastern and central US to evaluate the interplay between potential bioenergy grass (Miscanthus, Cave-in-Rock, and Alamo switchgrass) production and water quantity and quality. Our results suggest that certain regions could achieve high and stable biomass productivity in tandem with decreased demand for land and water on a per unit of ethanol production. These regions, primarily occupied by crops and forests, include the southern Midwest for Miscanthus, the northern Midwest for Cave-in-Rock, and the southern US for Alamo. However, the estimated land area suitable for growing energy grasses is limited by either too dry or too cold environmental conditions in some specific regions, particularly for Miscanthus, despite Miscanthus having the highest biomass productivity and the lowest water consumption requirement among the grasses on a per unit of ethanol production basis. One advantage of establishing bioenergy grasses is their ability to mitigate nitrogen leaching through decreased runoff and/or decreased inorganic nitrogen concentration in the soil water. Growing bioenergy grasses on croplands/grasslands consumes more water than the crops/grasses. Increase in ET can aggravate dry condition, which limits the bioenergy grasses’ productivity in the Great Plains. However, dry condition may not limit their productivity in other areas (e.g., the Midwest) due to root growth towards deeper, moister soils. In Chapter 5, I apply ISAM to estimate NEE and N2O emission with the growth of Miscanthus, Cave-in-Rock, and Alamo in the USA with and without application of N fertilizer, and with and without consideration of land cover and land use change (LCLUC). The model is further improved by implementing spatially varied N translocation rate and temporally varied N demand per unit of C assimilation for all bioenergy grasses and biological N fixation for Miscanthus. Implementation of these bioenergy grasses’ specific N dynamics improves the model’s ability to capture the response of biomass yields of bioenergy grasses to N fertilization especially in N-limited regions. The modeled belowground carbon stock in root and rhizome, soil respiration, NEE and N2O emission with the growth of bioenergy grasses are well evaluated with observational data, indicating that the model is basically able to capture multiyear variability in belowground carbon stock in root and rhizome, heterotrophic respiration, NEE and N2O emission, except for possible overestimated carbon and N2O emission during the year of establishment. Over the time period 2000-2012, growth of bioenergy grasses with N fertilizer and with LCLUC acts as a 0.25 kgCm-2yr-1 carbon source in the eastern US. This carbon source is mainly contributed by LCLUC, which turns the bioenergy grass-soil system from a carbon sink to a carbon source due to a large amount of carbon emission with the conversion of existing forest and grassland to bioenergy grasses. Unlike forest and grassland, conversion of cropland to bioenergy grasses is close to C neutral but with large spatial variability. For example, the bioenergy grass-soil system acts as a carbon sink in most parts of the southern Midwest, the South Atlantic and the Middle Atlantic. Application of N fertilizer is able to slightly mitigate carbon emission to the atmosphere with the growth of bioenergy grasses, but significantly increases N2O emission. In contrast, the effect of LCLUC on N2O is not significant. Conversion of forest to bioenergy grass decreases N2O emission due to input of difficult-to-decompose woody litter, whereas conversion of grassland to bioenergy grass increases N2O emission due to input of easily decomposed litter. Finally, Chapter 6 provides an overall summary and direction for further work related to research presented in this thesis