The success of a bioenergy industry based on dedicated crops depends on both high productivity and yield stability of feedstock supply. For this industry to have a major impact on the bioenergy demands of the United States, large-scale deployment of dedicated biomass crops will be required. However, limited land resources results in an inevitable conflict for its use when optimizing economic, social and environmental objectives. Since dedicated biomass crops are in their infancy, it is of great importance to be able to forecast their productivity and stability under different environments, with particular emphasis on marginal land. Empirical yield data, in contrast to the major US grain crops and commercial forestry, is sparse. As a result, predictions from empirically based yield models will have considerable uncertainties both spatially and temporally until a much larger and broader database of field studies is available. This necessitates the development of mechanistically rich models that may project yield from first principles, to avoid dependence on the limited empirical yield data. Such a model is also important for predicting fine scale productivity, e.g. at the scale of fields around a proposed biorefinery. Equally, it allows projection of how alteration of specific crop characteristics in a breeding program, such as increased leaf angle or altered shoot : root partitioning, would affect yield and yield stability. Here, we use such a detailed biochemical and biophysical crop model to develop spatial forecasts for the United States of growth, yield, and yield stability of Panicum virgatum (switchgrass) and Miscanthus × giganteus (Miscanthus).
A recent quantitative analysis of the P. virgatum literature showed that measured shoot dry matter yields ranged from 1 to almost 40 Mg ha−1 with most results in the 10–14 Mg ha−1 range (Wullschleger et al., 2010). Miscanthus × giganteus has been studied mostly in Europe where biomass productivity ranged from 10 to 40 Mg ha−1 (Miguez et al., 2008) with peak shoot dry biomass levels of 50 Mg ha−1 (Heaton et al., 2004, 2010; Clifton-Brown et al., 2004). No published US studies of this crop report yields beyond a third year, which is generally considered the first year in which the yield potential of this perennial is realized. Although the limited data for these crops provide bounds for the expected productivity, there is a critical need for spatially explicit forecasts that also consider the year-to-year variability. An estimate of this temporal variability will be critical information for biorefineries planning to use dedicated crops as feedstocks for cellulosic biofuel which will need to take account of potential fluctuations in supply. Typically it takes around 3 years after planting for these crops to reach their maximum annual yield, reflecting the time needed for the below-ground perennating organs to develop fully, but depending on the location yields might be higher in year four or later. For planning new plantings and feedstock supply, the model must therefore also be able to predict the dynamics of yield increase over this period and its spatial variability.
Evaluating yield stability, or the ability of a genotype to maintain relative performance across a range of environments (Tollenaar & Lee, 2002), depends on field testing of biomass crops across a range of environments involving geographic and temporal extents. However, long-term studies of P. virgatum and M. × giganteus that evaluate yield stability across a wide range of geographies are lacking. Substantial commitment to the production of biofuels from crop biomass feedstocks in the future will require evaluating not only their biomass productivity potential but also yield stability of biomass production both in response to inter-annual variability in weather and in response to anticipated changing climate. The latter is significant, given that a biorefinery established in the United States today may have a life expectancy of 30–50 years. Siting of a bioenergy facility, whether it is combusting the biomass to provide heat and power, or converting it to a liquid fuel, will require a stable predictable supply, so that the processing plant can be used year round and year after year. A supply that varies markedly between years would result in periods of closure and would be a suboptimal use of investment. The ability to predict biomass production of different bioenergy crop alternatives is crucial for evaluating their economic, agronomic, and environmental feasibility (Sommerville et al., 2010). Since land suitable for cultivating biomass crops is limited, there is a need to determine strategic regions where biomass production and profitability are maximized while negative environmental impacts are minimized (Zhang et al., 2010).
Previous Miscanthus × giganteus and Panicum virgatum biomass production simulation models
Biophysical models of varying complexity have been developed with the objective of simulating spatial and temporal variation in yield of M. × giganteus and P. virgatum. For example, Grassini et al. (2009) developed a model specifically for P. virgatum based on empirical relationships and the concept of radiation use efficiency (RUE) that was able to capture variation in date of anthesis and above-ground biomass at three locations in the United States. Wullschleger et al. (2010) performed a comprehensive analysis of P. virgatum potential in the United States and derived a regression-based model to predict biomass productivity in different regions.
For M. × giganteus, Clifton-brown et al. (2000), using a simple model based on RUE, developed potential productivity for Ireland with yields ranging from 16 Mg ha−1 in northern Ireland to 26 Mg ha−1 in southern Ireland, where total annual solar radiation and the length of the growing season due to higher temperatures are longer. These predictions, however, were only based on radiation and temperatures and ignored limitations due to water stress. Price et al. (2004) using a similar approach, but including effects due to water stress, produced yields in the range 7–24 Mg ha−1 for England and Wales. In addition, Price et al. (2004) estimated an inter-annual coefficient of variation in yield of 10–25% (i.e. standard deviation divided by the mean, multiplied by 100). Interannual variability is typically poorly estimated from short-duration field trials, but as noted above, it is a crucial component in planning for feedstock availability for a biorefinery. In a later study, incorporation of site-specific information about soil water availability significantly improved predictions (Clifton-Brown et al., 2004), showing that the rainfed potential for M. × giganteus biomass production in Europe ranged from 17 Mg ha−1 in Sweden to 41 Mg ha−1 in Portugal. In the absence of water limitation, i.e. irrigation as needed, it was estimated that the peak yield was of 60 Mg ha−1, which reflects the maximum potential of M. × giganteus and it is close to the highest values measured in Italy and Greece (50 and 54 Mg ha−1, respectively) and in central Illinois reaching 60 Mg ha−1 (Heaton et al., 2008).
Nutrient recycling, and therefore agronomic sustainability, in these crops is achieved by translocation of nitrogen to the underground perennating organ in the fall. This requires that the shoot is allowed to complete senescence and dry-down before harvest. After this point, it is inevitable that mass will be lost with time until harvest. Clifton-Brown et al. (2004) estimated this to be 0.36% loss per day and therefore 27.5% if harvest is delayed by 90 days. The model MISCANMOD, developed by Clifton-Brown et al. (2004), has been further refined (now MISCANFOR; Hastings et al., 2009) and incorporated improved descriptions of the relationship between potential and actual evapotranspiration, which impacts calculation of water stress; variable RUE which depends on temperature, nutrient, and water stress; and additional modifications that reflect recent findings in M. × giganteus physiology such as photoperiod sensitivity (Hastings et al., 2009). Their results suggest that although M. × giganteus can be highly productive in southern Europe, a ± 20% variability should be expected due to year-to-year fluctuations in weather patterns.
More detailed predictions particularly of growth, establishment, and inter-annual variability of M. × giganteus and P. virgatum require representation of detailed biophysical and physiological processes underlying carbon, water, and nutrient dynamics in a coupled and layered soil, plant and microclimate continuum as presented, for example, in wimovac (Windows Intuitive Model of Vegetation response to Atmospheric and Climate Change) (Humphries, 2003). This is arguably also a more suitable framework to guide future experiments and crop improvement programs (Humphries & Long, 1995). Such detailed models allow exploration of the value of potential genetic and agronomic modifications, as well as impacts of fine scale spatial and temporal variation in weather and soil. For example, using wimovac it was shown that, theoretically, M. × giganteus can increase its productivity by 4 Mg ha−1 if the threshold temperature for growth could be lowered by 2 °C and the degree days requirements were increased so that flowering occurred uniformly (Clifton-Brown et al., 2001). Miguez et al. (2009) showed that in addition to peak productivity, wimovac was able to accurately simulate CO2 uptake, leaf area index (LAI), and partitioning between leaf, stem, root, and rhizome for M. × giganteus.
Here, we develop a new model, BioCro, which is a generic vegetation model based on the previously published wimovac (Humphries & Long, 1995) as adapted for M. × giganteus in Miguez et al. (2009). Based on wimovac's objectives of ease of use, modularity, and interactivity, we developed a new model and implemented it in r (R Development Core Team, 2006) and further parameterized it to simulate P. virgatum, in addition to M. × giganteus. Although previous semi-mechanistic models have been developed for one or other of these feedstocks, few models accommodate both within the same model structure and assumptions about process. Combining both within the same framework and assumptions avoids the danger of confounding differences in model structure with biological differences when comparing the two feedstocks. The new model was necessary for conducting simulations at a regional level (conterminous United States) since it is computationally more efficient, it is cross-platform and therefore more easily integrated with other software. The main algorithms in wimovac were implemented in the c programming language (Kernighan & Ritchie, 1988), and the interface was written in r (R Development Core Team, 2006). r was chosen because it is cross-platform, and it allows for access to optimization methods (parameter estimation, Monte Carlo, and Bayesian) as well as a range of graphics engines. Thus, BioCro has incorporated the biochemical, physiological, and environmental biophysics mechanism implemented in wimovac, plus parameter estimation capabilities and graphical procedures used to evaluate the agreement between the observed and simulated data. BioCro made it possible to run the model efficiently millions of times to allow optimization routines and hourly timesteps, across multiple years with a high degree of spatial resolution (details below).
Our objectives were the following:
- Test and parameterize simulations of M. × giganteus and P. virgatum against the only ‘long-term’ (>5 years) replicated side-by-side yield trials of the two species, which have currently been confined in the United States to Illinois.
- Map rainfed M. × giganteus and P. virgatum establishment, mature yields, and yield stability in relation to soil and weather variability over the past 32 years across the conterminous United States taking into account soils and climate.
- Compare M. × giganteus biomass productivity to representative maize total biomass production.
- Test predictions of P. virgatum biomass productivity against observed data from 30 published field trials.