Eric Casella

Greenhouse gas emissions from four bioenergy crops in England and Wales: Integrating spatial estimates of yield and soil carbon balance in life cycle analyses

Accurate estimation of the greenhouse gas (GHG) mitigation potential of bioenergy crops requires the integration of a significant component of spatially varying information. In particular, crop yield and soil carbon (C) stocks are variables which are generally soil type and climate dependent. Since gaseous emissions from soil C depend on current C stocks, which in turn are related to previous land management it is important to consider both previous and proposed future land use in any C accounting assessment. We have conducted a spatially explicit study for England and Wales, coupling empirical yield maps with the RothC soil C turnover model to simulate soil C dynamics. We estimate soil C changes under proposed planting of four bioenergy crops, Miscanthus (Miscanthus×giganteus), short rotation coppice (SRC) poplar (Populus trichocarpa Torr. & Gray ×P. trichocarpa, var. Trichobel), winter wheat, and oilseed rape. This is then related to the former land use – arable, pasture, or forest/seminatural, and the outputs are then assessed in the context of a life cycle analysis (LCA) for each crop. By offsetting emissions from management under the previous land use, and considering fossil fuel C displaced, the GHG balance is estimated for each of the 12 land use change transitions associated with replacing arable, grassland, or forest/seminatural land, with each of the four bioenergy crops. Miscanthus and SRC are likely to have a mostly beneficial impact in reducing GHG emissions, while oilseed rape and winter wheat have either a net GHG cost, or only a marginal benefit. Previous land use is important and can make the difference between the bioenergy crop being beneficial or worse than the existing land use in terms of GHG balance.

Modelling supply and demand of bioenergy from short rotation coppice and Miscanthus in the UK.

Biomass from lignocellulosic energy crops can contribute to primary energy supply in the short term in heat and electricity applications and in the longer term in transport fuel applications. This paper estimates the optimal feedstock allocation of herbaceous and woody lignocellulosic energy crops for England and Wales based on empirical productivity models. Yield maps for Miscanthus, willow and poplar, constrained by climatic, soil and land use factors, are used to estimate the potential resource. An energy crop supply-cost curve is estimated based on the resource distribution and associated production costs. The spatial resource model is then used to inform the supply of biomass to geographically distributed demand centres, with co-firing plants used as an illustration. Finally, the potential contribution of energy crops to UK primary energy and renewable energy targets is discussed.

Development and evaluation of ForestGrowth-SRC a process-based model for short rotation coppice yield and spatial supply reveals poplar uses water more efficiently than willow

Woody biomass produced from short rotation coppice (SRC) poplar (Populus spp.) and willow (Salix spp.) is a bioenergy feedstock that can be grown widely across temperate landscapes and its use is likely to increase in future. Process‐based models are therefore required to predict current and future yield potential that are spatially resolved and can consider new genotypes and climates that will influence future yield. The development of a process‐based model for SRC poplar and willow, ForestGrowth‐SRC, is described and the ability of the model to predict SRC yield and water use efficiency (WUE) was evaluated. ForestGrowth‐SRC was parameterized from a process‐based model, ForestGrowth for high forest. The new model predicted annual above ground yield well for poplar (r2 = 0.91, RMSE = 1.46 ODT ha−1 yr−1) and willow (r2 = 0.85, RMSE = 1.53 ODT ha−1 yr−1), when compared with measured data from seven sites in contrasting climatic zones across the United Kingdom. Average modelled yields for poplar and willow were 10.3 and 9.0 ODT ha−1 yr−1, respectively, and interestingly, the model predicted a higher WUE for poplar than for willow: 9.5 and 5.5 g kg−1 respectively. Using regional mapped climate and soil inputs, modelled and measured yields for willow compared well (r2 = 0.58, RMSE = 1.27 ODT ha−1 yr−1), providing the first UK map of SRC yield, from a process‐based model. We suggest that the model can be used for predicting current and future SRC yields at a regional scale, highlighting important species and genotype choices with respect to water use efficiency and yield potential.

Estimating the supply of biomass from short-rotation coppice in England, given social, economic and environmental constraints to land availability.

Background: Biomass has been identified as an important source of renewable energy. However, growing demand for dedicated energy crops could lead to conflicts with food production and ecosystem services. This study uses a geographic information systems-embedded modeling approach to assess the spatial supply of short-rotation coppice, taking into account social, economic and environmental constraints. Results: Results suggest that 7.5 million tons of biomass (from short-rotation coppice) is realistically available in England. Such production would require 0.8 million ha and could be grown almost entirely on poor quality marginal lands. Conclusion: We therefore conclude that short-rotation coppice energy crops have the potential to play an important role in meeting UK renewable energy targets without compromising environmental sustainability or food production.

Estimating UK perennial energy crop supply using farm-scale models with spatially disaggregated data

To achieve the UK Governments aim of expansion in the growth of perennial energy crops requires farmers to select these crops in preference to conventional rotations. Existing studies estimating the total potential resource have either only simplistically considered the farmer decision‐making and opportunity costs, for example using an estimate of annual land rental charge; or have not considered spatial variability, for example using representative farm types. This paper attempts to apply a farm‐scale modelling approach with spatially specific data to improve understanding of potential perennial energy crop supply. The model main inputs are yield maps for the perennial energy crops, Miscanthus and willow grown as short‐rotation coppice (SRC), and regional yields for conventional crops. These are used to configure location specific farm‐scale models, which optimize for profit maximization with risk aversion. Areas that are unsuitable or unavailable for energy crops, due to environmental or social factors, are constrained from selection. The results are maps of economic supply, assuming a homogenous farm‐gate price, allowing supply cost curves for the UK market to be derived. The results show a high degree of regional variation in supply, with different patterns for each energy crop. Using estimates of yields under climate change scenarios suggests that Miscanthus supply may increase under future climates while the opposite effect is suggested for SRC willow. The results suggest that SRC willow is only likely to able to supply a small proportion of the anticipated perennial energy crop target, without increases in market prices. Miscanthus appears to have greater scope for supply, and its dominance may be amplified over time by the effects of climate change. Finally, the relationship to the demand side of the market is discussed, and work is proposed to investigate the factors impacting how the market as a whole may develop.

Analysis of Geometric Primitives in Quantitative Structure Models of Tree Stems

One way to model a tree is to use a collection of geometric primitives to represent the surface and topology of the stem and branches of a tree. The circular cylinder is often used as the geometric primitive, but it is not the only possible choice. We investigate various geometric primitives and modelling schemes, discuss their properties and give practical estimates for expected modelling errors associated with the primitives. We find that the circular cylinder is the most robust primitive in the sense of a well-bounded volumetric modelling error, even with noise and gaps in the data. Its use does not cause errors significantly larger than those with more complex primitives, while the latter are much more sensitive to data quality. However, in some cases, a hybrid approach with more complex primitives for the stem is useful.

The potential for bioenergy crops to contribute to meeting GB heat and electricity demands

The paper presents a model system, which consists of a partial equilibrium model and process‐based terrestrial biogeochemistry models, to determine the optimal distributions of both Miscanthus (Miscanthus × giganteus) and short rotation coppice willow (SRC) (Salix. viminalis L. x S. viminalis var Joruun) in Great Britain (GB), as well as their potential contribution to meet heat and electricity demand in GB. Results show that the potential contribution of Miscanthus and SRC to heat and electricity demand is significant. Without considering farm‐scale economic constraints, Miscanthus and SRC could generate, in an economically competitive way compared with other energy generation costs, 224 800 GWh yr−1 heat and 112 500 GWh yr−1 electricity, with 8 Mha of available land under Miscanthus and SRC, accounting for 66% of total heat demand and 62% of total electricity demand respectively. Given the pattern of heat and electricity demand, and the relative yields of Miscanthus and SRC in different parts of GB, Miscanthus is mainly favoured in the Midlands and areas in the South of GB, whereas SRC is favoured in Scotland, the Midlands and areas in the South of GB.

High-resolution spatial modelling of greenhouse gas emissions from land-use change to energy crops in the United Kingdom

We implemented a spatial application of a previously evaluated model of soil GHG emissions, ECOSSE, in the United Kingdom to examine the impacts to 2050 of land‐use transitions from existing land use, rotational cropland, permanent grassland or woodland, to six bioenergy crops; three ‘first‐generation’ energy crops: oilseed rape, wheat and sugar beet, and three ‘second‐generation’ energy crops: Miscanthus, short rotation coppice willow (SRC) and short rotation forestry poplar (SRF). Conversion of rotational crops to Miscanthus, SRC and SRF and conversion of permanent grass to SRF show beneficial changes in soil GHG balance over a significant area. Conversion of permanent grass to Miscanthus, permanent grass to SRF and forest to SRF shows detrimental changes in soil GHG balance over a significant area. Conversion of permanent grass to wheat, oilseed rape, sugar beet and SRC and all conversions from forest show large detrimental changes in soil GHG balance over most of the United Kingdom, largely due to moving from uncultivated soil to regular cultivation. Differences in net GHG emissions between climate scenarios to 2050 were not significant. Overall, SRF offers the greatest beneficial impact on soil GHG balance. These results provide one criterion for selection of bioenergy crops and do not consider GHG emission increases/decreases resulting from displaced food production, bio‐physical factors (e.g. the energy density of the crop) and socio‐economic factors (e.g. expenditure on harvesting equipment). Given that the soil GHG balance is dominated by change in soil organic carbon (SOC) with the difference among Miscanthus, SRC and SRF largely determined by yield, a target for management of perennial energy crops is to achieve the best possible yield using the most appropriate energy crop and cultivar for the local situation.

Non-intersecting leaf insertion algorithm for tree structure models

We present an algorithm and an implementation to insert broadleaves or needleleaves into a quantitative structure model according to an arbitrary distribution, and a data structure to store the required information efficiently. A structure model contains the geometry and branching structure of a tree. The purpose of this work is to offer a tool for making more realistic simulations of tree models with leaves, particularly for tree models developed from terrestrial laser scanning (TLS) measurements. We demonstrate leaf insertion using cylinder-based structure models, but the associated software implementation is written in a way that enables the easy use of other types of structure models. Distributions controlling leaf location, size and angles as well as the shape of individual leaves are user definable, allowing any type of distribution. The leaf generation process consist of two stages, the first of which generates individual leaf geometry following the input distributions, while in the other stage intersections are prevented by carrying out transformations when required. Initial testing was carried out on English oak trees to demonstrate the approach and to assess the required computational resources. Depending on the size and complexity of the tree, leaf generation takes between 6 and 18 min. Various leaf area density distributions were defined, and the resulting leaf covers were compared with manual leaf harvesting measurements. The results are not conclusive, but they show great potential for the method. In the future, if our method is demonstrated to work well for TLS data from multiple tree types, the approach is likely to be very useful for three-dimensional structure and radiative transfer simulation applications, including remote sensing, ecology and forestry, among others.

Understanding the effect of disturbance from selective felling on the carbon dynamics of a managed woodland by combining observations with model predictions

The response of forests and terrestrial ecosystems to disturbance is an important process in the global carbon cycle in the context of a changing climate. blackThis study focuses on the effect of selective felling (thinning) at a managed forest site. Previous statistical analyses of eddy covariance data at the study site had found that disturbance from thinning resulted in no significant change to net ecosystem carbon uptake. In order to better understand the effect of thinning on carbon fluxes we use the mathematical technique of four-dimensional variational data assimilation. Data assimilation provides a compelling alternative to more common statistical analyses of flux data as it allows for the combination of many different sources of data, with the physical constraints of a dynamical model, to find an improved estimate blackof the state of a system. We develop blacknew observation operators to assimilate daytime and nighttime net ecosystem exchange observations with a daily time-step model, increasing blackobservations available by a factor of 4.25. Our results support previous analyses, with a predicted net ecosystem carbon uptake for the year 2015 of 426 ± 116g C m−2 for the unthinned forest and 420 ± 78g C m−2 for the thinned forest despite a model-predicted reduction in gross primary productivity of 337g C m−2. We show that this is likely due to reduced ecosystem respiration post-disturbance compensating for a reduction in gross primary productivity. This supports the theory of an upper limit of forest net carbon uptake due to the magnitude of ecosystem respiration scaling with gross primary productivity.