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.

This paper presents a method for reconstructing automatically the quantitative structure model of every tree in a forest plot from terrestrial laser scanner data. A new feature is the automatic extraction of individual trees from the point cloud. The method is tested with a 30-m diameter English oak plot and a 80-m diameter Australian eucalyptus plot. For the oak plot the total biomass was overestimated by about 17 %, when compared to allometry (N = 15), and the modelling time was about 100 min with a laptop. For the eucalyptus plot the total biomass was overestimated by about 8.5 %, when compared to a destructive reference (N = 27), and the modelling time was about 160 min. The method provides accurate and fast tree modelling abilities for, e.g., biomass estimation and ground truth data for airborne measurements at a massive ground scale.

Xylem vulnerability to cavitation is a promising criterion for identifying trees with high drought tolerance, but traditional techniques for measuring cavitation resistance are unsuitable for screening large numbers of genotypes. We tested the potential of the new Cavitron technique for high throughput screening of cavitation resistance in five poplar (Populus spp.) and four willow (Salix spp.) clones. The Cavitron technique enabled the screening of three to four clones per day with sufficient accuracy to reveal significant differences between clones. Because intraspecific screening may be better carried out through the identification of correlated and more easily measured traits, we attempted to identify accessible parameters that correlate to cavitation resistance. Variability in vulnerability to cavitation across clones was poorly correlated with anatomical traits such as vessel diameter, vessel wall strength, wood density and fiber wall thickness; however, a striking correlation was established between cavitation resistance and aboveground biomass production, indicating a possible trade-off between xylem safety and growth potential.

Quantifying soil organic carbon stocks (SOC) and their dynamics accurately is crucial for better predictions of climate change feedbacks within the atmosphere-vegetation-soil system. However, the components, environmental responses and controls of the soil CO₂ efflux (R_s) are still unclear and limited by field data availability. The objectives of this study were (1) to quantify the contribution of the various R_s components, specifically its mycorrhizal component, (2) to determine their temporal variability, and (3) to establish their environmental responses and dependence on gross primary productivity (GPP). In a temperate deciduous oak forest in south east England hourly soil and ecosystem CO₂ fluxes over four years were measured using automated soil chambers and eddy covariance techniques. Mesh-bag and steel collar soil chamber treatments prevented root or both root and mycorrhizal hyphal in-growth, respectively, to allow separation of heterotrophic (R_h) and autotrophic (R_a) soil CO₂ fluxes and the R_a components, roots (R_r) and mycorrhizal hyphae (R_m). Annual cumulative R_s values were very similar between years (740 ± 43 g C m⁻² yr⁻¹) with an average flux of 2.0 ± 0.3 μmol CO₂ m⁻² s⁻¹, but R_s components varied. On average, annual R_r, R_m and R_h fluxes contributed 38, 18 and 44%, respectively, showing a large R_a contribution (56%) with a considerable R_m component varying seasonally. Soil temperature largely explained the daily variation of R_s (R² = 0.81), mostly because of strong responses by R_h (R² = 0.65) and less so for R_r (R² = 0.41) and R_m (R² = 0.18). Time series analysis revealed strong daily periodicities for R_s and R_r, whilst R_m was dominated by seasonal (~150 days), and R_h by annual periodicities. Wavelet coherence analysis revealed that R_r and R_m were related to short-term (daily) GPP changes, but for R_m there was a strong relationship with GPP over much longer (weekly to monthly) periods and notably during periods of low R_r. The need to include individual R_s components in C flux models is discussed, in particular, the need to represent the linkage between GPP and R_a components, in addition to temperature responses for each component. The potential consequences of these findings for understanding the limitations for long-term forest C sequestration are highlighted, as GPP via root-derived C including R_m seems to function as a C "overflow tap", with implications on the turnover of SOC.