The actual cause of decreased growth under deep shade remains unclear. For example, reduced growth activity can be directly induced by light signals like low PAR or decreased red/far-red ratio (Ballaré and Casal 2000); or growth might decline in response to a declining need for new xylem vessels under shade due to a reduction of total leaf area (Schmid et al. 2017). To resolve the question of whether tree growth can indeed decrease in response to an up-regulation of C-storage, the usage of knock-out mutants for C-storage may be informative, similar to studies on the diurnal C-storage regulation in Arabidopsis (Gibon et al. 2004, Rolland et al. 2006, Smith and Stitt 2007, Gibon et al. 2009, Sulpice et al. 2009). Whatever the physiological reasons for the growth and NSC dynamics in tree saplings under shade observed here, the net result after three growing seasons are saplings with severely reduced growth, but tissue NSC reserves that are on par with unshaded saplings.
The Total AGB of the gardens was 1696.49 Kgs/2.27 ha. Total BGB was about 441.08 Kgs/2.27 ha and total biomass was 2137.57 Kgs/2.27 ha i.e. approximately 941.66 Kg ha -1 . Arc view 10.2.1, software of Geographic Information System (GIS) was used for predicting the biomass. The data helped to us analyse and interpret better, and eventually conceptualize the above and below ground biomass in the entire area of gardens. GIS based map shows the location and value of above and below ground biomass for each tree species in the study area. Green colour represents highest density of above ground and below ground biomass, yellow represents moderate while red indicates scarce or limited above and below ground biomass (Fig 2 A-F). The green colour actually represents the volume of biomass and not the density or the number of trees present in the study area. In Prabhat Udyan, (Fig 2 A and 2 B) the green colour is concentrated towards the north of the garden and it is epitomized by two large Samanea saman trees (> 60 inch diameter). Rest all the trees belonging to Class I-III appears as yellow or scattered in the red zone. As these trees are young, the biomass is not clearly visualized as green colour in the map but instead appears as yellow, orange or red respectively, in decreasing order of their biomass. Similarly in Gol maidan, the density of vegetation was high compared to other two gardens. Moreover, there are around 33 trees in the diameter Class VI (> 60 inches) depictinglarger area of green colour in the map (Fig 2 C and D). These trees are with huge volume of biomass and appear to be > 50 years of age.In the
This paper hypothesises that the constraint on CCS is therefore not cost related or supply chain related (i.e. build rate limited), particularly in later years. The key remaining possibility is that the residual emissions from CCS make it an unfavourable option in climate change mitigation scenarios; even these low levels of emissions are sufficiently high to conflict with extremely constrained global carbon budgets. This hypothesis is supported by previous works produced by UKERC  and IEAGHG , who both reported a capture rate of 90% for coal based power generation with CCS. IEAGHG  demonstrated that increasing the capture rate from 90% to 98% would not increase but rather reduce (-3%) the cost per tonne of CO 2 avoided for oxycombustion and IGCC applications. Capture technology
The soil total organic carbon concentrations differed sig- nificantly (P < 0.05) between regions, systems and soil depths (Table 1). Soil C concentration decreased with soil depth from the surface. Similar trends with depth have been noted by Cifuentes-Jara  and Dawoe . The topsoil, 0–20 cm, contained approximately 58.8 % of the soil organic C in the 0–60 cm soil profile. This undoubtedly reflects the great mass of litter fall in cocoa ecosystems. In addition, the high C concentration in the topsoil is in accordance with the presence of 80–85 % mat of lateral roots of cocoa trees being predominantly found within the top 0–30 cm [23–25], although visible roots were excluded in sampling for the current study. The soil C concentration range of 0.6–2.0 % lies within the soil C concentration range of 0.4–2.6 %, reported by Dawoe  for 15 and 30 year old cocoa ecosystems in the Ashanti region, Ghana.
where c is the speed of light in vacuum, n is the correction factor that applied if light travels in a medium; is the range, and is the time that light travels from the sensor to a target and back to the sensor (also known as TOF). Hence, with n =1 and the flying height is 1000 m, the TOF for a single pulse is 6.7 µs. For precise mapping, the position and orientation of the aircraft have to be recorded at certain level of accuracy. The laser light source is more directional and coherent than sunlight which allows the laser pulses to keep high spatial coherence when it hits and transmits back. The spatial coherence is dependent with the wavelength of the laser source that shorter the wavelength can create longer the coherence length. The multispectral ALS systems now integrate eye-safe lasers with wavelengths in the range of 0.4 to 1.6 µm. The strength of a reflected laser pulse is recorded as intensity values which measures the reflectivity of surface targets at the laser-specific wavelength. The intensity data provides complementary information to the 3D measurement which helps differential objects with similar elevation but different reflectivity. Meanwhile, the scanner emits pulses with certain scanning angles to optimize both the across- and along- track range (see Figure 2.3). A relationship between swath width sw of a scanner and the scanning angle can be established as
The use of species occurrence data and climate model variables in this study potentially present uncertainty in interpretation of the results. Dataset acquired from observations and herbarium often shows strong geographic bias (sampling bias) due to some areas being visited more often than others because of their ac- cessibility  . The availability of presence distribution data for species poses challenge as most of herbaria in the region lack sufficient data for model- ling . Such challenges has however been overcome by use of cross-validation in maxentmodelling technique which uses few data points. Species occurrence data can be biasedly distributed on the landscape which can contribute to local biasness and over-smoothing affecting reliability of the model  . We, how- ever, rely on maxent ability in using jackknifing to achieve a robust new estima- tor, called jackknife kriging, which retains ordinary kriging simplicity and global unbiasedness while at the same time reducing local bias and over-smoothing tendency . The climate model consist of potentially highly correlated va- riables; however, maxent can only select one of variables in a pair of highly cor- related variables and still model performance is not affected  . However, the selection of variables by maxent is associated with the risk of diminishing importance of other predictor in the pairs of variables  . It is also impor- tant to note that there are confounding factors that may affect the distribution of agroforestry tree species on the slopes of Mount Kilimanjaro and Taita Hills. These factors include but not limited to soil, market forces and land use plan and management.
ing water at a high flow rate until microseismic events are induced. Ideally, the same brine from the storage formation should be injected to avoid geochemical reactions around the injection well that may alter rock properties. However, inject- ing brine would imply having a large facility on the surface to store the brine from the storage formation that would have been pumped previously. The test has to be closely monitored with pressure, temperature, deformation and microseismic- ity monitoring. The hydraulic properties of the storage for- mation and caprock can be determined from the interpreta- tion of injection as a hydraulic test (Cooper and Jacob, 1946; Hantush, 1956). If heterogeneities are present in the storage formation, their effect is only detectable for a limited period of time (Wheatcraft and Winterberg, 1985; Butler and Liu, 1993). For this reason, it is extremely important to contin- uously measure pore pressure changes during injection. As for the geomechanical properties of the storage formation and caprock, they can be derived from the interpretation of the vertical displacement at the top of the storage forma- tion and the caprock. Additionally, measuring the pressure evolution in the caprock, which undergoes a pressure drop in response of the pressure buildup in the storage formation (Hsieh, 1996), also gives information on the geomechanical
forests (76 tC/ha; 77 tC/ha per unit of tree cover). A high fraction of those forest-like areas in the city of Leipzig certainly contributed to a high city-wide average of 68 tC/ha per unit of tree cover compared to our Berlin case study of around 24 tC/ha per unit of tree cover (Strohbach and Haase 2012). Therefore including such less urbanized land of high tree coverage in our calculations could have substantially affected and increased the average urban forest carbon density of the city of Berlin. Schreyer et al. (2014) calculated the carbon densities of urban trees for selected urban Berlin structure types. Those calculations were extrapolated across the total city including those dense woodlands resulting in an average density of 11.53 tC/ha for the city of Berlin. Similar differences were shown for the city of Karlsruhe, Germany, which stated urban forest carbon estimates of 9.5 tC/ha carbon for highly urbanized areas, and an exponential increase to a total average of 32.3 tC/ha, if state and city forests were included as they are part of the administrative boundaries of Karlsruhe (Kändler et al. 2011). Tree density is influenced by various factors in different case studies such as land use, differences between countries and city development. For example, our results of Berlin had an average range of 10–40 trees/ha across densely built areas (excluding parks and forest-like areas), which is close to the average of 30.7 trees/ha in the city of Karlsruhe, Germany (Kändler et al. 2011). Residential areas of Cambridge, UK, showed a range from 33.7 to 55.7 trees/ha (Wilson et al. 2015). Almost 80 % of 167 cities in the state of Gujarat, India, showed values below 30 trees/ha compared to its capital Gandhinagar with an average of 152 trees/ha (Singh 2013). For selected US cities, the average tree density had a large range from below 25 (Casper, Wyoming) up to 280 trees/ha (Atlanta, Georgia) (Nowak et al. 2008). Hence, tree density differences certainly have a large impact on carbon density values, which needs to be considered for comparisons between and within cities. Additionally, carbon density would slightly increase, if we included root biomass. Though, our case study excluded it since little research has been conducted on the carbonstorage of urban tree root systems and high uncertainty surrounds the research that has been conducted (Nowak and Crane 2002; Johnson and Gerhold 2003).
simulation findings may provide a feasible approach to analyze model dynamics, however, it should be kept in mind that the simulation aboveground carbonstorage on various climate change scenarios are complex flow processes. The users may improve the accuracy of the dynamic model by appropriately considering the possible shortcomings, particularly in regard to tree growth calculation. Looking at the periodical annual increment of each tree species (Table 1), it seems that the growth rate are too slow and there is no obvious annual increment difference among them. The PAI for Palaquium sp. is only limited to 0.22 – 0.41 cm/ year, while Myristica sp. and Syzygium sp. is about 0.21 – 0.43 cm/year. Those relatively small annual increments have also been reported by other studies, such as Santoso (2008), and Wahjono and Anwar (2008), who conducted measurements on permanent sample plots (PSPs) in 199 Figure 8. Projection of future climate change scenarios (Constant year 2000 concentrations, B1, A1T, A2,
The estimates given in this paper are based on limited ﬁeld data and become more uncertain as they reﬁne from national to regional and state estimates. More ﬁeld measurements are needed in urban areas to help improve carbon accounting and other functions of urban forest ecosystems. In particular, more ﬁeld data are needed to assess regional variation in forest struc- ture; long-term permanent plot data are needed to assess urban forest growth, regeneration, and mortality; and improved satellite monitoring of urban cover types is needed to more accurately assess changes in urban forest cover. In addition, research needs to develop bet- ter urban tree biomass equations, improve estimates of tree decomposition and maintenance emissions, and investigate the eﬀect of urban soils on carbonstorage and ﬂux in cities. A better understanding and account- ing of urban ecosystems can be used to develop man- agement plans and national policies that can signiﬁcantly improve environmental quality and human health across the nation.
resources that could have been used for these purposes (Newell 1991, Koenig and Knops 1998, Obeso 2002), but this has been called into question by observations that NSC levels may not decline or may even increase in masting years (Korner 2003). We found a positive correlation between 2011 residual BAI and acorns/stem, but this correlation did not exist among defoliated trees. This difference may arise because of reduced carbon availability or increased belowground storage allocation, making nothing but the minimal level of growth and reproductive output possible. In contrast to effects on growth, there was some evidence that reproduction did strain carbon resources, as early May root NSC levels were negatively correlated with acorns/stem across both treatments. However, neither belowground nor aboveground NSC increment appeared to be affected by reproductive output. One reason for this is that trees may only reproduce heavily when resource levels are high, and so may avoid or mitigate tradeoffs with other processes (Sánchez-Humanes et al. 2011). Alternatively, just as with growth and storage, trees may vary substantially in carbon availability, and while a tradeoff between growth, storage, and reproduction may exist for each tree, variation among trees is driven by differences in resource availability (Chapter 3), leading to a positive relationship between all these processes.
The fitted carbon equations yielded coefficients of deter- mination of between 0.85 and 0.86, and standard errors of the estimate near 20%. Among the models we tested the one resulting in the best fit was equation 1, though all four were satisfactory (Table 3). Schumacher et al.  obtained higher R 2 values and lower Syx% values in modeling the total biomass of A. angustifolia individuals. This is likely due to the fact that the authors used data from a homogeneously aged stand, e.g. 27 years. Sanquetta et al.  found satisfactory fits for the bio- mass in the bole as a function of DBH and H, with low standard errors of the estimate and high coefficients of determination, though unsatisfactory in making partial biomass estimates in each of the compartments.
The findings so far show that introducing carbon cap- ture and storage only makes sense if done on a large scale. Given the great volume of investment required and the implications for other options for reducing greenhouse gases, the decision about whether CCS should be made a central pillar of energy policy will have to be thoroughly considered on a solid scientific basis. It is already recognised today that new technolo- gies will have to satisfy numerous technological, struc- tural, economic, ecological and social criteria before they can be regarded as viable options for a sustainable future energy supply. So they will be subjected to a rig- orous selection process before their suitability as future key technologies is accepted. As well as detailed inves- tigation of the potential, the achievable future costs, the implications for industrial policy and social impact of a technology, differentiated life cycle assessments (LCAs) of the whole system represent a suitable instrument for assessing the practicability of new technologies against various sustainability criteria. Very detailed LCAs are already available for the various technologies based on renewables, which represent one of the main other options for avoiding greenhouse gases. Suitable data for making a solid assessment of capability and envi- ronmental and system impact are also already available for numerous technologies in the field of efficiency (e.g. modern combined heat and power).
density of the gas and the adsorption isotherm at 25ºC), and the capacity obtained from the scale-up measurements at 21ºC are represented.
From these results, different aspects are worthy of mention. Firstly, comparing the amount of gas storage by compression and by adsorption, it can be clearly observed that the presence of an adsorbent with suitable properties has important advantages over the empty tank. Thereby, in all the cases, the amount of gas stored in a tank which contains the carbon adsorbent is higher than the amount of gas stored by compression in an empty tank, in both, gravimetric and in volumetric terms. Even in the case of H 2 storage, where much higher pressures are needed for reaching viable storage amounts, the results obtained with the adsorbent are higher when compared with the compression process. At 20 bar, the carbon filled tank can store 376 g l -1 , 104 g l -1 and 2.4 g l -1 of CO 2 , CH 4 and H 2 , respectively, while the amounts stored by compression are only 40 g l -1 , 12 g l -1 and 1.6 g l -1 , respectively. Therefore, in the cases of CO 2 and CH 4 almost 10 times more gas can be stored by adsorption, in comparison with the amount obtained by compression. In the particular case of H 2 , adsorption allows to store 1.5 times more, as compared to compression, even when unfavourable conditions are used (only 20 bar at room temperature).
There is an increasing tendency among the environmentalists to view holistically the ever decreasing tree cover and subsequent fall in carbon sinks. This decline in carbon sinks has resulted in concomitant increase in carbon emission and a subsequent rise in global temperatures (A. Waran and A. Patwardhan, 2001). Thus increase in CO 2 is amongst the most dreaded problems among the urban tree cover because of the vehicular pollution. In the
proportion of the sequestered carbon will be released back into the atmosphere with the disposal of pulp and paper products. Consequently, the value of the sequestered carbon will be offset by the value of post-harvest carbon emissions. Tomich et al. (1997) report that if the half-life of pulp and paper products is 2.5 years, 80 percent of the value of the sequestered carbon will be offset by post-harvest emissions at a zero discount rate, and that this effect diminishes as the discount rate increases. This prompted us to also use our model to investigate the effect on the profitability of the Eucalypt forest of different debit regimes based on the rate at which carbon decays in forest products, for a range of discount rates. Some carbon is lost when trees are harvested and when raw timber is processed and converted into forest products, however the fate of the remaining carbon depends on its end use. This is illustrated in Figure 1. For example, carbon in durable forest products such as construction timber may be stored for decades, while carbon in less resilient products such as pulp and paper will be stored for far less time. In this paper, we compare the profitability of the forest with full payment of debits at harvest, and with partial payment of debits at harvest followed by annual payments post-harvest as the carbon decays in a durable forest product.
ABSTRACT: Acetaldehyde and ethanol are already present in detectable levels at the time of harvest, under aerobic conditions and in healthy, undamaged fruit. Both metabolites can be detected, at different concentrations, in all cultivars. Several hours after harvest, the levels of acetaldehyde in cultivars Summit, Těchlovan and Kordia were 6.41, 9.78 and 22.00 mg/l, respectively. Both ethanol and acetaldehyde accumulate to significant levels in anaerobically stored cherries, particularly in atmospheres with high levels of CO 2 . The highest levels of ethanol observed, after 31 days of exposure to anaerobic conditions, were in the cultivars Těchlovan (1,159 mg/l) and Summit (1,168 mg/l); both concentrations are are broadly similar. The metabolites decreased after a return to aerobic conditions, but remained higher than the levels first observed. Sweet cherries stored in anaerobic conditions are also sensitive to the development of off-flavours in the first 24 hours after opening the storage box. The very slow ripening of the fruit under anaerobic conditions was satisfactorily quantified by measuring the higher degree of fruit firmness, when the usual, visual attributes of ripeness were almost undetectable. Stems also remained green, in contrast to the usual browning that occurs under normal atmospheres. Discrimination analysis of various parameters observed gave a good resolution of different cultivars.
Containing the cost impacts of a 5 year delay would require both rapid (and risky) ‘catch up’ development of CCS during the 2030s and accelerated early uptake of a range of other low carbon technologies during the 2020s to fill the gap left by CCS (e.g. rapid replacement of gas heating during the 2020s as well as very rapid growth of biomass value chains to serve both heat and industrial energy needs). More realistically, if broad strategy remains focused on early decarbonisation of the power sector, delay to CCS would lead to greater reliance on nuclear and offshore wind, with associated pressure to deliver very demanding deployment. Even with successful unit cost reductions, this would increase system risk and costs both before and after 2030.
We approximated the PyC pool for northern peatlands by using discrete peatland inception ages and their distribution from Loisel et al., (2014) and the herein identi ﬁ ed PyC – age relationship. For the total northern peatland C stock of 436 Pg (Loisel et al., 2014), we calculated a PyC pool of 62·3 (±21·8) Pg or 14·3 (±5·0)% of the peatland C. The av- erage PyC content of 14·3% calculated for northern peatlands this way is slightly higher than in our direct mea- surements (13·5%) because carbon in northern peatlands (Loisel et al., 2014) is on average older (4,252 years) than in our sample set (3,527 years). The amount of northern peatland PyC is within the same order of magnitude as the global PyC pool estimated previously for all mineral and or- ganic soils (Bird et al., 2015; Reisser et al., 2016; Santín et al., 2016). Our PyC estimate for northern peatlands is as- sociated with some uncertainties: The signi ﬁ cant linear rela- tionship between peat age and PyC explains only 30% of the variability in % PyC. It is also not known whether the ob- served PyC increase with age holds true for a wider range of natural peatlands that were not covered by our data set. Yet our estimate is based on sound methodology and the most comprehensive one reported hitherto.