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New knowledge on soil structure highlights its importance for hydrology and soil organic matter (SOM) stabilization, which however remains neglected in many wide used models. We present here a new model, KEYLINK, in which soil structure is integrated with the existing concepts on SOM pools, and elements from food web models, that is, those from direct trophic interactions among soil organisms. KEYLINK is, therefore, an attempt to integrate soil functional diversity and food webs in predictions of soil carbon (C) and soil water balances. We present a selection of equations that can be used for most models as well as basic parameter intervals, for example, key pools, functional groups’ biomasses and growth rates. Parameter distributions can be determined with Bayesian calibration, and here an example is presented for food web growth rate parameters for a pine forest in Belgium. We show how these added equations can improve the functioning of the model in describing known phenomena. For this, five test cases are given as simulation examples: changing the input litter quality (recalcitrance and carbon to nitrogen ratio), excluding predators, increasing pH and changing initial soil porosity. These results overall show how KEYLINK is able to simulate the known effects of these parameters and can simulate the linked effects of biopore formation, hydrology and aggregation on soil functioning. Furthermore, the results show an important trophic cascade effect of predation on the complete C cycle with repercussions on the soil structure as ecosystem engineers are predated, and on SOM turnover when predation on fungivore and bacterivore populations are reduced. In summary, KEYLINK shows how soil functional diversity and trophic organization and their role in C and water cycling in soils should be considered in order to improve our predictions on C sequestration and C emissions from soils., This article is based upon work from COST Actions FP1305 (BioLink) and ES1406 (KEYSOM), supported by COST (European Cooperation in Science and Technology), and their Short Term Scientific Mission (STSM) programs. Omar Flores’ work was funded by FPU PhD grant program of the Spanish Ministry of Science, Innovation and Universities. Jorge Curiel Yuste received funding from the Spanish Ministry of Economy and Competitiveness (MINECO) under projects IBERYCA (CGL2017-84723-P) and the BC3 María de Maeztu excellence accreditation (MDM-2017-0714). Jorge Curiel Yuste also received funding from the Basque Government through the BERC 2018-2021 program., We also acknowledge institutional support from the Unit of Information Resources for Research at the Unit of Information Resources for Research at the "Consejo Superior de Investigaciones Científicas" (CSIC) for the article-processing charges contribution.

Quantifying the output uncertainty and tracking down its origins is key to interpreting the results of modelling studies. We performed such an uncertainty analysis on the predictions of forest growth and yield under climate change. We specifically focused on the effect of the interannual climate variability. For that, the climate years in the model input (daily resolution) were randomly shuffled within each 5-year period. In total, 540 simulations (10 parameter sets, nine climate shuffles, three global climate models, and two mitigation scenarios) were made for one growing cycle (80 years) of a Scots pine (Pinus sylvestris L.) forest growing in Peitz, Germany. Our results show that, besides the important effect of the parameter set, the random order of climate years can significantly change results such as basal area and produced volume, as well as the response of these to climate change. We stress that the effect of weather variability should be included in the design of impact model ensembles and in the accompanying uncertainty analysis. We further suggest presenting model results as likelihoods to allow risk assessment. For example, in our study, the likelihood of a decrease in basal area of >10% with no mitigation was 20.4%, whereas the likelihood of an increase >10% was 34.4%.

Altres ajuts: COST Actions BIOLINK (FP1305) and KEYSOM (ES 1406). Jorge Curiel Yuste received funding by the Basque Government through the BERC 2018-2021 program. The relatively poor simulation of the below-ground processes is a severe drawback for many ecosystem models, especially when predicting responses to climate change and management. For a meaningful estimation of ecosystem production and the cycling of water, energy, nutrients and carbon, the integration of soil processes and the exchanges at the surface is crucial. It is increasingly recognized that soil biota play an important role in soil organic carbon and nutrient cycling, shaping soil structure and hydrological properties through their activity, and in water and nutrient uptake by plants through mycorrhizal processes. In this article, we review the main soil biological actors (microbiota, fauna and roots) and their effects on soil functioning. We review to what extent they have been included in soil models and propose which of them could be included in ecosystem models. We show that the model representation of the soil food web, the impact of soil ecosystem engineers on soil structure and the related effects on hydrology and soil organic matter (SOM) stabilization are key issues in improving ecosystem-scale soil representation in models. Finally, we describe a new core model concept (KEYLINK) that integrates insights from SOM models, structural models and food web models to simulate the living soil at an ecosystem scale.

Many studies have shown the importance of ectomycorrhizal fungi (EM) in forests both for nutrient availability and for carbon (C) and nutrient cycling in the soil. Yet so far they are not incorporated in forest ecosystem growth and yield models. Recent research suggests phosphorus (P) shortage could be a major constraints to forest productivity in the future. For a realistic simulation of future forest ecosystem functioning, inclusion of detailed soil P cycling and the trees-EM interaction is necessary. We developed a full ecosystem P model that simulates P uptake by roots and EM, allocation within trees, physiological deficiency effects on C assimilation and allocation, release through litter decomposition, coupled with water, C and nitrogen (N) fluxes accounted for in the mechanistic forest stand model ANAFORE. Our results confirm the importance of incorporating EM in forest ecosystem models and suggest that the lack of incorporation of P in models may result in an under- or overestimation of forest growth. This new model has the potential of being used to assess the response of trees and/or stands to nutrient availability under different climate and management scenarios. With the current parameterization it is functional as a scientific research tool to investigate hypotheses.

For a sustainable future, we must sustainably manage not only the human/industrial system but also ecosystems. To achieve the latter goal, we need to predict the responses of ecosystems and their provided services to management practices under changing environmental conditions via ecosystem models and use tools to compare the estimated provided services between the different scenarios. However, scientific articles have covered a limited amount of estimated ecosystem services and have used tools to aggregate services that contain a significant amount of subjective aspects and that represent the final result in a non-tangible unit such as 'points'. To resolve these matters, this study quantifies the environmental impact (on human health, natural systems and natural resources) in physical units and uses an ecosystem service valuation based on monetary values (including ecosystem disservices with associated negative monetary values). More specifically, the paper also focuses on the assessment of ecosystem services related to pollutant removal/generation flows, accounting for the inflow of eutrophying nitrogen (N) when assessing the effect of N leached to groundwater. Regarding water use/provisioning, evapotranspiration is alternatively considered a disservice because it implies a loss of (potential) groundwater. These approaches and improvements, relevant to all ecosystems, are demonstrated using a Scots pine stand from 2010 to 2089 for a combination of three environmental change and three management scenarios. The environmental change scenarios considered interannual climate variability trends and included alterations in temperature, precipitation, nitrogen deposition, wind speed, Particulate matter (PM) concentration and CO2 concentration. The addressed flows/ecosystem services, including disservices, are as follows: particulate matter removal, freshwater loss, CO2 sequestration, wood production, NOx emissions, NH3 uptake and nitrogen pollution/removal. The monetary ecosystem service valuation yields a total average estimate of 361-1242 euro ha(-1) yr(-1). PM2.5 (

Although ectomycorrhizal fungi play an important role in forest ecosystem functioning, they are usually not included in forest growth or ecosystem models. Simulation is hampered by two main issues: a lack of understanding of the ecological functioning of the ectomycorrhizal fungi and a lack of adequate basic data for parameterization and validation. Concerning these issues, much progress has been made during the past few years, but this information has not found its way into the forest and soil models. In this paper, state-of-the-art insight into ectomycorrhizal functioning and basic values are described in a manner transparent to nonspecialists and modelers, together with the existing models and model strategies. As such, this paper can be the starting point and the motivator to include ectomycorrhizal fungi into existing soil and forest ecosystem models.

Forest management requires prediction of forest growth, but there is no general agreement about which models best predict growth, how to quantify model parameters, and how to assess the uncertainty of model predictions. In this paper, we show how Bayesian calibration (BC), Bayesian model comparison (BMC) and Bayesian model averaging (BMA) can help address these issues. We used six models, ranging from simple parameter-sparse models to complex process-based models: 3PG, 4C, ANAFORE, BASFOR, BRIDGING and FORMIND. For each model, the initial degree of uncertainty about parameter values was expressed in a prior probability distribution. Inventory data for Scots pine on tree height and diameter, with estimates of measurement uncertainty, were assembled for twelve sites, from four countries: Austria, Belgium, Estonia and Finland. From each country, we used data from two sites of the National Forest Inventories (NFIs), and one Permanent Sample Plot (PSP). The models were calibrated using the NFI-data and tested against the PSP-data. Calibration was done both per country and for all countries simultaneously, thus yielding country-specific and generic parameter distributions. We assessed model performance by sampling from prior and posterior distributions and comparing the growth predictions of these samples to the observations at the PSPs. We found that BC reduced uncertainties strongly in all but the most complex model. Surprisingly, country-specific BC did not lead to clearly better within-country predictions than generic BC. BMC identified the BRIDGING model, which is of intermediate complexity, as the most plausible model before calibration, with 4C taking its place after calibration. In this BMC, model plausibility was quantified as the relative probability of a model being correct given the information in the PSP-data. We discuss how the method of model initialisation affects model performance. Finally, we show how BMA affords a robust way of predicting forest growth that accounts for both parametric and model structural uncertainty. (C) 2012 Elsevier B.V. All rights reserved.

The contribution of selected sources of uncertainty to the total variance of model simulation results of stem biomass increment calculated from annual stem biomass predictions of European beech (Fagus sylvatica L.) was quantified. Sources of uncertainty were defined as the selected variables that influence the total variance of the model results. Simulations were made: (i) for ten regional climate models (RCMs) based on the IPCC scenario A1B and providing an ensemble of climate projections up to 2100; (ii) with two forest model types (FMTYPEs); (iii) for four forest management intensities (MANFORs); and (iv) for three time windows (TIMEWINDs), each spanning 15 years, starting in 2019, in 2049 and in 2079. Both models, the empirical SIBYLA model and the process-based ANAFORE model, were calibrated using experimental tree growth data from four plots in central Slovakia between 1989 and 2003. Three of these plots, representing the four MANFORs, were subject to different prior intensities of thinning while one was left untouched as a control. The FMTYPE explained most of the total variance in the simulation results (39.9%), followed by MANFOR (i.e. thinning intensity; 22.2%) and TIMEWIND (12.0%), while the effect of RCMs on model uncertainty was limited (

• Increasing atmospheric concentrations of phytotoxic ozone (O(3) ) can constrain growth and carbon sink strength of forest trees, potentially exacerbating global radiative forcing. Despite progress in the conceptual understanding of the impact of O(3) on plants, it is still difficult to detect response patterns at the leaf level. • Here, we employed principal component analysis (PCA) to analyse a database containing physiological leaf-level parameters of 60-yr-old Fagus sylvatica (European beech) trees. Data were collected over two climatically contrasting years under ambient and twice-ambient O(3) regimes in a free-air forest environment. • The first principal component (PC1) of the PCA was consistently responsive to O(3) and crown position within the trees over both years. Only a few of the original parameters showed an O(3) effect. PC1 was related to parameters indicative of oxidative stress signalling and changes in carbohydrate metabolism. PC1 correlated with cumulative O(3) uptake over preceding days. • PC1 represents an O(3) -responsive multivariate pattern detectable in the absence of consistently measurable O(3) effects on individual leaf-level parameters. An underlying effect of O(3) on physiological processes is indicated, providing experimental confirmation of theoretical O(3) response patterns suggested previously.

A stand-scale forest model has been developed that dynamically simulates, besides carbon (C) and water (H2O) fluxes, wood tissue development from physiological principles. The forest stand is described as consisting of trees of different size cohorts (for example, dominant, co-dominant and suppressed trees), either of the same or of different species (deciduous or coniferous). Half-hourly C and H2O fluxes are modeled at the leaf, tree and stand level. In addition to total growth and yield, the model simulates the daily evolution of tracheid or vessel biomass and radius, parenchyma and branch development. From these data early and latewood biomass, wood tissue composition and density are calculated. Simulation of the labile C stored in the living tissues allows for simulation of trans-seasonal and trans-yearly effects, and improved simulations of long-term effects of environmental stresses on growth. A sensitivity analysis was performed to indicate the main parameters influencing simulated stem growth and wood quality at the tree and stand level. Case studies were performed for a temperate pine forest to illustrate the main model functioning and, more in particular, the simulation of the wood quality. The results indicate that the ANAFORE model is a useful tool for simultaneous analyses of wood quality development and forest ecosystem functioning.

We present a mechanistic model of wood tissue development in response to changes in competition, management and climate. The model is based on a refinement of the pipe theory, where the constant ratio between sapwood and leaf area (pipe theory) is replaced by a ratio between pipe conductivity and leaf area. Simulated pipe conductivity changes with age, stand density and climate in response to changes in allocation or pipe radius, or both. The central equation of the model, which calculates the ratio of carbon (C) allocated to leaves and pipes, can be parameterized to describe the contrasting stem conductivity behavior of different tree species: from constant stem conductivity (functional homeostasis hypothesis) to height-related reduction in stem conductivity with age (hydraulic limitation hypothesis). The model simulates the daily growth of pipes (vessels or tracheids), fibers and parenchyma as well as vessel size and simulates the wood density profile and the earlywood to latewood ratio from these data. Initial runs indicate the model yields realistic seasonal changes in pipe radius (decreasing pipe radius from spring to autumn) and wood density, as well as realistic differences associated with the competitive status of trees (denser wood in suppressed trees).

Recent temperature increases have elicited strong phenological shifts in temperate tree species, with subsequent effects on photosynthesis. Here, we assess the impact of advanced leaf flushing in a winter warming experiment on the current year's senescence and next year's leaf flushing dates in two common tree species: Quercus robur L. and Fagus sylvatica L. Results suggest that earlier leaf flushing translated into earlier senescence, thereby partially offsetting the lengthening of the growing season. Moreover, saplings that were warmed in winter-spring 2009-2010 still exhibited earlier leaf flushing in 2011, even though the saplings had been exposed to similar ambient conditions for almost 1 y. Interestingly, for both species similar trends were found in mature trees using a long-term series of phenological records gathered from various locations in Europe. We hypothesize that this long-term legacy effect is related to an advancement of the endormancy phase (chilling phase) in response to the earlier autumnal senescence. Given the importance of phenology in plant and ecosystem functioning, and the prediction of more frequent extremely warm winters, our observations and postulated underlying mechanisms should be tested in other species.

A greenhouse experiment was conducted to investigate whether small differences in UV-B irradiance would lead to changes in competition between cocksfoot (Dactylis glomerata L. cv. Athos) and white clover (Trifolium repens L. cv. Mervi). Plants were grown in greenhouses covered with different thicknesses of UV-transmittant plexi (3 and 5 mm) resulting in 82 % and 88 % of ambient UV-B radiation. Aboveground biomass was harvested at 4-week intervals and the vertical distribution of biomass, leaf thickness and specific leaf area were determined. Tillering, stubble and root biomass and crop height were also measured. There was only one significant effect: at 88 % of ambient UV-B radiation a larger fraction of the biomass was present in the lower layers and a smaller fraction was present in the upper layers.

The penetration of natural and artificial UV-BBE (Biologically Effective UV-B, Caldwell 1971) and PAR (400–700 nm) in a grass canopy with increasing LAI was followed during 2 months. Overall, the transmission of UV-BBE sunlight is significantly higher than of PAR sunlight. This is mainly due to the higher proportion of diffuse light in the UV-B. Under cloudy conditions no difference between UV-BBE and PAR could be found. Sun angle and intensity of the radiation were less important in determining the penetration of light. Artificial light penetrates much more through the canopy, resulting in higher irradiation levels in the lower part of the canopy, but a lower UV-BBE/PAR ratio (since UV-B transmittance of the leaves is lower). The UV-BBE/PAR ratio reaching the leaves was influenced by LAI, sun angle, percent diffuse light and leaf angle. The large differences in UV-BBE/PAR ratio per unit leaf area under natural and artificial light conditions are important in understanding the influence of UV-B on plants.

Habitat destruction is a key determinant of species extinction. Its principle components, habitat fragmentation and habitat loss, affect species persistence in landscapes, but few models have investigated how dispersal type and intraspecific competition (crowding effect) modulate this. If dispersal type or crowding changes extinction thresholds, then fragmentation and habitat loss might act differently on different species. Here we generate fragmented landscapes by varying the orthogonal neighbouring correlation between suitable and unsuitable habitat. We show that local dispersers undergo a strong negative impact of habitat fragmentation (lower occupancy of suitable sites), but only under severe habitat loss. In contrast, global dispersal eliminated fragmentation effects through random establishment. Both fragmentation and crowding effects enhanced the extinction risk of species that spread their seeds at close range, while long-range dispersers were not influenced. However, fragmentation improved the persistence of global dispersers that are highly sensitive to crowding, probably through reduced intraspecific competition. With respect to species conservation, our results suggest that random habitat destruction is a more serious threat to species persistence than clustered habitat destruction. Moreover, as species responses to habitat fragmentation depend on dispersal type and sensitivity to crowding, fragmented landscapes with different properties may accommodate different species.

Budburst phenology is a key driver of ecosystem structure and functioning, and it is sensitive to global change. Both cold winter temperatures (chilling) and spring warming (forcing) are important for budburst. Future climate warming is expected to have a contrasting effect on chilling and forcing, and subsequently to have a non-linear effect on budburst timing. To clarify the different effects of warming during chilling and forcing phases of budburst phenology in deciduous trees, (i) we conducted a temperature manipulation experiment, with separate winter and spring warming treatments on well irrigated and fertilized saplings of beech, birch and oak, and (ii) we analyzed the observations with five temperature-based budburst models (Thermal Time model, Parallel model, Sequential model, Alternating model, and Unified model). The results show that both winter warming and spring warming significantly advanced budburst date, with the combination of winter plus spring warming accelerating budburst most. As expected, all three species were more sensitive to spring warming than to winter warming. Although the different chilling requirement, the warming sensitivity was not significantly different among the studied species. Model evaluation showed that both one- and two- phase models (without and with chilling, respectively) are able to accurately predict budburst. For beech, the Sequential model reproduced budburst dates best. For oak and birch, both Sequential model and the Thermal Time model yielded good fit with the data but the latter was slightly better in case of high parameter uncertainty. However, for late-flushing species, the Sequential model is likely be the most appropriate to predict budburst data in a future warmer climate.

Phenology models are tools to analyze changes in the timing and duration of the growing season. During the past three decades different budburst models have been developed, but, so far, no consensus model has been found to accurately predict budburst date across different tree species. The aim of this study was to estimate the performance of six different temperature-driven models of leaf budburst (thermal time, thermal period fixed, sequential, parallel, alternating, unified) for four temperate tree species in Belgium (birch, chestnut, oak, beech). The models were parameterized using a Bayesian approach. The performance of these models was compared using Bayesian model comparison (BMC) and root mean square error (RMSE). Model comparison showed that the two models that do not include a calculation of chilling requirement were the best for the studied four tree species. The Sequential model (SM) was the third most plausible model for predicting budburst, having a higher probability to be correct than the other two-phase models combining a chilling phase with a forcing phase. This suggested that in our budburst observation dataset, the chilling requirement was probably always fulfilled, making the date of budburst controlled by forcing temperature. We cannot rule out that in warmer regions or future warmer conditions, chilling may become insufficient and a sequential pattern of chilling and forcing may become most appropriate to simulate budburst date. Parameter analysis showed that the last month prior to budburst had the greatest impact on determining the date of bud opening in the case of birch and chestnut, whereas the last 3 months were the main determinants for oak and beech, the two later flushing species. Validation showed that the models that fitted the parameterization data well had much poorer performance when tested with independent data. This indicates that other factors (e.g. photoperiod) might affect the budburst process and/or model parameterization (determining the sensitivity of budburst to temperature) substantially change between different localities. (C) 2012 Elsevier BM. All rights reserved.

Numerous phenology models developed to predict the budburst date of trees have been merged into one Unified model (Chuine, 2000, J. Theor. Biol. 207, 337347). In this study, we tested a simplified version of the Unified model (Unichill model) on six woody species. Budburst and temperature data were available for five sites across Belgium from 1957 to 1995. We calibrated the Unichill model using a Bayesian calibration procedure, which reduced the uncertainty of the parameter coefficients and quantified the prediction uncertainty. The model performance differed among species. For two species (chestnut and black locust), the model showed good performance when tested against independent data not used for calibration. For the four other species (beech, oak, birch, ash), the model performed poorly. Model performance improved substantially for most species when using site-specific parameter coefficients instead of across-site parameter coefficients. This suggested that budburst is influenced by local environment and/or genetic differences among populations. Chestnut, black locust and birch were found to be temperature-driven species, and we therefore analyzed the sensitivity of budburst date to forcing temperature in those three species. Model results showed that budburst advanced with increasing temperature for 13 days °C−1, which agreed with the observed trends. In synthesis, our results suggest that the Unichill model can be successfully applied to chestnut and black locust (with both across-site and site-specific calibration) and to birch (with site-specific calibration). For other species, temperature is not the only determinant of budburst and additional influencing factors will need to be included in the model.

Ground-level ozone (O3) has gained awareness as an agent of climate change. In this respect, key results are comprehended from a unique 8-year free-air O3-fumigation experiment, conducted on adult beech (Fagus sylvatica) at Kranzberg Forest (Germany). A novel canopy O3 exposure methodology was employed that allowed whole-tree assessment in situ under twice-ambient O3 levels. Elevated O3 significantly weakened the C sink strength of the treesoil system as evidenced by lowered photosynthesis and 44% reduction in whole-stem growth, but increased soil respiration. Associated effects in leaves and roots at the gene, cell and organ level varied from year to year, with drought being a crucial determinant of O3 responsiveness. Regarding adult individuals of a late-successional tree species, empirical proof is provided first time in relation to recent modelling predictions that enhanced ground-level O3 can substantially mitigate the C sequestration of forests in view of climate change. Empirical proof corroborates substantial mitigation of carbon sequestration in the treesoil system of a forest site under enhanced O3 impact for adult beech.

The purpose of this study was to expand an existing semi-mechanistic forest model, ANAFORE (ANAlysing Forest Ecosystems), to allow for the prediction of log quality and the accompanying uncertainty as influenced by climate and management. The forest stand is described as consisting of trees of different cohorts, either of the same or of different species (deciduous or coniferous). In addition to photosynthesis, transpiration, total growth and yield, the model simulates the daily evolution in vessel biomass and radius, parenchyma and branch development. From these data early and latewood biomass, wood tissue composition, knot formation and density are calculated. The new version presented here, includes the description of log quality, including red heart formation of beeches. A Bayesian optimisation routine for the species parameters was added to the stand model. From a given range of input parameters (prior), the model calculates an optimised range for the parameters (posterior) based on given output data, as well as an uncertainty on the predicted values. A case study was performed for Slovenian beech forests to illustrate the main model functioning and more in particular the simulation of the wood quality. The results indicate that the ANAFORE model is a useful tool for analyzing wood quality development and forest ecosystem functioning in response to management, climate and stand characteristics. However, the Bayesian optimization showed that the remaining uncertainty on the input parameters for the chosen stand was very large, due to the large number of input parameters in comparison to the limited stand data.

The process model SECRETS was adapted to simulate coppice growth of poplar. The effects of soil type, irrigation, nitrogen fertilization and rotation cycle on growth and yield were studied. Simulated average production on an agricultural soil was 12.4 t ha −1 year −1 . Poplar growth was strongly reduced on sandy soils ( 6 t ha −1 year −1 ). Irrigation increased yield on all soil types by 25% while fertilization increased yield by 26%. The highest yield was simulated for the combined irrigation/fertilization ( 22.45 t ha −1 year −1 ). Length of rotation cycle greatly influenced yield; optimal rotation cycle in terms of yield was 3 or 4 years. The results clearly indicate a useful role for process models in predicting effects of management strategies on poplar yield in short rotation coppice.

For the implementation of the Kyoto Protocol, governments of annex I countries need to develop strategies and policies for greenhouse gas reduction. Land use, land use change and forestry (LULUCF) offer CO2 emission reduction opportunities both home and abroad. Selection of effective forestry opportunities is a complex decision process based on multiple information concerning the greenhouse gas emission reduction potential, the environmental impacts and the cost efficiency of potential scenarios. In this paper, a decision support framework to evaluate forestry scenarios for greenhouse gas emission reduction was presented and tested on five different scenarios (existing and new multifunctional forest in Flanders, Belgium, energy crop with short rotation poplar, energy crop with annually harvested Miscanthus, forest plantation in the subtropics, and conservation of tropical rainforest). The framework is organized as a serial connection of a carbon accounting module, an environmental module and an economic module. Modules include a combination of models and quantitative assessments procedures. In order to make scenarios comparable, the environmental and economic modules calculate their outputs on a functional unit basis of 1 ton CO2 emission reduction. The framework is universally applicable, straightforward, transparent and quantitative. Data requirements are medium, but applicability is fairly complex due to the interdisciplinary character of the tool. Further developments would require automated data flows between models and a user interface. As to the results of the scenario analysis, the only attractive possibility for sinks in Flanders is the establishment of new multifunctional forests. This even yields a net benefit because it replaces the generally loss-making agriculture and, in addition, yields other environmental and recreational benefits. The establishment of bioenergy plantations is a very efficient way of reducing CO2 as far as land occupation and environmental impacts are concerned. However, it also turns out to be a very expensive option. Plantation forestry in the tropics is advantageous when evaluated over longer periods of time. Conservation of tropical forest does not come into consideration as a CDM project, but is nevertheless economically attractive for Flanders since the cost per ton CO2 emission reduction is in the neighborhood of the world market price.

There are several very accurate methods to determine leaf angles in closed canopies. However, these are generally very time-consuming or require special equipment. Average canopy leaf angles were derived from simple height and blade length measurements. An exponential relationship between the height/length ratio and the average blade leaf angle was used. The method was tested for two grass species, Dactylis glomerata and Festuca arundinacea, grown under different UV-B levels. The results clearly show that the method is reasonably accurate and able to identify UV-B induced changes in leaf angle. To get these results only 50 measurements of leaf blade height and length were necessary to calculate the allometric relationship, after which 10 length and height measurements from a canopy were used to calculate the average canopy leaf angle.

The raw material properties of wood develop as the tree grows, laying down wood cells with specific properties, and forming the stem structure that is focal for timber quality. This development is influenced by genetic and environmental factors and forest management practices. It is desirable in growth and yield models intended for the economic assessment of management practices to include some indication of wood quality and how it is affected by genetics, environmental factors and silvicultural measures. This paper reviews approaches and models that allow us to consider the development of wood quality in combination with tree growth, and thus to include wood quality in the assessment of the value of the yield. We present such models as classified into three categories based on their complexity and information needs: quality indicators, static quality models, and dynamic quality models. We illustrate three advanced dynamic quality models and their applications with example case studies. These include empirical, hybrid, and mechanistic models applied to predictions of both sawn timber and fibre properties. Finally, we consider the current challenges for wood quality modelling in connection with growth models. Las propiedades de la madera se desarrollan cuando crece el arbol, estableciendose las células de la madera con propiedades específicas, y formandose la estructura del tronco que es central para la calidad de la madera. Esta desarrollo se ve influido por factores genéticos y ambientales y por las prácticas de gestión forestal. Es deseable incluir en los modelos de crecimiento y producción destinados a la evaluación económica de las prácticas de gestión una indicación de la calidad de la madera y cómo se ve afectada por factores genéticos y ambientales y por las medidas silvícolas. En el sector forestal, la comprensión del desarrollo y la variación de la calidad de la madera es importante en distintos programas de gestión y planificación que se pueden clasificar en (1) la gestión de las operaciones de cosecha, y (2) la planificación silvícola a largo plazo. Este artículo discute los principales enfoques y modelos que nos permiten estudiar estos dos problemas en términos cuantitativos. Comenzamos la revisión discutiendo el concepto de «modelo de calidad de madera», y luego clasificamos los enfoques sobre la base de su complejidad, el principio subyacente y el uso previsto. Mostramos tres modelos dinámicos avanzados de calidad y sus aplicaciones con estudios de casos. Estos incluyen modelos empíricos, híbridos y mecánicistas aplicadaos a las predicciones tanto de la madera aserrada como a las propiedades de la fibra. Por último, consideramos los retos actuales para el modelado de la calidad de la madera en relación con la gestión forestal.