Cabinteely, Ireland
Cabinteely, Ireland

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McCullagh A.,University College Dublin | Black K.,University College Dublin | Black K.,FERS Ltd | Nieuwenhuis M.,University College Dublin
European Journal of Forest Research | Year: 2017

Two models, Carbware (CW) and Growfor (GF), of different resolution and based on different frameworks were evaluated in relation to stand-level forecasts of volume and basal area using Ireland’s National Forest Inventory (NFI) data. CW is a distance-independent single-tree model that is based on diameter increment. GF is a stand-level dynamic empirical model that uses the von Bertalanffy–Richards growth equation in a state-space framework. NFI data were used as input to the models, and each model’s projections were compared to NFI data at the next measurement cycle. The NFI is a permanent sampling system with the objective to assess the composition and extent of the forest estate. A subset of the NFI was used in the study, single-species even-aged plots comprising Sitka spruce and lodgepole pine. The accuracy and performance of the CW and GF models were analysed using residual analysis and standard statistical techniques. Results show that both models require improvement, though the study has raised concerns regarding the suitability of the NFI data for this type of investigation. © 2017 Springer-Verlag Berlin Heidelberg

Groen T.A.,University of Twente | Verkerk P.J.,European forest Institute | Bottcher H.,International Institute For Applied Systems Analysis | Grassi G.,European Commission - Joint Research Center Ispra | And 9 more authors.
Environmental Science and Policy | Year: 2013

Under the United Nations Framework Convention for Climate Change all Parties have to report on carbon emissions and removals from the forestry sector. Each Party can use its own approach and country specific data for this. Independently, large-scale models exist (e.g. EFISCEN and G4M as used in this study) that assess emissions and removals from this sector by applying a unified approach to each country, still often based on country specific data.Differences exist between the national reported values and the calculations from the large scale models. This study compares these models with national reporting efforts for 24. EU countries for the period 2000-2008, and identifies the most likely causes for differences. There are no directly identifiable single input parameters that could be targeted to fully close the gap between country and model estimates. We found that the method applied by the country (i.e. stock-difference or gain-loss) contributes significantly to differences for EFISCEN and was the best explaining variable for G4M, although for the latter it was not significant. Other variables (biomass expansion factors, harvest volumes and the way harvest losses are treated) were not found to provide a conclusive explanation for the differences between the model estimations and the country submissions in an over-all analysis. However, at the level of individual countries several different causes for differences were identified. This suggests that to really close the gap between country submissions and large scale models, close collaboration between modellers and country experts is needed, calling for openness and willingness to share relevant data and to compare GHG inventories with independent estimates. This would enable to improve the confidence both in historical GHG inventories and in the models which are needed to project the future forest sink for several policy issues. © 2013 Elsevier Ltd.

Donlan J.,University of Limerick | Black K.,FERS Ltd | Hendrick E.,Kildare Street | O'Driscoll E.,Drima Marketing | Byrne K.A.,University of Limerick
Carbon Management | Year: 2013

Background: The Kyoto Protocol allows carbon in forests to contribute to emission-reduction targets. This has been extended to include carbon in harvested wood products (HWP). A projected baseline approach is proposed to identify business as usual and incentivize activities that reduce emissions or increase removals relative to reference level. In this article, we describe methods to calculate the projected reference level. Results: Wood product categories were derived using a top-down approach based on timber harvest data and sawmill statistics. The average of the overall projected forest management reference level for 2013-2020 was -206.81 Gg CO2-e including HWP and -72.72 Gg CO2-e excluding carbon stock change in HWP. Conclusion: These results highlight the potential of HWP to contribute to GHG mitigation strategies. © 2013 Future Science Ltd.

Black K.,FERS Ltd. | Black K.,University of Ulster | Creamer R.E.,Teagasc | Xenakis G.,University of Edinburgh | Cook S.,University of Ulster
Geoderma | Year: 2014

Forest soils store large amounts of carbon (C), and stock changes in this C pool may significantly increase the CO2 concentration in the atmosphere. However, estimation of soil organic carbon (SOC) stocks and stock changes following land use transition to forestry is subject to large uncertainty. Many currently used geochemical modelling approaches, such as YASSO, are used to estimate regional changes in forest SOC stocks, but these are difficult to calibrate to reflect regional conditions because of limited availability of sufficient SOC data. In addition, most model frameworks give little consideration regarding the appropriate use of geospatial climatic and topographical data, as dependent variables in the model. As a result, many regional models may exhibit spatial autocorrelation (SAC) of residuals, which contributes to overall model error. In this paper, we develop a method for assessing SOC stock changes in Irish forests by compiling a spatial SOC database and using these data to calibrate and improve on an existing YASSO model. Careful consideration was given to the use of available climatic and digital elevation GIS data in YASSO with the aim of reducing SAC of model residuals and to more precisely predict soil- and site-specific variations in SOC stock changes following transition to forestry.Analysis of the complied national SOC database shows that stock changes in afforested mineral soils may increase or decrease depending on previous land use and soil type. During refinement of the YASSO model, conventional statistical approaches confirmed that model performance can be improved by using climatic GIS data at the appropriate scale (resolution), together with additional use of novel topographical spatial data. The current YASSO model does not use these topographical factors as dependent variables, nor is there any consideration given to the spatial or temporal resolution of GIS datasets used. Use of GIS geo-statistical approaches to determine if SAC was reduced, as the YASSO model accuracy was improved on, produced conflicting results. We suggest that the use of Anselin Local Moran's I outlier analysis may not be suitable for this purpose because it may falsely detect spatial outliers due to the presence of neighbouring points with very high or low residual values. In contrast, semi-variogram analysis appeared to be the most useful geo-statistical measure of the spatial dependency, distribution and scale at which residual SAC occurs. Use of fine resolution (50. m) slope and topographical position index (TPI) raster datasets to predict forest SOC stocks significantly improved the final YASSO model accuracy and precision. In addition, semi-variogram analysis confirmed that the final YASSO model residuals exhibited no spatial dependency and residual error was uniformly distributed over the entire sample area, from which the SOC database was derived. However, the final YASSO model we describe requires considerable refinement using more intensive sampling studies and independent validation before it can be applied at a national level. In the future, particular emphasis should be directed to sampling forest brown earth soils, which are suggested to result in a net emission of C following transition from grassland to forest land. © 2014 Elsevier B.V.

Saunders M.,University College Dublin | Tobin B.,University College Dublin | Black K.,University College Dublin | Black K.,FERS Ltd. | And 3 more authors.
Agricultural and Forest Meteorology | Year: 2012

Commercial forest plantations need to be actively managed, through tree removal, in order to improve wood quality, maintain productivity and provide an economic return, although this could compromise an important role for forests in carbon sequestration and greenhouse gas mitigation. The impact of forest thinning on net primary productivity (NPP) and net ecosystem exchange (NEE) was assessed using a combination of biometric and eddy covariance (EC) techniques. Two thinning operations were performed in close succession, which reduced the basal area of the stand by 17% and 11% and removed a timber volume of 48m 3ha -1 and 50m 3ha -1, respectively. Annual rates of NPP ranged from 13.24 (±3.96) to 18.94 (±4.88)tCha -1 and 13.22 (±3.72) to 17.77 (±5.30)tCha -1 for the pre- and post-thinning periods, respectively. Estimates of NEE varied between 8.44 (±1.34) to 8.87 (±1.48)tCha -1 and 6.75 (±1.19) to 10.33 (±1.41)tCha -1 in the pre- and post-thinning periods. Forest thinning did not have a significant impact on carbon stocks or fluxes when pre-thinning (2002-2006) and post-thinning (2007-2009) estimates of NPP and NEE were compared, however the range of inter-annual variability in NEE increased after thinning. The partitioning of annual NEE carbon budgets into gross primary productivity (GPP) and ecosystem respiration (R eco) together with an analysis of key physiological parameters suggested that the impacts of forest thinning are largely dependent on temperature. An expected decrease in GPP after the initial thinning in 2007 was not observed due, in part, to the higher mean annual air temperatures and incident photosynthetic active radiation (PAR) and a compensatory increase in photosynthesis by the remaining trees. A continual decline in R eco, was observed in the years subsequent to the first thinning and was attributed to both biomass removal and climatic factors.Inter-annual variations in climate had a significant impact on NEE, GPP and R eco. Annual mean air temperature, total precipitation and total incident PAR were all shown to influence the processes driving CO 2 exchange. Overall, these results suggest that the impacts of the thinning practices, as implemented in this study, are dependent on climate and under similar conditions are unlikely, in the short-term, to compromise a role for forest ecosystems in carbon sequestration and greenhouse gas mitigation. © 2012 Elsevier B.V..

Tene A.,University College Dublin | Tene A.,Japan Forestry and Forest Products Research Institute | Tobin B.,University College Dublin | Dyckmans J.,University of Gottingen | And 4 more authors.
Tree Physiology | Year: 2011

A thinning experiment stand at Avoca, Ballinvalley, on the east coast of the Republic of Ireland was used to test a developed methodology aimed at monitoring drought stress, based on the analysis of growth rings obtained by coring. The stand incorporated six plots representing three thinning regimes (light, moderate and heavy) and was planted in the spring of 1943 on a brown earth soil. Radial growth (early- and latewood) was measured for the purpose of this study. A multidisciplinary approach was used to assess historic tree response to climate: specifically, the application of statistical tools such as principal component and canonical correlation analysis to dendrochronology, stable isotopes, ring density proxy, blue reflectance and forest biometrics. Results showed that radial growth was a good proxy for monitoring changes to moisture deficit, while maximum density and blue reflectance were appropriate for assessing changes in accumulated temperature for the growing season. Rainfall also influenced radial growth changes but not significantly, and was a major factor in stable carbon and oxygen discrimination, mostly in the latewood formation phase. Stable oxygen isotope analysis was more accurate than radial growth analysis in drought detection, as it helped detect drought signals in both early- and latewood while radial growth analysis only detected the drought signal in earlywood. Many studies have shown that tree rings provide vital information for marking past climatic events. This work provides a methodology to better identify and understand how commonly measured tree proxies relate to environmental parameters, and can best be used to characterize and pinpoint drought events (variously described using parameters such as like moisture deficit, accumulated temperature, rainfall and potential evaporation). © The Author 2011. Published by Oxford University Press. All rights reserved.

Black K.,FERS Ltd | Black K.,University College Dublin
Forestry | Year: 2016

There is an increasing resource assessment and management requirement for single-tree-based stand models due to a shift towards mixed-species and 'back to nature' forestry. This paper describes the validation and calibration of the CARBWARE stand simulator, a distance-independent single-tree growth modelling framework specifically modified to include spatially explicit climatic and soil factors. Tree growth functional analysis suggests that stand competition factors contribute to most (21-33 per cent) of the observed variation in diameter increment, followed by tree size factors (17-20 per cent). Edaphic and other site factors, particularly soil nutrition and moisture status, explained 4-11 per cent of the observed site-to-site variation. The developed empirical relationships may provide a deterministic framework for improving developed ecological site classification systems. Independent validation showed that the CARBWARE simulator provides a robust and un-bias estimate of single-tree and stand-based variables for both pure and intimate mixed stands. However, refinements to the simulator are still required, particularly improvement to climatic and genetic response factors, mortality functions for mixed-species stands and inclusion of regeneration/recruitment models. © 2015 Institute of Chartered Foresters, 2015. All rights reserved. For Permissions, please e-mail:

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