scholarly journals Using JABOWA-3 for forest growth and yield predictions under diverse forest conditions of Nova Scotia, Canada

2012 ◽  
Vol 88 (06) ◽  
pp. 708-721 ◽  
Author(s):  
M. Irfan Ashraf ◽  
Charles P.-A. Bourque ◽  
David A. MacLean ◽  
Thom Erdle ◽  
Fan-Rui Meng
Keyword(s):  
Climate Change ◽  
Nova Scotia ◽  
Model Prediction ◽  
Prediction Accuracy ◽  
Basal Area ◽  
Forest Growth ◽  
Growth And Yield ◽  
Individual Tree ◽  
Forest Conditions ◽  
Forest Growth And Yield

Empirical growth and yield models developed from historical data are commonly used in developing long-term strategic forest management plans. Use of these models rests on an assumption that there will be no future change in the tree growing environment. However, major impacts on forest growing conditions are expected to occur with climate change. As a result, there is a pressing need for tools capable of incorporating outcomes of climate change in their predictions of forest growth and yield. Process-based models have this capability and may, therefore, help to satisfy this requirement. In this paper, we evaluate the suitability of an ecological, individual-tree-based model (JABOWA-3) in generating forest growth and yield projections for diverse forest conditions across Nova Scotia, Canada. Model prediction accuracy was analyzed statistically by comparing modelled with observed basal area and merchantable volume changes for 35 permanent sample plots (PSPs) measured over periods of at least 25 years. Generally, modelled basal area and merchantable volume agreed fairly well with observed data, yielding coefficients of determination (r2) of 0.97 and 0.94 and model efficiencies (ME) of 0.96 and 0.93, respectively. A Chi-square test was performed to assess model accuracy with respect to changes in species composition. We found that 83% of species-growth trajectories based on measured basal area were adequately modelled with JABOWA-3 (P > 0.9). Model-prediction accuracy, however, was substantially reduced for those PSPs altered by some level of disturbance. In general, JABOWA-3 is much better at providing forest yield predictions, subject to the availability of suitable climatic and soil information.

Integrating biophysical controls in forest growth and yield predictions with artificial intelligence technology

2013 ◽  
Vol 43 (12) ◽  
pp. 1162-1171 ◽  
Author(s):  
M. Irfan Ashraf ◽  
Zhengyong Zhao ◽  
Charles P.-A. Bourque ◽  
David A. MacLean ◽  
Fan-Rui Meng
Keyword(s):  
Artificial Intelligence ◽  
Nova Scotia ◽  
Soil Water ◽  
Tree Growth ◽  
Forest Growth ◽  
Growth And Yield ◽  
Individual Tree ◽  
Artificial Intelligence Technology ◽  
Growth And Yield Models ◽  
Forest Growth And Yield

Growth and yield models are critically important for forest management planning. Biophysical factors such as light, temperature, soil water, and nutrient conditions are known to have major impacts on tree growth. However, it is difficult to incorporate these biophysical variables into growth and yield models due to large variation and complex nonlinear relationships between variables. In this study, artificial intelligence technology was used to develop individual-tree-based basal area (BA) and volume increment models. The models successfully account for the effects of incident solar radiation, growing degree days, and indices of soil water and nutrient availability on BA and volume increments of over 40 species at 5-year intervals. The models were developed using data from over 3000 permanent sample plots across the province of Nova Scotia, Canada. Model validation with independent field data produced model efficiencies of 0.38 and 0.60 for the predictions of BA and volume increments, respectively. The models are applicable to predict tree growth in mixed species, even- or uneven-aged forests in Nova Scotia but can easily be calibrated for other climatic and geographic regions. Artificial neural network models demonstrated better prediction accuracy than conventional regression-based approaches. Artificial intelligence techniques have considerable potential in forest growth and yield modelling.

scholarly journals How random are predictions of forest growth? The importance of weather variability

2020 ◽  
Author(s):  
Joanna Horemans ◽  
Olga Vindušková ◽  
Gaby Deckmyn
Keyword(s):  
Climate Change ◽  
Uncertainty Analysis ◽  
Climate Models ◽  
Global Climate ◽  
Basal Area ◽  
Random Order ◽  
Forest Growth ◽  
Global Climate Models ◽  
Growth And Yield ◽  
Weather Variability

Quantifying the output uncertainty and tracking down its origins is key to interpreting the results of model studies. We perform such an uncertainty analysis on the predictions of forest growth and yield under climate change. We specifically focus on the effect of the inter-annual 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, 9 climate shuffles, 3 global climate models and 2 mitigation scenarios), were made for one growing cycle (80 years) of a Scots pine 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, and 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 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%, while the likelihood of an increase >10% was 34.4%.

Predicting forest growth and yield in northeastern Ontario using the process-based model of TRIPLEX1.0

2005 ◽  
Vol 35 (9) ◽  
pp. 2268-2280 ◽  
Author(s):  
Xiaolu Zhou ◽  
Changhui Peng ◽  
Qing-Lai Dang ◽  
Jiaxin Chen ◽  
Sue Parton
Keyword(s):  
Black Spruce ◽  
Basal Area ◽  
Tree Height ◽  
Field Measurements ◽  
Forest Growth ◽  
Growth And Yield ◽  
Inventory Data ◽  
Yield Data ◽  
Yield Table ◽  
Forest Growth And Yield

Process-based carbon dynamic models are rarely validated against traditional forest growth and yield data and are difficult to use as a practical tool for forest management. To bridge the gap between empirical and process-based models, a simulation using a hybrid model of TRIPLEX1.0 was performed for the forest growth and yield of the boreal forest ecosystem in the Lake Abitibi Model Forest in northeastern Ontario. The model was tested using field measurements, forest inventory data, and the normal yield table. The model simulations of tree height and diameter at breast height (DBH) showed a good agreement with measurements for black spruce (Picea mariana (Mill.) BSP), jack pine (Pinus banksiana Lamb.), and trembling aspen (Populus tremuloides Michx.). The coefficients of determination (R2) between simulated values and permanent sample plot measurements were 0.92 for height and 0.95 for DBH. At the landscape scale, model predictions were compared with forest inventory data and the normal yield table. The R2 ranged from 0.73 to 0.89 for tree height and from 0.72 to 0.85 for DBH. The simulated basal area is consistent with the normal yield table. The R2 for basal area ranged from 0.82 to 0.96 for black spruce, jack pine, and trembling aspen for each site class. This study demonstrated the feasibility of testing the performance of the process-based carbon dynamic model using traditional forest growth and yield data and the ability of the TRIPLEX1.0 model for predicting growth and yield variables. The current work also introduces a means to test model accuracy and its prediction of forest stand variables to provide a complement to empirical growth and yield models for forest management practices, as well as for investigating climate change impacts on forest growth and yield in regions without sufficient established permanent sample plots and remote areas without suitable field measurements.

scholarly journals A Novel Modelling Approach for Predicting Forest Growth and Yield under Climate Change

PLoS ONE ◽  
2015 ◽  
Vol 10 (7) ◽  
pp. e0132066 ◽  
Author(s):  
M. Irfan Ashraf ◽  
Fan-Rui Meng ◽  
Charles P.-A. Bourque ◽  
David A. MacLean
Keyword(s):  
Climate Change ◽  
Forest Growth ◽  
Growth And Yield ◽  
Modelling Approach ◽  
Forest Growth And Yield

scholarly journals Mixed Regional Shifts in Conifer Productivity under 21st-Century Climate Projections in Canada’s Northeastern Boreal Forest

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 248
Author(s):  
Tyler Searls ◽  
James Steenberg ◽  
Xinbiao Zhu ◽  
Charles P.-A. Bourque ◽  
Fan-Rui Meng
Keyword(s):  
Climate Change ◽  
Model Validation ◽  
21St Century ◽  
Black Spruce ◽  
Abies Balsamea ◽  
Acer Rubrum ◽  
Forest Growth ◽  
Balsam Fir ◽  
Growth And Yield ◽  
Climate Projections

Models of forest growth and yield (G&Y) are a key component in long-term strategic forest management plans. Models leveraging the industry-standard “empirical” approach to G&Y are frequently underpinned by an assumption of historical consistency in climatic growing conditions. This assumption is problematic as forest managers look to obtain reliable growth predictions under the changing climate of the 21st century. Consequently, there is a pressing need for G&Y modelling approaches that can be more robustly applied under the influence of climate change. In this study we utilized an established forest gap model (JABOWA-3) to simulate G&Y between 2020 and 2100 under Representative Concentration Pathways (RCP) 2.6, 4.5, and 8.5 in the Canadian province of Newfoundland and Labrador (NL). Simulations were completed using the province’s permanent sample plot data and surface-fitted climatic datasets. Through model validation, we found simulated basal area (BA) aligned with observed BA for the major conifer species components of NL’s forests, including black spruce [Picea mariana (Mill.) Britton et al.] and balsam fir [Abies balsamea (L.) Mill]. Model validation was not as robust for the less abundant species components of NL (e.g., Acer rubrum L. 1753, Populus tremuloides Michx., and Picea glauca (Moench) Voss). Our simulations generally indicate that projected climatic changes may modestly increase black spruce and balsam fir productivity in the more northerly growing environments within NL. In contrast, we found productivity of these same species to only be maintained, and in some instances even decline, toward NL’s southerly extents. These generalizations are moderated by species, RCP, and geographic parameters. Growth modifiers were also prepared to render empirical G&Y projections more robust for use under periods of climate change.

Ontario's forest growth and yield modelling program: Advances resulting from the Forestry Research Partnership

2008 ◽  
Vol 84 (5) ◽  
pp. 694-703 ◽  
Author(s):  
Mahadev Sharma ◽  
John Parton ◽  
Murray Woods ◽  
Peter Newton ◽  
Margaret Penner ◽  
...  
Keyword(s):  
Tree Species ◽  
Stand Density ◽  
Forest Growth ◽  
Growth And Yield ◽  
Research Partnership ◽  
Wood Supply ◽  
Permanent Sample Plots ◽  
Forestry Research ◽  
Research Activities ◽  
Forest Growth And Yield

The province of Ontario holds approximately 70.2 million hectares of forests: about 17% of Canada’s and 2% of the world’s forests. Approximately 21 million hectares are managed as commercial forests, with an annual harvest in the early part of the decade approaching 200 000 ha. Yield tables developed by Walter Plonski in the 1950s provide the basis for most wood supply calculations and growth projections in Ontario. However, due to changes in legislation, policy, and the planning process, they no longer fully meet the needs of resource managers. Furthermore, Plonski`s tables are not appropriate for the range of silvicultural options now practised in Ontario. In October 1999, the Canadian Ecology Centre- Forestry Research Partnership (CEC-FRP) was formed and initiated a series of projects that collectively aimed at characterizing, quantifying and ultimately increasing the economically available wood supply. Comprehensive, defensible, and reliable forecasts of forest growth and yield were identified as key knowledge gaps. The CEC-FRP, with support from the broader science community and forest industry, initiated several new research activities to address these needs, the results of which are outlined briefly in this paper. We describe new stand level models (e.g., benchmark yield curves, FVS Ontario, stand density management diagrams) that were developed using data collected from permanent sample plots and permanent growth plots established and remeasured during the past 5 decades. Similarly, we discuss new height–diameter equations developed for 8 major commercial tree species that specifically account for stand density. As well, we introduce a CEC-FRP-supported project aimed at developing new taper equations for plantation grown jack pine and black spruce trees established at varying densities. Furthermore, we provide an overview of various projects undertaken to explore measures of site productivity. Available growth intercept and site index equations are being evaluated and new equations are being developed for major commercial tree species as needed. We illustrate how these efforts are advancing Ontario’s growth and yield program and supporting the CEC-FRP in achieving its objective of increasing the supply of fibre by 10% in 10 years while maintaining forest sustainability. Key words: permanent sample plots (PSPs), permanent growth plots (PGPs), normal yield tables, sustainable forest management, NEBIE plot network, forest inventory, Forest Vegetation Simulator

scholarly journals Stand-Level Components of a Growth and Yield Model for Nothofagus Mixed Forests from Southern Chile

Forests ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 810
Author(s):  
Sebastian Palmas ◽  
Paulo C. Moreno ◽  
Wendel P. Cropper ◽  
Alicia Ortega ◽  
Salvador A. Gezan
Keyword(s):  
Forest Type ◽  
Geographical Area ◽  
Basal Area ◽  
Forest Growth ◽  
Model Systems ◽  
Growth And Yield ◽  
Permanent Plots ◽  
Stand Age ◽  
Yield Model ◽  
Mixed Forests

Reliable information on stand dynamics and development is needed to improve management decisions on mixed forests, and essential tools for this purpose are forest growth and yield (G&Y) models. In this study, stand-level G&Y models were built for cohorts within the natural mixed second-growth Nothofagus-dominated forests in Chile. All currently available (but limited) data, consisting of a series of stratified temporary and permanent plots established in the complete range of this forest type, were used to fit and validate these models. Linear and nonlinear models were considered, where dominant stand age, number of trees, and the proportion of basal area of Nothofagus species resulted in significant predictors to project future values of stand basal area for the different cohorts (with R2 > 0.51 for the validation datasets). Mortality was successfully modeled (R2 = 0.79), based on a small set of permanent plots, using the concept of self-thinning with a proposed model defined by the idea that, as stands get closer to a maximum density, they experience higher levels of mortality. The evaluation of these models indicated that they adequately represent the current understanding of dynamics of basal area and mortality of Nothofagus and companion species in these forests. These are the first models fitted over a large geographical area that consider the dynamics of these mixed forests. It is suggested that the proposed models should constitute the main components of future implementations of G&Y model systems.

Development and Use of Bounding Functions in a Forest Growth Model

1993 ◽  
Vol 8 (1) ◽  
pp. 24-27
Author(s):  
K. Leroy Dolph ◽  
Gary E. Dixon
Keyword(s):  
Computer Simulation ◽  
Simulation Models ◽  
Height Growth ◽  
Forest Growth ◽  
Growth And Yield ◽  
Forest Growth Model ◽  
Individual Trees ◽  
Computer Simulation Models ◽  
Forest Growth And Yield

Abstract Erroneous predictions of forest growth and yield may result when computer simulation models use extrapolated data in repeated or long-term projections or if the models are used outside the range of data on which they were built. Bounding functions that limit the predicted diameter and height growth of individual trees to maximum observed values were developed to constrain these erroneous predictions in a forest growth and yield simulator. Similar techniques could be useful for dealing with extrapolated data in other types of simulation models. West. J. Appl. For. 8(1):24-27.

scholarly journals An Individual-Tree Growth and Yield Prediction System for Uneven-Aged Shortleaf Pine Stands

2000 ◽  
Vol 24 (2) ◽  
pp. 112-120 ◽  
Author(s):  
Michael M. Huebschmann ◽  
Lawrence R. Gering ◽  
Thomas B. Lynch ◽  
Onesphore Bitoki ◽  
Paul A. Murphy
Keyword(s):  
Basal Area ◽  
Growth Patterns ◽  
Growth And Yield ◽  
Prediction System ◽  
Pinus Echinata ◽  
Shortleaf Pine ◽  
Individual Tree ◽  
Basal Area Growth ◽  
Area Growth ◽  
Pine Stands

Abstract A system of equations modeling the growth and development of uneven-aged shortleaf pine (Pinus echinata Mill.) stands is described. The prediction system consists of two main components: (1) a distance-independent, individual-tree simulator containing equations that forecast ingrowth, basal-area growth, probability of survival, total and merchantable heights, and total and merchantable volumes and weights of shortleaf pine trees; and (2) stand-level equations that predict hardwood ingrowth, basal-area growth, and mortality. These equations were combined into a computer simulation program that forecasts future states of uneven-aged shortleaf pine stands. Based on comparisons of observed and predicted stand conditions in shortleaf pine permanent forest inventory plots and examination of the growth patterns of hypothetical stands, the simulator makes acceptable forecasts of stand attributes. South. J. Appl. For. 24(2):112-120.

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