Articles | Volume 14, issue 14
https://doi.org/10.5194/bg-14-3525-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/bg-14-3525-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
26 Jul 2017
Research article |
| 26 Jul 2017
Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments
Department of Forest Resources and Environmental Conservation,
Virginia Tech, Blacksburg, VA, USA
Evan B. Brooks
Department of Forest Resources and Environmental Conservation,
Virginia Tech, Blacksburg, VA, USA
Annika L. Jersild
Department of Forest Resources and Environmental Conservation,
Virginia Tech, Blacksburg, VA, USA
Eric J. Ward
Climate Change Science Institute and Environmental Sciences Division,
Oak Ridge National Laboratory, Oak Ridge, TN, USA
Randolph H. Wynne
Department of Forest Resources and Environmental Conservation,
Virginia Tech, Blacksburg, VA, USA
Timothy J. Albaugh
Department of Forest Resources and Environmental Conservation,
Virginia Tech, Blacksburg, VA, USA
Heather Dinon-Aldridge
State Climate Office of North Carolina, North Carolina State
University, Raleigh, NC, USA
Harold E. Burkhart
Department of Forest Resources and Environmental Conservation,
Virginia Tech, Blacksburg, VA, USA
Jean-Christophe Domec
Bordeaux Sciences Agro, UMR 1391 INRA-ISPA, Gradignan CEDEX, France
Nicholas School of the Environment, Duke University, Durham, NC, USA
Thomas R. Fox
Department of Forest Resources and Environmental Conservation,
Virginia Tech, Blacksburg, VA, USA
Carlos A. Gonzalez-Benecke
Department of Forest Engineering, Resources and Management, Oregon
State University, Corvallis, OR, USA
Timothy A. Martin
School of Forest Resources and Conservation, University of Florida,
Gainesville, FL,
USA
Asko Noormets
Department of Forestry and Environmental Resources, North Carolina
State University, Raleigh, NC, USA
current address: Department of Ecosystem Science and Management, Texas A&M University, College Station, TX, USA
David A. Sampson
Decision Center for a Desert City, Arizona State University, Tempe, AZ, USA
Robert O. Teskey
Warnell School of Forestry and Natural Resources, University of
Georgia, Athens, Athens, GA, USA
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Cited
35 citations as recorded by crossref.
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- Bayesian multi-level calibration of a process-based maize phenology model M. Viswanathan et al. 10.1016/j.ecolmodel.2022.110154
- Predicting Adaptive Genetic Variation of Loblolly Pine (Pinus taeda L.) Populations Under Projected Future Climates Based on Multivariate Models M. Lu et al. 10.1093/jhered/esz065
- Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation I. Fer et al. 10.5194/bg-15-5801-2018
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- The policy and ecology of forest-based climate mitigation: challenges, needs, and opportunities C. Giebink et al. 10.1007/s11104-022-05315-6
- Improving Pinus taeda site index from rotation to rotation with silvicultural treatments T. Albaugh et al. 10.1016/j.foreco.2022.120581
- Assessing the response of forest productivity to climate extremes in Switzerland using model–data fusion V. Trotsiuk et al. 10.1111/gcb.15011
- Forest soil classification for intensive pine plantation management: “Site Productivity Optimization for Trees” system R. Cook et al. 10.1016/j.foreco.2024.121732
- A review of the growth behaviour of stands and trees in even-aged, monospecific forest P. West 10.1186/s13595-024-01250-x
- Introducing 3-PG2Py, an open-source forest growth model in Python X. Song & Y. Song 10.1016/j.envsoft.2022.105358
- Assessing Ecosystem State Space Models: Identifiability and Estimation J. Smith et al. 10.1007/s13253-023-00531-8
- Biological and market responses of pine forests in the US Southeast to carbon fertilization J. Henderson et al. 10.1016/j.ecolecon.2019.106491
- Fertilization increased leaf water use efficiency and growth of Pinus taeda subjected to five years of throughfall reduction L. Samuelson et al. 10.1139/cjfr-2017-0357
- Creating Landscape-Scale Site Index Maps for the Southeastern US Is Possible with Airborne LiDAR and Landsat Imagery R. Gopalakrishnan et al. 10.3390/f10030234
- A mid‐century ecological forecast with partitioned uncertainty predicts increases in loblolly pine forest productivity R. Thomas et al. 10.1002/eap.1761
- Macrosystems EDDIE Teaching Modules Increase Students’ Ability to Define, Interpret, and Apply Concepts in Macrosystems Ecology A. Hounshell et al. 10.3390/educsci11080382
- Simulation of climate change and thinning effects on productivity of Larix olgensis plantations in northeast China using 3-PGmix model Y. Xie et al. 10.1016/j.jenvman.2020.110249
- From prairie to crop: Spatiotemporal dynamics of surface soil organic carbon stocks over 167 years in Illinois, U.S.A. N. Li et al. 10.1016/j.scitotenv.2022.159038
- The effect of climate variability factors on potential net primary productivity uncertainty: An analysis with a stochastic spatial 3-PG model H. Restrepo et al. 10.1016/j.agrformet.2022.108812
- r3PG – Anrpackage for simulating forest growth using the 3‐PG process‐based model V. Trotsiuk et al. 10.1111/2041-210X.13474
- Parameterizing Lognormal state space models using moment matching J. Smith et al. 10.1007/s10651-023-00570-x
- Forest hydrology modeling tools for watershed management: A review G. Sun et al. 10.1016/j.foreco.2022.120755
- Modelling carbon flows from live biomass to soils using the full Carbon Accounting Model (FullCAM) D. Forrester et al. 10.1016/j.envsoft.2024.106064
- Projecting U.S. forest management, market, and carbon sequestration responses to a high-impact climate scenario J. Baker et al. 10.1016/j.forpol.2022.102898
- Targeting Extreme Events: Complementing Near-Term Ecological Forecasting With Rapid Experiments and Regional Surveys M. Redmond et al. 10.3389/fenvs.2019.00183
- Bayesian hybrid analytics for uncertainty analysis and real‐time crop management E. Meenken et al. 10.1002/agj2.20659
- Impacts of climate change on biological rotation of Larix olgensis plantations for timber production and carbon storage in northeast China using the 3-PGmix model Y. Xie et al. 10.1016/j.ecolmodel.2020.109267
- Growth and yield drivers of loblolly pine in the southeastern U.S.: A meta-analysis H. Restrepo et al. 10.1016/j.foreco.2018.12.007
- Perspective: Advancing the research agenda for improving understanding of cyanobacteria in a future of global change M. Burford et al. 10.1016/j.hal.2019.04.004
- Species interactions under climate change in mixed stands of Scots pine and pedunculate oak M. Bouwman et al. 10.1016/j.foreco.2020.118615
- Evapotranspiration and water yield of a pine‐broadleaf forest are not altered by long‐term atmospheric [CO2] enrichment under native or enhanced soil fertility E. Ward et al. 10.1111/gcb.14363
34 citations as recorded by crossref.
- Calibration of the process-based model 3-PG for major central European tree species D. Forrester et al. 10.1007/s10342-021-01370-3
- Effects of climate on the growth of Swiss uneven-aged forests: Combining >100 years of observations with the 3-PG model D. Forrester et al. 10.1016/j.foreco.2021.119271
- Bayesian multi-level calibration of a process-based maize phenology model M. Viswanathan et al. 10.1016/j.ecolmodel.2022.110154
- Predicting Adaptive Genetic Variation of Loblolly Pine (Pinus taeda L.) Populations Under Projected Future Climates Based on Multivariate Models M. Lu et al. 10.1093/jhered/esz065
- Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation I. Fer et al. 10.5194/bg-15-5801-2018
- Projecting U.S. Forest Management, Market, and Carbon Sequestration Responses to a High-Impact Climate Scenario J. Baker et al. 10.2139/ssrn.4075804
- Bayesian calibration of a carbon balance model PREBAS using data from permanent growth experiments and national forest inventory F. Minunno et al. 10.1016/j.foreco.2019.02.041
- Realized ecological forecast through an interactive Ecological Platform for Assimilating Data (EcoPAD, v1.0) into models Y. Huang et al. 10.5194/gmd-12-1119-2019
- The policy and ecology of forest-based climate mitigation: challenges, needs, and opportunities C. Giebink et al. 10.1007/s11104-022-05315-6
- Improving Pinus taeda site index from rotation to rotation with silvicultural treatments T. Albaugh et al. 10.1016/j.foreco.2022.120581
- Assessing the response of forest productivity to climate extremes in Switzerland using model–data fusion V. Trotsiuk et al. 10.1111/gcb.15011
- Forest soil classification for intensive pine plantation management: “Site Productivity Optimization for Trees” system R. Cook et al. 10.1016/j.foreco.2024.121732
- A review of the growth behaviour of stands and trees in even-aged, monospecific forest P. West 10.1186/s13595-024-01250-x
- Introducing 3-PG2Py, an open-source forest growth model in Python X. Song & Y. Song 10.1016/j.envsoft.2022.105358
- Assessing Ecosystem State Space Models: Identifiability and Estimation J. Smith et al. 10.1007/s13253-023-00531-8
- Biological and market responses of pine forests in the US Southeast to carbon fertilization J. Henderson et al. 10.1016/j.ecolecon.2019.106491
- Fertilization increased leaf water use efficiency and growth of Pinus taeda subjected to five years of throughfall reduction L. Samuelson et al. 10.1139/cjfr-2017-0357
- Creating Landscape-Scale Site Index Maps for the Southeastern US Is Possible with Airborne LiDAR and Landsat Imagery R. Gopalakrishnan et al. 10.3390/f10030234
- A mid‐century ecological forecast with partitioned uncertainty predicts increases in loblolly pine forest productivity R. Thomas et al. 10.1002/eap.1761
- Macrosystems EDDIE Teaching Modules Increase Students’ Ability to Define, Interpret, and Apply Concepts in Macrosystems Ecology A. Hounshell et al. 10.3390/educsci11080382
- Simulation of climate change and thinning effects on productivity of Larix olgensis plantations in northeast China using 3-PGmix model Y. Xie et al. 10.1016/j.jenvman.2020.110249
- From prairie to crop: Spatiotemporal dynamics of surface soil organic carbon stocks over 167 years in Illinois, U.S.A. N. Li et al. 10.1016/j.scitotenv.2022.159038
- The effect of climate variability factors on potential net primary productivity uncertainty: An analysis with a stochastic spatial 3-PG model H. Restrepo et al. 10.1016/j.agrformet.2022.108812
- r3PG – Anrpackage for simulating forest growth using the 3‐PG process‐based model V. Trotsiuk et al. 10.1111/2041-210X.13474
- Parameterizing Lognormal state space models using moment matching J. Smith et al. 10.1007/s10651-023-00570-x
- Forest hydrology modeling tools for watershed management: A review G. Sun et al. 10.1016/j.foreco.2022.120755
- Modelling carbon flows from live biomass to soils using the full Carbon Accounting Model (FullCAM) D. Forrester et al. 10.1016/j.envsoft.2024.106064
- Projecting U.S. forest management, market, and carbon sequestration responses to a high-impact climate scenario J. Baker et al. 10.1016/j.forpol.2022.102898
- Targeting Extreme Events: Complementing Near-Term Ecological Forecasting With Rapid Experiments and Regional Surveys M. Redmond et al. 10.3389/fenvs.2019.00183
- Bayesian hybrid analytics for uncertainty analysis and real‐time crop management E. Meenken et al. 10.1002/agj2.20659
- Impacts of climate change on biological rotation of Larix olgensis plantations for timber production and carbon storage in northeast China using the 3-PGmix model Y. Xie et al. 10.1016/j.ecolmodel.2020.109267
- Growth and yield drivers of loblolly pine in the southeastern U.S.: A meta-analysis H. Restrepo et al. 10.1016/j.foreco.2018.12.007
- Perspective: Advancing the research agenda for improving understanding of cyanobacteria in a future of global change M. Burford et al. 10.1016/j.hal.2019.04.004
- Species interactions under climate change in mixed stands of Scots pine and pedunculate oak M. Bouwman et al. 10.1016/j.foreco.2020.118615
Discussed (final revised paper)
Latest update: 14 Dec 2024
Short summary
To improve predictions of future forest productivity, we introduce an analytical approach that uses data from numerous research experiments that have occurred across the southeastern US to calibrate a mathematical forest model and estimate uncertainty in predictions. As a result, predictions using the model are consistent with a rich history of forest research in a region that supplies a large fraction of wood products to the world.
To improve predictions of future forest productivity, we introduce an analytical approach that...
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