Impacts and uncertainties of climate change projections on Eucalyptus plantations productivity across Brazil

Elli, Elvis Felipe; Sentelhas, Paulo César; Bender, Fabiani Denise

doi:10.1016/j.foreco.2020.118365

Cited by 58 publications

(34 citation statements)

References 52 publications

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“…Regarding climate change data, we used the information of climate change forecast with statistical models developed in Latta et al [33]. Unlike the data from Garcia-Gonzalo et al [11], Álvarez-Miranda et al [12], Garcia-Gonzalo et al [13], which originated from a process-based modeling, statistical models of forest growth under climate change are much more common (e.g., Elli et al [34]). Hence, the method developed in this study can easily be extended to other forest systems.…”

Section: Discussionmentioning

confidence: 99%

Multistage Sample Average Approximation for Harvest Scheduling under Climate Uncertainty

Bagaram

Tóth

2020

Forests

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Forest planners have traditionally used expected growth and yield coefficients to predict future merchantable timber volumes. However, because climate change affects forest growth, the typical forest planning methods using expected value of forest growth can lead to sub-optimal harvest decisions. In this paper, we propose to formulate the harvest planning with growth uncertainty due to climate change problem as a multistage stochastic optimization problem and use sample average approximation (SAA) as a tool for finding the best set of forest units that should be harvested in the first period even though we have a limited knowledge of what future climate will be. The objective of the harvest planning model is to maximize the expected value of the net present value (NPV) considering the uncertainty in forest growth and thus in revenues from timber harvest. The proposed model was tested on a small forest with 89 stands and the numerical results showed that the approach allows to have superior solutions in terms of net present value and robustness in face of different growth scenarios compared to the approach using the expected growth and yield. The SAA method requires to generate samples from the distribution of the random parameter. Our results suggested that a sampling scheme that focuses on generating high number of samples in distant future stages is favorable compared to having large sample sizes for the near future stages. Finally, we demonstrated that, depending on the level of forest growth change, ignoring this uncertainty can negatively affect forest resources sustainability.

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Section: Discussionmentioning

confidence: 99%

Multistage Sample Average Approximation for Harvest Scheduling under Climate Uncertainty

Bagaram

Tóth

2020

Forests

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“…Regarding climate change data, we used the information of climate change forecast with statistical models developed in [30]. Unlike the data from [11][12][13], which originated from a process-based modeling, statistical models of forest growth under climate change are much more common (for e.g [31]). Hence, the method developed in this study can easily be extended to other forest systems.…”

Section: Discussionmentioning

confidence: 99%

Multistage Sample Average Approximation for Harvest Scheduling under Climate Uncertainty

Bagaram¹,

Tóth²

2020

Preprint

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Forest planners have traditionally used expected growth and yield coefficients to predict future merchantable timber volumes. However, because climate change affects forest growth, the typical forest planning methods using expected value of forest growth can lead to sub-optimal harvest decisions. We proposed in this paper to formulate the harvest planning with growth uncertainty due to climate change problem as a multistage stochastic optimization problem and use sample average approximation (SAA) as a tool for finding the best set of forest units that should be harvested in the first period even though we have a limited knowledge of what future climate will be. The objective of the harvest planning model is to maximize the expected value of the net present value (NPV) considering the uncertainty in forest growth and thus in revenues from timber harvest. The proposed model was tested on a small forest with 89 stands and the numerical results showed that the approach allows to have superior solutions in terms of net present value and robustness in face of different climate scenarios compared to the approach using the expected growth and yield. The SAA methods requires to generate samples from the distribution of the random parameter. Our results suggested that a sampling scheme that focuses on generating high number of samples in distant future stages is favorable compared to having large sample sizes for the near future stages. Finally, we demonstrated that, depending on the level of forest growth change, ignoring this uncertainty can negatively affect forest resources sustainability.

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“…Eucalyptus forests are the largest cultivated and natural hardwood flora worldwide, a genus belonging to the Myrtaceae family [ 58 ] ( Figure 4 ). Eucalyptus genus includes more than 700 species distributed around the world [ 59 ].…”

Section: Eucalyptus In a General Viewmentioning

confidence: 99%

“…Eucalyptus genus includes more than 700 species distributed around the world [ 59 ]. For example, Brazil [ 58 ], Australia [ 60 ], Egypt [ 61 ], China [ 62 ], Portugal, the European Union, and Spain [ 63 ] are the countries where Eucalyptus plantations are widely distributed in their landscapes. As a general description, the Eucalyptus genus comprises perennial and evergreen plants that vary from small shrubs to tall trees [ 64 ].…”

Section: Eucalyptus In a General Viewmentioning

confidence: 99%

Toward a Better Understanding of Metal Nanoparticles, a Novel Strategy from Eucalyptus Plants

Sawalha

Abiri

Sanusi

et al. 2021

Plants

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Nanotechnology is a promising tool that has opened the doors of improvement to the quality of human’s lives through its potential in numerous technological aspects. Green chemistry of nanoscale materials (1–100 nm) is as an effective and sustainable strategy to manufacture homogeneous nanoparticles (NPs) with unique properties, thus making the synthesis of green NPs, especially metal nanoparticles (MNPs), the scientist’s core theme. Researchers have tested different organisms to manufacture MNPs and the results of experiments confirmed that plants tend to be the ideal candidate amongst all entities and are suitable to synthesize a wide variety of MNPs. Natural and cultivated Eucalyptus forests are among woody plants used for landscape beautification and as forest products. The present review has been written to reflect the efficacious role of Eucalyptus in the synthesis of MNPs. To better understand this, the route of extracting MNPs from plants, in general, and Eucalyptus, in particular, are discussed. Furthermore, the crucial factors influencing the process of MNP synthesis from Eucalyptus as well as their characterization and recent applications are highlighted. Information gathered in this review is useful to build a basis for new prospective research ideas on how to exploit this woody species in the production of MNPs. Nevertheless, there is a necessity to feed the scientific field with further investigations on wider applications of Eucalyptus-derived MNPs.

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Impacts and uncertainties of climate change projections on Eucalyptus plantations productivity across Brazil

Cited by 58 publications

References 52 publications

Multistage Sample Average Approximation for Harvest Scheduling under Climate Uncertainty

Multistage Sample Average Approximation for Harvest Scheduling under Climate Uncertainty

Multistage Sample Average Approximation for Harvest Scheduling under Climate Uncertainty

Toward a Better Understanding of Metal Nanoparticles, a Novel Strategy from Eucalyptus Plants

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