Maritime Pine Extract

Keyword(s):  
2021 ◽  
Vol 11 (4) ◽  
pp. 1748
Author(s):  
Giovanna Concu

Timber buildings are experiencing a rapid diffusion due to their good performance and their sustainability; however, some steps of structural timber production process, such as drying, are energy-intensive and environmentally impactful, and many wood species are also affected by low yield. Therefore, it would be important to determine the quality of the green material, that is, in wet condition, before undergoing the most impactful and expensive production steps. This paper describes a research aimed at quantifying the variation of the dynamic modulus of elasticity MoEdyn, which is commonly used for structural timber mechanical grading, from wet to dry condition in Sardinian maritime pine boards to be used for the production of laminated timber, and to examine the relationship between wet and dry MoEdyn. The MoEdyn was determined from measurements of the velocity of sonic waves propagating through the boards. The results show that the dry MoEdyn can be estimated starting from boards sonic testing in the wet condition, so providing a basis for implementing Sardinian maritime pine pre-grading in order to obtain the reduction of manufacturing costs, the abatement of environmental impact, and the increase of structural grade yield.


2021 ◽  
Vol 168 ◽  
pp. 113581
Author(s):  
J. Santos ◽  
J. Pereira ◽  
N. Ferreira ◽  
N. Paiva ◽  
J. Ferra ◽  
...  

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 368
Author(s):  
Cristina Alegria ◽  
Natália Roque ◽  
Teresa Albuquerque ◽  
Paulo Fernandez ◽  
Maria Margarida Ribeiro

Research Highlights: Modelling species’ distribution and productivity is key to support integrated landscape planning, species’ afforestation, and sustainable forest management. Background and Objectives: Maritime pine (Pinus pinaster Aiton) forests in Portugal were lately affected by wildfires and measures to overcome this situation are needed. The aims of this study were: (1) to model species’ spatial distribution and productivity using a machine learning (ML) regression approach to produce current species’ distribution and productivity maps; (2) to model the species’ spatial productivity using a stochastic sequential simulation approach to produce the species’ current productivity map; (3) to produce the species’ potential distribution map, by using a ML classification approach to define species’ ecological envelope thresholds; and (4) to identify present and future key factors for the species’ afforestation and management. Materials and Methods: Spatial land cover/land use data, inventory, and environmental data (climate, topography, and soil) were used in a coupled ML regression and stochastic sequential simulation approaches to model species’ current and potential distributions and productivity. Results: Maritime pine spatial distribution modelling by the ML approach provided 69% fitting efficiency, while species productivity modelling achieved only 43%. The species’ potential area covered 60% of the country’s area, where 78% of the species’ forest inventory plots (1995) were found. The change in the Maritime pine stands’ age structure observed in the last decades is causing the species’ recovery by natural regeneration to be at risk. Conclusions: The maps produced allow for best site identification for species afforestation, wood production regulation support, landscape planning considering species’ diversity, and fire hazard mitigation. These maps were obtained by modelling using environmental covariates, such as climate attributes, so their projection in future climate change scenarios can be performed.


2004 ◽  
Vol 76 (2) ◽  
pp. 121-130 ◽  
Author(s):  
Célia Miguel ◽  
Sónia Gonçalves ◽  
Susana Tereso ◽  
Liliana Marum ◽  
João Maroco ◽  
...  

2011 ◽  
Vol 70 (1-3) ◽  
pp. 107-111 ◽  
Author(s):  
C. Pereira ◽  
F. Caldeira ◽  
José M. F. Ferreira ◽  
M. A. Irle
Keyword(s):  

2015 ◽  
Vol 24 (11) ◽  
pp. 1302-1313 ◽  
Author(s):  
M. J. Serra-Varela ◽  
D. Grivet ◽  
L. Vincenot ◽  
O. Broennimann ◽  
J. Gonzalo-Jiménez ◽  
...  

2017 ◽  
Vol 405 ◽  
pp. 219-228 ◽  
Author(s):  
José Riofrío ◽  
Miren del Río ◽  
Hans Pretzsch ◽  
Felipe Bravo

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