Browsing by Author "Jones, Dennis"
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- Life Cycle Assessment of Maritime Pine Wood: A Portuguese Case StudyPublication . Ferreira, José; Jones, Dennis; Esteves, Bruno; Cruz-Lopes, Luisa; Pereira, Helena; Domingos, IdalinaLife Cycle Assessment has become one of the most recognized and internationally accepted method for examining the environmental performance of forest products and processes. The main aim of this study was to evaluate the potential environmental impact associated with different commercial outputs of maritime pine wood (round, industrial, and residual) produced in the Portuguese forest under natural regeneration. Identifying the hotspots in the life cycle (cradle-to-gate) of each sort of maritime pine was another objective of the study. SimaPro software was used for this study, whilst the CML-IA (baseline) method was chosen to assess the environmental impacts. The study showed that round wood presented the highest values in all impact categories and industrial wood presented the lowest values except in photochemical oxidation where residual wood was the best co-product when economic allocation is chosen. The major hot spots appeared to be the felling and hauling processes due to fossil fuel combustion in the chainsaw and forwarder, respectively. The co-product that should be more environmentally friendly considerably depends on the allocation procedure chosen.
- Proof-of-Principle That Cellular Automata Can Be Used to Predict Infestation Risk by Reticulitermes grassei (Blattodea: Isoptera)Publication . Sequeira, João G. N.; Nobre, Tânia; Duarte, Sónia; Jones, Dennis; Esteves, Bruno; Nunes, LinaOver the past few decades, species distribution modelling has been increasingly used to monitor invasive species. Studies herein propose to use Cellular Automata (CA), not only to model the distribution of a potentially invasive species but also to infer the potential of the method in risk prediction of Reticulitermes grassei infestation. The test area was mainland Portugal, for which an available presence-only dataset was used. This is a typical dataset type, resulting from either distribution studies or infestation reports. Subterranean termite urban distributions in Portugal from 1970 to 2001 were simulated, and the results were compared with known records from both 2001 (the publication date of the distribution models for R. grassei in Portugal) and 2020. The reported model was able to predict the widespread presence of R. grassei, showing its potential as a viable prediction tool for R. grassei infestation risk in wooden structures, providing the collection of appropriate variables. Such a robust simulation tool can prove to be highly valuable in the decisionmaking process concerning pest management.