Search

Society's awareness of environmental issues increases every day. In this context, the concept of carbon footprint (CF) arises as a calculation tool that quantifies greenhouse gasses (GHG) emitted during the life cycle (LC) of a product. This calculation method is used in many productive sectors throughout the world; however, the Chilean fisheries sector has not notified the use of this tool or initiatives in that sense. This study performs a calculation of the CF of artisanal gillnet hake (Merluccius gayi gayi) fishery of the Caleta Portales, located in Valparaíso, Chile. The ISO 14040: 2006 methodology was used. The analysis was limited from the boat departure until the catch is landed, as a gate-to-gate life cycle assessment (LCA). The fuel consumption data and information related to the fleet were used as the main source of information. The Caleta Portales hake landings were 1,340.484 kg in 2011 and 703,411 kg in 2012. This fleet released into the atmosphere in 2011, 0.47 CO2 equivalent per kg of hake landed, and 0.58 kg CO2 eq, in 2012. It is the first result of CF reported in a Chilean fishery. This result can lead to an increase in the competitiveness of this hake fishery, as it can generate a positive impact on encouraging consumers to prefer the consumption from those places that have calculated the CF and are less than other food products. [ABSTRACT FROM AUTHOR]

In the present study, the performance of neuronal networks models in monthly landing forecasting of jack mackerel ( Trachurus murphyi) in central-southern Chile (32°S-42°S) was assessed. Thus, monthly estimations for 10 environmental variables, fishing effort (fe) and jack mackerel landings for the period 1973-2008 were used. A preliminary analysis was done in order to remove strongly correlated variables. Sea surface temperature ( SST) and fe are established as input variables, then, a non-linear cross correlation analysis was performed to estimate the lag between the input variables and jack mackerel landings. Two models were adjusted: model one includes both training and testing cases randomly selected using all data involved in the analysed period; for model 2, the data is divided into two time series: the first from 1973 to 2002 used for training and the second between 2003 and 2008 used for validation. The external validation process for model 1 showed an explained variance of 92%, with a standard forecasting error of 30%. The explained variance for model 2 was 81%, with a standard forecasting error of 38%. Finally, the sensitivity analysis for both models showed the fe as the most influential variable to jack mackerel landings, which presents functionality depending on anthropogenic effects rather than environmental conditions. [ABSTRACT FROM AUTHOR]