User’s Guide to ADMB2R: A Set of AD Model Builder Output Routines Compatible with the R Statistics Language

ADMB2R is a collection of AD Model Builder routines for saving complex data structures into a file that can be read in the R statistics environment with a single command.1 ADMB2R provides both the means to transfer data structures significantly more complex than simple tables, and an archive mechanism to store data for future reference. We developed this software because we write and run computationally intensive numerical models in Fortran, C++, and AD Model Builder. We then analyse results with R. We desired to automate data transfer to speed diagnostics during working-group meetings. We thus developed the ADMB2R interface to write an R data object (of type list) to a plain-text file. The master list can contain any number of matrices, values, dataframes, vectors or lists, all of which can be read into R with a single call to the dget function. This allows easy transfer of structured data from compiled models to R. Having the capacity to transfer model data, metadata, and results has sharply reduced the time spent on diagnostics, and at the same time, our diagnostic capabilities have improved tremendously. The simplicity of this interface and the capabilities of R have enabled us to automate graph and table creation for formal reports. Finally, the persistent storage in files makes it easier to treat model results in analyses or meta-analyses devised months—or even years—later. We offer ADMB2R to others in the hope that they will find it useful. (PDF contains 30 pages)

User’s Guide to C2R: A Set of C Language Output Routines Compatible with the R Statistics Language

C2R is a collection of C routines for saving complex data structures into a file that can be read in the R statistics environment with a single command.1 C2R provides both the means to transfer data structures significantly more complex than simple tables, and an archive mechanism to store data for future reference. We developed this software because we write and run computationally intensive numerical models in Fortran, C++, and AD Model Builder. We then analyse results with R. We desired to automate data transfer to speed diagnostics during working-group meetings. We thus developed the C2R interface to write an R data object (of type list) to a plain-text file. The master list can contain any number of matrices, values, dataframes, vectors or lists, all of which can be read into R with a single call to the dget function. This allows easy transfer of structured data from compiled models to R. Having the capacity to transfer model data, metadata, and results has sharply reduced the time spent on diagnostics, and at the same time, our diagnostic capabilities have improved tremendously. The simplicity of this interface and the capabilities of R have enabled us to automate graph and table creation for formal reports. Finally, the persistent storage in files makes it easier to treat model results in analyses or meta-analyses devised months—or even years—later. We offer C2R to others in the hope that they will find it useful. (PDF contains 27 pages)

User’s Guide to For2R: A Module of Fortran 95 Output Routines Compatible with the R Statistics Language

For2R is a collection of Fortran routines for saving complex data structures into a file that can be read in the R statistics environment with a single command.1 For2R provides both the means to transfer data structures significantly more complex than simple tables, and an archive mechanism to store data for future reference. We developed this software because we write and run computationally intensive numerical models in Fortran, C++, and AD Model Builder. We then analyse results with R. We desired to automate data transfer to speed diagnostics during working-group meetings. We thus developed the For2R interface to write an R data object (of type list) to a plain-text file. The master list can contain any number of matrices, values, dataframes, vectors or lists, all of which can be read into R with a single call to the dget function. This allows easy transfer of structured data from compiled models to R. Having the capacity to transfer model data, metadata, and results has sharply reduced the time spent on diagnostics, and at the same time, our diagnostic capabilities have improved tremendously. The simplicity of this interface and the capabilities of R have enabled us to automate graph and table creation for formal reports. Finally, the persistent storage in files makes it easier to treat model results in analyses or meta-analyses devised months—or even years—later. We offer For2R to others in the hope that they will find it useful. (PDF contains 31 pages)

The Vietnamese shrimp trade: livelihoods analysis of stakeholders and market chain analysis

Aquaculture and capture fisheries in Vietnam have been increasing fast in the last decade, especially aquaculture growth rate is 12% for the 1999 – 2003 period, contributing a significant part into the hunger eradication and poverty reduction1. Vietnam is to be ranked into one of the countries potential to produce the aquatic economic in the world, and the fact is that, after 40 years of establishing, the fisheries sector has made remarkable contributions to the country. By the list, at the moment the aquatic products make up about 4 - 5% of GDP and create job opportunities for over 3 three million employees (VASEP, 2004), in which the largest contribution is from shrimp farming. [PDF contains 124 pages.]

Results of bottom trawl surveys carried out in Vietnamese waters (20-200 m) in 1996-1997

Bottom trawl surveys were conducted in the southwest monsoon season in 1996 (survey 1) and in the northeast monsoon season in 1996-97 (survey 2) throughout Vietnamese waters. The surveys mainly covered the depth zone 50-200 m but in the northeast monsoon season the depth zone 20-50 m was included in the northern and southern areas. Overall, 273 trawl hauls were conducted. The total biomass for Vietnamese waters in the depth zone 20-200 m was estimated at 700 000 t . Biomass estimates are given for the most abundant species. A relatively higher mean catch-per-unit effort (CPUE) was obtained from survey 2 than from survey 1 and in partcular at depth ranges 50-100 and 100-200 m in south Vietnam. Overall, the dominant families were Monacanthidae (34%), Carangidae (15%), Trichiuridae (9%) and Synodontidae (6%).