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)

Water quality monitoring in the Mersey

This is the Water Quality Monitoring in the Mersey Estuary report produced by the Environment Agency in 2001. This report focuses on the Water Quality Monitoring Programme held in the Mersey Estuary. Since the mid-1960s water samples have been collected at approximately monthly intervals along the length of the estuary between Warrington and New Brighton and in later years further off-shore. This data-set provides an invaluable resource to determine how the very large capital spending of recent years has resulted in the dramatic improvements in water quality that we are now able to record. Initially, the interest was focused on parameters such as dissolved oxygen, BOD, nutrients and suspended solids. Over the last decades, as analytical methods have improved, toxic metals and persistent organic compounds have been included in the routine monitoring programme at a limited number of sites. Moreover, with the introduction of the European Water Framework Directive monitoring programmes it was an opportune time to review the Mersey monitoring strategy. This revised monitoring programme required data from several other components (water, sediments, flora, fauna, fish and birds. This report also contains information about Routine monthly surveys, Special surveys, Chloralkali Directive, UKNMP, British Geological Survey, EDMAR and NERC Environmental Diagnostics Thematic Programme.

Making agri-food systems work for the rural poor in eastern and southern Africa

The purpose of this output was to use the results of the baseline and participatory diagnostics analysis to develop alternative innovations for agricultural production, natural resource management and food security. The farming systems in the project areas were analysed to identify the innovations that communities had been using for agricultural production, natural resource management and food security. The innovative strategies were examined for their contribution to sustainable agriculture, food security and natural resource management. Comparative analysis of the agricultural productivity, food security and natural resource management in the different areas where the innovations have been put in place was undertaken. The best practices would be identified, which should be scaled-up, modified or sustained. The willingness and perceptions of the farmers to adopt the innovations would then be assessed.