10 resultados para module choice

em Aquatic Commons


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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)

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Both chemical and biological methods are used to assess the water quality of rivers. Many standard physical and chemical methods are now established, but biological procedures of comparable accuracy and versatility are still lacking. This is unfortunate because the biological assessment of water quality has several advantages over physical and chemical analyses. Several groups of organisms have been used to assess water quality in rivers and these include Bacteria, Protozoa, Algae, macrophytes, macroinvertebrates and fish. Hellawell (1978) provides an excellent review of the advantages and disadvantages of these groups, and concludes that macroinvertebrates are the most useful for monitoring water quality. Although macroinvertebrates are relatively easy to sample in shallow water (depth < 1m), quantitative sampling poses more problems than qualitative sampling because a large number of replicate sampling units are usually required for accurate estimates of numbers or biomass per unit area. Both qualitative and quantitative sampling are difficult in deep water (depth > 1m). The present paper first considers different types of samplers with emphasis on immediate samplers, and then discusses some problems in choosing a suitable sampler for benthic macroinvertebrates in deep rivers.

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While an overwhelming majority of sub-Saharan African countries exhibit serious weaknesses in statistics pertaining to crop and livestock sectors, the deficiencies in terms of nationally representative data on the fishery sector are even more acute. The very little data available on the sector are essentially derived from case studies of selected fisheries, and the limited nationally representative data available are generally derived from a few questions included in the livestock section of household surveys. These do not permit the detailed characterization of the fishery production systems. As a consequence in many countries the decision-makers and planners lack the most basic information about the role and importance of the fisheries sector to their national economy. As part of an initiative called the Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) project, a collaboration was developed between the World Bank and the WorldFish Center to address this situation. This report provides detail on pilot testing of a fisheries module for living standards measurement surveys.

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Based upon a global comparison of over 400 fisheries, the Principal Components Analysis (PCA) methodology was used to identify factors affecting the choice of growth estimation methods. Of the six factors examined, the growth rate (K) and asymptotic length (L8) explained most of the variations. Financial resources, i.e., Gross National Product (GNP), and latitude were also important factors.

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This study was carried out to identify factors that influence choice of fishing location and carry out profitability analysis of Chilimira and Gillnet in different fishing locations. A survey using semi-structured questionnaire was administered to 99 Gillnet and 101 Chilimira fishers in Nankumba Peninsula in Mangochi District. The logit model was used to determine the factors influencing choice of fishing location among the fishers. The study showed that 92.1% of Chilimira fishers are operating in offshore areas while 69.7% Gillnet fishers are operating in inshore areas. Chilimira offshore fishers have higher daily average gross margins than their inshore counterparts and Gillnet fishers. However, they incurred more operating costs than the inshore Chilimira and Gillnet fishers. Furthermore, they find their fishing occupation more rewarding as evidenced by the higher returns to labour. The factors that influenced fisher’s choice of fishing location were Age of the fisher, type of fishing vessel and gear, possession of motor sail engine and access to information about previous day’s catch rates. Finally the study concluded that artisanal fishers in Malawi use different criteria in deciding where to fish. The criterion involves a complex interaction of biological, technological, personal and economical factors and time. However, the resource constrained artisanal fisher will need support to enable him exploit offshore fishery resources. Consequently the study recommends that appropriate fishery development interventions by the government and other stakeholders must adapt to the economics and lifestyles driving the artisanal fishers to fish in particular locations and therefore, build on this foundation to improve the existing fishing technologies.

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An account of fishing vessel construction materials is given, with information on essential features, and a material account. Materials discussed in detail are steel, wood, aluminium, glass reinforced plastic, and ferro-cement.