16 resultados para Gross output

em Aquatic Commons


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In 1948, the U.S.S.R. began a global campaign of illegal whaling that lasted for three decades and, together with the poorly managed “legal” whaling of other nations, seriously depleted whale populations. Although the general story of this whaling has been told and the catch record largely corrected for the Southern Hemisphere, major gaps remain in the North Pacific. Furthermore, little attention has been paid to the details of this system or its economic context. Using interviews with former Soviet whalers and biologists as well as previously unavailable reports and other material in Russian, our objective is to describe how the Soviet whaling industry was structured and how it worked, from the largest scale of state industrial planning down to the daily details of the ways in which whales were caught and processed, and how data sent to the Bureau of International Whaling Statistics were falsified. Soviet whaling began with the factory ship Aleut in 1933, but by 1963 the industry had a truly global reach, with seven factory fleets (some very large). Catches were driven by a state planning system that set annual production targets. The system gave bonuses and honors only when these were met or exceeded, and it frequently increased the following year’s targets to match the previous year’s production; scientific estimates of the sustainability of the resource were largely ignored. Inevitably, this system led to whale populations being rapidly reduced. Furthermore, productivity was measured in gross output (weights of whales caught), regardless of whether carcasses were sound or rotten, or whether much of the animal was unutilized. Whaling fleets employed numerous people, including women (in one case as the captain of a catcher boat). Because of relatively high salaries and the potential for bonuses, positions in the whaling industry were much sought-after. Catching and processing of whales was highly mechanized and became increasingly efficient as the industry gained more experience. In a single day, the largest factory ships could process up to 200 small sperm whales, Physeter macrocephalus; 100 humpback whales, Megaptera novaeangliae; or 30–35 pygmy blue whales, Balaenoptera musculus brevicauda. However, processing of many animals involved nothing more than stripping the carcass of blubber and then discarding the rest. Until 1952, the main product was whale oil; only later was baleen whale meat regularly utilized. Falsified data on catches were routinely submitted to the Bureau of International Whaling Statistics, but the true catch and biological data were preserved for research and administrative purposes. National inspectors were present at most times, but, with occasional exceptions, they worked primarily to assist fulfillment of plan targets and routinely ignored the illegal nature of many catches. In all, during 40 years of whaling in the Antarctic, the U.S.S.R. reported 185,778 whales taken but at least 338,336 were actually killed. Data for the North Pacific are currently incomplete, but from provisional data we estimate that at least 30,000 whales were killed illegally in this ocean. Overall, we judge that, worldwide, the U.S.S.R. killed approximately 180,000 whales illegally and caused a number of population crashes. Finally, we note that Soviet illegal catches continued after 1972 despite the presence of international observers on factory fleets.

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An attempt was made to study the input-output relationships and economics of pangas monoculture and carp-pangas polyculture in Bangladesh. By analyzing the data collected from 50 pangas farms and 55 carp-pangas farms, the study has investigated the production systems of two technologies and the effects of fingerling stocking and applications of feed and fertilizer on fisheries income. The data were collected from the fishermen of Trishal and Bhaluka of Mymensingh district, and Kahaloo and Adamdighee of Bogra district during 2001-02. For pangas monoculture, the stocking density was 31,561 per ha while it was 55,017 per ha in carp-pangas polyculture. Most of the farmers used urea, TSP and lime before stocking. Rice and wheat bran happened to be the most common feed ingredients for both types of culture in general. Other important ingredients used were mustard oil-cakes, rice polish, wheat flour, fish meal, bone meal, soybean meal and poultry litter. In terms of quantities, rice bran and wheat bran dominated the farmers list. Rice and wheat bran together constituted about 60% of all studied feeds. Feed cost constituted 59.13% of total costs for pangas monoculture and 67.44% for carp-pangas polyculture. Per ha productions of pangas and carp-pangas in a single culture cycle were 15,508 kg and 19,745 kg, respectively. Per ha gross profits were estimated to be Tk 310,311 and Tk 464,418 for pangas monoculture and carp-pangas polyculture, respectively. Net profit appeared to be Tk 264,216 per ha for pangas monoculture and Tk 416,509 per ha for carp-pangas polyculture. The BCRs calculated were 1.46 and 1.68 for monoculture and polyculture, respectively. The break-even costs per kg of fish were estimated at Tk 36.93 for pangas and Tk 30.93 for mixed species which was much lower than the prices the producers received. Break-even productions were estimated at 10,702 kg per ha for pangas monoculture and 11,784 kg per ha for carp-pangas polyculture. Fingerling and feed cost, and pond size significantly explained the variation of income from pangas monoculture. These factors have significantly influenced the income from the crop. Functional analysis shows that 1% increase in the feed cost might increase 0.51% of pangas income and 0.41% in carp-pangas income. No other inputs had shown this much of responses to increasing income from a fish.

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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)

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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)

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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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Polydora nuchalis Woodwick, 1953 (Polychaeta: Spionidae) is a protandric hermaphrodite commonly inhabiting intertidal mud flats in southern California. The species exhibits lecithotrophic larval development and adelphophagia. Reproduction of P. nuchalis was monitored for a year at four sites: Catalina Harbor, San Gabriel River, Huntington Harbour, and Malibu Lagoon. Females deposited from 11 to 31 egg capsules in their tubes, with up to 230 eggs per capsule. An average of 3% of the eggs developed into larvae: the remaining were nurse eggs serving as food for the developing larvae. Reproductive output was quantified by determining the number and size of larvae and nurse eggs for individual capsules. Significant differences among the four populations were found for all the quantified variables. In addition, two size classes of nurse eggs were found to exist in capsules from all of the sites. Egg capsules were found throughout the year at San Gabriel River, but none were found during the winter months at the remaining three sites. Size/frequency data for juveniles and adults of the Catalina Harbor population indicate an annual cycle of recruitment. The laboratory experiment consisted of a 3 x 3 x 2 £actor1al design with replication testing the effects of temperature, salinity, and food supply on growth and reproduction of P. nuchalis. Increasing temperature resulted in significantly increased survivorship, growth rates, and percentage reproduction. It also produced a significant decrease in the size of the nurse eggs and the volume of food per larva. The number of egg capsules was maximum at the intermediate temperature. Increasing the salinity resulted in significant increases in survivorship and Class I nurse egg size. Increaaing food availability produced a significant increase in the percentage of worms reproducing. The interactive effect of salinity and £ood level produced significant changes in the number of larvae per capsule and the number of nurse eggs per capsule. However, the number of nurse eggs per larva did not differ significantly among the experimental treatment groups. (PDF contains 129 pages)

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Histopathologic studies of lesions found in commercially important North Atlantic marine fishes are uncommon. As part of a comprehensive Northeast Fisheries Center program ("Ocean Pulse") to evaluate environmental and resource health on the U.S. Continental Shelf from Cape Hatteras to Nova Scotia, grossly visible lesions of the gills, integument, muscle, and viscera of primarily bottom-dwelling fishes were excised and examined using light microscopy. Several gadid and pleuronectid fishes accounted for most of the lesions observed. Most pathological examinations were incidental to samples taken for age and growth determination and evaluation of predator/prey relationships. Several gadids, with either gill, heart, or spleen lesions, were sampled more intensively. Gill lesions principally affected gadids and were caused by either microsporidans or an unidentified oocyte-like cell. The majority of gastrointestinal lesions consisted of encapsulated or encysted larval worms or microsporidan-induced cysts. Few heart lesions were found. Integumental lesioos included ulcers, lymphocystis, and trematode metacercariae. Liver lesions almost always consisted of encapsulated or encysted larval helminths. Necrotic granulomata were seen in muscle and microsporidan-induced granulomata in spleen. Although not numerous, histologically interesting lesions were noted in integument, heart, liver, spleen, and muscle of several fish species. Histologic study of tissues excised from a variety of demersal and pelagic fishes from the eastern North Atlantic (France, Germany, Spain) revealed assorted integumental, renal, hepatic, and splenic lesions. Small sample size and non-random sampling precluded obtaining a meaningful quantitative estimate of the prevalence of the observed lesions in the population at risk; however, a useful census has been made of the types of lesions present in commercially important marine fishes. (PDF file contains 20 pages.)

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Aspects of the Nigerian fishing industry are outlined to explain the concept of fishing systems viability which is often influenced by a combination of factors including biological productivity, as well as technical, economic and social factors. The productivity of the aquatic environments can be increased by the construction and installation of artificial reefs and fish aggregating devices. These man-made structures provide shelters, food and breeding grounds for fin fish and shell fish. The habitat enhancement techniques are appropriate, efficient, cheap and simple strategic options for increase in fish production. Recommendations for effective utilization and long term management are outlined.

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This contribution is a summary of the results of the study conducted by the University of the Philippines in the Visayas team from November 1990 to June 1991. The purpose of this research is to estimate demand and output supply elasticities in gillnet and seine fishing in Guimaras Strait (Philippines) and adjacent waters.

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Ring seines are lightly constructed purse seines adapted for operation in the traditional sector. Fish production and energy requirement in the ring seine operations, off Cochin, Kerala, India are discussed in this paper, based on data collected during 1997- 1998. The results reflect the Gross Energy Requirement (GER) situation that existed during 1997-1998. Mean catch per ring seiner per year worked out to be 211.9 t of which sardines (Sardinella spp.) constituted 44.3%, followed by Indian mackerel (Rastrelliger kanagurta) 29.7%, carangids 11.4%, penaeid prawns 2.2%, pomfrets 1.1% and miscellaneous fish 11.3%. Total energy inputs into the ring seine operations were estimated to be 1300.8 GJ. Output by way of fish production was determined to be 931.85 GJ. GER is the sum of all non-renewable energy resources consumed in making available a product or service and is a measure of intensity of non-renewable resource use. GER per tonne of fish landed by ring seiners was estimated to be 6.14. Among the operational inputs, kerosene constituted 73.4% of the GER, followed by petrol (12.7%), diesel (6.7%) and lubricating oil (2.4%). Fishing gear contributed 3.8%, engine 0.8% and fishing craft 0.3% of the GER. Energy ratio for ring seining was 0.72 and energy intensity 1.40.

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Logbook set and trip summary data (containing catch and cost information, respectively) collected by NOAA’s National Marine Fisheries Service (NMFS) were analyzed for U.S. pelagic longline vessels that participated in Atlantic fisheries in 1996. These data were augmented with vessel information from the U.S. Coast Guard. Mean fish weights and ex-vessel prices from NMFS observers and licensed seafood dealers, respectively, were used to estimate gross revenues. Comparisons revealed that net returns varied substantially by vessel size and fishing behavior (i.e. sets per trip, fishing location, season, and swordfish targeting). While the calculated economic effects of proposed regulations will depend on the descriptive statistic chosen for analysis, which itself depends on the type of analysis being conducted, results show that considering heterogeneity within this fleet can have a significant effect on predicted economic consequences.

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Energy is a key input into the fish harvesting process. Efficient use of energy helps in reducing operational costs and environmental impact, while increasing profits. Energy optimisation is an important aspect of responsible fishing as enunciated in the Code of Conduct for Responsible Fisheries. Gross Energy Requirement (GER) is the sum of all non-renewable energy resources consumed in making available a product or service and is expressed in energy units per physical unit of product or service delivered. GER is a measure of intensity of non-renewable resource use and it reflects the amount of depletion of earth’s inherited store of non-renewable energy in order to create and make available a product or service. In this study, GER in fish harvesting up to the point of landing is estimated in selected fish harvesting systems in the small-mechanised sectors of Indian fisheries and compared with reported results from selected non mechanised and motorised fishing systems to reflect the situation during 1997-1998. Among the fish harvesting systems studied, GER t fish-1 ranged from 5.54 and 5.91 GJ, respectively, for wooden and steel purse seiners powered by 156 hp engines; 6.40 GJ for wooden purse seiner with 235 hp engine; 25.18 GJ for mechanised gillnet/line fishing vessel with 89 hp engines; to 31.40 and 36.97 GJ, respectively, for wooden and steel trawlers powered by 99-106 hp engines.

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EXTRACT (SEE PDF FOR FULL ABSTRACT): An empirically derived multiple linear regression model is used to relate a local-scale dependent variable (either temperature, precipitation, or surface runoff) measured at individual gauging stations to six large-scale independent variables (temperature, precipitation, surface runoff, height to the 500-mbar pressure surface, and the zonal and meridional gradient across this surface). ...The area investigated is the western United States. ... The calibration data set is from 1948 through 1988 and includes data from 268 joint temperature and precipitation stations, 152 streamflow stations (which are converted to runoff data), and 24 gridded 500-mbar pressure height nodes.

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A simple costs and return analysis was done to determine the profitability of pond fish culture in three selected thanas namely Nertakona Sadar, Purbadhala and Kendua under Netrakona district. Cobb-douglas production function model was used to estimate the contribution of key variables to the production process of pond fish culture. It was found that cost of pond fish production was Tk. 10,103/ha/yr and the per hectare fish yield was 943 kgl/yr and the average gross and net return were Tk. 49,515 and Tk. 39,412 respectively. It was found that medium and small farms had the higher yield because of efficient use of production inputs compared to large farms. It was also observed that ownership of pond, number of species and human labour had negative impact on pond fish output, while depth of pond water, farm size, fish seed, fertilizer and artificial feed had significant positive effects on pond fish output.