Multiple stable reference points in oyster populations: implications for reference point-based management

In the second of two companion articles, a 54-year time series for the oyster population in the New Jersey waters of Delaware Bay is analyzed to examine how the presence of multiple stable states affects reference-point–based management. Multiple stable states are described by four types of reference points. Type I is the carrying capacity for the stable state: each has associated with it a type-II reference point wherein surplus production reaches a local maximum. Type-II reference points are separated by an intermediate surplus production low (type III). Two stable states establish a type-IV reference point, a point-of-no-return that impedes recovery to the higher stable state. The type-II to type-III differential in surplus production is a measure of the difficulty of rebuilding the population and the sensitivity of the population to collapse at high abundance. Surplus production projections show that the abundances defining the four types of reference points are relatively stable over a wide range of uncertainties in recruitment and mortality rates. The surplus production values associated with type-II and type-III reference points are much more uncertain. Thus, biomass goals are more easily established than fishing mortality rates for oyster population

Canadian, Mexican, and U. S. Fisheries: Recent Developments

The creation of extended zones (EEZ's) has shifted some aspects of fisheries management and policy from the arena of international negotiations to the economic and political decision making process within the coastal state. The transition from a world of international commons to one of coastal state jurisdiction raises a variety of issues. The one of concern here is a broad welfare question: Given the transfer of assets from the international commons to the coastal state, how well (efficiently) has the state used these new assets to increase the flow of income and Gross National Product (GNP)?

Maryland commercial fisheries statistics, 1946-1950

This bulletin is the second of a series begun in 1948, designed to present detailed records of the commercial fish catch of the state. The statistics set forth were derived from records obtained only from licensed commercial fishermen and contain no data pertaining to small unlicensed units of fishing gear, or to the sports fishery.

Role of fisheries extension officers in FFDA at Balasore

The first FFDA in Orissa state was established in the year 1976. The study was conducted in Balasore district of the State. The findings revealed that all of the FEOs were not well qualified but trained and experienced. The duration of training obtained varied from 3 to 10 months. An FEO has a large area under his jurisdiction. Cycle was used by most of the FEOs to visit the fish farmers which resulted in infrequent visits. The FEOs were not satisfied with the working conditions, promotion avenues and conveyance facilities. There was interference of political bodies in leasing out ponds. The amount of loan sanctioned was not adequate to start fish farming. They also revealed that marketing aspects were totally neglected by the FFDA.

Semi-intensive shrimp farming system of Kerala, India: a production function analysis

An analysis of the factor-product relationship in the semi-intensive shrimp farming system of Kerala, farm basis and hectare basis, we are attempted and the results reported in this paper. The Cobb-Douglas model, in which the physical relationship between input and output is estimated, and the marginal analysis then employed to evaluate the producer behaviour, was used for the analysis. The study was based on empirical data collected during November 1994 to May 1996, covering three seasons, from 21 farms spread over Alappuzha, Ernakulam and Kasaragod districts of the state. The sample covered a total area of 61.06 ha. Of the 11 explanatory variables considered in the model, the size of the farm, casual labour and chemical fertilizers were found statistically significant. It was also observed that the factors such as age of pond, experience of the farmer, feed, miscellaneous costs, number of seed stocked and skilled labour contributed positively to the output. The estimated industry production function exhibited unitary economies of scale. The estimated mean output was 3937 kg/ha. The test of multi-co-linearity showed that there is no problem of dominant variable. On the basis of the marginal product and the given input-output prices, the optimum amounts of seed, feed and casual labour were estimated. They were about 97139 seed, 959 kg of feed and 585 man-days of casual labour per farm. This indicated the need for reducing the stocking density and amount of feed from the present levels, in order to maximise profit. Based on the finding of the study, suggestions for improving the industry production function are proposed.