The secret life of a slab of chocolate You might celebrate World Chocolate Day today 7 July by sitting back and feasting on your favourite slab of sugary goodness. At Stats SA, we love our chocolate. But we also love data and all things statistical. Stats SA has already delved deep into the data to give you insight into the price behaviour of beer and cake.
It will also be important to update this information when fleets or vessels change e. There is no single method for such evaluation since it depends on the type of the fishery. Nevertheless, a direct and full appreciation of daily fishing operations is fundamental to the data collection design.
For example, an examination of fish handling practices is necessary to decide what level of species detail should be feasible for reporting in fishing logbooks. This is one of a number of points in the management process where involvement of fishers and other industry representatives can be helpful.
Compliance with data supply and willingness to assist in data collection are the two biggest administrative problems for management. It is clear from experience that two attributes of a fishery enhance the ability to collect accurate and timely data: It is important, therefore, to select indicators and variables that are directly related to the objectives, in order to limit the task both for fishers and landings enumerators.
However, in some cases more information than that strictly required for the analyses may be necessary to validate data. In developing the data collection system, the implications for fisheries management of not collecting certain types of data will also need to be considered.
For example, production data with detailed species, product and size grade information may be needed for the dynamic bioeconomic models necessary to set optimal quotas. However, if such types of data are found too expensive to collect, management may need to reconsider the use of quotas as a control for the fishery.
Following some decisions on what data are possible to collect, it is then necessary to decide on what data are essential, and what are only desirable. Catch and effort data are critical to construct the most important indicators in most fisheries. Other data types relating to details of vessel activity, may not be considered necessary in particular cases.
When beginning data collection systems, initial emphasis should be on the harvesting sector for all data domains operational, biological, economic and socio-culturalwith processing and other secondary and tertiary sectors being constructed subsequently according to available resources and management goals.
Each data type may be used for a variety of indicators. Catch, for instance, may be used both in calculations of revenue for economic purposes, and as a rough measure of resource depletion.
Using various models, effort can be linked both to fishing costs and to fishing mortality.
This is useful because it is not possible to measure these variables like costs and mortality directly all the time, if at all. Because different indicators may make different uses of the same types of data, attention should be given to recording data in a manner that allows their use for different purposes.
For example, data on effort, an economic variable, should ideally be recorded in a form proportional to variable costs of fishing, such as travel distance and number of fishing days. Alternatively, for compliance control purposes, the fishing position may also be needed.
For biological purposes, effort data may be needed by set or haul and in a form proportional to fishing mortality. The selection of a data type also depends on the available analyses. Many fish populations dynamics models require catch in weight and number by species, as well as other data on the biology of each species e.
A bioeconomic model may require data not only on the specific fishery production and prices, but also on other economic sectors for comparative purposes.warning against fraudulent use of the african development bank’s (afdb) name.
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