Pavel Prikhodko, Ph.D. Machine Learning
The fisheries and aquaculture sector is usually divided into commercial and recreational fishing and involves the harvesting, processing and marketing of fish. The commercial part of this sector includes the raising of fish or the capture of wild fish. Fishing methods vary from using large nets to poles and lines, trolling with single lines and traps or pots.
The fishing industry in the United States is primarily concentrated on the Pacific Coast, with some of the major domestic species of fish including cod and pollock. Recent trends shaping this sector include a shift toward more sustainable and responsible fishing. According to statistics published at Statista.com, pollock landings amounted to approximately 3.36 billion pounds in 2016, while cod landings totaled 711 million pounds in the same year.
The landings of flatfish and hakes amounted to approximately 590.61 and 576.09 million pounds respectively. In 2016, crabs landings totaled about 317.35 million pounds. Shimp landings amounted to 270.79 million pounds in 2016, while sea herring and rockfish landings reached 191.55 million pounds and 165.04 million pounds respectively.
If we examine the purposes of domestic fishery landings for fresh and frozen fish in the U.S. in 2016, we find that the greatest number of fish landed were for human consumption, while a lesser amount of fishery landings were intended for bait and animal food.
Next, let’s examine the number of seafood industry landings in the U.S. in 2016 by state. Alaska came in first by a large margin with 5.6 billion pounds of seafood landed. Louisiana came in second place with approximately 1.2 billion pounds of seafood landings. Washington and Virginia came next, landing 551.9 million pounds and 363.3 million pounds respectively. Mississippi rounded out the top five, where seafood landings amounted to 304 million pounds in 2016.
About Pavel Prikhodko
Pavel Prikhodko, Ph.D. Machine Learning
Pavel has worked for many years as a researcher and developer on a wide range of applications (varying from mechanics and manufacturing to social data, finance and advertising), building predictive systems and trying to find stories that data can tell.
In his free time, he enjoys being with his family.
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