Estimation of fishing power and fishing capacity on coastal stow net fishery in the Korean waters

Kim, Pyungkwan; Lee, Kyounghoon; Kim, Do-Hoon; Lee, Geonho; H, An; Kim, Seonghun; Yang, Yaguang

doi:10.3796/ksft.2015.51.4.583

Cited by 7 publications

(2 citation statements)

References 6 publications

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“…Multi-layer system models have been developed, including C2R, C2W, C2WH, and C2GS2 models [43,44]. Among them, the C2R model was the first model widely used in marine fisheries assessment and was recommended by the World Food and Agriculture Organization [45].…”

Section: Data Development Analysis (Dea)mentioning

confidence: 99%

Evaluating Fishing Capacity Based on DEA and Regression Analysis of China’s Offshore Fishery

Liu

Sun

Lyu

et al. 2021

JMSE

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The analysis of offshore fishing capacity is of great significance and practical value to the sustainable utilization and conservation of marine fishery resources. Based on the 2004–2020 China Fishery Statistical Yearbook, data envelopment analysis (DEA) was applied for measuring fishing capacity using a number of fishing vessels, total power, total tonnage, and the number of professional fishermen as the input measures and the annual catch as the output measure. Capacity utilization had a calculated range from 80.7 to 100%, and its average is 93.5%. In the first four years of 2003–2007, the excess investment rate of fishing vessels, total tonnage, total power, and fishermen was low (<5%). There was a consistent sharp upward trend in 2007, a gradual downward trend from 2007 to 2015, and an upward trend after reaching a low point in 2015, with the highest gross tonnage of fishing vessels reaching 25.5%. Four regression models that incorporate machine learning algorithms are used, including Lasso, Ridge, KNN, and Polynomial Features. The goodness of fit for the four models was used as the evaluation index, and the offshore annual catch based on the evaluation index was proposed. The forecasting annual catch of the polynomial model can reach 0.98. Furthermore, a comparative simulation of the DEA incorporating the polynomial model was carried out. The results show that DEA can evaluate input factors under the conditions of a given range, and the polynomial model has more advantages in forecasting annual catches. Furthermore, the combined application of DEA and polynomial model was used to analyze and discuss the management policies of China’s offshore fishery, which can provide help and reference for future management.

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Section: Data Development Analysis (Dea)mentioning

confidence: 99%

Evaluating Fishing Capacity Based on DEA and Regression Analysis of China’s Offshore Fishery

Liu

Sun

Lyu

et al. 2021

JMSE

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“…The trends of the nominal (N.) catch per unit effort (CPUE) can be in uenced through several factors in addition to stock abundance, including the shing location, target species, and environmental conditions. Therefore, standardizing the N. CPUE has become an essential step when studying sheries management (Kim et al 2015). In our previous study, we described the habitat preferences of immature albacore tuna and its critical role (Mondal et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Projected climate change impact on immature albacore tuna habitats in the Indian Ocean by using sea surface temperature and chlorophyll data: Ensemble forecasting

Mondal

Hsu

Lee

et al. 2022

Preprint

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This study employed an arithmetic mean model with sea surface temperature and chlorophyll data (2-month lag) to determine the projected impact of climate change on the immature albacore tuna habitat in the Indian Ocean. Albacore tuna fishing data from Taiwanese longline fishery from 2000 to 2016 were used. Standardization of the nominal catch per unit effort data was performed to prevent bias and overestimation resulting from various temporal and spatial factors. In the Indian Ocean, potential immature albacore habitats exhibited significant habitat suitability index (HSI) changes in response to future climate change levels. As the water stratifies in a projected warm climate, low HSI areas were enlarged, and potential immature albacore habitats exhibited a net southward shift. Although the CMIP5 climate model sea surface temperature projections generated different HSI patterns for immature albacore, our results from the ensemble median immature albacore habitat forecasts provided information useful for assessing risks and adaptation strategy options for albacore fishery resources under climate change. The trends of the potential immature albacore habitat distribution could also be cautiously used to inform resource stakeholders’ decisions.

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Change of relative Fishing Power Index by technological Development in the Offshore Red Snow Crab Trap Fishery

Oh¹,

Seo²,

Hwang³

et al. 2017

JFMSE

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Estimation of fishing power and fishing capacity on coastal stow net fishery in the Korean waters

Cited by 7 publications

References 6 publications

Evaluating Fishing Capacity Based on DEA and Regression Analysis of China’s Offshore Fishery

Evaluating Fishing Capacity Based on DEA and Regression Analysis of China’s Offshore Fishery

Projected climate change impact on immature albacore tuna habitats in the Indian Ocean by using sea surface temperature and chlorophyll data: Ensemble forecasting

Change of relative Fishing Power Index by technological Development in the Offshore Red Snow Crab Trap Fishery

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