Forecasting for recreational fisheries management: a derby fishery case study with Gulf of Mexico Red Snapper

Farmer, Nicholas A.; Froeschke, John T.; Records, David

doi:10.1093/icesjms/fsaa005

“…Pada bidang meteorologi dan klimatologi, metode ini digunakan dalam studi limpasan jangka panjang di Amerika Serikat (Valipour 2015) dan peramalan rata-rata suhu bulanan Zaria, Nigeria (Kajuru dan Muhammed 2019). Pada bidang perikanan, metode ARIMA dan SARIMA digunakan untuk Peramalan perikanan rekreasi, studi kasus kakap merah di Teluk Meksiko (Farmer et al 2020)…”

Section: Pendahuluanunclassified

Model Peramalan Jumlah Tangkapan Ikan Kakap Yang Didaratkan Di Ppi Oeba Kupang Nusa Tenggara Timur

Edo,

Nalle

2024

Jurnal Marfis

0

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Snapper is one of the economically important fish targeted by fishers in the island of Timor. Domestic and foreign markets for fillet and fresh snapper is considerably large. The export volume in 2021 was 4,172,056 kg, and the total value reached 12,452,211 USD. Along with the increasing snapper production in Kupang City and NTT, it is necessary to forecast the future snapper production. Thus, it can be a reference for policy makers in the region in designing fisheries development. This study aims to produce forecasting model for snapper catches at PPI Oeba Kupang. This research applied Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA), as a forecasting method. The data of snapper production analyzed in this study consisted of 72 months, started from January 2016 to December 2021. Data were obtained from the UPT of the Department of Marine and Fisheries of NTT Province at PPI Oeba Kupang. Data have a non-stationary pattern to the variance, hence a logarithmic transformation is needed, as well as seasonal differences analysis. However, there is no need for non-seasonal differences since the data are stationary with respect to the mean. The results of the identification of the Autocorrelation function and Partial Autucorrelation function Plots are ARIMA models with seasonal factor period 4, ARIMA (P,D,Q) = (1,0,1), while the SARIMA order (P, D, Q) = (2,1,1)4. Based on parameter testing, verification, examination, and testing of suitable models, the best model obtained was (0,0,1)(2,1,1)4. Keywords: Autoregressive Integrated Moving Average, focecasting model, Seasonal Autoregressive Integrated Moving Average, Snapper.

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“…Pada bidang meteorologi dan klimatologi, metode ini digunakan dalam studi limpasan jangka panjang di Amerika Serikat (Valipour 2015) dan peramalan rata-rata suhu bulanan Zaria, Nigeria (Kajuru dan Muhammed 2019). Pada bidang perikanan, metode ARIMA dan SARIMA digunakan untuk Peramalan perikanan rekreasi, studi kasus kakap merah di Teluk Meksiko (Farmer et al 2020)…”

Section: Pendahuluanunclassified

Untitled

2024

Jurnal Marfis

0

View full text Add to dashboard Cite

Snapper is one of the economically important fish targeted by fishers in the island of Timor. Domestic and foreign markets for fillet and fresh snapper is considerably large. The export volume in 2021 was 4,172,056 kg, and the total value reached 12,452,211 USD. Along with the increasing snapper production in Kupang City and NTT, it is necessary to forecast the future snapper production. Thus, it can be a reference for policy makers in the region in designing fisheries development. This study aims to produce forecasting model for snapper catches at PPI Oeba Kupang. This research applied Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA), as a forecasting method. The data of snapper production analyzed in this study consisted of 72 months, started from January 2016 to December 2021. Data were obtained from the UPT of the Department of Marine and Fisheries of NTT Province at PPI Oeba Kupang. Data have a non-stationary pattern to the variance, hence a logarithmic transformation is needed, as well as seasonal differences analysis. However, there is no need for non-seasonal differences since the data are stationary with respect to the mean. The results of the identification of the Autocorrelation function and Partial Autucorrelation function Plots are ARIMA models with seasonal factor period 4, ARIMA (P,D,Q) = (1,0,1), while the SARIMA order (P, D, Q) = (2,1,1)4. Based on parameter testing, verification, examination, and testing of suitable models, the best model obtained was (0,0,1)(2,1,1)4.

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Marine recreational fisheries — current state and future opportunities

Hyder

¹

,

Maravelias

²

,

Kraan

³

et al. 2020

ICES Journal of Marine Science

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Marine recreational fisheries (MRF) have important social and economic benefits, but can impact fish stocks and the environment. The diverse and dispersed nature of these fisheries makes them challenging to study; a lack of data has made it more difficult to include them in fisheries management and the varied motivations of fishers makes their response to management measures hard to predict. Research into MRF is growing rapidly, so this themed article set aims to bring together MRF research to highlight the current evidence base and identify future opportunities. New survey methods were presented alongside analyses of existing data, which highlighted issues with methods, reconstruction of missing data, and factors influencing catch and effort. The manuscripts demonstrated the biological and economic impacts of MRF, and its self-subsidizing nature was recognized. Novel approaches for management, including improving compliance, were identified. Finally, the lack of funding for MRF was highlighted. Key research gaps are: governance that embeds MRF in fisheries management; integration of novel approaches and traditional surveys; risk-based approaches to identify impacts; understanding fish welfare; management that balances economic, social, and biological impacts and allows allocation between sectors; and understanding social benefits and their impacts on management and compliance.

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Forecasting for recreational fisheries management: a derby fishery case study with Gulf of Mexico Red Snapper

Cited by 3 publications

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Model Peramalan Jumlah Tangkapan Ikan Kakap Yang Didaratkan Di Ppi Oeba Kupang Nusa Tenggara Timur

Model Peramalan Jumlah Tangkapan Ikan Kakap Yang Didaratkan Di Ppi Oeba Kupang Nusa Tenggara Timur

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Marine recreational fisheries — current state and future opportunities

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