Decision Support System in Fisheries Industry: Current State and Future Agenda

Andreas Tri Panudju, Sapta Rahardja, Mala Nurilmala, - Marimin

Abstract


Decision Support Systems (DSS) are systems that assist decision-makers and aim to synthesize domain and technical knowledge and package it so non-scientists can use and comprehend it. This study aims to compile initial empirical studies that can objectively reject or confirm the central hypothesis. The materials were retrieved after applying the filtered query across all sources. All search engine providers use five query strings. In each example, five findings were collected, sorted, and compared to one another, and 152 papers were generated. Seventy-six documents were discovered after applying the inclusion and exclusion criteria. Each of the 70 papers was independently examined and analyzed. The method of study followed a specific procedure explicitly developed to minimize the risk of researcher bias. It is primarily concerned with whether fisheries have decision-making systems and what empirical outcomes these systems produce, particularly in real-time analysis. The result shows a dearth of research on intelligent DSS, which accounts for less than 3% of all DSS types discussed in the article. This study offers academics and professionals an overview of the implementation of DSS. These new contributions imply the form of several different new contributions to further research. Furthermore, the possibility of identifying research gaps increases once merged with geoinformation technology or spatial information. We introduced a new model that combines spatial logistics techniques with GIS-based tracing and tracking. The envisioned logistics ensure spatial and logistical traceability in the process of fish products.

Keywords


Decision support systems; fishery; systematic literature review; future research agenda

Full Text:

PDF

References


P. B. Keenan and P. Jankowski, “Spatial Decision Support Systems: Three decades on,†Decis. Support Syst., vol. 116, pp. 64–76, 2019, doi: 10.1016/j.dss.2018.10.010.

W. A. Teniwut and C. L. Hasyim, “Decision support system in supply chain: A systematic literature review,†Uncertain Supply Chain Manag., vol. 8, no. 1, pp. 131–148, 2020, doi: 10.5267/j.uscm.2019.7.009.

Safriyana, Marimin, E. Anggraeni, and I. Sailah, “Spatial-intelligent decision support system for sustainable downstream palm oil based agroindustry within the supply chain network: A systematic literature review and future research,†Int. J. Supply Chain Manag., vol. 9, no. 3, pp. 283–307, 2020.

B. M. Mathisen, P. Haro, B. Hanssen, S. Björk, and S. Walderhaug, Decision Support Systems in Fisheries and Aquaculture: A systematic review. dspace.fudutsinma.edu.ng, 2019. [Online]. Available: http://arxiv.org/abs/1611.08374.

K. Kawamoto, C. A. Houlihan, E. A. Balas, and D. F. Lobach, “Improving clinical practice using clinical decision support systems: A systematic review of trials to identify features critical to success,†Br. Med. J., vol. 330, no. 7494, pp. 765–768, 2019, doi: 10.1136/bmj.38398.500764.8f.

Ã. Cobo, I. Llorente, L. Luna, and M. Luna, “A decision support system for fish farming using particle swarm optimization,†Comput. Electron. Agric., vol. 161, no. March, pp. 121–130, Jun. 2019, doi: 10.1016/j.compag.2018.03.036.

Z. Andreopoulou, C. Koliouska, and C. Zopounidis, “Multicriteria and Clustering: Classification Techniques in Agrifood and Environment,†Cooperative Management, 2017, doi: 10.1007/978-3-319-55565-2.

A. Mohammed and Q. Wang, “Multi-criteria optimization for a cost-effective design of an RFID-based meat supply chain,†Br. Food J., vol. 119, no. 3, pp. 676–689, 2019, doi: 10.1108/BFJ-03-2016-0122.

T. Nishida, K. Itoh, A. Caton, and D. Bartlett, “Geoinformatics for fisheries management,†Geoinformatics Mar. Coast. Manag., pp. 297–322, 2016, doi: 10.1201/9781315181523.

E. Georgiades et al., “Regulating Vessel Biofouling to Support New Zealand’s Marine Biosecurity System – A Blue Print for Evidence-Based Decision Making,†Front. Mar. Sci., vol. 7, 2020, doi: 10.3389/fmars.2020.00390.

L. V. Weatherdon, A. K. Magnan, A. D. Rogers, U. R. Sumaila, and W. W. L. Cheung, Observed and projected impacts of climate change on marine fisheries, aquaculture, coastal tourism, and human health: An update, vol. 3, no. APR. frontiersin.org, 2016. doi: 10.3389/fmars.2016.00048.

H. Riniwati, N. Harahab, and Z. Abidin, “A vulnerability analysis of coral reefs in coastal ecotourism areas for conservation management,†Diversity, vol. 11, no. 7, 2019, doi: 10.3390/d11070107.

C. Kelly et al., “Capturing big fisheries data: Integrating fishers’ knowledge in a web-based decision support tool,†Front. Mar. Sci., vol. 9, no. December, pp. 1–11, 2022, doi: 10.3389/fmars.2022.1051879.

M. James, T. Mendo, E. L. Jones, K. Orr, A. McKnight, and J. Thompson, “AIS data to inform small scale fisheries management and marine spatial planning,†Mar. Policy, vol. 91, pp. 113–121, 2018, doi: 10.1016/j.marpol.2018.02.012.

A. K. Nayak, P. Kumar, A. K. Saxena, M. Kumar, and et al Nayak A.K, Kumar P., Aquaculture Development in Kumaon Hills: a Spatial Decision Support System Approach, vol. 7. krishi.icar.gov.in, 2017. [Online]. Available: http://krishi.icar.gov.in/jspui/handle/123456789/11268.

S. Brodie et al., “Seasonal forecasting of dolphinfish distribution in eastern Australia to aid recreational fishers and managers,†Deep. Res. Part II Top. Stud. Oceanogr., vol. 140, pp. 222–229, 2017, doi: 10.1016/j.dsr2.2017.03.004.

A. J. Hobday, C. M. Spillman, J. Paige Eveson, and J. R. Hartog, “Seasonal forecasting for decision support in marine fisheries and aquaculture,†Fish. Oceanogr., vol. 25, pp. 45–56, 2016, doi: 10.1111/fog.12083.

C. van der Geest and C. van der Geest, “Redesigning Indian Ocean fisheries governance for 21st century sustainability,†Glob. Policy, vol. 8, no. 2, pp. 227–236, 2017, doi: 10.1111/1758-5899.12447.

Z. Provot, S. Mahévas, L. Tissière, C. Michel, S. Lehuta, and B. Trouillet, “Using a quantitative model for participatory geo-foresight: ISIS-Fish and fishing governance in the Bay of Biscay,†Marine Policy, vol. 117, p. 103231, Jul. 2020, doi: 10.1016/j.marpol.2018.08.015.

L. Dediu, L. M. Moga, and V. Cristea, “The barriers for the adoption of traceability systems by Romanian fish farms,†AACL Bioflux, vol. 9, no. 6, pp. 1323–1330, Dec. 2016.

R. Yasmin, M. Islam, and M. Alam, “A Study on Potential Application of Geographic Information Systems (GIS) in Fisheries and Aquaculture of Bangladesh,†World J. Fish Mar. Sci., vol. 4, no. 6, pp. 609–619, 2012, [Online]. Available: http://idosi.org/wjfms/wjfms4(6)12/9.pdf.

S. Lombardo, S. Israel, and D. Wood, “The Environment-Vulnerability-Decision-Technology Framework for Decision Support in Indonesia,†IEEE Aerosp. Conf. Proc., vol. 2022-March, 2022, doi: 10.1109/AERO53065.2022.9843544.

T. C. Pablo Vicente, A. Silvana, G.-G. Carina Soledad, and R.-M. Germania, “Methodology for systematic literature review applied to engineering and education,†IEEE Glob. Eng. Educ. Conf., pp. 1364–1373, 2019.

T. S. Shomefun, A. Ademola, C. O. A. Awosope, and A. I. Adekitan, “Critical review of different methods for siting and sizing distributed-generators,†Telkomnika (Telecommunication Comput. Electron. Control., vol. 16, no. 5, pp. 2395–2405, 2019, doi: 10.12928/telkomnika.v16i5.9693.

I. Ahmed, “Systematic review on evaluating planning process in agile development methods,†Telkomnika (Telecommunication Comput. Electron. Control., vol. 18, no. 6, pp. 2970–2976, 2020, doi: 10.12928/telkomnika.v18i6.16425.

S. W. Chong, T. J. Lin, and Y. Chen, “A methodological review of systematic literature reviews in higher education: Heterogeneity and homogeneity,†Educ. Res. Rev., vol. 35, 2022, doi: 10.1016/j.edurev.2021.100426.

M. Vidoni, “A systematic process for Mining Software Repositories: Results from a systematic literature review,†Inf. Softw. Technol., vol. 144, 2022, doi: 10.1016/j.infsof.2021.106791.

N. B. Armada, R. T. M. Bacalso, R. M. P. Rosales, and A. T. Lazarte, “Right-sizing as a strategy for allocating fishing effort in a defined marine ecosystem: A Philippines case study,†Ocean Coast. Manag., vol. 165, no. August, pp. 167–184, 2018, doi: 10.1016/j.ocecoaman.2018.08.018.

A. K. Carlson, D. I. Rubenstein, and S. A. Levin, “Linking Multiscalar Fisheries Using Metacoupling Models,†Frontiers in Marine Science, vol. 7. frontiersin.org, 2020. doi: 10.3389/fmars.2020.00614.

K. Friedman, S. M. Garcia, and J. Rice, “Mainstreaming biodiversity in fisheries,†Mar. Policy, vol. 95, pp. 209–220, 2018, doi: 10.1016/j.marpol.2018.03.001.

Y. F. Hernández-Julio, M. J. Prieto-Guevara, and W. Nieto-Bernal, “Fuzzy clustering and dynamic tables for knowledge discovery and decision-making: Analysis of the reproductive performance of the marine copepod Cyclopina sp.,†Aquaculture, vol. 523, 2020, doi: 10.1016/j.aquaculture.2020.735183.

J. Melbourne-Thomas et al., “Integrated modelling to support decision-making for marine social-ecological systems in Australia,†ICES J. Mar. Sci., vol. 74, no. 9, pp. 2298–2308, 2017, doi: 10.1093/icesjms/fsx078.

J. Jossart, S. J. Theuerkauf, L. C. Wickliffe, and J. A. Morris, “Applications of Spatial Autocorrelation Analyses for Marine Aquaculture Siting,†Frontiers in Marine Science, vol. 6. frontiersin.org, 2020. doi: 10.3389/fmars.2019.00806.

G. W. Lailossa, K. B. Artana, N. Pujawan, and A. A. B. Dinariyana, “Model of strategy quality improvement of tuna and other species in the cold chain system (Fuzzy expert systems approach),†AACL Bioflux, vol. 9, no. 5, pp. 1154–1166, 2016.

A. J. Lynch et al., “Grand Challenges in the Management and Conservation of North American Inland Fishes and Fisheries,†Fisheries, vol. 42, no. 2, pp. 115–124, 2017, doi: 10.1080/03632415.2017.1259945.

Y. A. Nada and Y. H. Ellawady, “Analysis, Design, and Implementation of Intelligent Fuzzy Expert System for Marine Wealth Preservation,†International Journal of Computer Applications, vol. 161, no. 2. researchgate.net, pp. 15–20, 2017. doi: 10.5120/ijca2017913114.

Z. Provot, S. Mahévas, L. Tissière, C. Michel, S. Lehuta, and B. Trouillet, “Using a quantitative model for participatory geo-foresight: ISIS-Fish and fishing governance in the Bay of Biscay,†Mar. Policy, vol. 117, no. June, p. 103231, 2020, doi: 10.1016/j.marpol.2018.08.015.

L. P. Sousa and F. L. Alves, “A model to integrate ecosystem services into spatial planning: Ria de Aveiro coastal lagoon study,†Ocean Coast. Manag., vol. 195, 2020, doi: 10.1016/j.ocecoaman.2020.105280.

É. E. Plagányi, T. Skewes, N. Murphy, R. Pascual, and M. Fischer, “Crop rotations in the sea: Increasing returns and reducing risk of collapse in sea cucumber fisheries,†Proc. Natl. Acad. Sci. U. S. A., vol. 112, no. 21, pp. 6760–6765, 2015, doi: 10.1073/pnas.1406689112.

A. J. Hobday, C. M. Spillman, J. Paige Eveson, and J. R. Hartog, “Seasonal forecasting for decision support in marine fisheries and aquaculture,†Fish. Oceanogr., vol. 25, pp. 45–56, Apr. 2016, doi: 10.1111/fog.12083.

R. L. Stephenson et al., “A practical framework for implementing and evaluating integrated management of marine activities,†Ocean Coast. Manag., vol. 177, pp. 127–138, 2019, doi: 10.1016/j.ocecoaman.2019.04.008.

D. S. Hidayat, W. S. Satuti, D. I. Sensuse, D. Elisabeth, and L. M. Hasani, “Decision support system for fish quarantine measures in Indonesia,†VINE J. Inf. Knowl. Manag. Syst., 2022, doi: 10.1108/VJIKMS-08-2021-0144.

A. T. Panudju, M. Nurilmala, and Marimin, “The conceptual design of intelligent spatial decision support system for the fishery-industry logistic,†IOP Conf. Ser. Earth Environ. Sci., vol. 1063, no. 1, 2022, doi: 10.1088/1755-1315/1063/1/012029.

B. D. Ratner et al., “A framework to guide research engagement in the policy process, with application to small-scale fisheries,†Ecol. Soc., vol. 27, no. 4, 2022, doi: 10.5751/ES-13604-270445.

M. P. Turschwell et al., “A review of support tools to assess multi-sector interactions in the emerging offshore Blue Economy,†Environ. Sci. Policy, vol. 133, no. June 2021, pp. 203–214, 2022, doi: 10.1016/j.envsci.2022.03.016.

I. Granado et al., “Categories Studies by Topic,†J. Clean. Prod., vol. 320, no. August, p. 128661, 2021, doi: 10.1016/j.jclepro.2021.128661.

S. B. Bricker, T. L. Getchis, C. B. Chadwick, C. M. Rose, and J. M. Rose, “Integration of ecosystem-based models into an existing interactive web-based tool for improved aquaculture decision-making,†Aquaculture, vol. 453, pp. 135–146, Feb. 2016, doi: 10.1016/j.aquaculture.2015.11.036.

Berona, Elyzer & Buntag, Daibey & Tan, Mary Jane & Coronado, Armin. (2016). Web-Based Decision Support System for Water Quality Monitoring and Prediction for Outdoor Microalgae Cultivation. IOSR Journal of Computer Engineering. 18. 2278-661. 10.9790/0661-1803061620.

R. P. Canale and S. C. Chapra, “Decision Support Models for Assessing the Impact of Aquaculture on River Water Quality,†J. Environ. Eng., vol. 142, no. 10, p. 03116001, Oct. 2016, doi: 10.1061/(ASCE)EE.1943-7870.0001115.

K. El-Nemr Moataz and M. K. El-Nemr, “Fish farm management and microcontroller based aeration control system,†Agric. Eng. Int. CIGR J., vol. 15, no. 1, pp. 87–99, 2013.

M. Føre et al., “Precision fish farming: A new framework to improve production in aquaculture,†Biosyst. Eng., vol. 173, pp. 176–193, 2018, doi: 10.1016/j.biosystemseng.2017.10.014.

A. R. Keshtkar, Z. Oros, S. Mohammadkhan, S. Eagdari, and H. Paktinat, “Multi-criteria analysis in Artemia farming site selection for sustainable desert ecosystems planning and management (case study: Siahkouh Playa, Iran),†Environ. Earth Sci., vol. 75, no. 16, 2016, doi: 10.1007/s12665-016-5998-2.

F. O’Donncha and J. Grant, “Precision aquaculture,†IEEE Internet Things Mag., 2019, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8982744/.

R. S. Porchelvi and J. Irine, “A multi-objective linear programming model for small-scale fish farmers in polyculture fish farming,†International Journal of Pure and Applied Mathematics, vol. 119, no. 7. acadpubl.eu, pp. 361–369, 2018. [Online]. Available: https://acadpubl.eu/jsi/2018-119-7/articles/7a/41.pdf.

Ramadiani, R. Kurniawan, A. H. Kridalaksana, and M. L. Jundillah, Decision Support Systems Selection of Soang Superior Brood Using Weighted Product (WP) and Simple Additive Weighting (SAW) Method, vol. 125. repository.unmul.ac.id, 2019. doi: 10.1051/e3sconf/201912523004.

Nguyen Thanh Tung and Tong Phuoc Hoang Son, “GIS-Based Multi-Criteria Evaluation Models for Selection of Suitable Sites for Pacific Oyster (Crassostrea gigas) Aquaculture in the Central Region of Vietnam,†Journal of Environmental Science and Engineering A, vol. 8, no. 4. davidpublisher.com, 2019. doi: 10.17265/2162-5298/2019.04.002.

O. G. Ogiy, V. Y. Osipov, A. B. Tristanov, and N. A. Zhukova, “The Process of Managing Labor Potential of the Fishery Complex as an Object of Modeling Using Artificial Neural Networks,†AIP Conf. Proc., vol. 2661, no. October, 2022, doi: 10.1063/5.0107815.

C. Seliger, M. Haslauer, G. Unfer, and S. Schmutz, “Aquazone: An integrative tool for sustainable fish farm zoning,†Sustain., vol. 13, no. 3, pp. 1–25, 2021, doi: 10.3390/su13031470.

R. Ranjan, K. Sharrer, S. Tsukuda, and C. Good, “Effects of image data quality on a convolutional neural network trained in-tank fish detection model for recirculating aquaculture systems,†Comput. Electron. Agric., vol. 205, p. 107644, 2023, doi: 10.1016/j.compag.2023.107644.

A. J. Hobday et al., “Ethical considerations and unanticipated consequences associated with ecological forecasting for marine resources,†ICES J. Mar. Sci., vol. 76, no. 5, pp. 1244–1256, Oct. 2019, doi: 10.1093/icesjms/fsy210.

C. P. Paukert et al., “Designing a global assessment of climate change on inland fishes and fisheries: knowns and needs,†Rev. Fish Biol. Fish., vol. 27, no. 2, pp. 393–409, 2017, doi: 10.1007/s11160-017-9477-y.

R. Xiang, C. Jones, R. Mamon, and M. Chavez, “Modelling exchange-driven fish price dynamics,†J. Model. Manag., vol. 16, no. 4, pp. 1054–1069, 2021, doi: 10.1108/JM2-04-2020-0101.

K. Friedman et al., “Informing CITES Parties: Strengthening science-based decision-making when listing marine species,†Fish Fish., vol. 21, no. 1, pp. 13–31, 2020, doi: 10.1111/faf.12411.

J. Trammell, M. Krupa, P. Williams, and A. Kliskey, “Using comprehensive scenarios to identify social–ecological threats to salmon in the Kenai river watershed, Alaska,†Sustain., vol. 13, no. 10, 2021, doi: 10.3390/su13105490.

F. O’Donncha and J. Grant, “Precision Aquaculture,†IEEE Internet Things Mag., vol. 2, no. 4, pp. 26–30, 2020, doi: 10.1109/iotm.0001.1900033.

A. J. Lynch, W. W. Taylor, and A. M. McCright, “Stakeholder Views of Management and Decision Support Tools to Integrate Climate Change into Great Lakes Lake Whitefish Management,†Fisheries, vol. 41, no. 11, pp. 644–652, 2016, doi: 10.1080/03632415.2016.1232960.

A. Ronald, V. Yesmaya, and M. Danaparamita, “Personal security tracking based on android and web application,†Telkomnika (Telecommunication Comput. Electron. Control., vol. 16, no. 2, pp. 771–775, 2019, doi: 10.12928/telkomnika.v16i2.7625.

M. Asrol, Marimin, Machfud, and M. Yani, “Method and approach mapping of fair and balanced risk and value-added distribution in supply chains: A review and future agenda,†Int. J. Supply Chain Manag., vol. 7, pp. 74–95, 2019.

V. Yesmaya, A. Ronald, and M. Hidajat, “Property exhibition decision support system based on web application,†Telkomnika (Telecommunication Comput. Electron. Control., vol. 16, no. 2, pp. 766–770, 2019, doi: 10.12928/telkomnika.v16i2.7601.

S. Parjuangan, R. Ali, and A. Purnama, “Real-time monitoring and warning system in urban rivers,†Telkomnika (Telecommunication Comput. Electron. Control., vol. 17, no. 3, pp. 1521–1525, 2019, doi: 10.12928/telkomnika.V17I3.10397.

S. Heripracoyo and R. Kurniawan, “Big data analysis with MongoDB for decision support system,†Telkomnika (Telecommunication Comput. Electron. Control., vol. 14, no. 3, pp. 1083–1089, 2019, doi: 10.12928/telkomnika.v14i2.3115.

Q. Zhou, Z. Yin, Q. Ying, and S. Wang, “Intelligent Data Mining and Decision System for Commercial Decision Making,†Telkomnika Indones. J. Electr. Eng., vol. 12, no. 1, Jan. 2020, doi: 10.11591/telkomnika.v12i1.3977.

R. M. Sampurno, K. B. Seminar, and Y. Suharnoto, “Weed control decision support system based on precision agriculture approach,†Telkomnika (Telecommunication Comput. Electron. Control., vol. 12, no. 2, pp. 475–484, Jun. 2019, doi: 10.12928/telkomnika.v12i2.1982.




DOI: http://dx.doi.org/10.18517/ijaseit.13.2.17914

Refbacks

  • There are currently no refbacks.


Published by INSIGHT - Indonesian Society for Knowledge and Human Development