Marine Fish Production Research Articles

Pemberdayaan Kampung KB Sekip Jaya Pada Program Dapur Sehat Atasi Stunting

Kampung KB Sekip Jaya is prepared as a pilot Kampung KB for stunting prevention. Through the Dapur Sehat Atasi Stunting (DASHAT) programme, Kampung KB Sekip Jaya was implementing a government programme in the form of 100 days to overcome stunting by providing additional nutritionally balanced food to children who are identified as stunted. Together with the Kampung KB Activity Groups (BKB, BKR, BKL and UPPKA), the P3A and P2KB Office of Deli Serdang Regency, the Sekip Village Government and other community groups, the DASHAT programme was implemented as an effort to prevent stunting and build quality families. In its implementation, the DASHAT programme in Kampung KB Sekip Jaya faces various problems, namely: 1) DASHAT management was still conventional so that the 100-day programme to overcome stunting was not supported by the DASHAT work programme, 2) Community empowerment has not been maximised, especially in involving nutritionists and culinary experts, 3) Community empowerment, especially the Activity Group Kampung KB, was not in accordance with its function so that the target of the DASHAT programme was still limited, 4) Human resources involved in the DASHAT programme have low knowledge and understanding of DASHAT management and stunting prevention. The solutions offered through this activity were: 1) training and practice of DASHAT management, 2) training and practice of food production made from local raw materials for each Activity Group Kampung KB as a superior product that sells well. The method used was partner assistance through training and practice of DASHAT management, training and production practices so that partners have a productive business unit that can be used as a source of financing for the family and for the Kampung KB Sekip Jaya. The results of the activity show that the participants' insights have increased, especially in preparing food menus for 100 days to overcome stunting, the availability of work programs in DASHAT management, the practice of processed marine fish production and the superior product of Kampung KB Sekip Jaya in the form of fish biscuits as a product that will be produced by the UPPKA group as the spearhead of the productive economy in Kampyng KB Sekip Jaya.

A comprehensive approach to strategic planning for marine aquaculture in Egypt: SWOT-AHP analysis

Egypt, a leading producer of aquaculture in the Mediterranean, faces challenges in developing its marine aquaculture sector. To address these issues, this study utilized the SWOT-AHP methodology for a quantitative assessment of strengths, weaknesses, opportunities, and threats. Interviews were conducted with 26 experts, including marine fish farmers, academics, and officials. The results revealed Egypt's global leadership as the largest producer of meagre and the third-largest producer of marine species such as seabass and seabream. This success can be attributed to strengths such as competitive pricing for meagre, extended marine aquaculture sites, suitable weather conditions, and moderate winters not available to competitors, contributing to the production of high-quality marine fish with a large market size. This provides a significant marketing opportunity when focusing on this high-priced large-size category. However, experts' concerns about the development of marine aquaculture lead them to believe that weaknesses and threats outweigh strengths and opportunities. These concerns include reliance on limited and costly trash fish, a decrease in demand for aquafeed due to rising prices, poor water quality, and a dependency on wild seeds. Pollution and climate change are also major concerns. In summary, the implementation of the SWOT-AHP approach ensures the development of an effective strategic plan for marine aquaculture in Egypt. This approach contributes to maximizing opportunities and addressing challenges that may hinder the sector's future growth. Collaborative execution among stakeholders (farmers, academics, and officials) is expected to facilitate a transition to sustainable practices, promoting innovation, and continuous improvement of the strategy.

Analisis Pemasaran dalam Rangka Peningkatan Efisiensi Pasar Ikan Tangkap

Marine fish production in PPN Palabuhanratu fluctuates every year. This condition impacts fish prices at the producer and consumer levels. The low cost of fish is one of the problems with PPN Palabuhanratu. In addition, perishable products affect the importance of an efficient marketing process so that fish can reach consumers fresh. The research aims to analyze the marketing to increase fish catches, especially efficiency in madidihang tuna in PPN Palabuhanratu. This research was conducted from November 2022 to June 2023, interviewing ten respondents from 10 sample ships. The study used three methods (purposive sampling, accidental sampling, and snowball sampling). Primary and secondary data are used in this research to calculate marketing margin, fishermen's share, and profit-cost ratio. The result is four marketing channels, the most efficient being the marketing channel from traders, fishermen, and consumers. This is because this channel has the smallest margin value, the highest fishermen's share value, and the profitable value of cost benefits.

Forecasting of Fish Production of India using ARIMA Models

Fish is an essential component of the diet of the most popular in India. Fish production in India has increased at a higher rate compared to other food items. The most widely used time series models ARIMA was applied for modelling and forecasting of total, inland and marine fish production in India for a period from 1978-79 to 2021-22. The collected data during the third decade was found highly consistent and stable with less variability. The lowest mean fish production growth rate was observed in all three categories viz., Inland, Marine and Total fish production, in the fourth decade of study period, i.e., 2003-2012. Present study revealed that the model ARIMA (0,1,3) was best fit for forecasting marine fish production of India while ARIMA (0,2,1) was best model for both inland and overall fish production. The model forecasted that the fish production in all three categories would be in increasing trend for the next five years from 2022-23 to 2026-27 with highest estimated marine, inland and total fish production of 42.18 lakh tonne 162.78 lakh tonne and 211.64 lakh tonnes respectively during the year 2026-27.

Does fish production influence the GDP and food security in Gulf Cooperation Council countries? Evidence from the dynamic panel data analysis

The significance of fish production in human life has been proven in terms of food security as well as in increasing fishery income and economic growth. This study investigates the model of the long-run connection between marine fish production, economic growth, and food security. The study applies an annual panel data set comprising six Gulf Cooperation Council countries, covering the sample period 2000–2019. We have selected marine fish production as most of the fish production in the Gulf region is originated from marine sources with broad geographic distribution and better accessibility of marine fish production data. Testing the recently developed panel unit root tests, cointegration test, and the heterogeneous dynamic panel data named as a mean group, pooled mean group, and dynamic fixed effect estimation techniques on cross-country panel data, the result confirms the theoretical hypothesis of a positive relationship between fish export and GDP and fish import and food security. The Hausman tests suggest that the pooled mean group results are the appropriate ones. Further studies need to re-investigate the potential of fish production in explaining the economic growth and food security to meet the fast-growing population in Gulf Cooperation Council countries.

Predictive modeling of marine fish production in Brunei Darussalam's aquaculture sector: A comparative analysis of machine learning and statistical techniques

The aquaculture industry has witnessed significant global growth, offering opportunities for sustainable fish production. This research delves into the application of data analytics to develop an appropriate predictive model, utilizing diverse machine learning and statistical techniques, to forecast marine fish production within Brunei Darussalam's aquaculture sector. Employing a machine learning-based algorithm, the study aims to achieve enhanced prediction accuracy, thereby providing novel insights into fish production dynamics. The primary objective of this research is to equip the industry with alternative decision-making tools, leveraging predictive modeling, to identify trends and bolster strategic planning in farm activities, ultimately optimizing marine fish aquaculture production in Brunei. The study employs various time series and machine learning techniques to generate a precise predictive model, effectively capturing the inherent seasonal and trend patterns within the time-series data. To construct the model, the research incorporates notable algorithms, including autoregressive integrated moving average (ARIMA), long short-term memory (LSTM), linear regression, random forest, multilayer perceptron (MLP), and Prophet, in conjunction with correlation analysis. Evaluation of the model's performance and selection of the optimal forecasting model are based on mean absolute percentage error (MAPE) and root mean squared error (RMSE) metrics, ensuring a robust analysis of time series data. Notably, this pioneering research stands as the first-ever attempt to forecast marine fish production in Brunei Darussalam, setting a benchmark unmatched by any existing baseline studies conducted in other countries. The experiment's results reveal that straightforward machine learning and statistical techniques, such as ARIMA, linear regression, and random forest, outperform deep learning methods like MLP and LSTM when forecasting univariate time series datasets.

Analysis of Trade, Investment, and Global Value Chain on the Gross Domestic Product of Fisheries Sector in Indonesia

Purpose: This study aims to analyze the indicators that play a role in the Indonesian marine and fisheries sector's development (investment in the fisheries sector, trade openness, fisheries net exports, global value chain (GVC) and fish production) towards the gross domestic product (GDP) of the fisheries sector. Theoretical framework: The theoretical framework builds on the New Trade Theory (NTT), a sustainable ocean economy, and global value chains. Design/methodology/approach: The panel vector autoregressive (PVAR) method is used to achieve the research gols. The data used is panel data for groups of provinces in Indonesia. Findings: The results of the estimated PVAR show that the investment of the fisheries sector has a negative and significant impact on the GDP of the fisheries sector, and GVC and fish production has a positive and significant impact on fisheries GDP. Whereas, Net-export and Trade openness have an insignificant impact on the GDP of fisheries. Research, Practical & Social implications: There is a need for an effective maritime policy to reduce the environmental impact of the development of coastal resources and preserve the diversity of fish production to face future threats. This is due to the constraints of international trade in the form of information or communication, technology, and regulations. Originality/value: Research related to the effect of investment in the fisheries sector, trade openness, fisheries net exports, GVC, and fish production on the GDP fishery in Indonesia and the data used is panel data for groups of provinces in Indonesia have never been done by other researchers.

Sustainable intensification of small-scale mariculture systems: Farm-level insights from the coastal regions of India

This study undertakes a comprehensive assessment of selected mariculture enterprises in the coastal regions of India, centered on long-term sustainability as the key focus. This is juxtaposed against India's ambitious blue economy targets and policy thrust that pin on the expansion of mariculture as a promising avenue for enhancing marine fish production. Farm-level, region-specific, techno-economic, and socio-cultural factors associated with, and conditional on, sustainable intensification of mariculture-based production systems are examined in detail. The Principles-Criteria-Indicators (PCI) approach is used to establish the linkage between identified farm-level indicators and various dimensions of sustainability. While the selected enterprises were assessed to be technically and economically viable in general, glaring gaps were evident on key indicators of sustainability such as the legitimacy of access over water bodies, use of quality seed and feed, institutional credit access, market access, and fair marketing practices, optimal stocking density, mechanization, use of renewable energy, adoption of environmental-friendly culture practices, farm surveillance, crew safety, and social protection. This indicates the need for taking proactive measures to ensure the long-term sustainability of mariculture, particularly in the initial stages of establishment when such interventions are easy to adopt. Based on the insights obtained from the analysis, a broad set of strategies, policy options, and institutional interventions critical to scaling-up coastal mariculture enterprises along the east and west coasts of India are presented.

Faktor-Faktor yang Mempengaruhi Permintaan Ikan Laut di Desa Jetis, Kecamatan Nusawungu, Kabupaten Cilacap

Cilacap Regency has a fairly high production of marine fish, but it is not matched by the high level of consumption of marine fish by its people. However, based on data per village, Jetis Village in Nusawungu District is the highest village in terms of consumption of marine fish when compared to other areas. Therefore, it is interesting to study the factors that influence the demand for marine fish in Jetis Village, Nusawungu District, Cilacap Regency. The purpose of this study was to analyze the effect of the price of marine fish, the price of other goods, income, and the number of household members on the demand for marine fish in Jetis Village, Nusawungu District, Cilacap Regency. This study uses primary data obtained through interviews and distributing questionnaires to 96 households in Jetis Village. Data were analyzed using multiple linear regression model. The results showed that the price of marine fish and income, did not have a significant effect on the demand for marine fish in Jetis Village. Meanwhile, the price of other goods and the number of household members have a significant influence on the demand for marine fish in Jetis Village. The implication of this research is that although the price of marine fish has no significant effect on the demand for marine fish, the stability of the price of marine fish is still important for the community so that the price of marine fish will remain affordable. Since the prices of other goods are complementary to the consumption of marine fish, it is important to pay attention to price stability for other goods, such as the price of chicken meat, the price of freshwater fish, and the price of eggs.

Impact of Climate Change on Aquatic Marine Fish Production: A Multivariate and Causality Analysis

Impact of Climate Change on Aquatic Marine Fish Production: A Multivariate and Causality Analysis

Impact of climate change on aquatic marine fish production: a multivariate and causality analysis

Impact of climate change on aquatic marine fish production: a multivariate and causality analysis

The impact of climate variables on marine fish production: an empirical evidence from Bangladesh based on autoregressive distributed lag (ARDL) approach.

Several studies have highlighted the significant impact of climate change on agriculture. However, there have been little empirical enquiries into the impact of climate change on marine fish production, particularly in Bangladesh. Hence, this study aims to investigate the impact of climate change on marine fish production in Bangladesh using data from 1961 to 2019. Data were obtained from the Food and Agriculture Organization, Bangladesh Meteorological Department, the World Development Indicators, and the National Oceanic and Atmospheric Administration. The autoregressive distributed lag (ARDL) model was used to describe the dynamic link between CO2 emissions, average temperature, Sea Surface Temperature (SST), rainfall, sunshine, wind and marine fish production. The ARDL approach to cointegration revealed that SST (β = 0.258), rainfall (β =0.297), and sunshine (β =0.663) significantly influence marine fish production at 1% and 10% levels in the short run and at 1% level in the long run. The results also found that average temperature has a significant negative impact on fish production in both short and long runs. On the other hand, CO2 emissions have a negative impact on marine fish production in the short run. Specifically, for every 1% rise in CO2 emissions, marine fish production will decline by 0.11%. The findings of this study suggest that policymakers formulate better policy frameworks for climate change adaptation and sustainable management of marine fisheries at the national level. Research and development in Bangladesh's fisheries sector should also focus on marine fish species that can resist high sea surface temperatures, CO2 emissions, and average temperatures.

Bioeconomic Modelling in Sustainable Fisheries Management of Commercial Marine Fisheries in Kelantan, Malaysia

Bioeconomic modelling is an important issue in sustainable fisheries management of commercial marine fisheries in Kelantan, Malaysia. Previous studies focus on the impact of trawling on fisheries, tourism and the socio-environment, which overfishing is a direct threat to local fishing communities. In addition, bottom trawl fishing may affect fishing, environment and socio-economic management objectives. Moreover, fishing activities lead to changes in the structure of marine habitats and affect the diversity, composition, biomass and productivity of related biota. Finally, the previous research discussed on challenges of the fisheries industry in peninsular Malaysia. Studies focused on bioeconomic modelling in sustainable management of commercial marine fisheries in terms of fishing gear, climate changes, and anthropogenic disturbances are still limited. This study aims to investigate the sustainability of marine fish production and analyse the potential effect of climate changes and anthropogenic disturbances that affect fisheries activities. The theory and practice of the bioeconomic surplus production model by Gordon - Schafer (GS) are used to calculate the total biology and economic value. The result of the study indicated that trawl nets, anchovy purse seinses, climate changes and anthropogenic disturbances affect the sustainable management of commercial marine fisheries in Kelantan, Malaysia.

Fisheries Sector in Kerala: Emerging Issues and Policy Responses

The present paper discusses the status of the fisheries sector in Kerala with emphasis on emerging issues and policy responses. In Kerala, still, the traditional fishermen dominate the field although in recent times the State has been witnessing the emergence of a dual structure with the traditional sector on the one side and the modern fishing sector on the other. There has been a mismatch between fish production and consumption in the State. The fishermen’s per capita income growth has been volatile in the State while the per capita income of an average Keralite has almost remained stable. Marine fish production forms the major share of total fish production in the State. It is obvious that at the all India level, the overall fish production registered a remarkable increase from 107.62 lakh tones in 2015-16 to 137.5 lakh tonnes in 2019-20, a growth of over 30 per cent. But at the same time, Kerala’s fish production did not make a substantial improvement which is evident from the fact in 2019-20, its fish production actually declined to 6.8 lakh tones from 7.27 lakh tones in 2015-16. Climate changes, oceanic plastic waste, infrastructural bottlenecks, the vulnerability of fishermen have been identified as emerging issues.

Forecasting of the different hydro-climatic variables and impact of climate change on marine fish production in West Bengal, India

Forecasting of the different hydro-climatic variables and impact of climate change on marine fish production in West Bengal, India

Molecular Characterization of Cryptosporidium spp. in Cultivated and Wild Marine Fishes from Western Mediterranean with the First Detection of Zoonotic Cryptosporidium ubiquitum.

Simple SummaryCryptosporidium is a widespread pathogen that infects a broad range of vertebrates, including humans, in which it is one of the main causes of diarrhea worldwide. Marine fishes also harbor Cryptosporidium species, including zoonotic ones. The goal of this study is to evaluate the presence of Cryptosporidium species in edible marine fishes using molecular tools. The area of study, located in the Western Mediterranean, is an important area for marine fish production and capture. The following three groups were studied: cultivated fish, wild fish that aggregate in the surroundings of marine fish farms and wild fish from extractive fisheries. Results show that the most affected group is the group of wild fish from the vicinity of fish farms. Two species were mainly identified, C. molnari (fish specific) and zoonotic C. ubiquitum. The presence of zoonotic C. ubiquitum in two European sea bass (Dicentrarchus labrax) highlights a potential risk for fish consumers.Fish not only harbor host-specific species/genotypes of Cryptosporidium, but also species like zoonotic C. parvum or anthroponotic C. hominis, which can pose a risk for fish consumers. This study aims to investigate fish cryptosporidiosis in an important aquaculture and fishery area of the Western Mediterranean (Comunidad Valenciana, Spain). We analyzed 404 specimens belonging to the following three groups: cultivated fish (N = 147), wild synanthropic fish (N = 147) and wild fish from extractive fisheries (N = 110). Nested PCR targeting the 18S rRNA gene, followed by sequencing and phylogenetic analysis, were performed. Positive isolates were also amplified at the actin gene locus. An overall prevalence of 4.2% was detected, with the highest prevalence in the synanthropic group (6.1%). C. molnari was identified in thirteen specimens from seven different host species. Zoonotic C. ubiquitum was detected in two European sea bass (Dicentrarchus labrax). One isolate similar to C. scophthalmi was detected in a cultivated meagre (Argyrosomus regius), and one isolate, highly divergent from all the Cryptosporidium species/genotypes described, was identified from a synanthropic round sardinella (Sardinella aurita). This study contributes to increasing the molecular data on fish cryptosporidiosis, expanding the range of known hosts for C. molnari and identifying, for the first time, zoonotic C. ubiquitum in edible marine fishes, pointing out a potential health risk.

Numerical Flow Modeling of Artificial Ocean Upwelling

Artificial Upwelling (AU) of nutrient-rich Deep Ocean Water (DOW) to the ocean's sunlit surface layer has recently been put forward as a means of increasing marine CO2 sequestration and fish production. AU and its possible benefits have been studied in the context of climate change mitigation as well as food security for a growing human population. However, extensive research still needs to be done into the feasibility, effectiveness and potential risks, and side effects associated with AU to be able to better predict its potential. Fluid dynamic modeling of the AU process and the corresponding inorganic nutrient transport can provide necessary information for a better quantification of the environmental impacts of specific AU devices and represents a valuable tool for their optimization. Yet, appropriate capture of all flow phenomena relevant to the AU process remains a challenging task that only few models are able to accomplish. In this paper, simulation results obtained with a newly developed numerical solution method are presented. The method is based on the open-source modeling environment OpenFOAM. It solves the unsteady Reynolds-Averaged Navier-Stokes (RANS) equations with additional transport equations for energy, salinity, and inorganic nutrients. The method aims to be widely applicable to oceanic flow problems including temperature- and salinity-induced density stratification and passive scalar transport. The studies presented in this paper concentrate on the direct effects of the AU process on nutrient spread and concentration in the ocean's mixed surface layer. Expected flow phenomena are found to be captured well by the new method. While it is a known problem that cold DOW that is upwelled to the surface tends to sink down again due to its high density, the simulations presented in this paper show that the upwelled DOW settles at the lower boundary of the oceans mixed surface layer, thus keeping a considerable portion of the upwelled nutrients available for primary production. Comparative studies of several design variants, with the aim of maximizing the amount of nutrients that is retained inside the mixed surface layer, are also presented and analyzed.

Comparative study of statistical and machine learning techniques for fish production forecasting in Andhra Pradesh under climate change scenario

The present study emphasizes the forecast of Andhra Pradesh's total marine fish production and the catch of commercially important fishes, viz., Indian Mackerel, Oil Sardine, Horse Mackerel, and Lesser Sardines for the next 5 years by different statistical and machine learning approaches under climate change scenario. Forecasting is done with and without the inclusion of climatic and environmental parameters in different models. Exogenous variables, i.e., climatic parameters such as Sea Surface Temperature (SST), wind speed, and environmental parameters such as Chlorophyll-a, diffusion attenuation coefficient, and Photo-synthetically Active Radiation (PAR), were used in the model. The following models like Non-linear Autoregressive (NAR) Artificial Neural Network (ANN) (NNAR-ANN), Auto-Regressive Integrated Moving Average (ARIMA), Empirical Mode Decomposition based Artificial Neural Network (EMD-ANN), are used to predict the fish catch data using time series quarterly catch data of commercially important fishes and total fish catch without the inclusion of climatic and environmental variables. Auto Regressive Integrated Moving Average method with inclusion of exogenous variables (ARIMAX) and Non-Linear Auto Regression with exogenous variables (NARX) models were used to forecast along with quarterly average data of environmental and climatic variables. The model developed predicts the total fish catch and also the catch of commercially important fish for the next 20 quarters. The developed model forecasts are compared based on the error measure, i.e., MAPE (Mean Absolute Percentage Error), and the results showed that the NARX model outperformed other models like ARIMAX, ARIMA, NNAR-ANN, and EMD-ANN. Implementation of management strategies considering the impact of climate change on fisheries will enhance sustainable fisheries and pave a pathway for the mitigation of climate change.

Comparison of Exponential Smoothing Methods for Forecasting Marine Fish Production in Pekalongan Waters, Central Java

Pekalongan waters, a part of the Java Sea, has potency to develop marine fisheries sector to increase regional income and community livelihoods. The fluctuation of marine fish production every year requires serious attention in planning and policy strategies for the utilization of the fishery resources. Time series fish production data can be used to predict fish production in the following years through the forecasting process. The data used in this study is fish production data from Pekalongan Fishing Port, Central Java, from January 2011 to December 2020. The method used is data exponential smoothing by comparing three exponential smoothing methods consisting of single/simple exponential smoothing, double exponential smoothing and Holt-Winters’ exponential smoothing. The criterion that used to measure the forecasting performance is the mean absolute percentage error (MAPE) value. The smaller MAPE value shows the better the forecasting result. The smallest MAPE value is obtained by finding the optimal smoothing constant value which is usually calculated using the trial and error method. However, in this study, the constant value was calculated using the add-in solver approach in Microsoft Excel. The forecasting results obtained show that forecasting using the Holt Winter Exponential Smoothing method is reasonable with a MAPE value of 37.878.

Developing an Ensembled Machine Learning Prediction Model for Marine Fish and Aquaculture Production

The fishing industry is identified as a strategic sector to raise domestic protein production and supply in Malaysia. Global changes in climatic variables have impacted and continue to impact marine fish and aquaculture production, where machine learning (ML) methods are yet to be extensively used to study aquatic systems in Malaysia. ML-based algorithms could be paired with feature importance, i.e., (features that have the most predictive power) to achieve better prediction accuracy and can provide new insights on fish production. This research aims to develop an ML-based prediction of marine fish and aquaculture production. Based on the feature importance scores, we select the group of climatic variables for three different ML models: linear, gradient boosting, and random forest regression. The past 20 years (2000–2019) of climatic variables and fish production data were used to train and test the ML models. Finally, an ensemble approach named voting regression combines those three ML models. Performance matrices are generated and the results showed that the ensembled ML model obtains R2 values of 0.75, 0.81, and 0.55 for marine water, freshwater, and brackish water, respectively, which outperforms the single ML model in predicting all three types of fish production (in tons) in Malaysia.