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Review

Recirculating Aquaculture Systems (RAS) for Cultivating Oncorhynchus mykiss and the Potential for IoT Integration: A Systematic Review and Bibliometric Analysis

by
Dorila E. Grandez-Yoplac
1,
Miguel Pachas-Caycho
2,
Josseph Cristobal
2,
Sandy Chapa-Gonza
1,
Roberto Carlos Mori-Zabarburú
1 and
Grobert A. Guadalupe
3,4,*
1
Instituto de Investigación, Innovación y Desarrollo para el Sector Agrario y Agroindustrial (IIDAA), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Calle Higos Urco N° 342-350-356—Calle Universitaria N° 304, Chachapoyas 01001, Peru
2
Instituto de Investigación en Negocios Agropecuarios (INNA), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Calle Higos Urco N° 342-350-356—Calle Universitaria N° 304, Chachapoyas 01001, Peru
3
Grupo de Investigación en Seguridad Alimentaria (GISA), Facultad de Ingeniería y Ciencias Agrarias (FICA), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Calle Higos Urco N° 342-350-356—Calle Universitaria N° 304, Chachapoyas 01001, Peru
4
Instituto Universitario de Ingeniería de Alimentos Food-UPV, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(15), 6729; https://doi.org/10.3390/su17156729
Submission received: 3 June 2025 / Revised: 17 July 2025 / Accepted: 21 July 2025 / Published: 24 July 2025
(This article belongs to the Special Issue Sustainability in Aquaculture)
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Versions Notes

Abstract

The objective of this research was to conduct a comprehensive review of rainbow trout (Oncorhynchus mykiss) culture in recirculating aquaculture systems (RAS), identify knowledge gaps, and propose strategies oriented towards intelligent and sustainable aquaculture. A systematic review and bibliometric analysis of 387 articles published between 1941 and 2025 in the Scopus database was carried out. Since 2011, there has been a sustained growth in scientific production, with the United States, Denmark, Finland, and Germany standing out as the main contributors. The journals with the highest number of publications were Aquacultural Engineering, Aquaculture, and Aquaculture Research. The conceptual analysis revealed the following three thematic clusters: experimental studies on physiology and metabolism; research focused on nutrition, growth, and yield; and technological developments for water treatment in RAS. This evolution reflects a transition from basic approaches to applied technologies oriented towards sustainability. There was also evidence of a thematic transition toward molecular tools such as proteomics, transcriptomics, and real-time PCR. However, there is still limited integration of smart technologies such as the IoT. It is recommended to incorporate self-calibrating multi-parametric sensors, machine learning models, and autonomous systems for environmental regulation in real time.

1. Introduction

Aquaculture is currently the fastest-growing food production sector worldwide [1] and plays an important role in global food security by providing 49% of all aquatic products consumed [2]. Within this context, rainbow trout (Oncorhynchus mykiss) culture represents an important part of global aquaculture, standing out with 1.72% in 2020 [3]. Its high growth rate and nutritional content make it an attractive option for human consumption [4]. It is the dominant salmonid in Europe and North America, although its production faces challenges related to animal welfare, culture densities, and water quality [3,5]. In addition, it is exposed to diseases such as infectious pancreatic necrosis [6] and environmental risks such as heavy metal accumulation, excessive use of water resources, waste generation, and impacts on natural aquatic ecosystems [7].
In response to these challenges, recirculating aquaculture systems (RAS) have emerged as a sustainable technological alternative for the culture of high-value aquatic species. RAS represent an intensive technology that allows the reuse of more than 95% of water through mechanical and biological processes, such as biofiltration and disinfection [2,8]. These systems significantly reduce water consumption and environmental impact [7,9], improving biosecurity, disease control, and efficiency in aquaculture production [10]. However, they face technical and economic challenges such as high energy consumption, operating costs, and nitrate accumulation, which can affect water quality and generate off-flavours in fish [3,11].
Several studies have documented the incorporation of IoT-based smart monitoring platforms that advance the application of RAS for different aquaculture species, reporting benefits in terms of efficiency, mortality reduction, and water quality control [12,13]. However, the available literature shows a concentration of research on species such as Atlantic salmon (Salmo salar), European sea bass (Dicentrarchus labrax), sea bream (Sparus aurata), yellowtail kingfish (Seriola lalandi), Arctic char (Salvelinus alpinus), tilapia (Oreochromis niloticus), and shrimp (Litopenaeus vannamei) [14,15,16,17,18,19]. In this context, there is a knowledge gap related to the systematisation of technological advances in aquaculture recirculation systems for rainbow trout, particularly regarding the incorporation of IoT-based smart monitoring platforms and real-time data analysis.
This research presents an original contribution by critically examining recent developments in RAS technologies and their applications in rainbow trout farming, analysing their implications for the sustainability, productivity, and resilience of the aquaculture sector. The objective of this research was to carry out an exhaustive study of rainbow trout farming in RAS, as well as to identify research gaps and propose strategies for future innovations in the framework of smart and sustainable aquaculture.

2. Materials and Methods

2.1. Literature Search and Screening

The PRISMA framework was used to perform a comprehensive systematic review of previously conducted research [20,21]. Scopus was selected as the database to be used for the collection of publications because of its extensive coverage of the scientific literature, its high indexing standards, and the availability of specialised tools for advanced bibliometric analysis [22]. Titles, abstracts, and keywords, including the following words, were included in the search criteria: trout or “Oncorhynchus mykiss” and RAS or “Recirculating Aquaculture Systems” or recirculating or recirculation. These articles were then reviewed to confirm their suitability for inclusion (Figure 1). Only studies written in English, available in full text form, in the final stage of publication, and document-type articles were included. Conference papers, reviews, errata, book chapters, short surveys, and letters were excluded.
A total of 432 papers were identified. In the screening phase, conference papers (n = 21), reviews (n = 9), errata (n = 4), book chapters (n = 3), short surveys (n = 1), and letters (n = 1) were excluded, leaving a total of 393 papers. In the eligibility phase, of the 393 articles, only papers in English were selected (selection lasted until 15 May 2025). Finally, 387 articles were included in the review. Subsequently, the abstracts of the remaining 387 articles were reviewed to identify those studies that implemented recirculating aquaculture systems (RAS) in trout culture. Finally, 49 studies were included in the review.

2.2. Data Synthesis and Analysis

A bibliometric analysis of the information was performed following the stages and studies of RStudio (Bibliometrix, version 4.3.3) described by Guadalupe et al. [23] (see Figure S1). The documents were exported in .cvs. format and analysed with the free software R (v 4.4.1), combined with the Bibliometrix package (version 4.3.3) [24]. The study findings were reviewed and analysed to ensure accurate interpretation. The results of the analyses are presented in tables and figures.

3. Bibliometric Analysis

3.1. Production per Year

The study of recirculating aquaculture systems (RAS) associated with trout began in 1940 with the first recorded publication on the subject. Between 1941 and 1980, research in this area was limited, resulting in no publications per year. From 1983 to 2010, there was a slight increase in activity, with the number of papers published annually ranging from 0 to 10. However, it was not until after 2011 that research in this field experienced a substantial boom, when the research output began to grow until it peaked at 28 publications in both 2020 and 2024 (Figure 2). In the first months of 2025 (January to 15 May 2025), 10 publications were produced.

3.2. Scientific Production by Country

Figure 3 shows the distribution of the corresponding countries of the authors of the 387 analysed publications on RAS applied to Oncorhynchus mykiss. The United States leads with the highest number of documents, mostly from single-country publications (SCP). Denmark, Finland, and Bulgaria also show strong national research activity. Germany, Poland, and Canada exhibit a balance between SCP and international collaborations (MCP), reflecting active research networks. Countries like Chile, Mexico, and Iran contribute fewer studies but indicate growing interest in using RAS for rainbow trout farming. European countries, particularly France and the UK, show a higher proportion of MCP, highlighting international cooperation in this research field.

3.3. Contribution of the Journals

The journals were analysed according to their number of publications and their ranking in the Scimago Journal & Country Rank (SJR) during the year 2024 (SJR, 2025). Figure 4 presents the 20 most relevant journals and the number of articles published. It was observed that 40% (8 journals) are located in the first quartile, 35% (7 journals) in the second quartile, 15% (3 journals) in the third quartile, and 10% (2 journals) in the fourth quartile. The journal Aquacultural Engineering tops the list with 65 publications, followed by Aquaculture with 38 publications and Aquaculture Research with 16 publications.

3.4. Authors’ Contributions

The expertise of an author in a specific field is usually determined by the number of scientific articles published and the number of citations received by each. According to this criterion, the most influential author in the field during the study period was Pederson PB, with 28 publications and 1096 citations (from 2010 to 2025), followed by Pederson L.F, with 21 publications and 593 citations (from 2010 to 2025), and Davidson J, with 17 publications and 868 citations (from 2009 to 2023). It was observed that most of the leading researchers in the subject came from the United States and Denmark, which is consistent with the progressive growth of these countries’ scientific production (see Figure 5).

3.5. Keyword Analysis

The keyword analysis was performed with the Bibliometrix program. The results reveal that the extracted terms can be grouped into three main clusters. Figure 6 presents the conceptual structure map generated by the Multiple Correspondence Analysis (MCA) method, which allows the identification and visualisation of thematic groupings within the analysed bibliographic corpus. This map represents the conceptual distribution of the publications related to the study of rainbow trout in the context of aquaculture and water treatment in recirculation systems.
Three main clusters are identified, which are represented by coloured polygons. The green cluster (left zone) groups terms related to controlled and experimental studies in animals, including “controlled study”, “nonhuman”, “experiment”, “animal”, “animals”, “metabolism”, and “rainbow trout”. This cluster represents a thematic line focused on physiological and metabolic studies in model animals, especially rainbow trout. Its location suggests a more traditional and experimental approach in laboratory conditions.
The blue cluster (upper zone) includes terms such as “diet”, “growth rate”, “survival”, “salmonidae”, “salmonid culture”, and “aquaculture system”. This group reflects research related to the nutrition, growth, and productive performance of salmonid species in aquaculture systems, with emphasis on diet and physiological parameters such as growth rate and survival. The term “Oncorhynchus mykiss” appears in connection to both the green and blue zones, acting as a node of intersection between physiological and productive performance studies.
The red cluster (lower right zone) includes terms such as “recirculating aquaculture system”, “water quality”, “biofiltration”, “denitrification”, “bioreactor”, “effluents”, and “pollutant removal”. This group represents a thematic line focused on water treatment and the microbiological and technological aspects associated with aquaculture recirculation systems. It shows approaches oriented towards sustainability, pollutant control (ammonia, nitrogen), and process efficiency in bioreactors.
Overall, the map reveals a clear thematic differentiation in the body of literature analysed, from basic and experimental studies on animals to applied approaches in aquaculture production and water quality management in recirculation systems. The spatial arrangement of the clusters reflects a conceptual evolution from fundamental laboratory research (left), through productive and physiological optimisation (top), to system-wide engineering solutions (bottom right). This gradient suggests a multidisciplinary progression in rainbow trout aquaculture research, integrating biological, nutritional, and technological dimensions. However, some notable thematic gaps are evident:
  • Disease diagnostics and fish health monitoring, which are crucial for maintaining stock health in intensive aquaculture, are underrepresented. Terms such as pathogen, disease, immune response, and diagnosis do not appear prominently, suggesting either a separate research stream not captured in this analysis or a gap in the integration of health diagnostics with RAS-focused trout research.
  • Energy efficiency and climate impact, which are increasingly central to the sustainability of aquaculture systems, are also not reflected among the prominent keywords. Terms like energy use, carbon footprint, and renewable energy are absent, indicating an opportunity for future research to address the environmental footprint of RAS technologies.
  • Similarly, socioeconomic and policy-related terms are missing, which may reflect the technical focus of the current literature, and there is a lack of interdisciplinary studies that assess adoption barriers, cost–benefit analysis, or farmer perceptions, particularly in low- and middle-income regions.

4. Aquaculture Recirculating Aquaculture Systems (RAS) for Oncorhynchus mykiss

Of the 387 scientific articles included in the bibliometric analysis, an exhaustive review of the content of each one identified 49 publications that have reported on trout culture under RAS. These articles were published between 2000 and 2025. Table 1 presents information on the year of publication, the country where the study was developed, the monitored water quality parameters (temperature, dissolved oxygen, nitrite, nitrate, ammonia, total suspended solids, electrical conductivity, turbidity), the system characteristics, the filter type, and the reference.
Since the first documented implementation of RAS in Oncorhynchus mykiss culture in Denmark in 2000 [25], a progressive standardisation of water physicochemical parameters has been observed. From 2000 to 2025, notable improvements were observed in the monitoring and control of water parameters in recirculating aquaculture systems (RAS) for Oncorhynchus mykiss. Early studies relied predominantly on manual or semi-automated techniques to measure basic parameters such as temperature and dissolved oxygen. However, over time, technological progress, particularly the integration of Internet of Things (IoT) systems and multisensor platforms, has enabled the real-time monitoring of a wider array of water quality variables, including pH, ammonia (NH4+), carbon dioxide (CO2), nitrate (NO3−), and turbidity.
These advancements have allowed for more precise environmental control, reductions in stress-related events, and the optimisation of feeding and waste management protocols. Moreover, the development of automated alarm systems and feedback loops in RAS has facilitated proactive interventions, improving fish health and operational sustainability. Nevertheless, parameters such as sulphates (SO42−), phosphates (PO43−), and trace metals remain under-monitored, representing an opportunity for future technological adoption in smart aquaculture.
Temperatures between 13 and 18 °C, a pH in the range of 7.0 to 7.5, and dissolved oxygen levels above 8 mg/L are the conditions recurrently reported in high-quality studies. These parameters are critical for maintaining a stable environment and reducing physiological stress in fish, which translates into improvements in feed conversion rate, growth, and survival. Recent studies, such as those of De Jesus Gregersen & Pedersen [67], confirm that these optimal values are replicable in both experimental and commercial systems.
The RAS used for rainbow trout culture vary widely in their configuration, from experimental tanks of 0.17 m3 [30] to large commercial units of more than 1000 m3 [72]. This structural diversity reflects the adaptability of RAS to different levels of production scale and budget. Tank design directly influences flow dynamics, gas exchange efficiency, and residue removal. Circular tanks with underdrainage, widely used in Denmark, Morocco, and Chile, facilitate the concentration of solids for efficient removal, while rectangular raceway configurations, observed in Finland [66], allow a more uniform distribution of water flow.
Water treatment in RAS relies heavily on an efficient combination of mechanical and biological filtration. Microscreen drum filters with mesh sizes between 45 and 100 μm are most commonly used for suspended solids removal [52,54,73,74], while fixed or fluidised bed biofilters play a critical role in the transformation of ammonium to nitrate by nitrifying bacteria. The incorporation of advanced technologies such as ozonation, UV sterilisation, and foam fractionation, reported in Denmark and Greece, evidences an effort to minimise microbial load and dissolved organic compounds. These advanced treatments are especially relevant for high-density systems, where the risks of toxic compound accumulation increase significantly.

5. Integration of IoT Technologies in RAS

Despite the technical development of RAS, there is still limited adoption of smart technologies for real-time monitoring of water quality. Although studies report constant monitoring of variables such as pH, oxygen, temperature, and turbidity, most of these processes still rely on manual measurements or individual equipment without connectivity [50,69]. The incorporation of IoT sensors connected to analysis platforms would allow continuous monitoring of critical parameters such as nitrite, ammonium, and solids, and more accurate management of the systems. This automation would not only improve operational efficiency, but also reduce the risks of filtration failures or biofiltration collapses.
The recent evolution of RAS has been largely driven by the incorporation of digital technologies, particularly those related to the Internet of Things (IoT). The incorporation of IoT technologies in aquaculture has enabled monitoring of key parameters such as dissolved oxygen, pH, temperature, and ammonia in real time using sensors connected to cloud platforms [75,76]. This enables remote, preventive, and efficient system management, improving productivity and reducing risks [77]. Data analytics and the use of artificial intelligence make it possible to model environmental conditions and optimise processes such as feeding and energy use, thus contributing to crop sustainability [77,78]. This digitisation not only optimises the management of aquaculture systems, but also presents new possibilities for process automation, the prediction of adverse events, and continuous improvement of culture productivity and sustainability.
Lee et al. [79] developed an environmental monitoring system for a fish farm using sensors for temperature, pH, and dissolved oxygen (DO), whose data were processed through a Raspberry Pi microcontroller connected to a Wi-Fi network [22]. Complementarily, Suhaili et al. [80] used the same processing platform along with an ESP8266 module to transmit real-time information from temperature, salinity, total dissolved solids (TDS), pH, and DO sensors to the Cayenne IoT platform. This integration not only facilitated remote data visualisation, but also allowed the automation of devices such as lighting systems, heaters, and automatic feeders [22]. Meanwhile, Libao et al. [81] evaluated the effectiveness of automated monitoring based on IoT sensors for pH, salinity, DO, and temperature, demonstrating a significant reduction in error margins compared to manual methods, as well as improved control of system conditions, higher productivity, and decreased mortality caused by pathogens. Connectivity technologies such as LoRaWAN and NB-IoT have gained popularity due to their low energy consumption and wide coverage, especially in rural areas [82]. Finally, visualisation platforms such as Grafana and cloud-based analytics solutions enable real-time predictive processing and monitoring, providing key tools for decision making in smart aquaculture systems. Table 2 presents an overview of sensor and communication technologies with potential for integration into Oncorhynchus mykiss culture in RAS, evaluated based on their monitoring and automation capabilities across key water quality parameters.
Combining RAS with smart technologies, such as the Internet of Things (IoT), represents a powerful strategy for moving towards more sustainable aquaculture. The integration of real-time sensors would improve production yields, reduce water and energy use, and reduce nitrogen emissions to the environment. The integration of Recirculating Aquaculture Systems (RAS) and Internet of Things (IoT) technologies aligns with and actively contributes to several Sustainable Development Goals (SDGs), particularly the following:
  • SDG 2 (Zero Hunger): Smart RAS can increase aquaculture productivity through precision control of environmental parameters, which enhances fish health and feed conversion efficiency. For example, the use of IoT-based water quality monitoring systems has been associated with up to 30% reductions in mortality rates and improved yield consistency in trout farming [83,84].
  • SDG 12 (Responsible Consumption and Production): RAS significantly reduce water use by up to 90% compared to traditional systems, while IoT enables real-time resource tracking (e.g., feed, energy, water) and optimisation. This fosters more efficient and sustainable production cycles with quantifiable metrics such as litres of water used per kilogram of fish produced [85].
  • SDG 13 (Climate Action): By reducing dependence on open water bodies and minimising effluent discharge, RAS mitigate the impact of climate-related variability (e.g., temperature or water availability). IoT platforms also allow the modelling and anticipation of environmental risks (e.g., oxygen drops), enabling adaptive responses to climate stressors [86].
  • SDG 14 (Life Below Water): The adoption of closed-loop systems like RAS reduces aquaculture’s ecological footprint, particularly regarding nutrient runoff, antibiotic use, and escape of farmed species. IoT integration ensures early detection of water quality issues, preventing the discharge of harmful effluents into natural water bodies [87].
Together, these technologies contribute not only to production efficiency, but also to environmental stewardship, positioning smart aquaculture as a critical enabler of sustainable food systems.

6. Current Challenges, Future Prospects, and Limitations

The analysis of bibliometric trends revealed a progressive transition from classical topics such as dissolved oxygen, growth response, and biofiltration to more sophisticated approaches focused on proteomics, transcriptomics, and molecular techniques such as real-time PCR (Figure 7). This evolution reflects a shift towards an aquaculture based on genomic and microbiological evidence for disease control and growth optimisation. Terms such as “water quality”, “nitrification”, “recirculating aquaculture system”, and “Oncorhynchus mykiss” have maintained a high frequency since 2013, evidencing sustained interest in the sustainability of trout culture in closed systems. However, the implementation of smart RAS still faces significant challenges, such as high installation costs, connectivity issues in rural areas, and the technical demands of sensor maintenance and calibration.
To address these challenges, the following future directions are proposed, integrating both existing experimental evidence and considerations of technological feasibility:
  • The development and validation of self-calibrating multi-parameter sensors capable of measuring pH, dissolved oxygen, ammonia, and turbidity in real time. Experimental systems such as those developed by Lee et al. [79] and Suhaili et al. [80] using Raspberry Pi and ESP8266 platforms have demonstrated the potential for low-cost sensor networks with wireless connectivity, enabling effective monitoring and automation in aquaculture environments.
  • The integration of Internet of Things (IoT) technologies in commercial-scale RAS through the use of energy-efficient communication protocols such as LoRaWAN and NB-IoT, which are particularly suitable for remote aquaculture facilities. These technologies allow for extended battery life, wide-area connectivity, and data transmission to cloud-based platforms for real-time analysis and decision making.
  • The application of machine learning and artificial intelligence for the prediction of critical variables and autonomous environmental control. Libao et al. [81] reported the successful implementation of automated management systems using AI-driven models, resulting in improved water quality stability, reduced mortality, and increased productivity in trout farming operations.
  • The feasibility of implementation in low- and middle-income regions should be addressed through modular system design, the use of open-source platforms, and training programs for local operators. This approach ensures cost-effectiveness and scalability, fostering technology adoption in diverse socioeconomic contexts.
These recommendations provide a roadmap toward the digital transformation of trout aquaculture, enhancing environmental sustainability, animal welfare, and operational efficiency.
This study has certain limitations that should be considered when interpreting the results. First, the bibliometric analysis was restricted to the Scopus database, which may exclude relevant articles indexed in other databases such as Web of Science or PubMed. Second, the use of automated tools such as Bibliometrix ensures analytical consistency but may omit nuanced interpretations that manual reviews can capture. Third, although the study highlights global trends, the analysis based on the authors’ nationalities may not fully reflect collaborative or multicentric contributions. Lastly, the integration of IoT and RAS technologies remains an evolving field, and some of the most recent innovations may not yet be fully captured in the indexed literature.

7. Conclusions

The bibliometric study conducted on 387 articles allows us to observe that, since 2011, there has been a growth in the number of research articles. The United States (with 430 publications), Denmark (with 200 publications), Finland (with 102 publications), and Germany (with 95 publications) were the countries with the highest production and collaboration in this field. Aquacultural Engineering (with 65 publications), Aquaculture (with 38 publications), and Aquaculture Research (with 16 publications) were the journals with the most publications on the subject. Finally, the conceptual analysis identified the following three thematic clusters: experimental animal studies focused on physiology and metabolism; research on the nutrition, growth, and performance of salmonid species; and technological approaches to water treatment in recirculation systems. These clusters reflect an evolution from basic studies to sustainable applications in aquaculture.
The analysis of bibliometric trends revealed a progressive transition from classical topics such as dissolved oxygen, growth response, and biofiltration to more sophisticated approaches focused on proteomics, transcriptomics, and molecular techniques such as real-time PCR. This evolution reflects a shift towards genomic and microbiological evidence-based aquaculture for disease control and growth optimisation. Future research should prioritise the development of self-calibrating multi-parametric sensors and the application of machine learning models for the prediction of critical variables and the incorporation of autonomous response systems that adjust environmental conditions in real time.
Policymakers should consider integrating these technologies into national aquaculture strategies and support capacity building through funding and training programs. Moreover, public–private partnerships could be encouraged to facilitate the implementation of smart aquaculture solutions, particularly in developing regions. At the practical level, aquaculture producers can adopt IoT-enabled monitoring tools to optimise resource use, reduce environmental impact, and improve fish health and productivity.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su17156729/s1, Figure S1: RStudio (Bibliometrix) stages and studies.

Author Contributions

Conceptualization, D.E.G.-Y.; methodology, D.E.G.-Y. and G.A.G.; validation, M.P.-C., J.C. and R.C.M.-Z.; formal analysis, D.E.G.-Y., G.A.G. and R.C.M.-Z.; investigation, D.E.G.-Y.; data curation, G.A.G., J.C., R.C.M.-Z. and M.P.-C.; writing—original draft preparation, D.E.G.-Y.; writing—review and editing, D.E.G.-Y., G.A.G., R.C.M.-Z. and J.C.; visualization, G.A.G., M.P.-C. and R.C.M.-Z.; supervision, J.C., R.C.M.-Z. and G.A.G.; project administration, S.C.-G.; funding acquisition, R.C.M.-Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by “Mejoramiento del servicio de promoción de la ciencia, tecnología e innovación tecnológica en centro de investigación en pesca y acuicultura “CIPA” de la UNTRM—distrito de Copallín de la Provincia de Bagua del Departamento de Amazonas”, grant number 2622092. The APC was funded by Vicerrectorado de Investigación: Universidad Na-cional Toribio Rodríguez de Mendoza de Amazonas.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

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Figure 1. PRISMA flowchart depicting systematic literature search and selection process (accessed on 15 May 2025).
Figure 1. PRISMA flowchart depicting systematic literature search and selection process (accessed on 15 May 2025).
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Figure 2. Annual evolution of publications.
Figure 2. Annual evolution of publications.
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Figure 3. Distribution of authors’ corresponding countries in scientific publications. SCP: single-country publications; MCP: multiple-country publications.
Figure 3. Distribution of authors’ corresponding countries in scientific publications. SCP: single-country publications; MCP: multiple-country publications.
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Figure 4. Main journals with publications related to subject.
Figure 4. Main journals with publications related to subject.
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Figure 5. Author contributions: (a) most relevant authors and (b) authors’ production over time.
Figure 5. Author contributions: (a) most relevant authors and (b) authors’ production over time.
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Figure 6. Analysis of factorial correspondence of keywords.
Figure 6. Analysis of factorial correspondence of keywords.
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Figure 7. Issues related to culture of Oncorhynchus mykiss in RAS investigated over time.
Figure 7. Issues related to culture of Oncorhynchus mykiss in RAS investigated over time.
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Table 1. Oncorhynchus mykiss farming in RAS from 2000 to 2025.
Table 1. Oncorhynchus mykiss farming in RAS from 2000 to 2025.
CountryYearWater ParametersTank SystemFilterReference
Denmark2000-T°: 15.3 ± 0.5
-OD: 7.9 ± 0.4		2 of 1.5 m3Fluidised bed biofilter[25]
United States2002-T°: 14.3 ± 0.6
-pH: 7.07–7.22		2 of 1.5 m3Drum filter[26]
United States2005-T°: 15.8–18.1
-pH: 7.1–7.2		5 of 7.8 m3Drum filter[27]
Greece2005-T°: 16.0–16.3
-pH: 7.5–7.6
-OD: 8.2–8.7
-NO2−: 0.071–0.079		6 of 2.5 m3Biofilter[28]
Denmark2007-T°: 16.4–17.7
-pH: 6.0–6.1
-OD: ≥8
-NH3: <0.006
-NO2−: 1.0 ± 0.9		12 of 0.3 m3Biofilter[29]
Greece2008-T°: 16.6 ± 0.03
-pH: 7.18 ± 0.005
-OD: 9.1 ± 0.01
-NH3: 0.503 ± 0.0139
-NO2−: 0.187 ± 0.0084		12 of 0.17 m3Mechanical and biological filters, UV sterilization[30]
France2009-T°: 12
-pH: 7.33 ± 0.17
-OD: 8.4 ± 3.1		2 of 4.65 m3Biofilter[31]
France2009-T°: 12.6 ± 0.8
-pH: 7.36 ± 0.21		3 of 3.6 m3Moving bed filter[32]
United States2009-T°: 13.2 ± 0.0
-pH: 7.53 ± 0.03
-OD: 10.0 ± 0.0
-SST: 2.7 ± 0.1		6 of 1.6 m3Microscreen drum filter[33]
United States2010-T°: 13.9 ± 0.1
-pH: 7.17 ± 0.01
-OD: 10.1
-SST: 7.99 ± 0.37		6 of 9.5 m3Drum filter[34]
United States2010-T°: 15.2
-pH: 7.60 ± 0.02
-OD: 9.8
-SST: 3.4 ± 0.4		6 of 6.3 m3Ozonification[35]
United States2011-T°:14.0 ± 0.1
-pH: 7.47 ± 0.1
-OD: 10.7 ± 0.1		6 of 9.5 m3Drum filter and fluidised sand biofilter[36]
Denmark2012-T°: 18.0
-pH: 7.2–7.4		12 of 4.7 m3Biofilter[37]
United States2013-T°: 13.1 ± 0.1
-pH: 7.71 ± 0.01
-OD: 10.4
-SST: 8.9 ± 1.2		6 of 9.5 m3Drum filter and fluidized sand biofilter[38]
Canada2013-T°: 15.2 ± 0.4
-pH: 7.4 ± 0.4		4 of 1.5 m3Filter with sieves[39]
United States2014-pH: 7.080 ± 0.0122 of 9.5 m3Biofilters[40]
Netherlands2014-T°: 15.7 ± 0.1
-pH: 6.9 ± 7.8
-OD: 9.0
-Conductivity: 905–2230		6 of 9.5 m3Trickling filters[41]
United States2014-T°: 15.5
-pH: 7.59 ± 0.01
-OD: 10.1
-SST: 6.6 ± 1.1
-Conductivity: 1215 ± 8		6 of 9.5 m3Drum filter and fluidized sand biofilter[42]
Denmark2015-T°: 18.0
-pH: 7.2–7.4
-OD: 7.2–8.5		4 of 8.5 m3Cellulose acetate filters[43]
Denmark2015-T°: 18 ± 0.4
-pH: 7.2–7.4		12 of 1.7 m3Biofilter[44]
France2015-T°: 16.4
-SST: 2.9 ± 0.32		10 of 6.5 m3Drum filter[45]
Denmark2017-pH: 7.2–7.6
-OD: 8 		2 of 8.5 m3Drum filter[46]
Netherlands2017-T°: 16.0 ± 0.1
-pH: 7.3 ± 7.4
-OD: 8.1 ± 0.1
-Conductivity: 394.1 ± 0.4
-NO2−: 0.42 ± 0.03
-NO3−: 23.5 ± 0.3		6 of 6.5 m3Trickling filters[47]
Denmark2017-T°: 16–18
-pH: 7.2–7.5
-OD: 7–7.5		6 of 1.7 m3Submerged fixed-bed biofilter and trickling filter[48]
Denmark2017-pH: 6.9–8.01 of 8.5 m3 Filters and ozonation[49]
Norway2018-T°: 19 ± 0.3
-pH: 7.3–7.4		6 of 1.8 m3 Biofilters[50]
Germany2018-T°: 16
-pH: 6.5–8.5
-NTU: 2.2 ± 0.6
-SST: 3.9		10 of 3.3 m3Drum filter[51]
United States2019-T°: 13.8 ± 0.1
-pH: 7.0 ± 0.04
-OD: 9.9 ± 0.01
-SST: 11.0 ± 0.5
-NO2−: 0.042 ± 0.005
-NO3−: 201 ± 11		6 of 9,5 m3Drum filter and fluidised sand biofilter[52]
Bulgaria2019-T°: 15.9 ± 204 of 0.8 m3Mechanical filters (sedimentation tanks), moving-bed biofilter, and pumping sections[53]
Germany2019-T°: 14.5 ± 0.3
-pH: 6.5–8.5
-OD: 10.7 ± 0.2
-NTU: 2.1 ± 0.6		20 of 6 m3Drum filter[54]
Finland2020-T°: 15.5 ± 0.7
-pH: 7.2
-OD: 7.6–8.2		2 of 2.5 m3Sand filter[55]
Bulgaria2020-T°: 16.8–17.9
-pH: 7.4–8.11
-OD: 7.35–8.32
-Conductivity: 263–269		10 of 3 m3Mechanical filter (sedimentation tank) and biological filter[56]
Finland2020-T°: 16 °C.
-pH: 7.2
-NTU: 2.0 ± 0.3		8 of 5 m3Drum filter with panels[57]
Denmark2020-T°: 15.8 ± 0.5
-pH: 7.1–7.5
-NO3−: 91.5–98.2		12 of 1.7 m3 Fixed-bed biofilter and trickling filter[58]
Denmark2021-T°: 13.9 ± 0.5-Drum filters and fixed-bed biofilters[59]
Morocco2022-T°: 16.8 ± 0.5
-pH: 7.0
-OD: 8.0–10.0
-NO2−: 0.45
-NO3−: 65		17 of 4.5 m3Drum filter[60]
Finland2022-T°: 16.1 ± 0.8
-pH: 7.1 ± 0.3
-OD: 98.2 ± 7.0
-NO2−: 0.03 ± 0.01
-NO3−: 40.4 ± 5.0
-NH3: 0.001		10 of 4.5 m3Drum filter[61]
Denmark2022-T°: 18.0–19.9
-pH: 7.4 ± 0.3
-OD: 8.9 ± 1.3		2 of 5 m3Drum filter[62]
Denmark2022-NTU: 7.02 ± 2.56
-NO2−: 119 ± 24.5
-NO3−: 57.5 ± 2.57		12 of 0.8 m3Biofilter[63]
Denmark2022-T°: 17 ± 2
-pH: 7.0–7.3
-NTU: 7.02 ± 4.34		12 of 1.2 m3 Ozonation and foam fractionation[64]
Finland2022-pH: 7.79
-NO2−: 0.21
-NO3−: 0.75		6 of 2.5 m3Bead filter and moving bed biofilter filled with helicoidal floating biomedia[65]
Finland2023-T°: 12.8
-pH: 7.5
-NO2−: 0.105 ± 0.108
-NO3−: 44.2 ± 65.4		2 of 5 m3Drum filter[66]
Denmark2023-T°: 18 ± 1
-pH: 7.0–8.5
-OD: 6.68–10.88
-NTU: 33.8 ± 6.6		1 of 0.5 m3Cylindrical biofilter[67]
Denmark2023-T°: 16–17
-pH: 7.0–7.4
-NTU: 6.5–7.7		1 of 0.8 m3Biofilter[68]
Czech Republic2023-T°: 15.4 ± 1.0
-pH: 7.13 ± 0.38
-NH3: 0.95 ± 0.59		12 of 3.6 m3Biofilters[69]
Chile2024-T°: 8 ± 18
-NH3: 0.25 ± 0.65		4 of 10 m3Biofilters[70]
Denmark2024-T°: 18.3 ± 0.7
-pH: 7.0–7.4		12 of 5 m3Biofilters[71]
Greece2025-T°: 11.5–16.5
-NO2−: 0.3 ± 0.8
-NO3−: 9–15
-NH3: 0.50 ± 0.85		12 of 1181 m3 Interconnected fixed-bed and floating-bed biofilters[72]
T°: temperature (°C), OD: dissolved oxygen (mg/L−1), NO2−: nitrite (mg/L−1), NO3−: nitrate (mg/L−1), NH3: ammonia (mg N/L), SST: total suspended solids (mg/L), electrical conductivity (μS), NTU: turbidity (FNU).
Table 2. Technology with potential for integration into Oncorhynchus mykiss culture in RAS.
Table 2. Technology with potential for integration into Oncorhynchus mykiss culture in RAS.
Sensor/TechnologyT°O2pHNH4+CO2NO3−TurbidityAutomation
LoRaWAN🔴🟡🟡🟢🟡🔴🟡High
WiFi/Ethernet🟡🟡🟡🟢🔴🟡🟢Medium
NB-IoT🟢🟡🔴🟡🟡🟢🔴Under
Optical Sensors🟡🟢🟡🟡🟡🟡🟡Under
Multi-Parameter Sensors🔴🔴🟡🟡🟡🟡🟡High
🔴 High: high frequency of measurement and/or automation. 🟡 Medium: intermediate frequency of measurement and/or automation. 🟢 Under: low frequency of measurement and/or automation.
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MDPI and ACS Style

Grandez-Yoplac, D.E.; Pachas-Caycho, M.; Cristobal, J.; Chapa-Gonza, S.; Mori-Zabarburú, R.C.; Guadalupe, G.A. Recirculating Aquaculture Systems (RAS) for Cultivating Oncorhynchus mykiss and the Potential for IoT Integration: A Systematic Review and Bibliometric Analysis. Sustainability 2025, 17, 6729. https://doi.org/10.3390/su17156729

AMA Style

Grandez-Yoplac DE, Pachas-Caycho M, Cristobal J, Chapa-Gonza S, Mori-Zabarburú RC, Guadalupe GA. Recirculating Aquaculture Systems (RAS) for Cultivating Oncorhynchus mykiss and the Potential for IoT Integration: A Systematic Review and Bibliometric Analysis. Sustainability. 2025; 17(15):6729. https://doi.org/10.3390/su17156729

Chicago/Turabian Style

Grandez-Yoplac, Dorila E., Miguel Pachas-Caycho, Josseph Cristobal, Sandy Chapa-Gonza, Roberto Carlos Mori-Zabarburú, and Grobert A. Guadalupe. 2025. "Recirculating Aquaculture Systems (RAS) for Cultivating Oncorhynchus mykiss and the Potential for IoT Integration: A Systematic Review and Bibliometric Analysis" Sustainability 17, no. 15: 6729. https://doi.org/10.3390/su17156729

APA Style

Grandez-Yoplac, D. E., Pachas-Caycho, M., Cristobal, J., Chapa-Gonza, S., Mori-Zabarburú, R. C., & Guadalupe, G. A. (2025). Recirculating Aquaculture Systems (RAS) for Cultivating Oncorhynchus mykiss and the Potential for IoT Integration: A Systematic Review and Bibliometric Analysis. Sustainability, 17(15), 6729. https://doi.org/10.3390/su17156729

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