Abstract
Introduction:
Aquaponics, an innovative integration of recirculating aquaculture systems (RAS) and soilless cultivation systems, has gained increasing attention as a sustainable food production strategy for urban and peri-urban environments. Despite advancements in aquaponics research, several challenges persist, including plant nutrient efficiency, reliable water and microbial quality, system design optimization, and technology implementation. The current study aimed to generate insights into enhancing the application of aquaponics as a food production system, to explore phenotypic assessment techniques for detecting biotic and abiotic conditions, and to evaluate plant performance with respect to nutrient and microbial dynamics, as well as physiological characteristics related to chlorophyll content and leaf area development.
Materials and methods:
A vertical aquaponic system equipped with LED lighting was operated alongside a hydroponic Nutrient Film Technique (NFT) system in a climate-controlled chamber to analyze nutrient flow and its correlation with the development of tilapia and tatsoi, used as model organisms for fish and plants, respectively. The control was a Hydroponic unit using the Hoagland & Arnon solution for plant fertilization. Both the control and aquaponic systems were operated in triplicate. The experiment lasted forty-five days.
Results and conclusion:
Tilapia growth parameters were optimal, with a Specific Growth Rate (SGR) of 5.25% and a Feed Conversion Ratio (FCR) of 0.96. The highest total biomass yield in plants was observed in the aquaponic system, reaching 11.27 kg, with a productivity of 4.5 kg/m2. Significant differences were observed in aquaponics plants, with superior values for average length and wet weight (p < 0.05), as well as chlorophyll content (p < 0.01) and green leaf expansion (p < 0.001). Phenotypic assessment in plants provides valuable insights into plant development and potential yield limitations, particularly in aquaponic systems, where plant production is a primary driver of system viability and expansion. The absence of Oomycetes and Enterobacteriaceae in aquaponic fish tanks suggests the potential to reduce root fungal and microbial pathogens. The results emphasize aquaponics’ capacity to improve plant growth and overall system health by promoting microbial quality, all while ensuring optimal fish production. This supports its role as a sustainable and effective system.
1 Introduction
Food security plays a vital role in a nation’s prosperity and economic growth. By 2050, the global population is projected to reach about 10 billion, presenting a major challenge: how to sustainably supply enough food without exhausting natural resources or harming the environment (Barrett, 2020; United Nations (UN), 2024; Song et al., 2023). It is crucial to meet rising global food demand while maintaining sustainable production methods. Developing countries face additional difficulties in ensuring food and nutritional security for this expanding population, especially amid the impacts of climate change (Iqbal et al., 2025). Therefore, local, regional, and global initiatives that promote sustainable food production—by using resources efficiently and minimizing environmental impact—are essential (Sarkar et al., 2020). A major global goal is enhancing sustainability in city-based food production, where land, property, and water are limited, and production density is high, making efficient systems vital. This approach helps reduce transportation costs and time, encouraging nearby or urban food systems. In this regard, aquaponics offers a practical solution due to its suitability for operations at various scales (Proksch et al., 2019).
Aquaponics combines recirculating aquaculture systems (RAS) with soilless cultivation methods such as hydroponics and is gaining popularity among researchers, entrepreneurs, producers, and consumers (Pantanella, 2018). These systems act as mini-ecosystems due to their symbiotic environment, improving resource and land use by enabling multiple forms of production, encouraging diversification, adaptation, and sustainable practices—especially suited for urban farming (Semenova et al., 2022). They promote water conservation, higher productivity, and food security while reducing pollution and energy use, making them a promising sustainable approach to food production. They can address sustainability challenges by lowering pesticide use, easing pressure on fish stocks, and reducing environmental pollution, without sacrificing productivity, comparable to hydroponics (Okomoda et al., 2023). Nair et al. (2024) highlight the growing body of aquaponics research, driven by its potential to address food security, water conservation, and sustainable agriculture, especially in climate-affected areas. The field has matured with standardized protocols (Basumatary et al., 2023). It offers ethical benefits by providing local, nutritious food and jobs in urban areas, making it suitable for use in city buildings as a safe, eco-friendly green technology that promotes ecological balance and integrates plant cultivation into urban environments (Proksch et al., 2019; Zhang et al., 2022).
Even though there are many advantages to aquaponic systems, one of the main challenges in upscaling these systems is balancing the conditions required for the growth of multiple species, resulting in a dynamic, highly complex system (Reyes Lastiri et al., 2018). Several authors described the system and technical management as primary challenges to upscaling the aquaponic system (Arakkal Thaiparambil and Radhakrishnan, 2022; Yep and Zheng, 2019), where abiotic factors such as temperature, pH, waste accumulation, dissolved oxygen, conductivity, and nutrient content play a crucial role in the water quality that impact the success of aquaponics (Palm et al., 2015)—highlighting the importance of maintaining optimal conditions for the physicochemical parameters for plant, fish, and microbial growth (Somerville et al., 2014). Another important point is nutrient equilibrium and management during the culture process (Nair et al., 2024), which can influence the microbiological community, which has a vital role in the nitrification and mineralization of the nutrients in the systems (Kasozi et al., 2021; Schmautz et al., 2021) and can condition or promote the growth of plants (Piñero et al., 2023). In urban areas, having an easy tool to understand the health and behavior of plants and fish is a priority. In plants, external color, shape, and growth are responses to biotic and abiotic conditions in the system. Plant phenotyping assessment enables rapid evaluation of numerous plants under diverse biotic and abiotic conditions (Lobos et al., 2017). This approach is essential for accelerating crop improvement and adaptation to changing environments (Schneider et al., 2026). Phenotypic assessment can significantly improve the performance of aquaponic systems, enabling better management of critical parameters such as temperature, pH, and electrical conductivity (Lobos et al., 2017). By optimizing these factors, aquaponic systems can achieve improved sustainability, productivity, and resource efficiency, especially in urban areas, where the implementation of easy and rapid decision-making tools can be a pivotal characteristic for the adoption and implementation of aquaponic systems (Al Tawaha et al., 2025; Alizaeh et al., 2025).
Aquaponics has ongoing research gaps in the standardization of design protocols and the integration of real-time automation technologies to enhance management and system stability. Further insights into microbial dynamics and long-term ecological effects are necessary to ensure the sustainability of these production systems (Faisal et al., 2025). This study aims to advance efforts by providing comprehensive information on vertical systems and integrating phenotypic assessments, nutrient dynamics, and microbiology to better understand nutrient flows and identify biotic and abiotic factors. It specifically explores strategies to improve nutrient management and system performance, emphasizing the effectiveness of imaging-based phenotypic techniques for assessing plant health, growth trends, and potential yield limitations in aquaponics. A central focus is on how accurately these approaches detect stress early, enabling timely interventions and improved system control.
2 Materials and methods
2.1 Experimental setup and operation
The bioassay included a complete aquaponic system as the experimental treatment, with three replicates. Each replicate contained a 70-liter fish tank, a 100-liter settling tank, and a 100-liter biofilter, all integrated with three hydroponic rafts into a single experimental unit. The fish tank was constantly aerated with an aquarium pump (Hagen-Elite maxima, 30 gal, 115 V with a 4.5 W); the water entered the settling tank, passed through the biofilter, and was pumped (Sicce Sycra-1.0, 950 L/h, Italy) to the hydroponic vertical bed composed of three rafts of Nutrient Film Technique (NFT) units, with 10 plants space per raft and a total area of 1.39 m2, and at the end, the water returned to the fish tank in cascade (see Figure 1a). The control replicates contained a hydroponic unit with a vertical system, a 70-liter nutrient-solution container, and one NFT raft with space for 10 plants, with continuous aeration and recirculation driven by the same pumps used in aquaponics (see Figure 1b). Plant nutrition was provided using the standard nutrient solution (Hoagland and Arnon, 1950). These systems were housed in a climate-controlled chamber illuminated by LED lights (RX30, Heliospectra, Sweden) featuring eight monochromatic LEDs (blue, red, green) and one white (polychromatic) LED. The lighting followed a 12:12 dark-to-light cycle, with the chamber temperature held at 25 °C and the relative humidity at 70%.
Figure 1
Nile tilapia (Oreochromis niloticus) were used in the experiment, with 33 fish per tank, an initial weight of 0.5 g, and a length of 1.5 cm. A commercial feed ration with 40% protein (Skretting Nutra-120, Stavanger, Norway) was implemented three times daily, every 3 hours. Daily fish consumption was recorded, and weekly increases in fish biomass were measured using a precision scale (OHAUS, Adventurer ARA520, NJ, USA) with 10 fish per tank. Measurements were performed in a water-filled container to minimize stress. Feeding rates were adjusted weekly after biometry, according to the Daily Protein Intake (DPI) (Fimbres-Acedo et al., 2019). Tatsoi (Brassica rapa subsp. narinosa) was used as a model plant. Tatsoi seeds were sown in small rock wool cubes and transplanted into hydroponic units after 14 days. Before the experiment, the aquaponic systems functioned only with fish for twenty-five days to stimulate the biofilter. The experiment lasted for 45 days.
2.2 Sampling and nutrient analyses
Water samples were obtained from the fish tank every 3 days before feeding and analyzed immediately for NH4-N and NO3-N concentrations using HACH reaction kits (Loveland, CO, USA), namely Ammonium LCK505 (0.5–5.0 mg/L NH4-N) and Nitrate LCK340 (5–35 mg/L NO3-N). Parameters such as temperature, pH (0.00 to 14.00), total dissolved solids (TDS - 0.00 to 10.00 ppt), and electric conductivity (EC - 0.00 to 20.00 mS/cm) were measured in situ twice daily: 8:00 a.m. and 5:00 p.m., using a portable water multiparameter (HANNA-HI 98130, Woonsocket, RI, USA). Elemental analysis was performed at the initial, middle, and final stages of the experiment. The samples were collected from the biofilter using a plastic tube (50 mL) and sent to be analyzed by LMI, a certified laboratory for nutrient analyses (Helsingborg, Sweden).
2.3 Tilapia analyses
The following equations were used to obtain growth and production parameters using data extracted from the weekly biometrics.
Absolute growth (AG): It is expressed as:
Where wf is the final weight/length of the organisms, and wi is the initial weight/length, and t is the time.
Relative growth rate (RGR): Equation 1 is additionally divided by the initial weight/length and multiplied by 100. Accordingly, the result is a percentage increase:
Specific growth rate (SGR) (%/day):
Its results are given in percentage increase per day.
Feed conversion ratio (FCR):
It is a metric to measure how efficiently the organisms convert feed mass and assimilate it into the body mass (Lugert et al., 2016). These thorough analyses offered a detailed insight into the fish’s growth and production rates, enriching the research’s depth.
2.4 Plant analyses
Three plant analyses were conducted weekly during the experiment, with 10 plants per unit. Chlorophyll Fluorescence (ChlF) was measured at ambient temperature in dark-adapted plants for 20 min using a portable modulated fluorometer (Chl Fluorometer, Heiz Walz GmbH, Inc., Effeltrich, Germany); the middle portions of mature, healthy, fully expanded leaves were targeted for measurements. Leaf chlorophyll content was measured using a chlorophyll meter (SPAD-502; Minolta Camera Co., Japan). Measurements were collected from three mature leaves from the middle to upper portion of each plant and averaged. Normalized Difference Vegetation Index (NDVI) was calculated using a handy spec field on mature, healthy, fully expanded leaves at wavelengths between 305 and 2,150 nm, with 1 nm intervals. In the final experiment, 10 plants per replicate were used, and leaves and roots were separated with a scalpel to measure their wet weights. Then, the samples were placed in a paper bag and transported to a dry oven with horizontal airflow (Termarks®, Norway) at 70 °C for 48 h.; all samples were weighed to obtain the dried weight of leaves and roots. The growth factors included weight gain (g) and specific growth rate (SGR), which were calculated using Equations 1, 2. To analyze the production of fish and plants in an aquaponic system, specific equations described by Colt et al. (2022) were used and are presented below.
Feed-to-area ratio (FAR):
The average feed-to-area ratio (FAR) can be measured over a given period (typically over the fish’s production cycle) or on a specific day:
The daily feed input will vary with time and fish size, and the equation is
Area to volume ratio (AVR):
Volume ratio (VR): This parameter is the ratio of volumes for plant and fish growth:
Area ratio (AR): this parameter is the ratio of the areas for plant and fish growth:
Plant-to-fish number ratio (PFR): this parameter indicates the balance between plants and fish:
Plant-to-Fish Ratio on a Mass basis (PFRM): This parameter is far more significant than the PFR#, but it changes over time:
2.5 Computer visualization
The experiment was conducted in a Phenocave chamber (Leiva et al., 2021), with a 2 × 2 m workspace and a camera positioning precision of 0.5 mm. An RGB digital single-lens reflex (DSLR) camera, Canon 1300D (Canon USA. Inc., Huntington, NY, USA), was used to photograph the plants weekly to monitor changes in growth. For image acquisition, plants from hydroponic/aquaponic units were captured with a Canon EF-S 18–55 mm STM lens from a top-down angle. Image analysis was performed using ImageJ software, following the procedure described by Leiva et al. (2021).
2.6 Microbial analysis
The water samples for microbial analysis were collected on three occasions during the cultivation period: at the beginning, middle, and end. On each occasion, 50 mL was centrifuged, and the pellet was resuspended in 750 μL RNA/DNA Shield (Zymo Research). DNA and RNA were extracted using a Zymobiomics DNA/RNA miniprep Kit (Zymo Research) according to the manufacturer’s protocol. Quantification of the occurrence of nitrification bacteria in the biofilter, Oomycetes in the hydroponic parts, and Enterobacteriaceae in the fish tank was performed using the automated QX200TM Droplet DigitalTM PCR system (Bio-Rad, Hercules, CA, USA). A reaction mix composed of 10 μL QX200 EvaGreen Digital PCR Supermix, 0.5 μL each of forward and reverse species-specific primers, 7 μL DNase/RNase-free MilliQ water, and 2 μL cDNA sample was prepared (final volume 20 μL). Samples were placed in the automated droplet generator. The plate containing droplets was sealed with pierceable aluminum foil using a PX1 PCR plate sealer (Bio-Rad, Hercules, CA, USA) set to 180 °C for 5 s. The plate was then moved to a Touch Thermal Cycler (Bio-Rad, Hercules, CA, USA) and run with the following thermal conditions: Enzyme activation at 95 °C for 5 min, followed by 40 cycles of denaturation at 95 °C for 30 s, annealing, and extension for 1 min with the temperature specific for the primer used. The procedure concluded with the signal being stabilized at 4 °C for 5 min, at 90 °C for 5 min, and then held indefinitely at 4 °C. After the thermal cycle, the plate was collocated in a QX microplate reader (Bio-Rad, Hercules, CA, USA). QuantaSoft™ software (Version 1.7) was used to run the instrument and analyze the data.
2.7 Statistical analysis
The experiment with aquaponic and hydroponic systems was conducted in triplicate. The physicochemical parameters, inorganic nutrient levels (ammonium and nitrate), nutrient concentrations, fish and plant production parameters were analyzed using Minitab 16 Statistical Software (Minitab 16.1.1., Minitab Inc.). One-way ANOVA and Tukey post hoc tests (p ≤ 0.05) were employed to identify significant differences among the parameters. Statistical analyses of the collected plant parameter data were carried out using the agricolae package in R (R Core Team, 2012) and the Statistical Tool for Agricultural Research (STAR) Version 2.0.1 (International Rice Research Institute, IRRI, Philippines). Area under plant growth progression values (AUGPC) for green leaf area, chlorophyll, NDVI, and quantum yield (QY) traits were calculated using the agricolae package in R, with ggplot2, gridExtra, factoextra, and ggbiplot.
3 Results
3.1 Water quality and nutrient content
The physicochemical parameters observed during the experiment in hydroponic and aquaponic systems were comparable with respect to temperature and pH. However, electrical conductivity (EC), total dissolved solids (TDS), and phosphate showed significant differences (p < 0.05). The highest EC measure was 1.83 mS/cm in hydroponic systems, TDS was 0.90 mg/L, and phosphate levels ranged from 32 to 35 mg/L (Table 1).
Table 1
| Parameters | Aquaponic | Hydroponic |
|---|---|---|
| Temperature (°C) | 24.27 ± 0.15 | 24.43 ± 0.06 |
| pH | 6.10 ± 0.17 | 5.70 ± 0.01 |
| EC (mS/cm) | 0.30 ± 0.01b | 1.83 ± 0.06a |
| Total Dissolved Solids (TDS) (mg/L) | 0.10 ± 0.01b | 0.90 ± 0.01a |
| PO4−P (mg/L) | 2.6–2.8b | 32–35a |
The mean values of physicochemical parameters measured in both aquaponic and hydroponic systems.
The parameters include temperature (°C), pH, Total dissolved solids (TDS), and phosphate (PO4–P).
The accumulation of inorganic nitrogen compounds differs significantly between systems (p < 0.05). In aquaponics, nitrate levels gradually increased from 6.73 mg/L to 22.3 mg/L at the end of the experiment. In the hydroponic control system, nitrate fluctuations were influenced by periodic replacements of the nutrient solution. Ammonium levels also fluctuated in both systems, increasing in aquaponics as the systems matured and varying in hydroponics due to changes in nutrient solution (Table 2).
Table 2
| Systems | I | II | III | |||
|---|---|---|---|---|---|---|
| NO3–N | NH4–N | NO3–N | NH4–N | NO3–N | NH4–N | |
| Aquaponic | 6.73 ± 2.8b | 3.3 ± 1.3b | 20.7 ± 0.6b | 5.7 ± 0.4b | 22.3 ± 3.1b | 8.87 ± 1.8b |
| Hydroponic | 186.7 ± 5.8a | 26.7 ± 0.6a | 206.7 ± 11.6a | 22.0 ± 5.7a | 223.3 ± 11.6a | 28.3 ± 3.1a |
Inorganic nutrient (ammonium and nitrate) concentrations (mg/L, mean ± standard error) in aquaponic and hydroponic systems were presented at the experiment’s initial (I), middle (II), and final (III) stages. Different letters in the same column indicate significant differences (p < 0.05) between the aquaponic and hydroponic systems.
The letters a and b indicate significant differences between systems.
To evaluate nutrient flow during tatsoi production, fourteen nutrients were measured at the initial, middle, and final stages. Hoagland’s nutrient solution showed the highest values (p < 0.05). In aquaponics, macronutrient accumulation increased gradually, while micronutrients such as Copper, Iron, and Zinc showed no significant change across the stages and systems (p > 0.05). However, B showed a significant difference between the initial and final stages (Table 3). A decline in macronutrient levels was observed in the final stage of the aquaponic system due to plant growth, yet plant development remained optimal throughout the experiment.
Table 3
| Nutrients (mg/L) | I | II | III | |||
|---|---|---|---|---|---|---|
| A | H | A | H | A | H | |
| N | 9.7 ± 1.0b | 213.3 ± 8.7a | 26.0 ± 0.9b | 228.7 ± 11.6a | 31.2 ± 4.8b | 251.7 ± 14.5a |
| P | 0.9 ± 0.1b | 32.0 ± 2.0a | 2.6 ± 0.2b | 34.0 ± 1.7a | 0.1 ± 0.08b | 30 ± 1.7a |
| K | 2.3 ± 0.1b | 266.7 ± 24.3a | 2.2 ± 0.7b | 273.3 ± 11.5a | 0.3 ± 0.1b | 221.0 ± 16.5a |
| Mg | 2.6 ± 0.1b | 38.3 ± 0.7a | 3.7 ± 0.2b | 43.3 ± 2.5a | 3.7 ± 0.3b | 40.7 ± 1.2a |
| Ca | 25.7 ± 0.6b | 176.6 ± 6.7a | 31.0 ± 1.0b | 190.0 ± 10.0a | 23.7 ± 1.2b | 170 ± 10a |
| B | 0.3 ± 0.2 | 0.5 ± 0.01 | 0.01 ± 0.0b | 0.6 ± 0.01a | 0.001 ± 0.0 b | 0.47 ± 0.01a |
| Cu | 0.2 ± 0.2 | 0.1 ± 0.1 | 0.04 ± 0.0 | 0.03 ± 0.0 | 0.03 ± 0.0 | 0.04 ± 0.01 |
| Fe | 0.7 ± 0.1 | 0.3 ± 0.3 | 0.1 ± 0.01 | 0.03 ± 0.0 | 0.05 ± 0.0 | 0.01 ± 0.005 |
| Mn | 0.001 ± 0.0b | 0.4 ± 0.0a | 0.03 ± 0.01b | 0.5 ± 0.03a | 0.01 ± 0.0 | 0.4 ± 0.01 |
| Mo | 0.017 ± 0.0 | 0.016 ± 0.0 | 0.002 ± 0.0b | 0.01 ± 0.0a | 0.00 | 0.00 |
| Na | 13.7 ± 0.6a | 11.7 ± 0.7b | 17.0 ± 0.0a | 10.3 ± 0.6b | 9.7 ± 3.4 | 8.4 ± 0.5 |
| S | 4.2 ± 0.1b | 72.7 ± 2.7a | 5.2 ± 0.26b | 77.0 ± 3.6a | 2.6 ± 0.9b | 72 ± 2.0a |
| Si | 2.1 ± 0.06b | 2.3 ± 0.06a | 2.8 ± 0.25b | 2.4 ± 0.1a | 2.4 ± 0.3a | 1.7 ± 0.1b |
| Zn | 0.2 ± 0.26 | 0.1 ± 0.03 | 0.07 ± 0.01 | 0.3 ± 0.01 | 0.02 | 0.13 ± 0.1 |
Nutrient concentrations were assessed in both aquaponic and hydroponic systems throughout the experimental period.
Different letters in the same line indicate significant differences (p < 0.05) between the aquaponic and hydroponic systems. The evaluation was conducted systematically across three distinct stages: the initial stage (I), the middle stage (II), and the final stage (III). Each stage included measurements from aquaponic systems, denoted as “A,” and hydroponic systems, denoted as “H”. The letters a and b indicate significant differences between systems.
3.2 System performance
Fish growth and physiological traits were monitored throughout the experiment to evaluate the effects of the aquaponic system on fish performance and production. The final recorded fish weight was 21 g, with an absolute growth of 19 g and a specific growth rate of 5.25% per day. The feed conversion ratio (FCR) was 0.96, and no mortality was observed during the study. The average feed-to-area ratio was 35.82, with a daily value of 38.38 (Table 4).
Table 4
| Fish parameters | Data |
|---|---|
| Initial weight (g) | 1.98 ± 0.02 |
| Final weight (g) | 21.01 ± 0.40 |
| Total growth (g) | 19.03 ± 0.44 |
| Absolute growth (AG) | 0.42 |
| Relative growth rate (RGR) | 961.1 |
| Specific growth rate (SGR %/day) | 5.25 |
| Initial length (cm) | 1.05±0.02 |
| Final length (cm) | 10.47±0.10 |
| Feed Conversion Ratio (FCR) | 0.96 |
| Survival (%) | 100 |
| Feed-to-area ratio (FAR) | 1612.15 |
| Feed-to-area ratio (FAR) average | 35.82 |
| Feed-to-area ratio (FAR) daily | 38.38 |
Fish parameters obtained during the experiment. Mean data are presented based on fish parameters obtained during the experiment in aquaponics systems.
Mean data are presented based on fish parameters measured during the experiment in aquaponic systems. To calculate AG, SGR, FCR, FAR, FAR average, and FAR daily, Equations 1 to 7 were used. The letters a and b indicate significant differences between systems.
Plant production parameters were monitored throughout the experiment to assess the effects of aquaponic and hydroponic systems. In aquaponics, the plant-to-fish mass ratio (PFRM) and plant-to-fish number ratio (PFR) were determined at 5.20 and 0.86, respectively. The highest total biomass yield, 11.27 kg (p < 0.05), was observed in the aquaponic treatment, with a productivity of 4.51 kg per square meter (p < 0.05). A comparison of growth parameters between aquaponics and hydroponics showed significant differences in average length (22.3 ± 5.09) and wet weight (130.68 ± 41.00), with higher values in the aquaponic system (p < 0.05), and no significant difference in dry weight between systems (Table 5).
Table 5
| Plant production | Aquaponics | Hydroponics |
|---|---|---|
| Area-to-volume ratio (AVR) | 5.95 | — |
| Volume ratio (VR) | 0.297 | — |
| Area ratio (AR) | 1.98 | — |
| Plant-to-fish number ratio (PFR) | 0.86 | — |
| Plant-to-fish mass ratio (PFRM) | 5.2 | — |
| Biomass plants total (kg) | 11.27a | 2.88b |
| Biomass plants/m2 (kg) | 4.51 | 3.46 |
| Length average (cm) | 22.3 ± 5.09a | 14.5 ± 1.38b |
| Wet weight (g) | 130.68 ± 41.00a | 95.9 ± 60.08b |
| Dry weight (g) | 7.84 ± 3.03 | 7.15 ± 4.25 |
Plant production parameters, including the values obtained during the experiment for aquaponic and hydroponic treatments. Different letters in the same line indicate significant differences (p < 0.05) between the systems.
The Area-to-volume ratio (AVR), Volume ratio (VR), Area ratio (AR), Plant-to-fish number ratio (PFR), and Plant-to-fish mass ratio (PFRM) were calculated only for aquaponics using equations 8 to 12. For both systems, the total plant biomass, production per square meter, average plant length, and wet and dry weights were determined at the final period. The letters a and b indicate significant differences between systems.
3.3 Plant growth and phenotypic assessment
Plant growth and physiological traits were analyzed throughout the crop growth period to assess the impact of the aquaponic system on tatsoi yield. Significant differences were observed in leaf area, NDVI, and leaf chlorophyll content between aquaponic and hydroponic systems, and there was no effect of cultivating tatsoi across the three rafts (Table 6). Plants in the aquaponic system significantly outperformed those in the hydroponic system in terms of green leaf area. Green leaf area was 1.9- and 1.8-fold higher (p < 0.001) in the aquaponic system than in the hydroponic system. During the third and fourth weeks of crop growth, leaf area increased by 3.8- and 2.6-fold (p < 0.001), with a clear demarcation in the progression of green leaf area growth under the aquaponics\system (Figure 2). Normalized difference vegetation index was significantly higher under aquaponic system during the first week of plant growth (p < 0.01), greater vegetation of crop was evident during first (1.1 folds), second (1.0 folds), third (1.1 folds), fourth (1.1 folds) and fifth (1.0 folds) weeks of plant growth, associated with significantly greater growth progression for NDVI (p < 0.05) during the crop growth period under aquaponic system (Figure 2). Quantum yield (QY) did not vary significantly between treatments. However, the quantum efficiency of PSII was marginally higher under the aquaponic system during the crop growth period, with a 1.1-fold increase across all plant growth periods, except during the second week, when it increased by 1.0-fold, with greater progression in QY. Leaf chlorophyll content was significantly improved with aquaponic treatment, varying from a 1.2-fold increase during the fourth week (p < 0.05) to a 1.4-fold increase during the first, second, third, and fifth weeks (p < 0.001), with consequent improvement in the progression of leaf chlorophyll content across the plant growth period.
Table 6
| Measurements | Time | Mean | Pr(>F) | ||
|---|---|---|---|---|---|
| Hydroponics | Aquaponics | Treatment | Raft | ||
| Leaf area | Week 1 | 5515.56 ± 992.69b | 10628.22 ± 590.77a | *** | NS |
| Week 2 | 6158.78 ± 971.06b | 10788.19 ± 537.71a | *** | NS | |
| Week 3 | 168189.89 ± 50572.71b | 634993.08 ± 64745.17a | *** | NS | |
| Week 4 | 622264.11 ± 85397.53b | 1605358.02 ± 158148.99a | *** | NS | |
| NDVI | Week 1 | 0.68 ± 0.03b | 0.75 ± 0a | * | NS |
| Week 2 | 0.73 ± 0.04 | 0.75 ± 0.01 | NS | NS | |
| Week 3 | 0.69 ± 0.03 | 0.74 ± 0.01 | NS | NS | |
| Week 4 | 0.7 ± 0.04 | 0.74 ± 0.01 | NS | NS | |
| Week 5 | 0.7 ± 0.05 | 0.73 ± 0.01 | NS | NS | |
| QY | Week 1 | 0.75 ± 0.04 | 0.81 ± 0.01 | NS | NS |
| Week 2 | 0.8 ± 0.01 | 0.82 ± 0 | NS | NS | |
| Week 3 | 0.77 ± 0.03 | 0.82 ± 0 | NS | NS | |
| Week 4 | 0.77 ± 0.04 | 0.82 ± 0 | NS | NS | |
| Week 5 | 0.76 ± 0.06 | 0.81 ± 0 | NS | NS | |
| Chlorophyll | Week 1 | 29.4 ± 3.79b | 41.58 ± 0.82a | ** | NS |
| Week 2 | 32.5 ± 2.94b | 45.92 ± 2.27a | ** | NS | |
| Week 3 | 33.4 ± 3.87b | 46.98 ± 2.17a | ** | NS | |
| Week 4 | 36.23 ± 5.04b | 44.77 ± 2.24a | * | NS | |
| Week 5 | 35.62 ± 5.59b | 48.63 ± 2.13a | ** | NS | |
Variation in leaf area, NDVI, quantum yield (QY), and chlorophyll content measurements executed in the study [Asterisk represents significance at the level of p < 0.05 (represented by *), p < 0.01 (represented by **), and p < 0.001 (represented by ***) levels, respectively]. Different letters in the same line indicate significant differences between the aquaponic and hydroponic systems.
The letters a and b indicate significant differences between systems.
Figure 2
PCA analysis revealed plant growth patterns in aquaponics, with PC1 accounting for 53% of the variation, followed by PC2 (23%) and PC3 (8%) (Figure 3). PC1 had positive loadings on leaf area, NDVI, and chlorophyll (CHL) at multiple time points (Table 7). Root dry weight, root fresh weight, and shoot biomass had small negative loadings (Table 7). NDVI and QY had negative loadings on PC2, whereas greater shoot biomass and plant height had positive loadings on PC2. PC3 had negative loadings on root dry weight and root fresh weight, whereas leaf area at multiple time points and NDVI had positive loadings.
Figure 3
Table 7
| Trait | PC1 | PC2 | PC3 |
|---|---|---|---|
| Leaf area.TP1 | 0.102 | 0.328 | 0.103 |
| Leaf area.TP2 | 0.085 | 0.345 | 0.096 |
| Leaf area.TP3 | 0.183 | 0.234 | 0.153 |
| Leaf area.TP4 | 0.171 | 0.256 | −0.073 |
| NDVI.TP1 | 0.223 | −0.064 | −0.051 |
| NDVI.TP2 | 0.198 | −0.190 | −0.156 |
| NDVI.TP3 | 0.147 | 0.100 | 0.212 |
| NDVI.TP4 | 0.220 | −0.187 | −0.041 |
| NDVI.TP5 | 0.210 | −0.210 | −0.052 |
| QY.TP1 | 0.222 | −0.158 | −0.084 |
| QY.TP2 | 0.214 | −0.074 | 0.059 |
| QY.TP3 | 0.237 | −0.142 | −0.016 |
| QY.TP4 | 0.224 | −0.177 | −0.069 |
| QY.TP5 | 0.227 | −0.146 | 0.001 |
| Chlorophyll.TP1 | 0.197 | 0.083 | 0.063 |
| Chlorophyll.TP2 | 0.195 | 0.180 | −0.054 |
| Chlorophyll.TP3 | 0.208 | 0.138 | −0.037 |
| Chlorophyll.TP4 | 0.226 | −0.027 | −0.040 |
| Chlorophyll.TP5 | 0.214 | 0.062 | −0.221 |
| Leaf.area.Absolute.AUGPC | 0.182 | 0.254 | 0.039 |
| NDVI.AbsoluteAUGPC | 0.247 | −0.082 | 0.021 |
| QY.AbsoluteAUGPC | 0.234 | −0.133 | −0.075 |
| CHL.AbsoluteAUGPC | 0.237 | 0.133 | −0.030 |
| QY.AbsoluteAUGPC | 0.234 | −0.133 | −0.075 |
| Plant.height | 0.174 | 0.261 | 0.018 |
| Root.dry.weight.plant | −0.066 | 0.037 | −0.612 |
| Root.fresh.weight.plant | −0.056 | 0.000 | −0.535 |
| Shoot.dry.weight.plant | −0.052 | 0.276 | −0.251 |
| Shoot.fresh.weight.plant | −0.038 | 0.325 | −0.261 |
PCA loadings for leaf area, NDVI, quantum yield (QY), and chlorophyll content measurements were executed in the study.
3.4 Microbial and Oomycete activity
The content of nitrifying bacteria increased in the aquaponic system throughout the cultivation period, and their presence was confirmed by the end of the study. Human pathogens from the Enterobacteriaceae family were not detected in the fish tanks, indicating they were not a source of these pathogens. Additionally, root pathogens (Oomycetes) were not found during cultivation in the aquaponic system. Their presence was only observed in the middle and final stages of the hydroponic system (Table 8).
Table 8
| Group | I | II | III | |||
|---|---|---|---|---|---|---|
| A | H | A | H | A | H | |
| Nitrification bacteria | − | − | + | |||
| Enterobacteriaceae | − | − | − | |||
| Oomycetes | − | − | − | + | − | + |
The occurrence of root pathogens (Oomycetes), nitrification bacteria, and human pathogens (Enterobacteriaceae) in aquaponic (A) and hydroponic systems (H).
The samples were collected at the beginning (I), middle (II), and end (III) of the experiment. The symbols + or – indicate positive and negative occurrences, respectively.
4 Discussion
4.1 Parameters, water quality, and nutrients
The physicochemical parameters measured during the experiment remained within optimal ranges for fish and plant growth (Table 1) (Rakocy, 2012). The nutrient levels observed in this study align with established aquaponics standards, with nitrate concentrations below 20 mg/L and phosphate levels below 3 mg/L (Supajaruwong et al., 2021). Although ammonia and nitrate levels varied in the aquaponic system (Table 2), plant production continued to meet acceptable growth conditions (Table 5). Nutrient balance is crucial in aquaponic systems for achieving high crop yields and quality and for improving nutrient use efficiency (Krastanova et al., 2022; Yang and Kim, 2019). The optimal iron level is 5 mg/L (Rakocy, 2012); external iron supplementation is recommended in aquaponic systems to ensure optimal plant performance (Sorin et al., 2016; Kasozi et al., 2019). During hydroponic solution formulation, iron depletion was detected, likely due to component quality (Table 3); adding these supplements requires close system management and careful selection of chelating agents, as Fe-chelate bioavailability is environment-dependent (Kasozi et al., 2019). It is known that Brassica species, including tatsoi, need iron supplements for healthy growth. In this study, iron deficiency symptoms appeared only at the final stage, with fewer than 5% of plants showing signs like black discoloration. In the case of phosphorus (P) levels, the aquaponic system falls within the typical range (3.68–7.0 mg/L) (Da Silva Cerozi and Fitzsimmons, 2016). The levels started at 0.9 mg/L, increased to 2.6 mg/L midway, then decreased to 0.1 mg/L by the end. Despite these fluctuations, plant growth differed significantly (p < 0.05) from the hydroponic system. No notable differences in micronutrients like copper (Cu) and zinc (Zn) were observed between aquaponic and hydroponic setups. Moreover, plants in the aquaponic system showed significantly improved growth parameters (p < 0.05) compared to those in hydroponics. This improvement could be partly due to the combination of nutrients, nitrates, and light exposure, which is related to the system’s position. The chamber has a combination of eight monochromatic LEDs (blue, red, and green) and a white LED (polychromatic), with a 12:12 dark: light photoperiod, which can influence plant growth. This explanation aligns with the results reported by Heo et al. (2024), who used hydroponic and aquaponic systems with LED lighting to promote the growth of herbs such as basil and lemon balm. They observed a leaf yield of 60%–70%, and notably, hooker chives in aquaponics experienced 200% greater leaf growth than in hydroponics. Results in this experiment indicate that successful aquaponics relies on a combination of lighting, feeding schedules, water quality management, and nutrient circulation within the system.
4.2 System performance and fish parameters
The nutrient cycle in aquaponic systems is often complex, so it is necessary to understand and control key nutrients to ensure a good crop yield (Eck et al., 2019). Therefore, implementing standardized metrics such as the FCR is essential for effective nutrient management, as it balances nutrient inputs with transfers between system components and accounts for water quality and temperature, feed quality, feeding practices, and overall system efficiency (Boyd et al., 2007; Eck et al., 2019). In aquaponics, tilapia typically achieves an efficient FCR of 1.0 to 1.8. For economic and sustainability reasons, an FCR below 1 is ideal (Kloas et al., 2015); in this study, the value was 0.96 (Table 4), attributable to water quality, minimal feed waste from the feeding regimen based on daily protein intake (DPI), and high-performance diets. Similarly, Yang and Kim (2020) reported FCR values of 0.9–1.4 for diverse vegetables and herbs in aquaponic systems. Hu et al. (2015) reported an FCR of 2.0 in Pak choi-based aquaponics at a density of 30 kg/m3. In an experiment with high densities of 150, 300, and 450 fish/m3, the FCRs were 1.45 ± 0.13, 1.66 ± 0.1, and 1.86 ± 0.07, respectively, in aquaponic systems (Ani et al., 2022); Higher densities lead to higher FCRs. On the other hand, the SGR obtained was a 5.25% higher value, compared with the values found in a study focused on a semi-intensive aquaponic system with and without a biofilter, with values of 1.37 to 1.89 (Silva et al., 2017), and with values of 0.10% to 1.14% in an aquaponic system fed with spirulina (Arthrospira platensis) (Siringi et al., 2021). Yang and Kim (2019) found that a uniform feeding regimen can increase N use efficiency by improving water quality and nutrient availability, thereby enhancing the quality and/or yield of vegetables and herbs in aquaponics. These results align with the DPI methodology used in the experiment and can contribute to optimal environmental control that supports fish survival (100%), FCR, SGR, and plant growth. Another important factor in the success of aquaponics production is monitoring the systems; it is indispensable to track various parameters related to the integration of fish and plants. The feed-to-area ratio (FAR) is an uncommon parameter used in aquaponics experiments. Semenova et al. (2022) found that only nearly 25% of the experiments calculated this parameter. The importance of this parameter lies in its role in explaining the relationship between the total feed input (g) and the plant growth area (m2). In the present study, the value was 1612.15 compared with 56–169 obtained by Al-Hafedh et al. (2008), using an aquaponic system combined with tilapia and lettuce. The optimal FAR depends on the fish species and plant nutrient requirements (Colt and Schuur, 2021; Colt et al., 2022). In the aquaponic system, the plant-to-fish ratio (PFR) was 0.86; the system contained 34 organisms and 30 plants. Semenova et al. (2022) calculated the PFRM using data from warm- and cold-feeding species and various plant species. They obtained PFRM values of 3.1 for all plants and all feeds, 4.1 for warm feed, and 5.1 for pak-choi with warm feed species. For the present study, the value was 5.2. The Area-to-volume ratio (AVR) in aquaponic systems was 5.95; this parameter indicates the area dedicated to the growth of fish and plants. In our case, the fish area is smaller than the hydroponics area. This also influences the total biomass in the aquaponic systems versus hydroponics; the total number of plants per unit system varies from 30 to 10 plants in aquaponics and hydroponics, respectively, even though the final length and wet weight were significantly different between systems (p < 0.05), with the higher values obtained in aquaponics, with no difference in dry weight between systems (Table 5). Vertical aquaponics is a viable solution for reducing pressure on agricultural resources and mitigating climate change, especially in urban areas (Cowan et al., 2022). According to the results, maintaining a balance of conditions is one of the key factors for production success. It is important to monitor the FCR, the SGR, and the survival of fish, including the performance of other indices that is more related to the engineering of the systems, such as the Plant-to-fish mass ratio (PFRM) and the Plant-to-fish number ratio (PFR), which can help to systematize the upscaling of the system (Colt et al., 2022).
4.3 Phenotypic assessment and plant production
Plants display visible traits that help us understand their reactions to environmental stresses, both biological and non-biological. These traits serve as an effective early warning system. This is especially important in aquaponic systems, where plant production directly impacts system success and growth (Yang and Kim, 2019). In the present study, phenotypic assessment of plants provides indispensable information on growth conditions, morphology, and yield. Conducting detailed, non-destructive phenotypic assessments offers valuable insights into how plants develop and identifies potential yield constraints. For example, leaf area and chlorophyll values were higher than those in hydroponics throughout the experiment (Table 6). NDVI is a vital indicator of plant health, measuring green vegetation cover and enabling early detection of stress (Beisel et al., 2018; Semenova et al., 2022; Serrano-Trujillo et al., 2022). Nevertheless, in aquaponically grown tatsoi, both NDVI and QY showed marginal improvements (≥1-fold), indicating enhanced foliar-level photosynthetic efficiency. This suggests that increasing fish stocking densities in aquaponics may boost photosynthetic performance and potentially improve yields (Jaszczuk et al., 2023) (Table 6). In this study, we measured the leaf chlorophyll content of tatsoi using the Soil and Plant Analyzer Development (SPAD). Its effectiveness in evaluating leaf chlorophyll in aquaponics has been well established (Taha et al., 2024). SPAD meters quantify chlorophyll levels and can detect stress-induced leaf damage. They are widely used for their non-destructive, cost-effective, and real-time assessment of leaf health (Taha et al., 2024; Uddling et al., 2007). Chlorophyll content of tatsoi under aquaponics was increased by 1.2-fold, indicating sufficient accumulation of fish stock density in the current investigation, a crucial factor in photosynthetic capacity, growth stage, and nitrogen status evaluation, which is critical in plant productivity, and it is strongly related to leaf chlorophyll content of leafy vegetables in aquaponics. In this study, chlorophyll content in aquaponics-grown tatsoi remained consistently above 40 (Table 6), exceeding the values reported for pak-choi cultivated in various aquaponic systems, which ranged from 22.3 to 32.1 (Sundar and Chen, 2020). Increased leaf chlorophyll content under aquaponics corroborates greater carbon assimilation per plant and, thereby, greater leaf area in aquaponics. Comparable chlorophyll levels have been recorded in aquaponics tatsoi cultivated under different growing conditions, including values of 33.69–34.51 in floating and conventional systems, 32.54–36.39 with varied split fertilizer applications, and 31.97–36.97 when combining cultivation methods with split fertilization strategies (Kartika et al., 2021). The observed variation in chlorophyll content and fluorescence may be influenced by differences in nutrient solution composition and microbial activity within the systems. Further research is needed to better understand the role of these factors in optimizing plant health and productivity in aquaponic environments. PCA analysis sufficiently delineated the hydroponic and aquaponic systems, with PC1 contributing more to the data; higher leaf area, NDVI, and leaf chlorophyll content were associated with PC1, suggesting that PC1 represents overall plant vigor and photosynthetic activity. Root dry weight, root fresh weight, and shoot biomass with marginally negative values indicate less influence on PC1. Overall, PC1 indicates that it represents above-ground traits.
In aquaponic systems, the microbial community is fundamental for maintaining system functions and balance. Their performance in promoting plant growth or acting as pathogens, as well as their effects on nutrient balance and water quality, depends on factors that can favor one type of microorganism over another (Kasozi et al., 2021). Analysis of nitrifying bacteria in aquaponic systems revealed their presence only in the final stage. This suggests that these bacteria appear during the system’s last phase (Table 8). Additionally, detecting nitrifying bacteria at the end of the cultivation cycle underscores the need to optimize the system to achieve earlier nitrification, possibly by using water from a mature system or by enriching it with fish effluents and nutrients, especially key elements such as phosphorus, nitrogen, and iron. Pathogenic Oomycetes can inhabit both organic and inert surfaces, significantly impacting plant health or disease progression (Larousse and Galiana, 2017). Their mobile dispersal form, especially in liquid environments, warrants special attention (McGehee et al., 2024). Recirculating nutrient systems and uniform distribution among plants can promote the spread of Oomycetes (Laevens et al., 2024; Redekar et al., 2020). Environmental contamination sources in hydroponic systems are similar to those in conventional agriculture (Avila-Vega et al., 2014; Orozco et al., 2008; Sela Saldinger et al., 2023), which may explain the presence of root pathogens during stages II and III in hydroponic setups (Table 8). The absence of these pathogens in aquaponic systems suggests the potential to suppress root-related fungal pathogens, such as Oomycetes, aligning with other studies indicating that aquaponics may help suppress waterborne pathogens (Suárez-Cáceres et al., 2021). Enterobacteriaceae, a family of bacteria that affects humans and can cause infections in the stomach, lungs, wounds, or blood, were not detected in the system. This underscores the potential of aquaponic systems to inhibit the growth of human pathogens under the studied conditions (Kasozi et al., 2021). Overall, biocontrol strategies, system monitoring, and microbiological analysis are essential for the success of urban aquaponic systems. These tools help scale up and integrate sustainable production methods. By understanding how these factors interact, operators can optimize their systems for better sustainability and higher yields.
5 Conclusion
Vertical aquaponic systems hold significant potential for urban and peri-urban areas, where space constraints make aquaponics an effective solution for sustainable vegetable cultivation. However, achieving optimal outcomes in these systems hinges on careful management and precise control of system parameters. This is especially important for timely responses to changes that can affect the health of both plants and fish. Phenotypic assessments are an effective tool for quickly identifying abiotic and biotic stresses impacting plants and fish. The integration of technology for early detection supports ongoing system success by facilitating rapid intervention and adjustment. Such approaches are critical for maintaining system stability and ensuring consistent productivity. Water quality, best management practices, and robust control measures are essential to minimize pathogen risks. In hydroponic systems, practices like reusing plastic containers and altering nutrient solutions, if mishandled, can promote the growth of Oomycetes. These findings underscore the need for strict water-quality management and effective pathogen-control strategies to safeguard both plant and fish health. Advancing productivity in aquaponic systems requires developing new strategies informed by a comprehensive understanding of the microbiological community and its dynamics. Implementing technologies that enable rapid responses to emerging issues will further enhance system productivity and sustainability over time.
Statements
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Ethics statement
The animal study was approved by the Swedish Board of Agriculture, Dnr 5.2.18-10997/2024, for the ethical use of fish and permission number 2023-11-22 6.2.18-17898/2023 for the use of greenhouse facilities for the experiment with fish. The study was conducted in accordance with the local legislation and institutional requirements.
Author contributions
YEFA: Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. VT: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – review & editing. FL: Data curation, Formal analysis, Investigation, Writing – review & editing. MK: Data curation, Formal analysis, Writing – review & editing. AC: Funding acquisition, Resources, Supervision, Validation, Writing – review & editing. SK: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – review & editing.
Funding
The author(s) declared that financial support was received for this work and/or its publication. The Carl Trygger Foundation, grant number CTS 20:1172, supported this work.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Summary
Keywords
Crop optimization, Climate-controlled production, Urban production, Tatsoi, Nutrient content, Microbial community, Oomycetes
Citation
Fimbres-Acedo YE, Thuraga V, Leiva F, Karlsson M, Chawade A and Khalil S (2026) Plant growth and nutritional profiles of aquaponic crops based on phenotypic analyses. Front. Sustain. Food Syst. 10:1865450. doi: 10.3389/fsufs.2026.1865450
Received
26 April 2026
Revised
17 July 2026
Accepted
17 July 2026
Published
18 August 2026
Volume
10 - 2026
Edited by
Adolfo Jatobá, Catarinense Federal Institute - Araquari Campus, Brazil
Reviewed by
Priscila Sarai Flores-Aguilar, Autonomous University of Queretaro, Mexico
Shahadat Hossain, Universiti Malaysia Terengganu Institut Akuakultur Tropika, Malaysia
Updates
Copyright
© 2026 Fimbres-Acedo, Thuraga, Leiva, Karlsson, Chawade and Khalil.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Yenitze Elizabeth Fimbres-Acedo, yfimbres@cibnor.mxyenitze.fimbres@slu.se
Disclaimer
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