Research Article | | Peer-Reviewed

An Evaluation of User Satisfaction and Economics Performance of the Closed-System Tilapia Farming Control System

Received: 8 January 2026     Accepted: 20 February 2026     Published: 16 April 2026
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Abstract

Tilapia farming in Thailand faces critical challenges, including high mortality rates from water quality shock (20% production loss) and limited monthly farmer incomes of 2,700-9,100 Baht. This study evaluated the economic performance of a closed-system tilapia farming control device and user satisfaction among small-scale farmers. The objective was to assess whether adoption is associated with reduced production time and operating costs. Data were collected from 20 members of the Ban Pla Fish Farming Cooperative in Phayao Province who implemented the closed-system tilapia farming control device. Data collection utilized structured questionnaires covering satisfaction levels, operational performance, and financial outcomes, with analysis employing descriptive statistics and economic cost-effectiveness analysis. Based on the results of this study, the closed-system tilapia farming control device demonstrated exceptional performance across all evaluation categories. User satisfaction reached very high levels, with system functionality receiving the highest ratings. Technology achieved significant operational improvements: cultivation cycles reduced from 180 to 150 days (16.7% improvement), labor requirements decreased from 3 to 1 worker per pond (66.7% reduction), and aerator usage declined from 7 to 5 hours daily (28.6% reduction). Most critically, during the study period, the system eliminated fish mortality from water quality shock entirely. Economic performance analysis showed strong investment viability with Net Present Value of 747,975 Baht, Internal Rate of Return of 40.05%, and 2-year payback period. The closed-system tilapia farming control device represents a transformative solution for modernizing Thailand's tilapia sector through accessible technological innovation.

Published in International Journal of Economics, Finance and Management Sciences (Volume 14, Issue 2)
DOI 10.11648/j.ijefm.20261402.13
Page(s) 139-152
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Economic Cost-Effectiveness, Tilapia Farming Control Device, Tilapia, Water Condition Assessment Device

1. Introduction and Objective
1.1. Introduction
Tilapia is the most widely accepted freshwater fish species among fish farmers in Thailand, providing high nutritional value as an important economic aquaculture commodity. Thailand exported 6,251.83 tons valued at 256.34 million Baht, with 68.17% being fresh frozen tilapia exported to Asean countries (26.5%), Middle East (23.3%), United States (19.6%), European Union (14.1%), United Kingdom (7.8%), Japan (4.6%), and other countries (4.1%). Imports totaled 680.94 tons valued at 119.92 million Baht, with 98.73% being frozen tilapia primarily from China (45.30%). Regarding the tilapia production situation in 2023, production volume reached 256,484 tons, valued at 11,890 million Baht, with a farming area of 533,066 rai and an average yield of 481 kilograms per rai . However, production remains insufficient, requiring imports. Therefore, smart tilapia farming using the closed-system tilapia farming control system will increase domestic production by reducing losses from water quality shock and mortality.
In recent years, fish farmers have encountered problems with fish mortality due to water contamination and variable weather conditions affecting water quality, causing fish to experience water shock from sudden climate changes and resulting in significant losses. Truong et al. (2019) stated that environmental conditions severely impact aquaculture operations, affecting growth rates, survival rates, and disease risks, which influence business management and farmers' incomes . Fish farmers also face high production costs, with profits of 30,000-100,000 Baht per pond and farming periods of 6-11 months, resulting in monthly incomes of 2,700-9,100 Baht. Farmers must also manage risks from fish mortality due to water shock, diseases, and high electricity costs. Despite significant advances in smart aquaculture, IoT-based monitoring, automated feeding, and intensive production systems, several critical research gaps remain in tilapia aquaculture management. Existing studies have demonstrated the effectiveness of Internet of Things (IoT) platforms for real-time monitoring of dissolved oxygen, temperature, and potential of Hydrogen (pH), as well as automated aeration and feeding systems that reduce labor and improve operational efficiency . Other research has focused on closed, semi-closed, biofloc, and recirculating aquaculture systems to enhance productivity, reduce disease, improve feed efficiency, and increase profitability . However, most of these studies emphasize biological performance metrics, system-level production outcomes, or economic comparisons, while relatively few integrate real-time water quality intelligence with adaptive control mechanisms designed specifically to prevent equipment failure and optimize aerator operation at the farm level . Moreover, current sensor-based systems often rely on conventional electrochemical dissolved oxygen sensors, which require frequent calibration, cleaning, and replacement, increasing maintenance costs and limiting long-term reliability in commercial pond environments. While artificial intelligence and optimization frameworks have been proposed to improve feeding strategies and production efficiency , their integration with robust hardware-level control systems that directly manage aeration infrastructure and electrical load protection remains limited. Additionally, many advanced production systems report higher energy consumption or increased capital investment without adequately addressing practical risk factors such as aerator motor burnout, unstable power conditions, and delayed response to abnormal water quality events. Thus, to solve these problems, the research team has developed the closed-system tilapia farming control system featuring innovative optical sensors for measuring dissolved oxygen (DO) levels. These optical sensors offer longer lifespans than previous sensors, require minimal cleaning and maintenance, and incorporate a complex internal neural network that allows customers to configure up to 16 different functions. The device includes a water aerator control system designed according to customer needs, providing superior operational capabilities compared to market alternatives. The system features dual processors working in coordination and electrical power technology designed to prevent water aerator motor burnout through delayed group operations, electrical current monitoring, and automatic motor shutdown when abnormalities are detected. This device was tested with the Ban Pla Fish Farming Cooperative in Ban Tam Subdistrict, Mueang Phayao District, Phayao Province to gather information for improving quality and increasing income for fish farmers.
Therefore, this study addresses a critical gap in tilapia aquaculture by developing a farm-ready water quality assessment and aeration control system that integrates long-life optical dissolved oxygen sensors, intelligent data processing, and built-in electrical protection. The closed-system tilapia farming control system links real-time water condition monitoring with adaptive, motor-protective aerator control and is validated through on-farm testing. To assess its practical value, system adoption was evaluated in terms of production time, operational cost reduction, and farmer satisfaction, providing evidence to support smart aquaculture adoption and improved farm profitability.
1.2. Objectives
The objective of this study is to assess whether adoption is associated with reducing production time and operation costs.
2. Literature Review
2.1. Closed-System Tilapia Farming Control System
The closed-system tilapia farming control system represents an innovative oxygen measurement system utilizing advanced optical sensors for dissolved oxygen level monitoring in aquatic environments. These optical sensors demonstrate superior longevity compared to conventional sensors while requiring minimal maintenance through automated sensor head cleaning technology. The device features a sophisticated internal neural network architecture enabling configuration of up to 16 distinct operational functions through dual processors operating in coordination. The electrical power management system solves motor burnout issues in water aerators through sequential group operations, continuous electrical current monitoring, and automated motor shutdown protocols when system abnormalities are detected.
The closed-system tilapia farming control system incorporates five primary operational features. First, real-time water quality monitoring continuously measures and displays pH, salinity, and dissolved oxygen levels via digital interface, automatically activating response protocols upon detection of parameter deviations. Second, environmental monitoring functionality automatically controls water aerator operations based on detected temperature differentials between surface and subsurface water layers. Third, the system provides independent water aerator control capabilities, allowing customized operational scheduling according to specific requirements. Fourth, oxygen generator management includes continuous dissolved oxygen monitoring with automatic start/stop functionality based on predetermined threshold values. Fifth, the device employs advanced sensor technology with extended operational lifespans and automated maintenance protocols requiring minimal user intervention.
The closed-system control system, when compared to Internet of Things-based systems (LoT), Biofloc Technology systems (BFT), and Recirculating Aquaculture system (RAS), features advanced optical sensors, neural network architecture with dual processors, and automated equipment protection. It monitors multiple parameters (pH, salinity, dissolved oxygen, temperature) with sophisticated automation capabilities. IoT-based systems focus on remote monitoring via 3G/4G networks and web browsers, offering user-friendly operation for controlling aerators and feeders with high farmer satisfaction. BFT systems deliver superior biological performance (22% higher weight, 128% greater growth, 18% better feed conversion) with the lowest production costs ($1.45/kg) but higher energy consumption (50-60% more than alternatives). RAS systems excel in sustainability, saving 95-99% water with 60-70% lower disease losses. Despite higher initial costs, break-even occurs in 3.5-4 years, with aquaponics integration providing additional revenue .
2.2. Economic Cost-Effectiveness Assessment Concepts
Economic cost-effectiveness assessment utilizes cost-benefit analysis principles, which lead to economic cost-effectiveness evaluation . There are three time-adjusted economic assessment methods: Net Present Value (NPV), Benefit-Cost Ratio (BCR), and Internal Rate of Return (IRR). Additionally, Payback Period (PB) is used as another economic assessment tool. The discount rate for government-supported projects is based on the 5-year government bond yield plus the policy inflation rate. The time period of the analysis valuation depends on the lifespan and full maintenance cycle of the product or devices.
2.3. Related Research
Ardarsa et al. (2021) concluded that Internet of Things (IoT) technology utilizes microcontrollers to process sensor data and transmit information via 3G/4G networks to web browsers, enabling remote monitoring and control of aerators and fish feeders with both automatic and manual capabilities, with fish farmers expressing high satisfaction with these technological solutions . In addition, Hadkhuntod and Sangkudluo (2022) demonstrated that automated feeding systems using ESP8266 Arduino boards with Hypertext Preprocessor (PHP) and MariaDB can schedule feeding three times daily and dispense food at controlled rates of 1 kilogram per minute, effectively replacing human labor in fish farming operations . Furthermore, Biofloc Technology (BFT) systems demonstrate remarkable efficiency advantages. Misund et al. (2024) found that BFT shows the lowest production costs at 1.45 USD/kilogram among intensive systems with 23% better feed efficiency than conventional Recirculating Aquaculture Systems . Luo et al. (2014) concluded that BFT achieves 22% higher individual weight, 128% greater total weight gain, 112% higher specific growth rate, and 18% better feed conversion ratio compared to Recirculating Aquaculture Systems, with 100% survival rates in both systems . Suárez-Puerto et al. (2021) found that BFT systems generate 0.73 USD profit per USD invested with a 48% contribution margin, requiring 25-30% less feed but consuming 50-60% more energy than green water technology systems . Moreover, Rodríguez-Hernández et al. (2025) concluded that Recirculating Aquaculture Systems (RAS) achieve exceptional water conservation, saving 95-99% of water compared to flow-through systems. Despite requiring 25-30% higher initial investment, these systems reach break-even within 3.5-4 years through 60-70% lower disease losses and 15-20% improved feed conversion. Integration with aquaponics enables production of 8-12 kilogram/m²/year of leafy greens alongside fish . Kadota et al. (2024) concluded that combined closed-system nursery and pond grow-out approaches for common carp production reduce bird predation losses by 15-20%, shorten traditional 3-year production cycles by 8-12 months, and improve feed conversion ratios by 0.3-0.5 points. Production costs averaged 2.02 USD/kilogram, while net revenue increased by 22-25% through higher productivity and reduced pond area requirements . Mihály-Karnai et al. (2024) concluded that semi-closed containment systems reduce lice infestations by 85-90% but increase energy costs by 30-35%, while land-based systems show 40-50% lower mortality rates despite challenges in maintaining stable water temperatures, particularly in northern latitudes . Lal et al. (2024) demonstrated that closed-loop systems produce fish with 18-22% higher omega-3 fatty acid content and 12-15% greater protein retention compared to traditional pond systems. However, this study concluded that system-specific optimization of feeding regimes is crucial, as excessive stocking densities in intensive systems can reduce nutritional quality by up to 30% . Anani and Agbo (2019) concluded that Aquaculture Research and Development Centre Farmmade Tilapia Starter Diet (ARDECFEED) offers a cost-effective alternative for tilapia hatchery operators, particularly benefiting small-scale fish farmers by reducing production costs while maintaining comparable growth performance . Turlybek et al. (2025) found that behavioral analysis using 2.5-unit grid resolution optimally captures feeding responses, which show 35-40% greater entropy variation than non-feeding periods . Sriprateep et al. (2025) developed the Adaptive Artificial Multiple Intelligence System (AAMIS) integrated with Design of Experiments methodology to optimize Nile tilapia aquaculture. AAMIS achieved superior performance with a Hypervolume score of 0.62 and reduced life cycle costs to $39,880 while attaining the lowest environmental impact score (3,460.76). The system effectively balances yield maximization with cost minimization by optimizing water quality, feeding rates, and stocking densities, outperforming traditional optimization methods like Grey Wolf Optimizer and Artificial Bee Colony . Fitzsimmons and Feldman (2024) concluded that tilapia aquaculture advancement encompasses genetic improvements, alternative feed ingredients replacing fishmeal (soy, insects, seaweed), and modern systems including In-Pond Raceways and AI-managed facilities. The industry's sustainability credentials—integrated farming, low trophic feeding, and minimal chemical use—position tilapia as aquaculture's cornerstone species despite market challenges from misinformation and tariffs . Arumugam et al. (2023) reviewed tilapia aquaculture advancements in India, highlighting farming strategies including BFT, RAS, cage culture, and Integrated Multi-Trophic Aquaculture (IMTA), alongside monosex and Genetically Improved Farmed Tilapia (GIFT) strain production, alternative feed sources replacing fishmeal, and disease management through vaccines, probiotics, and immunostimulants. Government initiatives such as India's Blue Revolution (Neel Kranti) and Pradhan Mantri Matsya Sampada Yojana support production expansion, positioning tilapia farming as crucial for food security and economic development . Finally, Misund et al. (2024) found that while 82% of studies report biological performance metrics, only 19% include complete cost analyses, highlighting a gap in comprehensive economic assessment of aquaculture systems .
Previous studies have demonstrated the economic viability of various aquaculture interventions in Thailand and internationally. Wongwitwichote et al. (2021) assessed a Department of Fisheries project over a 5-year lifespan with a 5% discount rate, finding a net present value of 13.13 million Baht, benefit-cost ratio of 5.48, and Internal Rate of Return of 94% . Similarly, the Department of Fisheries, Ministry of Agriculture and Cooperatives (2020) reported expected returns of approximately 10,872 Baht per case for sex-reversed tilapia production with a 73.75% survival rate, yielding an average net return of 4,580 Baht per case and a 42.13% profit margin, alongside non-monetary benefits from farmer participation throughout project implementation . Setawong (2016) documented significant profitability improvements following business optimization, where production costs increased from 128,640 to 167,790 Baht, yet revenue rose from 660,000 to 1,620,000 Baht, resulting in net profits increasing from 531,360 to 1,452,210 Baht—a 173% return rate . Toopmongkol et al. (2022) evaluated the Laem Phak Bia Environmental Research and Development Project, calculating Net Present Value of 14,685,072 Baht for local fishery groups with research funding of 2,995,340 Baht, generating Social Return on Investment of 1:4.9 Baht, demonstrating how technology transfer improved community self-reliance and environmental development capabilities . Internationally, Cala-Delgado et al. (2024) found that integrating photovoltaic energy systems in aquaculture operations achieved NPV of USD 31,735.32 and IRR of 9.88%, with feed costs representing 39-43% of total production costs and labor comprising 20% . Finally, Tongsiri et al. (2020) examined Biofloc technology implementation in Chiang Mai, Thailand, reporting a net profit of 19,137 Baht per pond, benefit-cost ratio of 1.19, NPV of 1.43 million Baht, IRR of 45%, and payback period of 7.14 years .
Technology Clinic, Rajamangala University of Technology Lanna, Nan (2023) reported that the Mae Sa-Pong Sanook Large-Scale Tilapia Community Enterprise Group demonstrated strong interest and cooperation in workshop participation, successfully applying acquired knowledge to develop new tilapia farming concepts featuring convenient and manageable recirculating water systems as prototypes for individual members. The group also diversified their product line by developing seasoned crispy fried tilapia skin from production by products, complementing existing products including ground tilapia chili paste, sun-dried tilapia, and tilapia sausage, while simultaneously developing new branding, labeling formats, and expanded distribution channels . Similarly, the Faculty of Agricultural Technology, Sakon Nakhon Rajabhat University (2020) found that all technology transfer participants could apply acquired knowledge, with 96.67% successfully using this knowledge to develop their tilapia farming businesses under sufficiency economy philosophy principles. Participants expressed high satisfaction with the technology transfer project aimed at increasing income through improved tilapia farming business management. However, participants encountered significant challenges in production management, including inconsistent product availability, difficulties meeting product quality standards, and insufficient investment capital .
Kingcha (2022) found that pond-based tilapia farmers were predominantly male (61.54%), aged 41-60 years with primary education and average experience of 8.95 years. These farmers utilized personal investment capital (51.28%), participated in group cooperation (64.10%), owned land (79.49%), and maintained average farming areas of 21.40 rai with 2.00 ponds averaging 3.25 rai each. Farmers conducted one annual production cycle during June-September, stocking 5-6 cm fingerlings at 5,444 fish per rai for average periods of 235.97 days (7-8 months), achieving 62.31% survival rates, yields of 3,474.93 kg per rai, and average fish size of 1.07 kg. Feed conversion ratio averaged 1.27, with selling prices of 86.03 Baht per kg through wholesale markets. Total production costs averaged 318,702.28 Baht per rai, comprising variable costs of 309,479.15 Baht per rai (97.11%), primarily feed costs of 217,942.19 Baht per rai (68.39%) and fixed costs of 9,223.13 Baht per rai (2.89%). Average production costs were 91.71 Baht per kg, resulting in net losses of 19,769.30 Baht per rai and return on investment of -6.20%. Primary constraints included high costs (61.54%), market size mismatches (35.90%), insufficient cooperative networks (30.77%), low product prices (28.21%), and inadequate working capital (17.95%) . Moreover, Lianyong (2019) examined tilapia cage farmers, finding them predominantly male (52.50%) with average age of 53 years and primary education. Farmers utilized household labor with average annual household income of 200,000 Baht. Cage sizes were 3.0×6.0×2.5 meters and 5.0×5.0×2.5 meters, averaging 9.25 cages per farmer. Fish stocking density averaged 50 fish per cubic meter at 31-35 grams per fish, with culture periods of 5.3 months and survival rates of 83.83%. Average farming costs were 67,731.24 Baht per cage per cycle or 49.51 Baht per kg, with feed representing the highest cost component (82.31%), followed by fingerlings, labor, and cage construction. Average production was 35.84 kg per cubic meter, with income of 2,664.06 Baht per cubic meter and net profit of 880.55 Baht per cubic meter (24.57 Baht per kg). Average selling price was 74.53 Baht per kg, with return on investment of 53.45% . Suwitthayaporn (2015) compared earthen pond polyculture and cage monoculture systems. Earthen pond farmers averaged 1.07 rai, achieving yields of 604.13 kg per rai (192.49 kg tilapia and 411.64 kg other species), with total costs of 20,033.68 Baht per rai, net profits of 1,737.08 Baht per rai or 2.88 Baht per kilogram, and rearing periods of 13.53 months. Farmers stocked 6,132 fish per rai (2,545 tilapia and 3,587 other species) with 35.21% survival rates, with major costs including feed, fingerlings, and household labor. Red tilapia cage farming utilized average cage sizes of 19.62 cubic meters in monoculture, producing 28.53 kg per cubic meter with total costs of 1,870.46 Baht per cubic meter, net profits of 199.80 Baht per cubic meter or 7.00 Baht per kg, and rearing periods of 4.76 months. Farmers stocked 48.88 fish per cubic meter with 75.32% survival rates, with feed and fingerlings as major costs .
3. Research Methodology
This quantitative research utilized assessment forms to collect data from 20 members of the Ban Pla Fish Farming Cooperative, Ban Tam Subdistrict, Mueang Phayao District, Phayao Province, who had implemented the closed-system tilapia farming control system. The assessment form included general information, fish farming data, satisfaction with the closed-system freshwater fish farming control system, knowledge and understanding of the innovation, financial information, marketing information, and suggestions.
The assessment form development process involved literature review of academic documents and related research, questionnaire design covering research objectives, expert validation, content revision for accuracy and clarity, final expert review for completeness, and data collection from the 20 respondents.
Data analysis employed descriptive statistics including frequency, percentages, means, and standard deviations, along with economic cost-effectiveness analysis using NPV, IRR, BCR, and PB. The project duration is 5 years, based on the full maintenance time of the closed-system tilapia farming control system.
4. Results
The statistical and Cost-Effectiveness analysis results from 20 respondents are summarized below.
4.1. General Information
Gender distribution showed 12 males (60%) and 8 females (40%). Age groups included 61-70 years (8 people, 40%), 51-60 years (7 people, 35%), under 50 years (3 people, 15%), and over 70 years (2 people, 10%).
Cooperative membership duration in the Ban Pla Fish Farming Cooperative showed 12 members (60%) with 6-10 years of membership and 8 members (40%) with fewer than 5 years. Participation in the closed-system freshwater fish farming control system was equally divided, with 10 respondents (50%) involved and 10 respondents (50%) not involved.
Fish farming experience revealed 18 respondents (90%) with fewer than 10 years of experience, while 1 person each had 11-20 years and over 20 years of experience (5% each).
Occupations included 17 fish farmers (85%), 2 general laborers (10%), and 1 rice farmer (5%).
4.2. Fish Farming Information
Pond ownership showed 18 respondents (90%) with 1-5 fishponds, while 1 person each (5%) owned 5-10 ponds and more than 10 ponds, respectively.
Farming duration revealed equal distribution with 7 people each (35%) using 180-day and 210-day cycles. Additionally, 1 person (5%) used the longest duration of 280 days, and 1 person (5%) used the shortest duration of 110 days.
Daily feeding patterns showed 12 people (60%) feeding 51-100 kilogram per pond daily, followed by 5 people (25%) feeding 0-50 kilogram, and 3 people (15%) feeding over 100 kilograms.
Labor requirements indicated 11 respondents (55%) used 1 worker for pond care, 6 people (30%) used 2 workers, 1 person (5%) used 20 workers, with remaining 2 people (10%) using other arrangements.
Table 1. Number and percentage of respondents classified by hours of water aerator use.

Hours of water aerator use (Hours)

Frequency

Percentages

0

1

5.00

1

1

5.00

2

1

5.00

3

2

10.00

4

2

10.00

5

4

20.00

6

2

10.00

6.5

1

5.00

7

6

30.00

Total

20

100.00

From Table 1, it was found that most respondents (6 people, 30%) used the water aerator for 7 hours per day, while One person did not use the water aerator.
Table 2. Number and percentage of respondents classified by total yield per fish harvest.

Total yield per fish harvest (Kilogram)

Frequency

Percentage

0 -3,000

4

20.00

3,000 – 6,000

6

30.00

6,000 – 9,000

9

45.00

More than 9,000 Kilogram

1

5.00

Total

20

100.00

From Table 2, it was found that most respondents (9 people, 45%) had a total yield per fish harvest of 6,000-9,000 kilogram, followed by 6 people (30%) with 3,000-6,000 kilogram, 4 people (20%) with 0-3,000 kilogram, and 1 person (5%) with more than 9,000 kilogram, respectively.
Table 3. Number and percentage of respondents classified by monthly electricity costs.

Monthly electricity (Baht)

Frequency

Percentage

0 – 500

5

25

500 - 1,000

5

25

1,000 - 1,500

4

20

1,500 - 2,000

3

15

More than 2,000 Baht

3

15

Total

20

100.00

From Table 3 shows that most respondents (5 people, 25% each) had monthly electricity costs for fish farming between 0-500 Baht and 500-1,000 Baht. 4 people (20%) had monthly electricity costs for fish farming between 1,000-1,500 Baht, and 3 people (15% each) had monthly electricity costs for fish farming between 1,500-2,000 Baht and more than 2,000 Baht, respectively.
Table 4. Number and percentage of respondents classified by loss (fish mortality) from fish farming.

Loss (fish mortality) from fish farming

Frequency

Percentage

Loss

6

30.00

Not loss

14

70.00

Total

20

100.00

From Table 4, it was found that most respondents experienced loss (fish mortality) from fish farming (14 people, 70%), while 6 people (30%) did not experience any loss (fish mortality) from fish farming.
4.3. Satisfaction with the Freshwater Fish Farming Control System Innovation
Table 5. Mean and standard deviation of satisfaction with the closed-system tilapia farming control system for closed-system freshwater fish farming.

Satisfaction with the Freshwater Fish Farming Control System Innovation

Mean (X̅)

Standard Deviation (S.D.)

Interpretation

The closed freshwater fish farming control system

1. The closed freshwater fish farming control system has a structure that is easy to use

4.45

1.00

Very High level

2. The closed freshwater fish farming control system has a strong structure

4.20

0.83

Very High level

3. The closed freshwater fish farming control system has a safety system

4.55

0.60

Very High level

4. The installation system of the closed freshwater fish farming control system is not complicated

4.55

0.69

Very High level

5. The closed freshwater fish farming control system can check data via mobile device

4.70

0.66

Very High level

6. The closed freshwater fish farming control system Modernity is a touch screen

4.75

0.64

Very High level

Total

4.53

0.74

Very High level

The use of the closed freshwater fish farming control system

1. The closed freshwater fish farming control system is easy to use and convenient

4.65

0.59

Very High level

2. The operating system is a touch system that helps with easy use

4.70

0.57

Very High level

3. There is a manual for using the closed freshwater fish farming control system, making it convenient to use

4.80

0.52

Very High level

4. The closed freshwater fish farming control system has a real-time data connection. (Modern)

4.90

0.31

Very High level

Total

4.76

0.50

Very High level

Performance of closed freshwater fish farming control system

1. Can check the oxygen level of the fish pond accurately and precisely

4.65

0.75

Very High level

2. Can check the pH level of the fish pond accurately and precisely

4.65

0.59

Very High level

3. Can check the temperature level of the water in the pond accurately and precisely

4.65

0.49

Very High level

4. Can check the temperature level in the air or humidity in the air

4.50

0.95

Very High level

5. There is a warning command to activate the water aerator in the fish pond

4.60

0.68

Very High level

6. There is a safety system in use

4.80

0.41

Very High level

7. Can set the on/off time

4.85

0.49

Very High level

8. The closed freshwater fish farming control system helps save labor in fish farming

4.60

0.60

Very High level

9. The closed freshwater fish farming control system helps reduce electricity costs in fish farming

3.95

1.32

Very High level

10. The closed freshwater fish farming control system helps to turn on and off the water beater

4.60

0.68

Very High level

11. The closed freshwater fish farming control system can reduce the time in fish farming

4.55

0.94

Very High level

Total

4.58

0.72

Very High level

Quality of closed freshwater fish farming control system

1. The closed freshwater fish farming control system helps produce quality fish

4.80

0.41

Very High level

2. Closed freshwater fish farming control system helps to check the water quality accurately.

4.70

0.57

Very High level

3. Closed freshwater fish farming control system helps to reduce the cost of fish feed

4.25

0.79

Very High level

4. Closed freshwater fish farming control system helps to reduce the loss in fish farming.

4.75

0.64

Very High level

5. Closed freshwater fish farming control system helps to control the time to catch from the pond more accurately.

4.55

0.76

Very High level

Total

4.61

0.63

Very High level

Grand Total

4.60

0.67

Very High level

From Table 5, perception of respondents with the closed-system tilapia farming control system showed an overall mean of 4.60 (standard deviation = 0.67) across all four aspects, indicating the highest satisfaction level. When categorized by aspect, system operation ranked highest (mean = 4.76, SD = 0.50), followed by product quality (mean = 4.61, SD = 0.63), device efficiency (mean = 4.58, SD = 0.72), and device features (mean = 4.53, SD = 0.74). All aspects demonstrated the highest satisfaction levels.
4.4. Financial Data
Table 6. Number and percentage of respondents classified by income from fish sales per harvest.

Income from fish sales per harvest (Baht)

Frequency

Percentage

0 – 100,000

9

45.00

100,000 – 200,000

2

10.00

200,000 – 300,000

5

25.00

300,000 – 400,000

1

5.00

More than 400,000 Baht

3

15.00

Total

20

100.00

From Table 6, it was found that most respondents (9 people, 45%) had an income from fish sales per harvest of 0-100,000 Baht, followed by 5 people (25%) with 200,000-300,000 Baht, 3 people (15%) with more than 400,000 Baht, 2 people (10%) with 100,000-200,000 Baht, and 1 person (5%) with 300,000-400,000 Baht, respectively.
Table 7. Number and percentage of respondents classified by fish farming cost per harvest.

Fish farming cost per harvest (Baht)

Frequency

Percentage

0 - 50,000

2

10.00

50,000 - 100,000

2

10.00

100,000 -150,000

2

10.00

150,000 - 200,000

1

5.00

200,000 - 250,000

4

20.00

250,000 - 300,000

2

10.00

More than 300,000 Baht

7

35.00

Total

20

100.00

From Table 7, it was found that most respondents (7 people, 35%) had fish farming costs per harvest of more than 300,000 Baht. The lowest cost was 0-50,000 Baht (2 people, 10%).
Table 8. Number and percentage of respondents classified by investment in fish farming per harvest.

Investment in fish farming per harvest (Baht)

Frequency

Percentage

0 - 50,000

3

15.00

50,000 - 100,000

2

10.00

100,000 - 150,000

1

5.00

150,000 - 200,000

2

10.00

200,000 - 250,000

5

25.00

More than 250,000 Baht

7

35.00

Total

20

100.00

From Table 8, it was found that most respondents (7 people, 35%) invested more than 250,000 Baht in fish farming per harvest, followed by 5 people (25%) investing 200,000-250,000 Baht, 3 people (15%) investing 0-50,000 Baht, 2 people (10% each) investing 50,000-100,000 Baht and 150,000-200,000 Baht, and 1 person (5%) investing 100,000-150,000 Baht, respectively.
Source of fish farming funds: All 20 respondents (100%) obtained fish farming funds from the Ban Tam Fish Farming Cooperative.
Table 9. Income from fish sales per harvest and Fish farming experience Cross table Results.

Income from fish sales per harvest (Baht)

Total

0 – 100,000

100,000 – 200,000

200,000 – 300,000

300,000 – 400,000

More than 400,000 Baht

Fish farming experience (Year)

1-5 years

Count

1

2

2

1

0

6

% within Income

16.7%

33.3%

33.3%

16.7%

0.0%

100.0%

% within Experience

12.5%

66.7%

50.0%

33.3%

0.0%

30.0%

% of Total

5.0%

10.0%

10.0%

5.0%

0.0%

30.0%

6-10 years

Count

7

1

1

1

2

12

% within Income

58.3%

8.3%

8.3%

8.3%

16.7%

100.0%

% within Experience

87.5%

33.3%

25.0%

33.3%

100.0%

60.0%

% of Total

35.0%

5.0%

5.0%

5.0%

10.0%

60.0%

11-15 years

Count

0

0

1

0

0

1

% within Income

0.0%

0.0%

100.0%

0.0%

0.0%

100.0%

% within Experience

0.0%

0.0%

25.0%

0.0%

0.0%

5.0%

% of Total

0.0%

0.0%

5.0%

0.0%

0.0%

5.0%

4.00

Count

0

0

0

1

0

1

% within Income

0.0%

0.0%

0.0%

100.0%

0.0%

100.0%

% within Experience

0.0%

0.0%

0.0%

33.3%

0.0%

5.0%

% of Total

0.0%

0.0%

0.0%

5.0%

0.0%

5.0%

Total

Count

8

3

4

3

2

20

% within Income

40.0%

15.0%

20.0%

15.0%

10.0%

100.0%

% within Experience

100.0%

100.0%

100.0%

100.0%

100.0%

100.0%

% of Total

40.0%

15.0%

20.0%

15.0%

10.0%

100.0%

From Table 9, it shows that farmers with 1-5 years of experience tend to earn in the lower-to-middle income ranges (100,000-300,000 Baht), with two-thirds earning between 100,000-300,000 Baht. Farmers with 6-10 years of experience show the widest income distribution. Notably, 87.5% of the lowest earners (0-100,000 Baht) come from this group, but they also represent all farmers earning over 400,000 Baht, suggesting high variability in outcomes for this experience level. For higher experience levels (11-15 years), data is limited, but the one farmer earned 200,000-300,000 Baht. Overall, experience level alone doesn't guarantee higher income; other factors likely influence earnings significantly.
4.5. Marketing Channels
Most respondents (15 people, 75%) sold fish through the Ban Tam Fish Farming Cooperative, while other channels such as direct sales accounted for 5 people (25%).
All respondents (20 people, 100%) sold fresh fish.
4.6. Economic Cost-Effectiveness Analysis Results
Table 10. Summary of Economic and Fish Farming Analysis Results.

No.

Description

Before used device

After used device

Results

1.

Income from the end of the pond

1,800 x 60 = 108,000 Baht (Chance of fish dying from water shock approximately 20%)

2,000 x 60 = 120,000 Baht (No chance of fish dying from water shock)

additional = 120,000 – 108,000 = 12,000 Baht, an increase of 11.11 percent

2.

Total cost of fishing farming

2,000 × 50 = 100,000 Baht

2,000 × 50 = 100,000 Baht

Not change

3.

Raising period

180 days

150 days

Reduce 30 days

4.

Total cost of raising fish per round (Daily cost of raising 100,000/180 = 555 Baht)

180 × 555.55 = 100,000 Baht

150 × 555.55 = 83,250 Baht

decreased by 16,750 Baht (reduce cost as 16.75%)

5.

workers to take care of the pond

3 workers

1 worker

Reduce 2 workers

6.

Hours of water aerator usage

7 hours

5 hours

Reduce 2 hours

Remark: Data from interviews with fish farmers show that the average fish price sold at the Ban Tam Fish Farming Cooperative Group was 60 Baht per kilogram. The fish farming cycle is one crop only.
The total project investment is 610,000 Baht, which is supported by a government organization. The cost of capital is 3.41% (the cost of capital equals the 5-year government bond yield of 1.41% plus the policy inflation rate of 2%, based on data from Thai Bond Market Association and Bank of Thailand . The device has a lifespan of 5 years when properly maintained. The fish farming cycle was reduced from 6 months to 5 months, with a 1-month pond rest period. Average income per pond is 1,500 kilogram from 10 ponds. Average selling price is 60 Baht per kilogram. Total farming costs average 50 Baht per kilogram. The economic cost-effectiveness analysis results are as follows:
1) Payback Period of 2 years and 1 month
2) Net Present Value (NPV) of 747,975.17 Baht
3) Internal Rate of Return (IRR) of 40.05%
4) Benefit-Cost Ratio (BCR) of 2.23 times.
Based on economic cost-effectiveness analysis results, the author concluded that the firm's freshmen should adopt the closed-system tilapia farming control system device.
5. Discussion
Based on the results of this study, farmers with 1-5 years of experience earned mostly 100,000-300,000 Baht per harvest, while those with 6-10 years showed the widest income distribution, ranging from under 100,000 to over 400,000 Baht, indicating that experience alone does not guarantee higher earnings. The wide income variability among farmers with 6-10 years of experience suggests that experience alone does not determine financial outcomes, consistent with Misund et al. (2024), who concluded that only 19% of aquaculture studies include complete cost analyses, highlighting gaps in understanding income determinants . Other influencing factors may include pond size, technology adoption, and market access, as supported by Lianyong (2019), who found that feed costs represented 82.31% of total farming expenses, significantly affecting net profitability regardless of experience level . Furthermore, Fitzsimmons and Feldman (2024) emphasized that modern farming systems and alternative feed ingredients substantially influence production economics beyond farmer experience alone . The closed-system tilapia farming control system demonstrated exceptional performance across all evaluation categories. This finding shows very high satisfaction levels among users. This finding aligns with Ardarsa et al. (2021), who reported that farmers expressed high satisfaction with smart agricultural technology for tilapia farms using Internet of Things foundations . The device's real-time monitoring capabilities for dissolved oxygen, pH, and temperature parameters mirror the three-sensor approach that was validated. The reduction in labor requirements from 3 to 1 worker per pond operation supports findings from Hadkhuntod and Sangkudluo (2022), who demonstrated that Internet of Things-based systems effectively replace workers in fish farming operations . This worker efficiency improvement contributes significantly to the overall cost reduction of 16.75% observed in the current study. The closed-system tilapia farming control system eliminates fish mortality from water shock, compared to the previous 20% loss rate, represents a substantial improvement in production stability. This finding corresponds with Rodríguez-Hernández et al. (2025), who reported 60-70% lower disease losses in advanced aquaculture systems . The automated aerator control system improved energy efficiency by reducing daily operation time from 7 to 5 hours while preserving ideal water quality. Production data showed strong performance with an average harvest of 1,500 kilograms per pond across 10 ponds, reaching a density of 2,000 fish per pond and selling at 60 Baht per kilogram. These outcomes align well with Lianyong's (2019) findings, which documented average yields of 35.84 kilograms per cubic meter and market prices of 74.53 Baht per kilogram in cage-based systems . The widespread adoption of the Ban Tam Fish Farming Cooperative as the main distribution channel (used by 75% of participants) reflects successful market connectivity. This community-centered marketing strategy mirrors effective frameworks outlined in Technology Clinic, Rajamangala University of Technology Lanna, Nan (2023), where local enterprises effectively implemented innovative tilapia cultivation methods through joint efforts . Complete reliance on cooperative financing demonstrates robust institutional backing, consistent with the knowledge transfer achievements documented by Faculty of Agricultural Technology, Sakon Nakhon Rajabhat University (2017), where 96.67% of participants effectively implemented learned techniques to enhance their operations .
The equal distribution of participation in the closed-system control suggests potential for expanded adoption. The predominant demographic of farmers with fewer than 10 years of experience (90%) indicates successful technology transfer to relatively newer practitioners, supporting the accessibility of innovation. This finding contrasts with Kingcha (2022), who concluded average farming experience of 8.95 years among traditional pond farmers , suggesting the closed-system tilapia farming control system appeals to farmers across experience levels. The maintenance of total farming costs at 50 Baht per kilogram while achieving increased production efficiency demonstrates the system's cost-effectiveness. This performance compares favorably with Misund et al. (2024), who identified Biofloc Technology Systems achieving the lowest production costs of 1.45 USD/kilogram among intensive systems . The device's ability to maintain cost stability while improving production metrics indicates sustainable financial performance for adopting farmers.
The closed-system tilapia farming control system demonstrated exceptional economic performance with Internal Rate of Return of 40.05%, significantly exceeding the findings of Wongwitwichote et al. (2021), who reported IRR of 94% for fisheries technology projects . However, the current study's IRR of 40.05% remains substantially higher than typical investment benchmarks, indicating strong project viability. Net Present Value of 747,975.17 Baht and Benefit-Cost Ratio of 2.23 times demonstrate robust economic returns, surpassing the BCR of 1.19 reported by Tongsiri et al. (2020) for Biofloc Technology implementation . The 2.1 years payback period represents a relatively quick capital recovery, contrasting with the 7.14-year payback period found in Tongsiri et al. (2020) study . This accelerated return on investment can be attributed to the device's ability to reduce farming cycles from 180 to 150 days while maintaining production quality, resulting in increased annual production capacity.
6. Conclusion
A comprehensive study examined the closed-system tilapia farming control system through field testing with 20 fish farmers in Phayao Province, focusing on user perception and economic viability of this cutting-edge aquaculture technology.
Based on the results of this study, it demonstrated outstanding performance across all evaluation categories. User satisfaction reached high levels, with system functionality receiving the most positive feedback. The technology delivered notable operational improvements: cultivation cycles decreased from 180 to 150 days (16.7% improvement), worker requirements reduced from 3 to 1 worker per pond (66.7% reduction), and aerator usage declined from 7 to 5 hours per day (28.6% reduction). Most importantly, the system entirely eliminated fish mortality from water quality shock, removing what had been a 20% production loss.
Economic evaluation confirmed strong investment viability with 610,000 Baht capital expenditure generating excellent returns: Net Present Value of 747,975.17 Baht, Internal Rate of Return of 40.05%, Benefit-Cost Ratio of 2.23, and payback period of 2 years 1 month. Per-cycle improvements included 12,000 Baht increased revenue (11.11%) and 16,750 Baht reduced expenses (16.75%), totaling 28,750 Baht enhanced profitability per cycle.
The compelling combination of operational efficiency gains, economic returns, and user acceptance creates an evidence-based framework for policy intervention. Government agricultural agencies should prioritize smart farming initiatives, while financial institutions can design supportive adoption programs. Additionally, the study demonstrates that sophisticated monitoring technologies can deliver measurable benefits to resource-constrained farmers without requiring extensive technical expertise. The closed-system tilapia farming control system represents more than incremental improvement—it offers a viable pathway for modernizing tilapia production through economically sustainable innovation that directly enhances farmer livelihoods while advancing environmental sustainability goals.
7. Suggestions for Future Studies
Future research could explore applying this model to larger groups and saltwater fish.
8. Limitations of This Study
This study on a single cooperative with 20 respondents may limit generalizability across diverse farming contexts.
Abbreviations

IoT

Internet of Things

pH

Potential of Hydrogen

DO

Dissolved Oxygen

BFT

Biofloc Technology Systems

RAS

Recirculating Aquaculture System

NPV

Net Present Value

BCR

Benefit-Cost Ratio

IRR

Internal Rate of Return

PB

Payback Period

PHP

Hypertext Preprocessor

AAMIS

Artificial Multiple Intelligence System

IMTA

Integrated Multi-Trophic Aquaculture

GIFT

Genetically Improved Farmed Tilapia

Author Contributions
Suphattana Tachochalalai: Formal analysis, Validation, Writing – review & editing
Somkid Yakean: Conceptualization, Formal analysis, Methodology, Validation, Writing – original draft, Writing – review & editing
Sukhamit Theerasanee: Conceptualization, Methodology, Writing – review & editing
Kittisak Srepirote: Conceptualization, Methodology, Writing – review & editing
Jaruwan Ketkhanla: Conceptualization, Methodology, Writing – review & editing
Piyachon Ketsuwan: Conceptualization, Methodology, Writing – review & editing
Funding
This research was supported by the University of Phayao and the Thailand Science Research and Innovation Fund (Fundamental Fund 2025, Grant No. 5048/2567).
Conflicts of Interest
The authors declare that there is no conflicts of interest.
References
[1] Boon-Ek P. (2025). Tilapia and product situation in 2023 and trends in 2024.
[2] T. T. Ngoc Huyen, T. K. Duy, N. M. Trang, L. V. Quoc Danh and A. Dinh. (2019). A Simple dissolved Oxygen sensor using low-cost visible Spectro sensor. International Journal of Innovative Technology and Exploring Engineering (IJITEE). Vol. 8 (7C2), pp. 31-34.
[3] Ardarsa P., Apinantanakon W., Srichaiwong P., and Chansanam W. (2021). Development of smart agricultural technology for Tilpia farming by using the Internet of Things. Journal of MCU Ubon Review. 6(3), September-December 2021, pp. 531-544.
[4] Hadkhuntod P., and Sangkudluo T. (2020). Development of the Smart farm weighing-based fish feeding system. NSRU Science and Technology Journal. 14(20), July – December 2020, pp. 97-111.
[5] Luo, G., Gao, Q., Wang, C., Liu, W., Sun, D., Li, L., and Tan, H. (2014). Growth, digestive activity, welfare, and partial cost-effectiveness of genetically improved farmed tilapia (Oreochromis niloticus) cultured in a recirculating aquaculture system and an indoor Biofloc system. Aquaculture, 422-423, pp. 1-7,
[6] Suárez-Puerto, B., Delgadillo-Díaz, M., Sánchez-Solís, M. J., and Gullian-Klanian, M. (2021). Analysis of the cost-effectiveness and growth of Nile tilapia (Oreochromis niloticus) in Biofloc and green water technologies during two seasons. Aquaculture, 538, 736534.
[7] Kadota, M., Torisawa, S., and Takagi, T. (2024). Quantitative assessment and analysis of fish behavior in closed systems using information entropy. Fishes. 9(6), 224.
[8] Rodríguez-Hernández, M. E., Martínez-Castellanos, G., López-Méndez, M. C., Reyes-Gonzalez, D., and González-Moreno, H. R. (2025). Production costs and growth performance of tilapia (Oreochromis niloticus) in intensive production systems: A review. Sustainability. 17(4), 1745.
[9] Misund, A., Thorvaldsen, T., Strand, A. V., Oftebro, T. L., and Dahle, S. W. (2024). Opportunities and challenges in new production systems for salmon farming in Norway-Industry perspective. Marine Policy. 170, 106394.
[10] Sriprateep, K., Pitakaso, R., Khonjun, S., Luesak, P., Jutagate, A., Kaewta, C., Srichok, T., Kosacka-Olejnik, M., and Matitopanum, S. (2025). Optimizing Nile tilapia growth and production costs in earthen ponds using multi-objective adaptive artificial intelligence systems. Aquaculture Reports, 41, 102716.
[11] Turlybek, N., Nurbekova, Z., Mukhamejanova, A., Baimurzina, B., Kulatayeva, M., Aubakirova, K. M., and Alikulov, Z. (2025). Sustainable aquaculture systems and their impact on fish nutritional quality. Fishes. 10(5), 206.
[12] Stephen A. Ross, Randolph W. Westerfield, and Bradford D. Jordan (2010). Fundamentals of corporate finance. Alternate Edition, 9th ed., McGraw-Hill International Edition.
[13] Lal, J., Vaishnav, A., Deb, S., Kashyap, S., Debbarma, P., Devati, Gautam, P., Pavankalyan, M., Kumari, K., and Verma, D. K. (2024). Re-circulatory aquaculture systems: A pathway to sustainable fish farming. Current Research International. 24(5), pp. 799-810.
[14] Mihály-Karnai, L., Szűcs, I., Fehér, M., Szőllősi, L., Bozánné Békefi, E., and Kertész-Molnár, S. (2024). Combining nursery closed-system and pond grow-out common carp (Cyprinus carpio) production is a profitable business in Hungary. Aquaculture Reports. Vol 36, 102189.
[15] Anani, F. A., and Agbo, N. W. (2019). Growth performance and cost-effectiveness of farm-made and commercial Tilapia starter diets in Nile tilapia (Oreochromis niloticus L.) fingerling production in Ghana. Turkish Journal of Fisheries and Aquatic Sciences, 19(12), pp. 1001-1007.
[16] Fitzsimmons, K., and Carvalho Filho, J. (Eds.). (2025). Proceedings of the 13th International Symposium on Tilapia in Aquaculture (ISTA 13). International Symposium on Tilapia in Aquaculture.
[17] Arumugam, M., Jayaraman, S., Sridhar, A., Venkatasamy, V., Brown, P. B., Abdul Kari, Z., Tellez-Isaias, G., and Ramasamy, T. (2023). Recent advances in tilapia production for sustainable developments in Indian aquaculture and its economic benefits. Fishes, 8(4), 176.
[18] Wongwitwichote A., Kuldilok K., and Sukkumnoed D. (2021). Economic worthiness of the Tilapia collaborative farming extension project in Chon Buri province. Khon Kaen Agriculture Journal. 49(2): pp. 430-441.
[19] Department of Fisheries, Ministry of Agriculture and Cooperative (2020). Evaluation report of the project to develop alternative careers in fisheries: Raising sex-reversed Tilapia in earthen ponds. Office Department of Fisheries, Ministry of Agriculture and Cooperative.
[20] Setwong J. (2016). The Optimization of the Tilapia farming management on all supply chain. Independent Study, Master of Business Administration, Logistics Management Department, Graduate School, University of the Thai Chamber of Commerce.
[21] Toopmongkol T., Intaraksa A., and Phewnil O. (2022). Social return on investment of local fishing households from environmental knowledge creation at The King’s Royally Initiated Laemphakbia Environmental projects, Phetchaburi province. Journal of Social Sciences, Srinakharinwirot University. 25(1). January – June 2022, pp. 24-37.
[22] Cala-Delgado, D. L., da Costa, J. I., and Garcia, F. (2024). Economic analysis of red tilapia (Oreochromis sp.) production under different solar energy alternatives in a commercial Biofloc system in Colombia. Fishes, 9(12), 505.
[23] Tongsiri, S., Somkane, N., Sompong, U., and Thiammueang, D. (2020). A cost and benefit analysis of Nile tilapia culture in biofloc technology, the environmental friendly system: The case of selected farm in Chiang Mai, Thailand. Maejo International Journal of Energy and Environmental Communication, 2(1), pp. 45-49.
[24] Technology Clinic, Rajamangala University of Technology Lanna, Nan (2023). Report on the performance of increasing the potential of safe Nile Tilapia production with the innovation of dense Nile Tilapia farming in a Recirculating Water system: Mae Sa-Pong Sanuk Large-Scale Nile Tilapia community enterprise: Platform for increasing the potential of community businesses (Business Community Enterprise: BCE). Fund Supported by the Office of the Permanent Secretary, Ministry of Higher Education, Science, Research and Innovation for fiscal year 2023.
[25] Faculty of Agricultural Technology, Sakon Nakhon Rajabhat University (2017). Project performance report: Project to transfer technology for managing Tilapia farming business to increase income based on the sufficiency economy philosophy. 28-29 April 2017 at the Community Hall, Village No. 3, Ban Un Nam, Nangua Subdistrict, Na Wa District, Nakhon Phanom Province.
[26] Kingcha P. (2022). The study on cost and financial sea bass in earthern ponds Prachuap Khiri Khan Province year 2021. Prachuap Khiri Khan Fisheries Provincial, Office Department of Fisheries, Ministry of Agriculture and Cooperative.
[27] Lianyong N. (2019). The study on cost and financial return of Nile Tilapia cage culture in the Chao Phraya River in Ang Thong Province. Ang Thong Provincial Fisheries, Office Department of Fisheries, Ministry of Agriculture and Cooperatives.
[28] Suwitthayaporn I. (2015). The study of costs and returns of Tilapia culture investment in suitable commercial area for Tilapia culture in Phitsanulok province. Phitsanulok Provincial Fisheries Office, Department of Fisheries, Ministry of Agriculture and Cooperatives.
[29] Bank of Thailand (2025). Announcement of the monetary policy committee on monetary policy objectives for 2025.
[30] Thai Bond Market Association (2025). Government bond yield curve.
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    Tachochalalai, S., Yakean, S., Theerasanee, S., Srepirote, K., Ketkhanla, J., et al. (2026). An Evaluation of User Satisfaction and Economics Performance of the Closed-System Tilapia Farming Control System. International Journal of Economics, Finance and Management Sciences, 14(2), 139-152. https://doi.org/10.11648/j.ijefm.20261402.13

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    Tachochalalai, S.; Yakean, S.; Theerasanee, S.; Srepirote, K.; Ketkhanla, J., et al. An Evaluation of User Satisfaction and Economics Performance of the Closed-System Tilapia Farming Control System. Int. J. Econ. Finance Manag. Sci. 2026, 14(2), 139-152. doi: 10.11648/j.ijefm.20261402.13

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    AMA Style

    Tachochalalai S, Yakean S, Theerasanee S, Srepirote K, Ketkhanla J, et al. An Evaluation of User Satisfaction and Economics Performance of the Closed-System Tilapia Farming Control System. Int J Econ Finance Manag Sci. 2026;14(2):139-152. doi: 10.11648/j.ijefm.20261402.13

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  • @article{10.11648/j.ijefm.20261402.13,
      author = {Suphattana Tachochalalai and Somkid Yakean and Sukhamit Theerasanee and Kittisak Srepirote and Jaruwan Ketkhanla and Piyachon Ketsuwan},
      title = {An Evaluation of User Satisfaction and Economics Performance of the Closed-System Tilapia Farming Control System},
      journal = {International Journal of Economics, Finance and Management Sciences},
      volume = {14},
      number = {2},
      pages = {139-152},
      doi = {10.11648/j.ijefm.20261402.13},
      url = {https://doi.org/10.11648/j.ijefm.20261402.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijefm.20261402.13},
      abstract = {Tilapia farming in Thailand faces critical challenges, including high mortality rates from water quality shock (20% production loss) and limited monthly farmer incomes of 2,700-9,100 Baht. This study evaluated the economic performance of a closed-system tilapia farming control device and user satisfaction among small-scale farmers. The objective was to assess whether adoption is associated with reduced production time and operating costs. Data were collected from 20 members of the Ban Pla Fish Farming Cooperative in Phayao Province who implemented the closed-system tilapia farming control device. Data collection utilized structured questionnaires covering satisfaction levels, operational performance, and financial outcomes, with analysis employing descriptive statistics and economic cost-effectiveness analysis. Based on the results of this study, the closed-system tilapia farming control device demonstrated exceptional performance across all evaluation categories. User satisfaction reached very high levels, with system functionality receiving the highest ratings. Technology achieved significant operational improvements: cultivation cycles reduced from 180 to 150 days (16.7% improvement), labor requirements decreased from 3 to 1 worker per pond (66.7% reduction), and aerator usage declined from 7 to 5 hours daily (28.6% reduction). Most critically, during the study period, the system eliminated fish mortality from water quality shock entirely. Economic performance analysis showed strong investment viability with Net Present Value of 747,975 Baht, Internal Rate of Return of 40.05%, and 2-year payback period. The closed-system tilapia farming control device represents a transformative solution for modernizing Thailand's tilapia sector through accessible technological innovation.},
     year = {2026}
    }
    

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  • TY  - JOUR
    T1  - An Evaluation of User Satisfaction and Economics Performance of the Closed-System Tilapia Farming Control System
    AU  - Suphattana Tachochalalai
    AU  - Somkid Yakean
    AU  - Sukhamit Theerasanee
    AU  - Kittisak Srepirote
    AU  - Jaruwan Ketkhanla
    AU  - Piyachon Ketsuwan
    Y1  - 2026/04/16
    PY  - 2026
    N1  - https://doi.org/10.11648/j.ijefm.20261402.13
    DO  - 10.11648/j.ijefm.20261402.13
    T2  - International Journal of Economics, Finance and Management Sciences
    JF  - International Journal of Economics, Finance and Management Sciences
    JO  - International Journal of Economics, Finance and Management Sciences
    SP  - 139
    EP  - 152
    PB  - Science Publishing Group
    SN  - 2326-9561
    UR  - https://doi.org/10.11648/j.ijefm.20261402.13
    AB  - Tilapia farming in Thailand faces critical challenges, including high mortality rates from water quality shock (20% production loss) and limited monthly farmer incomes of 2,700-9,100 Baht. This study evaluated the economic performance of a closed-system tilapia farming control device and user satisfaction among small-scale farmers. The objective was to assess whether adoption is associated with reduced production time and operating costs. Data were collected from 20 members of the Ban Pla Fish Farming Cooperative in Phayao Province who implemented the closed-system tilapia farming control device. Data collection utilized structured questionnaires covering satisfaction levels, operational performance, and financial outcomes, with analysis employing descriptive statistics and economic cost-effectiveness analysis. Based on the results of this study, the closed-system tilapia farming control device demonstrated exceptional performance across all evaluation categories. User satisfaction reached very high levels, with system functionality receiving the highest ratings. Technology achieved significant operational improvements: cultivation cycles reduced from 180 to 150 days (16.7% improvement), labor requirements decreased from 3 to 1 worker per pond (66.7% reduction), and aerator usage declined from 7 to 5 hours daily (28.6% reduction). Most critically, during the study period, the system eliminated fish mortality from water quality shock entirely. Economic performance analysis showed strong investment viability with Net Present Value of 747,975 Baht, Internal Rate of Return of 40.05%, and 2-year payback period. The closed-system tilapia farming control device represents a transformative solution for modernizing Thailand's tilapia sector through accessible technological innovation.
    VL  - 14
    IS  - 2
    ER  - 

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  • Abstract
  • Keywords
  • Document Sections

    1. 1. Introduction and Objective
    2. 2. Literature Review
    3. 3. Research Methodology
    4. 4. Results
    5. 5. Discussion
    6. 6. Conclusion
    7. 7. Suggestions for Future Studies
    8. 8. Limitations of This Study
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  • Abbreviations
  • Author Contributions
  • Funding
  • Conflicts of Interest
  • References
  • Cite This Article
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