Resource-use efficiency analysis of rainbow trout farms in rural areas: a case study from Adana, Taurus Mountains, TürkiyeABSTRACT:
Assessing resource-use efficiency is essential for evaluating the sustainability of fish farms. This study examined the technical efficiency of Oncorhynchus mykiss (rainbow trout) farms operating in the Taurus Mountains region of Adana, Türkiye. Using Data Envelopment Analysis (DEA), both average and individual efficiency coefficients for these farms were calculated. The findings indicated that the average efficiency coefficient was 0.732 (73.2%), suggesting that farms could achieve the same production levels with 26.8% less input usage. Additionally, 50% of the surveyed farms operated at full efficiency. Analysis of excessive input use revealed that the highest inefficiencies were in labour costs (33%), chemical use (29.1%), and feed consumption (23%). These results provided significant contributions to improving efficiency in rainbow trout farming and can support the development of more sustainable aquaculture practices.
Key words:
rural aquaculture; rainbow trout farms; efficiency analysis; data envelopment method
RESUMO:
Revelar a eficiência do uso de recursos é vital para comparar e avaliar a sustentabilidade de uma fazenda de peixes. Os dados obtidos podem ser usados tanto em nível regional quanto para uso nacional. Neste estudo, os níveis de eficiência técnica de fazendas de trutas Oncorhynchus mykiss operando na área das Montanhas Taurus da província de Adana na Turquia foram calculados, e suas estruturas econômicas, técnicas e de equipamentos foram reveladas. A Análise Envoltória de Dados foi aplicada aos dados obtidos, e os coeficientes de eficiência médios e individuais dessas empresas foram calculados. De acordo com os resultados, o coeficiente de eficiência médio das empresas foi de 0,732 (73,2%), o que mostra que as empresas poderiam obter a mesma produção se reduzissem seu uso de insumos em 26,8%. Além disso, 50% das empresas produzem em um nível de eficiência total. Em avaliações conduzidas sobre o uso excessivo de insumos por meio de análise, foi calculado que os maiores insumos excedentes estavam em custos de mão de obra (33%), uso de produtos químicos (29,1%) e uso de ração (23%). Estatísticas descritivas aplicadas aos dados da pesquisa definiram as características básicas dos produtores de trutas e os fatores que afetam a produção de trutas. A pesquisa revelou resultados que fornecerão contribuições significativas aos esforços de desenvolvimento rural, à indústria pesqueira e aos pesquisadores.
Palavras-chave:
aquicultura rural; fazendas de truta arco-íris; análise de eficiência; método de envoltório de dados
INTRODUCTION
Aquaculture, the process of cultivating animals and plants in aquatic environments in a controlled manner, is one of the fastest-growing food industries in the world (GARLOCK et al., 2019; JOLLY, 2023). Aquaculture-based production of aquatic products has shown an increasing trend, accounting for approximately 51% of the total global supply of aquatic products with a production of 94.4 million tons (FAO, 2024). Notably, inland waters accounted for 38 percent of the total aquatic animal production, with 84 percent coming from aquaculture and 16 percent from capture fisheries. Inland aquaculture accounted for 62.6% of the total production of farmed aquatic animals, with finfish representing 89.7% of inland aquaculture output. Trout is one of the top 15 most cultivated species, holding a 1.5% share among cultured fish species (FAO, 2024).
In Türkiye, 72% of aquaculture production takes place in marine waters, while 28% occurs in inland waters. Oncorhynchus mykiss (rainbow trout) is the most commonly cultivated species in freshwater aquaculture, positioning Türkiye as one of the leading countries in Europe in this sector. In 2023, out of the 154,333 tons of fish cultivated in inland waters, 154,006 tons consisted of rainbow trout (ÇÖTELİ, 2024; TUIK, 2023). A significant portion of rainbow trout production in Türkiye is conducted in small and medium-sized rainbow trout farms, most of which are located in high and mountainous rural areas, numbering close to 2000. Rainbow trout production is extremely important for contributing to the national economy and providing solutions to employment issues in regions with limited economic opportunities.
In today’s world, the increasing problem of resource scarcity has heightened the importance of closely monitoring the production performance of businesses, especially regarding the sensible use of natural and human resources. Activities in the aquaculture sector, heavily reliant on natural and human resource use, play a role in ensuring food security for societies. However, aquaculture enterprises potentially cause negative environmental impacts through the inputs they utilize. In this context, measuring the performance efficiency of aquaculture businesses, considering their input usage, has become increasingly important (ÖZDEN, 2010).
For businesses to increase their profitability, they must increase efficiency (optimal use of inputs) to reduce production costs. Therefore, measuring efficiency and investigating the factors causing inefficiency in production are crucial for improving both agricultural production in general and sustainable aquaculture practices (ILIYASU & MOHAMED, 2015).
Maximizing productivity at the farm level is essential to ensure profitability in agricultural production (KELLY et al., 2012). For both theorists and policymakers, calculating productivity plays a vital role in assessing the success of decision-makers. Knowing where to invest to enhance productivity and regain competitiveness is essential. Identifying the reasons for the effectiveness and performance of an organization significantly contributes to developing strategies for the sector (DUARTE et al., 2019). Production function analyses in aquaculture are often limited to evaluating the profitability and return rates of new investments, estimating economies of scale, determining the optimal intensity of input usage, and improving current management (HATCH & TAI, 1997). In this regard, polyculture with fish farming provides a suitable environment for the application of output-based efficiency measurement techniques such as DEA.
This research analyzed the production efficiency of rainbow trout producer businesses operating in the mountainous region of Taurus in Adana, Türkiye. In this context, the input levels used and the output levels obtained are assessed together to reveal the efficiency of the rainbow trout farms operating in the region. As a result of this measurement, average excess input use and the proportional excess use of specific inputs have been calculated.
MATERIALS AND METHODS
Material and data collection
Data necessary for measuring the efficiency metrics of rainbow trout farms operating in the mountainous region of Taurus, Adana, were obtained through a producer survey developed by the authors. In this context, pre-prepared survey forms were administered to rainbow trout farmers using a face-to-face method.
Field studies for the face-to-face survey were conducted from March to December 2022 by visiting all available and currently operating rainbow trout farms (n=10) located in the districts of Feke, Kozan, Saimbeyli, and Pozanti in Adana province. The geographical distribution of the surveyed farms within Adana province is presented in figure 1. Since the study assessed the production efficiency of the entire active sector in the region, we included all existing farms in the research rather than selecting a random subset.
The standardized survey form used in the research consists of three sections. The first section includes questions aiming to reveal at the socio-demographic and economic structures of the operators engaged in rainbow trout production. The second section contains questions related to fundamental information about rainbow trout farms. The final section consists of questions regarding the usage status of the resources necessary for production. Thus, the input costs of the businesses (labor, insurance, feed, chemicals, maintenance, repair, fry, rent, water fees, electricity, fuel, and supplies) were expressed in terms of Turkish Lira (₺) per cubic meter, and the costs incurred for one cubic meter of pond area were determined.
Data regarding the input levels in production and the output quantities from these inputs for a production period in rainbow trout farms were collected through surveys. This data allowed for the calculation of the input and output amounts per cubic meter of production area. In our calculations, the output is represented by Gross Production Value (GPV) and is obtained by multiplying the unit sales price of rainbow trout by the total production of each farm.
Analytical framework
Efficiency analysis is one of the most used criteria in business performance measurements. Efficiency refers to the ability to produce goods or services at the minimal possible cost while ensuring consistent quality. It is an economic concept that evaluates the relationship between resources utilized and the output generated (WHITE JR, 1999). Efficiency assesses the extent to which resources are utilized to maximize value for money. It focuses on the relationship between resource inputs-such as labor, capital, or equipment costs-and the resulting intermediate outputs or ultimate health outcomes (PALMER & TORGERSON, 1999). Measuring efficiency allows for the identification of a business’s position within the current competitive environment (AKAN & ÇALMAŞUR, 2012). Since the aim is to ensure the optimal level of input usage, it is necessary to identify the factors causing any inefficiency.
Technical efficiency calculations have been developed through the analysis established by FARRELL (1957), which uses two inputs (X1, X2) and a single output (Y). In this context, the input oriented technical efficiency ratio of a firm is calculated as follows:
TE = OQ/OP.
Where:
OQ: efficient input usage (minimum required inputs).
OP: actual input usage.
The technical efficiency value ranges between zero and one; a value of 1 indicates that the firm is technically fully efficient. Values between zero and one define technical inefficiency, whereas values closer to one define the ability of the farmer to use the minimum inputs necessary to reach the given output level (COELLI et al., 2005; HAQ & BOZ, 2019; (KELLY et al., 2012; MISRA & MISRA, 2014).). One of the most used frontier estimation methods is the DEA, which employs mathematical programming (COELLI et al., 2003). DEA generalizes the concept of relative efficiency, defined by FARRELL (1957), for a single output to multiple outputs. DEA is a linear programming-based method used to evaluate the efficiencies of decision-making units, creating a productivity frontier defined by Pareto-efficient units that also includes inefficient ones (benchmarking criteria) (GOMES et al., 2023). Since its inception, the DEA method has undergone numerous theoretical developments and has been applied particularly in banking, healthcare, agriculture, transportation, education, and manufacturing (ILIYASU & MOHAMED, 2015). The DEA method does not specify a fixed form for the production function, and input and output data are not treated as dimensional issues. However, challenges related to input or output factors may arise during evaluation, often referred to as ‘slack’ or ‘crowding’ problems (LIN et al., 2024).
In addition to estimating efficiency scores, input slack values were obtained for each decision-making unit using the DEA model. These slack values represent the amount by which each input could be reduced without reducing output. To assess the contribution of each input to overall inefficiency, the slack values were normalized by the actual observed input values, and the average proportional excess use was calculated across all inefficient units. This analysis enabled the identification of specific input categories (e.g., labor, feed, chemicals) that contributed most significantly to inefficiency in rainbow trout production.
The DEA model can be defined as follows:
Min 0, λ θ, subject to
-yi + Y λ ≥ 0
θ Xi - X λ ≥ 0
N1’ λ = 1
λ ≥ 0 (1)
Xi: input vector of the Decision Making Units to be analyzed
yi: output vector of the Decision Making Units to be analyzed
θ: efficiency score of the i-th unit
λ: N x 1 constant vector
Y: output matrix
X: input matrix
In the study, the DEAP software version 2.1, prepared by COELLI et al. (2005), was utilized to estimate DEA efficiency scores, and these scores were calculated under constant returns to scale (CRS) assumptions. CRS means that the output increases proportionally with all input changes. It is also known as a general measure owing to its technical efficiency input/output configuration, which contributes to identifying inefficiency. In addition to CRS, the DEAP software automatically calculates efficiency scores under Variable Returns to Scale (VRS) and determines Scale Efficiency (SE), which is derived as the ratio of CRS to VRS efficiency scores. The VRS model isolates pure technical efficiency, excluding the effect of scale, while SE reflects whether a decision-making unit is operating at an optimal size.
RESULTS AND DISCUSSION
The descriptive characteristics of the 10 surveyed rainbow trout farms are summarized in table 1. These include demographic information of the producers, production experience, credit usage, cooperative membership, and other economic attributes.
By subtracting the total input levels from the GPV, the average gross profit/loss status of the rainbow trout farms operating in the mountainous region of Taurus in Adana province was determined. All calculations related to this section are presented in table 2.
When examining the input and output levels in rainbow trout farms, it is observed that maintenance and repair expenses constitute the largest share among the inputs used in these businesses. This indicated that the facilities have infrastructural issues, necessitating continuous maintenance and repair due to aging and wear and highlighting the need for renewal within the businesses. The second largest share of input usage in rainbow trout farms comes from feeding costs. The dependency on imports for feed supply in Türkiye’s animal production, coupled with rising foreign exchange rates, has made feed expenses one of the most significant input factors for all animal production activities. In their study LASNER et al. (2016) establishes a benchmarking model for profitability, productivity, and energy efficiency of rainbow rainbow trout farming in Türkiye, Germany, and Denmark. They found that no matter which aquaculture technique is used, feed remains the most critical expense, accounting for approximately 40-70% of total cash costs. According to the finding of BOZOĞLU & CEYHAN (2009) in trout production in Türkiye feed and diesel fuel expenses constitute a significant portion of total costs, accounting for more than 46.8% of overall expenditures. However, the continuous increase in feed costs and low feed efficiency negatively impact the economic sustainability of trout and sea bass farming in the Black Sea region.
In the research, labor costs are seen as an important input factor, despite the use of family labor. Even though we are living in a post-modern technological age, the artisanal nature of agricultural, forestry, and aquaculture production activities, particularly in rural areas, necessitates a labor-intensive structure.
By jointly evaluating the input costs incurred in production with the obtained GPV, the results of the efficiency analysis allowed us to determine the relationship between the output level and the inputs used.
Table 3 presents the efficiency coefficients obtained through DEA, which measure the production performance of the rainbow trout farms included in the research relative to their input usage levels. Accordingly, it was found that four farms reached the ideal efficiency score of 1, while one farm has a very close score to full efficiency. Besides, three farms have efficiency score between 0.5 and 1 and two farms has efficiency scores less than 0.5. The average efficiency coefficient for all the businesses was calculated as 0.732. This suggested that on average, the businesses could achieve the same level of production with 27% less input. In a study by CİNEMRE et al. (2006), DEA was used to predict the efficiency measurements of sample farms, and the average technical, allocative, and cost efficiencies for trout farms were measured as 0.82, 0.83, and 0.68, respectively. When these two data sets are examined in relation to each other, the trout farms in the Black Sea region operate more efficiently from a technical perspective compared to those in the Adana region.
Within the scope of the research, the level of contribution of excessive input items to inefficiency was also calculated, and the results are presented in table 4. Accordingly, in the businesses surveyed, labor usage accounts for the largest share of excessive input usage at 33%. Other input elements contributing to excessive usage are chemicals, at 29.1%, and feed, at 23%. Following these: electricity usage at 18.8%, supply usage at 17%, water usage at 14%, maintenance and repair at 13.7%, and fuel usage at 11.7%. As a result, it was concluded that the businesses could achieve the same level of production with significantly less labor and reduce feed and chemical usage. In the input-based analyses, when examining the levels of maintenance and repair expenses in rainbow trout farms, this category constituted the largest share among all inputs. This indicated that businesses have a persistent need for ongoing maintenance and repair due to structural aging and wear. Although, maintenance and repair costs were high in absolute terms for all 10 farms, the DEA model did not identify this input as having high excess usage because there was limited variation across the sample; most farms exhibited similarly high maintenance costs. As a result, the optimization algorithm could not benchmark any unit as more efficient in this category, thereby yielding minimal or no slack for maintenance. Although, maintenance and repair expenses rank first among all input costs, they have been confirmed as necessary expenditures under the category of essential input usage.
According to the research results, labor costs are calculated to be the most significant component under the excessive input usage category, accounting for 33%. This situation can be considered a technically solvable issue. In this regard, especially in developed countries with strong purchasing power, modern mechanization tools, technological programs, and digital equipment are utilized to address the problem of excessive labor utilization (DIKEL & DEMIRKALE, 2022). However, it should not be overlooked that rainbow trout production typically occurs in high-altitude areas where unemployment is prevalent. Therefore, its significance in combating unemployment elevates the status of rainbow trout farming. Based on the findings from our study, labor costs are observed to be an important expenditure, ranking third with a cost of 21.16 TL/m³, which constitutes 13.64% of total costs. Business size, production capacity, and production methods are significant factors that considerably affect labor quantity (DIKEL, 2009). The relatively small scale of production capacity in businesses in the Adana region, especially since many are family-owned, influences mechanization or modernization efforts, altering investment costs. Consequently, the possibilities for using automated equipment and digitalization are naturally diminished.
Feed costs, conversely, represent one of the most important expenditure items for businesses, potentially accounting for 50-70% of operating period expenses (DIKEL et al., 2010). Feeding is the primary factor determining cost in fish farming; hence it is crucial that feeding is conducted in a controlled manner to maximize efficiency. With the continuous development of intensive aquaculture, the proportion of expenses related to feed costs has also increased (DE VERDAL et al., 2018). All these factors influence the amount of feed the fish receive and; consequently, the efficiency of their production. Considering the high feed costs and the limited availability, or declining sustainability, of feed raw materials, controlled feeding becomes even more critical. Inadequate feeding can result in stunted growth, delays in reaching market size, deterioration of fish health, lowered immunity, and increased vulnerability to diseases. Conversely, overfeeding (rapid feeding or continuing to feed after satiety) can lead to waste from uneaten feed and, additionally, can degrade water quality (EVLIYAOĞLU, 2022). Therefore, while feed usage may not rank first in excessive input usage, it remains among the highest ranks. One conclusion, that can be drawn from this is that businesses must be more mindful of their feed usage. Training and supervising staff enhances the effectiveness of feed inputs. In his study measuring the aquaculture value chain in Türkiye YILDIRIM, (2023) highlighted the importance of equipping trout producers with the necessary knowledge and skills to optimize resource utilization, adopt sustainable practices, and enhance overall operational efficiency.
In the input-based analyses, when examining the levels of maintenance and repair expenses in rainbow trout farms, this category constituted the largest share among all inputs. This indicated that businesses have a persistent need for ongoing maintenance and repair due to structural aging and wear. Although, maintenance and repair expenses rank first among all input costs, they have been confirmed as necessary expenditures under the category of essential input usage. Specifically, under this category, maintenance and repair inputs do not rank in the top positions of excessive input usage, accounting for only 13.7%, and instead place fifth among at least seven other inputs. Thus, it is possible to further reduce input usage in this category through modern aquaculture systems. The production systems used directly influence this aspect. Energy costs in rainbow trout farms (including electricity and fuel) have also been found to be close to labor costs. Due to the continuous increase in energy costs, further increases in this category are expected in t future. Energy input represents a significant expenditure category in the rainbow trout farming sector, as it does in all production sectors. In our study, the energy expenses examined under fuel and electricity constitute 13.56% of the total inputs. By reducing this input, this approach would not only facilitate the use of technology but also partially lower costs through modernization. The most important action to reduce energy input is to utilize alternative energy sources, with solar energy and wind energy being at the forefront. TYEDMERS & PELLETIER (2007) reported that in aquaculture production, 0 to 3 kWh of electricity is used per kg of output depending on the intensity of the operation for feed distribution and certain activities. Additionally, VO et al. (2021) indicated that these values could increase with the size and efficiency of the businesses. LASNER et al. (2016) also highlighted that trout farms with lower levels of mechanization face a disadvantage due to the opportunity costs of their own labor, which can account for up to 11% of total costs for some smaller family farms.
The sample size of this study, comprising 10 rainbow trout farms, is a limitation that should be considered when interpreting the results. The scattered distribution of rainbow trout farms in the region, accessibility challenges, and the limited number of active producers constrained the sample size. However to enhance the study’s applicability we ensured that the selected farms represented a range of sizes and management structures. Despite this limitation, the DEA method remains a robust approach for assessing efficiency, even with relatively small datasets.
CONCLUSION
This research focuses on evaluating the technical efficiency structures of local rainbow trout production farms operating in the mountainous region of Taurus in Adana province, with an emphasis on the production economics of these businesses. The aim is to enhance the economic activities of rainbow trout farms in the area.
In high-altitude mountainous rural regions where economic activities are limited, poverty caused by unemployment is a significant issue. Utilizing the potential of natural resources to generate income and provide employment is effective in addressing this problem. In these areas, the suitability of both surface and groundwater for aquaculture presents an ideal option for the sustainable use of water resources in trout farming (WOYNAROVICH et al., 2011). The mountainous regions of Adana, which form the field component of this study, possess all the necessary conditions for rainbow trout farming, and represent an important natural resource potential. This could play a crucial role in the livelihoods of the local population through both small and industrial-scale trout farming. According to the findings of the study conducted by YILDIRIM & ÇANTAŞ (2022) on the production and economic indicators of rainbow trout farming in Türkiye, the Eastern Anatolia region, which accounts for a significant portion of rainbow trout production, generates added value through the aquaculture sector, including employment, technological advancements, and export revenues. This, in turn, is expected to increase employment in a region with high unemployment rates and have a direct impact on social life. Additionally, as the professional competencies of individuals working in these enterprises improve, it is anticipated that production performance and quality will be directly affected in the coming years.
In mountainous regions where water resources can support profitable rainbow trout farming, environmental protection is crucial for sustainable aquaculture. Inefficient production, characterized by excessive input usage, is one of the foremost factors contributing to the degradation of natural resources. The results obtained from the research also provide significant data regarding sustainability.
In biological industries, such as aquaculture, where biophysical factors contribute to a complex production environment, the effects of inefficiency can be more substantial as they contribute to the industry’s environmentally destructive impacts. Overall aquaculture, and specifically trout farming, has a biological production process characterized by rapid technological advancement. Consequently, firms in this sector must not only keep pace with technological limitations but also use the correct combination of input factors, which is both challenging and essential (GARLOCK et al., 2019). Conducting regional analyses containing detailed microeconomic data for aquaculture is critical for making managerial decisions and enhancing competitiveness (LASNER et al., 2016). In fish farms, where there is a greater control over outputs compared to inputs, the excessive use of nearly all inputs is a prevalent issue. Fish producers should manage their input usage practices carefully to reduce production costs and increase revenue and profit while complying with sustainability parameters (ILIYASU & MOHAMED, 2015). The excessive inputs identified from the research results indicate the presence of a sensitive structure in terms of natural resource economics due to potential environmental issues arising from this production model, which interacts closely with nature.
To conduct a comprehensive analysis of rainbow trout farms and businesses it is essential to evaluate their technical efficiency, cost structures, environmental impact, technological integration, and socioeconomic contributions. Our study has highlighted various inefficiencies in rainbow trout farming operations in the Taurus Mountains of Adana, Türkiye. Based on these findings, we proposed several analytical approaches that future research and industry stakeholders should consider.
Our findings revealed that labor costs (33%) and feed costs (23%) were the most significant contributors to excessive input use. Analyzing how input costs correlate with output (Gross Production Value per cubic meter) can help farm operators identify opportunities for cost reduction. Implementing strategies such as optimized labor management, automated feeding systems and alternative feed sources should be considered in future research to enhance profitability. Furthermore, an analysis of the economies of scale in trout farming could help determine the optimal farm size for maximum efficiency. To businesses in their current position to improve, they will be able to have more efficient production opportunities by having modern equipment with credit resources that they will use for a more modern and sustainable production in the future. The environmental impact and sustainability of rainbow trout farming should be considered a critical area of focus. As trout production depends extensively on natural water resources, inefficient farming practices can lead to excessive use of chemicals (29.1%) and energy (18.8%), resulting in water pollution and increased carbon emissions. To develop more sustainable production systems, conducting a Life Cycle Assessment (LCA) is essential. An LCA can provide comprehensive data on key indicators such as carbon footprint, water consumption, and waste generation. The process involves two main phases: inventory analysis and impact assessment. During the inventory analysis, relevant data on resource use and emissions-including greenhouse gas emissions, water usage, and waste output-are collected and analyzed. Subsequently, a water footprint analysis can be conducted, followed by an impact assessment to evaluate the environmental implications of the farming system. This information can serve as a foundation for implementing more sustainable practices in rainbow trout farming.
Additionally, future research should explore the implementation of bio filtration systems, integrated aquaculture techniques, and eco-friendly water management solutions to reduce the environmental burden of rainbow trout farming. Another crucial factor to consider is the role of technology in enhancing farm efficiency. While traditional rainbow trout farming methods dominate the industry modern solutions such as AI-driven feeding systems, smart sensors for water quality monitoring and automated harvesting technologies can significantly improve efficiency and reduce resource wastage. Trout farmers often collaborate both formally and informally for purchasing, processing, and marketing their products. They may also establish organizations that advocate for their interests at various levels-local, regional, national, or international. These groups, which include clubs, associations, and federations, are prevalent worldwide and share similar objectives and activities (WOYNAROVICH et al., 2011). Notably, strong organizations for trout farmers exist in countries such as Australia, Austria, Canada, Denmark, Germany, the United Kingdom, and the United States. In Türkiye, the Trout Producers’ Association is a prominent organization actively supporting these efforts. Drawing on decades of experience, these organizations can provide valuable insights and practical initiatives for representing, coordinating, and supporting the shared interests of their members. They can also make substantial financial contributions to enhance technological advancements, equipment installation, and training for small-scale producers.
Our study highlighted the importance of rainbow trout farming in rural, mountainous regions where economic opportunities are limited. Assessing the employment generation, income contribution, and market accessibility of rainbow trout farms can help guide policy interventions aimed at supporting small-scale producers. Additionally, evaluating the impact of government subsidies, training programs, and access to financial resources on farm productivity would provide a clearer understanding of the role of public policy in sustaining the rainbow trout farming industry.
Taking this into consideration, the findings of this study provide constructive critiques regarding the effective use of production inputs, which should be communicated with producers, the region, and even managers. Following this study, conducting similar research in other regions with intensive rainbow trout production holds significant importance for revealing the current status in terms of production management and the economic development of the sector.
ACKNOWLEDGEMENTS
This study was financially supported by the Coordination Unit of the Scientific Research Projects of the Çukurova University via the project FDK-2019-11535.
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CR-2025-0098.R4
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DATA AVAILABILITY
All data generated or analysed during this study are included in this published article
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DECLARATION OF USE OF ARTIFICIAL INTELLIGENCE
No artificial intelligence tools or applications were used in the preparation, analysis or writing of this study.
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RESTRICTIONS
Although, this study was conducted with a relatively low sample size due to the scattered structure of businesses, accessibility issues, and the numerical status of producers, it established the rainbow trout production economy of the region, provide a foundation for larger-scale research, and enable comparisons with similar studies in other regions.
Edited by
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Editor
Rudi Weiblen (0000-0002-1737-9817)Carlos Eduardo Copatti (0000-0002-0114-0334)
All data generated or analysed during this study are included in this published article
