Understanding Small-Scale Aquaculture Producers’ Perceptions of Challenges Across Production Systems in Manabí, Ecuador

Abstract

Aquaculture plays a key role in food security and rural development, yet small-scale producers face heterogeneous structural, economic, and institutional constraints. This study analyzes aquaculture producers’ perceptions of the main challenges affecting small-scale aquaculture in the province of Manabí, Ecuador. A total of 98 producers were surveyed, including 37.14% Backyard, 45.71% Transitional, and 17.14% Commercial farms, using a Likert-type questionnaire to assess the perceived importance of different constraints. A structured survey was administered to 98 producers, including 20 Likert-scale variables. Differences among systems were evaluated using non-parametric univariate tests (Kruskal–Wallis with Dunn post hoc comparisons), and multivariate techniques (Principal Component Analysis and discriminant analysis) were applied to identify underlying perception patterns. Significant differences were mainly associated with biological input supply, market conditions, and structural production constraints, particularly, between Backyard farms and the other systems. In contrast, feed costs, energy consumption, and regulatory requirements emerged as transversal constraints across all systems. Multivariate analysis identified two main perception dimensions associated with market/input factors and structural/managerial limitations, showing a moderate differentiation among systems, with partial overlap between Transitional and Commercial farms and clearer separation of Backyard farms. These findings provide insights into system-specific and transversal constraints, contributing to the design of more adaptive and context-sensitive governance strategies for small-scale aquaculture.

1. Introduction

Small-scale aquaculture is increasing relevance as a strategic component of food systems and rural development in developing countries. It contributed to food security, rural employment, and local livelihoods in communities highly dependent on aquatic resources [1,2,3,4]. By providing a stable source of animal protein, aquaculture complements capture fisheries and strengthened local food system resilience [5,6]. In the coastal province of Manabí, Ecuador, small-scale aquaculture represents a key livelihood strategy linked to family farming and local value chains. Production systems are predominantly small and family-managed [7], operating with low capitalization and limited technological intensity [3,8]. These characteristics shaped exposure to price volatility, environmental pressures, and regulatory requirements [9].

Research and public policies in Ecuador have mainly focused on marine shrimp farming, while freshwater systems based on native species have received less analytical and institutional attention [10,11]. This situation has created a knowledge gap regarding the structural conditions, challenges, and development pathways of small-scale freshwater aquaculture [6].

Recent research has described aquaculture challenges as multidimensional [12]. Market volatility, rising feed and energy costs, and value-chain asymmetries shape economic viability [4,9,12,13]. Administrative procedures, sanitary standards, and access to public support influence formalization and innovation processes [14,15]. Social factors such as labor availability and generational succession affected long-term sustainability [6]. These dimensions interacted and generated differentiated pressures across production systems [12]. These challenges are not exclusive to aquaculture but are also widely observed in small-scale farming systems, where constraints related to access to resources, market integration, and institutional support require differentiated policy responses [16].

From a theoretical perspective, this study is grounded in system-based and social–ecological approaches, which conceptualize aquaculture as a complex system where environmental, economic, and institutional factors interact dynamically. These frameworks emphasize that production systems are shaped by multiple interdependent constraints and that actors’ perceptions play a key role in understanding system functioning and adaptive capacity [17]. In this context, perception-based analyses provide valuable insights into how constraints are experienced and prioritized, complementing traditional technical and economic assessments. Typological and comparative studies have shown that production scale, species composition, technological intensity, and degree of formalization structured heterogeneous aquaculture realities [8,18]. System-based approaches provide analytical tools to capture this diversity and support differentiated governance strategies [17,18].

Within this context, farmers’ perceptions have emerged as relevant analytical lens. Survey-based studies have applied Likert-type instruments to assess perceived constraints, risks, and governance conditions [19]. Rather than relying exclusively on parametric comparisons, recent research increasingly employs non-parametric statistical tests and multivariate techniques to analyze perception-based survey data, particularly when variables are measured using ordinal Likert scales [20]. Non-parametric methods such as the Kruskal–Wallis test allow robust comparisons across groups without assuming normality, while dimensionality-reduction techniques such as Principal Component Analysis (PC) help identify structured perception gradients underlying complex survey responses. When combined with multivariate classification approaches, such as discriminant analysis, these methods allow researchers to evaluate whether production systems differ not only in individual constraints but also in their overall perception profiles. These studies aim to capture the multidimensional nature of structural and institutional constraints and to evaluate their distribution across producer typologies. The combined use of univariate and multivariate approaches became a well-established methodological strategy in research grounded in predefined production systems [21,22]. This framework generated system-sensitive evidence relevant to governance design in territorially heterogeneous contexts.

The integration of producers’ perceptions of the multidimensional challenges of aquaculture within a previously defined typology remains underdeveloped, particularly in small-scale farming context. A structured comparison of perception profiles across production systems contributed to strengthening adaptive and differentiated governance strategies.

Despite the growing body of literature on aquaculture systems, most studies have focused on technical, economic, or production-oriented indicators, with limited attention to how producers themselves perceive the constraints affecting their activity. In addition, although typological approaches have been widely used to characterize heterogeneity in aquaculture systems, the integration of perception-based data within predefined production system frameworks remains underexplored. This gap limits the understanding of how multidimensional constraints are experienced across different production systems and reduces the capacity to design context-sensitive and adaptive governance strategies [13,18].

In this context, the study is guided by the following research question: How do small-scale aquaculture producers perceive the main challenges affecting their activity, and to what extent do these perceptions differ across production systems? Addressing this question allows for a better understanding of the heterogeneity of constraints and supports the development of more adaptive and system-specific governance strategies.

The contribution of this study lies in the integrated analysis of perception-based constraints across predefined aquaculture production systems. Specifically, this research combines (i) the application of a typology-based framework to capture system heterogeneity, (ii) the use of both univariate and multivariate statistical approaches to analyze perception data, and (iii) the identification of system-specific and transversal constraints with direct implications for adaptive governance. Rather than proposing a novel methodological technique, the study contributes by applying and integrating these approaches within the context of small-scale freshwater aquaculture in Ecuador, a setting that remains underrepresented in the literature.

The objectives of this study were to identify the main challenges perceived by aquaculture producers in Manabí, to evaluate statistically significant differences in the perceived importance of these challenges across production systems using non-parametric univariate analysis, and to explore whether the multidimensional perception structure summarized through principal components allows multivariate discrimination among systems. The study aims to generate system-specific evidence to support adaptive governance and context-sensitive policy design for small-scale aquaculture. The remainder of this paper is organized as follows. Section 2 describes the research methodology, including the study area, survey design, and statistical analysis. Section 3 presents the main results. Section 4 discusses the findings in relation to existing literature and outlines their theoretical and practical implications. Finally, Section 5 provides the main conclusions of the study.

2. Research Methodology

2.1. Study Area and Survey

Manabí province was selected as a case study because it is one of Ecuador’s main coastal provinces in terms of aquaculture activity and exhibits a high diversity of production systems, species composition, and degrees of formalization [23].

The identification of the main challenges affecting aquaculture enterprises was conducted using a participatory approach. A working group of experts was formed, consisting of three university professors with experience in aquaculture, three technicians from the sector, and three aquaculture producers [22,24]. This composition enabled the integration of scientific, technical, and experiential knowledge, as recommended in studies on aquaculture governance and sustainability [6,25]. Through joint working sessions, the group identified and agreed upon a set of challenges considered relevant for the performance and sustainability of aquaculture enterprises [14,15].

Based on this initial selection, a structured questionnaire was developed within the framework of the Alternative Species Network, jointly coordinated by the University of Córdoba (Spain), the Higher Polytechnic Agricultural School of Manabí (ESPAM), and the Quevedo State Technical University (UTEQ), the latter two located in Ecuador.

The survey included 90 questions related to productive, structural, and social aspects of aquaculture enterprises. The questionnaire was designed based on previous studies and validated instruments reported in the literature. Additionally, it was reviewed by experts in aquaculture and rural development to ensure clarity and relevance of the items. The full questionnaire is provided as Supplementary Material. Twenty items specifically focused on assessing the perceived importance of existing challenges (

Table 1. Definition and coding of perceived aquaculture challenge variables.

Items	Description	Abbreviation
P1	Difficulties in the supply of larvae.	P1_LSUP
P2	Difficulties in the supply of fingerlings	P2_FSUP
P3	High price of larvae.	P3_LPRI
P4	High price of fingerlings.	P4_FPRI
P5	Low selling price of shrimp.	P5_SSELL
P6	Low selling price of fish.	P6_FSELL
P7	Limited number of shrimp buyers.	P7_SBUY
P8	Limited number of fish buyers.	P8_FBUY
P9	Limited number of larvae buyers.	P9_LBUY
P10	Limited number of fingerling buyers.	P10_FGBUY
P11	High cost of formulated feed.	P11_FEEDC
P12	Reduction or insufficiency of public subsidies.	P12_SUBS
P13	Security problems related to theft.	P13_SECUR
P14	Difficulties related to administrative procedures and management.	P14_ADMIN
P15	Insufficient surface area of culture ponds.	P15_PONDS
P16	Lack of generational succession on the farm.	P16_SUCC
P17	Environmental problems affecting aquaculture activities.	P17_ENVIR
P18	Sanitary and regulatory requirements applicable to the farm.	P18_REGUL
P19	Difficulties in maintaining farm infrastructure.	P19_INFRA
P20	High fuel and/or energy consumption.	P20_ENERG

). These items were organized into three main groups of challenges, defined as a priori during the instrument design phase, rather than derived from any exploratory statistical procedure [18]. This classification was established during the questionnaire design phase by the working group, with the aim to group challenges according to their economic, production-related, and socio-institutional nature, and of facilitating their interpretation from a systems-based perspective. This approach is consistent with previous studies that conceptualize aquaculture challenges as a multidimensional phenomenon and rely on a priori conceptual classifications to organize and interpret farmers’ perceptions, without assuming an underlying factorial structure [6,8,9].

The first group, Market Conditions and Input Costs, comprised five items related to market functioning and the costs of key production inputs: P3_LPRI, P4_FPRI, P5_SSELL, P6_FSELL and P11_FEEDC. This block encompassed aspects related to output prices, market access, and the cost of key inputs, highlighting the central role of market conditions and production costs in determining the economic viability of aquaculture enterprises, particularly in small-scale contexts [9,13].

The second group, Farm Production and Operational Constraints, consisted of nine items associated with production, technical, and operational limitations at the farm level: P1_LSUP, P2_FSUP, P9_LBUY, P13_SECUR, P14_ADMIN, P15_PONDS, P16_SUCC, P19_INFRA and P20_ENERG. This block included aspects related to the supply of biological inputs, production infrastructure, operational management, and energy consumption, capturing internal constraints that condition production efficiency and the capacity for technological adaptation in aquaculture units [3,8].

The third group, Socio-Institutional and Environmental Factors, integrated six items linked to social, institutional, and environmental factors influencing farm performance and sustainability: P7_SBUY, P8_FBUY, P10_FGBUY, P12_SUBS, P17_ENVIR and P18_REGUL. This block considered aspects related to market structure, public support, regulatory frameworks, and environmental pressures, in line with recent approaches highlighting the relevance of these dimensions for understanding the structural and long-term challenges of small-scale aquaculture [6,14,17].

The importance of each challenge was measured using a five-point Likert-type scale, ranging from 1 (low importance) to 5 (high importance). The questionnaire was pre-tested and refined to ensure clarity, avoid ambiguity, and promote consistent interpretation among respondents, following established recommendations for survey-based perception studies [26,27]. After validation, the survey was administered between 2022 and 2023 to a stratified sample of 98 aquaculture producers in the province of Manabí, Ecuador. The sampling design aimed to represent the diversity of aquaculture production systems present in the territory and was based on the three predefined systems (Backyard, Transitional, and Commercial). Stratified sampling has been shown to be particularly appropriate for perception studies conducted in heterogeneous small-scale aquaculture contexts [8,17].

The sampling frame was constructed from local aquaculture producer records and field identification in the study area. Farms were included if they were actively engaged in aquaculture production at the time of the survey. Producers were assigned to each production system according to the typology criteria described above, based on structural and management characteristics such as farm size, level of capitalization, technological intensity, and market orientation. The final sample included 37.14% Backyard farms, 45.71% Transitional farms, and 17.14% Commercial farms. Although the survey aimed to achieve proportional representation across systems, the final distribution reflects the actual availability and accessibility of producers in the study area. Participation was voluntary.

2.2. Typology of Aquaculture Enterprises in Manabi

The present study is based on a typology of aquaculture production systems defined through field observations, expert knowledge, and empirical characterization of the study area. This typology identifies three representative aquaculture production systems in the province of Manabí, Ecuador: Backyard, Transitional, and Commercial (Figure 1).

These systems were defined according to a combination of structural, productive, and management-related criteria, including farm size, level of capitalization, degree of technological intensity, and market integration. This classification provides an analytical framework to capture heterogeneity across aquaculture systems and to facilitate the comparison of perception patterns among producers.

Backyard systems are characterized by small-scale, predominantly family-based enterprises with low capitalization, limited infrastructure, and low technological intensity [16,23,28]. Production relied largely on local resources and is mainly oriented toward local markets or household consumption, with weak integration into formal value chains. This typology is commonly referred to in the local context as backyard production, a term used to describe small household-level operations characterized by informal management practices and minimal external inputs [29,30]. Farms within this system primarily cultivated native fish species adapted to local environmental conditions, such as Chame (Dormitator latifrons), Vieja azul (Andinoacara rivulatus), Vieja Colorada (Cichlasoma festae) and Guanchiche (Hoplias microlepis) [11,31]. Conceptually, this system was associated with high vulnerability to market, environmental, and institutional constraints.

Transitional systems include small and medium-sized enterprises with intermediate levels of intensification and formalization [9]. These farmers combined traditional practices with the partial adoption of technical and organizational improvements and show greater market orientation and increasing integration into structured value chains. High-yield species predominate, mainly tilapia (Oreochromis spp.) and shrimp (Litopenaeus vannamei), along with a smaller proportion of native species [6]. This system represented a transitional stage, in which productivity gains coexisted with persistent limitations related to financing, technical assistance, and regulatory compliance [6,9].

Commercial systems encompass more consolidated enterprises, primarily focused on shrimp (Litopenaeus vannamei) production, characterized by larger production scale, higher capitalization, and more intensive use of technologies and standardized management practices [1,13]. These enterprises operate as formalized businesses integrated into national and international markets [3,9]. While they exhibit higher levels of productivity and organizational maturity, they also face specific challenges related to higher operational costs and stricter sanitary and environmental requirements associated with intensive aquaculture systems [10,13].

2.3. Statistical Analysis

Although Likert-scale data are ordinal in nature, they were treated as continuous variables for the multivariate analyses, an approach commonly adopted in applied and social sciences when scales include five or more response categories and sample sizes are adequate [32]. This assumption allows the application of techniques such as Principal Component Analysis (PCA) and discriminant analysis, facilitating the identification of underlying patterns in perception data.

However, this approach represents a methodological simplification that may influence the results, particularly in multivariate contexts. In this study, PCA was conducted using Pearson correlations, which may not fully capture the ordinal structure of the data. Alternative approaches, such as the use of polychoric correlation matrices or ordinal factor analysis, could provide a more appropriate representation of relationships among ordinal variables.

Despite this limitation, previous research has shown that PCA based on Pearson correlations can yield robust and interpretable results when applied to Likert-type data with sufficient categories. Therefore, the results of the multivariate analysis should be interpreted with caution, acknowledging this assumption and its potential implications.

Given the ordinal nature of Likert-scale responses and the presence of three independent production systems, differences in the perceived importance of each challenge were evaluated using the non-parametric Kruskal–Wallis test. This test compares the distribution of scores among groups without assuming normality or homoscedasticity, making it appropriate for ordinal perception data.

The Kruskal–Wallis test was applied independently to each of the twenty variables (P1–P20), using the three aquaculture production systems (Backyard, Transitional, and Commercial) as grouping factors. To account for multiple comparisons, p-values were adjusted using the Holm sequential correction, which controls the family-wise error rate. Values represent median scores of perceived importance. Pairwise differences among production systems were assessed using Dunn’s multiple comparison test with Holm-adjusted p-values.

Variables showing statistically significant differences after multiple-testing adjustment were interpreted as system-specific constraints, while variables with high scores and no significant differences were interpreted as cross-cutting constraints shared between production systems.

However, while univariate tests identify differences at the level of individual variables, they do not capture the multidimensional structure of farmers’ perception profiles. Given that aquaculture challenges are conceptually interrelated and operate simultaneously within production systems, a multivariate approach was also implemented.

Principal Component Analysis (PCA) is widely used to reduce dimensionality and identify latent structures in multivariate datasets [20]. To reduce the dimensionality of the perception dataset and identify the main underlying gradients structuring farmers’ responses, PCA was performed on the standardized variables (P1–P20). This method transforms the original correlated variables into a smaller set of orthogonal components that capture the maximum variance in the dataset, allowing the identification of the main perception dimensions. The number of principal components retained for interpretation was determined based on the proportion of explained variance, interpretability criteria, and the objective of obtaining a parsimonious representation of the perception structure. The first two principal components were selected for descriptive and graphical purposes, as they captured the main underlying gradients and allowed a clear and interpretable representation of the data structure.

Subsequently, a canonical discriminant analysis was conducted using all principal components as predictors and the three production systems (Backyard, Transitional, and Commercial) as the grouping factor. This approach ensures that the full variability of the dataset is considered in the classification procedure and avoids potential information loss associated with restricting the analysis to a reduced number of components. This procedure allowed the evaluation of whether the multidimensional perception structure captured by the principal components was able to statistically discriminate among aquaculture production systems.

The discriminant analysis provided canonical functions summarizing the multivariate separation among systems and identified the perception dimensions contributing most strongly to system differentiation.

All statistical analyses were performed using STATGRAPHICS Centurion XVI.I. and STATISTICA ver 12.0 (StatSoft, Inc., Tulsa, OK, USA).

3. Results

3.1. Differences in Perceived Challenges According to the Typology of Aquaculture Enterprises in Manabí

To address the study’s comparative objective, differences in the perceived importance of each challenge across the three production systems were examined using the non-parametric Kruskal–Wallis test for each Likert item (P1–P20), with Holm adjustments to control for multiple testing (

Table 2. Comparison of perceived challenge scores among aquaculture production systems (median scores).

Variable 1	Systems			H	p (Holm) 2
	Backyard	Transition	Commercial
P1_LSUP	0.00 a	1.00 b	2.00 b	30.022	***
P2_FSUP	2.00 a	0.00 b	0.00 b	30.846	***
P3_LPRI	0.00 a	1.00 b	2.00 b	21.095	***
P4_FPRI	2.00 a	0.00 b	0.00 b	28.821	***
P5_SSELL	0.00 a	3.00 b	3.00 b	28.569	***
P6_FSELL	3.00 a	0.00 b	0.00 b	27.917	***
P7_SBUY	0.00 a	1.00 b	3.00 b	26.309	***
P8_FBUY	3.00 a	0.00 b	0.00 b	24.589	***
P9_LBUY	0.00 a	0.00 a	0.00 a	12.174	*
P10_FGBUY	0.00 a	0.00 a	0.00 a	2.888	n.s.
P11_FEEDC	3.00 a	3.00 a	3.00 a	1.081	n.s.
P12_SUBS	1.00 a	1.00 a	1.50 a	2.370	n.s.
P13_SECUR	1.00 a	1.00 a	1.50 a	0.148	n.s.
P14_ADMIN	1.00 a	1.00 a	1.50 a	7.431	n.s.
P15_PONDS	2.00 a	1.00 b	1.00 ab	11.268	*
P16_SUCC	1.00 a	1.00 a	1.50 a	8.166	n.s.
P17_ENVIR	2.00 a	2.00 a	2.50 a	3.950	n.s.
P18_REGUL	2.00 a	2.00 a	2.50 a	1.144	n.s.
P19_INFRA	1.00 a	1.00 a	2.00 a	4.634	n.s.
P20_ENERG	3.00 a	3.00 a	3.00 a	4.568	n.s.

¹ See Table 1; ² * p < 0.05; *** p < 0.001; n.s. not significant. ^a, ^b, Different letters indicate significant differences between groups.

). The first group of variables was related to biological input supply and input prices. In particular, variables related to the supply of biological inputs, such as difficulties in the supply of larvae (P1_LSUP) and fingerlings (P2_FSUP), as well as their associated prices (P3_LPRI, P4_FPRI), showed clear differences among systems after Holm adjustment (p < 0.05).

A second group of variables was associated with market and commercialization conditions. Specifically, variables related to selling prices (P5_SSELL and P6_FSELL) and the availability of buyers (P7_SBUY, P8_FBUY) also differed significantly across production systems, indicating that market access and price conditions are perceived differently depending on the level of market integration of each system.

Pairwise comparisons using Dunn’s test indicated that most differences were driven by contrasts between the Backyard system and the other two production systems, while Transitional and Commercial farms showed more similar perception profiles.

A third difference was related to structural farm characteristics. The variable associated with available pond surface area (P15_PONDS) also differed significantly among systems, suggesting that farm scale remains an important factor distinguishing production models.

In contrast, several variables showed homogeneous perception patterns across systems, indicating transversal constraints affecting aquaculture producers regardless of production model. Feed costs (P11_FEEDC), energy consumption (P20_ENERG), and regulatory requirements (P18_REGUL) received similar assessments across the three production systems.

3.2. Multivariate Discrimination of Production Systems Based on Farmers’ Perceptions

To explore the multivariate structure of farmers’ perceptions, a Principal Component Analysis (PC) was applied to the twenty Likert variables (P1–P20). The PC in

Table 3. Principal component (PC) loading matrix after Varimax rotation.

Variable 1	Loading	Eigenvalue	Explained Variance (%)	Accumulate	PC
P2_FSUP	−0.833	5.968	29.84	29.84	1
P4_FPRI	−0.871
P6_FSELL	−0.886
P8_FBUY	−0.832
P5_SSELL	0.837
P7_SBUY	0.809
P1_LSUP	0.677
P3_LPRI	0.6
P9_LBUY	0.734	2.867	14.34	44.18	2
P12_SUBS	0.653
P16_SUCC	0.627
P19_INFRA	0.784
P11_FEEDC	0.769	1.879	9.393	53.569	3
P18_REGUL	0.692
P14_ADMIN	0.77	1.34	6.7	60.268	4
P15_PONDS	0.796
P13_SECUR	0.847	1.196	5.981	66.249	5
P17_ENVIR	0.533
P10_FGBUY	−0.799	1.051	5.255	71.504	6
P20_ENERG	0.546

¹ See Table 1.

showed that the first two principal components explained 44.18% of the total variance (PC1 = 29.84%; PC2 = 14.34%). These components were therefore retained as synthetic variables summarizing the multidimensional perception structure, although they represent a partial (44.18%) but interpretable proportion of the total variance.

The loading matrix indicated two clearly interpretable dimensions. PC1 was mainly associated with market and biological input constraints, including access to larvae and fingerlings, the number of buyers, and selling prices. PC2 was related to structural and managerial constraints at the farm level, including generational succession, public support, infrastructure maintenance, and commercialization channels.

After Varimax rotation, the variables with the highest loadings on PC1 were mainly related to biological input supply and market conditions. These included difficulties in the supply of fingerlings (P2_FSUP), the price of fingerlings (P4_FPRI), fish selling prices (P6_FSELL), the availability of fish buyers (P8_FBUY), shrimp selling prices (P5_SSELL), and the availability of shrimp buyers (P7_SBUY). In contrast, PC2 was primarily associated with variables reflecting structural and institutional constraints affecting farm management. These included the availability of larvae buyers (P9_LBUY), access to public subsidies (P12_SUBS), generational succession (P16_SUCC), and infrastructure maintenance (P19_INFRA).

Using these reduced dimensions, a discriminant analysis was conducted to evaluate whether the perception structure summarized by the PC could differentiate the three production systems (Figure 2). The discriminant model used PC1 and PC2 as predictors and the production system cluster as the grouping variable. The discriminant analysis showed a moderate classification accuracy (approximately 70%), indicating some degree of differentiation among production systems. However, the graphical representation revealed partial overlap, particularly between Transitional and Commercial farms, while Backyard farms appeared more clearly separated.

The canonical discriminant analysis confirmed a statistically significant multivariate differentiation among systems. The first discriminant function explained 82.32% of the between-group variance, with an eigenvalue of 0.956 and a canonical correlation of 0.699. The Wilks’ Lambda test confirmed the statistical significance of this function (Λ = 0.420; χ² = 54.34; df = 12; p < 0.001). These results indicate that the linear combination of perception variables summarized in this dimension effectively discriminated among production systems.

The discriminant space revealed a partial separation pattern. The centroid of the Backyard system was located at positive values along the first discriminant axis. This position indicates a perception profile strongly associated with constraints related to market access and biological input supply. In contrast, Transitional and Commercial systems appeared closer to each other along this axis, reflecting more similar perception structures. However, a certain degree of overlap between Transitional and Commercial systems was observed, indicating moderate rather than complete separation among groups

This pattern was also visible in the discriminant plot (Figure 3 and Figure 4), where Backyard farms formed a clearly differentiated group, while Transitional and Commercial systems partially overlapped. These results suggest that differences among production systems are better interpreted as gradients of perceived constraints rather than strictly separated categories.

The structure coefficients provided further insight into the variables contributing to system differentiation (

Table 4. Structure coefficients of the canonical discriminant functions.

Variable 1	r (LD1)	r (LD2)	|r|	Ranking
P4_FPRI	0.874	0.052	0.874	1
P2_FSUP	0.859	0.06	0.859	2
P7_SBUY	−0.853	−0.071	0.853	3
P5_SSELL	−0.85	−0.063	0.85	4
P6_FSELL	0.82	−0.05	0.82	5
P1_LSUP	−0.79	0.043	0.79	6
P8_FBUY	0.769	−0.045	0.769	7

¹ See Table 1.

). The variables showing the strongest correlations with the first discriminant function were mainly related to biological input supply and market conditions. These included the price of fingerlings (P4_FPRI), difficulties in fingerling supply (P2_FSUP), the availability of shrimp buyers (P7_SBUY), shrimp selling prices (P5_SSELL), fish selling prices (P6_FSELL), difficulties in larvae supply (P1_LSUP), and the availability of fish buyers (P8_FBUY).

The strongest contributors were variables related to biological input supply and pricing, particularly the price of fingerlings (P4_FPRI) and difficulties in fingerling supply (P2_FSUP). These were followed by variables related to commercialization conditions, such as the availability of shrimp buyers (P7_SBUY) and shrimp selling prices (P5_SSELL). Additional contributions were observed for fish selling prices (P6_FSELL), constraints in larvae supply (P1_LSUP), and the availability of fish buyers (P8_FBUY). Overall, these results indicate that system differentiation was primarily driven by variables related to input availability and market access, highlighting the central role of commercialization conditions and biological inputs in shaping perception patterns among aquaculture producers.

Based on the combined interpretation of the univariate and multivariate results, a set of good management practices was identified for each production system (

Table 5. Suggested management implications derived from perception patterns.

System (Variables)	Producers	Technical Advisors	Governance/Public Policy
Backyard (P15_PONDS)	Improve pond use; gradual management improvements; focus on native species	Provide simple technical guidance; low-cost technologies; support local knowledge	Small infrastructure support; seed access programs; recognition of smallholder role
Transitional (P5_SSELL, P6_FSELL, P7_SBUY, P8_FBUY)	Improve marketing strategies; diversify buyers; basic farm management	Training on planning and market analysis; cost management; value chain integration	Support cooperative marketing; strengthen producer organizations; facilitate market access
Commercial (P1_LSUP, P2_FSUP; P14_ADMIN)	Secure biological inputs; improve administrative capacity; efficiency planning	Specialized advisory services; support regulatory compliance; operational optimization	Administrative simplification; innovation incentives; stable input supply chains
All farms (Transversal)	Efficient energy and input use; risk reduction strategies	Dissemination of best practices; technical coordination across systems	Adaptive governance; proportional regulation; reduction in transaction costs

).

4. Discussion

The results indicate that aquaculture challenges are not homogeneous across production systems, but rather reflect different combinations of structural, market, and institutional constraints. The analytical approach adopted in this study made it possible to identify both individual constraints and broader perception patterns. Non-parametric comparisons revealed statistically significant differences among systems, while multivariate analysis captured the overall perception structure underlying farmers’ responses.

The results reveal a clear distinction between system-specific constraints and transversal sectoral pressures. Significant differences were detected mainly in variables related to biological input supply, market conditions, and structural production factors. These results are consistent with previous studies highlighting the internal heterogeneity of small-scale aquaculture systems and the role of scale, technological intensity, and value-chain integration in shaping production constraints [8,9,17,33,34]. Variables associated with seed availability, input prices, selling prices, and market access differed significantly across systems. Post hoc comparisons showed that these differences were primarily driven by contrasts between Backyard farms and the other two production systems. Transitional and Commercial farms displayed more similar perception profiles.

In contrast, several constraints appeared consistently across systems. Feed costs, energy expenditure, and regulatory requirements were perceived as important challenges by producers regardless of production type. These results suggest that some pressures operate at the sectoral level rather than at the system level. Similar structural constraints have been widely documented in aquaculture systems, particularly in developing regions where energy prices, input costs, and regulatory compliance represent major operational challenges [3,6,12].

The multivariate analysis further clarified the structure of farmers’ perceptions. Principal Component Analysis reduced the twenty variables into two main dimensions that summarized the perception structure. The first dimension was associated with market conditions and biological input supply, while the second dimension reflected structural and managerial constraints at the farm level. These dimensions provide a simplified representation of how producers perceive the challenges affecting their activity. It should be noted that the retained components explain a moderate proportion of total variance, and that including additional components could potentially improve classification performance, although at the expense of interpretability.

The discriminant analysis indicated a moderate capacity to differentiate of production systems based on perception profiles, with an overall classification accuracy of approximately 70%. The moderate separation observed is therefore not attributable to the number of principal components retained for visualization, as the full set of components was included in the classification procedure. However, the results also revealed partial overlap among systems. The Backyard system appeared more clearly separated, whereas Transitional and Commercial farms showed greater overlap in the discriminant space. These findings suggest that the multivariate differentiation among systems is meaningful but not definitive. Rather than representing clearly distinct categories, production systems can be better interpreted as gradients of constraints, with shared characteristics across groups.

This pattern suggests that production systems should be interpreted as gradients of constraints rather than strictly discrete categories. Similar interpretations have been proposed in recent typological studies of agricultural and aquaculture systems, where production models often share characteristics while differing in the relative importance of specific pressures [8,17]. In this context, the typology used in this study functions as an analytical framework that captures structured variation without imposing rigid boundaries between systems.

The identification of system-specific perception profiles provides useful insights for the design of differentiated management strategies. Pattern-based approaches have been increasingly applied in agri-food systems to tailor interventions to the structural characteristics of production units [22]. The results of this study support the relevance of adapting technical and governance strategies to the specific conditions faced by each production system.

These findings provide a basis for the development of system-specific management strategies (Table 5). The variables contributing to system differentiation were mainly associated with biological input supply and pricing, including difficulties in larvae and fingerlings supply (P1_LSUP and P2_FSUP) and the price of fingerlings (P4_FPRI), as well as with market conditions, such as selling prices (P5_SSELL and P6_FSELL) and buyer availability (P7_SBUY and P8_FBUY). These results suggest that factors related to input availability and marked access play a central role in shaping differences among production systems, particularly in Transitional and Commercial farms. In contrast, the characterization of Backyard farms was more strongly supported by the univariate results and their structural features, especially limitations in pond surface area (P15_PONDS) and their low-capital, low-intensity production profile. For Backyard farms, producers emphasized structural constraints, particularly limited pond surface area. This result reflects the small physical scale and limited expansion capacity typical of low-capitalized rural operations [3,8]. Under these conditions, incremental improvements may be more realistic than rapid intensification strategies. Interventions focusing on improved farm management, the use of native and locally adapted species, and accessible technical advisory services could strengthen system resilience. Public policies aimed at small-scale infrastructure support, seed availability, and low-cost production tools may therefore be particularly relevant for this system [4,6,14].

In Transitional farms, the perception profile was mainly shaped by market-related variables. Selling prices (P5_SSELL and P6_FSELL) and buyer availability (P7_SBUY and P8_FBUY) emerged as key concerns, highlighting the importance of commercialization conditions in these systems. These farms appear more exposed to price volatility and intermediary dependence, a pattern widely discussed in aquaculture value-chain research [9,13,17]. Vulnerability in this system is therefore less associated with structural limitations and more with incomplete market integration. Strengthening managerial capacities, improving production planning, and diversifying commercialization channels could reduce this vulnerability. Policies supporting producer organizations and collective marketing strategies may also improve market access and stability.

In Commercial farms, producers expressed greater concern regarding biological input supply and administrative requirements. In particular, issues related to seed availability (P1_LSUP and P2_FSUP) and administrative management (P14_ADMIN) were identified as key constraints. These factors reflect the higher operational complexity of more intensive production models. Intensified aquaculture systems are typically more sensitive to disruptions in input availability and regulatory compliance [5,13,17]. For this system, improved planning of biological input procurement and stronger administrative management appear particularly important. Technical advisory services and more coherent regulatory frameworks may also help improve operational efficiency and compliance.

Overall, the results indicate that uniform policy approaches are unlikely to address the diversity observed across production systems. Heterogeneous aquaculture sectors require governance strategies that combine transversal measures addressing shared structural constraints with system-specific interventions. Adaptive governance frameworks that reduce transaction costs, strengthen sector coordination, and align the actions of producers, advisors, and public authorities may therefore contribute to improving the resilience and long-term sustainability of small-scale aquaculture systems [3,6,9,14,17]. These findings provide a basis for interpreting how different types of constraints shape production systems and may inform context-specific management and policy strategies.

4.1. Theoretical Implications

This study contributes to the literature on small-scale aquaculture by providing a perception-based analysis of multidimensional constraints across production systems. Unlike studies that focus on technical or economic performance indicators, this research highlights the relevance of farmers’ perceptions as a complementary analytical dimension for understanding system heterogeneity.

The results support the interpretation of production systems as gradients of constraints rather than strictly discrete categories, reinforcing recent typological approaches in agricultural and aquaculture research. In addition, the integration of non-parametric and multivariate techniques demonstrates the usefulness of combining univariate and dimensionality-reduction methods to capture both individual constraints and overall perception structures.

4.2. Practical Implications

The findings of this study provide relevant insights for policymakers, technical advisors, and aquaculture producers. The identification of system-specific and transversal constraints suggests that uniform policy approaches may be insufficient in heterogeneous small-scale aquaculture contexts.

The results indicate that factors related to input availability and market access play a central role in shaping differences among production systems. In this context, the findings may inform the design of system-specific strategies.

For Backyard systems, the results suggest that interventions could focus on improving basic infrastructure, facilitating access to inputs, and supporting low-cost technical assistance adapted to small-scale production conditions. In Transitional systems, the findings indicate that improving market access, strengthening producer organizations, and supporting commercialization strategies may help reduce dependence on intermediaries. For Commercial systems, ensuring stable access to biological inputs and improving administrative and regulatory processes may be relevant areas for action.

In addition, transversal constraints such as feed costs, energy use, and regulatory requirements highlight the need for coordinated actions at the sectoral level. Overall, these findings should be interpreted as reasoned implications derived from perception-based data, which may inform future policy design and empirical validation.

Producers’ perceptions provided insight into how constraints were prioritized, but they did not establish causal relationships between perceived problems and technical or economic performance. Future research could apply structural equation modeling (SEM) to explore how market, production, and institutional factors interact and influence farm outcomes. Such approaches would strengthen the empirical basis for more targeted and performance-oriented policy design, both in Manabí and in other vulnerable contexts with comparable structural conditions.

In addition, the use of Likert-scale data treated as continuous variables and the application of PCA based on Pearson correlations represent methodological simplifications. Future research could explore the use of alternative approaches, such as polychoric correlations or ordinal factor analysis, to assess the robustness of the multivariate results.

5. Conclusions

This study analyzed perception of small-scale aquaculture systems producers in Manabí (Ecuador) regarding the main challenges affecting their activity, using a combination of non-parametric statistical tests and multivariate analysis.

The results confirm that these challenges are multidimensional and vary across production systems. Significant differences among systems were mainly associated with biological input supply, market conditions, and structural production constraints. Backyard farms were primarily limited by structural factors, Transitional farms by market-related pressures, and Commercial farms by input supply and administrative requirements. At the same time, feed costs, energy consumption, and regulatory demands emerged as transversal constraints affecting all systems.

The multivariate analysis identified two main perception dimensions related to market conditions and biological input availability, while the second dimension captured structural and managerial factors. Although the discriminant analysis showed only moderate separation among systems, the results support interpreting production systems as gradients of constraints rather than strictly discrete categories

Overall, these findings highlight the need for adaptive governance strategies that combine transversal measures with system-specific interventions. The analytical framework proposed in this study provides a useful tool for supporting context-sensitive policy design in heterogeneous small-scale aquaculture systems

This study has some limitations that should be acknowledged. First, the results are based on farmers’ perceptions, which do not establish causal relationships between constraints and performance. Second, the use of Likert-scale data treated as continuous variables and PCA based on Pearson correlations represents a methodological simplification. Future research could apply alternative approaches, such as structural equation modeling, polychoric correlations, or ordinal factor analysis, and extend the analysis to other geographical contexts to assess the robustness and generalizability of the findings.