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Article

Factors Influencing Willingness to Collaborate on Water Management: Insights from Grape Farming in Samarkand, Uzbekistan

by
Sodikjon Avazalievich Mamasoliev
1,
Motoi Kusadokoro
2,
Takeshi Maru
3,
Shavkat Hasanov
4 and
Yoshiko Kawabata
3,*
1
Department of Symbiotic Science of Environment and Natural Resources, United Graduate School of Agricultural Science, Tokyo University of Agriculture and Technology, 3-5-8 Saiwai, Fuchu, Tokyo 183-8509, Japan
2
Division of Studies in Sustainable and Symbiotic Society, Institute of Agriculture, Tokyo University of Agriculture and Technology, 3-5-8 Saiwai, Fuchu, Tokyo 183-8509, Japan
3
Division of International Environmental and Agricultural Science, Institute of Agriculture, Tokyo University of Agriculture and Technology, 3-5-8 Saiwai, Fuchu, Tokyo 183-8509, Japan
4
Department of Economics and Business, Faculty of Economics and Management, Samarkand Agroinnovations and Research University, 7 Amir Temur, Dahbet Town, Okdarya District, Samarkand 141001, Uzbekistan
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(15), 6991; https://doi.org/10.3390/su17156991 (registering DOI)
Submission received: 11 June 2025 / Revised: 24 July 2025 / Accepted: 28 July 2025 / Published: 1 August 2025
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Abstract

Water is essential for ecological balance, environmental sustainability, and food security, particularly in arid regions where effective water management increasingly depends on farmer cooperation. The Samarkand region of Uzbekistan, known for its favorable climate and leading role in grape production, is facing rising drought conditions. This study explores the factors influencing grape farmers’ willingness to collaborate on water management in the districts of Ishtikhan, Payarik, and Kushrabot, which together produce 75–80% of the region’s grapes. A quantitative survey of 384 grape-producing households was conducted across 19 county citizens’ gatherings (38.7% of such gatherings), and structural equation modeling was employed to analyze a framework consisting of four dimensions: norms, environmental concerns, economic barriers, and the intention to adopt sustainable practices. The results indicate that norms and environmental concerns positively influence collaboration, suggesting a collective orientation toward sustainability. In contrast, economic barriers such as high costs and limited financial capacity significantly hinder cooperative behavior. Furthermore, a strong individual intention to adopt sustainable practices was associated with a greater likelihood of collaboration. These findings highlight the critical drivers and constraints shaping collective water use in agriculture and suggest that targeted policy measures and community-led efforts are vital for promoting sustainable water governance in drought-prone regions.

1. Introduction

Water is a crucial, limited natural resource essential for ecological balance, environmental sustainability, and food security, as well as a fundamental resource for societal development, particularly in arid areas [1,2,3,4,5]. Globally, water use has increased by approximately 1% per year over the past 40 years, particularly in middle- and low-income countries [6]. The increasing demand for water resources in agriculture requires the implementation of strategies that enhance efficiency in the usage process. As Rastegaripour et al. [7] highlight, climate variability and increasing water demand require innovative water management strategies, particularly in arid and semi-arid regions where water shortages directly impact agricultural productivity. As indicated in the United Nations World Water Development Report [6], approximately 25% of global croplands are currently experiencing agricultural economic water scarcity. This scarcity is not primarily caused by hydrological constraints but rather by a lack of institutional and financial capacity for irrigation. Despite scientific and technological advances such as mineral fertilizers, new plant varieties, and increased automation in agriculture, water remains a key limiting factor in global food production [8,9]. Therefore, it is imperative to develop effective water resource management systems, especially in regions where the regulation and oversight of groundwater resources are weak [10]. In this regard, the direct participation of farmers in water management, based mainly on cooperative relations, has yielded positive results in various global contexts [11]. However, as Azadi et al. [12] emphasize, social barriers such as fragmented governance structures and resistance to behavioral change often hinder the success of cooperative water management, necessitating the implementation of participatory governance frameworks to build trust among stakeholders. Therefore, to mitigate the serious water crisis and ensure the sustainable growth of water resources, it is necessary to optimize cooperation to improve water use efficiency.
In regions where resources are scarce or the management system does not meet the needs of multiple users, the risk of conflict is substantial. Prudent management of groundwater resources, which are common in water-scarce areas with high demand, is especially essential in arid and semi-arid regions [13]. In these regions, conflicts, environmental instability, and drought disrupt the functioning of resource management institutions and reduce resource availability [14]. Such conflicts are often observed in Central Asian countries, where the goals of water use and the behavior of water users diverge [15,16]. Considering the complexity of cooperative water resources management, modern research and practical reforms for the optimization of water management are necessary in Central Asian countries [17]. Pronti et al. [18] demonstrate that economic constraints remain a critical barrier to adopting water-saving technologies, reinforcing the need for targeted financial incentives to encourage collective irrigation management in resource-scarce regions, including Uzbekistan.
Uzbekistan, a post-Soviet country located in the heart of Central Asia, is particularly vulnerable to water scarcity due to its arid climate and reliance on an outdated Soviet-era irrigation system [19]. Due to shortages and infrastructure problems, groundwater—primarily intended for drinking—is increasingly being used for agriculture [20]. Uzbekistan’s irrigation infrastructure, much of it developed during the Soviet era to support cotton monoculture, remains largely outdated [21]. These large-scale systems prioritized agricultural expansion over water efficiency or crop-specific needs, resulting in long-term environmental degradation and challenges for alternative crops such as grapes. After the collapse of the Soviet Union in 1991, land resources in Uzbekistan were widely redistributed to households for small-scale agricultural use. This process primarily took place in mountainous and foothill regions, where irrigation infrastructure was underdeveloped. In contrast, areas with developed irrigation systems are often underutilized due to water shortages. This situation has contributed to the decline in the efficiency of water delivery services for farmers. Simultaneously, the rapid increase in the number of small-scale farming households has further strained and accelerated the deterioration of these service systems. Also, the tendency of water user associations to prioritize large farmers—based on long-standing state directives—has hindered the development of cooperative relationships with households [22]. In response to water-related challenges, farmers increasingly turned to groundwater as an alternative source. Agriculture, which consumes 90% of the nation’s water supply, relies heavily on irrigation [23]. Water usage varies annually, with an average of 52–53 km3 allocated for agriculture, of which 7.8 km3 is groundwater [24]. Approximately 23% of cultivated land uses water-saving technologies, mainly in wheat, cotton, and fruit production. Grapes, a water-intensive crop, are widely grown in Uzbekistan’s foothills, particularly in the Samarkand region, which is known for its grape farming. However, managing water resources for grape production is increasingly challenging because of declining groundwater levels [25]. As Sharifzadeh et al. [26] note, although some farmers are actively adopting sustainable practices, others hesitate because of governance uncertainties. Supporting proactive farmers can significantly enhance cooperation in irrigation management. In 2023, Uzbekistan produced approximately 1.73 million tons of grapes, with the Samarkand region contributing 634.9 thousand tons (36.7% of the total national production). However, challenges in water resources management have led to decreased grape yields, exacerbated by climate change, drought, and declining groundwater availability.
Despite the importance of cooperative water management in the Samarkand region, there is limited research on the factors influencing farmers’ willingness to collaborate on water use. Studies explore factors such as social norms and environmental concerns in other regions, including agent-based modeling in the Aral Sea basin, to understand water-sharing dynamics [27]. Research in Kazakhstan and Kyrgyzstan highlights the role of institutional support in overcoming economic barriers to cooperation [28]. However, most studies in Samarkand tend to focus on hydro-economic modeling or irrigation efficiency [29], neglecting the socio-behavioral aspects of cooperation. This study aims to address this gap by examining the determinants of farmers’ willingness to collaborate, with a focus on social norms, environmental concerns, economic barriers, and the intention to adopt sustainable practices.
This study has three main objectives: (1) to assess the impact of norms, environmental concerns, economic barriers, and the intention to adopt sustainable practices on the willingness to collaborate on water usage, including informal water-sharing agreements, joint investments in irrigation infrastructure, and participation in water user associations; (2) to develop a comprehensive structural equation model (SEM) to illustrate the relationships among these factors; and (3) to provide actionable insights for policymakers and stakeholders to enhance collective water management practices in the Samarkand region, Uzbekistan.

2. Materials and Methods

2.1. Study Area, Sampling, and Data Collection

In this study, we investigate how small-scale grape farmers in Samarkand, Uzbekistan, engage in collaborative water use in the context of increasing environmental and economic pressures. The northern part of the Samarkand region is characterized by mountains and foothills, and the climatic conditions are very favorable for viticulture, with an altitude ranging from 700 to 800 m above sea level. The empirical work focuses on three agriculturally significant districts—Ishtikhan; Payarik; and Kushrabot—located in the Zarafshan River Basin; where irrigation systems are highly dependent on groundwater and channels that are based on precipitation in Figure 1. The selection of counties was carried out to cover various conditions of water use in grape cultivation. Attention was given to identifying differences in water distribution and cooperation practices in the chosen areas. Additionally, the study allowed for examining relationships among farmers under water scarcity conditions in regions with limited water resources. Furthermore, areas with different social and ecological conditions were selected to analyze efficient water use and competition dynamics. This approach provided a basis for broadly highlighting various water-related problems and social interactions. Data were collected between January and March 2024 using structured face-to-face interviews with 384 grape households (n = 384). One of the major reforms in agriculture involved changing the form of ownership in rural areas from state- and collectively owned farms to individual business-oriented farmers and households, locally known as dehkan households, which are the Uzbek version of the small, subsistence-oriented household plots and the basis of rural families [30]. The sample was identified using a snowball sampling approach, starting with registered members of water user associations and expanding to their networks. This method was chosen because formal lists of collaborating farmers were unavailable, and snowball sampling enabled access to relevant but hard-to-reach participants. The number of samples from each district represented roughly 10% of the total households, which mainly grow cash crops, such as grapes, and use groundwater for irrigation of vineyards. The structured questionnaire was first drafted in English and then translated into Uzbek, followed by a back-translation to ensure semantic consistency. A pilot test with 20 households assisted in refining the clarity and flow of the items. The questionnaire was designed in line with the objectives of the study, and the survey was conducted following relevant domestic and international laws and regulations, including the “Act on the Protection of Personal Information” and “Guidelines for the Ethics Committee on Research Involving Human Subjects at Tokyo University of Agriculture and Technology.” The questionnaire consisted of three components. The first gathered demographic and farm-level characteristics (Table 1); the second assessed attitudinal and behavioral constructs on a five-point Likert scale (ranging from 1, “strongly disagree,” to 5, “strongly agree”) (Table 2); and the third focused on farmer experiences of water access, collective practices, and environmental constraints.
Table 1 presents descriptive summary statistics of respondents and provides a comprehensive overview of the demographic and economic characteristics of the households involved, highlighting the significance of grape farming as a major.

2.2. Conceptual Framework and Hypothesis Settings

Effective strategies for water management are necessary not only to ensure environmental sustainability but also to promote equitable resource distribution and mitigate conflicts [2,13,14,15]. The importance of cooperation in water management is increasingly recognized, especially in regions such as Central Asia, where fragmented governance and resistance to change hinder progress [12]. In this context, key factors that influence farmers’ willingness to collaborate in water management include norms, environmental concerns, the intention to adopt sustainable practices, and economic barriers. Norms, which are shared expectations about appropriate behavior within a community, can significantly promote cooperative behavior in water management [31]. In regions such as Uzbekistan, where informal and formal water-sharing norms coexist, these norms can shape collective action [32]. Environmental concerns also drive farmers to adopt sustainable practices. As farmers become more aware of environmental degradation, their motivation to cooperate in water management increases [33]. Similarly, farmers’ intention to adopt sustainable practices reflects their readiness to engage in innovative water use strategies, supported by information and enabling systems [34]. However, economic barriers, such as high costs for water-saving technologies, remain a significant obstacle to cooperation [10]. Based on this conceptual framework, the following hypotheses are proposed:
H1. 
Norms positively influence grape farmers’ willingness to collaborate in water usage.
H2. 
Environmental concerns positively influence the willingness to collaborate in water usage.
H3. 
The intention to adopt sustainable practices positively influences the willingness to collaborate.
H4. 
Economic barriers negatively influence the willingness to collaborate.
These relationships are grounded in theories, such as those of planned behavior [35] and collective action [31]. Their aim is to explore how behavioral and contextual factors can enhance cooperative water management. The proposed model (Figure 2) seeks to provide valuable insights into the drivers and barriers to collaboration, particularly in water-scarce regions such as the Samarkand region of Uzbekistan, where water resource management is critical for sustaining agricultural productivity and livelihoods.
To understand how farmers’ behavioral and contextual conditions influence their willingness to collaborate on water management, we employed latent constructs informed by the theory of planned behavior and collective action theory. These constructs included willingness to collaborate (WTC), intention to adopt sustainable practices (IA), perceived social norms (N), environmental concerns (EC), and perceived economic barriers (EB). Each construct was measured using a group of observed indicators designed to capture underlying attitudes and motivations. To ensure data quality and construct validity, we applied several diagnostics and statistical tests. Initially, we assessed missing values and outliers, using Grubbs’ [36] test to detect univariate outliers in continuous variables such as landholding size and yield, and the robust Iglewicz and Hoaglin [37] method to flag extreme values based on modified Z-scores derived from the median and median absolute deviation. Approximately 4% of the socioeconomic variables had missing values. These were addressed using mean substitution within districts after confirming robustness via multiple imputation checks, such as 10 iterations via chained equations. To assess potential multicollinearity among latent predictors, we extracted factor scores using lavPredict() and computed Variance Inflation Factors (VIFs). All VIF values were below 1.15, well within accepted thresholds [38], indicating no collinearity problem. The dimensionality and structure of the latent constructs were explored using exploratory factor analysis (EFA). Table 2 shows that principal component extraction with varimax rotation was applied to the 17 Likert-scale items.
We used EFA to empirically test how items should be grouped into theoretically informed latent factors. The Kaiser–Meyer–Olkin (KMO) measure yielded a value of 0.773, indicating sampling adequacy for factor analysis, and Bartlett’s test of sphericity was significant (χ2 = 1651.2, p < 0.001), confirming the appropriateness of factor analysis. EFA identified five distinct factors explaining 59.2% of the total variance. All factor loadings exceeded 0.55, suggesting strong associations between observed variables and their respective constructs (Table 3). Following the EFA, we tested reliability and internal consistency using Cronbach’s alpha for each construct. All values exceeded the 0.7 threshold, indicating acceptable internal coherence among items within each factor. In the subsequent SEM analysis, we calculated composite reliability (CR) and average variance extracted (AVE) to further confirm convergent validity. These steps ensured that the measurement model was both empirically and theoretically sound.

2.3. SEM Specification and Model Fit

To investigate how behavioral intentions, social norms, environmental concern, and economic constraints influence farmers’ willingness to collaborate in water usage, we employed SEM. We chose to use SEM because it allows for the simultaneous estimation of latent constructs and their interrelationships, making it well-suited to theory-driven analysis with multiple dependent and mediating factors. First, EFA was conducted to identify latent variables and uncover the underlying structure of the data without imposing predefined relationships. The EFA used varimax rotation and yielded five distinct factors from the 17 survey items—willingness to collaborate; environmental concerns; norms; economic barriers; and intention to adopt sustainable practices—explaining a cumulative variance of 59%. The sample was adequate for factor analysis, with a KMO measure of 0.77 and a significant Bartlett’s test of sphericity, indicating suitability for structure detection [39,40]. The factor loadings and related statistics are provided in Table 3. After uncovering these factors, CFA was employed to validate the factor structure identified in the EFA. The CFA confirmed that the relationships between observed variables and their corresponding latent constructs were strong and statistically sound. Model fit indices supported the adequacy of the measurement model: the root mean square error of approximation (RMSEA) was 0.052 (90% CI: 0.039–0.066), and the Tucker–Lewis Index (TLI) was 0.946, with both indices indicating a good fit [41,42]. The AVE ranged from 0.49 to 0.77, and CR ranged from 0.74 to 0.91, indicating acceptable convergent validity and internal consistency [43]. Cronbach’s alpha values were above 0.7 for all constructs, with an overall scale alpha of 0.872, indicating high reliability [44,45]. Each of the latent variables—norms (N); environmental concerns (EC); the intention to adopt sustainable practices (IA); and economic barriers (EB)—was assessed via multiple survey items; as summarized in Table 3. Willingness to cooperate was measured by three items reflecting openness to collaboration. ECs included three items capturing perceptions of climate change. IA was gauged by three items on the intention to adopt water-saving practices. EB was assessed using four items on financial constraints, and N was captured by four statements on social and community expectations. Descriptive statistics for these items and constructs show moderate to high agreement levels with the statements and indicate a generally positive orientation towards cooperative water management. Following the validation of the measurement model, the second step of the SEM procedure involved estimating the structural model to examine how the latent variables interact to shape farmers’ willingness to collaborate. The estimation was conducted using the maximum likelihood estimation method via the lavaan package in R and the SEM module in Stata 18.
The general form of the model is expressed as follows:
IAi = γ1 × Ni + γ2 × ECi + δi
WTCi = β1 × Ni + β2 × ECi + β3 × EBi + β4 × IAi + εi
where IAi denotes the intention of farmer i to adopt sustainable practices, WTCi represents the willingness of farmer i to collaborate, and N, EC, and EB are the exogenous latent predictors. The residuals δi and εi represent unexplained variation.
To assess the adequacy of the structural model, we examined several fit indices. The comparative fit index (CFI) was 0.96, the TLI was 0.95, and the RMSEA was 0.048. These metrics fall within accepted thresholds, indicating strong model performance and a well-fitting model both in absolute and residual terms [42]. By employing these tests, our analysis ensured that the theoretical constructs were not only statistically validated but also accurately reflected in the observed data. This methodological rigor allowed for a robust assessment of how internal motivations, perceived norms, environmental concerns, and economic barriers jointly shape farmers’ willingness to collaborate, yielding both empirical clarity and theoretical insight into the dynamics of cooperative water management.

3. Results

We rigorously validated the proposed model for cooperative water management among grape farmers in the Samarkand region using a sequence of statistical techniques, including EFA, CFA, and SEM. These methods confirmed that the survey items reliably captured the intended latent constructs, with stable factor structures and strong internal consistency.
The reliability and validity of the measurement model were confirmed through several indicators in Table 3. Cronbach’s alpha values for all constructs ranged from 0.72 to 0.89, demonstrating good internal consistency. CR scores exceeded the 0.70 benchmark, and AVE values were all above 0.50, confirming convergent validity. We verified discriminant validity using the Fornell–Larcker criterion, which indicated that each construct captured a unique aspect of the theoretical framework and did not overlap with other constructs. The structural model explained 53% of the variance in the dependent variable, willingness to collaborate (R2 = 0.53), which represents a moderate-to-strong level of explanatory power in behavioral research.
The constructs WTC, EC, N, EB, and IA demonstrated high construct validity, and we deemed them suitable for further analysis.
The descriptive statistics provide a clear socioeconomic profile of the sample. On average, respondents had 28.1 years of experience in agriculture, including viticulture, and most cultivated a modest amount of land—0.69 hectares in total, with 0.55 hectares specifically allocated to vineyards. These figures are indicative of a smallholder farming structure, where grape production plays a central role in household livelihoods. The average annual income from grape farming was 3.62 thousand US dollars (45.55 million UZS), accounting for over half of the total household income, which averaged 6.55 thousand US dollars (82.34 million UZS). Household size averaged around 6.41 members, suggesting a reliance on family labor, particularly during harvest and irrigation periods. This demographic and economic context underscores the relevance of coordinated water management in maintaining productivity and income stability.
The SEM analysis showed that the model provided a good fit to the data. Table 4 provides the key fit indices, including the chi-square to degrees of freedom ratio (χ2/df = 1.891), the RMSEA (0.048), and incremental fit indices, the TLI (0.95), CFI (0.96), IFI (0.96), and the NFI (0.92), all of which met or exceeded commonly accepted thresholds in the measurement model. A second model, the SEM, yielded identical values, confirming the robustness of the model specification. These fit statistics validate the theoretical structure of the model and affirm that the hypothesized relationships are supported by the empirical data.
Further support for the model’s validity is reflected in the correlation matrix presented in Figure 3. Strong inter-item correlations within the WTC construct—such as between WTC1 and WTC2 (r = 0.7879) and WTC1 and WTC3 (r = 0.7896)—indicate a high degree of internal consistency. This suggests that farmers who expressed agreement with one statement about collaboration were also likely to agree with related items, reinforcing the reliability of the construct.
Conversely, some correlations are negative, such as between WTC1 and EB1, which correlates to −0.2780. This negative correlation indicates an inverse relationship: as farmers’ willingness to collaborate increases, they tend to perceive fewer economic barriers related to water usage, and vice versa. This suggests that overcoming economic concerns might increase farmers’ willingness to collaborate. This visual representation confirms the presence of several strong relationships, particularly among the WTC items, suggesting that these factors are closely related. The negative correlations, such as those between WTC and EB, further underscore the need to address economic barriers to improve collaboration. These findings emphasize the importance of examining multicollinearity, as high correlations between some variables may affect the precision of the regression coefficients in later analyses. As Hair Jr. et al. [46] point out, identifying and addressing these relationships ensures the reliability of the subsequent regression results and contributes to a clearer understanding of the factors influencing willingness to collaborate on water usage in this study.
The analysis of the standardized path coefficients, detailed in Table 5 and Figure 4, confirmed all four hypothesized relationships in the model. The path diagram in Figure 4 further visualizes these relationships, with unstandardized coefficients for the paths between the constructs. For example, the path from N to WTC is 0.44, suggesting a moderate-to-strong influence of N on WTC. EC had a weaker effect on WTC (0.12), and IA showed a similar but slightly stronger influence on WTC (0.15). In contrast, EB had the most significant negative impact on WTC (−0.18), highlighting the key role that economic constraints play in shaping farmers’ willingness to cooperate on water management.
In Table 5, the magnitude of relationships from the path diagram is quantified by significance levels. First, we find that norms (N) have a positive and statistically significant influence on WTC (β = 0.534, p < 0.001). This result emphasizes the power of communal expectations and shared behavioral standards in guiding farmers’ decisions. In a context such as Samarkand, where community-based agricultural practices and informal institutions remain strong, the presence of prevailing norms that endorse cooperation can substantially increase the likelihood of farmers engaging in joint water management activities. These norms often manifest through observed behavior within the community, where consistent participation in cooperative practices by peers may motivate others to behave in the same way to maintain social cohesion or avoid reputational loss. Second, EC also has a significant positive relationship with WTC (β = 0.153, p < 0.05). This indicates that farmers who are more attuned to environmental changes—such as reduced river flows; unpredictable rainfall; and increasing water scarcity—are more likely to perceive cooperation as a necessary adaptation strategy. The recognition that individual water access is increasingly vulnerable to climate-related stressors appears to encourage collective action as a pragmatic solution to maintaining agricultural productivity and resource security. Third, IA exhibits a positive but relatively weaker effect on WTC (β = 0.179, p < 0.05). Although statistically significant, the lower magnitude of this relationship suggests that despite many farmers expressing a desire to adopt environmentally friendly practices—such as drip irrigation or reduced pesticide use—the intention alone is insufficient to drive cooperative behavior. It must be reinforced by enabling conditions, including technical support, peer networks, or policy incentives. This result implies that behavioral intentions must be accompanied by practical tools, knowledge sharing, and institutional backing to translate into collective action. Finally, economic barriers (EB) were negatively associated with willingness to collaborate (β = −0.22, p < 0.001), revealing a critical constraint to cooperation. Limited financial capacity, whether caused by small farm size, lack of access to credit, or insufficient income to invest in water-efficient infrastructure, reduces farmers’ ability to participate in shared irrigation systems or joint maintenance activities. In many cases, although farmers may be conceptually supportive of collaborative water management, their participation is hindered by their inability to afford essential inputs, such as pipes, pumps, or contribution fees for canal repairs. This result underscores the importance of addressing structural and financial obstacles if cooperation is to be scaled up meaningfully. Therefore, to promote the adoption of water-saving technologies, it is essential to expand financial support mechanisms, improve farmers’ access to affordable credit, and design policies tailored to the specific agro-ecological and socio-economic conditions of each region [18]. This will strengthen partnerships between small-scale producers and provide an impetus for future collaborative financial reforms.
These results from both the SEM analysis and the path diagram confirm that our proposed model is valid. The significant path coefficients show that norms, environmental concerns, and the intention to adopt sustainable practices positively influence farmers’ willingness to collaborate on water usage, whereas economic barriers have a negative impact. This suggests that addressing economic barriers and promoting positive norms and environmental awareness are the keys to improving collaborative water management. These results provide useful insights for policymakers, agricultural stakeholders, and researchers working to enhance water resource management through cooperation.

4. Discussion

The analysis of the variables and their impact on the willingness to collaborate in water management is structured around four key hypotheses, each supported by empirical data and theoretical foundations. The path coefficients obtained for each hypothesis help us to understand the strength and direction of these relationships. Below, we explain each hypothesis and its significance.

4.1. Social Norms

The findings highlight the central role of social norms in shaping farmers’ willingness to collaborate on water management. This aligns closely with Ostrom’s [31] theory of collective action, which underscores the importance of shared expectations, trust, and informal rules in facilitating cooperative behavior among resource users. Similar conclusions were drawn by recent studies emphasizing the role of informal institutions and community-based mechanisms in strengthening cooperation and collective action in water governance systems [49]. In the case of Samarkand’s grape-producing regions—where water scarcity is a growing concern—social norms appear to serve as a strong enabling factor for collective water governance. This suggests that fostering a culture of collaboration through community-led initiatives, social recognition, or local leadership may be more effective than purely technical or economic interventions. Studies, such as Sharifzadeh et al. [26], emphasize that farmers’ engagement in environmental practices often depends on perceptions of the security and legitimacy of local governance, particularly when water availability is uncertain. Moreover, the influence of subjective norms as explored by Abani and Kelboro [50] points to the role of respected individuals and peer networks in encouraging or deterring participation in conservation behaviors. Therefore, strengthening these relational dimensions, for instance, by recognizing highly engaged farmers or supporting informal leadership within water user groups, may enhance cooperation and contribute to more sustainable water use in semi-arid agricultural systems.

4.2. Environmental Concerns

Environmental concerns also play a significant role for grape households. Grape households’ concerns about environmental issues negatively impacting viticulture prospects were first noted with the outbreak of plant diseases and later drought and temperature fluctuations, for example, extreme cold and heat, which posed risks to agronomic planning. Most households try to maintain as much moisture in the soil as possible, although this results in disease and irregular water use. In these observed problems, environmental awareness was shown to motivate cooperative behavior. This finding is consistent with the literature linking environmental awareness to sustainable agricultural practices [8,48,51]. In Samarkand, where ecological degradation threatens agricultural productivity, raising awareness about the consequences of water use could encourage farmers to engage in collective water management. However, although environmental concerns are important, they may need to be combined with other motivating factors, such as economic incentives, to help drive significant behavioral change.

4.3. Intention to Adopt Sustainable Production Practices

The widespread adoption of sustainable production practices, especially in agriculture, requires the abandonment or radical reform of traditional production methods in an era of increasing climate change impacts [52]. In this regard, farmers’ intention to adopt sustainable practices is important. We found that the intention to adopt sustainable practices was correlated positively with the cooperative use of water resources, and this aligns with Rogers’ [53] “Diffusion of Innovations Theory,” which suggests that individuals who perceive the benefits of an innovation are more likely to adopt it. Grape households that are inclined to adopt sustainable practices also tend to see the value in cooperating for better water management. The moderate path coefficient indicates that although the intention to adopt sustainable practices is significant, it is not the only driver of collaboration. Effective communication strategies and practical support for adopting these practices are necessary to maximize the impact on farmers’ willingness to collaborate.

4.4. Economic Barriers

The impact of EB on the formation of cooperative relationships often stems not only from differences in financial literacy or economic understanding but also from perceptions of unfairness in how costs and benefits are shared. In our findings, concerns about equitable benefit distribution clearly stood out as a central issue, indicating that farmers may hesitate to engage in collective water management when they anticipate unequal returns or imbalanced risk-sharing. Rather than framing economic barriers solely in terms of access to capital, we emphasize the importance of perceived fairness in financial arrangements. High costs associated with water-saving technologies or shared infrastructure can deter participation, especially if benefits are expected to accrue unevenly among members. Addressing these concerns requires more than generic financial incentives. We therefore suggest that policymakers consider tailored mechanisms that directly respond to local realities and farmers’ expressed concerns. Collaborative funding models, such as those discussed by Pronti et al. [18], can promote trust and inclusion by ensuring that costs and benefits are distributed transparently and proportionally. These models may involve collective investment agreements, shared decision-making frameworks, and conditional subsidies based on fairness criteria. Additionally, practical tools like micro-irrigation leasing schemes can lower entry barriers for smallholders, while volumetric-based subsidies, linked to actual water use, can reduce free-riding risks and strengthen accountability within cooperative structures. Such targeted approaches are more likely to address the underlying economic disincentives and foster sustained collaboration.

4.5. Policy and Stakeholder Implications for Collaborative Water Management

Enhancement of the water management system stems from the optimization of state policies related to water resources [54]. The results highlight the need to address multiple factors to enhance collaboration in water management in Samarkand. As difficult as it is for a single policy to be effective, effective strategies for water resources management and monitoring, especially in the groundwater sector, require that spatial and temporal dynamic factors be addressed [55]. Strengthening norms that promote cooperation, raise environmental awareness, reduce economic barriers, and foster the intention to adopt sustainable practices are essential to an effective strategy. This requires the formulation and implementation of effective mechanisms that incentivize farmers among all relevant state structures. In fact, interventions focused on building a collaborative culture, such as community-led initiatives, can significantly boost willingness to collaborate. Although environmental concerns alone may not drive large-scale change, they are critical to a broader strategy that combines practical support for sustainable farming. Addressing economic constraints through financial incentives or collaborative funding models is essential for overcoming barriers to adoption. Furthermore, communication strategies that emphasize the benefits of sustainable practices, along with providing access to resources and training, can support farmers in adopting water-efficient practices and increase their willingness to collaborate. By addressing these factors in tandem, policies can create an environment that fosters collaboration and improves water management practices.
This study highlights the critical factors influencing grape farms’ willingness to collaborate on water management in the Samarkand region. The integration of norms, environmental concerns, economic barriers, and the intention to adopt sustainable practices provides a comprehensive understanding of the dynamics at play. The findings underscore the importance of addressing economic challenges, fostering positive norms, and raising environmental awareness to enhance collaborative water management. By leveraging these insights, policymakers and stakeholders can design effective strategies that promote sustainable agricultural practices and improve water resource management in the region. The robust analytical framework and significant findings provide a solid foundation for future research and practical interventions aimed at promoting sustainability in water resource management.

5. Conclusions

This study elucidates the key factors influencing collaborative water management among grape farmers in the Samarkand region, emphasizing the roles of social norms, environmental concerns, and economic barriers. It underscores the need for integrated policy approaches that address financial constraints and support sustainable practices, with examples including subsidies and accessible technologies. Although the results offer valuable insights for future research and policymaking, we note that the study has limitations related to sampling and regional focus, indicating the need for broader comparative studies across agricultural contexts and locations to enhance generalizability. Future researchers could investigate factors affecting cooperative water management in diverse agricultural settings. Comparative and longitudinal studies would provide deeper insights into the effectiveness and sustainability of collaborative interventions over time. In conclusion, this research advances the understanding of cooperative water management in the Samarkand region of Uzbekistan and delivers actionable insights for promoting sustainable agricultural water use. By addressing interlinked social, economic, and environmental factors, policymakers can foster more effective collaboration, benefiting both farmers and the wider community.

Author Contributions

Conceptualization, S.A.M. and Y.K.; methodology, S.A.M.; software, S.A.M. and T.M.; validation, S.A.M., S.H. and Y.K.; formal analysis, S.A.M. and M.K.; investigation, Y.K. and T.M.; resources, Y.K. and S.H.; data curation, S.A.M.; writing—original draft preparation, S.A.M.; writing—review and editing, Y.K., T.M., M.K. and S.H.; visualization, S.A.M.; supervision, Y.K.; project administration, Y.K.; funding acquisition, Y.K. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Japan International Cooperation Agency, Tokyo, Japan, within the Project for Human Resource Development Scholarship framework, and by the Japan Society for the Promotion of Science KAKENHI, Grant number 23K28291.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Review Committee and the permission of the President of Tokyo University of Agriculture and Technology (No. 201103-0255, 17 January 2024).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to the requirement of the “Act on the Protection of Personal Information” and “Guidelines for the Ethics Committee on Research Involving Human Subjects at Tokyo University of Agriculture and Technology”.

Acknowledgments

The authors thank the Department of Agriculture and Statistics (the Branch of Agricultural and Environmental Statistics) in Samarkand, Uzbekistan, and the small-scale grape producers and stakeholders in the region for their cooperation in this study. We thank Amanda Fitzgibbons for editing a draft of this manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
SEMStructural Equation Model
NNorms
ECEnvironmental Concern
IAIntention to Adopt
EBEconomic Barriers
WTCWillingness to Collaborate
EFAExploratory Factor Analysis
CFAConfirmatory Factor Analysis
KMOKaiser–Meyer–Olkin Measure of Sampling Adequacy
CRComposite Reliability
AVEAverage Variance Extracted
RMSEARoot Mean Square Error of Approximation
TLITucker–Lewis Index
CFComparative Fit Index
IFIIncremental Fit Index
NFINormed Fit Index
Z-scoreStandard Score
UZSUzbekistan Sums

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Sustainability 17 06991 g001
Figure 1. (a) Map of Uzbekistan and location of Samarkand region. (b) Study area map of Samarkand region, including water channels and surveyed households’ locations. Source: The authors, using QGIS 3.40.1 software, and channel data were obtained from the HydroSHEDS database https://www.hydrosheds.org/products/hydrorivers (accessed on 20 July 2025).
Figure 1. (a) Map of Uzbekistan and location of Samarkand region. (b) Study area map of Samarkand region, including water channels and surveyed households’ locations. Source: The authors, using QGIS 3.40.1 software, and channel data were obtained from the HydroSHEDS database https://www.hydrosheds.org/products/hydrorivers (accessed on 20 July 2025).
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Figure 2. Proposed conceptual framework.
Figure 2. Proposed conceptual framework.
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Figure 3. Correlation matrix of statements. Note: The heatmap color-codes the correlations for easy interpretation, with red highlighting positive correlations, blue indicating negative correlations, and white representing weak or near-zero correlations.
Figure 3. Correlation matrix of statements. Note: The heatmap color-codes the correlations for easy interpretation, with red highlighting positive correlations, blue indicating negative correlations, and white representing weak or near-zero correlations.
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Figure 4. Model testing results of the structural equation model.
Figure 4. Model testing results of the structural equation model.
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Table 1. Descriptive summary statistics (n = 384).
Table 1. Descriptive summary statistics (n = 384).
VariablesUnitMeanSDMaxMin
Experience in Agricultureyear28.109.28506
Family membersperson6.411.83152
Total Landhectare0.690.403.090.15
Vineyard Landhectare0.550.363.030.10
Total IncomeUSD, thousand6.552.7418.580.80
Income from Grape FarmingUSD, thousand3.622.3915.110.40
Source: Survey results of study areas, 2024. Note: 1 USD = 12,574.88 Uzbekistan sums (UZS) (Central Bank of Uzbekistan, August 2024).
Table 2. Statements of latent constructs.
Table 2. Statements of latent constructs.
VariableCodeStatements
Willingness to collaborationWTC1I am willing to collaborate with other grape farmers in our region to collectively manage water resources for mutual economic benefits.
WTC2I believe that collaborating with other grape farmers in water usage can lead to improved economic outcomes.
WTC3I consider water usage collaboration to be important in our territory.
Environmental concernsEC1I have noticed changes in local climate patterns that have impacted grape farming operations.
EC2I believe that climate change poses a significant threat to grape farming.
EC3I am inclined to cooperate with other grape farmers in water use due to the possible impacts of climate change.
Intention to adoptIA1I implement sustainable agricultural practices on my grape farm to conserve water resources.
IA2I am open to adopting new agricultural practices that can enhance water efficiency in grape farming.
IA3I believe that sustainable agriculture practices can improve the overall profitability of grape farming.
Economic barriersEB1Concerns about the equitable distribution of economic benefits are a significant barrier to collaboration in water usage.
EB2Collaborating on the economic aspects of water usage may lead to financial disputes among farmers.
EB3Water costs significantly impact the profitability of my grape farming operations.
EB4I am willing to share financial resources or investments with other grape farmers to improve water infrastructure.
NormsN1My fellow grape farming peers encourage and support collaborative water usage practices.
N2There is a general expectation among grape farmers in my community to engage in collaborative water management.
N3Local agricultural organizations or authorities actively promote and support collaborative water usage among grape farmers.
N4I am aware of government policies or regulations that encourage collaboration in water usage among grape farmers.
Table 3. Measurement model and validation indices.
Table 3. Measurement model and validation indices.
Latent VariablesMeanSDEFACFAAVEComposite ReliabilityCronbach’s Alpha
Willingness To Collaborate (WTC) 0.770.910.90
WTC13.811.290.860.93
WTC23.861.250.830.84
WTC33.821.040.820.84
Environmental Concerns (EC) 0.620.830.83
EC14.200.950.830.83
EC24.240.910.860.86
EC34.101.000.680.68
Intention To Adopt (IA) 0.490.740.73
IA13.771.020.630.62
IA24.220.920.800.81
IA34.170.920.660.67
Economic Barriers (EB) 0.520.810.82
EB13.800.880.800.83
EB23.830.880.770.78
EB33.631.060.710.69
EB43.890.980.620.60
Norms (N) 0.510.810.81
N13.551.220.740.84
N23.371.320.740.80
N33.301.290.640.62
N43.761.220.650.55
Note: Descriptive statistics and measurement validation indices for the five latent constructs used in the structural equation model (SEM): Willingness to Collaborate (WTC), Environmental Concerns (EC), Intention to Adopt (IA), Economic Barriers (EB), and Norms (N). The indicators include item-level means and standard deviations (SD), factor loadings from exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), average variance extracted (AVE), composite reliability (CR), and Cronbach’s alpha. All constructs meet or approach standard thresholds for convergent validity (AVE ≥ 0.50) and internal consistency (CR ≥ 0.70; α ≥ 0.70).
Table 4. Model fit indices.
Table 4. Model fit indices.
Fit IndicesThe Measurement ModelThe Structural ModelRecommended ValuesSource
ChiSq/df1.8911.891<3[46]
RMSEA0.0480.048<0.08[47]
TLI0.950.95>0.9[48]
IFI0.960.96>0.9[46]
CFI0.960.99>0.9[47]
NFI0.920.92>0.9[48]
Note: Table reports model fit statistics for both the measurement model and the structural equation model (SEM). Fit indices include the chi-square to degrees of freedom ratio (ChiSq/df), root mean square error of approximation (RMSEA), Tucker–Lewis Index (TLI), Incremental Fit Index (IFI), Comparative Fit Index (CFI), and Normed Fit Index (NFI). All values meet or exceed the commonly accepted thresholds recommended in the literature, indicating good model fit.
Table 5. Path coefficients and hypothesis testing results.
Table 5. Path coefficients and hypothesis testing results.
PathValueStd. Err.pResult
N → WTC0.5340.0720.000H1 Supported
EC → WTC0.1530.0610.013H2 Supported
IA → WTC0.1790.0660.007H3 Supported
EB → WTC−0.2220.0640.000H4 Supported
Note: Standardized path estimates from the structural equation model (SEM) show the direction and significance of the hypothesized relationships between latent variables and Willingness to Collaborate (WTC). All four hypotheses (H1–H4) are supported at p < 0.05.
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MDPI and ACS Style

Mamasoliev, S.A.; Kusadokoro, M.; Maru, T.; Hasanov, S.; Kawabata, Y. Factors Influencing Willingness to Collaborate on Water Management: Insights from Grape Farming in Samarkand, Uzbekistan. Sustainability 2025, 17, 6991. https://doi.org/10.3390/su17156991

AMA Style

Mamasoliev SA, Kusadokoro M, Maru T, Hasanov S, Kawabata Y. Factors Influencing Willingness to Collaborate on Water Management: Insights from Grape Farming in Samarkand, Uzbekistan. Sustainability. 2025; 17(15):6991. https://doi.org/10.3390/su17156991

Chicago/Turabian Style

Mamasoliev, Sodikjon Avazalievich, Motoi Kusadokoro, Takeshi Maru, Shavkat Hasanov, and Yoshiko Kawabata. 2025. "Factors Influencing Willingness to Collaborate on Water Management: Insights from Grape Farming in Samarkand, Uzbekistan" Sustainability 17, no. 15: 6991. https://doi.org/10.3390/su17156991

APA Style

Mamasoliev, S. A., Kusadokoro, M., Maru, T., Hasanov, S., & Kawabata, Y. (2025). Factors Influencing Willingness to Collaborate on Water Management: Insights from Grape Farming in Samarkand, Uzbekistan. Sustainability, 17(15), 6991. https://doi.org/10.3390/su17156991

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