Next Article in Journal
Analysis of the Water Quality Status and Its Historical Evolution Trend in the Mainstream and Major Tributaries of the Yellow River Basin
Previous Article in Journal
Characterization and Quantification of Dam Seepage Based on Resistivity and Geological Information
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

At What Price Are Farmers Willing to Reduce Water Usage? Insights from the Aosta Valley

1
Research Centre for Agricultural Policies and Bioeconomy, CREA-Council for Agricultural Research and Economics, Via della Navicella, 2/4, 00184 Roma, Italy
2
Department of Agricultural Sciences, University of Naples Federico II, Via Università, 100, Portici, 80055 Naples, Italy
3
Institut Agricole Régional, Regione La Rochère, 1/A, 11100 Aosta, Italy
*
Author to whom correspondence should be addressed.
Water 2024, 16(17), 2412; https://doi.org/10.3390/w16172412
Submission received: 2 July 2024 / Revised: 18 August 2024 / Accepted: 22 August 2024 / Published: 27 August 2024
(This article belongs to the Section Water, Agriculture and Aquaculture)

Abstract

:
As climate change and decreasing precipitation worsen water scarcity, understanding farmers’ willingness to reduce water usage is crucial. This study examines this issue in the Aosta Valley, a region facing unique challenges due to its mountainous terrain and high water management costs. The aim is to evaluate farmers’ willingness to reduce water usage and the economic incentives needed to encourage water-saving strategies. To gather the data, 100 farmers participated in a survey that included a discrete choice experiment. The findings revealed that 75% of farmers were unwilling to reduce their water usage even with proposed monetary compensation (EUR 100–120 per hectare per year). On average, the additional compensation farmers would accept for a 10% reduction in water usage was estimated at EUR 360 per hectare per year. This high compensation demand suggests a disconnect between individual desires and economic feasibility. The key reasons for their reluctance included the belief that their current water usage is already optimized, inadequate compensation for potential economic losses and concerns about water shortage. The study highlights the need to understand the socio-cultural context when designing water management policies. Combining economic incentives with social and educational initiatives is likely more effective for promoting sustainable water practices.

1. Introduction

The global rise in temperature and concurrent decrease in precipitation have exacerbated the scarcity of water resources [1]. Human water consumption is mainly distributed among agriculture (76%), industrial applications (15%) and domestic use (9%) [2]. During water shortages, industrial and domestic water use have the priority for resource allocation at the expense of agriculture, posing significant threats to food security [1]. As a result, knowing that the demand for irrigation water is inelastic because it is essential for farming practices, the described condition reduces the supply of this good, which in turn affects its value [3].
To address the issue of irrigation water scarcity, there are multiple possible approaches. Some focus on increasing the availability of irrigation water by introducing new supply systems, such as water recycling and reuse techniques [4,5], or rainwater harvesting [6]. Others aim at reducing irrigation water usage by improving irrigation [7] or agronomic techniques [8,9]. Additionally, it is crucial to implement policy interventions that influence the decisions of resource users, promoting greater efficiency and thereby reducing water scarcity [1]. In this context, within Europe, the European Commission introduced the Water Framework Directive in 2000 to encourage more rational water use based on the “polluter pays principle”. In accordance with this principle, farmers who pollute or use the resource are responsible for bearing the costs associated with such actions [10]. However, this strategy has proven ineffective for two main reasons: firstly, there is uncertainty surrounding water pricing policy estimates [11,12,13], and secondly, the policy exacerbates the financial burden on farmers already grappling with water scarcity issues, amplifying their costs.
Mountainous regions in particular face significant financial burdens due to challenging topography, which increases costs and heavily impacts farmers’ livelihoods, rendering farmers more susceptible to economic pressures [14]. Therefore, further increases in water-related costs can exert a disproportionately heavy impact on their livelihoods, posing a tangible risk to their activity’s survival [15]. A territory that exemplifies this situation is the Aosta Valley in the Northwest region of Italy located in the Western Alps. The region’s unique topography, climate and water resources significantly influence agricultural practices and water usage patterns. Despite historically favorable hydrological conditions of this region [16], the ongoing decrease in precipitation and glacier retreat [17] require the readjustment of water management practices. This would imply a shift in farmers’ attitudes and practices regarding water utilization, prompting an in-depth exploration of the intricate interplay of socio-economic, environmental and institutional factors [18]. At the core of this lies the need to understand farmers’ willingness to reduce water usage by adopting water-saving practices, which requires a comprehensive assessment of their perceptions and inclinations toward water-saving strategies in the context of changing climatic realities.
The approach of understanding farmers’ inclination toward sustainable behaviors was utilized in various studies focused on designing effective policies. Giannoccaro et al. [19] evaluated farmers’ adoption of a new payment scheme aimed at conserving irrigation water. Their study introduced an outcomes-based agri-environmental payment system for farmers in the Apulia region of southern Italy and found that farmers are generally willing to save water under the proposed scheme. Similar approaches have been frequently employed in evaluating the environmental measures of the Common Agricultural Policy. Some studies have explored farmers’ inclination to adopt voluntary sustainability measures that promote high environmental standards using a choice experiment approach. For example, Hannus et al. [20] and Niskanen et al. [21] conducted their research among German and Finnish farmers, respectively, underlying the scarce attractiveness of these measures mainly due to the sustainability standard required. Schulz et al. [22] explored German farmers’ perceptions of the “greening” measures in the Common Agricultural Policy, finding that farmers generally view these measures as costly constraints, with perceptions varying based on individual farmer characteristics. Boufous et al. [23] carried out a meta-analysis of studies investigating farmers’ readiness to adopt sustainable agricultural practices, with a particular emphasis on their Willingness to Accept (WTA) values. Their aim was to identify potential gaps in the current literature, and they found a scarcity of research specifically related to the adoption of water conservation practices.
This gap in the literature is particularly significant in the context of mountain agriculture. Most research on mountainous farming focuses on the environmental or cost implications of adopting sustainability measures rather than on farmers’ willingness to implement them. For instance, Saltos et al. [24] explored the side effects of sustainability measures for endangered species conservation in mountain vineyards in Portugal, revealing the limitations of these measures for biodiversity. Similarly, Roessiger et al. [25] identified a funding strategy to stabilize mountain forests in central Europe, finding that funding at or above 50% of planting costs greatly enhances the presence of minor or missing tree species, such as fir and beech. Zavalloni et al. [26] investigated land use and public good provision for Italian mountain farmers under the CAP, discovering that while generic income support reduces land abandonment, it harms overall welfare, whereas targeted policies improve welfare by optimizing land use and expanding forest areas. Lastly, Borsotto et al. [15] assessed the impact of introducing an irrigation water payment system on mountain farmers’ income, underscoring the challenges of implementing the European WFD in these regions.
To address this gap, our study is designed to specifically tackle the unique challenges faced by Italian farmers in mountainous regions, where economic pressures are intensified by challenging topography and climate change. Therefore, the main objectives of this research are threefold: (i) to analyze the farmers’ willingness to reduce water usage; (ii) to explore the motivations and barriers that influence farmers’ decision-making processes to reduce water use; and (iii) to quantify potential economic incentives that encourage a more efficient use of water resources. By combining economic valuation with socio-cultural insights, this research aims to provide an effective model to link farmers’ characteristics and policy interventions.
This study offers a comprehensive view of the perceptions of Aosta Valley farmers regarding water resources, their availability and strategies to promote sustainable use. In the regional context, there is a consolidated approach in water resource management—an approach that has become an expression of the peculiarities of the territory. Here, water irrigation management emerges not only as a practical matter but as a complex interplay of cultural and territorial factors [18]. However, the deep connection between social fabric and agricultural practices often contributes to establishing resistance to change, slowing the adoption of new environmentally sustainable practices [27,28].
This paper is organized as follows. The “Materials and Methods” section outlines the study’s approach, including data collection and analysis. The “Results” section presents the key findings on farmers’ willingness to adopt water-saving measures. The “Discussion” section interprets these results in the context of existing literature and offers policy recommendations. The “Conclusions” section summarizes the insights and suggests future research areas.

2. Materials and Methods

2.1. Study Area Information

Aosta Valley, the smallest region in Italy located in the Northwest and surrounded by the highest massifs of the Alps, spans approximately 3200 km2, with 90% of its terrain being mountainous (Figure 1). Altitudes range from 350 m above sea level (ASL) in the lower valley bordering Piedmont to the towering 4810 m ASL of Mont Blanc on the border with France [16]. This glaciated region consists of 13 lateral valleys and a central valley traversed by the Dora Baltea river, nourished by around 200 perennial glaciers spread across a high-altitude expanse of 190 km2 [29,30].
Hydrographically, the region boasts over 700 natural and artificial lakes spanning 9.5 km2. The valley’s water supply is supplemented by three aquifers at its base and numerous springs in the lateral valleys [16]. The utilized agricultural area covers around 56,000 hectares, with 98% dedicated to permanent meadows (9500 hectares) and pastures (45,000 hectares), while the remaining portion is allocated to orchards (280 hectares), vineyards (370 hectares) and other minor crops, such as potatoes, cereals and vegetables [33]. Animal husbandry is the main agricultural production in Aosta Valley, centered around the breeding of three native dual-purpose cattle breeds adapted to the region’s challenging mountainous conditions. The production of hay from permanent meadows, crucial for winter cow feed, is sustained through irrigation, as the region’s low rainfall would otherwise hinder agricultural output in terms of both quantity and quality [29]. The milk production is largely dedicated to crafting Fontina PDO cheese, a renowned product of Aosta Valley.
Since the establishment of irrigation canals in the region, water management responsibilities have been delegated to water users’ associations—private, non-profit entities formed by irrigation water users who voluntarily collaborate to address management challenges and water supply needs [16]. Water management in Aosta Valley is marked by a high degree of fragmentation and decentralization, with around 160 water users’ associations overseeing irrigation across the 74 municipalities of the region [16,34]. The primary task of these associations is organizing the corvée, an age-old system involving regular maintenance of irrigation networks through activities like drain and stream cleaning, as well as addressing storm-induced damages.

2.2. Methodology

This study employed a discrete choice experiment (DCE) to analyze farmers’ willingness to reduce water usage and quantify potential economic incentives to encourage the adoption of water-saving strategies. DCEs are widely recognized in environmental economics and natural resource management for investigating preferences and decision-making processes [35]. The theoretical basis for DCEs lies in Lancaster’s theory of value [36] and the random utility theory [37]. According to these theories, the utility provided by a good or service is the sum of the utilities of its individual attributes, and individuals choose the alternative that maximizes their utility.
The use of DCE is particularly suitable for this study, as it allows for the examination of multiple attributes and levels within a single experimental design, providing a comprehensive understanding of farmers’ preferences and trade-offs. This method is effective in capturing the complexity of decision-making processes related to water usage and the adoption of new practices, which are influenced by various economic, social and environmental factors. Furthermore, the DCE method is suitable for our study, as it explores stated preferences, which is crucial, given that real preferences cannot be directly observed due to the hypothetical and predictive nature of our research.
Similar to other studies [19,21,38], this DCE examined the relationship between farmers’ preferences for different water policy scenarios and their water usage, with economic incentives as a benefit. In a hypothetical but realistic scenario, farmers were offered the opportunity to voluntarily participate in a regional policy where a portion of the single payment scheme would compensate for the costs associated with adopting more sustainable irrigation techniques. The attributes in the policy scenario included (i) adherence to guidelines from a regional irrigation advisory service, requiring farmers to implement the recommendations over a specified period [39,40]; (ii) reduction in current irrigation water usage, applied as a specific percentage compared to current usage [19,41]; (iii) a decrease in irrigated agricultural land by excluding selected plots from irrigation, optimizing water usage by avoiding less essential irrigation [42]; and (iv) monetary compensation for the expenses incurred from adopting more sustainable production techniques [19,21]. In the study, three levels per attribute were chosen, as they provide sufficiently accurate estimates while maintaining methodological robustness [43,44]. The attribute levels were determined based on input from a focus group consisting of regional public officials and researchers from a local research institute of agricultural science. Their goal was to ensure that these levels were coherent and realistic, closely reflecting a potential irrigation policy that could be implemented in the area. For the monetary compensation attribute, the levels were specifically based on similar existing policies in the region, ensuring they were feasible and consistent with the regional economic capacity (Table 1).
Through repeated evaluations of bundles with varying attribute levels, respondents assessed the trade-offs between different compensation program scenarios. Each choice set presented respondents with two scenarios that varied in attribute levels, along with a status quo option where they could choose not to participate. To manage the complexity of the design, we employed a D-optimal design, resulting in eight choice sets divided into four blocks (i.e., questionnaires). Each respondent was then randomly assigned to one of the four blocks, with each block containing two choice sets (for the full list of choice sets, see Appendix A). To estimate the marginal value of the attributes, a mixed logit model with random parameters was used [45]. This econometric approach assumes a distribution of preference weights among respondents and estimates the parameters for each attribute level, revealing unobserved preference heterogeneity [46,47].
In analytical terms, the perceived utility associated with the j-th scenario by the i-th respondent (Uij) is considered as a linear and additive function of the j-th scenario attributes:
Uij = βxj + εij
where the xj vector comprises the different levels of the scenario’s attributes included in the choice set. The parameter vector β represents the direction of the magnitude of the statistical association between each scenario attribute and the perceived utility. Specifically, β indicates how the changes in each attribute level affect the overall utility derived from the scenario. Finally, εij represents the stochastic term, accounting for the unobserved factors affecting the perceived utility. McFadden introduced the use of the conditional logit model to estimate the marginal utility of attributes by assuming that the stochastic term follows a Gumbel distribution, allowing for a probabilistic framework to model choice behavior [37]. However, the conditional logit model assumes homogeneity in preferences across individuals, which may not always be realistic. To address this limitation, Train extended the model by incorporating random parameters, leading to the development of the mixed logit model [46]. This model allows for heterogeneity in preferences by assuming that the parameter vector β may vary across individuals (βi) according to a determined distribution. This flexibility enables the mixed logit model to capture more complex substitution patterns and provide more accurate estimates of individual-specific preferences and marginal utilities.
Marginal utilities in a logit framework are accounted for as marginal probabilities, indicating how a change in an attribute level affects the likelihood of a scenario being chosen. By using these estimates, the marginal willingness to accept each attribute can be calculated, offering insights into the acceptability of a compensation policy for promoting sustainable irrigation practices. This approach quantified the relative importance of different attributes by presenting respondents with hypothetical choice scenarios [47].
Analyzing farmers’ choices regarding their willingness to accept these measures allowed us to gain insights into their preferences and the value they place on water resource restoration initiatives. This experimental approach systematically varied attributes [45] to understand the trade-offs farmers are willing to make, providing valuable insights into their inclination toward adopting sustainable water management practices.
All data analysis was conducted using Stata, a powerful statistical software widely used for data management, statistical analysis and graphical representation in various fields, including economics and social sciences [48]. Stata provides robust tools for handling complex datasets, making it suitable for the detailed analysis required in discrete choice experiments.

2.3. Questionnaire Design and Administration

The survey questionnaire consisted of three sections. The experimental section included two choice sets with three alternatives each (Figure 2). In this section, respondents who preferred the status quo over the other alternatives in both choice sets were asked to provide an open-ended explanation for their choice, helping to understand their reasons for declining compensation and the proposed strategies. The second section of the questionnaire collected basic personal information about the respondents, while the final section included farm characteristics, particularly concerning water management and payment practices (Appendix B).
For this study, a sample size of 100 local farmers was selected to ensure a representative analysis of the Aosta Valley, a region characterized by specific geographic and structural features, which have led to a highly uniform agricultural reality (the data collection follows the principles of the Declaration of Helsinki, ensuring respect for the privacy and confidentiality of information collected from farmers). The sample was selected to be representative of the local agricultural landscape, considering both types of farming and farm size. Data collection was conducted through face-to-face interviews between September and November 2021. This approach was designed to capture a comprehensive overview of the various production sectors present within the Aosta Valley. Table 2 presents the descriptive data of the farms involved in the study. The sample includes a diverse range of agricultural activities, with 73 livestock farms, 12 fruit farms, 9 wine farms and 6 vegetable farms. On average, livestock farms have the largest utilized agricultural area (UAA) and irrigated UAA, highlighting their significant land use and water needs. The collected data revealed that the most used irrigation technology was the sprinkler system on grasslands and orchards, primarily because it provides uniform water distribution, which is essential for maintaining consistent moisture levels in these types of crops. Sprinkler irrigation is gradually replacing flooding on many permanent meadows, which are the fodder base for livestock farming in Aosta Valley, due to its efficiency in water use and ability to maintain optimal growing conditions. Flooding was still common on grasslands, orchards and vegetable gardens, as it is a traditional method suited for crops that can tolerate or thrive with a large amount of water at once. Drip irrigation was predominantly used on orchards, vineyards and vegetable gardens because it delivers water directly to the root zone, minimizing water waste and ensuring that these typically high-value crops receive precise amounts of moisture [10]. Moreover, 88% of farmers contributed a payment to local water users’ associations, as stipulated by Regional Law no. 3/2001, aimed at covering management, operational and routine maintenance expenses incurred by these associations.
Figure 3 provides descriptive statistics of the respondents’ characteristics, highlighting demographic details, such as gender, age and education level. The sample included a diverse range of respondents, with a notable representation across different age brackets and educational backgrounds.

3. Results

The data collected showed that among 100 farmers, a significant majority, totaling 75 individuals, preferred the status quo option in both choice sets provided. This indicates that a substantial portion of the sample is disinclined toward accepting compensation to adopt more sustainable irrigation water strategies. Among those who preferred the status quo option in both choice sets, 59 were livestock farmers (81% of livestock farmers), 7 were fruit farmers (58% of fruit farmers), 3 were vegetable farmers (50% of vegetable farmers), and 6 were wine producers (67% of wine producers).
To better understand their perspective, the reasons for their choices were categorized, as shown in Table 3. Many farmers expressed the view that they already optimize their use of water resources, making further improvements unfeasible. Additionally, some farmers believed that the proposed compensation was insufficient to cover potential economic losses resulting from such changes. Other respondents highlighted competition for water resources due to the introduction of hydroelectric power plants, which has negatively impacted their willingness to adopt water-saving measures. Some farmers already perceived a reduction in water availability due to climate change and were therefore unwilling to implement actions that involve reducing their current water usage. Finally, some respondents cited the structural characteristics of the regional soil as a limiting factor for the implementation of alternative irrigation strategies beyond those currently employed.
Although the largest portion of respondents preferred the status quo option, the estimates from the mixed logit model revealed some significant findings (Table 4). The attributes that significantly impacted farmers’ choices (p < 0.05) were the reduction in irrigation water and the monetary compensation. Conversely, guidance from the regional irrigation advisory service and the reduction in irrigated land did not significantly affect their choices.
The additional monetary compensation that farmers would be willing to accept for a unit increase or decrease in a specific attribute was estimated using the marginal Willingness to Accept (mWTA), calculated by dividing the estimated coefficients of the attributes by the coefficient of the monetary compensation. The results indicate that farmers would be willing to accept approximately EUR 36 ha−1 y−1 to reduce their irrigation water usage by 1%. Consequently, for reductions of 10% and 20%, as presented in the choice experiment, farmers would be willing to accept, on average, EUR 360 and EUR 720 ha−1 y−1, respectively, significantly exceeding the compensation values proposed in the policy scenario.
To better understand the relationship between compensation levels and farmers’ willingness to reduce irrigation, we employed a detailed analytical approach. Specifically, a findings–response model was applied to the estimates derived from the mixed logit model to assess how changes in compensation levels impact farmers’ willingness to reduce irrigation by 10%. This model focuses on analyzing the marginal changes in acceptance rates as compensation is incrementally increased. By systematically adjusting the compensation (expressed in EUR/ha), the findings–response model calculates the resulting variation in the proportion of farmers willing to accept the proposed reduction in irrigation. This method provides a nuanced understanding of how sensitive farmers’ willingness to participate is to different compensation levels, identifying the point at which a significant majority of farmers are inclined to agree. The results of this analysis are presented in Figure 4, showing the percentage variation in farmers’ willingness to reduce their irrigation volumes in response to varying compensation levels. The figure illustrates the non-linear relationship between compensation and acceptance, with notable increases in willingness to accept observed as compensation reaches higher thresholds. It is noteworthy that with a compensation of EUR 100 ha−1 y−1, approximately 20% of farmers would be willing to accept the reduction. When the compensation ranges from EUR 110 (average of proposed value) to EUR 450 ha−1 y−1, the percentage of farmers willing to accept the reduction remains constant at 22%. However, with a compensation of EUR 500 ha−1 y−1, almost all farmers would be willing to accept the reduction.
To investigate the heterogeneity in water attribute preferences among respondents, we employed a two-step approach, as in Weituschat et al. [49]. In the first step, the marginal WTA for water reduction was derived from the mixed logit model. In the second step, it was analyzed how these individual marginal WTA values relate to various socio-structural characteristics of the sample. The methodology involved a heteroskedasticity-robust regression model [50]. This model regresses the marginal WTA (from the mixed logit model) on the socio-structural characteristics, with adjustments made to handle heteroskedasticity potentially introduced by outliers. The results, reported in Table 5, can be interpreted within the context of a conventional regression model.
Considering the characteristics of farmers, it can be observed that as age increases, their willingness to accept also increases, suggesting that older farmers are less inclined to reduce the use of irrigation water in exchange for compensation. The level of education of farmers also has an impact on their WTA, as compared to individuals with an academic degree, those with a high school diploma or secondary school education are less inclined to reduce the use of irrigation water, and their marginal WTA is EUR 13.14 y−1 and EUR 15.25 y−1, respectively, compared to those with an academic degree, whose marginal WTA is EUR 0.56 y−1.
Regarding the farm characteristics, it emerges that fruit and wine-growing farmers are more inclined to accept a reduction in water use compared to livestock farmers. In particular, the marginal WTA for livestock farms is EUR 22.78 y−1, while for wine and fruit farms, it is EUR 3.64 y−1 and EUR 0.99 y−1, respectively. Another factor that influences WTA is the irrigation system adopted: farms that use drip irrigation are less likely to reduce the use of water compared to those that use sprinkler irrigation systems. The marginal WTA for farms that adopt drip irrigation is estimated at EUR 28.5 y−1, while for those that use sprinkler irrigation, it is EUR 8.12 y−1.

4. Discussion

4.1. Discussion on Survey

In this study, the prevailing idea is that farmers are not inclined to change their irrigation water management, even when they are provided with economic incentives. Confirming this resistance to change is the fact that the water resource in the region is not scarce [17]. Despite this evidence, farmers tend to exhibit conservative behavior.
The cultural and territorial context outlined so far led 75% of the respondents to prefer the status quo scenario in the choice sets presented in the questionnaire, opting not to make any changes in their irrigation management. Among the reasons they provided to justify their choice, the one most frequently cited was that they already optimize water for their crops, indicating a perception among respondents of adopting virtuous and sustainable water use behaviors. However, this perception often aligns with the use of traditional techniques [51], hindering the transition to alternative irrigation management methods.
The second most frequently provided motivation concerned the inadequate amount of proposed economic support. According to farmers, implementing the required measures for policy adherence would result in a reduction in their production not adequately compensated by the support amount. While increasing public support for policy adherence may seem impractical due to regional budget constraints, this result underscores the perceived lack of congruity between the public support amount and the economic damage resulting from reduced agricultural production.
The third most frequently cited motivation related to issues associated with the installation of hydroelectric plants in the regional territory. This result stems from conflicting uses between energy and agricultural use of water [18]. The increase in hydroelectric plants in previous years has reduced water available for irrigation, making farmers less inclined to accept a policy imposing further restrictions on water use.
Another reason cited by farmers for not adhering to the policy was related to the perception of the consequences of climate change. This result reaffirms previous studies, suggesting that farmers’ increasing awareness and concern about the consequences of climate change for water resources often do not translate into adopting more sustainable practices. Instead, this awareness manifests as resistance to change, contradicting the expectation of greater adoption of sustainability-oriented agricultural approaches [52].
Another motivation adduced by farmers concerned the agronomic plan. Farmers highlight the difficulty of implementing measures on lands with characteristics leading to poor water retention. However, consultation of soil data suggests that the situation described by them is not widespread in Aosta Valley [53]. Only four respondents provided this motivation, and they may operate under specific conditions.
The mixed logit estimate of the choice experiment revealed that the decisive attribute in the decision to adhere to the proposed policy is the reduction in water used for irrigation. This means that, compared to other factors considered, the reduction in water use is the only element playing a crucial role in the decision to adopt the proposed policy. Examining the results of the marginal WTA from the mixed logit model, the required monetary compensation to reduce water use is particularly high. Consequently, farmers attribute a very high value to water, much higher than the amount that could be feasibly allocated to implement this type of policy. This underscores the need for different strategies that do not involve only funding the reduction in water usage but also incorporating technological innovations, enhancing water-use efficiency and promoting alternative agricultural practices. Such strategies might include investments in advanced irrigation systems, farmer education programs on sustainable water management and policies that incentivize crop diversification and soil conservation. The survey also revealed a certain discrepancy in the perception of the attribute related to advisory services. Not all farmers gave this attribute a negative connotation. An increase in the years of following the regional advisory service was often seen as an advantage, improving the farmers’ willingness to accept the policy scenario. This suggests that there are promising prospects for regional advisory services to incentivize farmers to use water more effectively.
Analyzing the results from the heteroskedasticity-robust regression models, the marginal WTA of respondents to reduce water use in relation to their characteristics yielded results very close to our expectations and previous studies. Older individuals are less inclined to reduce water use, likely due to their connection to tradition and habits. Similarly, farmers with lower levels of education are less inclined to reduce water use, probably because they are less informed and therefore less sensitive to environmental issues. This confirms observations in previous research regarding the influence of age and education on the propensity to adopt new production and business management strategies [54,55,56].
Livestock farmers show less willingness to reduce water use for irrigation compared to fruit and grape growers. This reluctance in the livestock sector to modify production practices is likely due to the sector’s resistance compared to others. The cause of this resistance is that, in this region, livestock farms are traditionally linked to Fontina cheese production, which restricts the use of hay from other regions. Livestock farmers fear that changing their irrigation habits may compromise the necessary forage availability for livestock during winter months, making external purchases difficult.
Those using drip irrigation are less willing to accept a reduction in water use compared to those using sprinkler irrigation, as they have already optimized resource use, and further reduction would harm production.

4.2. Policy Implications

This study provides some reflections on the policy implications related to the sustainable management of water resources in agriculture. The results challenge the effectiveness of economic incentives as the sole tool to influence farmers’ decisions to adhere to more sustainable water management policies. Beyond economic aspects, other crucial factors clearly emerge from our analysis, such as the socio-cultural context, operator characteristics, perception of climate change and growing awareness of resource scarcity.
In this scenario, it is essential to identify additional avenues of action to promote sustainable water resource management. The finding that the perception of the effects of climate change does not always translate into virtuous behaviors but often generates resistance to change suggests that policies should focus on social levers in addition to economic incentives. This means developing strategies such as training programs and awareness initiatives that can positively influence farmers’ perceptions and guide them toward adopting common change-oriented solutions. Such strategies could include, for example, promoting technical training on the advantages of adopting water-saving irrigation technologies for different types of farming [57], broadcasting high-quality video content on water sustainability strategies [58] or adapting existing online open platforms to provide courses for farmers’ training [59]. This perspective paves the way for training initiatives that, when integrated into agricultural policies, could significantly contribute to water sustainability in the agricultural sector [60].
Furthermore, when training paths prove less effective, consultancy services emerge as a potentially effective lever for change. If structured and implemented appropriately, these services could play a key role in directing farmers toward more efficient and sustainable irrigation practices [61,62]. Previous studies confirm that consultancy can facilitate transition processes, emphasizing the importance of integrating informational and training support into business management policies [63] to address the current deficiency [64]. By adopting a multifaceted approach, it is possible to achieve more sustainable water usage while addressing the economic and structural concerns of farmers [3].

4.3. Limitations and Future Works

This study has several limitations that future research should address. First, hypothetical bias may affect the results of discrete choice experiments (DCEs), as survey responses might not accurately represent farmers’ real-world behavior. Farmers might overstate their willingness to accept compensation or adopt new practices in a hypothetical scenario compared to actual decisions, where risk and uncertainty are more significant factors. Second, the survey’s sample size was relatively small, with 100 farmers participating. A larger sample would provide more reliable estimates and more generalizable conclusions. Future studies should aim for a larger and more diverse group of farmers to better capture a range of perspectives and behaviors, including an in-depth analysis of socio-economic factors like risk aversion, environmental attitudes, farm size, income levels and resource access. This would help in designing more effective policies. Additionally, the study did not present specific alternatives or technologies for reducing water usage. Providing detailed information on the available technologies and best practices could significantly impact farmers’ willingness to adopt water-saving measures. Further research should investigate how specific technological solutions and support systems influence farmers’ decision-making processes.

5. Conclusions

This study focused on the emerging challenges faced by farmers in mountainous regions, particularly in Aosta Valley, Italy, due to altered hydrological dynamics caused by climate change. The research aimed to understand farmers’ willingness to adopt water-saving measures and the motivations and barriers influencing their decision-making processes. Data were collected through questionnaires employing a discrete choice experiment from a sample of 100 regional farmers to explore their preferences for different water policy scenarios. The study revealed that a significant majority of farmers (75 out of 100) prefer the status quo, indicating resistance to adopting more sustainable irrigation strategies despite the compensation offered. The key reasons included perceived optimization of current water use, insufficient proposed compensation, competition from hydroelectric plants, climate-change-induced water scarcity and regional soil characteristics. The mixed logit model indicated that, on average, farmers would be willing to accept approximately EUR 360 ha−1 y−1 to reduce their irrigation water usage by 10%. However, with a compensation of EUR 500 ha−1 y−1, almost all farmers would be willing to accept the reduction.
Recognizing that such a high incentive would be impractical to implement, the study concludes that economic incentives alone are insufficient for sustainable water management among farmers. A multifaceted approach, incorporating socio-cultural factors, individual characteristics and climate change perceptions, is recommended. Effective policies should combine economic incentives with social initiatives, such as training programs and awareness campaigns, to positively influence farmers’ practices [60].
Despite some limitations, such as hypothetical bias that could have affected discrete choice experiments, the study shows that farmers in Aosta Valley are resistant to changing their irrigation water management practices, even when economic incentives are offered. However, the increasing pressures of climate change on water resources are becoming more apparent. This underscores the inevitable need for regions like the Aosta Valley to adopt new approaches in the future to ensure sustainable water use.

Author Contributions

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

Funding

RESERVAQUA project was co-financed by the European Union, European Regional Development Fund, the Italian State, the Swiss Confederation and the Cantons, under the Interreg V-A Italy-Switzerland Cooperation Programme 2014–2020 (Project id. 551749).

Data Availability Statement

Data are contained within the article.

Acknowledgments

This study is part of the activities of the RESERVAQUA project that operated under the INTERREG V-A Italy-Switzerland Cooperation Programme 2014–2020. The project’s goal is to develop an integrated management strategy for mountain regions and rural areas to ensure the sustainable use and quality protection of Alpine water resources in the future, benefiting both mountain and lowland regions.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Appendix A. Detailed Overview of the Four Experimental Blocks and the Corresponding Eight Choice Sets Used in the Survey

Block 1—Choice set 1.
Scenario 1Scenario 2Status Quo
Advisory service51No service
Water reduction20%10%No reduction
Decrease in irrigated land5%2%No reduction
Monetary compensationEUR 120 ha−1 y−1EUR 100 ha−1 y−1EUR 0 ha−1 y−1
Which do you prefer?Water 16 02412 i001Water 16 02412 i001Water 16 02412 i001
Block 1—Choice set 2.
Scenario 1Scenario 2Status Quo
Advisory service51No service
Water reduction10%20%No reduction
Decrease in irrigated land5%2%No reduction
Monetary compensationEUR 100 ha−1 y−1EUR 120 ha−1 y−1EUR 0 ha−1 y−1
Which do you prefer?Water 16 02412 i001Water 16 02412 i001Water 16 02412 i001
Block 2—Choice set 1.
Scenario 1Scenario 2Status Quo
Advisory service15No service
Water reduction10%20%No reduction
Decrease in irrigated land5%2%No reduction
Monetary compensationEUR 100 ha−1 y−1EUR 120 ha−1 y−1EUR 0 ha−1 y−1
Which do you prefer?Water 16 02412 i001Water 16 02412 i001Water 16 02412 i001
Block 2—Choice set 2.
Scenario 1Scenario 2Status Quo
Advisory service51No service
Water reduction20%10%No reduction
Decrease in irrigated land5%2%No reduction
Monetary compensationEUR 120 ha−1 y−1EUR 100 ha−1 y−1EUR 0 ha−1 y−1
Which do you prefer?Water 16 02412 i001Water 16 02412 i001Water 16 02412 i001
Block 3—Choice set 1.
Scenario 1Scenario 2Status Quo
Advisory service51No service
Water reduction10%20%No reduction
Decrease in irrigated land5%2%No reduction
Monetary compensationEUR 120 ha−1 y−1EUR 100 ha−1 y−1EUR 0 ha−1 y−1
Which do you prefer?Water 16 02412 i001Water 16 02412 i001Water 16 02412 i001
Block 3—Choice set 2.
Scenario 1Scenario 2Status Quo
Advisory service15No service
Water reduction20%10%No reduction
Decrease in irrigated land5%2%No reduction
Monetary compensationEUR 100 ha−1 y−1EUR 120 ha−1 y−1EUR 0 ha−1 y−1
Which do you prefer?Water 16 02412 i001Water 16 02412 i001Water 16 02412 i001
Block 4—Choice set 1.
Scenario 1Scenario 2Status Quo
Advisory service15No service
Water reduction10%20%No reduction
Decrease in irrigated land5%2%No reduction
Monetary compensationEUR 120 ha−1 y−1EUR 100 ha−1 y−1EUR 0 ha−1 y−1
Which do you prefer?Water 16 02412 i001Water 16 02412 i001Water 16 02412 i001
Block 4—Choice set 2.
Scenario 1Scenario 2Status Quo
Advisory service51No service
Water reduction10%20%No reduction
Decrease in irrigated land2%5%No reduction
Monetary compensationEUR 100 ha−1 y−1EUR 120 ha−1 y−1EUR 0 ha−1 y−1
Which do you prefer?Water 16 02412 i001Water 16 02412 i001Water 16 02412 i001

Appendix B. Example of the Survey Questionnaire Includes Three Sections: The Experimental Section (Block 1), a Section for Collecting Basic Personal Information about the Respondents and a Final Section Covering Farm Characteristics

We are a group of researchers from the Policy and Bioeconomy Center of the Council for Agricultural Research and Economics (CREA) and the University of Naples. We are conducting a study on agricultural water use in the Aosta Valley area on behalf of the Institut Agricole Régional of Aosta.
  • Section 1 (Choice Experiment)
In the upcoming agricultural policy, the single payment will include compensation for the costs arising from more sustainable production techniques, and the decision to adopt such techniques will be voluntary. Non-adherence will result in a reduction in the single payment disbursed to the farmer.
  • Which scenario would you choose among the proposed ones?
Scenario 1Scenario 2Status Quo
Advisory service51No service
Water reduction20%10%No reduction
Decrease in irrigated land5%2%No reduction
Monetary compensationEUR 120 ha−1 y−1EUR 100 ha−1 y−1EUR 0 ha−1 y−1
Which do you prefer?Water 16 02412 i001Water 16 02412 i001Water 16 02412 i001
2.
Which of these second set of scenarios would you adopt?
Scenario 1Scenario 2Status Quo
Advisory service51No service
Water reduction10%20%No reduction
Decrease in irrigated land5%2%No reduction
Monetary compensationEUR 100 ha−1 y−1EUR 120 ha−1 y−1EUR 0 ha−1 y−1
Which do you prefer?Water 16 02412 i001Water 16 02412 i001Water 16 02412 i001
(If the respondent preferred the status quo scenario in both choice sets).
3.
Why would you prefer not to adopt the voluntary measures described?
  • Section 2 (General Information)
4.
Gender: F/M
5.
Year of Birth:
6.
Education Level:
-
None
-
Primary School
-
Secondary School
-
High School Diploma
-
University Degree
  • Section 3 (Farms’ Data)
7.
Municipality where the business is located:
8.
Farm Area
  • Utilized Agricultural Area:
  • Irrigated Agricultural Area:
9.
Farming Type:
  • If livestock, specify number of heads:
10.
Do you make an annual contribution to the Consortia (role)? Yes/No
If yes, how much?
11.
Do you incur additional expenses for water allocation for irrigation? Yes/No
If yes, what are they?
How much?
12.
Do you participate in the maintenance activities of the Consortia networks through the voluntary corvée system? Yes/No
If not, what is the annual amount you contribute?
13.
Does the water you use for irrigation come exclusively from the Consortia? Yes/No
If not, what are the other sources?
14.
What irrigation techniques do you use for your land?
-
Flooding
-
Sprinkler (rain)
-
Drip irrigation
-
Other

References

  1. FAO. The State of the World’s Land and Water Resources for Food and Agriculture 2021—Systems at Breaking Point; FAO: Rome, Italy, 2022. [Google Scholar] [CrossRef]
  2. Carpenter, S.R.; Stanley, E.H.; Vander Zanden, M.J. State of the world’s freshwater ecosystems: Physical, chemical, and biological changes. Annu. Rev. Environ. Resour. 2011, 36, 75–99. [Google Scholar] [CrossRef]
  3. Buttinelli, R.; Cortignani, R.; Caracciolo, F. Irrigation water economic value and productivity: An econometric estimation for maize grain production in Italy. Agric. Water Manag. 2024, 295, 108757. [Google Scholar] [CrossRef]
  4. Radcliffe, J.C. Current status of recycled water for agricultural irrigation in Australia, potential opportunities and areas of emerging concern. Sci. Total Environ. 2022, 807, 151676. [Google Scholar] [CrossRef] [PubMed]
  5. El-Fakharany, Z.M.; Salem, M.G. Mitigating climate change impacts on irrigation water shortage using brackish groundwater and solar energy. Energy Rep. 2021, 7, 608–621. [Google Scholar] [CrossRef]
  6. Wen, L.Z.; Suhaimi, H.; Abas, P.E. Techno-economic feasibility of rainwater harvesting system for vertical aquaponics in Brunei Darussalam. AIP Conf. Proc. 2022, 2676, 030001. [Google Scholar] [CrossRef]
  7. Belaidi, S.; Chehat, F.; Benmehaia, M.A. The adoption of water-saving irrigation technologies in the Mitidja plain, Algeria: An econometric analysis. New Medit 2022, 21, 53–72. [Google Scholar] [CrossRef]
  8. Alam, A.U.; Ullah, H.; Himanshu, S.K.; Tisarum, R.; Cha-um, S.; Datta, A. Seed priming enhances germination and morphological, physio-biochemical, and yield traits of cucumber under water-deficit stress. J. Soil Sci. Plant Nutr. 2023, 23, 3961–3978. [Google Scholar] [CrossRef]
  9. Alam, A.; Ullah, H.; Thuenprom, N.; Tisarum, R.; Cha-Um, S.; Datta, A. Seed priming with salicylic acid enhances growth, physiological traits, fruit yield, and quality parameters of cantaloupe under water-deficit stress. S. Afr. J. Bot. 2022, 150, 1–12. [Google Scholar] [CrossRef]
  10. Zucaro, R. Condizionalità Ex-Ante per le Risorse Idriche: Opportunità e Vincoli per il Mondo Agricolo; INEA: Roma, Italy, 2014. [Google Scholar]
  11. Berbel, J.; Borrego-Marin, M.M.; Exposito, A.; Giannoccaro, G.; Montilla-Lopez, N.M.; Roseta-Palma, C. Analysis of irrigation water tariffs and taxes in Europe. Water Policy 2019, 21, 806–825. [Google Scholar] [CrossRef]
  12. Galioto, F.; Guerra, E.; Raggi, M.; Viaggi, D. The impact of new regulations on water pricing in the agricultural sector: A case study from Northern Italy. Agric. Econ. Rev. 2017, 18, 77–95. [Google Scholar]
  13. Gómez-Limón, J.A.; Martin-Ortega, J. The economic analysis in the implementation of the Water-Framework Directive in Spain. Int. J. River Basin Manag. 2013, 11, 301–310. [Google Scholar] [CrossRef]
  14. MacDonald, D.; Crabtree, J.R.; Wiesinger, G.; Dax, T.; Stamou, N.; Fleury, P.; Lazpita, J.G.; Gibon, A. Agricultural abandonment in mountain areas of Europe: Environmental consequences and policy response. J. Environ. Manag. 2000, 59, 47–69. [Google Scholar] [CrossRef]
  15. Borsotto, P.; Moino, F.; Novelli, S. Modeling change in the ratio of water irrigation costs to farm incomes under various scenarios with integrated fadn and administrative data. Int. J. Agric. Food Syst. 2021, 2020, 1–19. [Google Scholar] [CrossRef]
  16. Zucaro, R.; Seroglia, G. Monitoraggio dei Sistemi Irrigui delle Regioni Centro Settentrionali, Rapporto Sullo Stato Dell’irrigazione in Valle D’aosta; INEA: Roma, Italy, 2009. [Google Scholar]
  17. RAVA. Progetto di Aggiornamento del Piano di Tutela delle Acque, Relazione Generale, Assessorato Opere Pubbliche, Territorio ed Edilizia Residenziale Pubblica; 2019; 175p. Available online: https://www.regione.vda.it/territorio/allegati/progetti_via_1290_PTA%20VdA%20-%20RelGen.pdf (accessed on 16 April 2024).
  18. Novelli, S.; Moino, F.; Borsotto, P. External Benefits of Irrigation in Mountain Areas: Stakeholder Perceptions and Water Policy Implications. Land 2022, 11, 1395. [Google Scholar] [CrossRef]
  19. Giannoccaro, G.; Roselli, L.; Sardaro, R.; de Gennaro, B.C. Design of an incentive-based tool for effective water saving policy in agriculture. Agric. Water Manag. 2022, 272, 107866. [Google Scholar] [CrossRef]
  20. Hannus, V.; Venus, T.J.; Sauer, J. Acceptance of sustainability standards by farmers—Empirical evidence from Germany. J. Environ. Manag. 2020, 267, 110617. [Google Scholar] [CrossRef]
  21. Niskanen, O.; Tienhaara, A.; Haltia, E.; Pouta, E. Farmers’ heterogeneous preferences towards results-based environmental policies. Land Use Policy 2021, 102, 105227. [Google Scholar] [CrossRef]
  22. Schulz, N.; Breustedt, G.; Latacz-Lohmann, U. Assessing Farmers’ Willingness to Accept “Greening”: Insights from a Discrete Choice Experiment in Germany. J. Agric. Econ. 2014, 65, 26–48. [Google Scholar] [CrossRef]
  23. Boufous, S.; Hudson, D.; Carpio, C. Farmers’ willingness to adopt sustainable agricultural practices: A meta-analysis. PLOS Sustain. Transform. 2023, 2, e0000037. [Google Scholar] [CrossRef]
  24. Santos, M.; Garcês, C.; Ferreira, A.; Carvalho, D.; Travassos, P.; Bastos, R.; Cunha, A.; Cabecinha, E.; Santos, J.; Cabral, J.A. Side effects of European eco schemes and agri-environment-climate measures on endangered species conservation: Clues from a case study in mountain vineyard landscapes. Ecol. Indic. 2023, 148, 110155. [Google Scholar] [CrossRef]
  25. Roessiger, J.; Kulla, L.; Murgaš, V.; Sedliak, M.; Kovalčík, M.; Cienciala, E.; Šebeň, V. Funding for planting missing species financially supports the conversion from pure even-aged to uneven-aged mixed forests and climate change mitigation. Eur. J. For. Res. 2022, 141, 517–534. [Google Scholar] [CrossRef]
  26. Zavalloni, M.; D’Alberto, R.; Raggi, M.; Viaggi, D. Farmland abandonment, public goods and the CAP in a marginal area of Italy. Land Use Policy 2021, 107, 104365. [Google Scholar] [CrossRef]
  27. Anastasiadis, S.; Chukova, S. An inertia model for the adoption of new farming practices. Int. Trans. Oper. Res. 2019, 26, 667–685. [Google Scholar] [CrossRef]
  28. Giri, S. Water quality prospective in Twenty First Century: Status of water quality in major river basins, contemporary strategies and impediments: A review. Environ. Pollut. 2021, 271, 116332. [Google Scholar] [CrossRef]
  29. Cerutti, A.V. Le Pays de la Doire et Son Peuple; Musumeci Editeur: Quart, Italy, 1995; Quart; 406p. [Google Scholar]
  30. Janin, B. Le Val d’Aoste. In Tradition et Renouveau; Musumeci Editeur: Quart, Italy, 1991; 743p. [Google Scholar]
  31. Wikivoyage. Available online: https://upload.wikimedia.org/wikipedia/commons/0/07/Aosta_Valley_in_Italy.svg (accessed on 22 August 2024).
  32. Mappe Regione VdA. Available online: https://mappe.regione.vda.it/pub/geocartosct/ (accessed on 22 July 2024).
  33. CREA. L’agricoltura Nella Valle d’Aosta in Cifre; CREA: Roma, Italy, 2021. [Google Scholar]
  34. Trione, S. L’agricoltura Nella Valle d’Aosta in Cifre 2016; Consiglio per la Ricerca in Agricoltura e L’analisi dell’economia Agraria: Roma, Italy, 2017; 113p. [Google Scholar]
  35. Louviere, J.J.; Flynn, T.N.; Carson, R.T. Discrete Choice Experiments Are Not Conjoint Analysis. J. Choice Model. 2000, 3, 57–72. [Google Scholar] [CrossRef]
  36. Lancaster, K.J. A new approach to consumer theory. J. Political Econ. 1966, 74, 132–157. [Google Scholar] [CrossRef]
  37. McFadden, D. Economic Choices. Am. Econ. Rev. 2001, 91, 351–400. [Google Scholar] [CrossRef]
  38. Raina, N.; Zavalloni, M.; Targetti, S.; D’Alberto, R.; Raggi, M.; Viaggi, D. A systematic review of attributes used in choice experiments for agri-environmental contracts. Bio-Based Appl. Econ. 2021, 10, 137–152. [Google Scholar] [CrossRef]
  39. Altobelli, F.; Lall, U.; Dalla Marta, A.; Caracciolo, F.; Cicia, G.; D’Urso, G.; Del Giudice, T. Willingness of farmers to pay for satellite-based irrigation advisory services: A southern Italy experience. J. Agric. Sci. 2018, 156, 723–730. [Google Scholar] [CrossRef]
  40. Altobelli, F.; Marta, A.D.; Heinen, M.; Jacobs, C.; Giampietri, E.; Mancini, M.; Cimino, O.; Trestini, S.; Kranendonk, R.; Chanzy, A.; et al. Irrigation Advisory Services: Farmers preferences and willingness to pay for innovation. Outlook Agric. 2021, 50, 277–285. [Google Scholar] [CrossRef]
  41. Conrad, S.A.; Rutherford, M.B.; Haider, W. Profiling Farmers’ Preferences about Drought Response Policies Using a Choice Experiment in the Okanagan Basin, Canada. Water Resour. Manag. 2017, 31, 2837–2851. [Google Scholar] [CrossRef]
  42. Jørgensen, S.L.; Termansen, M.; Pascual, U. Natural insurance as condition for market insurance: Climate change adaptation in agriculture. Ecol. Econ. 2020, 169, 106489. [Google Scholar] [CrossRef]
  43. Louviere, J.J.; Hensher, D.A.; Swait, J.D. Stated Choice Methods: Analysis and Applications; Cambridge University Press: Cambridge, UK, 2000. [Google Scholar]
  44. Hensher, D.A.; Rose, J.M.; Greene, W.H. Applied Choice Analysis: A Primer; Cambridge University Press: Cambridge, UK, 2005. [Google Scholar]
  45. Hauber, A.B.; González, J.M.; Groothuis-Oudshoorn CG, M.; Prior, T.; Marshall, D.A.; Cunningham, C.; IJzerman, M.J.; Bridges JF, P. Statistical Methods for the Analysis of Discrete Choice Experiments: A Report of the ISPOR Conjoint Analysis Good Research Practices Task Force. Value Health 2016, 19, 300–315. [Google Scholar] [CrossRef]
  46. Revelt, D.; Train, K. Mixed logit with repeated choices: Households’ choices of appliance efficiency level. Rev. Econ. Stat. 1998, 80, 647–657. [Google Scholar] [CrossRef]
  47. Louviere, J.J. Choice experiments: An overview of concepts and issues. In The Choice Modelling Approach to Environmental Valuation; Edward Elgar Publishing: Cheltenham, UK, 2001; Volume 13. [Google Scholar]
  48. StataCorp. Stata Statistical Software: Release 16; StataCorp LLC: College Station, TX, USA, 2019. [Google Scholar]
  49. Weituschat, C.S.; Pascucci, S.; Materia, V.C.; Caracciolo, F. Can contract farming support sustainable intensification in agri-food value chains? Ecol. Econ. 2023, 211, 107876. [Google Scholar] [CrossRef]
  50. Li, G. 1985. Robust regression. In Exploring Data Tables, Trends, and Shapes; Hoaglin, D.C., Mosteller, C.F., Tukey, J.W., Eds.; Wiley: New York, NY, USA, 1985; pp. 281–340. [Google Scholar]
  51. Bhujel, R.R.; Joshi, H.G. Understanding farmers’ intention to adopt sustainable agriculture in Sikkim: The role of environmental consciousness and attitude. Cogent Food Agric. 2023, 9, 2261212. [Google Scholar] [CrossRef]
  52. Fleming, A.; Vanclay, F. Farmer responses to climate change and sustainable agriculture. A review. Agron. Sustain. Dev. 2010, 30, 11–19. [Google Scholar] [CrossRef]
  53. D’Amico, M.E.; Pintaldi, E.; Sapino, E.; Quaglino, E.; Passarella, I.; Freppaz, M.; Navillod, E.; Rocco, R.; Casola, S. Carta dei Suoli della Valle d’Aosta: Note illustrative; Assessorato Opere Pubbliche Valle d’Aosta: Aosta, Italy, 2019. [Google Scholar]
  54. Alcon, F.; Tapsuwan, S.; Brouwer, R.; Yunes, M.; Mounzer, O.; de-Miguel, M.D. Modelling farmer choices for water security measures in the Litani river basin in Lebanon. Sci. Total Environ. 2019, 647, 37–46. [Google Scholar] [CrossRef]
  55. Doherty, E.; Mellett, S.; Norton, D.; McDermott TK, J.; Hora, D.O.; Ryan, M. A discrete choice experiment exploring farmer preferences for insurance against extreme weather events. J. Environ. Manag. 2021, 290, 112607. [Google Scholar] [CrossRef]
  56. Šumrada, T.; Japelj, A.; Verbič, M.; Erjavec, E. Farmers’ preferences for result-based schemes for grassland conservation in Slovenia. J. Nat. Conserv. 2022, 66, 126143. [Google Scholar] [CrossRef]
  57. Xiuling, D.; Qian, L.; Lipeng, L.; Sarkar, A. The Impact of technical training on farmers adopting water-saving irrigation technology: An empirical evidence from China. Agriculture 2023, 13, 956. [Google Scholar] [CrossRef]
  58. Zoundji, G.C.; Okry, F.; Van Mele, P.; Bentley, J.W.; Kwame Sackey, C. The potential of farmer training video for supporting agroecological vegetable production in Benin. Cogent Food Agric. 2024, 10, 2358607. [Google Scholar] [CrossRef]
  59. Zhang, Z.P.; Hua, B.; Liu, J.X.; Dai, H.B.; Miao, M.M. University MOOC should be added with farmer interested sections and provide individualized service to adapt to farmer training. PLoS ONE 2023, 18, e0288309. [Google Scholar] [CrossRef]
  60. Grigorieva, E.; Livenets, A.; Stelmakh, E. Adaptation of Agriculture to Climate Change: A Scoping Review. Climate 2023, 11, 202. [Google Scholar] [CrossRef]
  61. Alzahrani, K.; Ali, M.; Azeem, M.I.; Alotaibi, B.A. Efficacy of Public Extension and Advisory Services for Sustainable Rice Production. Agriculture 2023, 13, 1062. [Google Scholar] [CrossRef]
  62. Santini, A.; Di Fonzo, A.; Giampietri, E.; Martelli, A.; Cimino, O.; Dalla Marta, A.; Annosi, M.C.; Blanco-Velázquez, F.J.; Del Giudice, T.; Altobelli, F. A Step toward Water Use Sustainability: Implementing a Business Model Canvas for Irrigation Advisory Services. Agriculture 2023, 13, 1081. [Google Scholar] [CrossRef]
  63. Parikoglou, I.; Emvalomatis, G.; Thorne, F.; Wallace, M. Farm Advisory Services and total factor productivity growth in the Irish dairy sector. Eur. Rev. Agric. Econ. 2023, 50, 655–682. [Google Scholar] [CrossRef]
  64. Ingram, J.; Mills, J. Are advisory services “fit for purpose” to support sustainable soil management? An assessment of advice in Europe. Soil Use Manag. 2019, 35, 21–31. [Google Scholar] [CrossRef]
Figure 1. Aosta Valley region [31,32].
Figure 1. Aosta Valley region [31,32].
Water 16 02412 g001
Figure 2. Example choice set.
Figure 2. Example choice set.
Water 16 02412 g002
Figure 3. Descriptive statistics of respondents’ characteristics.
Figure 3. Descriptive statistics of respondents’ characteristics.
Water 16 02412 g003
Figure 4. A findings–response model of farmers’ willingness to reduce their irrigation water use by 10% as the compensation increases.
Figure 4. A findings–response model of farmers’ willingness to reduce their irrigation water use by 10% as the compensation increases.
Water 16 02412 g004
Table 1. Identification of attributes and levels.
Table 1. Identification of attributes and levels.
AttributesDescriptionLevels
Advisory ServiceAdoption of regional advisory service guidelines for a specified period5 years;
1 year;
No service
Water ReductionReduction in total irrigation water usage20%;
10%;
No reduction
Decrease in
Irrigated land
Reduction in the percentage of irrigated area by excluding specific plots5%;
2%;
No reduction
Monetary
Compensation
Monetary compensation for implementing the measureEUR 100 ha−1 y−1;
EUR 120 ha−1 y−1;
EUR 0 ha−1 y−1
Table 2. Farms’ descriptive data.
Table 2. Farms’ descriptive data.
Main
Production
FrequencyMean UAA *
(SD)
Mean Irrigated UAA *
(SD)
Fruit123.4 ha
(2.5)
2.4 ha
(1.6)
Wine99.7 ha
(5.1)
8.2 ha
(4.3)
Vegetable64.2 ha
(3.4)
3.4 ha
(3.6)
Livestock7343.7 ha
(61.8)
19.2 ha
(14.7)
Total10033.4 ha
(55.3)
15.2 ha
(14.5)
Note: * Utilized agricultural area.
Table 3. Reasons associated with the choice of the status quo option.
Table 3. Reasons associated with the choice of the status quo option.
Frequency n.Frequency %
They are already working toward water optimization2128
The economic support does not cover the damages1723
Issues related to the installation of hydropower stations68
Concerns regarding the impacts of climate change68
The soil characteristics do not allow for reduction45
No response was provided2128
Table 4. Mixed logit estimates.
Table 4. Mixed logit estimates.
AttributeCoef.Std.errp-ValueMarginal WTA (EUR y−1)
Advisory service0.2280.2600.3807.23
Water reduction−1.150 ***0.4310.008−36.39
Decrease in irrigated lands0.1320.1740.4504.34
Monetary compensation0.032 **0.0150.041
Note: (** p < 0.05; *** p < 0.01).
Table 5. Robust regression results.
Table 5. Robust regression results.
Coef.Std.errp-ValueMarginal WTA (EUR y−1)
  Age (years)0.453 ***0.1580.005
  Education
    Degree0 0.56
    Diploma12.584 **5.7660.03213.14
    Secondary school14.696 **6.3570.02315.25
    Primary school10.19911.1060.36110.76
  Main production
    Livestock0 22.78
    Fruit−21.794 ***6.1560.0010.99
    Vegetable−10,4808.1560.20212.30
    Wine−19.144 *10.9600.0843.64
  Irrigation technique
    Sprinkler irrigation0 8.12
    Drip irrigation20.372 *10.8080.06328.50
    Flooding−5.8496.1560.3452.27
    Association−7.308 *4.3450.0960.82
  Constant11.6087.0950.105
Note: (* p < 0.10; ** p < 0.05; *** p < 0.01).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Moino, F.; Caracciolo, F.; Borsotto, P.; Trione, S.; Chabloz, D.; Bassignana, M.; del Giudice, T.; Altobelli, F. At What Price Are Farmers Willing to Reduce Water Usage? Insights from the Aosta Valley. Water 2024, 16, 2412. https://doi.org/10.3390/w16172412

AMA Style

Moino F, Caracciolo F, Borsotto P, Trione S, Chabloz D, Bassignana M, del Giudice T, Altobelli F. At What Price Are Farmers Willing to Reduce Water Usage? Insights from the Aosta Valley. Water. 2024; 16(17):2412. https://doi.org/10.3390/w16172412

Chicago/Turabian Style

Moino, Francesca, Francesco Caracciolo, Patrizia Borsotto, Stefano Trione, Denise Chabloz, Mauro Bassignana, Teresa del Giudice, and Filiberto Altobelli. 2024. "At What Price Are Farmers Willing to Reduce Water Usage? Insights from the Aosta Valley" Water 16, no. 17: 2412. https://doi.org/10.3390/w16172412

APA Style

Moino, F., Caracciolo, F., Borsotto, P., Trione, S., Chabloz, D., Bassignana, M., del Giudice, T., & Altobelli, F. (2024). At What Price Are Farmers Willing to Reduce Water Usage? Insights from the Aosta Valley. Water, 16(17), 2412. https://doi.org/10.3390/w16172412

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop