How do farmers' perceptions and attitudes toward agricultural water consumption behaviors can lead to unsustainability; evidence from Mahabad plain, Lake Urmia, Iran

How do farmers' perceptions and attitudes toward agricultural water consumption behaviors can lead to unsustainability; evidence from Mahabad plain, Lake Urmia, Iran | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article How do farmers' perceptions and attitudes toward agricultural water consumption behaviors can lead to unsustainability; evidence from Mahabad plain, Lake Urmia, Iran Hamid Farahmand, Massoud Tajrishy, Mohammad Taghi Isaai, Mohammad Ghoreishi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2478328/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract There has been much attention paid to Lake Urmia's catastrophic desiccation by researchers and the government. An in-depth semi-structured interview and thematic analysis were used in this study to examine irrigation behavior and crop type selection decisions. 73% of farmers believe that there is no need to reduce their water consumption, 87% do not look for rain forecasts since they regard the government as responsible for water supply or have very few crop alternatives to choose from. In choosing the type of product, 77% only consider economics and do not consider environmental objectives, and 71% do not think drought conditions affect irrigation decisions. Educating farmers and increasing their collaboration role are therefore necessary. Therefore, these variables are the basis for extending psychological theories such as TPB to predict farmers' behavior to a much greater extent. While this study focused on one region, its findings are applicable to similar circumstances worldwide. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Lake Urmia, located in northwestern Iran, is one of the largest hypersaline lakes in the world, facing a severe drying crisis in recent years. This drying process will have serious implications for the environment, social, economic, and health services (AghaKouchak et al., 2015; Shadkam et al., 2016). The increase in water consumption due to non-environmental activities such as illegal over-extraction of groundwater resources, increased withdrawals of surface water from reservoirs and excessive consumption of surface water resources for irrigation in the basin as a result of unsustainable agricultural development are among the main causes of the catastrophe (Chaudhari et al., 2018; Gavahi et al., 2019; Khazaei et al., 2019; Nikraftar & Azizi, 2015; Saed et al., 2018; Shirmohammadi et al., 2020). To restore the lake's ecological water level, the government in Iran established the Urmia Lake Restoration National Committee (ULRNC), and launched the Urmia Lake Restoration Program (ULRP) in 2013. The program is expected to reach its target within 10 years. The most basic part of the program is to change cultivation patterns in favor of crops consuming less water. The plan is the most critical part of the ULRP as the agricultural sector consumes almost 90% of water resources (Tatar et al., 2019). The ULRP has been successful in stabilizing the lake's water level, but poor management in the agricultural sector could compromise the lake's sustainability (Parsinejad et al., 2022). Water consumption amount is the direct outcome of decisions made by farmers (Mancha & Yoder, 2015). Therefore, their behavior needs to be studied to see if farmers' water-saving behavior has improved (Abadi, 2019; Shafiei & Maleksaeidi, 2020). An answer to this question may shed light on the question of whether or not the lake can be sustainably restored. In the Urmia lake basin, numerous studies have been conducted on the behavior of humans regarding water consumption (Anbari et al., 2021; Mahdavi, 2021; Pouladi et al., 2019, 2021; Sadeghi et al., 2020; Shojaei‐Miandoragh et al., 2020). The purpose of these studies was to identify human factors contributing to Lake Urmia's dryness and to suggest solutions to resolve the problem. Global environmental threats are attributed to unsustainable human behavior (Vlek & Steg, 2007). Human behavior is determined by perception rather than reality (Arbuckle et al., 2013). Human perception of the water value and its relationship to consumption, as well as other water-related behaviors such as water conservation, are essential to saving water (Batra, 2019). Human behavior studies have become an essential part of water management to better address environmental challenges. Frameworks such as Social Ecological Systems (SES) (Ostrom, 2007; Poteete et al., 2010), and research areas such as socio-hydrology (Sivapalan et al., 2012) aim to investigate the interactions and feedbacks between human and natural elements within a system. The purpose of behavior studies is to better manage anthropogenic activities such as land use change and overexploitation of water resources. Anthropogenic activities can exacerbate the effects of climate change, such as a regional drought called anthropogenic drought (AghaKouchak et al., 2015; Mehran et al., 2017). Water-related values and attributes (e.g. environmental impacts and social norms) help policy makers to intervene in consumers' water consumption behavior (Etale et al., 2018). Given the complexity of human behavior and the non-linear interactions between system elements (Cumming & Peterson, 2017; Rocha et al., 2018), any institutional and policy change without encouraging individuals to adopt pro-environmental behaviors may fail to reduce environmental threats (Clayton & Brook, 2005; Winter et al., 2011); in other words “a good development policy is a good adaptation policy” (De la Torre et al., 2009). This is the case in Lake Urmia which has gained lots of attention due to its severe environmental crisis. Understanding human behaviors is the basis for water management programs such as encouraging pro-environmental behaviors (Yazdanpanah, Hayati, Hochrainer-Stigler, et al., 2014; Yazdanpanah, Hayati, Thompson, et al., 2014). Most behavioral studies use common and well-known social science theories without sufficient caution, since choosing an appropriate theory is very difficult and concepts are overlapping in these studies (Michie et al., 2005). Among current theories, the Theory of Planned Behavior (TPB) (Ajzen, 1985) is the most widely accepted theory in environmental studies (Kwon & Silva, 2020; Rahimi-Feyzabad et al., 2020) along with other less common theories like evolutionary theory (Hamilton, 1964), prospect theory (Kahneman & Tversky, 2018), Theory of Reasoned Action (TRA) (Hill et al., 1977), and value-belief-norm (VBN) theory (Stern et al., 1999). Base on literature, TPB-based models are used to study certain behaviors, such as transition from traditional to pressurized irrigation (Castillo et al., 2021), the acceptance of ULRP water policy plans (Mahdavi, 2021), the adoption of Conservation Agriculture (Lalani et al., 2016), water saving behavior (Yazdanpanah, Hayati, Hochrainer-Stigler, et al., 2014) as well as the adoption of environmental management accounting practices (Tashakor et al., 2019). A common limitation of all models is their inability to record large variations in behavior. This fact emerges from some studies showing that only a small number of behavioral variations can be explained (Azadi et al., 2019; Mahdavi, 2021; Savari et al., 2021; Schuitema et al., 2020; Tajeri moghadam et al., 2020; Valizadeh et al., 2020; Zhang et al., 2020). This fact requires the discovery of other factors that influence behavior. There are some limitations to TPB. The TPB should be used to study explicit behavior of interest (Ajzen, 2020), and is unable to predict general behaviors. Furthermore, it introduces perceived behavioral control instead of analyzing actual behavioral control resulting in poor understanding of various internal (e.g., skills and knowledge) and external factors (e.g., money and equipment) that shape behavior (Ajzen, 2020). Some researchers attempt to extend existing theories to describe more variations in farmers' behavior (Azadi et al., 2019; Mohammadinezhad & Ahmadvand, 2020; Rahimi-Feyzabad et al., 2020; Savari et al., 2021; Trautwein et al., 2021; Yazdanpanah, Hayati, Hochrainer-Stigler, et al., 2014) in the sense that they seek more plausible explanations for human behavior (Ghoreishi, Razavi, et al., 2021; Ghoreishi, Sheikholeslami, et al., 2021; Sniehotta et al., 2014). The extension of theories includes certain constructs such as emotions, social norms, knowledge, moral norms or the perception of risk. This fact makes the theories unsuitable for nudging (Marteau et al., 2011). Effective interventions to address the difficulty of changing consumer social behavior require a thorough understanding and analysis of psychological mechanisms and perceptions (Cauberghe et al., 2021). In other words, these theories should be used to study the relationship between a pre-defined factor and a behavior. They should not be used for behavioral modeling or strategy creation (Kwon & Silva, 2020). Despite the growing trend in the studies of psychological behavior, most studies have focused only on socioeconomic factors and poorly measured the psychological variables in the models (Foguesatto et al., 2020). Perception of farmers as a psychological factor has always received little attention. Therefore, there is a significant gap in studying the psychological factors of farmer behavior, because there is very little research in this area (Foguesatto et al., 2020). In this study, we seek to identify psychological variables that influence farmers' choice of crop and irrigation, so that in future studies it can be used as a basis to include important variables in the extension of well-known psychological theories such as TPB. The behavior is related to water consumption from both surface water and groundwater resources. This study is concerned with two behaviors: 1- determining the type of crop or tree to plant on a farm, and 2- determining irrigation time. These two behaviors are selected since they both significantly affect a farmer's water consumption. Behaviors are examined to understand the factors influencing farmers' perceptions of the value of water, the associated environmental crisis and their goal to adopt more sustainable agricultural practices. We use semi-structured interviews as a data collection method, and thematic analysis to explore and evaluate behavioral factors. A particular focus of this study is on farmers' perceptions, values, and concerns regarding water conservation behaviors in the Mahabad plains, a region located in the northwestern part of Iran. Study Area The Mahabd Plain is located southwest of Lake Urmia, Iran (Figure 1). The area is made up of 21 villages with a total farming population of around 10,000 (Valizadeh et al., 2020). The agricultural sector accounts for more than 90% of total water use (Madani, 2014), and is the primary source of income in this area. The agricultural area is about 12,000 hectares, of which 94% are apple, wheat, alfalfa and sugar beet plantations. Through the Mahabad irrigation canal, these areas depend on water supplied by the Mahabad Dam, having a storage capacity of 200 million cubic meters. Studied area is an important part of the Lake Urmia Basin because 1) a major river that flows into Lake Urmia is Mahabad River 2) there is no diversity of cultural values in Mahabad villages because the residents are mostly Kurd. Consequently, the behavioral complexities in this area are less, decreasing uncertainties of behavioral studies 3) Further, in this region, nudge theory-based behavior intervention plans can be employed. Methodology 4.1 Sampling and data collection Because psychological variables are hidden, they cannot be measured with a single statement (e.g. a question on a questionnaire) (Foguesatto et al., 2020). Therefore, other data collection methods should be used. Interview, allows a researcher to focus on the critical or relevant perspective of the respondent, which may not be taken into account in the study (Young et al., 2018). For complex behaviors, interviewing is preferred to other methods of filling the knowledge gap (Minichiello et al., 2008). Interviews fall into three categories: structured, unstructured, and semi-structured. A structured interview allows comparison of different results, but not the discovery of hidden ones (Punch, 2013). An unstructured interview allows for detailed analysis, but makes it difficult to analyze and compare data (Bryman, 2016). A semi-structured interview is preferred which avoids researcher bias, provides in-depth analysis, and produces comparable results (Hay, 2000). Semi-structured interviews provide the analytical flexibility that is vital to examine complex issues (Rose et al., 2018; Young et al., 2018). Very few studies have used semi-structured interviews to assess improvement in farmers' understanding of values and beliefs associated with water conservation behaviors (Young et al., 2018). The semi-structured interview can successfully generate the psychological variables needed for other modeling methods such as Structural Equation Modeling (SEM). To examine causal relations and influential factors in farmers' water-use decisions in the city of Mahabad, two researchers designed and conducted a semi-structured interview. In each of the eight regions, one or two villages were chosen to carry out the survey. Stratified random sampling method was used in each village. We continued our interviews until we reached a saturation point (i.e. new interviews did not result in significant results regarding the behavior factors affecting farmers' water consumption decisions). Besides, for the quantitative analysis, the total number of interviews exceeded the sample size estimated by Cochran's sampling formula, which was set at 140 farmers at the 95% confidence level (Cochran, 1977). Thus, we conducted 148 interviews in eight villages. The number of interviews in each village and the associated map are shown in Figure 2 and Table 1. Table 1 – Name of villages and number of interviews in each Village name Interview count Village name Interview count Yousefkandi 21 Khorkhoreh 5 QomQale 26 Qare qishlaq 24 Laj 20 Kik Abad and Gabazaleh 10 Agriqash 8 Qezelqopi 32 To enhance the clarity and intelligibility of the questions, we conducted two pilot surveys. After each pilot survey, we analyzed the content of the farmers' responses and their willingness to participate in the survey to improve the interview (see the final interview questions in Appendix A). The analysis and its modifications were carried out under the supervision of experts in the field of social and agricultural sciences. We have divided the final interview questions into three main categories, which are listed below: Farmers and their farm areas: these questions were used to start and warm up the interview. Farmers’ decision making on water use: These questions were designed to investigate the influential factors in farmers’ decision making on water use. Precipitation prediction, neighbors’ behaviors, and plants’ water demand were specifically the subjects of questions. However, farmers were free to talk about other factors. The responses to these questions are analyzed to reveal how farmers perceive the importance of water use for irrigation and how they affect their water consumption behavior. Farmers’ decision making on crop type: These questions were designed to investigate water-related factors in farmers’ decision on crop type. The responses to the questions are analyzed to reveal how farmers perceive water-related challenges and how that perception affects farmers’ crop type decisions. To conduct this interview, we divided our case study into eight regions, each represented by a village. To divide this area, the following criteria were taken into account: Accessibility to a surface irrigation canal (Figure 3a): Since there is no complete control over farmers' water consumption from the irrigation canal, upstream farmers can use water more than others. The case study was split in order to sample different water resource options. Crop types: As the upstream farms have easy access to the irrigation canal, they mainly cultivate orchard trees, unlike the farmers downstream (Figure 3b). The case study was divided to include all major crop types, including apple orchard, wheat, sugarbeet, and alfalfa. Groundwater table: Downstream farmers can access groundwater more easily than upstream farmers due to the depth of the groundwater table (Figure 3c). The case study was divided to include different groundwater table depths from 2 to 20 meters. 4.2 Data analysis We extracted the themes or patterns of the farmers’ responses from the interviews, using thematic analysis (Boyatzis, 1998). This thematic analysis is a well-known and flexible method to deal with the complexity of qualitative data (Braun & Clarke, 2006; Holloway & Todres, 2016). We began by transcribing the responses and reading the transcripts several times to familiarize ourselves with the data. This step is necessary to find patterns in responses. The texts were coded based on similar patterns that were extracted and categorized. Finally, we created a list of themes from the data in Excel. 4.3 Ethics statement This study involved human participants in accordance with the Helsinki Declaration of 1964 and its amendments. Prior to the interview, farmers provided written informed consent. The study informed respondents that participation was voluntary, their identities would remain confidential, and they could discontinue participation at any time. 4.4 Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Results And Analysis 5.1 Socio-economic characteristics of farmers A total of 148 farmers were interviewed for this study. All interviewees were men as women do not work outside the home due to their common culture. Table 2 shows the age distribution of the respondents. The farmers were on average 49.8 years old, with the majority (almost 61%) being between 40 and 50 years of age. Table 2 – frequency distribution of age of respondents Age Percent of total respondents 30-35 2.7 35-40 8.1 40-45 32.4 45-50 29.1 50-55 18.9 >55 8.8 Of the 148 respondents, 34% had orchard farms, 30% had crop farms, and 36% had mixes of orchard and crop farms. The diversity of the types of orchard trees and crops is shown in Figure 4. Apple tree presents in all investigated orchards. It takes 15 and 3 years for an apple and a peach tree respectively to become commercially beneficiary. Thus, peach trees are planted between apple trees to provide source of income for farmers in short time. In this way, farmers can have enough income to live on and keep investing in building apple orchards, which is why some farmers combine apple and peach fields. The average and standard deviation of area are 1.7 ha and 3.4 ha for crop farms and 3.4 ha and 5.9 ha for orchard trees, respectively. 5.2 Irrigation behavior 5.2.1 Irrigation decision criteria We extracted four criteria from farmers' responses to the question "How do you know when to start watering?" to examine their decisions about irrigation. The following criteria emerged: Fresh leaves: Farmers usually look at the leaves, and check them for freshness. The farmers decide to irrigate their fields if they feel that the leaves are not fresh enough. In other words, the freshness of the plant leaves is assessed using visual inspection. When farmers visually notice that plant leaves are not fresh, they irrigate crops or orchard trees with surface water. If there is no access to surface water, they use groundwater. Soil moisture: Farmers monitor soil moisture visually. If cracks are found in the soil, they dig a small hole to visually check the soil condition. They irrigate their crops with groundwater as there is no surface water. Fresh grass: Farmers check the grass condition under trees. If they find that the grass is drying out, they start irrigating with surface or groundwater. Fixed schedule: Regardless of all other factors, farmers have a fixed watering schedule. According to the farmers' responses, before the Urmia Lake Restoration Program in 2015, there was always enough water in the irrigation canals. This allowed them to easily open the entrance gates of their farms for irrigation. However, since the beginning of this program, the surface water supply from the Mahabd dam to the irrigation canal has decreased by about 40%. Therefore, the Urmia Lake Restoration Program can be an important factor in explaining groundwater use by farmers. Under the program, surface water is distributed to farmers, and they have access to water once a month. Based on our interviews, farmers fall into one or two of the above categories. For example, farmers can visually check both leaf freshness and soil moisture to make a watering decision. Figure 5 shows the frequency distribution of the criteria. As shown in Figure 5, approximately 71.5% of farmers use "fresh leaf" as either the only or one of their factors for deciding whether to irrigate. 21% of farmers use "fresh leaf" and "soil dryness” as the factors in making their decision. In addition to "fresh leaf”, the use of “Soil dryness " can help farmers make irrigation decisions because leaf freshness decreases dramatically around midday, which can lead to wrong irrigation decisions. To measure leaf freshness, farmers visually check leaf characteristics such as greenness, crispness, limpness, edge shape, and softness. The data is used as input to their decision-making algorithm, which is made up of past experiences, common sense, technical knowledge, cultural beliefs, and the impact of neighbors' decisions. In other words, farmers use mental shortcuts to determine irrigation time, which is common among farmers even in developed countries such as USA, where almost 75% of farmers use rules-of-thumb (like visual observation and “when neighbors begin to irrigate”) (NASS, 2017). This can lead to different irrigation behaviors among them. Mental shortcuts can reveal how farmers are moving toward a reduction in water consumption. To analyze farmers’ decision on irrigation behavior, this study looks at two distinct definitions of deficit irrigation; technical deficit irrigation and behavioral deficit irrigation. Technical deficit irrigation has a well-known definition in which irrigation is less than the total water requirement of a plant and the amount of irrigation is done based on patterns (Attia et al., 2021; Khapte et al., 2019). Behavioral deficit irrigation is defined, in this study, as a subjective analysis in a farmer's mind, and relates to the farmer's decision regarding irrigation time. Upon inferring that the freshness of the plants is insufficient, the farmer starts irrigation, so deficit irrigation is not considered. Deficit irrigation is assumed to be an effective strategy for environmental conservation behavior (Montazar, 2021; UT et al., 2020), and can increase water productivity as well as farmers’ profit (Du et al., 2015; Fereres & Soriano, 2007; Karandish, 2021). While the extent of technical deficit irrigation can be assessed to find out possible options for water conservation, it can be concluded that farmers do not incorporate deficit irrigation into their decision making process. If a farmer wanted to apply deficit irrigation in his decision algorithm, he would respond with a sentence such as "when I see insufficient plant freshness, I wait a few days before starting irrigation to apply deficit irrigation". This could be the subject of another study to find out why farmers do not allow deficit irrigation. About 11% of farmers use a fixed irrigation schedule. This fixed schedule implies that farmers do not follow the daily temperature variations that directly affect plant evapotranspiration, crops water needs and yields. Figure 6 shows the time series of total water and agriculture release from the Mahabd dam. Total water release indicates the water volume released for agricultural, industrial, rural and environmental purposes. The total amount of water released has not decreased since 2015, but the amount released for agriculture has decreased by almost 29%. This is due to the start of the Water Cap project to reduce agricultural consumption by 40% during the Urmia Lake Restoration Program in 2015. To compensate for the reduction of available surface water, farmers have been using groundwater, which may explain the dramatic increase in groundwater use as well as the number of wells. The reduction project did not bring any changes to farmers' irrigation criteria, probably due to the ease of access to shallow groundwater (Khalaj et al., 2019; Valizadegan & Yazdanpanah, 2018). In other words, it seems farmers have not adapted to reduce water consumption. 5.2.2 Interactions among farmers The farmer's answer to the question "If some of your neighbors start watering, will you start watering?" can show whether farmers' decisions are influenced by their neighbors or whether they are learning from their neighbors. Figure 7 shows the frequency distribution of farmers’ responses. Around 87% of farmers answered the question with "No". Lack of cooperative behavior among farmers can lead to unsustainable groundwater consumption, which can lead to increased competitive behavior and public tragedies in the future. Therefore, their cooperative behavior should be improved for future drought scenarios. 5.2.3 Forecasting future precipitation In response to the question "Do you predict or look for precipitation forecasts for the next year?", the farmers replied "yes" or "no". Figure 8 shows the frequency distribution of answers to this question. Nearly 87% of farmers do not forecast rain or look for rain forecasts. Surface water is supplied by the Mahabd dam which is filled with runoff from upstream precipitation. Groundwater is recharged by river flows, precipitation and irrigation seepage. Water is crucial to agricultural production, so it is expected that farmers have taken into account the precipitation forecast. Farmers do not look for data that can be attributed to water governance and technical knowledge. There are some reasons for the behavior. Irrigation water strongly influences agriculture both on crop farms and orchard trees. Crop choice is an annual decision, and farmers have to choose a crop each year. Orchard growers do not make an annual decision, but need a production estimate to decide on production plans. These decisions are based on watering volume. Farmers were expected to follow precipitation forecast sources. Interviews revealed, however, that most farmers do not rely on precipitation forecasts. There are some possible explanations for this behavior. Farmers in the Mahabad plain have access to reliable and scheduled water, including surface and underground water. During drought periods, a reliable source of groundwater replaced the deficit in surface water. In addition, surface water is provided by the government in a top-down structure which ignores the participatory role of farmers. Continuity of the process over the decades after the construction of Mahabad Dam led farmers to assume the government as the sole agent responsible for water supply. Thus, farmers do not see a direct relationship between precipitation distribution and their agricultural activities and incomes, so they ignore precipitation forecasts. If crop farmers do not have access to alternative crops, they are not in a decision-making position, and will not take into account decision parameters such as rainfall. Lack of sufficient and up-to-date technical knowledge can prevent farmers from changing their crop types. In this context, farmers' knowledge and experience were analyzed based on their responses to the question "How many other crops have you grown in recent years?" (Figure 9). The figure shows the diversity of crops that farmers are able to cultivate. To improve soil properties and reduce weed, disease and pest pressure, farmers have to practice crop rotation (Kremen et al., 2012). Therefore, those 86% of farmers who grow fewer than four different crops do not have many options to choose from each year. These farmers do not look for precipitation forecast data since it is of no use to them. Moreover, low crop diversity limits farmers' ability to adapt to hard water resource availability. Lack of agricultural knowledge or lack of financial sources may be the causes of low crop diversity. 5.3 Perception of farmers about water value in agriculture 5.3.1 Attitudes toward future Farmers’ response to the question "If you have a new farm, which factors will you consider when choosing a crop or orchard?" will reveal the most significant influencing factors involved in their decision to choose a crop. The answer indicates whether water consumption is a significant factor or not. The content of the responses is as follows: I look for a lot of income, so I will choose between high value crops and orchard trees. I look for crops or orchard trees that are financially and technically supported by the government. I invested in an apple orchard, so I will continue to cultivate it. I look for plants that are easy to cultivate. No, I am not looking for other crops. I follow other farmers in making decisions. I choose crops that consume less water. The response frequency distribution is shown in Figure 10. Since some farmers selected more than one factor, the sum of the factor frequencies exceeds 100%. The factors are: High income plants: Farmers choose the crop type or orchard tree that has economic value, and can generate high income for the farmer. Financial and technical support: Farmers look for a crop or orchard that receives technical and financial support from government agencies. Pursue investment in the apple orchard: Farmers definitely choose the apple tree. Low-difficulty cultivation: Farmers look for a type of crop or fruit that does not require much effort, such as deep plowing. Follow others: Farmers try to choose what other farmers usually choose. Plants with low water consumption: Farmers are concerned about water consumption and look for a type of crop or orchard with low needs for water. In accordance with respondents' responses, environmental crises or water-related issues have the least impact on farmers' decisions because they are motivated by economic gain. Analysis of the responses can reveal possible future plans for farmers, especially those facing an economic downturn in apple orchards. Having a long history of producing apples in the region, the apple orchard corresponds with the farmers' responses because it generates the highest income (Panah et al., 2018), and it allows them to access experience and technical support. Therefore, it is to be assumed that farmers have a high tendency to cultivate apple orchards. When a farmer decides to plant a field with an apple orchard, he invests about 15 years of his money and time to make the orchard profitable. Due to the high water use of apple trees, the percentage of farmers choosing to plant or replant a garden with apple orchards can cause a crisis of environmental overconsumption for decades. This means that water consumption in the region is expected to remain high. On the contrary, there is an opportunity hidden in their response themes. The government can boost farmer productivity by offering incentives and promoting crops with low water needs, high incomes, and technical support, like the pistachio tree, which is a little-known crop. Apart from financial support, which is a challenge for the government due to difficulties, it can provide technical support and introduce the pistachio as a high-income product. If the farmers’ attitude about the priority of water consumption changes the most sustainable environmental conditions will not happen. This change requires careful intervention based on social studies. For example, it is possible to alter farmers’ perception about the high income of apple orchards if all financial and health consequences of Lake Urmia desiccation and its main causes (Schmidt et al., 2021) are properly presented to them and incorporated into their economic decisions. 5.3.2 Self-evaluation of water consumption behavior Analysis of farmers' responses to the question "To what extent do you think water resources are available compared to the previous question?" revealed the following themes: Excessive water resources: Farmers believe that water resources are plentiful, and they do not need to include the status of water resources in their decision-making process. Behavior with low water consumption: Farmers think that they are consuming little water. No other option: Farmers believe that they are in a forced situation, and cannot behave in another way to take into account the water resources. Other technologies will solve the problem: Farmers think that other solutions such as drip irrigation will solve the water crisis, and they have no action to take. Farmers' perceptions of water scarcity can steer their actions towards more sustainable water management solutions (Fan et al., 2019). Community participation in water management can also be compromised if it is believed to have excessive water resources. Other studies indicate that drought periods and frequencies have increased and will increase in the future (Mirgol et al., 2021; Sobhani et al., 2019). In contrast, more than half of farmers feel that there are too many water resources in the basin. this means they do not consider it necessary to take water scarcity into account in their decisions. It's an alarm that needs a lot of attention to understand why some obvious and vague problems like the shrinking of Lake Urmia have not yet forced farmers to change their attitude to "having too much water". Much more dangerous is the attitude of those 18.8% of farmers who believe that they have already reduced their water consumption. Since farms are dominated by rudimentary technologies and farmers' knowledge of water management is low due to the lack of an integrated training program, they are likely to have a misconception about "low water consumption", which may lead them to take no further responsibility for water level reduction schemes. This attitude makes farmers do nothing even if the lake disappears from the earth. In other words, they believe that they have taken all the necessary actions to reduce water consumption. They are likely to blame the government or other farmers for environmental problems as well as their economic consequences. Therefore, this situation can lead to violent protests and security problems. There is a common misconception among people that technology is a perfect solution that can solve all water-related problems (De Châtel, 2007). Although only a limited number of farmers in Mahabad agree with this attitude, it can increase if serious environmental consequences directly affect their lives. Conclusion This study aimed to determine the water consumption behavior of farmers in Mahabad by discovering and assessing influential factors in crop choice and irrigation scheduling decisions. We found that reduction in water consumption plays no role in farmers’ irrigation scheduling. There is almost no interaction among them, and they do not look for precipitation forecast data to make decisions about annual crops. In addition, financial factors such as income and investments play a major role in the decisions of farmers, and there is no concern about reducing water consumption in their beliefs. They, also, do not consider themselves an influential factor when it comes to moving toward sustainable water management. This study showed a fatal gap in any program like ULRP that targets to save Lake Urmia; thus, to achieve a successful program for restoring the lake, we need to focus on farmers behaviors and perceptions. Future interventions should aim to emphasize importance of the farmers’ roles and perceptions. This will lead to change farmers’ beliefs to feel more responsible about environment protection. For example, targeted information campaigns can be suggested to achieve this goal. Moreover, workshops may have a contributed effect to supply farmers the required agricultural knowledge. Appendix A The final interview questions are: What are the current types of crops on your farm? How many crops have you grown in the past years? If you have orchards, how old are the trees? How do you know when to start irrigation? How do you react when one of your neighbors is irrigating his crops? Do you predict or look for prediction of the precipitation for next year? If you have a new farm, what factors influence your choice about the type of crop or orchard tree? Given the previous question, to what extent do you consider the available water resources? Declarations CRediT authorship contribution statemen Hamid Farahmand : Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing - original draft. Masoud Tajrishy : Supervision, Writing - editing. Mohammad Taghi Isaai : Supervision, Conceptualization. Mohammad Ghoreishi : Formal analysis, Writing - review & editing. Mohammadreza Mohammadi : Data curation, Investigation, Data visualization. References Abadi, B. (2019). How agriculture contributes to reviving the endangered ecosystem of Lake Urmia? The case of agricultural systems in northwestern Iran. Journal of Environmental Management, 236 , 54–67. AghaKouchak, A., Norouzi, H., Madani, K., Mirchi, A., Azarderakhsh, M., Nazemi, A., Nasrollahi, N., Farahmand, A., Mehran, A., & Hasanzadeh, E. (2015). 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16:07:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1204537,"visible":true,"origin":"","legend":"\u003cp\u003eSelected Villages for interview survey\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-2478328/v1/dcdcf1a1579e9c96e72969ea.png"},{"id":31760212,"identity":"edc56907-8487-4c16-b899-c031de1a74c6","added_by":"auto","created_at":"2023-01-18 16:15:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1900069,"visible":true,"origin":"","legend":"\u003cp\u003ea) accessibility of each village to surface water b) crop types in the study area c) the aquifer area in the study area\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-2478328/v1/cad2bc099290d15dd1fcdef9.png"},{"id":31758940,"identity":"aa8135b6-72b4-492a-9b72-cae5a5c33096","added_by":"auto","created_at":"2023-01-18 15:59:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":63008,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDiversity in types of a) orchard trees and b) crops of respondents\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-2478328/v1/307397851f7a0abe33bda81d.png"},{"id":31758943,"identity":"dcad1637-61eb-40fe-8bc9-256805f5346d","added_by":"auto","created_at":"2023-01-18 15:59:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":39541,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003epercentage frequency distribution for irrigation decision criteria\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-2478328/v1/04aca74e93cfc9950e872d58.png"},{"id":31758941,"identity":"2bd3268c-19bf-4324-b050-3e36bbd4c2f1","added_by":"auto","created_at":"2023-01-18 15:59:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":91948,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003etime series of total water and agriculture release from the Mahabd dam\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-2478328/v1/f6d9f6c11643c32575a50f38.png"},{"id":31757161,"identity":"d2f4b1a4-8ac5-4508-b2dd-d92336a11813","added_by":"auto","created_at":"2023-01-18 15:51:48","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":8528,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003epercent of farmers who start irrigation based on their neighbor’s irrigation\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-2478328/v1/1f0cc745a03de96e34350f32.png"},{"id":31758945,"identity":"abc3fc88-7f68-408a-9b5f-e3cf492db2d9","added_by":"auto","created_at":"2023-01-18 15:59:49","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":9214,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003epercent of farmers who look for precipitation forecast data\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-2478328/v1/7cec29a8539e2f4ab0b42941.png"},{"id":31757169,"identity":"b91cb507-f286-4c5d-b0e2-7bfc29607131","added_by":"auto","created_at":"2023-01-18 15:51:49","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":26172,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003epercentage frequency distribution for diversity of crops cultivated by farmers\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-2478328/v1/f9f27cd0a177edb11e2abb0e.png"},{"id":31758946,"identity":"46a63eb8-5176-4bd2-8368-92c3c4e817fb","added_by":"auto","created_at":"2023-01-18 15:59:49","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":44666,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003epercentage frequency distribution for crop choice criteria\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-2478328/v1/33f44699bba87701f3bcef6e.png"},{"id":31760351,"identity":"c2aa43aa-7b7b-4f44-9248-a8e8b88c45b4","added_by":"auto","created_at":"2023-01-18 16:23:49","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":41832,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003epercentage frequency distribution for self-evaluation of water consumption behavior\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-2478328/v1/59ed6a6b085d23501330305c.png"},{"id":32668791,"identity":"16319e21-e13d-4249-a713-0f8586159df1","added_by":"auto","created_at":"2023-02-08 20:29:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4080100,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2478328/v1/2f9cc142-e2bc-4037-b681-16427a98529a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"How do farmers' perceptions and attitudes toward agricultural water consumption behaviors can lead to unsustainability; evidence from Mahabad plain, Lake Urmia, Iran","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLake Urmia, located in northwestern Iran, is one of the largest hypersaline lakes in the world, facing a severe drying crisis in recent years. This drying process will have serious implications for the environment, social, economic, and health services\u0026nbsp;(AghaKouchak et al., 2015; Shadkam et al., 2016). The increase in water consumption due to non-environmental activities such as illegal over-extraction of groundwater resources, increased withdrawals of surface water from reservoirs and excessive consumption of surface water resources for irrigation in the basin as a result of unsustainable agricultural development are among the main causes of the catastrophe\u0026nbsp;(Chaudhari et al., 2018; Gavahi et al., 2019; Khazaei et al., 2019; Nikraftar \u0026amp; Azizi, 2015; Saed et al., 2018; Shirmohammadi et al., 2020).\u0026nbsp;To restore the lake\u0026apos;s ecological water level, the government in Iran established the Urmia Lake Restoration National Committee (ULRNC), and launched the Urmia Lake Restoration Program (ULRP) in 2013. The program is expected to reach its target within 10 years. The most basic part of the program is to change cultivation patterns in favor of crops consuming less water. The plan is the most critical part of the ULRP as the agricultural sector consumes almost 90% of water resources (Tatar et al., 2019). The ULRP has been successful in stabilizing the lake\u0026apos;s water level, but poor management in the agricultural sector could compromise the lake\u0026apos;s sustainability (Parsinejad et al., 2022). Water consumption amount is the direct outcome of decisions made by farmers\u0026nbsp;(Mancha \u0026amp; Yoder, 2015). Therefore, their behavior needs to be studied to see if farmers\u0026apos; water-saving behavior has improved (Abadi, 2019; Shafiei \u0026amp; Maleksaeidi, 2020). An answer to this question may shed light on the question of whether or not the lake can be sustainably restored. In the Urmia lake basin, numerous studies have been conducted on the behavior of humans regarding water consumption (Anbari et al., 2021; Mahdavi, 2021; Pouladi et al., 2019, 2021; Sadeghi et al., 2020; Shojaei‐Miandoragh et al., 2020). The purpose of these studies was to identify human factors contributing to Lake Urmia\u0026apos;s dryness and to suggest solutions to resolve the problem.\u003c/p\u003e\n\u003cp\u003eGlobal environmental threats are attributed to unsustainable human behavior (Vlek \u0026amp; Steg, 2007). Human behavior is determined by perception rather than reality\u0026nbsp;(Arbuckle et al., 2013). Human perception of the water value and its relationship to consumption, as well as other water-related behaviors such as water conservation, are essential to saving water (Batra, 2019). Human behavior studies have become an essential part of water management to better address environmental challenges. Frameworks such as Social Ecological Systems (SES)\u0026nbsp;(Ostrom, 2007; Poteete et al., 2010), and research areas such as socio-hydrology\u0026nbsp;(Sivapalan et al., 2012)\u0026nbsp;aim to investigate the interactions and feedbacks between human and natural elements within a system.\u003c/p\u003e\n\u003cp\u003eThe purpose of behavior studies is to better manage anthropogenic activities such as land use change and overexploitation of water resources. Anthropogenic activities can exacerbate the effects of climate change, such as a regional drought called anthropogenic drought (AghaKouchak et al., 2015; Mehran et al., 2017). Water-related values and attributes (e.g. environmental impacts and social norms) help policy makers to intervene in consumers\u0026apos; water consumption behavior (Etale et al., 2018). Given the complexity of human behavior and the non-linear interactions between system elements\u0026nbsp;(Cumming \u0026amp; Peterson, 2017; Rocha et al., 2018), any institutional and policy change without encouraging individuals to adopt pro-environmental behaviors may fail to reduce environmental threats\u0026nbsp;(Clayton \u0026amp; Brook, 2005; Winter et al., 2011); in other words \u0026ldquo;a good development policy is a good adaptation policy\u0026rdquo;\u0026nbsp;(De la Torre et al., 2009). This is the case in Lake Urmia which has gained lots of attention due to its severe environmental crisis.\u003c/p\u003e\n\u003cp\u003eUnderstanding human behaviors is the basis for water management programs such as encouraging pro-environmental behaviors (Yazdanpanah, Hayati, Hochrainer-Stigler, et al., 2014; Yazdanpanah, Hayati, Thompson, et al., 2014). Most behavioral studies use common and well-known social science theories without sufficient caution, since choosing an appropriate theory is very difficult and concepts are overlapping in these studies (Michie et al., 2005). Among current theories, the Theory of Planned Behavior (TPB) (Ajzen, 1985) is the most widely accepted theory in environmental studies (Kwon \u0026amp; Silva, 2020; Rahimi-Feyzabad et al., 2020) along with other less common theories like evolutionary theory (Hamilton, 1964), prospect theory (Kahneman \u0026amp; Tversky, 2018), Theory of Reasoned Action (TRA) (Hill et al., 1977), and value-belief-norm (VBN) theory (Stern et al., 1999). Base on literature, TPB-based models are used to study certain behaviors, such as transition from traditional to pressurized irrigation (Castillo et al., 2021), the acceptance of ULRP water policy plans (Mahdavi, 2021), the adoption of Conservation Agriculture (Lalani et al., 2016), water saving behavior (Yazdanpanah, Hayati, Hochrainer-Stigler, et al., 2014) as well as the adoption of environmental management accounting practices (Tashakor et al., 2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA common limitation of all models is their inability to record large variations in behavior. This fact emerges from some studies showing that only a small number of behavioral variations can be explained\u0026nbsp;(Azadi et al., 2019; Mahdavi, 2021; Savari et al., 2021; Schuitema et al., 2020; Tajeri moghadam et al., 2020; Valizadeh et al., 2020; Zhang et al., 2020). This fact requires the discovery of other factors that influence behavior. There are some limitations to TPB. The TPB should be used to study explicit behavior of interest (Ajzen, 2020), and is unable to predict general behaviors. Furthermore, it introduces perceived behavioral control instead of analyzing actual behavioral control resulting in poor understanding of various internal (e.g., skills and knowledge) and external factors (e.g., money and equipment) that shape behavior (Ajzen, 2020).\u003c/p\u003e\n\u003cp\u003eSome researchers attempt to extend existing theories to describe more variations in farmers\u0026apos; behavior (Azadi et al., 2019; Mohammadinezhad \u0026amp; Ahmadvand, 2020; Rahimi-Feyzabad et al., 2020; Savari et al., 2021; Trautwein et al., 2021; Yazdanpanah, Hayati, Hochrainer-Stigler, et al., 2014) in the sense that they seek more plausible explanations for human behavior (Ghoreishi, Razavi, et al., 2021; Ghoreishi, Sheikholeslami, et al., 2021; Sniehotta et al., 2014). The extension of theories includes certain constructs such as emotions, social norms, knowledge, moral norms or the perception of risk. This fact makes the theories unsuitable for nudging (Marteau et al., 2011). Effective interventions to address the difficulty of changing consumer social behavior require a thorough understanding and analysis of psychological mechanisms and perceptions (Cauberghe et al., 2021). In other words, these theories should be used to study the relationship between a pre-defined factor and a behavior. They should not be used for behavioral modeling or strategy creation (Kwon \u0026amp; Silva, 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite the growing trend in the studies of psychological behavior, most studies have focused only on socioeconomic factors and poorly measured the psychological variables in the models (Foguesatto et al., 2020). Perception of farmers as a psychological factor has always received little attention. Therefore, there is a significant gap in studying the psychological factors of farmer behavior, because there is very little research in this area\u0026nbsp;(Foguesatto et al., 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, we seek to identify psychological variables that influence farmers\u0026apos; choice of crop and irrigation, so that in future studies it can be used as a basis to include important variables in the extension of well-known psychological theories such as TPB. The behavior is related to water consumption from both surface water and groundwater resources. This study is concerned with two behaviors: 1- determining the type of crop or tree to plant on a farm, and 2- determining irrigation time. These two behaviors are selected since they both significantly affect a farmer\u0026apos;s water consumption. Behaviors are examined to understand the factors influencing farmers\u0026apos; perceptions of the value of water, the associated environmental crisis and their goal to adopt more sustainable agricultural practices. We use semi-structured interviews as a data collection method, and thematic analysis to explore and evaluate behavioral factors. A particular focus of this study is on farmers\u0026apos; perceptions, values, and concerns regarding water conservation behaviors in the Mahabad plains, a region located in the northwestern part of Iran.\u0026nbsp;\u003c/p\u003e"},{"header":"Study Area","content":"\u003cp\u003eThe Mahabd Plain is located southwest of Lake Urmia, Iran (Figure 1). The area is made up of 21 villages with a total farming population of around 10,000\u0026nbsp;(Valizadeh et al., 2020). The agricultural sector accounts for more than 90% of total water use\u0026nbsp;(Madani, 2014), and is the primary source of income in this area. The agricultural area is about 12,000 hectares, of which 94% are apple, wheat, alfalfa and sugar beet plantations. Through the Mahabad irrigation canal, these areas depend on water supplied by the Mahabad Dam, having a storage capacity of 200 million cubic meters.\u003c/p\u003e\n\u003cp\u003eStudied area is an important part of the Lake Urmia Basin because 1) a major river that flows into Lake Urmia is Mahabad River 2) there is no diversity of cultural values in Mahabad villages because the residents are mostly Kurd. Consequently, the behavioral complexities in this area are less, decreasing uncertainties of behavioral studies 3) Further, in this region, nudge theory-based behavior intervention plans can be employed.\u003c/p\u003e"},{"header":"Methodology","content":"\u003ch2\u003e4.1 Sampling and data collection\u003c/h2\u003e\n\u003cp\u003eBecause psychological variables are hidden, they cannot be measured with a single statement (e.g. a question on a questionnaire)\u0026nbsp;(Foguesatto et al., 2020). Therefore, other data collection methods should be used. Interview, allows a researcher to focus on the critical or relevant perspective of the respondent, which may not be taken into account in the study\u0026nbsp;(Young et al., 2018). For complex behaviors, interviewing is preferred to other methods of filling the knowledge gap\u0026nbsp;(Minichiello et al., 2008). Interviews fall into three categories: structured, unstructured, and semi-structured. A structured interview allows comparison of different results, but not the discovery of hidden ones\u0026nbsp;(Punch, 2013). An unstructured interview allows for detailed analysis, but makes it difficult to analyze and compare data\u0026nbsp;(Bryman, 2016). A semi-structured interview is preferred which avoids researcher bias, provides in-depth analysis, and produces comparable results\u0026nbsp;(Hay, 2000). Semi-structured interviews provide the analytical flexibility that is vital to examine complex issues\u0026nbsp;(Rose et al., 2018; Young et al., 2018). Very few studies have used semi-structured interviews to\u0026nbsp;assess\u0026nbsp;improvement in farmers\u0026apos; understanding of values and beliefs associated with water conservation behaviors\u0026nbsp;(Young et al., 2018). The semi-structured interview can successfully generate the psychological variables needed for other modeling methods such as Structural Equation Modeling (SEM).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo examine causal relations and influential factors in farmers\u0026apos; water-use decisions in the city of Mahabad, two researchers designed and conducted a semi-structured interview. In each of the eight regions, one or two villages were chosen to carry out the survey. Stratified random sampling method was used in each village. We continued our interviews until we reached a saturation point (i.e. new interviews did not result in significant results regarding the behavior factors affecting farmers\u0026apos; water consumption decisions). Besides, for the quantitative analysis, the total number of interviews exceeded the sample size estimated by Cochran\u0026apos;s sampling formula, which was set at 140 farmers at the 95% confidence level (Cochran, 1977). Thus, we conducted 148 interviews in eight villages. The number of interviews in each village and the associated map are shown in Figure 2 and Table 1.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;1\u0026nbsp;\u0026ndash; Name of villages and number of interviews in each\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVillage name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInterview count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVillage name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInterview count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eYousefkandi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eKhorkhoreh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eQomQale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eQare qishlaq\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaj\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eKik Abad and Gabazaleh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAgriqash\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eQezelqopi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cspan style=\"text-align: inherit;\"\u003eTo enhance the clarity and intelligibility of the questions, we conducted two pilot surveys. After each pilot survey, we analyzed the content of the farmers\u0026apos; responses and their willingness to participate in the survey to improve the interview (see the final interview questions in\u0026nbsp;Appendix A). The analysis and its modifications were carried out under the supervision of experts in the field of social and agricultural sciences. We have divided the final interview questions into three main categories, which are listed below:\u003c/span\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eFarmers and their farm areas: these questions were used to start and warm up the interview.\u003c/li\u003e\n \u003cli\u003eFarmers\u0026rsquo; decision making on water use: These questions were designed to investigate the influential factors in farmers\u0026rsquo; decision making on water use. Precipitation prediction, neighbors\u0026rsquo; behaviors, and plants\u0026rsquo; water demand were specifically the subjects of questions. However, farmers were free to talk about other factors. The responses to these questions are analyzed to reveal how farmers perceive the importance of water use for irrigation and how they affect their water consumption behavior.\u003c/li\u003e\n \u003cli\u003eFarmers\u0026rsquo; decision making on crop type: These questions were designed to investigate water-related factors in farmers\u0026rsquo; decision on crop type. The responses to the questions are analyzed to reveal how farmers perceive water-related challenges and how that perception affects farmers\u0026rsquo; crop type decisions.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo conduct this interview, we divided our case study into eight regions, each represented by a village. To divide this area, the following criteria were taken into account:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eAccessibility to a surface irrigation canal (Figure 3a): Since there is no complete control over farmers\u0026apos; water consumption from the irrigation canal, upstream farmers can use water more than others. The case study was split in order to sample different water resource options. \u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCrop types: As the upstream farms have easy access to the irrigation canal, they mainly cultivate orchard trees, unlike the farmers downstream (Figure 3b). The case study was divided to include all major crop types, including apple orchard, wheat, sugarbeet, and alfalfa.\u003c/li\u003e\n \u003cli\u003eGroundwater table: Downstream farmers can access groundwater more easily than upstream farmers due to the depth of the groundwater table (Figure 3c). The case study was divided to include different groundwater table depths from 2 to 20 meters.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e4.2 Data analysis\u003c/h2\u003e\n\u003cp\u003eWe extracted the themes or patterns of the farmers\u0026rsquo; responses from the interviews, using thematic analysis (Boyatzis, 1998). This thematic analysis is a well-known and flexible method to deal with the complexity of qualitative data (Braun \u0026amp; Clarke, 2006; Holloway \u0026amp; Todres, 2016). We began by transcribing the responses and reading the transcripts several times to familiarize ourselves with the data. This step is necessary to find patterns in responses. The texts were coded based on similar patterns that were extracted and categorized. Finally, we created a list of themes from the data in Excel.\u003c/p\u003e\n\u003ch2\u003e4.3 Ethics statement\u003c/h2\u003e\n\u003cp\u003eThis study involved human participants in accordance with the Helsinki Declaration of 1964 and its amendments. Prior to the interview, farmers provided written informed consent. The study informed respondents that participation was voluntary, their identities would remain confidential, and they could discontinue participation at any time.\u003c/p\u003e\n\u003ch2\u003e4.4 Competing Interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"Results And Analysis","content":"\u003ch2\u003e5.1 Socio-economic characteristics of farmers\u003c/h2\u003e\n\u003cp\u003eA total of 148 farmers were interviewed for this study. All interviewees were men as women do not work outside the home due to their common culture. Table 2 shows the age distribution of the respondents. The farmers were on average 49.8 years old, with the majority (almost 61%) being between 40 and 50 years of age.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;2\u0026nbsp;\u0026ndash; frequency distribution of age of respondents\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePercent of total respondents\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e30-35\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e35-40\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e40-45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e45-50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e50-55\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;55\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eOf the 148 respondents, 34% had orchard farms, 30% had crop farms, and 36% had mixes of orchard and crop farms. The diversity of the types of orchard trees and crops is shown in Figure 4. Apple tree presents in all investigated orchards. It takes 15 and 3 years for an apple and a peach tree respectively to become commercially beneficiary. Thus, peach trees are planted between apple trees to provide source of income for farmers in short time. In this way, farmers can have enough income to live on and keep investing in building apple orchards, which is why some farmers combine apple and peach fields. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe average and standard deviation of area are 1.7 ha and 3.4 ha for crop farms and 3.4 ha and 5.9 ha for orchard trees, respectively.\u003cbr\u003e\u003cbr\u003e\u003cspan style=\"text-align: inherit;\"\u003e\u003cstrong\u003e5.2 Irrigation behavior\u003c/strong\u003e\u003c/span\u003e\u003cbr\u003e\u003cbr\u003e\u003cspan style=\"text-align: inherit;\"\u003e\u003cstrong\u003e5.2.1\u0026nbsp;\u003c/strong\u003e\u003c/span\u003e\u003cspan style=\"text-align: inherit;\"\u003e\u003cstrong\u003eIrrigation decision criteria\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eWe extracted four criteria from farmers\u0026apos; responses to the question \u0026quot;How do you know when to start watering?\u0026quot; to examine their decisions about irrigation. The following criteria emerged:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eFresh leaves: Farmers usually look at the leaves, and check them for freshness. The farmers decide to irrigate their fields if they feel that the leaves are not fresh enough. In other words, the freshness of the plant leaves is assessed using visual inspection. When farmers visually notice that plant leaves are not fresh, they irrigate crops or orchard trees with surface water. If there is no access to surface water, they use groundwater.\u003c/li\u003e\n \u003cli\u003eSoil moisture: Farmers monitor soil moisture visually. If cracks are found in the soil, they dig a small hole to visually check the soil condition. They irrigate their crops with groundwater as there is no surface water. \u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFresh grass: Farmers check the grass condition under trees. If they find that the grass is drying out, they start irrigating with surface or groundwater.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFixed schedule: Regardless of all other factors, farmers have a fixed watering schedule.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAccording to the farmers\u0026apos; responses, before the Urmia Lake Restoration Program in 2015, there was always enough water in the irrigation canals. This allowed them to easily open the entrance gates of their farms for irrigation. However, since the beginning of this program, the surface water supply from the Mahabd dam to the irrigation canal has decreased by about 40%. Therefore, the Urmia Lake Restoration Program can be an important factor in explaining groundwater use by farmers. Under the program, surface water is distributed to farmers, and they have access to water once a month.\u003c/p\u003e\n\u003cp\u003eBased on our interviews, farmers fall into one or two of the above categories. For example, farmers can visually check both leaf freshness and soil moisture to make a watering decision. Figure 5 shows the frequency distribution of the criteria.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs shown in\u0026nbsp;Figure 5, approximately 71.5% of farmers use \u0026quot;fresh leaf\u0026quot; as either the only or one of their factors for deciding whether to irrigate. 21% of farmers use \u0026quot;fresh leaf\u0026quot; and \u0026quot;soil dryness\u0026rdquo; as the factors in making their decision. In addition to \u0026quot;fresh leaf\u0026rdquo;, the use of \u0026ldquo;Soil dryness \u0026quot; can help farmers make irrigation decisions because leaf freshness decreases dramatically around midday, which can lead to wrong irrigation decisions. To measure leaf freshness, farmers visually check leaf characteristics such as greenness, crispness, limpness, edge shape, and softness. The data is used as input to their decision-making algorithm, which is made up of past experiences, common sense, technical knowledge, cultural beliefs, and the impact of neighbors\u0026apos; decisions. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn other words, farmers use mental shortcuts to determine irrigation time, which is common among farmers even in developed countries such as USA, where almost 75% of farmers use rules-of-thumb (like visual observation and \u0026ldquo;when neighbors begin to irrigate\u0026rdquo;) (NASS, 2017). This can lead to different irrigation behaviors among them.\u003c/p\u003e\n\u003cp\u003eMental shortcuts can reveal how farmers are moving toward a reduction in water consumption. To analyze farmers\u0026rsquo; decision on irrigation behavior, this study looks at two distinct definitions of deficit irrigation; technical deficit irrigation and behavioral deficit irrigation. Technical deficit irrigation has a well-known definition in which irrigation is less than the total water requirement of a plant and the amount of irrigation is done based on patterns (Attia et al., 2021; Khapte et al., 2019). Behavioral deficit irrigation is defined, in this study, as a subjective analysis in a farmer\u0026apos;s mind, and relates to the farmer\u0026apos;s decision regarding irrigation time. Upon inferring that the freshness of the plants is insufficient, the farmer starts irrigation, so deficit irrigation is not considered. Deficit irrigation is assumed to be an effective strategy for environmental conservation behavior\u0026nbsp;(Montazar, 2021; UT et al., 2020), and can increase water productivity as well as farmers\u0026rsquo; profit\u0026nbsp;(Du et al., 2015; Fereres \u0026amp; Soriano, 2007; Karandish, 2021). While the extent of technical deficit irrigation can be assessed to find out possible options for water conservation, it can be concluded that farmers do not incorporate deficit irrigation into their decision making process. If a farmer wanted to apply deficit irrigation in his decision algorithm, he would respond with a sentence such as \u0026quot;when I see insufficient plant freshness, I wait a few days before starting irrigation to apply deficit irrigation\u0026quot;. This could be the subject of another study to find out why farmers do not allow deficit irrigation. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAbout 11% of farmers use a fixed irrigation schedule. This fixed schedule implies that farmers do not follow the daily temperature variations that directly affect plant evapotranspiration, crops water needs and yields.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 6 shows the time series of total water and agriculture release from the Mahabd dam. Total water release indicates the water volume released for agricultural, industrial, rural and environmental purposes. The total amount of water released has not decreased since 2015, but the amount released for agriculture has decreased by almost 29%. This is due to the start of the Water Cap project to reduce agricultural consumption by 40% during the Urmia Lake Restoration Program in 2015. To compensate for the reduction of available surface water, farmers have been using groundwater, which may explain the dramatic increase in groundwater use as well as the number of wells. The reduction project did not bring any changes to farmers\u0026apos; irrigation criteria, probably due to the ease of access to shallow groundwater (Khalaj et al., 2019; Valizadegan \u0026amp; Yazdanpanah, 2018). In other words, it seems farmers have not adapted to reduce water consumption. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2.2 Interactions among farmers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe farmer\u0026apos;s answer to the question \u0026quot;If some of your neighbors start watering, will you start watering?\u0026quot; can show whether farmers\u0026apos; decisions are influenced by their neighbors or whether they are learning from their neighbors.\u0026nbsp;Figure 7\u0026nbsp;shows the frequency distribution of farmers\u0026rsquo; responses. Around 87% of farmers answered the question with \u0026quot;No\u0026quot;. Lack of cooperative behavior among farmers can lead to unsustainable groundwater consumption, which can lead to increased competitive behavior and public tragedies in the future. Therefore, their cooperative behavior should be improved for future drought scenarios.\u003c/p\u003e\n\u003cp\u003e\u003cstrong style=\"text-align: inherit;\"\u003e5.2.3 Forecasting future precipitation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn response to the question \u0026quot;Do you predict or look for precipitation forecasts for the next year?\u0026quot;, the farmers replied \u0026quot;yes\u0026quot; or \u0026quot;no\u0026quot;. Figure 8 shows the frequency distribution of answers to this question. Nearly 87% of farmers do not forecast rain or look for rain forecasts.\u003c/p\u003e\n\u003cp\u003eSurface water is supplied by the Mahabd dam which is filled with runoff from upstream precipitation. Groundwater is recharged by river flows, precipitation and irrigation seepage. Water is crucial to agricultural production, so it is expected that farmers have taken into account the precipitation forecast. Farmers do not look for data that can be attributed to water governance and technical knowledge. There are some reasons for the behavior.\u003c/p\u003e\n\u003cp\u003eIrrigation water strongly influences agriculture both on crop farms and orchard trees. Crop choice is an annual decision, and farmers have to choose a crop each year. Orchard growers do not make an annual decision, but need a production estimate to decide on production plans. These decisions are based on watering volume. Farmers were expected to follow precipitation forecast sources. Interviews revealed, however, that most farmers do not rely on precipitation forecasts. There are some possible explanations for this behavior.\u003c/p\u003e\n\u003cp\u003eFarmers in the Mahabad plain have access to reliable and scheduled water, including surface and underground water. During drought periods, a reliable source of groundwater replaced the deficit in surface water. In addition, surface water is provided by the government in a top-down structure which ignores the participatory role of farmers. Continuity of the process over the decades after the construction of Mahabad Dam led farmers to assume the government as the sole agent responsible for water supply. Thus, farmers do not see a direct relationship between precipitation distribution and their agricultural activities and incomes, so they ignore precipitation forecasts.\u003c/p\u003e\n\u003cp\u003eIf crop farmers do not have access to alternative crops, they are not in a decision-making position, and will not take into account decision parameters such as rainfall. Lack of sufficient and up-to-date technical knowledge can prevent farmers from changing their crop types. In this context, farmers\u0026apos; knowledge and experience were analyzed based on their responses to the question \u0026quot;How many other crops have you grown in recent years?\u0026quot; (Figure 9).\u003c/p\u003e\n\u003cp\u003eThe figure shows the diversity of crops that farmers are able to cultivate. To improve soil properties and reduce weed, disease and pest pressure, farmers have to practice crop rotation (Kremen et al., 2012). Therefore, those 86% of farmers who grow fewer than four different crops do not have many options to choose from each year. These farmers do not look for precipitation forecast data since it is of no use to them. Moreover, low crop diversity limits farmers\u0026apos; ability to adapt to hard water resource availability. Lack of agricultural knowledge or lack of financial sources may be the causes of low crop diversity.\u0026nbsp;\u003cbr\u003e\u003cbr\u003e\u003cspan style=\"text-align: inherit;\"\u003e\u003cstrong\u003e5.3 Perception of farmers about water value in agriculture \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/span\u003e\u003cbr\u003e\u003cbr\u003e\u003cspan style=\"text-align: inherit;\"\u003e\u003cstrong\u003e5.3.1\u0026nbsp;\u003c/strong\u003e\u003c/span\u003e\u003cspan style=\"text-align: inherit;\"\u003e\u003cstrong\u003eAttitudes toward future\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eFarmers\u0026rsquo; response to the question \u0026quot;If you have a new farm, which factors will you consider when choosing a crop or orchard?\u0026quot; will reveal the most significant influencing factors involved in their decision to choose a crop. The answer indicates whether water consumption is a significant factor or not. The content of the responses is as follows:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eI look for a lot of income, so I will choose between high value crops and orchard trees.\u003c/li\u003e\n \u003cli\u003eI look for crops or orchard trees that are financially and technically supported by the government.\u003c/li\u003e\n \u003cli\u003eI invested in an apple orchard, so I will continue to cultivate it.\u003c/li\u003e\n \u003cli\u003eI look for plants that are easy to cultivate.\u003c/li\u003e\n \u003cli\u003eNo, I am not looking for other crops.\u003c/li\u003e\n \u003cli\u003eI follow other farmers in making decisions.\u003c/li\u003e\n \u003cli\u003eI choose crops that consume less water.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe response frequency distribution is shown in\u0026nbsp;Figure 10. Since some farmers selected more than one factor, the sum of the factor frequencies exceeds 100%. The factors are:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eHigh income plants: Farmers choose the crop type or orchard tree that has economic value, and can generate high income for the farmer.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFinancial and technical support: Farmers look for a crop or orchard that receives technical and financial support from government agencies.\u003c/li\u003e\n \u003cli\u003ePursue investment in the apple orchard: Farmers definitely choose the apple tree.\u003c/li\u003e\n \u003cli\u003eLow-difficulty cultivation: Farmers look for a type of crop or fruit that does not require much effort, such as deep plowing.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFollow others: Farmers try to choose what other farmers usually choose.\u003c/li\u003e\n \u003cli\u003ePlants with low water consumption: Farmers are concerned about water consumption and look for a type of crop or orchard with low needs for water.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn accordance with respondents\u0026apos; responses, environmental crises or water-related issues have the least impact on farmers\u0026apos; decisions because they are motivated by economic gain. Analysis of the responses can reveal possible future plans for farmers, especially those facing an economic downturn in apple orchards. Having a long history of producing apples in the region, the apple orchard corresponds with the farmers\u0026apos; responses because it generates the highest income (Panah et al., 2018), and it allows them to access experience and technical support.\u003c/p\u003e\n\u003cp\u003eTherefore, it is to be assumed that farmers have a high tendency to cultivate apple orchards. When a farmer decides to plant a field with an apple orchard, he invests about 15 years of his money and time to make the orchard profitable. Due to the high water use of apple trees, the percentage of farmers choosing to plant or replant a garden with apple orchards can cause a crisis of environmental overconsumption for decades. This means that water consumption in the region is expected to remain high.\u003c/p\u003e\n\u003cp\u003eOn the contrary, there is an opportunity hidden in their response themes. The government can boost farmer productivity by offering incentives and promoting crops with low water needs, high incomes, and technical support, like the pistachio tree, which is a little-known crop. Apart from financial support, which is a challenge for the government due to difficulties, it can provide technical support and introduce the pistachio as a high-income product.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIf the farmers\u0026rsquo; attitude about the priority of water consumption changes the most sustainable environmental conditions will not happen. This change requires careful intervention based on social studies. For example, it is possible to alter farmers\u0026rsquo; perception about the high income of apple orchards if all financial and health consequences of Lake Urmia desiccation and its main causes (Schmidt et al., 2021) are properly presented to them and incorporated into their economic decisions.\u003c/p\u003e\n\u003ch3\u003e5.3.2 Self-evaluation of water consumption behavior\u003c/h3\u003e\n\u003cp\u003eAnalysis of farmers\u0026apos; responses to the question \u0026quot;To what extent do you think water resources are available compared to the previous question?\u0026quot; revealed the following themes:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eExcessive water resources: Farmers believe that water resources are plentiful, and they do not need to include the status of water resources in their decision-making process.\u003c/li\u003e\n \u003cli\u003eBehavior with low water consumption: Farmers think that they are consuming little water.\u003c/li\u003e\n \u003cli\u003eNo other option: Farmers believe that they are in a forced situation, and cannot behave in another way to take into account the water resources.\u003c/li\u003e\n \u003cli\u003eOther technologies will solve the problem: Farmers think that other solutions such as drip irrigation will solve the water crisis, and they have no action to take.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFarmers\u0026apos; perceptions of water scarcity can steer their actions towards more sustainable water management solutions (Fan et al., 2019). Community participation in water management can also be compromised if it is believed to have excessive water resources. Other studies indicate that drought periods and frequencies have increased and will increase in the future (Mirgol et al., 2021; Sobhani et al., 2019). In contrast, more than half of farmers feel that there are too many water resources in the basin. this means they do not consider it necessary to take water scarcity into account in their decisions. It\u0026apos;s an alarm that needs a lot of attention to understand why some obvious and vague problems like the shrinking of Lake Urmia have not yet forced farmers to change their attitude to \u0026quot;having too much water\u0026quot;. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMuch more dangerous is the attitude of those 18.8% of farmers who believe that they have already reduced their water consumption. Since farms are dominated by rudimentary technologies and farmers\u0026apos; knowledge of water management is low due to the lack of an integrated training program, they are likely to have a misconception about \u0026quot;low water consumption\u0026quot;, which may lead them to take no further responsibility for water level reduction schemes. This attitude makes farmers do nothing even if the lake disappears from the earth. In other words, they believe that they have taken all the necessary actions to reduce water consumption. They are likely to blame the government or other farmers for environmental problems as well as their economic consequences. Therefore, this situation can lead to violent protests and security problems. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere is a common misconception among people that technology is a perfect solution that can solve all water-related problems (De Ch\u0026acirc;tel, 2007). Although only a limited number of farmers in Mahabad agree with this attitude, it can increase if serious environmental consequences directly affect their lives.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study aimed to determine the water consumption behavior of farmers in Mahabad by discovering and assessing influential factors in crop choice and irrigation scheduling decisions. We found that reduction in water consumption plays no role in farmers\u0026rsquo; irrigation scheduling. There is almost no interaction among them, and they do not look for precipitation forecast data to make decisions about annual crops. In addition, financial factors such as income and investments play a major role in the decisions of farmers, and there is no concern about reducing water consumption in their beliefs. They, also, do not consider themselves an influential factor when it comes to moving toward sustainable water management.\u003c/p\u003e\n\u003cp\u003eThis study showed a fatal gap in any program like ULRP that targets to save Lake Urmia; thus, to achieve a successful program for restoring the lake, we need to focus on farmers behaviors and perceptions. Future interventions should aim to emphasize importance of the farmers\u0026rsquo; roles and perceptions. This will lead to change farmers\u0026rsquo; beliefs to feel more responsible about environment protection. For example, targeted information campaigns can be suggested to achieve this goal. Moreover, workshops may have a contributed effect to supply farmers the required agricultural knowledge.\u003c/p\u003e"},{"header":"Appendix A","content":"\u003cp\u003eThe final interview questions are:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eWhat are the current types of crops on your farm?\u003c/li\u003e\n \u003cli\u003eHow many crops have you grown in the past years?\u003c/li\u003e\n \u003cli\u003eIf you have orchards, how old are the trees?\u003c/li\u003e\n \u003cli\u003eHow do you know when to start irrigation?\u003c/li\u003e\n \u003cli\u003eHow do you react when one of your neighbors is irrigating his crops?\u003c/li\u003e\n \u003cli\u003eDo you predict or look for prediction of the precipitation for next year?\u003c/li\u003e\n \u003cli\u003eIf you have a new farm, what factors influence your choice about the type of crop or orchard tree?\u003c/li\u003e\n \u003cli\u003eGiven the previous question, to what extent do you consider the available water resources?\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statemen\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHamid Farahmand\u003c/strong\u003e: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing - original draft. \u003cstrong\u003eMasoud Tajrishy\u003c/strong\u003e: Supervision, Writing - editing. \u003cstrong\u003eMohammad Taghi Isaai\u003c/strong\u003e: Supervision, Conceptualization. \u003cstrong\u003eMohammad Ghoreishi\u003c/strong\u003e: Formal analysis, Writing - review \u0026amp; editing. \u003cstrong\u003eMohammadreza Mohammadi\u003c/strong\u003e: Data curation, Investigation, Data visualization.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eAbadi, B. 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Predicting climate change mitigation and adaptation behaviors in agricultural production: A comparison of the theory of planned behavior and the Value-Belief-Norm Theory. \u003cem\u003eJournal of Environmental Psychology\u003c/em\u003e, \u003cem\u003e68\u003c/em\u003e, 101408.\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-2478328/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2478328/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThere has been much attention paid to Lake Urmia's catastrophic desiccation by researchers and the government. An in-depth semi-structured interview and thematic analysis were used in this study to examine irrigation behavior and crop type selection decisions. 73% of farmers believe that there is no need to reduce their water consumption, 87% do not look for rain forecasts since they regard the government as responsible for water supply or have very few crop alternatives to choose from. In choosing the type of product, 77% only consider economics and do not consider environmental objectives, and 71% do not think drought conditions affect irrigation decisions. Educating farmers and increasing their collaboration role are therefore necessary. Therefore, these variables are the basis for extending psychological theories such as TPB to predict farmers' behavior to a much greater extent. While this study focused on one region, its findings are applicable to similar circumstances worldwide.\u003c/p\u003e","manuscriptTitle":"How do farmers' perceptions and attitudes toward agricultural water consumption behaviors can lead to unsustainability; evidence from Mahabad plain, Lake Urmia, Iran","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-01-18 15:51:44","doi":"10.21203/rs.3.rs-2478328/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"11ce0fa1-10fb-4ff3-9624-877c58920da2","owner":[],"postedDate":"January 18th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-02-08T20:29:23+00:00","versionOfRecord":[],"versionCreatedAt":"2023-01-18 15:51:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2478328","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2478328","identity":"rs-2478328","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}