Determination of Farmers’ Willingness to Pay for Drought Adaptation Policies in Şanlıurfa, Turkey

Mustafa Hakkı Aydoğdu aDepartment of Agricultural Economics, Faculty of Agriculture, Harran University, Şanlıurfa, Turkey

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Mehmet Reşit Sevinç bDepartment of Bozova Vocational School, Harran University, Şanlıurfa, Turkey

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Mehmet Cançelik cDepartment of Social Science Vocational School, Harran University, Şanlıurfa, Turkey

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Abstract

In Şanlıurfa, Turkey, agriculture is the most important source of income. This study aimed to determine Şanlıurfa farmers’ willingness to pay for drought adaptation policies and the factors affecting their willingness. The data were obtained from face-to-face surveys with farmers, selected using a simple random sampling method. According to the results, 50.26% perceive a risk of drought, and 35.86% are willing to pay for adaptation policies. Among those willing to pay, the average amount was $22.63 ha−1 [$1 (U.S. dollar) = TRY 5.676 (Turkish lira)], and the average for all participants was $13.55 ha−1. This adds up to a total of $14,363,000 yr−1 for Şanlıurfa. This amount is 1.47% of the annual average income of the participants and is thus within their ability to pay. Age, amount of land farmed, education level, experience, and income were factors affecting willingness to pay. Many respondents, however, were unaware of drought adaptation policies. Because there is concern that drought risk is increasing, awareness needs to be increased, for example, through extension services. To the best of our knowledge, this is the first study of its kind, and the results may be useful for creating and applying drought adaptation policies in both Turkey and other regions with similar socioeconomic characteristics.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Mehmet Reşit Sevinç, rsevinc@harran.edu.tr

Abstract

In Şanlıurfa, Turkey, agriculture is the most important source of income. This study aimed to determine Şanlıurfa farmers’ willingness to pay for drought adaptation policies and the factors affecting their willingness. The data were obtained from face-to-face surveys with farmers, selected using a simple random sampling method. According to the results, 50.26% perceive a risk of drought, and 35.86% are willing to pay for adaptation policies. Among those willing to pay, the average amount was $22.63 ha−1 [$1 (U.S. dollar) = TRY 5.676 (Turkish lira)], and the average for all participants was $13.55 ha−1. This adds up to a total of $14,363,000 yr−1 for Şanlıurfa. This amount is 1.47% of the annual average income of the participants and is thus within their ability to pay. Age, amount of land farmed, education level, experience, and income were factors affecting willingness to pay. Many respondents, however, were unaware of drought adaptation policies. Because there is concern that drought risk is increasing, awareness needs to be increased, for example, through extension services. To the best of our knowledge, this is the first study of its kind, and the results may be useful for creating and applying drought adaptation policies in both Turkey and other regions with similar socioeconomic characteristics.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Mehmet Reşit Sevinç, rsevinc@harran.edu.tr

1. Introduction

Throughout the ages, humans have always forced the natural environment in which they live to change rather than adapting to it. As a result, both the structure and balance of the natural environment have been disrupted (Aydogdu 2016). Drought, which can be catastrophic for all living beings, is caused by natural factors and human activity (Başol et al. 2007) and may be expected to increase in the Anthropocene, in which human activity has become the dominant influence on climate and the environment (Van Loon et al. 2016). Drought can have more severe effects than other natural disasters because it affects large areas (Partigöç and Soğancı 2019). Globally, climate change and drought have garnered significant research attention of late (Wilhite and Glantz 1985; Van Loon et al. 2016; Aydogdu and Yenigün 2016; Tol 2018; Gracia et al. 2020). Although the precise definition of drought and its impacts varies (Wilhite and Glantz 1985), for different applications (e.g., agriculture, hydrology), overall drought occurs when the precipitation rates (or the available moisture/water at the surface) for a particular period is substantially lower than long-term averages. Even though drought may occur at any location, its impact varies considerably from region to region. Drought has meteorological, hydrological, agricultural, and socioeconomic effects (Wilhite and Glantz 1985). According to the Aqueduct Water Risk Atlas, created by the World Resources Institute (WRI), drought and water shortages occur in approximately one-quarter of the world; this issue will continue to rise, and unavoidable droughts may occur in some regions soon (Hofste et al. 2019). The WRI divides the world’s countries into four categories, according to drought and water stress. Turkey is part of the second category, in which there is a high probability of the occurrence of water shortage (Hofste et al. 2019). Summers in Turkey are expected to get ~6.5°C warmer and 40% drier with the changing climate (Bağçaci et al. 2021).

The first effects of drought often manifest in the agricultural sector (Akbaş 2014), which is crucial in every country to ensure food security, the provision of raw materials to other sectors, employment, and rural development (Mulazzani et al. 2020; Ali et al. 2020). The effects of drought on agriculture differ from those on the other sectors, owing to the uncertain start and end dates of the growing season, the cumulative increase in effects as drought persists simultaneous impact on more than one resource, and high economic consequences (Türkeş 2012). Because rural areas—where agriculture is usually widespread—generally contain poorer populations, the effects of drought are experienced more intensely (Akalın 2015), and it is essential to ensure economic benefits for those living in rural areas (El-Bilali et al. 2020). Fighting droughts is more difficult than mitigating other natural disasters because droughts start slowly and affect large areas (Kapluhan 2013). Developing drought adaptation policies for the agricultural sector is a crucial issue (Organisation for Economic Co-operation and Development 2019).

Among the drought adaptation policies that affect both dry and irrigated areas are afforestation, research, and development for diseases caused by pests that will occur due to water scarcity and heat waves; development of plant varieties with low water consumption or higher drought tolerance; agricultural insurance; good agricultural practices that are the processes that should be applied specifically to each region to make the agricultural production system socially viable, economically profitable, and efficient; protecting the health and welfare of living things; and giving importance by adapting to the environment, and educating farmers, for example, through agricultural extension activities. In irrigated areas, the effect of a drought is the inability to supply sufficient water to meet the crop water requirements. Irrigation efficiencies in the study area are between 30% and 45% on average (General Directorate of Water Management 2019). The low irrigation efficiency is due to problems in the open canal system and the high reliance (85%) on gravity, furrow, and wild irrigation versus much lower use (15%) of pressurized irrigation, sprinkler, and drip irrigation in the study area (Aydogdu 2019).

A study conducted in China analyzed the behavioral psychology of farmers in drought control. According to the results, it has been determined that water-saving irrigation measures, crop type, and cultivation structure are important. Economically, it has been stated that the rationality of drought control behavior depends on farmers’ willingness to pay (WTP) and their ability to pay (Dong et al. 2020). A study conducted in Zimbabwe was concluded that farmers have 2.56 to 5 times greater WTP for a drought-tolerant maize variety and suggested that awareness activities should be expanded in combating responding to drought conditions (Kassie et al. 2017). A study conducted in Ghana found that farmers who cultivated maize have a WTP for drought agricultural insurance and that age, education, income, land ownership, and awareness were among the affecting factors (Abugri 2016). A study conducted in Pakistan found that 30% of the participants have a WTP for agricultural insurance in combating climate change and drought (Arshad et al. 2016).

This study focused on determining the WTP of farmers in Şanlıurfa for drought adaptation policies, along with the factors affecting WTP. Previously, studies on climate change, natural resources, and sustainability have been conducted in the research area; however, to the best of our knowledge, there has been no study on the WTP for drought adaptation policies.

2. Study area

Turkey is located in the Mediterranean Sea region, which is expected to be severely affected by climate change (Aksoy and Can 2012). These effects will vary from region to region (Bağçaci et al. 2021). The central and southeastern Anatolia region of Turkey often experiences droughts (Öztürk 2012). Şanlıurfa is located in the southeastern Anatolia region [Güneydoğu Anadolu Bölgesi (GAP)], which is Turkey’s second-least-developed region (Doğan et al. 2020). The population of Şanlıurfa is 2.155 million, which is the eighth-highest regional population in Turkey (TURKSTAT 2021). Şanlıurfa has a decreasing altitude from north to south. Animal husbandry and crops such as wheat, barley, and lentil are generally produced in the north, and crops such as cotton, wheat, and corn are cultivated in the south where economically irrigable areas are located. Rainfall is around 900 mm in the northern parts of Şanlıurfa, around 450 mm in the center, and around 277–430 mm in the southern parts. The average temperature is 4°–6°C higher in the south than in the north (Çağlak et al. 2016). In Şanlıurfa as a whole, between the years 1929 and 2019, the average amount of precipitation was 463.6 mm, the average number of rainy days was 73.5, and the average temperature was 18.4°C (General Directorate of Meteorology 2020). The GAP region and Şanlıurfa are located in the Euphrates subbasin where 9 months of severe drought and 119 months of moderate to severe drought have occurred between 1984 and 2015 based on the Palmer drought severity index, which is given in Table 1 (General Directorate of Water Management 2019).

Table 1.

The drought information of the Euphrates subbasin from 1984 to 2015.

Table 1.

A projection analysis performed on the Atatürk Dam in GAP-Şanlıurfa, which is the source of irrigation, shows that the amount of surface water is expected to decrease by 23.3% overall and for the south from 55.1% to 67.2% between 2018 and 2100 (General Directorate of Water Management 2019). As an example of the consequences of reduced rainfall, precipitation from 1 October 2020 to 15 March 2021 was about one-half that of the previous year in Şanlıurfa (General Directorate of Meteorology 2021). This condition is expected to reduce crop yields in dry farming areas by 15%–20% in 2021. The geographical location of Şanlıurfa within GAP and Turkey is shown in Fig. 1 (Aydogdu 2019).

Fig. 1.
Fig. 1.

Turkey, GAP, and Şanlıurfa.

Citation: Weather, Climate, and Society 13, 3; 10.1175/WCAS-D-20-0163.1

In Turkey, in 2018, agriculture accounted for 6.2% of the gross domestic product (Turkey Ministry of Agriculture and Forestry 2020; Sevinç et al. 2019) and 17.7% of employment (TURKSTAT 2019; Sevinç et al. 2019). Şanlıurfa ranks first in the GAP region and third in Turkey in terms of agricultural potential. Şanlıurfa has an area of 1.06 million ha, the amount of irrigable area is 941 000 ha, and the economically irrigable area is 764 800 ha (KKA 2021). As of the end of 2019, irrigated agriculture has been conducted on an area of 481 800 ha (Turkey Ministry of Agriculture and Forestry 2020), which was 45.5% of the total land. In Şanlıurfa, 791 000 ha is used for cereals and other row crops, 20 200 ha for vegetables, and 157 400 ha for fruit, beverages, and spices (Turkey Ministry of Industry and Technology 2020). The livestock numbers are 2.5 million, along with annual average milk production of 426 000 t (Turkey Ministry of Industry and Technology 2020).

There is almost no night irrigation in the study area; that is, the water flows past the fields and is not used at all during the night. This causes the water to flow into the drainage channels during the night, increasing the amount of evapotranspiration due to higher daytime air temperatures. Therefore, irrigation efficiency can and should be increased. Loss and leakage rates can be reduced by improvement and repair of irrigation systems, and by switching to closed and pressurized water transmission systems. On the other hand, these are costly investments and put a burden on public finances. If farmer participation is provided with cofinancing, problems will be solved faster and the farmer’s sense of ownership will increase. In dry farming areas, development of drought-resistant crop varieties, dissemination of organic agriculture, providing technical and construction support for use of rainwater harvesting, providing support that will affect rural welfare such as training, and organizing for the geographically marked products and handicrafts are among the available drought adaptation policies.

3. Materials and methods

The data used in the research has been obtained from farmers through face-to-face surveys. We have used the same research method as that applied in a previous study (Aydoğdu et al. 2020); the difference is that we apply it to a different research question and a larger region. In 2019, 59 862 farmers were registered in the state farmer-registration system in Şanlıurfa (ŞTOM 2020). The sample volume has been determined as 382 by using the sampling size with a table of tolerable sampling errors (Bayram 2017), with a 95% confidence level and 5% error margin; 172 of the questionnaires were conducted in rain-fed and 210 in irrigated areas. Because statistical analyses based on randomly selected samples may lead to erroneous results, data analysis was carried out in two stages in this research, using StataCorp 14. In this research, the abovementioned adaptation policies were explained to the farmers in a general way and farmers were asked about their hypothetical WTP for some of these policies that would benefit them. The limitation of the research is existing of the possibility of generally described drought adaptation policies may not fully have understood by the farmers who were mostly the less educated in the study area.

In the first stage, the farmers’ willingness to participate in WTP for drought adaptation policies is determined, along with the factors affecting it. In the second stage, the statistical significance is determined by comparing the reference group and subgroups of these independent variables that affected WTP in the first stage. Therefore, Heckman’s two-stage model was used for the analysis, which is a statistical method applied to correct errors in randomly selected samples or randomly truncated dependent variables (Winship and Mare 1992). A two-stage prediction method is used to correct biases resulting from the use of nonrandomly selected samples to predict behavioral relationships as a specification error (Heckman 1979; Nawata 1994). The first stage of the method was a typical binary-selection probit model of whether farmers are accepted to involve in the WTP, modeled as shown here:
P(0,1)=β0+β1X1+β2X2++βnXn+εi.
Here, P indicates the declared variable (if the farmer has participated in the WTP, it assumes a value of 1; otherwise, it is 0); β0 is a constant; β0, …, βn are the regression coefficients of variables; X1, …, Xn are the explanatory variables; and εi is the error term. The second stage is the ordinary least squares approach, which attempts to determine the factors affecting the payment levels if the farmers accepted to participate in the WTP, which is modeled as follows:
Yi|P>0=β0+β1X1+β2X2++βnXn+βn+1λ+εi.
Here, Yi indicates the disclosed variable, that is, the level of payment of farmers; β0 is a constant; β0, …, βn are regression coefficients of variables; X1, …, Xn are descriptive variables; and εi is the error term. In Eq. (2), λ, or lambda, denotes the Mills ratio. More information is available about the model used in this study in the references listed in this section and also in studies by Greene (2002), Briggs (2004), Certo et al. (2016), Kyaw et al. (2018), Xiong et al. (2019), and Aydoğdu et al. (2020).

4. Research findings and discussion

The questionnaires have been administered to male farmers owing to the cultural norms specific to Şanlıurfa. 95.2% of the participants were married, 3.2% were single, and 1.6% were widowed. The average age of the participants was 45.9, the average household size was 8.09, and the average farming experience of the participants was 20.7 years. The descriptive statistics of the survey obtained from the participants are given in Table 2.

Table 2.

The descriptive statistics of the surveyed participants.

Table 2.

In 2019, the average exchange rate for $1 (U.S. dollars) was TRY 5.676 (Turkish lira; Para Çevirici 2019). Data obtained from farmers in irrigated and rain-fed farming areas are given in Table 3. Drought risk perception has been measured within the scope of general awareness, based on the farmers’ recent experiences, income losses, and concerns. Drought risk perception was higher than WTP in all participants. We found that those who had no information on these policies will be in favor of payment if these policies are of the kind that will increase their income. Besides, drought risk perception and WTP are determined to be higher in farmers in rain-fed areas, which is expected.

Table 3.

Data from irrigated and rain-fed farming areas. The numbers in square brackets are variable labels and/or indications of which labeled variables/rows were used to calculate the indicated variable (e.g., [3 = 2/1] indicates that the variable labeled 3 was calculated by dividing the variable labeled 2 by the variable labeled 1).

Table 3.

The average WTP of farmers involved in the survey was calculated as TRY 73.09 ha−1 ($12.88 ha−1) for irrigated areas, TRY 81.62 ha−1 ($14.38 ha−1) for rain-fed areas, and TRY 76.91 ha−1 ($13.55 ha−1) for all participants. These amounts are 1.01% of the annual average agricultural income of participants from irrigated areas, 2.98% for those in rain-fed areas, and 1.47% for all participants. These amounts do not exceed the farmers’ ability to pay.

According to Heckman’s two-stage estimation method, change in independent variables has two effects (Heckman 1976, 1979). In this research, the first stage is the estimation of participation of the farmer in WTP for drought adaptation policies based on the independent variables that are given in Table 4. In other words, Table 4 includes the analysis results based on if the farmer participates in WTP or not, that is, 1 (yes) or 0 (no). In the second stage (Table 5), the analysis continues by taking those who participated in WTP (1, yes) in the first stage (Table 4). The estimation results of the first stage of Heckman’s model are given in Table 4, which has the WTP of the surveyed farmers for drought adaptation policies. The likelihood ratio (LR) chi square, p value, and pseudo R2 values for the model were 38.58%, 0.007%, and 8.2%, respectively. The pseudo R2 value represents the measure of goodness of fit and indicates the effectiveness of the variables in explaining the dependent variable in the model.

Table 4.

The estimation results of Heckman’s first-stage model. One, two, and three asterisks indicate statistical significance at levels of 0.10, 0.05, and 0.01, respectively.

Table 4.
Table 5.

The estimation results of Heckman’s second-stage model. One, two, and three asterisks indicate statistical significance at levels of 0.10, 0.05, and 0.01, respectively.

Table 5.

A statistically significant relationship exists between the independent variables—including age, land amount, and education level—and the dependent variable—the willingness to pay for drought adaptation policies—based on the results given in Table 4. Farmers between the ages of 18 and 29 years were taken as a reference in the age variable groups. All the subgroups in the age variable have a lower willingness to participate in payment concerning the reference group. There was a statistically significant relationship for those between the ages of 50 and 59 years, at a significance level of p < 0.01, and those 60 and above, at a level of p < 0.05. Age has been determined to be an influencing factor in studies related to sustainable ecology and the agricultural environment conducted in Turkey, China, and France (Doğan et al. 2020; Ning et al. 2019; Di Pietro 2001). However, a study conducted in southern Turkey about farmers’ sustainable agriculture perception found that age was not an influencing factor (Hayran et al. 2018). The results of our research and cited studies show that in terms of a sustainable environment and life, where climate change and droughts are decisive, affect their attitudes, therefore their WTP varies depending on age. Depending on social norms, which are the measures of people’s activities and behavior, that serve to meet their material and spiritual needs, and that separate them from other beings, the WTP may increase if they believe that paying would make difference, sometimes drought is perceived as a fate that is beyond their attitudes and controls, which is a fatalistic attitude, so to pay would make no difference and thus WTP decreases.

The households of 1–4 people were taken as the reference group. All the subgroups of the household variable have a higher willingness to participate in payment concerning the reference group; however, there has not been statistical significance among them in our research. Similarly, household size has not been found to be an influencing factor in some studies on the perception of the farmers and the sustainability of agricultural resources in Turkey (Hayran et al. 2018; Aydogdu 2019). However, a similar study concluded that it was an influencing factor for sustainable agricultural income in Turkey (Doğan et al. 2020). The number of households is important both in terms of livelihood and labor. If there are individuals in the household who can work in nonagricultural jobs, WTP is lower or zero, but if the household depends on agricultural workers, then WTP increases.

In terms of experience, participants with farming experience between 1 and 10 years were taken as the reference group. All subgroups in the farming experience variable had a higher willingness to participate in payment concerning the reference group; however, there was no statistical significance among them in our research. A similar result was obtained from a study on natural resources (Aydogdu 2019); another study concluded that it was an influencing factor based on income in Turkey (Doğan et al. 2020). Experience is about what happened in the past. Our study and previous studies have found that those with more farming experience have experienced the impact of drought more in the past years and believe that participating in a planned adaptation policy will benefit them. In some studies, the results are statistically significant, while in others they are not.

For the amount of land variable, farmers whose land is 5 ha or less were taken as the reference group. All the subgroups in the land variable have a higher willingness to participate in payment concerning the reference group. There was a statistically significant relationship for those who have 15.1–20 ha of land, at a level of p < 0.05, and for those who have more than 20 ha of land, at a level of p < 0.1 in our research. The same results were obtained from studies on income related to agricultural sustainability conducted in Uganda, Taiwan, Indonesia, China, and Turkey (Ulimwengu and Sanyal 2011; Lin et al. 2019; Mutaqin and Usami 2019; Yang et al. 2019; Doğan et al. 2020). However, a study conducted in the south of Turkey found that it was not an influencing factor (Hayran et al. 2018). As the amount of land increases, participation and WTP increase. Therefore, priority can be given to large landowners in policies so they will set a good example. The positive results will encourage small landowners and may lead to increased participation. Because of the nature of the region being investigated, often small landowners follow what large landowners are doing.

For the agricultural income variable, farmers whose annual income is below TRY 50,000 have been taken as the reference group. Farmers with an income of TRY 50,000–99,000 have a higher WTP, whereas farmers with an income of more than TRY 100,000 have a lower WTP. However, there was no statistical significance between them in terms of this variable in our research. On the other hand, some studies concluded that income was an influencing factor in Iran, China, and Uganda (Maghsood et al. 2019; Yang et al. 2019; Ulimwengu and Sanyal 2011). Participation in policies is concerned with the ability to pay and loss of welfare. Whereas lower-income groups participate less because they do not have sufficient solvency, upper income-groups were not worried about much welfare loss.

In terms of the education variable, literate farmers were taken as the reference group. A linear relationship was detected between education level and WTP. However, a statistically significant relationship was only identified for university graduates, with a level of p < 0.05 in our research. Similar results were obtained in studies conducted in the United States, China, and Turkey, where education was found to be an influencing factor (Burch et al. 2020; Ning et al. 2019; Aydogdu and Bilgic 2016). However, a study conducted in the south of Turkey found that it was not an influencing variable (Hayran et al. 2018). This region is a more developed region than our study area, and the membership of the farmers to the organized structure is higher. Therefore, farmers can more easily access support and assistance on the issues they need. As the level of education increases, awareness increases, therefore, participation and WTP are increasing. It will be beneficial to give priority to this group in policies to set a good example followed by the other groups.

The second-stage estimation results of Heckman’s model are given in Table 5, which uses the outcome of Table 4. The second-stage results show the relationship between the factors that affected the payment level of the variables used in the model and their subgroups. The model’s second-stage values were calculated as 631.39 for the Wald chi square and 0.000 for the p value. From these values, it could be said that the model was statistically significant, as a whole. Further, the Mills ratio is calculated as 0.178, which is statistically meaningless. Therefore, based on these statistical results, it could be said that the model has no selection bias, indicating the reliability of the obtained results.

All of the subgroups in the age variable showed a higher WTP than the reference group. However, there was no statistical significance in any subgroup. In the household-size variables, all subgroups have a lower WTP than the reference group. There was an inverse relationship in this variable, where WTP decreased as the size of households increased. However, there was no statistical significance in any subgroup. For the farming-experience variable, all subgroups have a lower WTP than the reference group. The WTP decreased as the farmer’s experience increased. There was a statistically significant relationship for those who had 11–20 and 21–30 years of farming experience, at a level of p < 0.1, and for those who had more than 30 years of farming experience, at a level of p < 0.05.

For the amount of land variable, all subgroups had a higher WTP than the reference group. The WTP increased as the amount of land cultivated by the farmer increased. A statistically significant relationship was identified for those who had 5.1–10 ha of land, at a level of p < 0.05, and for those who had 10.1–15, 15.1–20, and 20.1 ha and above of land, at a level of p < 0.01. In terms of the farmers’ agricultural income, all subgroups had a higher WTP than the reference group at a level of p < 0.05 and p < 0.01. The WTP increased as farmers’ agricultural income increased. In terms of the farmers’ education level, all subgroups had a lower WTP than the reference group, other than for high-school graduates. As the education level of the farmer increased, WTP decreased (except for high-school graduates, who had a higher WTP). However, there was no statistical significance among them in any subgroup.

5. Conclusions

Droughts are likely to occur soon at levels that may have severe negative effects on agriculture, which is the main source of livelihood in the region. This will affect farmers more particularly over drier farming areas. The result of this research showed that the farmers in drier farming areas have about twofold more WTP than the farmers in irrigated areas. According to the other results of this research, half of the participants have a perception of drought risk, whereas the other half do not have an opinion on this issue or have a no-risk perception due to lack of sufficient understandable information, insufficient use of local knowledge among themselves, fatalistic attitude, and being in irrigated areas. The first method of combating drought is creating awareness based on drought monitoring by extension services. In this regard, regulations have started to be made by the state in Turkey. Next, the regulations should be supported. This has started to be done partially in Turkey, which includes the contents and quantities of the necessary financing and technical support and to determine under what conditions they will be given. The ability of the farmers should be taken into consideration in technical support planning, which seems ignored due to lack of socioeconomic research. Then, depending on the regulations and the supports, supervision is required that is under the planning stage in Turkey. Finally, evaluations will be necessary based on feedback obtained from the field for improvements. This will be the most important stage that shows how well the measures taken have been understood and applied, what results have been achieved.

Currently, farmers need more information about droughts and adaptation policies to increase their awareness. As long as this awareness is lacking, drought adaptation policies cannot be effective. Furthermore, to reduce the effects of drought on agricultural production, in addition to technological applications (water-saving pressurized irrigation systems, water-stress-resistant seed varieties, etc.), some financial measures (such as agricultural insurance, loans, and subsidies) must also be developed. These policies should be informed by the demands of farmers and their adaptation capacity, which depend on the region and needs of each farmer. Therefore, more detailed research is required to accomplish this but to begin with, priority should be given to young farmers, large landowners, and those with more education, who appear to be most receptive to adopting drought adaptation measures and most willing to pay for them.

In this study, the annual average WTP for all participants has been determined to be TRY 76.91 ha−1 ($13.55 ha−1). When the needs of the farmers are determined and awareness is raised through extension services, this amount may be expected to increase. The agricultural land assets of Şanlıurfa are 1.06 million ha, and the total WTP for farmers’ drought adaptation policies has been calculated as $14.363 million yr−1. Currently, drought practices and measures are mostly funded by the public budget. This amount is a potential cofinancing source that can be used by the public to pay for drought adaptation policies. In this way, both the burden on the public budget will decrease and the adoption and efficiency of adaptation policies will increase owing to the participation of farmers. To the best of our knowledge, this study is the first insight into this subject. The obtained results may be useful for drought adoption policies in both Turkey and other regions with similar socioeconomic features.

Fighting drought is a complex concept because many factors affect it; focusing on human attitudes will be one of the most effective ways in this struggle. There is need for a good follow-up study that involves conducting detailed qualitative interviews with farmers about their experiences with and beliefs about drought and their perceptions as to how to mitigate the risk. Such as, if they had a functioning drought early warning system, how would that affect their decisions? Or do they consider drought as an unavoidable fate? What is the frequency of information sharing among farmers and when? Whom do they see as a reliable source of information and why? How do these affect their attitudes?

Acknowledgments

The authors thank the editor-in-chief, editors, and anonymous reviewers for their encouraging comments and contributions to the development of this paper and to make it more understandable and legible for publication.

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    • Search Google Scholar
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  • Burch, C., and Coauthors, 2020: Revisiting a water conflict in southeastern Oklahoma 6 years later: A new valuation of the willingness to pay for ecosystem services. Sustainability, 12, 819, https://doi.org/10.3390/su12030819.

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    • Export Citation
  • Çağlak, S., T. Özlü, and S. Gündüz, 2016: Şanlıurfa ili iklim özelliklerinin enterpolasyon teknikleri ile analizi (Analysis of the climatic characteristics of Şanlıurfa with interpolation techniques). J. Int. Soc. Res., 9, 360372.

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  • Doğan, H. P., M. H. Aydoğdu, M. R. Sevinç, and M. Cançelik, 2020: Farmers’ willingness to pay for services to ensure sustainable agricultural income in the GAP-Harran Plain, Sanlıurfa, Turkey. Agriculture, 10, 152, https://doi.org/10.3390/agriculture10050152.

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    • Export Citation
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    • Export Citation
  • El Bilali, H., G. Calabrese, M. Iannetta, M. Stefanova, F. Paoletti, G. Ladisa, F. Bottalico, and R. Capone, 2020: Environmental sustainability of typical agro-food products: A scientifically sound and user friendly approach. New Medit, 19, 6983, https://doi.org/10.30682/nm2002e.

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    • Export Citation
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    • Export Citation
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    • Export Citation
  • Lin, H. I., J. L. Liou, and S. H. Hsu, 2019: Economic valuation of public meteorological information services—A case study of agricultural producers in Taiwan. Atmosphere, 10, 753, https://doi.org/10.3390/atmos10120753.

    • Search Google Scholar
    • Export Citation
  • Maghsood, F. F., H. Moradi, R. Berndtsson, M. Panahi, A. Daneshi, H. Hashemi, and A. R. M. Bavani, 2019: Social acceptability of flood management strategies under climate change using contingent valuation method (CVM). Sustainability, 11, 5053, https://doi.org/10.3390/su11185053.

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    • Export Citation
  • Mutaqin, D. J., and K. Usami, 2019: Smallholder farmers’ willingness to pay for agricultural production cost insurance in rural West Java, Indonesia: A contingent valuation method (CVM) approach. Risks, 7, 69, https://doi.org/10.3390/risks7020069.

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    • Export Citation
  • Aydogdu, M. H., and A. Bilgic, 2016: An evaluation of farmers’ willingness to pay for efficient irrigation for sustainable usage of resources: The GAP-Harran Plain case, Turkey. J. Integr. Environ. Sci., 13, 175186, https://doi.org/10.1080/1943815X.2016.1241808.

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    • Export Citation
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    • Search Google Scholar
    • Export Citation
  • Aydoğdu, M. H., M. R. Sevinç, M. Cançelik, H. P. Doğan, and Z. Şahin, 2020: Determination of farmers’willingness to pay for sustainable agricultural land use in the GAP-Harran Plain of Turkey. Land, 9, 261, https://doi.org/10.3390/land9080261.

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  • Başol, K., M. Durman, and H. Önder, 2007: Doğal Kaynakların ve Çevrenin Ekonomik Analizi (Economic Analysis of Natural Resources and Environment). 1st ed. Alfa Akademi, 195 pp.

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    • Export Citation
  • Burch, C., and Coauthors, 2020: Revisiting a water conflict in southeastern Oklahoma 6 years later: A new valuation of the willingness to pay for ecosystem services. Sustainability, 12, 819, https://doi.org/10.3390/su12030819.

    • Search Google Scholar
    • Export Citation
  • Çağlak, S., T. Özlü, and S. Gündüz, 2016: Şanlıurfa ili iklim özelliklerinin enterpolasyon teknikleri ile analizi (Analysis of the climatic characteristics of Şanlıurfa with interpolation techniques). J. Int. Soc. Res., 9, 360372.

    • Search Google Scholar
    • Export Citation
  • Certo, S. T., J. R. Busenbark, H. Woo, and M. Semadeni, 2016: Sample selection bias and Heckman models in strategic management research. Strateg. Manage. J., 37, 26392657, https://doi.org/10.1002/smj.2475.

    • Search Google Scholar
    • Export Citation
  • Di Pietro, F., 2001: Assessing ecologically sustainable agricultural land-use in the central Pyrénées at the field and landscape level. Agric. Ecosyst. Environ., 86, 93103, https://doi.org/10.1016/S0167-8809(00)00269-3.

    • Search Google Scholar
    • Export Citation
  • Doğan, H. P., M. H. Aydoğdu, M. R. Sevinç, and M. Cançelik, 2020: Farmers’ willingness to pay for services to ensure sustainable agricultural income in the GAP-Harran Plain, Sanlıurfa, Turkey. Agriculture, 10, 152, https://doi.org/10.3390/agriculture10050152.

    • Search Google Scholar
    • Export Citation
  • Dong, X., Y. Liu, and Q. Li, 2020: Psychoanalysis of farmers’ irrational drought-control behaviors. Rev. Argent. Clin. Psicol., 29, 194198.

    • Search Google Scholar
    • Export Citation
  • El Bilali, H., G. Calabrese, M. Iannetta, M. Stefanova, F. Paoletti, G. Ladisa, F. Bottalico, and R. Capone, 2020: Environmental sustainability of typical agro-food products: A scientifically sound and user friendly approach. New Medit, 19, 6983, https://doi.org/10.30682/nm2002e.

    • Search Google Scholar
    • Export Citation
  • General Directorate of Meteorology, 2020: Climate data of GAP-Şanlıurfa Province. Turkish State Meteorological Service, accessed 25 March 2020, https://mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?k=A&m=SANLIURFA.

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  • General Directorate of Water Management, 2019: Euphrates sub-basin drought management plan report. Turkey Ministry of Agriculture and Forestry Flood and Drought Management Department Rep., 128–134.

  • Gracia, A., J. Barreiro-Hurlé, Y. Pérez, and L. Pérez, 2020: Assessing the benefits of sustainability certification of biofuels: How much are consumers willing to pay? New Medit, 19, 318, https://doi.org/10.30682/nm2002a.

    • Search Google Scholar
    • Export Citation
  • Greene, W. H., 2002: Econometric Analysis. 5th ed. Pearson Education, 802 pp.

  • Hayran, S., A. Gül, and M. A. Sarıdaş, 2018: Farmers’ sustainable agriculture perception in Turkey: The case of Mersin province. New Medit, 3, 6978, https://doi.org/10.30682/nm1803f.

    • Search Google Scholar
    • Export Citation
  • Heckman, J., 1976: The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models. Ann. Econ. Soc. Meas., 5, 475492.

    • Search Google Scholar
    • Export Citation
  • Heckman, J., 1979: Sample selection bias as a specification error. Econometrica, 47, 153161, https://doi.org/10.2307/1912352.

  • Hofste, R. W., P. Reig, and L. Schleifer, 2019: 17 countries, home to one-quarter of the world’s population, face extremely high water stress. World Resources Institute, accessed 25 June 2020, https://www.wri.org/blog/2019/08/17-countries-home-one-quarter-world-population-face-extremely-high-water-stress.

  • Kapluhan, E., 2013: Türkiye’de kuraklık ve kuraklığın tarıma etkisi (Drought and its effects on agriculture in Turkey). Marmara Coğrafya Derg., 27, 487510.

    • Search Google Scholar
    • Export Citation
  • Kassie, G. T., A. Abdulai, W. H. Greene, B. Shiferaw, T. Abate, A. Tarekegne, and C. Sutcliffe, 2017: Modeling preference and willingness to pay for drought tolerance (DT) in maize in rural Zimbabwe. World Dev., 94, 465477, https://doi.org/10.1016/j.worlddev.2017.02.008.

    • Search Google Scholar
    • Export Citation
  • KKA, 2021: General land distribution and irrigation status. Karacadağ Development Agency—Investment Support Office, accessed 15 March 2021, https://www.investsanliurfa.com/sektorler/genel-arazi-dagilimi-ve-sulama-durumu--71.

  • Kyaw, N. N., S. Ahn, and S. H. Lee, 2018: Analysis of the factors influencing market participation among smallholder rice farmers in Magway region, central dry zone of Myanmar. Sustainability, 10, 4441, https://doi.org/10.3390/su10124441.

    • Search Google Scholar
    • Export Citation
  • Lin, H. I., J. L. Liou, and S. H. Hsu, 2019: Economic valuation of public meteorological information services—A case study of agricultural producers in Taiwan. Atmosphere, 10, 753, https://doi.org/10.3390/atmos10120753.

    • Search Google Scholar
    • Export Citation
  • Maghsood, F. F., H. Moradi, R. Berndtsson, M. Panahi, A. Daneshi, H. Hashemi, and A. R. M. Bavani, 2019: Social acceptability of flood management strategies under climate change using contingent valuation method (CVM). Sustainability, 11, 5053, https://doi.org/10.3390/su11185053.

    • Search Google Scholar
    • Export Citation
  • Mulazzani, L., R. Manrique, C. Stancu, and G. Malorgio, 2020: Food security and migration in Africa: A validation of theoretical links using case studies from literature. New Medit, 19, 1936, https://doi.org/10.30682/nm2002b.

    • Search Google Scholar
    • Export Citation
  • Mutaqin, D. J., and K. Usami, 2019: Smallholder farmers’ willingness to pay for agricultural production cost insurance in rural West Java, Indonesia: A contingent valuation method (CVM) approach. Risks, 7, 69, https://doi.org/10.3390/risks7020069.

    • Search Google Scholar
    • Export Citation
  • Nawata, K., 1994: Estimation of sample selection bias models by the maximum likelihood estimator and Heckman’s Two-Step Estimator. Econ. Lett., 45, 3340, https://doi.org/10.1016/0165-1765(94)90053-1.

    • Search Google Scholar
    • Export Citation
  • Ning, J., J. Jin, F. Kuang, X. Wan, C. Zhang, and T. Guan, 2019: The valuation of grassland ecosystem services in Inner Mongolia of China and its spatial differences. Sustainability, 11, 7117, https://doi.org/10.3390/su11247117.

    • Search Google Scholar
    • Export Citation
  • Organisation for Economic Co-operation and Development, 2019: OECD environmental performance reviews: Turkey 2019. Accessed 17 July 2020, https://read.oecd-ilibrary.org/environment/oecd-cevresel-performans-incelemeleri-turkiye-2019_653318da-tr#page1.

  • Öztürk, K., 2012: Küresel iklim değişikliği ve Türkiye’ye olası etkileri (Global climatic changes and their probable effect upon Turkey). Gazi Eğitim Fakültesi Derg., 22, 4765.

    • Search Google Scholar
    • Export Citation
  • Para Çevirici, 2019: 2019 yılı ortalama dolar kuru (2019 average dollar rate per year). Accessed 21 March 2020, https://paracevirici.com/doviz-arsiv/merkez-bankasi/gecmis-tarihli-doviz/2019/amerikan-dolari.

  • Partigöç, N. S., and S. Soğancı, 2019: Küresel iklim değişikliğinin kaçınılmaz sonucu: Kuraklık (An inevitable consequence of global climate change: Drought). Resilience, 3, 287299, https://doi.org/10.32569/resilience.619219.

    • Search Google Scholar
    • Export Citation
  • Sevinç, G., M. H. Aydoğdu, M. Cançelik, and M. R. Sevinç, 2019: Farmers’ attitudes toward public support policy for sustainable agriculture in GAP-Şanlıurfa, Turkey. Sustainability, 11, 6617, https://doi.org/10.3390/su11236617.

    • Search Google Scholar
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