Comparing the predictive power of the RANAS approach through planned behavior theory in applying sustainable water consumption measures among Minoodasht county farmers

Introduction
Agriculture is the main consumer of water and also the most vulnerable economic sector in the face of water shortage. This issue has become one of the main challenges in water management in the dry regions of the world where the socio-economic risks of water shortage are unavoidable; One of the most important solutions is to pay attention to the problem of changing the current behavior of water consumption and achieving a sustainable behavior of agricultural water consumption. Changing human behavior is complex and usually requires a combination of components to induce a person to try, adopt, and maintain a new behavior. There are different models for investigating behavior change and identifying the factors affecting behavioral intention. One of the models that has received the most attention from researchers is the theory of planned behavior. This theory was proposed by Ajzen in 1991. This theory has good power in predicting behavioral intentions due to considering individual, social and environmental factors; In fact, this model considers the complexity of relationships between human behavior and its determining factors, and ultimately considers human behavior as a result of his intentions. The constituent components of this theory include attitude, subjective norm and perceived behavior control. In addition to the theory of planned behavior, another model that is considered as one of the best methods to know the determinants of behavioral components and can be used to examine the key factors of behavior change is the Risk, Attitude, Norm, Ability and Self-regulation (RANAS) approach. The effectiveness of the RANAS for predicting behavior has been confirmed in comparison with some behavior change models; Because it creates a strong foundation for designing behavior change interventions. Therefore, The purpose of the research is to compare the predictive power of the theory of planned behavior with the RANAS approach to measure the intention to use sustainable agricultural water consumption measures in Minoodasht county.
 
Materials and Methods
The statistical population of the study was made up of 2358 farmers of Minoodasht county, Golestan province, of which 331 people were selected as a sample using Cochran's formula. Considering the distribution and dispersion of farmers in different districts and in order to obtain a representative sample of the studied statistical population, multi-stage random sampling was used. The data collection tool was a researcher-made questionnaire, which was used to measure the main components of behavioral intention from the planned behavior theory and the RANAS approach. The face validity of the questionnaire was used by a panel of PNU university experts and ministry of agriculture-jihad experts, and its reliability was confirmed by conducting a pre-test study by calculating Cronbach's alpha coefficient (0.603 ≤ α ≤ 0.971). It should be noted that in the descriptive statistics section, Interval of Standard Deviation from the Mean (ISDM) method was used to describe the frequency of the respondents' responses to each of the research variables. According to this formula, individuals' responses were categorized as low, moderate, and high according to Likert type scale used: A: Low= A≤ Mean-1/2 Sd; B: Moderate= Mean–1.2 Sd≤ B≤ Mean+1.2 Sd; C: High= Mean+1/2 Sd≤ C. In order to analyze the data SPSS₂₂ software was used.
 
Results and Discussion
The components of the planned behavior theory, i.e., attitude, subjective norm, and perceived behavioral control, were able to explain 88.5% of changes in behavioral intention, and the components of the RANAS approach, i.e., risk, attitude, subjective norm, ability, and self-regulation, were able to explain the 89.4% of behavioral intention changes. In the planned behavior theory, only the attitude variable (P=0.000, T=24.13, and B=0.935) has a direct, positive and significant effect on the behavioral intention variable. Also, the findings of this model showed that perceived behavior control has no significant relationship with behavioral intention, which were consistent with Pino et al. (2017), and Tavassoti et al. (2021). The respondents did not find it easy to use the methods and measures that lead to less water consumption in the fields, and they considered changing the irrigation method and adapting new irrigation methods to the cultivation pattern to be associated with risks. According to the farmers' understanding of the difficulty of the new behavior, they underestimated their own success in changing the new behavior. In fact, farmers' motivation to implement activities and measures to reduce water consumption in the farm due to their understanding of the poor condition of the internal environment (ability, knowledge and skills) as well as the external environment (opportunities, support, economic, financial, security and social issues) considered difficult. The results of the RANAS approach showed that based on the standardized beta coefficient, the attitude variable (0.752) had the greatest role and influence on the intention of farmers to sustainable water consumption, followed by risk (0.169) and self-regulation (0.154). The subjective norm (0.106) contributed to the prediction of the intention to accept the behavior of sustainable agricultural water consumption. The results of this approach in the field of attitude were consistent with Tajeri Moghadam et al. (2018), and Khani Filestan et al. (2020). The findings of this approach showed that sunjective norm has a significant effect on behavioral intention, which was consistent with Mohammadi et al. (2016) and Bakhshi et al. (2019). This study showed that the risk variable has a significant effect on behavioral intention, which is consistent with Tajeri Moghadam et al. (2018) and Hassani et al. (2017). The strongest predictive component in both models for the readiness to apply water-saving measures was attitude with coefficients (0.933) and (0.752); Also, the level of this variable was in a low state (64.4%). Therefore, it is suggested that the extension department, considering the high average age (69 years) and the low literacy level of the majority of farmers (84.9 %), should design and implement extension and educational programs that suit their conditions in the form of field visits, farm days, and demonstration farms of leading farmers so that they have a more positive attitude towards reducing water consumption.
 
Conclusion
Both models have a high power in explaining the prediction of behavioral intention. But the RANAS approach is better able to recognize behavioral determinants than the programmed behavior theory. Therefore, It is suggested that the extension department should use the RANAS approach to design intervention programs to change the behavior of farmers to reduce water consumption.