## 2 Methods and Variables
### Selection and Description of the Study Variables
This expectation is based on previous findings such as those in [22], which indicated that farmers who were driven by a higher learning motivation were more likely to engage in ESs. It should be noted that the former participation of farmers in PRPs may also influence their choice to participate in new projects. This question was added so as to incorporate a respective variable into the model but only four farmers demonstrated their participation in PRPs before the considered period. Therefore, it was decided to not include such a measure in the model, as it would provide very little information regarding the actual effect of this variable. Finally, the \"farmers' ability\" category is composed by some sociodemographic variables. The first variable is this of _Income_. Farmers with higher income may find it easier to secure and fund their participation in a PRP. Though, the empirical evidence of this relationship from the studies on ESs is somehow ambiguous. For instance, [PERSON] et al. [17] and [PERSON] et al. [19] found a positive relationship between income and farmers' willingness to pay for ES but [PERSON] et al. [22] found a negative relationship between income and participation in extension services which was statistically significant at the 0.10 level. In addition, the Full-Time Farmer (F-T Farmer) variable indicates whether the respondents are full-time farmers (1) or they are also engaged in other occupations (0). It is expected that additional income from off-farm activities may help farmers to enroll easier in PRP due to higher liquidity [19]. In addition, the variable Education (E) is incorporated in the model, in order to test if more educated farmers are more enthusiastic to participate in PRP. The positive relationship of education and farmers' engagement has been adequately documented by the past research on ES engagement [18]. Nevertheless, in the study of [PERSON] et al. [22] no statistically significant effect of education was found. Furthermore, the _Residence_ variable takes the value 1 for farmers that are located in areas of more than 10,000 inhabitants and 0 for those who live in less populated places. The variable seeks to reveal if the deficit of opportunities potentially associated with residents of less populated rural areas may have an effect on their inclination for joining PRP. This is also the case for ES, where farmers were found to show more willingness to participate in ES when there were plenty of available ES options and in a short distance, as well as higher friction with knowledge diffusors [20,38]. The next sociodemographic variable is this of _Gender_. Despite the fact that the gender of farmers has been understudied in relative literature [16], this is not the case for studies on ES, as nearly all of the reviewed studies incorporated such a measure in their models. It is really interesting, that in most of the studies no significant effect of this parameter likelihood to participate in extension services was found [19,20,22], except for the study of [PERSON] et al. [17] who found that female farmers were more likely to pay for participating in ES. The last variable is this of _Age_. Empirical research findings on farmers' decision-making denotes that as farmers grow older, they become less open to changes and behavioral shifts. As for the participation in ESs is concerned, findings are mixed with some studies confirming this negative relationship [20,22], while others finding no statistically significant relationship between variables [17,19].