This study was carried out to evaluate the socio-economic structure and organic farming activities of grape producers in Besni. A face-to-face survey was conducted with producers producing organic grapes. Data were obtained by interviewing a total of 50 producers. The number of organic products grown between 2005 and 2018 increased from 205 to 213 in Turkey. The number of producers engaged in cultivation in this area has increased more than 5 times compared to 2005. It reached nearly 80.000 in 2018. The area of organic vegetable production increased from 203.811 to 626.884 hectares in Turkey. While the production area increased 2.5 times, the production amount increased almost 4 times. Today, a variety of products in organic vegetable production, planting area, the number of farmers has increased in Turkey. The findings were obtained education level of the producers, the number of individuals in the family, record keeping, annual total income, newspaper reading, grape yield, grape cultivation area, property and rental land, credit usage, weed control, organic farming knowledge level, organic farming by using variables such as thoughts and grape variety. There are serious differences between conventional agriculture and organic farming. The family structure is effective in production activities. It has been determined that the thoughts of organic farming have changed depending on the age and experience of the producers. Organic grape producers have turned to organic farming due to their desire to sell their products at higher prices with state supports. Also, it has been suggested that organic farming is important in terms of the sustainability of living life all over the world. Increasing producer income and environmental awareness are among the main reasons for the transition to organic farming.

The main objective of this study is to determine the factors affecting the application of innovations for dairy farmers in the Milas district, Muğla province, Turkey. Data from 71 dairy farmers and the Multinominal logit model are used for this study. The dependent variable of the model is divided into three categories; “I don’t apply any agricultural innovations”, “I apply agricultural innovations only after other farmers apply and express their positive opinion” and “I apply agricultural innovations”. In conclusion of the analysis, the comparisons are done with the farmers who “do not apply any agricultural innovations,” and the farmers who “apply agricultural innovations only after other farmers apply and express their positive opinion”; it is determined that a one unit increase in educational level raised the likelihood of applying innovations after receiving positive opinion. When the farmers that “apply agricultural innovation”, and that “apply agricultural innovations only after other farmers apply and express their positive opinion” are compared, it is found that a one unit increase in the number of milking animals had increased the likelihood of applying the innovations.

In this research, it is aimed to determine the factors affecting the innovation level of agricultural enterprises. Konya province was chosen as the research area because it is one of the important agricultural production centres in Türkiye. Stratified random sampling method was used within the scope of the study. A total of 268 agricultural enterprise owners were interviewed face to face and survey data were compiled. Multiple regression model was applied in the analysis of the factors affecting the perception of innovations by the operator. In this research, considering the characteristics of the data collected by the questionnaire, the linear regression model, which uses functional forms in regression models, was used. In the study, innovation index was taken as the dependent variable in the linear regression model. Independent variables were determined as education, experience, number of parcels, amount of land, active capital, amount of land, agricultural income, following innovations, receiving training on agricultural issues, participating in agricultural activities, following events related to agriculture on social media and cooperation with institutions. As a result of the model, a positive relationship was determined between education, experience, number of parcels, amount of land, active capital, amount of land, agricultural income, following innovations, receiving training on agriculture-related issues, participating in agriculture-related activities, following agriculture-related events on social media and cooperation with institutions and innovation index.