Analysis of Agricultural Credit Performance of Turkey using K-means Clustering Algorithm

Abstract

Agriculture is a significant sector that supplies raw materials to many sectors as well as providing nutrients to humans and animals and ensures employment. The economic crises, rapid population growth, the rise in demand for food products have increased importance and necessity of agriculture. For this reason, agriculture must be supported in order not to be affected by adverse conditions and effects. Thus, agricultural credit is an important factor in the development of the production and investment structure of the agricultural sector. In this study, agricultural credit performance of 81 provinces in Turkey in 2018 was compared by taking into consideration the value of total agricultural production, total cultivated area and the amount of agricultural credit used. The data used in this study were collected from the Banking Regulation and Supervision Agency (BRSA) and the Turkish Statistical Institute. In order to determine relationships between the 81 provinces of Turkey, one of the nonhierarchical clustering method, i.e. the K-means clustering method was applied using SPSS Clementine data mining software. As a result, the credit performance of provinces was evaluated and similarities and differences were revealed using agricultural production value, total cultivated land, agricultural credit volume data.