Abstract

The aim of this article is to characterize the current situation of family farmers or smallholders in Brazil and establish a connection with the rural public policies that exist in the country. This study analyzed the most current available data regarding family farming in Brazil, which included almost 4.7 million smallholders and their characteristics. Two analytical tools for unsupervised learning were combined, Principal Component Analysis (PCA) and K-means clustering, which enabled the analysis of such a large database and the extraction of information concerning this sector. It was found that cooperative smallholders are considerably more likely to achieve higher incomes. A family farmer’s income and productivity are related to their region and are higher in the South and Southeast and lower in the Northeast region. Crop diversification presented a negative impact on family farming activity, although this practice is considered highly important for agricultural sustainability. These results confirm, based on the data, empirical findings regarding the sector and also reveal new information such as the negative impact that rural assistance services are demonstrated to have on smallholders’ income. Therefore, this study provides essential information to support policy makers in the process of formulating better and more efficient policies in order to strengthen smallholders in Brazil and guarantee food security in the future.

Key words: Family farm, rural programs, unsupervised learning, government support.