Estimation of cropland prices in Rio Grande do Sul by multiple linear regression and principal component analysis

Abstract

This study aimed to price croplands in Rio Grande do Sul State (southern Brazil) and point which variables had the most significant impact on prices. The main purpose was achieved using multiple linear regression and principal component analysis. The variables used in this study were planted area, production, price, and yield of the commodities soybean, wheat, and corn. The period under analysis was from January 1994 to December 2017 (biannual observations). Multiple linear regression showed that five variables contributed to land pricing, being three related to soybean and two to wheat. Multivariate analysis grouped the investigated variables into clusters and indicated their influence, in addition to providing information on land prices and reducing variable dimensionality from fourteen original variables to three principal components to be analyzed. The two analyses complemented each other so that the croplands’ price was explained by three variables, in which two corroborated in constructing the pricing model for croplands.

Principal Component Analysis of Selected Indicators of Wheat in European Countries

“…CAP), both for member states of the union, and for European countries that have acquired the status of EU membership [9]). This fact should always be taken into account in view of the constant increase in the population in the world, which is why it is necessary to increase the area under wheat [10,11]. Countries that have acquired the status of candidates for EU membership are recommended to constantly build and strengthen the legislative framework and to constantly work on monitoring the implementation of support for public policy measures) [12,13].…”