Global Spatial Autocorrelation of Corn-Belt Farmland Values and Rental Rates

Giving empirical application to spatial econometrics models is the main fall in the economic theory approach. Controlling for spatial correlation and dependence is becoming an important part in farmland valuation literature. Farmland is the most basic resources for agricultural production. Price of potential crops, location, productivity level, and some external investment factors have an influence on farmland values and rental rates. Values of farmland and rental rates are expected to be spatially clustered since agricultural production systems are similar, soil productivity indexes change slowly across the landscape. We test spatial correlation in US county-level non-irrigated farmland rental rates from 2009 to 2014 and state-level farmland values from 1950 to 2021 using spatial econometric diagnostic tools. Spatial correlation was detected in farmland values, percent changes in farmland values, and non-irrigated cash rental rates. Results indicated spatial spillovers in the farmland markets had differing interpretations pending geographic scale of datasets