Physical Sciences and Mathematics Commons^™

A Comparison Of Geostatistical And Spatial Autoregressive Approaches For Dealing With Spatially Correlated Residuals In Regression Analysis For Precision Agriculture Applications, Ignacio Colonna, Matías Ruffo, Germán Bollero, Don Bullock

Conference on Applied Statistics in Agriculture

Regressions such as Grain yield=f(soil,landscape) are frequently reported in precision agriculture research, and are typically computed using conventional OLS methods, implicitly ignoring spatial correlation of the residuals. This oversight can have a marked effect on the final conclusions derived from these regressions. A further issue is, which approach should be used to account for this problem? We investigated this question using a 2 year data set that includes sitespecific soil and topographic information and soybean yields and compare regression results from direct covariance representation and spatial autoregressive approaches. Our results show that the coefficients from both spatial approaches are in …