AUTHOR=Hawinkel Stijn , De Meyer Sam , Maere Steven 

TITLE=Spatial Regression Models for Field Trials: A Comparative Study and New Ideas

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2022.858711

DOI=10.3389/fpls.2022.858711

ISSN=1664-462X

ABSTRACT=Naturally occurring variability within a study region harbours valuable information on relationships between biological variables. Yet spatial patterns within these study areas, e.g. in field trials, violate the assumption of independence of observations, setting particular challenges in terms of hypothesis testing, parameter estimation, feature selection and model evaluation. We evaluate a number of spatial regression methods in a simulation study, including more realistic spatial effects than employed so far. Based on our results, we recommend Generalised Least Squares (GLS) estimation for experimental as well as for observational setups, and demonstrate how it can be incorporated into popular regression models for high-dimensional data such as regularised least squares. This new method is available in the BioConductor R-package $pengls$. Inclusion of a spatial error structure improves parameter estimation and predictive model performance in low-dimensional settings, and improves feature selection in high-dimensional settings by reducing ``red-shift'': the preferential selection of features with spatial structure. In addition, we argue that absence of spatial autocorrelation in the model residuals should not be taken as a sign of a good fit, since it may result from overfitting the spatial trend. Finally, we confirm our findings in a case study on prediction of winter wheat yield based on multispectral measurements.