Use of Mixed Effects Models accounting for residual spatial correlation to analyze soil properties variation in a field irrigated with treated municipal wastewater.

Knowledge about soil properties variation as effect of agronomic management is of great interest for assessing soil quality and should be investigated using appropriate methodological approaches. Irrigation with treated municipal wastewater (TWW) can be considered an important strategy to save limited freshwater resource and protect the environment. TWW composition varies among sites and over time, thus its effect should be monitored to avoid soil fertility decline in the medium to long term. In evaluating the effect of different agronomic managements on soil properties or plant response, data sampling and analysis play a crucial role to take into account variability that occurs at a scale smaller than the block size. Spatial dependence between observations and residuals may occur in the experimental fields and, if not properly considered, may result in erroneous conclusions about treatment significance (Hong et al., 2005; Littell et al., 2006). Linear mixed effects models (LMM) allow spatial components to be assessed and filtered from the total residual term of the model so improving the protection of the statistical tests (Rodrigues et al., 2013; Ventrella et al., 2016). In this study, LMMs accounting for residual autocorrelation were used to investigate the effect of a three year irrigation with TWW on soil properties with particular regard to organic carbon. Auxiliary information deriving from proximal geophysical sensors was also used to assess and describe main sources of variability of the experimental field.