© 2016 Elsevier Inc. Introduction: We have recently mapped ALS spatial risk in Ireland using Bayesian and cluster analysis methods at electoral division (ED) and small area (SA) levels. As a number of metal elements (both minerals and toxins) have been proposed as risk factors for ALS, here we extend this analysis to include soil constituents from the Irish National Soils Database as Bayesian conditional auto-regression covariates to determine associations with small area ALS risk. Methods: Data on 45 different soil parameters were obtained under license from National Soils Database (via Irish EPA). We interpolated average values of each soil constituent for each small area using ordinary kriging. All cases of ALS in Ireland from January 1995 to December 2013 were identified from the Irish ALS register and observed and age and gender standardised expected cases were calculated for each SA. Besag-York-Mollié (BYM) models were then built including each parameter from the national soils database in turn as a Bayesian covariate in the BYM model. Models were compared using the deviance information criterion (DIC) and separate models were built for ALS subtypes. Results: 1701 ALS patients were included - 959 (56%) were male, 938 (55%) had limb onset ALS. 315 Bayesian models were built in total. Of the 315 models built, only one resulted in a coefficient that did not overlap zero. For limb onset cases, total magnesium had a mean coefficient of 0.319 (credible interval 0.033-0.607). Discussion: We report the first spatial analysis of potential association between ALS and soil minerals using a population-based dataset collected over 18 years. Our sole non-zero finding is likely a random finding due to the high number of models built. We did not find any evidence to support soil mineral and toxin levels as risk factors for ALS. However as soil parameters are an ecological assessment of exposure in a given area, individual level measures of exposure are required.