Much of the current analysis on agricultural productivity is hampered by the lack of consistent, high quality data on soil health and how it is changing under past and current management. Historically, plot-level statistics derived from household surveys have relied on subjective farmer assessments of soil quality or, more recently, publicly available geospatial data. The Living Standards Measurement Study of the World Bank implemented a methodological study in Ethiopia, which resulted in an unprecedented data set encompassing a series of subjective indicators of soil quality as well as spectral soil analysis results on plot-specific soil samples for 1,677 households. The goals of the study, which was completed in partnership with the World Agroforestry Centre and the Central Statistical Agency of Ethiopia, were twofold: (1) evaluate the feasibility of integrating a soil survey into household socioeconomic data collection operations, and (2) evaluate local knowledge of farmers in assessing their soil quality. Although a costlier method than subjective assessment, the integration of spectral soil analysis in household surveys has potential for scale-up. In this study, the first large scale study of its kind, enumerators spent approximately 40 minutes per plot collecting soil samples, not a particularly prohibitive figure given the proper timeline and budget. The correlation between subjective indicators of soil quality and key soil properties, such as organic carbon, is weak at best. Evidence suggests that farmers are better able to distinguish between soil qualities in areas with greater variation in soil properties. Descriptive analysis shows that geospatial data, while positively correlated with laboratory results and offering significant improvements over subject assessment, fail to capture the level of variation observed on the ground. The results of this study give promise that soil spectroscopy could be introduced into household panel surveys in smallholder agricultural contexts, such as Ethiopia, as a rapid and cost-effective soil analysis technique with valuable outcomes. Reductions in uncertainties in assessing soil quality and, hence, improvements in smallholder agricultural statistics, enable better decision-making.