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.