How accurate are app-based farmer self-reports? Evidence from coffee production records in Burundi

Governments and donors increasingly collect agricultural data through digital tools, but evidence remains limited on how production estimates from farmer-mediated tools compare with those from enumerator surveys. We address this with coffee production data from about 300 farmers in Burundi. Farmers logged sales in a mobile application and answered an end-of-season in-person survey (CAPI); we benchmark both against records from six Coffee Washing Stations where cherry is weighed. App reports fall 21 per cent below the benchmark; the in-person survey stays within 4 per cent. The app's ranking accuracy varies with use: among farmers with above-median participation, the app ranks farmers about as well as the in-person survey (Spearman 0.82 against 0.87, not significant); below the median it does worse (0.60 against 0.83). The shortfall comes from unlogged transactions rather than understated amounts, and it grows with recorded production, so a constant correction cannot remove it. Our results suggest combining the two modes rather than replacing one with the other: app data can rank farmers where use is sustained, but aggregate production figures still require enumerator surveys or administrative records.

Authors

Publication Details

Published
2026-10-28
DOI
https://doi.org/10.31235/osf.io/4b7vy_v2
Primary Topic
Survey Methodology and Nonresponse
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

How accurate are app-based farmer self-reports? Evidence from coffee production records in Burundi

J.M. Fernández Sánchez, Elena Serfilippi, Carlos de los Ríos
Survey Methodology and Nonresponse
preprint

How accurate are app-based farmer self-reports? Evidence from coffee production records in Burundi

J.M. Fernández Sánchez, Elena Serfilippi, Carlos de los Ríos
preprint en

Abstract

Governments and donors increasingly collect agricultural data through digital tools, but evidence remains limited on how production estimates from farmer-mediated tools compare with those from enumerator surveys. We address this with coffee production data from about 300 farmers in Burundi. Farmers logged sales in a mobile application and answered an end-of-season in-person survey (CAPI); we benchmark both against records from six Coffee Washing Stations where cherry is weighed. App reports fall 21 per cent below the benchmark; the in-person survey stays within 4 per cent. The app's ranking accuracy varies with use: among farmers with above-median participation, the app ranks farmers about as well as the in-person survey (Spearman 0.82 against 0.87, not significant); below the median it does worse (0.60 against 0.83). The shortfall comes from unlogged transactions rather than understated amounts, and it grows with recorded production, so a constant correction cannot remove it. Our results suggest combining the two modes rather than replacing one with the other: app data can rank farmers where use is sustained, but aggregate production figures still require enumerator surveys or administrative records.

Zero hunger
Survey Methodology and Nonresponse
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon
How accurate are app-based farmer self-reports? Evidence from coffee production records in Burundi — J.M. Fernández Sánchez, Elena Serfilippi, et al. · (2026) | TGRS Research Map | TGRS