Lessons from agricultural surveys

Data collected in household and agricultural surveys are crucial to monitor changes in food production, livelihoods of people depending on agriculture, and environmental performance of agricultural systems.

Our research article published in Experimental Agriculture discusses common challenges in data collection and evaluates the reliability of survey data which has implications for monitoring food security status for example.

Similar topics are discussed by Andrew Dillon and colleagues and published in a guidebook on best practices in Agricultural Survey Design. The guidebook is based on lessons learned from the Living Standards Measurement Study program managed by the World Bank’s Development Data Group and other survey programs.

Some interesting challenges and lessons learned are:

  • Survey data needs to be carefully evaluated for outliers, missing values, values outside plausible ranges
  • It cannot be assumed that a variable reported in one survey will agree with the same variable in another survey
  • Land area measurement bias: small plot areas over-estimated, large plot areas under-estimated
  • Challenges to livestock measurements
  • Recall bias depends on frequency of visits, the type of events and the time period of the event
  • Merging several surveys has a range of challenges but increases sample size and representation of different systems
  • Longitudinal surveys over 10+ years very rare and actual household tracking not necessarily possible
  • Survey data to be viewed as complementary to other data sources, not a substitute

Among the survey datasets available publicly, I often use national agricultural surveys, the living standards measurements study – integrated surveys on agriculture (LSMS-ISA), and the Rural Household Multi-Indicator Survey – RHoMIS.


Posted on

June 14, 2022

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