Unveiling Rwanda’s Master Sampling Frame strategy for improving agricultural survey and data precision

This paper reflects on the efforts of the National Institute of Statistics of Rwanda (NISR) to enhance agricultural surveys in general and the reliability of crop statistics in particular by implementing the related Master Sampling Frame (MSF) strategy, a crucial method for achieving reliable agricultural statistics and robust data systems in the country.

A MSF is a frame that can be used for several surveys, possibly in different fields. The frame developed and implemented by NISR in Rwanda currently consists of two main components: a “List Frame” (LF) of large or specialised producers, for which exhaustive data are regularly collected, and an “Area Sampling Frame” (ASF) for other agricultural holders, to ensure completeness of the crop coverage. Introduced in 2012, the ASF was initially based on segments with physical boundaries and involved a complete enumeration of the plots in the sampled segments. Subsequent methodological reviews enhanced cost‑efficiency by adopting square segments and subsampling points within the sampled segments. This approach allowed NISR to reduce the number of points required per segment and to increase the number of sampled segments, leading to better coverage.

After the introduction, the paper’s second section covers the historical background and the recent transformative measures implemented by the Rwandan Government to revolutionise agricultural statistics production. It highlights the importance of accurate agricultural data for evidence‑based planning and decision‑making, enhancing the national Gross Domestic Product (GDP), and closing data gaps for key users and stakeholders. The third section discusses the implementation process of the MSF strategy for agricultural surveys in Rwanda. It highlights the methodologies and approaches used, including the incorporation of the Geographical Information System (GIS) in MSF building and use, the evolvement of the MSF surveys, challenges encountered during the implementation of the MSF, and other surveys that help to produce additional agriculture indicators. The fourth section presents the outcomes of using the MSF for agricultural surveys, comparing its advantages over the previous systems and the improvements in data quality and precision, as well as lessons learned. Finally, the paper concludes by highlighting and summarising key takeaways from the development and implementation process, best practices for future agricultural surveys in Rwanda and beyond, and recommendations for further improvements.

Mwizerwa, J.C., Mutebutsi, A., Abayisenga, A., Gallego, J. (2025). Unveiling Rwanda’s Master Sampling Frame strategy for improving agricultural survey and data precision. Statéco, 119.