Zimbabwe strengthens agricultural statistical capacity
In Zimbabwe, a key consideration in the agriculture sector is not only the collection of data, but also strengthening its use for practical evidence in planning, investment, and farmer support. Decisions on crop production, livestock, rural livelihoods, and food security rely on analysts who can manage and interpret data accurately. While there is existing capacity within institutions, there remains a need for continuously strengthening the capacity in the use of modern statistical tools to further enhance the translation of available data into actionable policy insights.
To address this gap, the Food and Agriculture Organization of the United Nations (FAO), in collaboration with the Zimbabwe National Statistics Agency (ZIMSTAT) and the Ministry of Agriculture, Mechanization and Water Resources Development, conducted a two-week STATA Statistical Software Training in Harare from 1 – 12 June 2026. The training focused on equipping government analysts and researchers with practical, hands-on skills to manage, analyse and present data using STATA, one of the most widely used statistical software packages in research, government and development work.
“The training was designed to be immediately applicable to participants’ day-to-day work, drawing on real household survey datasets relevant to Zimbabwe’s national context. It progressed from foundational concepts to advanced data management, analysis and output production techniques over ten structured working days,” said Patrice Talla, FAO Subregional Coordinator for Southern Africa and Representative to Zimbabwe.
The FAO Regional Office for Africa has been supporting Zimbabwe to strengthen agricultural statistics as part of broader efforts to improve evidence-based planning in the agrifood sector. Reliable agricultural statistics are essential for understanding the structure of farming systems, tracking productivity, guiding public investment, improving food security analysis, and ensuring that services and policies respond to the realities faced by farmers, especially smallholders. The STATA training forms part of this wider support by helping national institutions build the technical skills needed to analyse agricultural survey and census data more effectively.
The workshop also came at an opportune time as Zimbabwe is stepping up preparations for its first National Agricultural Census. The census aims to establish a comprehensive baseline of agricultural holdings across household and non-household sectors, as well as community-level characteristics that shape rural development outcomes. In April 2026, FAO and Government experts conducted a 9-day technical workshop to develop core census tools and strengthen national capacity, underlining the country’s growing momentum towards a credible, data-driven census process.
For agricultural institutions, STATA is more than a software package, it is a practical tool for turning raw data into evidence. It helps analysts organize large datasets, clean errors, combine data from different sources, run statistical analysis, and generate tables and outputs that can directly inform planning and reporting. In an agriculture context, these skills matter for analysing production trends, household survey data, food security indicators, and administrative data used to monitor programmes and guide investment decisions. Strengthening STATA skills therefore helps ensure that agricultural statistics are not only collected but also translated into insights that improve policy and programme delivery.
The 10-day workshop was strategically designed as a practical learning process rather than a purely theoretical course. Using real datasets relevant to Zimbabwe, the training combined instructor-led demonstrations with guided hands-on exercises, individual and group problem-solving, daily recap sessions and question-and-answer discussions.
Participants moved step by step through the full analytical workflows, from understanding the STATA interface and creating do-files, to cleaning and validating data, merging and reshaping datasets, applying labour-saving commands, producing descriptive tables, and exporting outputs. Pre- and post-training assessments were also built in to help track learning and reinforce knowledge retention.
“This training is important because agricultural data must do more than sit in reports, it must help us plan better, monitor results and respond to the needs of farmers. STATA gives us practical tools to analyse data more efficiently and with greater confidence, which is especially valuable as Zimbabwe strengthens its agricultural statistics systems,” said Brighton Mangaiso, Monitoring and Evaluation Officer in the Agriculture Ministry.
The training also created a space for collaboration between statisticians, analysts and monitoring officers from the two key national institutions, helping to build a shared understanding of good data management and reproducible analysis. This is especially important in agricultural statistics, where the quality of analysis can influence how resources are targeted, how surveys are designed, and how progress is measured across the sector.
“This workshop has strengthened our ability to manage and analyse agricultural data in a more systematic way. Beyond learning commands, we gained practical skills we can immediately apply to survey and census datasets, which will improve the quality, consistency and usefulness of the statistics we produce,” said Dominic Tafirenyika, Principal Statistical Officer at ZIMSTAT.
Looking ahead, the skills gained through the workshop are expected to support follow-up work on agricultural surveys, routine statistical production and preparations for the National Agricultural Census. The next steps will likely include continued practice using national datasets, application of STATA skills in ongoing analytical work, and further technical backstopping to strengthen end-to-end data management and analysis. As Zimbabwe advances toward census implementation readiness, investments in practical analytical skills such as these will be critical for producing credible agricultural statistics that can guide stronger policies, smarter investments and more effective support to farmers.
