GSARS Enhances Statistical Capacity with Specialized Workshops in Madagascar and Comoros

The Food and Agriculture Organization of the United Nations (FAO), through the Global Strategy for the Improvement of Agricultural and Rural Statistics (GSARS), has championed a robust training workshop in SPSS data processing and analysis tools aiming to enhance and refine the availability and quality of agricultural and rural statistics in developing nations. 

Amidst the backdrop of deficits in capacity witnessed in numerous developing countries, this training, delivered in two substantial phases, has employed workshops to heighten the institutional, human, and financial capacities of Agricultural Statistical Systems (SSA). The GSARS program has developed support modules to impart crucial knowledge on various activities in the process of data collection and processing from surveys. 

Expert trainer TIOTSOP Blaise has led capacity-building workshops introducing three GSARS II Modules in Madagascar and Comoros. Each module spanned five days, cumulating in a comprehensive three-week training period per country. However, to facilitate optimal assimilation and minimize continuous time away for participants, the mission was bifurcated into two phases: 

First Mission: Presented Modules 1 and 2 in Antananarivo and Module 1 in Moroni. 

Second Mission: Delivered Module 3 in Antananarivo and Modules 2 & 3 in Moroni. 

The training aimed to (i) significantly augment the capacity of National Statistical Systems (SSN) in adopting a quality approach in data processing and analysis and (ii) share experiences regarding data processing and analysis procedures through popularly utilized packages such as SPSS. 

In the workshops, participants from both Madagascar and Comoros made notable advancements in their proficiency with statistical software. They strengthened their skills in leveraging various functionalities and features for survey data processing and analysis, as explored in the first module. Moreover, their ability in using programming features of statistical software to streamline data handling and analysis was notably enhanced. Particularly in Madagascar, participants developed a keen awareness and understanding regarding the significance and optimal practices in quality assurance during the statistical survey process. This was covered in the second module, which also guided them on employing statistical softwares to identify errors in survey data, enabling them to undertake pertinent correction actions, thereby uplifting the overall data quality. This progression not only bolstered their practical skills but also underlined the crucial role of quality assurance in statistical processes, thereby enriching the participants' overall competency in managing and interpreting agricultural and rural data. 

The diversity of participants in the training sessions from both countries enriched the learning environment, highlighting a spectrum of professionals from statisticians to data scientists, each bringing distinct perspectives and varying degrees of familiarity with statistical software. The majority of participants in Madagascar were from the Ministry of Agriculture (11 out of 14), while in Comoros, participants represented a mix of sectors, with about 40% coming from the Ministry of Agriculture. Furthermore, efforts toward gender equity were visible, especially in Madagascar, where over 40% of participants were female, while in Comoros, the representation was lower with a single female participant. 

The Global Strategy remains committed to nurturing the development of robust, accurate, and reliable statistical systems in rural and agricultural contexts within developing nations, pivoting towards a future where data-driven policymaking propels sustainable agricultural production and rural development. This pivotal work is made possible with the generous support of our donors, The European Union and the Bill and Melinda Gates Foundation, whose contributions are instrumental in enabling these initiatives to thrive and generate tangible improvements in agricultural and rural statistical systems across developing nations.