Building the foundation of sustainable and efficient national statistical systems
Building the statistical capacities at national and regional levels is fundamental for countries to produce sufficient and reliable quantity and quality data to support evidence-based policy-making processes. However, in many low- and middle-income countries, statistical systems are often understaffed and workforces suffer from poor training, high turnover and limited career development opportunities. As a result, highly skilled employees capable of collecting, producing, disseminating and communicating official statistics are scarce.
One of the main objective of the Global Strategy is to provide training programmes in agricultural statistics to staff working in national statistical services to build a critical mass of agricultural statisticians and analysts that can support national and regional data production and use. To do so, the Global Strategy is working with countries on many different aspects to allow national statistical units to deliver their missions in modern data ecosystems:
- by establishing adequate human resources policies that allow the national statistical units to hire, develop and retain the skilled workforces that they need;
- by strengthening leadership and communication skills in national agricultural statistical agencies, both at organizational and individual levels, to ensure that data and statistics are reaching the desired audience and interpreted as intended;
- by facilitating access to training to ensure that staff in countries where there are no national training centers offering programmes in agricultural statistics. The project’s objective is to provide assistance to statistical training centers in developing, implementing and promoting their training programmes as well as strengthening their infrastructure capabilities. In addition, 50 students will be provided with a scholarships at postgraduate level in agricultural statistics in selected statistical training centers;
- by providing basic theoretical knowledge and skills in agricultural statistics to data producers, in particular to statisticians with no or limited background in agriculture statistics, and to economists or agronomists with basic knowledge in statistics, to pave the way for the development of sustainable agricultural statistics systems.