The Technical Centre for Agricultural and Rural Cooperation (CTA) confirms closure by end of 2020.
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A revolution underway


Potential of open and big data in Africa

Better statistics, better policies and better development results - this winning combination is still far from being a reality in Africa. However, substantial progress is being made and a ‘data revolution’ is underway, as promoted by the United Nations to improve decision making and measure sustainable development progress.

Agriculture has a pivotal role in African economies by providing employment, creating wealth and reducing poverty, malnutrition and food insecurity. Yet today this role is largely unrecognised due to the lack of reliable, comprehensive and streamlined statistical data.

It has not always been so. Until the late 1970s, states had some capacity to generate data that they used to prepare and monitor national agricultural and rural development policies. But this was followed by a decline because of the lack of funding for statistical data systems, particularly agricultural statistics. “A process such as the Millennium Development Goals (MDGs), which encouraged many donors to focus their aid on health and education sectors, does not have a very positive impact on agricultural statistics,” says Christophe Duhamel, Coordinator of the Global Office of the Global Strategy to Improve Agricultural and Rural Statistics (GSARS).

Access to grassroots data

The international community is more concerned about agricultural and food security issues and has been looking at the problem of the lack of availability of agricultural statistics. This led to the adoption of the GSARS by the United Nations Statistical Commission in 2010. With a 5-year budget of €76 million, currently partially financed by the Bill & Melinda Gates Foundation and the British Department for International Development, GSARS is striving to fulfil the need for top quality agricultural statistics. Its initiatives are focused in three areas: producing a minimum set of baseline data, better mainstreaming of agricultural statistics in national statistical systems and capacity building. The GSARS global office, hosted at FAO headquarters in Rome, will develop the methodology and coordinate actions in five regions: Africa, Asia-Pacific, Latin America-Caribbean, Middle East and the Commonwealth of Independent States. Technical assistance for Africa is handled by the African Development Bank (AfDB), while the Economic Commission for Africa takes care of training.

In practice, an evaluation of the statistical system of each country is systematically carried out to draw up a blueprint for potential activities, which will be part of a national statistical development strategy. For some Sahelian countries, the emphasis is often on livestock production, while for others it may be on fishing or crop production. “Technical assistance will thus be tailored to the priorities of the concerned country, but often everything is an urgent priority,” says Duhamel.

Regarding training, the priority is to offset the severe shortage of agricultural statistics - roughly half of all African ministries of agriculture have no statisticians. Two aspects are considered to cope with this situation: offering scholarships and capacity building within the network of regional statistical training centres in Abidjan, Dakar, Yaounde, Kampala and Dar es Salaam, through specific curricula designed for training future generations of agricultural statisticians.

“Many African countries manage to conduct an agricultural census every 10 years, which is essential because it is the foundation of all agricultural statistical systems. However, between two censuses there are not sufficient funds to cover current statistics collection. The aim of the GSARS is to strengthen survey systems to be able to collect information during this intercensal

period,” says the GSARS Coordinator. Survey systems could also be improved through the use of technology such as remote sensing or tablet computers. This would make it easier to stratify areas in order to identify land- use patterns or to target household surveys. Cape Verde

is one of the most advanced countries regarding the use of these technologies for agricultural statistics collection.

Collected agricultural statistics then have to be mainstreamed into the national statistical system. This requires improvements in coordination mechanisms between governments and donors to ensure some degree of sustainability in the statistical system development process.

Funding through the Comprehensive Africa Agriculture Development Programme (CAADP)

The challenge to achieve a high level of quality in the collection, processing and dissemination of statistical data is certainly great but an unprecedented financial effort has been engaged for this task. Two key factors could also accelerate the process. First, several sustainable development goal indicators are applicable to agriculture and sustainable development for the post-2015 period. This should give a boost, like the

MDGs had regarding health and education data. But there is one slight note of caution, agricultural statistics information is much more complicated to collect than health and education data because it requires on-farm and farming household surveys.

The CAADP could also kickstart the process. In a report on its first 10 years of activity, the New Partnership for Africa’s Development concluded that CAADP monitoring and implementation was hindered by the shortage of statistical data. GSARS therefore decided to integrate - and thus fund - an agricultural statistics development component in African national agricultural investment plans. This GSARS input ensures long-term financing and there is hope that the tangible results obtained in Malawi, Mozambique, Rwanda (see box) and Tanzania, will ramify to other areas. However, as the main donors are English-speaking agencies, it is important to ensure that francophone countries are not overlooked and will obtain sufficient support for these initiatives.

Prevalence of mobile phone technology

ICTs also offer cost-effective ways to collect and disseminate data. This technology has already proven effective in price collection. Market information systems (MIS) are rapidly developing throughout Africa. Farmers receive timely information by SMS that helps them make decisions, and the range of information disseminated is constantly expanding to include data on production, cropping practices, weather conditions, etc. Esoko is an ICT service that was initially launched in Ghana, but is now present in 11 African countries and concerns some 150,000 farmers. Moreover, N’Kalo (see Field Report), the RESIMAO MIS platform in West Africa and M-Farm in Kenya are continuing to develop. And there are many other examples of this trend. The point is to collect a variety of information for very quick dissemination to subscribers.

The success of ICTs in this field should, however, not overshadow the unanswered questions regarding how to make these services cost-effective and thus sustainable, since most of them are being developed with donor funding. Moreover, the specific impacts of these instruments on policies and producers are still not clear.

Mobile phones are also the gateway to big data in Africa. This is almost the only way for people to access available external data to meet their analysis and forecasting needs.

Hence it is essential to collaborate with mobile phone operators and their partners to develop innovative services tailored to local people’s needs.

The use of these data for development applications is clearly still in its infancy. In 2012, the mobile carrier Orange launched the Data for Development Challenge in Cote d’Ivoire and Senegal as a contribution to the development of projects based on telephone data to address transportation, health and agricultural needs. Over 500 researchers from around the world participated in this challenge. Using big data to foster development is also the approach taken by the United Nations in its Global Pulse innovation initiative, which is striving to find new ways to utilise data analysis technologies to revolutionise economic development and aid in the poorest countries.

Potential of open data

In recent years, systems involving open data - data that is freely available for everyone to use and share - have been taking shape in Africa. In 2012, AfDB launched the Africa Information Highway programme to facilitate wider public access to statistical data from 54 countries via an open data platform. Several countries, such as Burkina Faso, Kenya and South Africa, have also set up an open government data system.

Regarding agriculture, two major initiatives are worth noting: CIARD, a collaborative movement between institutions involved in research and rural development, and the Global Open Data Initiative for Agricultural and Nutrition. CIARD was founded in 2008 to promote open access to agricultural knowledge through its Checklist of Good Practices and the RING portal it launched in October 2013 to support global efforts to provide global unrestricted access to relevant agricultural and nutritional data.

These two major initiatives complement other existing specialised platforms, like CGIAR Consortium’s Cassavabase ( containing data on cassava crops, and Toto Agriculture (, an agricultural information sharing platform that was developed by the INSEAD Business School. This platform hosts information in over 100 languages on weather, soil, plants, etc., from 180 countries. Organisations such as FAO and the World Bank have also been pioneers in this field.

Open data, potentially combined with information from statistical databases and/or big data, may also provide practical solutions for farmers in ACP countries, e.g. via mobile applications. The hackathons launched by CTA and its Eastern African and Caribbean partners to develop applications and platforms to meet agricultural challenges are in the same vein.

Open data and big data offer many possibilities for data collection, access and use, but Duhamel points out that, “statistics is primarily an inferential science. Thus to produce relatively reliable statistics, it is essential to have a sample that is representative of the ‘population’ we want to study. The final data quality cannot be improved simply by having more data (big data), it’s the representativeness of the chosen sample that counts.” Regarding open data, he regrets that this tool is now mainly in the hands of computer specialists, at the expense of thematicians.