Critical data studies (Kenya)

Authored by Javiera Atenas

This introduction is thought in the context of Kenya

Information and Communication Technologies (ICT) are being used across the world to generate efficiency gains for farmers. This has led to an information and data explosion with an associated boom in new applications, tools, actors, business models, and entire industries. Agri-food systems are being transformed.

Beyond the technological developments, data for and from farmers has become a growth area, driving expectations and investments in big data (but also small data), blockchain technology, precision agriculture, farmer profiling and e-extension. Investing in data-driven agriculture is expected to increase agricultural production and productivity, help adapt to or mitigate the effects of climate change, bring about more economic and efficient use of natural resources, reduce risk and improve resilience in farming, and make agri-food market chains much more efficient. Ultimately, it will contribute to worldwide food and nutrition security.

Smallholders, in particular, have much to gain from data – small improvements in their operations are likely to provide larger gains at household level, proportionally, and, if the improvements are widely adopted, the whole agricultural sector in many countries that depend on smallholder agri-food systems can be transformed.

However, for smallholders to benefit from data-driven agriculture, tools and applications need to be designed for their specific situations and capacities; they – and the organizations that support them – need to grow their capacities to become smart data users and managers; measures are needed to ensure that farmer-generated data is not exploited or misused; and smallholders, usually the least powerful parts of a value chain, must grasp every opportunity to be included in the collective data flows within agri-food systems.

Based on data there are questions that can be answered much quicker. Such questions can be:

  • Where does our food come from?
  • Can we manage risks in our farm and take control measures against droughts or pests?
  • Are we able to predict problems such as floods or low yields?
  • Can we make informed decisions on what to grow, what treatment to apply, when to plant, treat or harvest?

Technologies today allow us to build services to answer these questions but data only offers these opportunities when it is usable. For this reason, we are going to devote the first unit to understand what is data and what does it mean ‘to be usable’, namely to open.

Reference for this text: Digital and Data-Driven Agriculture: Harnessing the Power of Data for Smallholders (2018). Ajit Maru, Dan Berne, Jeremy De Beer, Peter Ballantyne, Valeria Pesce, Stephen Kalyesubula, Nicolene Fourie, Chris Addison, Anneliza Collett, Juanita Chaves. Published 01 MAY 2018, available from https://doi.org/10.7490/f1000research.1115402.1, the material is CC-BY-SA)

  Visit our podcast series with international experts

   The full booklet with the content can be accessed here

   Go to the open data for empowerment (OD4E) workshop

 

Learning Outcomes

  1. Understanding the basics of open data
  2. Understanding the key principles of open data
  3. Understanding the basics of Open Science
  4. Understanding data ethics
  5. Understanding the concepts of data agency and sovereignty
  6. Developing ideas to innovate using open data

The big wins of Open Data for agriculture and nutrition

Icon for resources.

Key Complementary Resources

  1. Johnson, J. A. (2014) ‘From open data to information justice’, Ethics and Information Technology, vol. 16, no. 4, pp. 263–274. https://link.springer.com/article/10.1007/s10676-014-9351-8
  2. Baack, S. (2015) ‘Datafication and empowerment: how the open data movement re-articulates notions of democracy, participation, and journalism’, Big Data and Society, vol. 2, no. 2, pp. 1–11. https://journals.sagepub.com/doi/10.1177/2053951715594634
  3. Arzberger, P., et al., (2004) ‘Promoting access to public research data for scientific, economic, and social development’, Data Science Journal, vol. 3 (November), pp. 135–152. https://www.rug.nl/research/portal/publications/promoting-access-to-public-research-data-for-scientific-economic-and-social-development(baeac76c-ba55-4a05-aed4-73d92f8ef41b)/export.html
  4. Bezjak, S., Clyburne-Sherin, A., Conzett, P., Fernandes, P. L., Görögh, E., Helbig, K., ... & Ross-Hellauer, T. (2018). The open science training handbook https://www.fosteropenscience.eu/content/open-science-training-handbook
  5. Davies, T., Walker, S. B., Rubinstein, M., & Perini, F. (2019). The state of open data: Histories and horizons (p. 592). African Minds. https://stateofopendata.od4d.net/
  6. Gurin, J., Bonina, C., & Verhulst, S. (2019) Open Data Stakeholders - Private Sector. In T. Davies, S. Walker, M. Rubinstein, & F. Perini (Eds.), The State of Open Data: Histories and Horizons. Cape Town and Ottawa: African Minds and International Development Research Centre. Print version https://zenodo.org/record/2677777
css.php