P.C.S. Traoré, C.L. Mberi Kimpolo, S. Ndung’u, A.M. Nenkam, V.C. Ahonon, E.B. Chinedze, E. Claudius, M. Dia, R. Kouemo, R.K. Kyalo, M.R. Mahary, J.R. Nteupe, O.A. Ojo, Z.O. Olufunke, R.E. Omer, T.A. Randriamalalanirina, I. Uwerikowe, A.A.D. Wounfa, G.M. Wowo, D. Annerose, R. Tabo and A.M. Whitbread
DAKAR, KIGALI (AIMS/ICRISAT/Manobi Africa) – Standing up to Covid-19’s institutional and social gridlock, a team of 20 AIMS, ICRISAT and Manobi Africa scientists (55% women) engaged in an unprecedented learning experience in the largest intake of AIMS alumni to date. With no time to spare, these trailblazers powered up a cohort of the Mathematical Sciences for Climate Resilience (MS4CR) Internship Program into a nimble remote working team spanning 13 countries and 10 time zones.
This is important, because the food security challenges exacerbated by Covid-19 present a unique rehearsal opportunity to improve the resilience of food systems against future climate and market shocks, and simply transform how we produce food. But beyond the disciplinary silos and dominant discourses about climate vulnerability, injecting fresh thinking and cold analytical power is a great way to understand, predict and change the behavior of non-ergodic systems[1].
Leveraging the legacy of the CGIAR, world’s premier network of agricultural research centers and Africa’s growing digital startup economy, ICRISAT and Manobi Africa offer the real-world workbench to put artificial intelligence and machine learning to use in the agricultural and water sectors. Looking to transform the way we predict smallholder yields, optimize rural water distribution systems, aggregate user feedback on technologies, or de-risk agricultural investments, the team will build new algorithms and workflows to power inclusive business service platforms such as agCelerant and UtilitY85. This partnership provides AIMS with an opportunity to increase the contribution of African mathematical scientists to finding solutions to climate change-related challenges in Africa.
In turn, these internships offer AIMS alumni high quality work experience to solve very practical societal challenges. In addition to contributing their strong mathematical and computational skills to assigned projects, AIMS alumni also improve their domain knowledge in the fields of agriculture, water and food security. In fact, this hyper-local cradle of innovation may help AIMS and its eponymous Next Einstein Initiative deliver more than the next Einstein: a new generation of world polymaths[2].
agCelerant solutions are adopted by MDBs such as the Islamic Development Bank in the context of its Regional Rice Value Chain Program. agCelerant technical development is also facilitated by:
- the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) project on Capacitating African Stakeholders with Climate Advisories and Insurance Development (CASCAID);
- the European Commission’s Horizon 2020 research and innovation program under grant agreement No 776309 (NADiRA – Nurturing Africa’s Digital Revolution for Agriculture); and
- the International Fund for Agricultural Development (IFAD) under grant number 2000002575, implemented by Alliance Bioversity-CIAT with AR4D support from the European Commission for the year 2019.
The technical development of UtilitY85 solutions is financed by Manobi Africa through contracts with water utilities and agencies such Benin’s Agence Nationale d’Approvisionnement en Eau Potable en Milieu Rural (ANAEPMR) with funding support from the World Bank.
The MS4CR Internship Program, one of the Work Integrated Learning Programs under the AIMS Industry Initiative, is implemented with the aid of a grant from the International Development Research Centre, Ottawa, Canada, and with financial support from the Government of Canada, provided through Global Affairs Canada (GAC).
[1] Stochastic processes whose statistical behavior changes over time, analogous to a gear shift in car driving
[2] Individuals whose knowledge span a significant number of subjects, known to draw on complex bodies of knowledge to solve specific problems, such as Leonardo da Vinci.
