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The "State of AI in Climate Action" report explores the pivotal role of artificial intelligence (AI) in addressing the multifaceted challenges of climate change across Africa. As the continent contends with increasing threats such as extreme weather events, food insecurity, and biodiversity loss, the integration of AI offers transformative potential to enhance resilience, promote sustainable development, and protect vulnerable ecosystems and communities. This third report of the “State of AI in Africa” series, developed under the Researcher-in-Residence Program at the International Centre of Expertise in Montreal on Artificial Intelligence (CEIMIA), with the support of the International Development Research Centre (IDRC), provides an in-depth analysis of the current state of AI in climate action across sub-Saharan Africa. This report will enable policymakers, developers, researchers, entrepreneurs, and citizens to develop AI-enabled solutions that are both contextually relevant and culturally appropriate to address climate change.

Although Climate Change is a global phenomenon, the impact in Africa is anticipated to be greater than in many other parts of the world. This expectation is supported by many factors, including the relatively low shock tolerance of many African countries and the relatively high percentage of African workers engaged in the agricultural sector. High-income countries are increasingly turning their focus to climate change adaptation, and Artificial Intelligence (AI) is a critical tool in those efforts. Algorithms using AI are making better predictions on the short- and long-term effects of climate change, including predictions related to weather patterns, floods and droughts, and human migration patterns. It is not clear, however, that Africa is (or will be) maximally benefitting from those AI tools, particularly since they are largely developed by highly developed countries using data sets that are specific to those same countries. It is therefore important to characterize the efforts underway to use AI in a way that specifically benefits Africa in climate change adaptation. These efforts include projects undertaken physically in Africa as well as those that have Africa as their focus. In exploring AI projects in or about Africa, this chapter also looks at the sufficiency of such efforts and the variety of approaches taken by researchers working with AI to address climate change in Africa.

Increasing the milk production of small dairy producers is necessary to cover the increase in milk demand in Tanzania. Currently, the population of people in both Tanzania and the world has increased and is predicted to increase more in the year 2050. The use of multilevel association rule mining methods to mine strong patterns among smallholder dairy farmers could help in identifying the best dairy farming practices and increase their milk production by adopting them. This study employed multi-level association rule mining to discover strong rules in three clusters, resulting in three levels of rules in each cluster. These three clusters were high, medium, and low milk producers. Rules were obtained for feeding practices, milk production, and breeding and health practices. These rules represent strong patterns among smallholder dairy farmers that could help them improve their dairy farming practices and have a gradual increase in milk production, from low to medium and from medium to higher milk production. Smallholder dairy producers would be provided with recommendations on their dairy farming practices, using rules based on the cluster to which they belong that could help them achieve higher milk production.