The Digitalisation of African Agriculture Report 2018-2019

An inclusive, digitally-enabled agricultural transformation could help achieve meaningful livelihood improvements for Africa’s smallholder farmers and pastoralists. It could drive greater engagement in agriculture from women and youth and create employment opportunities along the value chain. At CTA we staked a claim on this power of digitalisation to more systematically transform agriculture early on. Digitalisation, focusing on not individual ICTs but the application of these technologies to entire value chains, is a theme that cuts across all of our work. In youth entrepreneurship, we are fostering a new breed of young ICT ‘agripreneurs’. In climate-smart agriculture multiple projects provide information that can help towards building resilience for smallholder farmers. And in women empowerment we are supporting digital platforms to drive greater inclusion for women entrepreneurs in agricultural value chains

An Analysis of Urban Land use land cover (LULC) Changes in Lilongwe City, Central Malawi (2002–2022)

Lilongwe, Malawi’s capital city, has grown nearly tenfold in the last 40 years with a 4-5% annual population growth rate, and the city’s population is projected to double over the next decade. Rural to urban migration and natural increase are the driving factors of the city’s urban expansion. Characterised by the urbanisation of poverty, Lilongwe is experiencing uncontrolled and unplanned urban expansion that has led to the growth of informal settlements. Urbanisation leads to land use land cover (LULC) changes that negatively impact the quality of life and the environment. Lilongwe faces many challenges, including high levels of poverty, inequality, poorly built infrastructure, lack of access to safe sanitation and clean water, urban flooding, and poor waste disposal. Effective land use planning is important in mitigating future urbanisation\u27s adverse effects. To prepare and plan for the inevitable future urban growth of the city, studies of historical land use land cover changes are essential in understanding the urbanisation trajectory of the city. This study used post classification change detection and the SLEUTH urban growth model to analyse land use land cover changes in Lilongwe from 2002 to 2022. Results revealed that Lilongwe’s urban growth is characterised by the expansion of built area coverage within and on the edges of already existing urban clusters. While urban growth is apparent in all parts of the city, it is concentrated in the northwest, southwest, and southeast

Revolutionizing Global Food Security: Empowering Resilience through Integrated AI Foundation Models and Data-Driven Solutions

Food security, a global concern, necessitates precise and diverse data-driven solutions to address its multifaceted challenges. This paper explores the integration of AI foundation models across various food security applications, leveraging distinct data types, to overcome the limitations of current deep and machine learning methods. Specifically, we investigate their utilization in crop type mapping, cropland mapping, field delineation and crop yield prediction. By capitalizing on multispectral imagery, meteorological data, soil properties, historical records, and high-resolution satellite imagery, AI foundation models offer a versatile approach. The study demonstrates that AI foundation models enhance food security initiatives by providing accurate predictions, improving resource allocation, and supporting informed decision-making. These models serve as a transformative force in addressing global food security limitations, marking a significant leap toward a sustainable and secure food future

Modelling of Floods in Urban Areas

This Special Issue publishes the latest advances and developments concerning the modelling of flooding in urban areas and contributes to our scientific understanding of the flooding processes and the appropriate evaluation of flood impacts. This issue contains contributions of novel methodologies including flood forecasting methods, data acquisition techniques, experimental research in urban drainage systems and/or sustainable drainage systems, and new numerical and simulation approaches in nine papers with contributions from over forty authors

Adoption of artificial intelligence based technologies in sub-saharan african agriculture

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceSub-Saharan Africa (SSA) is currently facing numerous agriculture related challenges such as climate change, lacking infrastructure, and limited institutional as well as economic support. However, current research does not provide holistic solutions to this problem. This study aims to shed light on this topic through the development of a model that can be used to assess the solution potential as well as high-level implementation requirements of selected artificial intelligence (AI) based agriculture technologies in the context of SSA. To thoroughly develop the above-mentioned model a design science approach was followed. First an in depth (systematic) literature review was conducted where the agriculture related challenges in SSA and state-of-the-art AI-based agriculture technologies are detailed. This step was followed by the creation of a model that aims to find a nexus between the researched challenges and available technologies as potential solutions. Furthermore, the framework outlines context specific technology adoption requirements. Lastly, expert interviews were conducted to validate and revise the proposed model. The final framework clearly highlights the positive impact AI based technologies can have in SSA’s agriculture and the basic conditions that need to be met to successfully implement them

Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

In this paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 98% were articles with at least 482 citations published in 903 journals during the past 30 years. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent

Data-driven appraisal of renewable energy potentials for sustainable freshwater production in Africa

Clean water scarcity plagues several hundred million people worldwide, representing a major global problem. Nearly half of the total population lacking access to safe and drinkable water lives in Africa. Nonetheless, the African continent has a remarkable yet untapped potential in terms of renewable energy production, which may serve to produce clean water from contaminated or salty resources and for water extraction and distribution. In this view, the analysis of possible scenarios relies on data-driven approaches due to the scale of the problem and the general lack of comprehensive, direct on-site experience. In this work, we aim to systematically review and map the renewable potentials against the freshwater shortage in Africa to gain insight on perspective possible policies and provide a readily usable and well-structured framework and database for further analyses. All reported datasets are critically discussed, organized in tables, and classified by a few metadata to facilitate their usability in further analyses. The accompanying discussion focuses on regions that, in the near future, are expected to significantly exploit their renewable energy potentials, and on the reasons at the basis of the local water shortage, including technological and distribution problems