Urbanisation is changing food systems globally, and in particular in sub-Saharan Africa and South Asia. This transformation can affect rural livelihoods in multiple ways. Evidence on what enabling conditions are needed to materialise the opportunities and limit risks is scattered. Here we review scientific literature to elaborate on how urbanisation affects food systems, and on the enabling conditions that subsequently shape opportunities for rural livelihoods. We find that urbanisation leads to a rising and changing food demand, both direct and indirect land use changes, and often to more complex market linkages. Evidence shows that a wide range of enabling conditions can contribute to the materialisation of opportunities for rural livelihoods in this context. Reviewed evidence suggests that the connectivity to urban centres is pivotal, as it provides access to finance, inputs, information, services, and off-farm employment. As a result, physical and communication infrastructure, the spatial pattern of urbanisation, and social networks connecting farmers to markets are identified as important enabling factors for the improvement of rural livelihood outcomes. Our findings suggest that coordinated and inclusive efforts are needed at different scales to make sure rural livelihoods benefit from urbanisation and food system transformation.

No abstract

No abstract

In the past decade, several efforts have been made to project armed conflict risk into the future. This study broadens current approaches by presenting a first-of-its-kind application of machine learning (ML) methods to project sub-national armed conflict risk over the African continent along three Shared Socioeconomic Pathway (SSP) scenarios and three Representative Concentration Pathways (RCPs) towards 2050. Results of the open-source ML framework CoPro are consistent with the underlying socioeconomic storylines of the SSPs, and the resulting out-of-sample armed conflict projections obtained with Random Forest classifiers agree with the patterns observed in comparable studies. In SSP1-RCP2.6, conflict risk is low in most regions although the Horn of Africa and parts of East Africa continue to be conflict-prone. Conflict risk increases in the more adverse SSP3-RCP6.0 scenario, especially in Central Africa and large parts of Western Africa. We specifically assessed the role of hydro-climatic indicators as drivers of armed conflict. Overall, their importance is limited compared to main conflict predictors but results suggest that changing climatic conditions may both increase and decrease conflict risk, depending on the location: in Northern Africa and large parts of Eastern Africa climate change increases projected conflict risk whereas for areas in the West and northern part of the Sahel shifting climatic conditions may reduce conflict risk. With our study being at the forefront of machine learning (ML) applications for conflict risk projections, we identify various challenges for this arising scientific field. A major concern is the limited selection of relevant quantified indicators for the SSPs at present. Nevertheless, ML models such as the one presented here are a viable and scalable way forward in the field of armed conflict risk projections, and can help to inform the policy-making process with respect to climate security.

The growing demand for food, water, and shelter change the way people use land. These changes have affected or even caused conflict in several locations. However, conflicts do not erupt in isolation; they are the result of multiple interacting causes. There is limited structural understanding of these causes. In this study, we systematically coded case studies that report on conflict related to land-use change, including deforestation in commodity frontiers, agricultural development on common land, and urban development. Based on an analysis of 62 cases, we identified population growth, overlapping land rights, ethnic fragmentation, and economic inequality as the most frequently reported root causes, while rises in land prices was the most often reported proximate cause. Reported institutional causes suggest that the problem is not necessarily the complete absence of governance mechanisms, but rather that governance mechanisms are not fully equipped to deal with the complexities of the observed land-use changes.

No abstract

Peer review statementThe paper is a non-peer reviewed pre-print submitted to EarthArXiv.

Decreasing yields due to water stress form a threat to rural livelihoods and can affect migration dynamics, especially in vulnerable regions that lack the capacity to adapt agriculture to water stress. But since migration is complex, non-linear and context-dependent, it is not feasible to predict the precise number of people that will migrate due to water stress. It is possible to map the different conditions that shape regional vulnerabilities and the number of people affected. This study presents a vulnerability approach to identify areas on the African continent where emigration associated with water stress is expected to be relatively high by 2050 under a middle-of-the-road scenario (SSP2) and compares the results with the 2010 situation. By utilizing among other indicators the water yield gap, the impact of water stress on rainfed agricultural crop yields is included, reflecting the impact of water stress on rural livelihoods depending on crop farming. The analysis was done on a water-province level, 393 in total. Clusters of potential emigration associated with the impacts of water stress on agriculture are projected for parts of the Sahel, the Horn of Africa and regions of Angola. The regions where migration associated with water stress is expected to be relatively high by 2050 are approximately the same as those of 2010, although more people are projected to be living in these water-stressed regions. By developing this vulnerability approach, this manuscript enlarges the current insights regarding future clusters of water stress-related migration.