This analysis is composed of two parts. The evolving structure of Kenya’s agrifood system (AFS) and its contribution to national development is assessed using a series of Social Accounting Matrixes (SAMs) for Kenya for the period 2009–2019. Economic performance is also assessed at subsector level to better understand the contributions of different agrifood value chains to Kenya’s development and economic transformation in recent years. The analysis reveals that the effects of AFS transformation stretches well beyond primary agriculture, creating jobs and income opportunities throughout the economy. In fact, the off-farm components of the AFS have grown more rapidly than primary agriculture, although differences in market structure and internationally tradable status contributed to varied patterns of growth across value chains. The analysis further reveals that it is the domestic market, not exports, that has driven the recent growth in Kenya’s AFS. Rapid urbanization and increased income generating opportunities in the rural nonfarm sector are causing dietary patterns to shift, which will continue to shape the transformation of the AFS in Kenya. A forward-looking analysis using IFPRI’s Rural Investment and Policy Analysis (RIAPA) model – an economywide modeling framework – assesses the potential impacts of future value chain growth on development outcome indicators. The findings reveal that value chains differ considerably in their effectiveness in achieving various development outcomes. Promoting only one value chain may also result in trade-offs across these development goals. For example, the coffee and tea value chains are highly effective at raising off-farm employment in the AFS, but they have weak impacts on diet quality. Likewise, cattle and dairy have strong off-farm GDP effects within the AFS, but are relatively ineffective at reducing poverty. By promoting and investing in several value chains simultaneously, policymakers can leverage synergies and mitigate trade-offs across development outcomes associated with specific value chains. The RIAPA analysis here suggests that joint promotion of the pulses and oilseeds, fruits and nuts, and cattle and dairy value chains will be most effective at impacting the full spectrum of development outcomes tracked in the model, including poverty, growth, jobs, and diets. However, the final value chain selection may change depending on the importance policymakers attach to the respective development outcomes.

Kenya experienced significant economic development in the 2009 to 2019 period. Gross domestic product (GDP)—an indicator of the economy’s size—expanded by an annual average of 5 percent (KNBS 2022). This exceeded population growth and helped raise household incomes, leading to a decline in poverty rates; more importantly, for the first time in at least three decades, the country experienced a decline in the absolute number of poor people (World Bank 2022). While the global COVID-19 pandemic caused negative economic growth in 2020, the economy recovered quickly in 2021. Kenya was also largely spared the adverse effects of the global commodity market disruptions arising from the Russia-Ukraine war that started in 2022 and from the global recession in 2023 (Arndt et al. 2023; Diao and Thurlow 2023). Kenya’s GDP growth is projected to reach 5.0 percent in 2023 and 5.3 percent in 2024 (World Bank 2023), suggesting that the economy is resuming its pre-pandemic growth trajectory. Agriculture remains an important sector, accounting for about one-quarter of GDP and nearly half of Kenya’s employment. It has thus played an important role in economic development. The sector has grown alongside the rest of the economy despite many challenges including climate variability (Ochieng et al. 2020), weak rural infrastructure (Benin and Odjo 2018), declines in farm size (Jayne et al. 2016), and limited access to farm inputs combined with poor agronomic management (Worku et al. 2020). In this brief, we look beyond primary agriculture to understand how Kenya’s broader agrifood system (AFS) is contributing to growth and transformation in the country.

The new Kenyan government faces a complex domestic and global environment, and it is widely expected to address key food and agricultural challenges with a new set of policies and programs. This policy brief presents key recommendations from a forthcoming book, Food Systems Transformation in Kenya: Lessons from the Past and Policy Options for the Future, which provides research-based “food for thought and action” to support the Kenyan government’s efforts to improve food security.

This paper assesses the structure of Rwanda’s current and evolving agrifood system and its contribu-tion to national development. The paper reiterates the point that Rwanda’s agrifood system stretches well beyond primary agriculture and creates jobs and income opportunities throughout the economy. While off-farm components of Rwanda’s agrifood system have generally grown more rapidly than pri-mary agriculture in recent years, growth varies across value chains of the agrifood system in the stud-ied period. The growth diagnostic in this paper reveals that it is domestic markets that have driven the recent growth in Rwanda’s AFS other than exports. The paper’s forward-looking analysis assesses potentially differential impacts of value-chain develop-ment efforts on broad development outcomes. The analysis measures the synergies and trade-offs of value-chain development in the context of an inclusive agricultural transformation. Such analysis is conducted using the Rwanda Computable General Equilibrium (CGE) model – an adaption of IFPRI’s Rural Investment and Policy Analysis (RIAPA) model to the Rwandan context. The modeling results indicate that value chains differ considerably in their effectiveness in achieving development goals and there are significant trade-offs among different development goals from pro-moting a specific value chain. The value chains that make a larger contribution to growth or job crea-tion are not necessarily effective in reducing poverty or improving dietary quality – for example, value chains for coffee and tea – while value chains that play an important role in improving dietary quality may contribute less to job creation – such as vegetables or fruits. While there is no single value chain that can achieve all development goals effectively, it is possible to select a diversified set of value chains that complement each other in achieving different development goals. This latter strategy is a more realistic approach to growth and development.

Substantial model variability exists regarding the likely meteorological impact of climate change on Kenya, particularly with respect to future precipitation levels. Significant regional differences are expected, largely due to Kenya’s diverse climate profile. Overall, temperatures are projected to increase while future precipitation levels are highly uncertain. Climate change is expected to significantly affect coastal areas, including because of sea level rise risks, stronger winds, and an overall warmer and drier climate. This will likely harm important ecosystems, including wetlands, mangroves, and coral reefs. Some models project that arid and semi-arid areas may become drier and hotter, which would exacerbate preexisting water scarcity and agricultural challenges for the already vulnerable communities living there. That said, these projections are not corroborated by all models. The climate change impact on other areas, particularly south and west of Mount Kenya, could generally be positive, as it would provide even better conditions for agriculture. The key climate change risk for Kenya is from extreme events, in particular droughts and floods. The frequency and intensity of such events is likely to increase because of climate change. They also often lead to adverse knock-on effects, such as soil erosion, land degradation, and pest breakouts. Overall, Kenya’s updated Nationally Determined Contribution (NDC) (2020) estimates that between 2010 and 2020, adverse climate change-related events led to annual socioeconomic losses of 3–5 percent of total gross domestic product (GDP).

This book provides a synthesis of the key issues and challenges facing agriculture and food production in Southern Africa. Southern Africa is facing numerous challenges from diverse issues such as agricultural transformations, growing populations, urbanization and climate change. These challenges place great pressure on food security, agriculture, water availability and other natural resources, as well as impacting biodiversity. Drawing on case studies from Lesotho, Malawi, Mozambique, Namibia, South Africa, Swaziland, Tanzania, Zambia and Zimbabwe, the chapters in this book consider these challenges from an interdisciplinary perspective, covering key areas in constraints to production, the most important building blocks of good farming practices, and established and emerging technologies. This book will be a valuable support for informing new policies and processes aimed at improving food production and security and developing sustainable agriculture in Southern Africa. This informative volume will be key reading for those interested in agricultural science, African studies, rural studies, development studies and sustainability. It will also be a valuable resource for policymakers, governmental and nongovernmental organizations, and agricultural practitioners. This title has been made available as Open Access under a Creative Commons Attribution-Non Commercial-No Derivatives (CCBY-NC-ND) license and can be accessed here: https://www.taylorfrancis.com/books/e/9780429401701

This open access book examines the interactions between India’s economic development, agricultural production, and nutrition through the lens of a “Food Systems Approach (FSA).” The Indian growth story is a paradoxical one. Despite economic progress over the past two decades, regional inequality, food insecurity and malnutrition problems persist. Simultaneously, recent trends in obesity along with micro-nutrient deficiency portend to a future public health crisis. This book explores various challenges and opportunities to achieve a nutrition-secure future through diversified production systems, improved health and hygiene environment and greater individual capability to access a balanced diet contributing to an increase in overall productivity. The authors bring together the latest data and scientific evidence from the country to map out the current state of food systems and nutrition outcomes. They place India within the context of other developing country experiences and highlight India’s status as an outlier in terms of the persistence of high levels of stunting while following global trends in obesity. This book discusses the policy and institutional interventions needed for promoting a nutrition-sensitive food system and the multi-sectoral strategies needed for simultaneously addressing the triple burden of malnutrition in India.

The present edition of Nature & Faune journal emphasizes the role of natural renewable resources within the framework of Africa’s agricultural transformation. Central to the transformation agenda is achieving greater prosperity which can improve peoples' lives and livelihoods. These improvements of lives and livelihoods cover their economic well-being; their environment; socio‐cultural and political sphere of influence. The conditions for modernizing Africa’s agriculture entail transforming not only production processes but also the products. This issue of the Journal highlights the unique features of Africa’s agriculture, including (i) the urgent need for improving productivity; (ii) the importance of the agricultural sector in Africa’s economies in terms of employment; and (iii) the climate-resilient opportunities within agriculture to cope with climate change challenges. It draws attention to the fact that the agriculture sector offers possibilities for increased productivity while also adapting to and mitigating climate change thus safeguarding also future production.

Smallholder farming systems contribute a substantial quantity of the food consumed in many lower and middle-income countries and contribute to the national and local economies. Despite the importance of smallholder farming, a transformation is needed in order to deliver food security and decent incomes for the farmers themselves and at the national level. This transformation must also be sustainable in terms of environmental impacts and social equity in order to be successful in the long term. The pressures of population growth, climate change, and land fragmentation compound the problem. Addressing these overlapping issues is a big challenge. One obstacle is the lack of good quality granular data linking these issues together. Household surveys are the workhorse method for gathering such data, but there are well-known problems that prevent household survey data from building up a “big picture” and delivering insights beyond the geographical boundary of each individual study. Such obstacles include the lack of access to datasets, differences in survey design, and respondent biases. Agile, data-oriented research tools can help to overcome these challenges. We use the term “agile” to imply methods that do not attempt exhaustive measurements, which are designed to be easy to use, and which entail some degree of flexibility in terms of adaptation to local conditions and integration with other tools or methods. Often these methods also nudge the behavior of tool users towards best practices. In recent years various research tools and approaches have been published which fit within our definition of “agile data-oriented research tools”. The domains these tools function in include monitoring and evaluation, intervention targeting, tailored information delivery, citizen science, credit scoring, and user feedback collection; all with the over-arching aim to improve data quality and access for those studying the sustainable development of smallholder farming systems. The goal of this Research Topic is to better define that niche, the ecosystem of tools and current practices, and to explore how such approaches can provide the underpinning knowledge required for the transformation of smallholder farming systems. One example of an agile data-oriented research tool is the Rural Household Multi-Indicator Survey (RHoMIS). It is a modular, digital system for building household surveys addressing the common topics in smallholder development. It was purposefully designed to give a broad overview of the farm system whist keeping survey duration to a minimum, to be user-friendly in implementation, and to be sufficiently flexible to function in a broad variety of locations and projects. Since 2015 it has been used by 30 organizations in 32 countries to interview over 34,000 households. The tool and database are open access and a community of practice is developing around the tool. We particularly welcome contributions that engage with the RHoMIS tool and data. However, we also describe the tool in order to provide an example of what is meant by an agile data-oriented research tool, and welcome contributions focusing on other tools or methodologies. We encourage the submission of manuscripts addressing the above topic, and those which fit within one of the following three sub-themes: (i) Perspectives or review articles which explore the niche, best practices, or promising approaches in agile data-oriented research tools for smallholder farm system transformation. Also, technology and code articles that describe new tools are welcomed. (ii) Original research articles presenting analyses based on data derived from agile data-oriented tools used at the project level. Examples include impact evaluations, adoption studies, targeting studies, or adaptive management, and should reflect on the additional benefit leveraged by the agile method applied. (iii) Original research articles that make use of the large amounts of data generated by such agile methods and/or link between agile data and other data sources. Examples include meta-analyses of data from multiple studies, layering data collected from different agile tools, or linking agile data to remote sensing or large-scale modeling outputs.