Impact of Agro-Processing Among Small-Scale Farmers in Sub-Saharan Africa

The potential of agro-processing among farmers in Sub-Saharan Africa (SSA) is immeasurable. Its effects range from the improvement of livelihoods to stimulating commercial agriculture. This research aimed to examine the impact of agro-processing among rural smallholder farmers in SSA, identifying specific themes based on regional influence, contemporary trends, technology and innovation, research methodologies, and existing gaps. This study is based on a Systematic Literature Review of 20 articles selected from a pool of 93 scientific documents focused on agro-processing and its impact on farmers, especially smallholder farmers in rural SSA. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was applied to select articles from the Web of Science database. The findings showed that in 50% of the studies reviewed, the income effect was the most significant factor, followed by market effects with varying degrees of impact, farmer groups in the form of cooperatives, and profitability as an indicator of price-driven value enhancement, all of which were prominent among smallholder farmers and along the value chains. Agribusiness and agro-processing development also benefit from complementary factors, such as technological innovation like the use of nanotechnology in the agri-food industry, training, extension services, and information sharing that directly influence product quality, shelf-life extension, food safety and security, postharvest management, and biofortification for nutritional improvement in food products. Emerging trends in commercialization, farmers' welfare, and policy reforms highlight the need for sustainable value chain activities. The impact of agro-processing spreads along the value chain stakeholders, underscoring the need for formal systems and solid market integration. Future research should be conducted on livestock products targeting individual countries in the region based on empirical analysis using mixed methods.