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The rise in demand for high-quality crops post-1980s has transformed the agrifood value chain (VC) in many developing nations from traditional to transitional and modern stages. Small and medium-sized enterprises (SME) in the midstream segment spearhead the VC transformation during the transitional stage by modernising procurement through contract-based consolidation of high-quality crops at the local level. Supermarkets and multinational companies in the downstream segment lead the transformation to the modern stage by consolidating crops of stringent quality at national, regional and global levels. This transformation opens the doors to new market choices for farmers to engage with modern SMEs and supermarkets as opposed to traditional wholesalers, thereby increasing their possibilities to earn a higher income and improve their livelihood. This thesis explores how transactional attributes influence farmers' preferences to engage with modern SMEs over traditional wholesalers to identify ways of effective market integration of smallholders during the early phase of transformation. Earlier research focused on farmers' market preference for supermarket VCs in the modern stage. In addition, this thesis explores whether farmers gain income from market participation when a VC transforms from traditional to transitional and modern stages. Previous studies used static approaches and investigated the income impacts of market participation with supermarkets and SMEs at a single point in time during the transitional or modern stages. This thesis utilises a dynamic approach to visualise four hypothetical future scenarios of the transitional and modern stages and evaluate income impact across each scenario. Nepal's vegetable VC was chosen as a study context on account of its transformation from the traditional to the transitional stage. The thesis provides useful and timely information on effective and profitable VC integration initiatives, to policymakers and market actors in Nepal and other developing nations in Asia and Africa, where VCs are transforming. [...]

Remote sensing offers a low-cost method for estimating yields at large spatio-temporal scales. However, the use of high spatial and temporal resolution remote sensors to map field-level yields in heterogeneous smallholder systems in the developing world is not well understood. Here we examined the ability of Sentinel-2 satellite imagery to map field-level maize yields across smallholder farms in two regions in Oromia district, Ethiopia. We specifically evaluated how effectively different indices, MTCI, GCVI, and NDVI, and different models, linear regression and random forest regression, can be used to map field-level yields. We also examined how generalizable our models were if trained in one region and applied to another region, where no data were used for model calibration. We found that linear regression models that use MTCI led to the highest yield prediction accuracies (R2 ranging from 0.24 to 0.47), particularly when using only localized data for training the model. These models were not very generalizable, especially when models were applied to regions that still had significant haze remaining in the imagery. Our results highlight the ability of Sentinel-2 imagery to map field-level yields in smallholder systems, though accuracies are limited in regions with high cloud cover and haze.