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Using the methodology developed by Graw and Husmann (2014), the map overlays three indicators (high agricultural potential, high poverty mass and high yield gaps) to identify areas with high potential for agricultural development and poverty reduction in Kenya. Data sources and thresholds: Agricultural potential: Suitability of currently available land area for rainfed crops, using maximising crop and technology mix, FGGD map 6.61 (2005), High: top 3 suitability classes (medium high, high and very high) Poverty mass: Number of poor people in Kenya (by district), KIHBS (2005/06), High: >300,000 per district Yield gap: Yield gap for a combination of major crops, FAO/IIASA - GAEZ (2000/05), High: < 0.25 (on a scale from 0-1, with the highest value in Kenya ca. 0.44) District boundaries: Kenya Central Bureau of Statistics (2003)
Mobile phones have become the most ubiquitous telecommunication technology in developing countries. To take advantage of this trend, businesses, government agencies and non-governmental organisations are increasingly turning their attention to the delivery of services through mobile phones (m-services) in areas such as health, education, agriculture and entertainment. In the agriculture sector, information services are most common while m-payments, virtual markets and supply chain management systems are also expanding. The use of mobile phones in agricultural service delivery is still at an early stage, however, and most of the services have yet to reach scale and long-term financial sustainability. The dissertation examines how m-services could facilitate the participation of farmers in agricultural innovation processes, including the development and adoption of agricultural technologies. Four types of services are identified: information and learning, financial services, access to inputs and access to output markets. Existing empirical evidence in this research area is still scarce. To date, most of the research has focused on mobile phones as such. Only a few studies have looked specifically at m-services and their findings are not clear-cut. Several of them highlight benefits for farmers, including improved management practices, higher productivity or higher prices, while others do not find positive impacts. Kenya is widely seen as frontrunner in the development of m-services in Sub-Saharan Africa. The growth of the vibrant technology scene was facilitated by a number of factors, including the improving network infrastructure, government regulations and a supportive innovation environment that offers access to innovation hubs, finance and human resources. The growing customer base provides a promising market for m-service developers and through the mobile payment service M-Pesa, many Kenyans are already familiar with the use of their mobile phone for non-call related activities. A range of m-services are available for Kenyan farmers. However, the reach and scale of these services is still limited despite the conducive environment and their impacts have not been assessed. The dissertation presents the case study of M-Farm, an m-service that offers price information and marketing services to Kenyan farmers. It examines how the service has impacted farmers' decision to adopt agricultural technologies and their ability to generate income from their use. Farmers were very enthusiastic about the positive impact of M-Farm on production decisions and income, but the study finds little other evidence to support this positive perception. Other constraints, such as risk of crop losses, lack of insurance and limited finances, were generally seen as more significant obstacles. The study also shows that the radio provides a viable alternative to disseminating price information in the early stages of production, while M-Farm becomes more important closer to the selling stage. Existing m-services in the developing world are barely scratching the surface of what is technology possible. The dissertation examines how current technology trends may impact m-service delivery to farmers in the future. Three trends are identified, i.e. the growing diversity of mobile connected devices to access m-services; the 'Internet of Things' which links objects and people through the network; and the increasing ubiquity of mobile networks and expanding user base. The dissertation presents two scenarios for the evolution of mobile technology trends (Status Quo and Big Leap) and assesses their implications for agricultural service delivery.
M-Farm provides daily crop price information via SMS, assists smallholder farmers to collectively sell their produce, and connects buyers and sellers via an internet- and mobile phone-enabled platform. The survey data was collected in May 2012 as part of a case study to assess the role of M-Farm in facilitating farmers' participation in agricultural technology innovation processes. Specifically, the survey aimed at assessing the extent to which M-Farm has influenced farmers' decision to adopt technologies and their ability to generate additional income through their use. The survey was undertaken in Rachuonyo and Migori districts in the Southwest of Kenya. The two study sites were chosen because farmers have access to two different bundles of services, thus allowing for a comparison between the two set-ups in a natural experiment setting. In Rachuonyo farmers only receive price information through M-Farm. In Migori they can access price information and also collectively sell their passion fruits through M-Farm. Different (although similar) questionnaires were used in the study sites.