9 pages, In agricultural research for development adoption of new technology tends to be cast in categories: adoption, partial adoption, dis-adoption or non-adoption. While these may serve for pragmatic classification and measures for project success or impact they fail to properly acknowledge the ongoing and independent efforts of farmers (and others) in experimentation and integration of knowledge across a range of sources. This paper explores responses to practices for cattle management introduced during a research project, at project close, and five years after the project has finished. We consider the perceptions and application of new knowledge by farmers, extension staff, and policy makers. By taking a longer-term view, we demonstrate how farming households adapt and integrate knowledge from different sources into their daily practice, influenced by local institutions and changing cultural expectations, as well as external researchers. We also consider the influence of changing government priorities and incentives in steering farm-management decisions. Results suggest that a focus on measures to build capacity and empower farmers with information to adapt and respond to change, regardless of project activities, is a much more important goal and indicator of impact than measuring adoption.
14 pages, via online journal, Designing effective policies for economic development often entails categorizing populations by their rural or urban status. Yet there exists no universal definition of what constitutes an “urban” area, and countries alternately apply criteria related to settlement size, population density, or economic advancement. In this study, we explore the implications of applying different urban definitions, focusing on Tanzania for illustrative purposes. Toward this end, we refer to nationally representative household survey data from Tanzania, collected in 2008 and 2014, and categorize households as urban or rural using seven distinct definitions. These are based on official administrative categorizations, population densities, daytime and nighttime satellite imagery, local economic characteristics, and subjective assessments of Google Earth images. These definitions are then applied in some common analyses of demographic and economic change. We find that these urban definitions produce different levels of urbanization. Thus, Tanzania's urban population share based on administrative designations was 28% in 2014, though this varies from 12% to 39% with alternative urban definitions. Some indicators of economic development, such as the level of rural poverty or the rate of rural electrification, also shift markedly when measured with different urban definitions. The periodic (official) recategorization of places as rural or urban, as occurs with the decennial census, results in a slower rate of rural poverty decline than would be measured with time-constant boundaries delimiting rural Tanzania. Because the outcomes of analysis are sensitive to the urban definitions used, policy makers should give attention to the definitions that underpin any statistics used in their decision making.
