Landini, Fernando (author), Beramendi, Maite (author), and University of La Cuenca del Plata
Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)
School of Psychology, University of Buenos Aires
Format:
Online journal article
Publication Date:
2019-07-24
Published:
Argentina: Taylor and Francis
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 101 Document Number: D10880
18 pages, online journal article, Purpose
This article aims at designing and validating a psychometric scale to assess extensionists’ and advisors’ beliefs about extension and innovation.
Design/Methodology/approach
The scale was developed by drawing upon results from a previous empirical research as well as insights from a literature review on extension and innovation approaches. The theoretical framework used to write the items was validated by 12 international experts from 11 countries. 608 Argentine extension workers completed the questionnaire. Replies were analysed using Exploratory and Confirmatory Factor Analysis.
Findings
The scale has a good fit and satisfactory level of internal consistency. Five factors were identified: Dialogue and horizontal coordination; Transfer of technology; Blame on farmers; Participatory, farmer-led extension; and Self-critical attitude.
Practical implications
The scale has multiple and different uses, including research, theory development, institutional practice, diagnosis, and teaching.
Theoretical implications
Results show that a horizontal, facilitative extension approach shares a common epistemology, as well as underlying values and assumptions, with territorial development and with an innovation systems perspective, and that both contrast with a traditional transfer of technology approach. Nonetheless, practitioners would not tend to see these two contrasting perspectives as contradictory but as complementary.
Originality/Value
The scale is the first validated psychometric instrument, based on an ample theoretical framework, that allows for a quantitative assessment of beliefs about extension and innovation.
4 pages., Online from publisher., "For the first time, a landmark report on digitalisation for agriculture (D4Ag) in Africa compiles and highlights data on digital solutions that are enabling the transformation of African agriculture."
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.
