Agricultural machinery manufacturers historically referred to the intermediate players for selling, maintenance, customer service and/or training of equipment appear to interact with farmers and end-users. Intermediate players have therefore faced the burden to master the technology, in constant evolution, and the associated training needs at the interface between sophisticated equipment and the end-user and its sociological characteristics (age, education, background, etc.).
During the period 2013-2019, the Agricultural Extension in South Asia (AESA) Network has served as a platform for collating the voices, insights, concerns, and experiences of people in the extension sphere of South Asia. Diverse professionals shared their concerns on the present and future of Extension and Advisory Services (EAS) in the form of blog conversations for AESA. Together, all of these individuals who are involved, interested and passionate about EAS, discussed ways to move beyond some of the seemingly intransigent problems that are hindering the professionalization of EAS.
This article surveys the trends in agricultural extension programmes and services found across the world, including privatization, decentralization, and pluralism. The general movement from top-down extension services to demand-driven programmes is explored along with its impact on the skills needed by extension professionals.
The Newsletter of the Tropical Agriculture Platform (TAP) provides regular updates on global activities by TAP and its partners, on the CDAIS projects and on upcoming related events. This issue specifically refers to the period from November 2018 to September 2019.
This research aims to add to the literature new insights about the interaction processes, which are implemented in different interactive extension approaches, by analysing how farmers attending different extension events shape a network of indirect interactions
Este trabajo describe la evolución desde los sistemas de transferencia de conocimientos agrarios más tradicionales, con transmisión lineal de la investigación a los usuarios, hasta sistemas que propicien en mayor medida la innovación, con la intervención de multiplicidad de actores entre los que se incluyen investigadores, agricultores, asesores, educadores, políticos, empresarios, etc.
On-farm agricultural innovation through incorporation of new technologies and practices requires access to resources such as knowledge, financial resources, training, and even emotional support, all of which require the support of different actors such as peers, advisors, and researchers. The literature has explored the support networks that farmers use and the overall importance ranking of different support actors, but it has not looked in detail at how these networks may differ for different farmers.
This paper examines the determinants of participation in an outsourced extension programs and its impact of smallholder farmers' net farm income in Msinga, KwaZulu-Natal, South Africa. A multi-stage sampling technique was used to obtain cross-sectional farm-level data from a sample of 300 farm households, using a structured questionnaire for the interview. The determinants and impacts of participation were estimated using the propensity score matching (PSM) to account for sample selection bias.
The paper is structured as follows. First, definitions and conceptualisations of trust are considered, before moving on review the literature on trust in rural network models of business support. Next, the empirical study design is described, which consisted of case studies of business advice programmes offered to artisanal food enterprises in Northern Ireland and displaying varying degrees of trust. The results of the empirical study are reported and then discussed, with reflections on how trust evolved in each case, and the ways in which trust was lost
The objective of the study was to identify a viable trade-off between low data requirements and useful household-specific prioritizations of advisory messages. At three sites in Ethiopia, Kenya, and Tanzania independently, we collected experimental preference rankings from smallholder farmers for receiving information about different agricultural and livelihood practices. At each site, was identified socio-economic household variables that improved model-based predictions of individual farmers’ information preferences.