This document is accompanyng the volume Public Agricultural Research in an Era of Transformation: The Challenge of Agri-Food System Innovation (available in TAPipedia here), which provides some of the groundwork in answering the question of how the CGIAR system and other public agricultural research organisations should adapt and respond to an era of transformation framed by the SDGs.
The Applied Research and Innovation Systems in Agriculture project (ARISA) started in December 2014 with the aim of increasing net farm income for 10,000 smallholder farming households in eastern Indonesia. The project was designed to address a key challenge in agricultural research for development: how to ensure that proven research outputs1 are available and accessible for use in farming communities.
This paper reflects on the experiences of the Applied Research and Innovation Systems in Agriculture (ARISA) project to caralyse agricultural innovation by bringing RIs and private sector (PS) actors together in partnerships. Facilitating partnerships to caralyse innovation requires capacity building of individuals as well as institutional change. This paper examines the approaches to parnering for innovation, successes, challenges and lessons learned
This note presents an outline of the main strands of the innovation systems research associated with the ARISA project. It begins by locating this in the current discourse on concepts and policy perspectives on innovation and capacity building before setting out key areas of research inquiry and research activities
Applied Research and Innovation Systems in Agriculture (ARISA) was implemented by CSIRO in collaboration with Indonesian partners. This multi-year program seeks to strengthen collaboration between public research organisations and agribusinesses in order to incubate and deliver technology and business solutions appropriate to smallholder farmers. The geographic focus of the program was Eastern Indonesia.
Agricultural Technology Management Agency (ATMA) is a single-window institutional arrangement for technology and information dissemination at the district level and an attempt was made to assess the dairy extension system in the context of ATMA in Guntur district of Andhra Pradesh during 2016. The study revealed that along with organized dairy extension services, ATMA is an important alternative to provide extension services to the dairy sector as animal husbandry sector is an existing allied sector for the ATMA.
This chapter examines the current state of agricultural extension reforms and their linkages to the agricultural research system reforms in India and identifies the policy options and strategic priorities for making it relevant, responsive, and efficient. It explores how the National Agriculture Research Systems (NARS) responded with its own set of reforms that were sought to increase its relevance and its linkages to the extension system reforms.
Agricultural Extension Reforms in South Asia: Status, Challenges, and Policy Options is based on agricultural extension reforms across five South Asian countries, reflecting past experiences, case studies and experiments. Beginning with an overview of historical trends and recent developments, the book then delves into country-wise reform trajectories and presents several cases testing the effectiveness of different types (public and private) and forms (nutrition extension, livestock extension) of extension systems.
Agricultural information is transferred through social interactions; therefore, ties to agricultural informants and network structures within farmers’ local neighborhoods determine their information-gathering abilities. This paper uses a spatial autoregressive model that takes account of spatial autocorrelation to examine such network connections, including friendship networks and advice networks, upon farmers’ knowledge-gathering abilities during formal agricultural training.
This study aims to determine the factors that influence group dynamics, and to find out whether there is a relationship between agricultural extension programs to farmer group dynamics. Data analysis method used is a Likert Scale and analyzed descriptively qualitatively. The results showed that the dynamics of the Sri Makmur Farmers Group were categorized as Less Dynamic. This is because the elements of the farmer group dynamics are not going well. Based on the results of a Likert Scale Research with Spearman Rank Correlation obtained a value of 0.221 at a confidence level of 95% (α 0.05).