The policy implications of cumulative innovation are essential to consider in order to mitigate risk and capitalise on opportunities as digitalisation transforms agriculture. One project that involves imagining the future of the sector and aims to develop the necessary tools and infrastructure is the Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO) Digiscape Future Science Platform (FSP). This paper explores the policy framework encompassing these tools and elucidates considerations for future governance in Australia.
In this paper is proposed to conduct a first stage AKIS diagnostic exercise developing a map of the system of the actors involved in water quality protection and catchment management that interact with the farming community. Specifically we will use the tool to understand: (a) Who are the players? (b) What roles do they have? (c) what is their position in the Innovation System. A key part in changing the regulatory or public incentive system is to change the behaviour not only of the farmers but also of the policy makers to facilitate the movement to a more localised approach.
Social structure, especially in the form of social networks, affects the adoption of agricultural technologies. In light of an increasing focus on new demand-driven agricultural extension approaches that leverage social networks as an opportunity, too little is known about (a) which network characteristics matter? and (b) how do specific network characteristics matter? This paper investigates the impact of social networks in relation to smallholder dairy production technology adoption in Ethiopia.
The study focuses on how levels of innovation, measured by complexity and investments requirements of the adopted technologies, relates to innovative behavior and complying with social responsibility practices, as two indicators of the farmer's behavior towards innovation. A typology of farmers with different technological
levels was constructed based on multivariate techniques, according to the adoption of seven technologies. The main objective of the study was to relate SR and innovative behavior to the technology clusters
This paper presents Thorvald II, a modular, highly re-configurable, all-weather mobile robot intended for applications in the agricultural domain. Researchers working with mobile agricultural robots tend to work in a wide variety of environments such as open fields, greenhouses, and polytunnels. Until now agricultural robots have been designed to operate in only one type of environment, with no or limited possibilities for customization.
In the AgriSpin project (2015-2017) fifteen organisations involved in innovation support tried to understand better how each of them made a difference in helping farmers to innovate. In principle, each partner organisation hosted a Cross Visits of 3 – 4 days, to present a number of interesting innovation cases in which it was involved. The visiting team, composed of colleagues from other partner organisations, interviewed key actors in each case, and gave feedback about pearls, puzzlings and proposals in these innovation processes.
Innovation is important for development in the private sector, but inevitably public sector also needs innovation to enhance services and processes, with research on employee-driven digital innovation in public organizations being limited. Was proposed a study in a public organization based on action design research (ADR) methodology to enhance theoretical knowledge and develop practice in relation to employee-driven digital innovation.
In this paper, presented at the 8th European IFSA Symposium ( Workshop 6: "Change in knowledge systems and extension services: Role of the new actors") in 2008, the authors discuss a conceptual framework that understands innovation processes as the outcome of collaborative networks where information is exchanged and learning processes happen. They argue that technical and economic factors used to analyse drivers and barriers alone are not sufficient to understand innovation processes.
3D Move To See (3DMTS) is a mutli-perspective visual servoing method for unstructured and occluded environments, like that encountered in robotic crop harvesting. This paper presents a deep learning method, Deep-3DMTS for creating a single-perspective approach for 3DMTS through the use of a Convolutional Neural Network (CNN). The novel method is developed and validated via simulation against the standard 3DMTS approach.
This paper describes a novel approach to create a collaborative space for grassland innovations contributing to profitability of European grassland farms while preserving environmental benefits. Innovative modes of collaboration between practice and science are enabled by an international thematic network across eight European member states.