The digital transformation in agriculture introduces new challenges in terms of data, knowledge and technology adoption due to critical interoperability issues, and also challenges regarding the identification of the most suitable data sources to be exploited and the information models that must be used.
The determination of bunch features that are relevant for bunch weight estimation is an important step in automatic vineyard yield estimation using image analysis. The conversion of 2D image features into mass can be highly dependent on grapevine cultivar, as the bunch morphology varies greatly. This paper aims to explore the relationships between bunch weight and bunch features obtained from image analysis considering a multicultivar approach.
Accurate and timely crop mapping is crucial for environment assessment, food security and agricultural production. However, for the areas with high landscape heterogeneity and frequent cloudy and rainy weather, the insufficient high-quality satellite images limit the accuracy of crop classification. The recently launched Chinese GF-6 wide field-of-view camera (WFV) with a revisit cycle of 4-day and spatial resolution of 16-meter shows great potential for agricultural monitoring.
The co-creation and sharing of knowledge among different types of actors with complementary expertise is known as the Multi-Actor Approach (MAA). This paper presents how Horizon2020 Thematic-Networks (TNs) deal with the MAA and put forward best practices during the different project phases, based on the results of a desktop study, interviews, surveys and expert workshops. The study shows that not all types of actors are equally involved in TN consortia and participatory activities, meaning TNs might be not sufficiently demand-driven and the uptake of the results is not optimal.
The Unites States Agency for International Development (USAID) Feed the Future De-veloping Local Extension Capacity (DLEC) project conducted a three-country study on youth and EAS in Rwanda, Niger and Gua-temala. These case studies provided a land-scape analysis to inform actions to strengthen the inclusion of youth in EAS to improve their livelihoods and increase the effective-ness of EAS systems.
This article presents a different dimension of the innovation systems approach, going beyond analysis and shedding light on how these processes can be facilitated in practice. This is based on 20 years' experience with innovations systems. The focus is on the role of facilitation in triggering the changes, as well as in integrating learning and knowledge management (KM) in the innovation process.
During May 2010 the International Centre for Tropical Agriculture (CIAT) hosted two events related to knowledge management (KM): The Knowledge Share Fair for Latin America and the Caribbean, funded by the Food and Agriculture Organization of the United Nations (FAO), and a regional meeting of the Knowledge Management for Development (KM4Dev) community. The Fair was attended by 200 professionals from more than 70 organizations and 18 countries and showcased more than 40 experiences related to KM in agriculture, development and food security.
This is a discussion of the processes that led Unilever to develop and implement a corporate sustainability strategy working with multiple stakeholders.
CABI’s Plantwise programme runs local plant clinics in 24 countries across Africa, Asia and Latin America where trained ‘plant doctors’ provide on-the-spot diagnosis and advice for farmers who bring samples to the clinics. A database that records each consultation and shares knowledge across clinics and countries continually builds the ability of the programme to respond to farmers’ needs. The programme embodies key principles of an innovation systems approach.
The process of knowledge brokering in the agricultural sector, where it is generally called agricultural extension, has been studied since the 1950s. While agricultural extension initially employed research push models, it gradually moved towards research pull and collaborative research models. The current agricultural innovation systems perspective goes beyond seeing research as the main input to change and innovation, and recognises that innovation emerges from the complex interactions among multiple actors and is about fostering combined technical, social and institutional change.