Extension agent is one of the important factors in the agricultural process to deliver technology information and agricultural programs from government to farmers. The good performance of agricultural extension agents will have an impact on improving the performance of farmers to increase agricultural production.In Langkat Regency, the extension agent performance was not still optimal. Factors affecting the performance of the extension agent consist of internal and external factors.
Innovation is considered as one of the key drivers for a competitive and sustainable agriculture and the European Commission highlights the importance of tailoring innovation support to farmers’ needs, especially in European Rural Development Policy (reg EU 1305/2013). The scientific literature offers a wide panorama of tools and methods for the analysis of innovation in agriculture but the lack of data on the state of innovation in the farms hampers such studies. A possibility to partially overcome this limit is the use of data collected by the Farm Accountancy Data Network (FADN).
This is a simple analytical tool that has been developed as part of Sidas action programme for capacity development. It is intended to provide guidance in project preparation and project assessment. It shall assist Sida staff and other actors to define needs for capacity development. It will thus help to identify factors that are important for sustainable development.
Farmers Training Center (FTC)-based farmer training is an emerging extension strategy geared towards human capital development through need-based, hands-on practical training in order to facilitate agricultural transformation and rural livelihood improvement. Although FTCs were established and made functional in the Tigray National Regional State and Alamata Woreda no systematic assessment of the relevance and effectiveness of the training were made.
This report deals with the adoption of technological innovations in the case of rice farming in Togo.
The following is a summary that introduces the report.
Angola has so much potential as an agricultural country, with up to 50 million hectares that could be cultivated. But why
"CDAIS is interesting for us because it is improving how we operate”, explains Francisco Venda, president of the Sementes do Planalto seed cooperative based in Bailundo. “We work with many partners, and the new skills have proved invaluable.” Since 2016, CDAIS has been working with this group, helping them to identify and agree their priority needs, and take steps to overcoming them. But much is yet to be done, though the high levels of energy and enthusiasm will ensure that progress will continue long after the project has ended.
Rice is produced in other parts of Angola, but not in the area around Bailundo, though conditions are favourable and there is much local demand. Building on the provision of technical expertise from other organisations, CDAIS is adding capacity development of another sort, of the ‘soft skills’ required to collaborate, learn, engage and adapt. “Now we will grow rice forever” says Marcos Satuala. “This innovation has given us a great thing – a new crop for us. And with CDAIS we can learn more, and grow more, for our families and to sell.”
“When I first heard about the CDAIS project two years ago, I knew immediately that it was just what our group of farmers was looking for” explains Edgar Somacumbi. “We have land, seeds, tractors and all the equipment we want, and a processing plant. But moving from being farmer to agro-entrepreneurs is a complex process and requires new skills. And this is where we needed help.” CDAIS is now supporting a group of farmers to improve how they organise themselves and to help them find solutions to their problems.
Georeferenced data are a key factor in many decision-making systems. However, their interpretation is user and context dependent so that, for each situation, data analysts have to interpret them, a time-consuming task. One approach to alleviate this task, is the use of semantic annotations to store the produced information. Annotating data is however hard to perform and prone to errors, especially when executed manually. This difficulty increases with the amount of data to annotate.