Most of today’s information services on the web are designed for PC users. There are few services fit to be accessed by mobile devices. In the countryside of China, most of the mobile phone users can not access the Internet. For this reason, was developed a General Agriculture Mobile Service Platform. The Platform is designed to make these information services fit to be accessed by mobile users, and to make those mobile phone users can use these services without Internet connection.
Digital Agriculture is an “intelligent” computer management and application system that includes many high technologies of, such as infomationization, digitization, network, automation and more so on. On the basis of analyzing the main content and meaning of “Digital Agriculture”, this paper designs the system construction of “Digital Agriculture” based on Internet, and discusses the function and structure of main core modules of System of Digital Agriculture in detail. Finally, this paper also analyzes the developing trend about system of “Digital Agriculture”
Rural growth is seen as an engine to drive the economy of developing countries and the use of Agriculture Market Information Services (AMIS) is believed to enable this growth. This paper is based on a literature study and investigates the spread and use of AMIS in the least developed countries (n=49) in terms of users, management, funding, infrastructure, and data. This paper investigates success as well as failure aspects, and discuss the role of new technologies.
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 Module is the first in a series of four that address capacity development competencies in FAO. It is intended to provide FAO staff and consultants with a basic understanding and knowledge of Capacity Development (CD), reflecting the international debate as well as FAO’s perspective on CD. It also provides some key concepts for adopting changes in responsibilities, behaviours and attitudes that are consistent with FAO’s new role in CD.
Society’s learning capacity in the field of sustainable land resource management is at stake and more emphasis on knowledge management is needed to guarantee that the accumulated knowledge is shared in such a way that the right actors have appropriate knowledge at the right time to take the best decisions. Efficient policies governing structures for national and regional knowledge management need to be formulated and the working procedures of the various actors in the field need to be defined more sharply.
This paper reviews the state of current scientific knowledge on the links between climate change, agriculture and food security, in terms of anticipating impacts, managing climate variability and risks, accelerating adaptation to progressive climate change, and mitigating greenhouse gas emissions from the agricultural sector.
This presentation by the Iowa State University Extension (USA) is on agriculture and extension in the USA, focussing on challenges in ICT and emerging paradigms.
The government of Ethiopia gives great attention to agriculture and rural development for the country’s economy development. Dairy development is one of the components of agricultural development. To improve dairy production in certain locality, dairy producers should able to access and use appropriate knowledge for the particular problem at the right time. This research was conducted to assess agricultural knowledge management system and its challenges and opportunities of knowledge management processes in Bure district.
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.