This PROLINNOVA report to the 3rd GFAR Programme-Committee meeting is composed of two parts.
The past 1 entitles ‘ PROLINNOVA genesis and growth’ describes historical background and
PROLINOVA in general while the part 2 entitles ‘2007 accomplishments’ narrates specific
accomplishments of PROLINNOVA during the period January-November 2007 . Further, the annex 1
lists contact addresses.
Development education, it combines various methodologies of education to promoting knowledge, so that agriculture sector needs development education to revive productivity through agriculture. ICT (Information communication technology) help to provide knowledge to the door step of farmers.
This decision guide is intended to help extension professionals and their organizations make informed decisions about which extension method and approach to use for providing information, technologies and services to rural producers and to facilitate interactions and knowledge flow. Expected users include field-based rural advisors, extension managers and programme planners.
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.
The first part of the working document on the global strategy brings together the ideas of some 40 experts involved in gender and participatory research who took part in the workshop ‘Repositioning Participatory Research and Gender Analysis in Times of Change’ in Cali, Colombia (June 16–18, 2010).The workshop participants firmly believe that gender responsive participatory research (GRPR) offers some of the most powerful and useful approaches for achieving sustainable development, including alleviating poverty, improving well being, achieving sustainable levels of natural resource use, and
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.
Fall Armyworm (Spodoptera frugiperda), or FAW, is an insect native to tropical and subtropical regions of the Americas. In the absence of natural controls or good management, it can cause significant damage to crops. It prefers maize, although it can feed on more than 80 additional species of crops including rice, sorghum, millet, sugarcane, vegetable crops and cotton.
This Final report identifies best-fit practices, and makes recommendations on how to target women advisory service providers in capacity development programmes.
Continually increasing food demand from a still–growing human population and the need for environmentally–friendly strategies for sustainable agricultural development require innovation and further enhancement of cropping systems’ factor productivity. The system of rice intensification (SRI) has been proposed as a suitable strategy to improve rice yields with reduced input requirements, most notably water and seed, while enhancing soil and water quality because agrochemical applications can be cut back.
The Colombian Ministry of Agriculture Colombia, an international research center and a national farmers’ organization developed a data-driven agricultural program that: (i) compiles information from multiple sources; (ii) interprets that data; and (iii) presents the knowledge to farmers through the local advisory services. Data was collected from multiple sources, including small-scale farmers. Machine learning algorithms combined with expert opinion defined how variation in weather, soils and management practices interact and affect maize yield of small-scale farmers.