13,036 research outputs found

    Organic Farming in Latin America and the Caribbean

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    In Latin America, 220’000 producers managed 6.4 million hectares of agricultural land organically in 2007. This constitutes 20 percent of the world’s organic land. The leading countries are Argentina (2'777'959 hectares), Brazil (1'765'793 hectares) and Uruguay (930'965 hectares). The highest shares of organic agricultural land are in the Dominican Republic and Uruguay with more than six percent and in Mexico and Argentina with more than two percent. Most organic production in Latin America is for export. Important crops are tropical fruits, grains and cereals, coffee and cocoa, sugar and meats. Most organic food sales in the domestic markets of the countries occur in major cities, such as Buenos Aires and São Paulo. Fifteen countries have legislation on organic farming, and four additional countries are currently developing organic regulations. Costa Rica and Argentina have both attained third country status according to the EU regulation on organic farming. In recognition of the growing importance of the organic sector to Latin America’s agricultural economy, governmental institutions have begun to take steps towards increasing involvement; governments are beginning to play a central role in the promotion of organic agriculture. The types of support in Latin American countries range from organic agriculture promotion programs to market access support by export agencies. In a few countries, limited financial support is being given to pay certification cost during the conversion period. An important process underway in many Latin America countries is the establishment of regulations and standards for the organic sector

    S-AVE Semantic Active Vision Exploration and Mapping of Indoor Environments for Mobile Robots

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    Semantic mapping is fundamental to enable cognition and high-level planning in robotics. It is a difficult task due to generalization to different scenarios and sensory data types. Hence, most techniques do not obtain a rich and accurate semantic map of the environment and of the objects therein. To tackle this issue we present a novel approach that exploits active vision and drives environment exploration aiming at improving the quality of the semantic map
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