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BioTerra: The new biodiversity monitoring system being developed by USAID Natural Wealth Program

BioTerra es un sistema innovador de monitoreo de la biodiversidad y sus amenazas desarrollado por el Programa Riqueza Natural de la Agencia de los Estados Unidos para el Desarrollo Internacional (USAID), y sus socios locales—el Centro Internacional de Agricultura Tropical (CIAT) y el Instituto de Investigación de Recursos Biológicos Alexander von Humboldt—para apoyar al gobierno colombiano en el cumplimiento de las metas y compromisos de conservación de la biodiversidad. Este sistema busca complementar y aunar esfuerzos existentes de monitoreo de la biodiversidad y sus amenazas, a nivel nacional y regional.

Learning more about soil ecology

Last April, the Soils and Landscapes for Sustainability Research Area at CIAT, in collaboration with the University of Minnesota (UMN) and the National University of Colombia at Palmira, held a training course entitled “Soil Ecology: Methodologies for the Assessment of Agroecosystems”.

Agriculture across borders

CATAS and CIAT Asia researchers held a 3-day joint workshop in April to prioritize key research areas, co-develop project concepts, and identify target funding sources. In the end, the new CATAS-CIAT cooperation portfolio came to include proposed projects on: 1) tropical crops such as cassava, forages, coffee, and tropical fruits; 2) sustainable farming systems including the role of microorganisms in enhancing productivity; 3) data-driven agronomy for sustainable agri-food systems; and 4) understanding consumer preferences for quality-traits to guide crop improvement and product development for tropical fruits.

CIAT’s data team proves its capacity by winning the 2018 Syngenta Crop Challenge in Analytics

CIAT’s team took part this year in the Syngenta Crop Challenge in analytics. After intense work in preparing our submission, we couldn’t lower the error of our model anymore. But when the team submitted its proposal to the Challenge, back in March this year, we did not really know what to expect from it, as we had no idea of the real potential of those datasets we worked on…

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