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dc.contributor.authorPorciello, Jaron
dc.description.abstractFood systems are complex, and as problems change and evolve, so will our understanding of their root causes and effective solutions. As donors mobilize to meet the targets set by UN Sustainable Development Goal 2 (SDG2): Zero Hunger by 2030, one of the most pervasive challenges they will face involves information: they need to know how much it will cost to fix these problems, what interventions have been researched, which are most effective in addressing them, and how those interventions will affect the rest of the economy. They must also be aware of potential synergies or trade-offs, where acting to achieve one objective can have strong impacts on achieving others, hampering attempts to establish a systematic approach to attaining the multiple objectives of SDG 2.en_US
dc.description.sponsorshipBill & Melinda Gates Foundation, BMZ Germanyen_US
dc.publisherCornell Universityen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.subjectmachine learning, AI, evidence synthesis,en_US
dc.titleCeres2030 technical note: Evidence synthesis, machine learning and partnershipen_US
dc.typetechnical reporten_US

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Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International