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Ceres2030 technical note: Evidence synthesis, machine learning and partnership

Author
Porciello, Jaron
Abstract
Food 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.
Sponsorship
Bill & Melinda Gates Foundation, BMZ Germany
Date Issued
2020-07Publisher
Cornell University
Subject
machine learning, AI, evidence synthesis,
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Type
technical report
Accessibility Hazard
none
The following license files are associated with this item:
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International