Ceres2030 technical note: Evidence synthesis, machine learning and partnership

dc.contributor.author	Porciello, Jaron
dc.date.accessioned	2021-12-31T19:31:20Z
dc.date.available	2021-12-31T19:31:20Z
dc.date.issued	2020-07
dc.description.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.	en_US
dc.description.sponsorship	Bill & Melinda Gates Foundation, BMZ Germany	en_US
dc.identifier.uri	https://hdl.handle.net/1813/110704
dc.language.iso	en_US	en_US
dc.publisher	Cornell University	en_US
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 International	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject	machine learning	en_US
dc.subject	AI	en_US
dc.subject	evidence synthesis	en_US
dc.title	Ceres2030 technical note: Evidence synthesis, machine learning and partnership	en_US
dc.type	technical report	en_US
schema.accessibilityHazard	none	en_US

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