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Sudano – Sahelian

Africa Rising M&E Components, Activities, and Outputs. Outputs. Program/Project Site Identification. Data/Analysis Platform. M&E Outputs FtF Indicators Outcome mapping (incl. nutrition & market effect) Cost/Benefit analyses RCT evaluation Adoption studies?. Ethiopian Highlands.

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Sudano – Sahelian

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  1. Africa Rising M&E Components, Activities, and Outputs Outputs • Program/Project Site Identification Data/Analysis Platform • M&E Outputs • FtF Indicators • Outcome mapping (incl. nutrition & market effect) • Cost/Benefit analyses • RCT evaluation • Adoption studies? Ethiopian Highlands Contextual Data (national/regional) - Statistics - HH survey & census - Spatial data Sudano – Sahelian Project Site Stratification (Development Domains) A B A C Derived Indicators - HH Typologies - Intensification Index - Sustainability Index - Nutrition index? East and Southern Africa Maize Mixed Ranking domains by key AR attributes A ________ C ________ B ________ FtF Indicators / reports by - Research sites - Country / National level - Project sites - Program / SSA • Action research site selection criteria • Site access • Existing activity/platforms • Research design • Intervention type • M&E approach • ……. • M&E approach Project/Activity/ Partner Inventory - Project DB (& maps) Identify research sites in priority domains that satisfy selection criteria SI Innovation Catalogue - Inventory (cross-site) - Characterization - Open access Innovation Inventory - Standard metadata - User interfaces Research Site Baseline survey Set up trials Monitoring Mid-line survey (?) End-line survey Communities/ Farms/Plots Site Data - Climate, soils, market access, etc - Community/HH survey data - Experimental data - Model input data • Performance Variables • (modeling & validation) • ∆Whole farm productivity • Technology performance • ∆ Yield • ∆ Labor prod.- by gender • ∆ NUE, WUE • - ∆Revenues, Costs, Profits + + + + + Project Planning & Management Improved insights into innovations , delivery platforms, and site selection + • Whole-farm models Learning

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