Taking Maize Agronomy to Scale in Africa TAMASA Taking Maize Agronomy to Scale in Africa

TAMASA Taking Maize Agronomy to Scale in Africa What would a better R&D/extension landscape look like? Current Potential Blanket recommendations Weak integration of available spatial data Coarse systems for monitoring adoption, yields Limited technology availability: blends Plot-specific recommendations Real-time integration of available spatial data High resolution, cost- effective systems for monitoring adoption, yields Location-specific blends How to move towards this vision? TAMASA activities should contribute to this movement policy/R&D private sector Data collection at scale Site-specific nutrient mgt. tool extension svcs farmers DSS for blends Site-specific maize variety tool

Maize fertilizer response is highly variable across space II III (Vanlauwe et al., Outlook on Agric 2011)

Site-specific nutrient management Improved recommendation NPK Based on QUEFTS Locally calibrated from nutrient omission trials Asks questions on observable plot characteristics, management history and farmer goals Current recommendation NP Plot-specific recommendations: Fertilizer rate and type* Planting density * conditional on availability

Maize Seed Variety Tool Identification of best varieties for a specific location with the farmers’ desired growth period and planting date Enhance farmers’ ability to predict the stage of maize development more efficient mgt, e.g. timing of weeding & fertilizer For each maize variety, the map will display the following information for individual point locations Flowering & maturity dates Obtained yield Other characteristics (color, drought/disease resistance, N-efficiency, etc.) At scale, opportunity to generate geospatial info on: Seed availability (for farmers) Seed demand (for producers, agrodealers)

Crop Health Tool In the field… William Wu/QED.ai, David Guerena/1 Acre Fund At scale, tool use enables early warning system, database on pests & disease incidence, etc Symptoms: Severe mottling of leaves Dead heart, Leaf necrosis Stunted growth Shortened internodes Barren ears. Diagnosis: Maize Lethal Necrosis

Predictive agronomy & soil science Describe, analyze & predict spatial & temporal variation in… soils, farmer actual & potential yield, benefits TZ yield predictions? c. 700 yields from farmer maize fields from Ethiopia in 2015 Collecting near-real time spatial data at high resolution is a challenge

Jordan Chamberlin j.chamberlin@cgiar.org Photo Credits (top left to bottom right): Julia Cumes/CIMMYT, Awais Yaqub/CIMMYT, CIMMYT archives, Marcelo Ortiz/CIMMYT, David Hansen/University of Minnesota, CIMMYT archives, CIMMYT archives (maize), Ranak Martin/CIMMYT, CIMMYT archives.