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

2. 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

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

4. 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

5. 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)

6. 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

7. 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

8. 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.