Developing technology extrapolation
domains for agronomic technology packages
Francis Muthoni
International Institute of Tropical Agriculture (IITA)
Africa RISING ESA Project review and planning meeting
11 – 12 September 2019, Dar es Salaam, Tanzania

Outline
 Example of extrapolation domains for AR technologies
 Data requirements for generating domains
 Overview of proposed work-plan

Why generate extrapolation domains?
 Different SI technology options are suited for specific biophysical
context
 Proper spatial targeting of SI technologies is required to:
 Reduce risk of failure
 Maximize impact
 Increase probability of adoption
 Rationalise investment of limited resources

Example: Extrapolation suitability index (ESI)

ESI for maize fertilizer packages in Tanzania
 Lower ESI value indicates high similarity with conditions in reference
trial sites
 Less risk for extrapolating a particular package of technologies
Muthoni et al 2019, GeoCarto Int., 34(4), 368 - 390

Data requirements from technology trials
 Availability of data from technology validation sites determines the
method & practicality of domains to be generated
 GPS location: Lat., Long., Altitude
 Crop varieties
 Inorganic fertilizers rates : N P rates
 Organic amendments: manure, crop residues
 Soil water conservation practices: Tied ridges, fanya juu/Chini
 Conservation Agri. practices: No tillage, minimum tillage….
 Grain/biomass yields
 Economics: Profitability

Activity 1.3.1.1: Extrapolation domains for CA
technologies

Activity 1.3.1.1: Extrapolation domains for CA
technologies
 A multivariate random Forests (MRF) model will be used to predict
grain yields for maize varieties grown with different CA practices
Hengl et al., 2017

Activity 1.3.1.2: Extrapolation domains for SWC
technologies

Activity 1.1.2.1: Monitoring impact of weather and
climate variability on maize-legume productivity

Activity 1.1.2.1: Monitoring impact of weather and
climate variability on maize-legume productivity

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