The ability to map actual and potential yields at scale would enable much more informed policy interventions and targeted investments. While methods exists, much empirical validation remains to be done for maize systems in sub-Saharan Africa.
To evaluate the prediction of actual and potential maize yields from remotely sensed predictors (NDVI, etc.) coupled with ground control data from Ethiopia &/or South Asia (Nepal, Bangladesh, India).
Student would need to have remote sensing & machine learning skills. We would expect to produce a paper from this work. Student would work closely with CIMMYT scientists in the Nairobi office.
Student would need to have remote sensing & machine learning skills.