In recent years the largest countries in sub-Saharan Africa have spent between 14% and 26% of combined annual public expenditures on agriculture. This reflects the fact that governments have prioritised access to fertiliser for rural smallholders.
The purpose of the programmes is to support smallholders so they can supply the growing food needs of the continent. However, governments’ budgets are limited, and fertiliser prices are increasing. As fertiliser programmes become more costly, what should governments do? Researchers have designed a tool that can support decisions about fertiliser use across sub-Saharan Africa.
They did this by focusing on a farmer’s internal rate of return from using fertiliser. The concept of a farmer’s returns is complicated because growing crops is inherently uncertain.
Farmers must plant seeds and use fertiliser before they know how good the weather will be or what price they will get for their harvest. The model accommodates these complexities by applying machine learning algorithms to data on maize crop trials, weather and soil.