One of the hardest decisions a government must make is who to support with the limited public funds at its disposal.
In recent years the largest countries in sub-Saharan Africa have spent between 14% and 26% of combined annual public expenditures on agriculture.
This, according to associate economics professor at Fordham University in the US, Andrew Simons, reflects the fact that the 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.”
Simons pondered that as fertiliser programmes become more costly, what should governments do?
In a recently published paper, Simons set out to answer this question with his colleagues Ellen McCullough at the University of Georgia and Julianne Quinn at the University of Virginia both in the US.
“We designed a tool that can support decisions about fertiliser use across sub-Saharan Africa. We 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.”
Simons said the model accommodates these complexities by applying machine learning algorithms to data on maize crop trials, weather and soil.
“Our hope is that the support tool we have designed helps governments answer tough questions such as who to target – and how – when budgetary resources are limited. We believe that better targeted policies can improve food security across the continent.”
According to Simons, to model the yield response to fertiliser they compiled numerous maize trial data sets spanning 17 countries over 13 years and eight agro-ecological zones.
“We matched all 21 000 of our trial observations with their corresponding growing conditions, like temperatures and precipitation in the months following planting. We also matched them with a newly available geospatial data set of soil characteristics (Africa Soil Information Service).”
The academic then modelled the yield response to the climate and site characteristics, and used this model to simulate the returns on investing in fertiliser across sub-Saharan Africa’s maize-growing regions.
“We found that on average, use of fertiliser results in a 1 800 kg/ha increase in maize yields. But the response varied considerably from year-to-year and within and between locations.
“Armed with these yield responses, we modelled site level farmer profitability across sub-Saharan Africa. The model simulated weather variables that influence maize yield response to fertiliser, and fertiliser and maize prices that influence profitability.”
Simons said often, high-level decisions about fertiliser subsidies are made by looking at average profits, and if an investment returns a certain amount over a span of years, it is an acceptable investment.
However, the academics proposed that from their findings, decision makers should view the decision differently.
“We determined which regions were ‘robustly profitable’. We defined these as areas achieving at least a 30% return on investment in at least 70% of the years.
“We compared these regions with those defined to be profitable based on a ‘naive’ definition of an average of 30% over all the years. This definition is commonly used in the literature and is often the basis of blanket fertiliser recommendations. But it ignores how frequently farmers may face returns below a threshold and therefore be unwilling to take on the risk of the investment.”
In about 25% of locations in sub-Saharan Africa our “robust profitability” criteria produced a different profitability assessment than the business-as-usual approach of focusing on average returns.
The experts also analysed the rising fertiliser prices.
“We analysed sensitivity by changing each of the variables in the yield and profitability model. For example, we adjusted certain inputs, such as the price of fertiliser, pH of the soil and the amount of precipitation.
“The purpose of an exercise like this is to understand which factors affect profitability the most. If changes in precipitation produce the greatest change in profitability at a particular site, then investments in irrigation may be the best policy for that location.”
Simons explained that farming was a complicated and uncertain endeavour.
“The tool we designed helps decision makers juggle these complexities. Understanding which factors affect the robust profitability of farmers the most will – hopefully – lead to a better distribution of resources and food security outcomes.”
