A New Instrument to Measure Food (In)Security: MAHFP

To increase food security is an important objective for ICCO Cooperation, in facts it is a separate theme in our strategy. It is therefore important to have appropriate tools in place to measure food security.

A New Instrument to Measure Food (In)Security: MAHFP


In this blog we highlight a tool that looks into food security: the Months of Adequate Household Food Provisioning (MAHFP). In the STARS program we wanted to understand how food security changes over the course of the year. In many countries where smallholder farmers grow a large share of their nation’s food supply, there is a ‘lean season’ in which stocks from the previous harvest are depleted but the new harvest is not yet in. These are the months that people must endure and when poverty and hunger are felt the hardest.

Simple questions?

To better understand these lean seasons, as well as to better understand how the STARS program impacts food security across the year, we decided to try out the MAHFP tool. The tool is exceedingly simple, consisting of only two questions:

1. Were there months, in the past 12 months, in which you did not have enough food to meet your family’s needs?
[Yes / No]

2. If yes, which were the months in the past 12 months during which you did not have enough food to meet your family’s needs?
[Jan / Feb / Mar / Apr / May / Jun / Jul / Aug / Sept / Oct / Nov / Dec]

Rainfall data, MAHFP and agricultural seasons in Rwanda

As simple as these questions are (even though care needs to be exercised in how to ask the second question, e.g. start with the current month and work your way back), the data resulting from these questions show this tool to be powerful. We will present results from the STARS program based on data collected in Rwanda in May 2017 (the minor lean season), where we collected data with a total of 1,063 smallholder households.

Figure one shows in orange the number of people that indicate facing food shortages disaggregated for each of the twelve months per year.  In the background, rainfall data are presented in grey. And below the bar charts there is a graph outlining the different agricultural seasons in Rwanda.

In Figure one it is predictable but nevertheless relevant to see how food shortages follow the climatic and corresponding agricultural seasons. Rwanda is characterized by having two marked agricultural seasons (called Season A and Season B), which are themselves determined by a bimodal annual rainfall pattern. For some crops there is even a short Season C. The two rainy seasons give rise to two planting and growing seasons in which farmers work their fields to secure the next crop. As the new crops are established, the old stocks are depleted and two lean seasons manifest themselves indicated by the peaks in the orange bars. As Figure 1 shows, most smallholder farmers face food shortages in the months surrounding April and again in the months surrounding November.

Even though we only present data on Rwanda, we can confirm that the data from other countries in the STARS program (Ethiopia, Burkina Faso, Senegal) show similar results (without the bimodal pattern).

Rainfall data, MAHFP and agricultural seasons in RwandaFigure one: Rainfall data, MAHFP and agricultural seasons in Rwanda

Purposes for the MAHFP data

These data on the Months of Adequate Household Food Provisioning have a number of purposes:

  • They can be used to gauge when food insecurity is at its height and how it fluctuates over the year. This can yield important information for the implementation of program activities, but it also shows when it would be best to collect deeper information on food insecurity like through the HFIAS and DDS (Dietary Diversity Score) tools. These tools are best applied during the lean season when food insecurity is at its height.
  • The data can also allow for a differentiation in the severity of food shortages that smallholder farmers face. Some farmers never face a shortage, some only in the lean seasons, and some all year round. Figure 2 shows the frequency distribution of farmers that face shortages for a period ranging from 0 to 12 months. This graph shows that 11% of the target population has no problems with food security, and that in the remaining 89% the majority suffers 2 – 4 months of food shortages per year.  It is noteworthy to see there is a small group that suffers from food insecurity all year long.

Frequency distribution of months with food shortages
Figure 2. Frequency distribution of months with food

  • And finally these data, when compared to an end-line measurement, can be used to show the impact of a food security program. A number of options exist for that like an increase in the percentage of farmers that face no shortages, a reduction in the percentage of farmers that face shortages over longer time periods, and an overall shift towards a reduction in the number of months of food shortages (the peak moves down, increasing the skewness and reducing the mean).

This brief presentation of the Months of Adequate Household Food Provisioning tool has hopefully shown how a simple tool that is easy to apply has the power to yield a lot of additional insight in the food security situation of the STARS program’s beneficiaries.

In one of my next blogs I will also share our experiences with the HFIAS tool (Household Food Insecurity Access Scale). HFIAS focuses on three aspects: 1) anxiety and uncertainty about the food supply; 2) insufficient quality of food; and 3) insufficient food intake. The HFIAS tool applies a 30 day recall period, meaning that fluctuations over time are not measured, and therefore not useable for the data described in this blog. HFIAS, instead, is a good tool to dive deeper into food insecurity periods. So stay tuned for the next blog!

More information about MAHFP:

Author Marco Dekker
More articles by Marco Dekker

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