Tackling Food Insecurity in Niger by Learning from Positive Deviants


By Assane Tchagam Mallam Boukar, Head of Solutions Mapping; Rachida Mani Kango Dan Koulou, Head of Exploration; Moustapha Sahirou Yacouba, Head of Experimentation, all UNDP Accelerator Lab Niger; Moctar Seydou, National Economist; Amadou Djibo, Communications Manager, both UNDP Niger

Nearly 20% of the Nigerien population suffer from food insecurity.

Driven by a combination of factors, including increasing degradation of natural resources (soil, water and biodiversity); rapid population increase; conflict between livestock breeders and farmers over access to land, water, and biomass; and insecurity in several regions of the country, the situation is further exacerbated by more frequent climatic events like poor rainfall.

Pearl millet and sorghum, the main food crops in the country, play a key role in mitigating food insecurity. While they generally do not require complex irrigation technology, the increasingly drier climate has a severe negative impact on these rainfed subsistence crops.

As we launched the pilot project, we set out to answer: How can we help farmers improve their production of pearl millet and sorghum?

With the support of the GIZ Data Lab, the University of Manchester and the UNDP Accelerator Labs, the UNDP Accelerator Lab Niger together with GIZ PromAP Niger have been at work for the last two years to identify and learn from positive deviants: Nigerien farmers who show consistently higher yields of pearl millet and sorghum than their peers.

We’ve worked through the first three stages of the data-powered positive deviance method, and now can report on the uncommon, yet successful practices of the positive deviants.

DPPD: remote data analysis and local encounters

The data-powered positive deviance method builds on the positive deviance approach: which is based on the observation that in every community, there are individuals or groups that find more appropriate or sustainable solutions to the problems they face than their peers. This method also posits that those who are confronted with a given problem can most likely best solve it.

Previously, we described how we identified those areas in the south of Niger where yields of pearl millet and sorghum were consistently higher. We analyzed a combination of remotely sensed, biophysical, and climate data to narrow down our focus to eighteen villages in the regions of Tahoua, Dosso, Maradi and Zinder.

To confirm that positively deviant practices were indeed being applied in these villages, we then conducted a two-stage semi-qualitative field enquiry:

  1. We first visited the different locations to collect initial information on and from the local farmers we identified as potential positive deviants.
  2. A local agricultural expert then conducted a more exhaustive investigation to confirm the presence of said farmers in the eighteen villages, and to further unpack some of the environmental factors that might explain superior yields, as well as the agricultural practices that farmers had developed to achieve such yields.
Research on a farm of positive deviants near the village of Guilmey (Photo taken by AccLab Niger).

What we learned

The agricultural expert surveyed and interviewed 179 local actors involved in the production of pearl millet and sorghum in four regions (Tahoua, Dosso, Maradi and Zinder). He then grouped farmers across three categories according to socioeconomic variables, which allowed him to establish a distinction between farmers in the same village:

  1. Large-scale producers with a yield between 500 and 1000 stacks per year who have access to several fields and to storage, and raise cattle.
  2. Medium-scale producers with a yield between 50 and 500 stacks per year who have access to a few fields and raise some cattle, but have limited access to storage.
  3. Small-scale producers with a yield less than 50 stacks per year and who have access to a few fields, but no access to storage, and raise no cattle.
Photo of farmers harvesting taken near Doutchi by the local agricultural expert during the field survey

We found 13 positive deviants in the large-scale producers group, 19 in the medium-scale group and three in the small-scale group. These 35 producers were considered to be positive deviants because of the combination of above-average yields and the various behaviors, practices and techniques that resulted in these exceptionally high yields, including:

  • Rainfall and irrigation: sowing in times likely to result in higher yields and reduced seed loss due to climatic factors (relying on the notion of so called “useful rain” of at least 14mm, after which it is best to sow grain), as well as the use of Zaï holes or stone cords to harness rainfall and avoid rain water runoff
  • Fertilizers and pest control: the application of improved technical instruments through farmer field schools, the use of organic fertilizer to complement mineral fertilizers and the use of plant protection products
  • Field cleaning practices: rather than cleaning fields after harvesting, keeping millet stalks in the field to protect the soil from wind erosion and for fertilization to restore organic matter — an ancestral technique used by many of the positive deviants
  • Assisted natural regeneration: a blend of active planting (e.g., Gao trees that help regenerate and fertilize the soil when they lose their leaves) and passive restoration to increase their yield, resulting in better water infiltration, reduced soil evaporation and recovery of degraded land
Gao trees found at positively-deviant farms near Bey Beyé (Photo taken by the local agricultural expert during the field survey).

The way forward

The ultimate goal of the DPPD method is to help non-positive deviants reach a similar level of ‘performance’ as positive deviants. Now that the positive deviants and their practices have been identified, the next stage is about enabling members of the community to apply these practices.

Two types of interventions in the DPPD method: community and policy. Source: DPPD handbook launch event

As we describe in the DPPD handbook, we believe that in some cases the scaling of individual practices must be paired with programmes, policies or other interventions of external support, training or financing to be effective. And while the practices described above should serve as a starting point, the choice, design and implementation of any intervention will ultimately depend on the interests, capabilities and resources of the respective stakeholders — first and foremost the positive deviants and the communities where they live.

With thanks to Andreas Pawelke, Jeremy Boy and Basma Albanna for their comments and suggestions.


The Data Powered Positive Deviance initiative was established on the belief that lessons on how to tackle complex sustainable development challenges are best learned from the people who face those challenges every day. It is with this mindset that the GIZ Data Lab, the University of Manchester Centre for Digital Development, and the United Nations Development Programme Accelerator Labs are conducting a series of pilots in different countries and domains to uncover effective, locally developed practices and innovations as a response to development challenges.


UNDP Accelerator Lab Niger: Assane Boukar, assane.boukar@undp.org
GIZ PromAP Niger: Damien Hauswirth, damien.hauswirth@eco-consult.com
UNDP Accelerator Labs: Jeremy Boy, jeremy.boy@undp.org
GIZ Data Lab: Catherine Vogel, datalab@giz.de
University of Manchester: Basma Albanna, basma.albanna@manchester.ac.uk



Data Powered Positive Deviance DPPD

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