Tackling Food Insecurity in Niger by Learning from 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.

  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
  • 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

About

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.

Contact

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

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Data Powered Positive Deviance DPPD

Data Powered Positive Deviance DPPD

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We are an international collective that is dedicated to utilizing big data to find effective locally developed solutions to complex problems.