The Data Powered Positive Deviance initiative (DPPD), 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 and the UNDP Accelerator Labs are conducting a series of pilots in different countries and domains to uncover effective, locally developed practices and innovation. Combining readily accessible digital data and ethnographic research, we aim to uncover successful practices and to understand them in their respective context. Can we identify public spaces in Mexico City that are safer for women and explain why? Can we find pastoralists in Somalia who can maintain their livestock, and therefore their livelihood, despite increased droughts? Can we find cattle breeders in the Ecuadorian Amazon who do not contribute to deforestation? Can we find farmers in Niger who achieve sustainably high yields of sorghum and pearl millet despite environmental hazards?
The Positive Deviance approach assumes that in every community, there are individuals or groups with uncommon behaviors or coping mechanisms that can find better solutions to the challenges they face than their peers, even though they have access to the same resources. Building on these local capacities and innovative energies, identifying these people, and promoting them to role models in their communities, has already proven successful in several different countries and sectors. Data Powered Positive Deviance, builds on this legacy, using readily available data—satellite imagery, social media data, or any other source of digitally recorded data—to analyze our target groups in comparable contexts (Albanna & Heeks, 2019). This allows to look for PDs across large geographical areas, and considering more structural variables, such as climatic conditions, infrastructure or socio-economic variables when analyzing practices and solutions. DPPD holds the promise of a more holistic, contextual understanding of Positive Deviants and their practices, which can enhance the effectiveness of scaling their solutions and the possibilities of scaling.
Sustained agriculture in Niger is under tremendous pressure, as climate change and the reduction of rainfall affect crop cycles. Produce is of lesser quality as crops grow in a drier context, which in turn aggravates food insecurity and could further destabilizes Niger and other countries in the Sahel region (WFP). Furthermore, harvested farmlands are often handed over to pastoralists, which can lead to conflict between communities if production cycles are too lengthy and plots remain in use.
Changing conditions for agricultural cultivation and diverse practices across agricultural zones, require a holistic understanding of the interplay structural, climatic and behavioral factors and their effect on produce.
The GIZ Data Lab, the UNDP Accelerator Lab Niger, the University of Manchester and the GIZ Niger Project PromAP set out to find Positive Deviants among farmers in southern Niger, who cultivate the rainfed crops sorghum and pearl millet. Positive Deviants were defined as those farmers, who achieved a higher yield on sorghum and pearl millet than other farmers facing similar conditions for cultivating those crops.
Instead of relying on a one-size-fits-all approach, DPPD could therefore allow more targeted, context-sensitive interventions based on local drivers of crop yield, existing capabilities, practices and adaptations and their interplay with contextual factors.
