gizdatalab/DPPD_Niger

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). # DPPD_Niger ## Positive Deviance 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? ## Background 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 soc …