UNDP has partnered with the Government of Telangana to jointly initiate the NextGenGov ‘Data for Policy’ initiative on Food Systems. The aim is to incorporate anticipatory governance models for future-fit food systems in Telangana using data-driven policymaking tools and ecosystem-driven approaches. UNDP is keen on augmenting learning capabilities, increasing the predictive or anticipatory capacity to feed-in to evidence-driven policies in the state, and create radical traceability and transparency across the system from producers to consumers by building provenance documentation around food that can help build trust in the system at the same time nurture sustainable and healthy practices. The goal is to design, develop and demonstrate anticipatory governance models for food systems in Telangana using digital public goods and community-centric approaches to strengthen data-driven policy making in the state.
The DiCRA platform for Telangana is envisioned as a Digital Public Good that will strategically feed into data-driven decision making in the state. The platform will have the capability to visualize and analyze high resolution geospatial data (both vector as well as raster layers). The digital platform will curate, integrate and visualise such critical datasets and assets to answer the basic question of - What is growing where? How much is there and the spatial and temporal changes within the state across various indicators relevant to Agriculture and Food Systems. The platform should be able to visualize over time the changes that have happened to the agriculture ecosystem in terms of crop diversity, changes in soil/ground water, tree cover, and other indicators at higher resolution to support policy decisions. Such a synthesis of data and analytics can help identify farms which are doing exceptionally well (Positive Deviance) through which repositories of good practices and indigenous knowledge can be documented. This also helps in identifying farms that are not doing good as per the defined indicators (Negative Deviance).
Positive and negative deviance would be particularly interesting to policy makers since they provide valuable intelligence on – which farms are having exceptionally high productivity compared to others? Which farms are most resilient (or most vulnerable) to extreme weather incidents? Such intelligence when combined with farmer-centric ethnographic research on the ground will provide insights and patterns on climate resilient agriculture practices that are already working well on the ground. The combination of data-driven and community-centric approaches when translated into policy insights can generate policy effects for strengthening climate resilient agriculture.
Data collaboration and synchronization is vital for implementing this technology solution. This platform aims to incorporate data from Open data platforms, Non-Public Domain datasets through data partnerships and incorporate data from open APIs.
Following is a depiction of the dataflow proposed under the project -
