Digital Innovation

Where we work: India

Digital Innovation’s work in India includes the following activities:

Digital Co-Lab: Digital agriculture case studies in India

Sustaining digital interventions in agriculture needs a holistic approach that includes the right policies, frameworks, ecosystem and capacities. We published a report highlighting some successful digital agriculture initiatives in leveraging digital technologies, improving value-chain processes and building capacity to bring about positive change among agriculture stakeholders and improve livelihoods.


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Digital Co-Lab: Empowering India’s Agri-Stack

At the core of digital transformation is open data. In India, a lot of work has been done by the Central and State Governments, academic, research institutions, and the private sector in conceptualizing different approaches and aspects of an “Agri-Stack” to digitally transform agriculture. Recognizing the need to integrate these efforts and incorporate use cases, in partnership with The Agri Collaboratory (TAC), we organized a consultation workshop in Delhi in November 2022. Participated by 70 stakeholders representing 54 organizations, the workshop enabled in-depth discussion on the design principles of thematic use cases and facilitated a broader debate on the specific building blocks needed.


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Digital Co-Lab: Enhancing agrometeorological advisories in India

Meghdoot is an Android mobile application that provides India’s official agrometeorological advisories to farmers. Meghdoot was developed by International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) to support the weather data dissemination effort by the India Meteorological Department (IMD). Since its release in 2021, Meghdoot has been downloaded more than 100,000 times. To improve the usability and performance of the application, we are enhancing the user interfaces and the overall user experience of the Meghdoot app by incorporating AI/large language models and applying human-centered design approaches.

Digital Co-Lab: Enhancing digital extension capacities in Odisha, India

While several digital platforms and applications developed for farmers collect data and information, more is needed to know about their use by the Extension and Advisory Services (EAS) to provide more relevant advice or design a data-informed extension. We developed a report focusing on what needs to be done to enhance the capacities of EAS based on in-depth reviews of farmers’ use of three digital farmer services available in Odisha and interactions with select stakeholders who are familiar with and are part of these services. We found that EAS stakeholders needed to be fully aware of the types of data and information available or how best they could be used. We identified that four specific types of capacities need to be strengthened coherently and systematically.


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Digital Co-Lab: Digital public infrastructure for agriculture

Agriculture faces the competing challenges of enhancing productivity and raising the incomes of smallholder farmers, while simultaneously also addressing concerns related to environmental sustainability. Digital technologies have the potential to tackle these challenges and transform agrifood systems in unprecedented ways. We led the development of a T20 policy brief that advocates for the conceptualization and development of digital public infrastructure for agriculture for a more equitable and responsible development pathway for agriculture in the G20 countries.


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Digital Twin: An AI-based farmers’ information demand prediction model

Various information and communication tools are used in two-way advisory services to interact with farmers. However, the utility and impact of such interactions are not well understood, primarily due to the lack of quality data at scale. To advance knowledge, we are developing a spatiotemporal prediction model of agricultural information demand in India by analyzing communications between farmers and the Kisan Call Centers. We processed more than 30 million records of communications that took place between 2004 and 2022 and validated macro trends in the data. We are further analyzing this database to identify spatiotemporal patterns of information needs, linking with explanatory variables such as climate and market trends that appear to trigger certain types of risks. We anticipate the model can predict future systemic shocks in advance and help decision-makers prepare for measures to manage the risks in advance.


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