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Connecting world-renowned researchers across academic boundaries with multidisciplinary practitioners to solve agri-food systems challenges.

Digital Agriculture

 

Our agri-food systems face a formidable challenge: feed an estimated global population of 10 billion people by 2050. Critical resources needed to meet this challenge are already severely strained.

Graphic of grand challenges and target outcomes for digital agriculture
Grand challenges and target outcomes for Digital Agriculture: Sustainably feeding and improving communities and the environment is one of the most significant challenges of our time. Digital Agriculture is a flexible, multi-scaled, integrative vision that has the potential to revolutionize agriculture. Source: Cornell University CIDA

Globally, most arable land is in production, and the planet’s supplies of freshwater and energy are being used unsustainably. Food security is further jeopardized by climate change, pre- and post-harvest losses and food waste. Agriculture and food systems require a radical transformation to sustainably feed 10 billion people.

Today’s farmers make decisions based on a combination of data, experience and recommendations from varied sources. Once a decision is implemented, the results may not be known until late in the production cycle. Further, because large numbers of different variables shape agricultural performance, it is very difficult to draw lessons from isolated empirical observations. Through the use of new and advanced technologies that autonomously collect, integrate and transmit information, digital agriculture (DA) is creating new tools and providing practical solutions to improve effective, real-time decision-making on farms and at many points throughout food systems.

A DA system has the potential to gather data more frequently and accurately, and to give farmers real-time feedback that provides value to their operations. Digital technologies include sensors, robotics, unmanned aviation systems, communication networks, artificial intelligence, machine learning and other advanced systems and devices. The ability to integrate data from different technologies and deliver it to the appropriate people in a readily digestible format is critical to support informed decision-making at many different points in farming operations.

Virtually all stages of agriculture and our food systems are being affected by our ability to autonomously collect, analyze and share large amounts of data. The impact of DA is being felt in the way plants and animals are selected through breeding, the way crops are managed in the field thanks to large-scale weather and disease forecasting, the way animals are fed, moved and monitored in an intensive livestock operation, the way fresh fruits and vegetables are produced and transported from farm to market and the way national and international food production and sourcing practices are monitored. The potential of DA affects people all along the value chain in our agri-food systems.

The Cornell Initiative for Digital Agriculture (CIDA) is pursuing a vigorous research agenda for DA through a collection of powerful interdisciplinary collaborations that will transform agriculture and foster a pipeline of practical innovations. CIDA defines DA as a holistic, systems-level approach to agriculture that incorporates tools and devices for sensing and automating activities at a range of scales, and leverages information and computational technologies (ICT) in a systems analytics framework. DA affects all components of the food system and provides new methods and tools for delivering relevant, timely and targeted information and services to farmers, consumers and policy-makers to enhance the productivity, profitability and the social, economic and environmental sustainability of agriculture, enabling the delivery of safe, nutritious and affordable food for all.

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