Systems Analytics

CIDA will pursue its activities with a systems perspective to tackle challenges at the farm, regional, national, and global levels, while considering the technical, social, economic, and environmental dimensions of agriculture. In this vision, systems analytics — advanced computing approaches that leverage artificial intelligence, machine learning and systems engineering — forms a pervasive component of our initiatives to capture, curate, interpret, and disseminate diverse data sets. Analytic frameworks will guide and optimize our technological innovations and facilitate scientific discovery and its translation into the field. These platforms will also provide effective interfaces for engagement of a broad range of stakeholders in the development and deployment of new technologies. Finally, these platforms will form the backbone of operational DA systems for producers, distributors, consumers, insurers, government agencies and policy-makers to consume natural resources more sustainably, improve distribution networks, and pursue socio-economic prosperity.


  • Harness systems engineering to discover and operate disparate information loops to sustainably optimize farm resources and production processes.
  • Develop methodologies to manage and deploy disparate, complex data structures and streams (climate, local weather, soil, plant/animal, machine/human).

Data management:

  • Build efficient platforms for the capture, storage, interpretation and dissemination of data. Connect scientists, engineers, producers, distributors, consumers, insurers, government agencies and policymakers.
  • Use data platform to develop policy and risk analytics to improve sustainability and inform decision-making for producers, distributors, consumers and policy makers.
  • Examine how attitudes, education and social and business relationships impact digital agriculture adoption, and how adoption may disrupt existing local social and economic relationships.

Data analytics:

  • Unleash the power of artificial intelligence, data analytics and networked resources for the intelligent farm of 2030.
  • Utilize available data, network and computing resources to reason autonomously and support robots and humans to optimally cooperate safely and securely in real time.
  • Support development of real-time, high-resolution yield forecasting, disease and practice modeling at scale utilizing artificial intelligence, spatial statistics and machine learning approaches.