Data Analytics for Climate & Crop Resources Sustainability
Data Analytics for Climate & Crop Resources Sustainability includes Big Data and Open Data to minimize impacts and maximize sustainable food resources.
For more information, please refer to Education.
Data Analytics, Big Data & Open Data for Sustainable Crop Resources
Climate change and extreme weather events impact crop production and food security. Digital analytics illustrates the impacts that climate change has on our natural resources. For more information, refer to our natural resources essential for food production.
Using these data sources, data analytics and modelling tools help to project the future impacts of climate change on food production. Adaptation plans can be formulated to mitigate these impacts and maximize resource sustainability.
Open Data & FAIR Data
Open Data is an essential component of the critical data needed for analysis and data analytics.
In Canada, Open Data and Big Data sources include climate, crop, soil, economic, and management data.
And globally, the Platform for Big Data in Agriculture at CGIAR supports the discovery of agriculture data and publications from global datasets. CGIAR’s Guardian platform supports the ability to map, visualize, and analyze data in a large number of publications and datasets.
Climate Change, Agriculture and Food Security (CCAFS) at CGIAR defines FAIR data as Findable, Accessible, Interoperable, and Reusable data.
The Open Data for Resilience Initiative (Open DRI) provides practices to reduce vulnerability and build resilience to natural hazards and the impacts of climate change across the globe.
Data Analytics and the Internet of Things (IoTs)
The Internet-of-Things comprised of various sensors can collect real-time data, track activities, and forecast future scenarios.
Extensive data analytics can be carried out on this data.
Output from these digital systems can improve management decision making, increase productivity, increase profitability, and help protect food security.
Data Analytics and Crop Modelling
Crop modelling tools can simultaneously simulate several parameters. These include direct and indirect climate parameters, various soil parameters, and management techniques.
Based on extensive data analytics, the outputs project future crop yields for key climate scenarios. They project the implications for global food security. Crop models can evaluate the benefits of adaptation measures.
The data analytics help project crop yields and profitability under various climatic, economic, and management scenarios for the major crops.
CropX
CropX combines in-soil data and satellite observations to improve global agricultural monitoring, optimize inputs, and monitor movement of water and agrichemicals in soil. Soil analytics is a key focus. Data is collected from weather observations, soil sensors, soil mapping, hydraulic models, aerial imagery, topography maps, user input, and crop models. Analytics activities help to minimize environmental pollution caused by leaching and runoff. In addition, these analytics help to reduce resource expenditures.
- Last Modified: May 17, 2024