This session by the Crop Modeling Community of Practice aired live at the virtual 2020 CGIAR Convention on Big Data in Agriculture.
The Ontologies Community of Practice is engaged in the development of ontologies for agricultural research. In a series of blog posts, we’ll take a look at ongoing ontologies projects and developments.
A new publication from scientists at the International Maize and Wheat Improvement Center highlights the need to better constrain parameters of global gridded crop models, particularly in the mid to high latitudes, to improve predictability.
CGIAR staff participated in a two-day training course to develop capabilities for spatial analysis, automatizing image processing, machine learning, and other approaches for working on large climate data sets. This blog was originally posted on the CIMMYT Intranet.
A team of geospatial scientists at ICRISAT, led by Murali Krishna Gumma, successfully developed and implemented a MODIS-based workflow to map major staple crops in Malawi and detect major spatio-temporal changes occurred between 2010/2011 and 2016/2017.
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