Data-driven agronomy


Data-driven agronomy refers to a set of complementary approaches that enhance traditional agronomy. Specifically, data-driven agronomy cuts across three main principles: (i) increased use of observational information  (ii) data mining  and (iii) contextualized information. This combination facilitates discoveries that can help provide farmers, technicians and researchers with new information on best practices within specific intersections of different crops, environmental, and socioeconomic conditions.

This Community of Practice is led by CIAT and has been launched as a part of the CGIAR Platform for Big Data in Agriculture.

The main purpose of this Community of Practice is to harness modern information technologies to advance agricultural practices, in relation to particular socio-economic and environmental contexts in a way that promotes the democratization and transparency of agricultural information.

This is part of the Platform’s Module #2: Convene which help increase the exposure of CGIAR’s work on big data, and build capacity internally and externally on big data approaches in agriculture.

This space can be used as a discussion area, share and request relevant information and contribute towards building the community as a whole.

Download the Work Plan





Daniel Jimenez

Daniel Jimenez

Community of Practice- Leader, CIAT