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Data-driven Agronomy


To the Data-Driven Agronomy Community of Practice

Data-driven agronomy refers to an approach or a set of approaches using digital technology to source, analyze and translate data into timely, practical and context-specific information to help farmers make the best choices for their farms.

As a community of practice we aim to collectively strengthen the innovation of technology and big data to tackle an array of agricultural challenges – including the closing of yield gaps – to reduce hunger and poverty and transform global agriculture.

The data-driven agronomy CoP, led by CIAT, builds capacity among farmers, researchers, rural advisory service providers and CGIAR strategic partners. This space can be used as a discussion area, share and request relevant information and contribute towards building the community as a whole.

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Community News

USAID case study highlights CIAT’s data-driven agronomy work

An overview of how CIAT is using data analytics to help farmers make better on-farm decisions.

Aligning needs with solutions: Data-driven agricultural innovation for Vietnam’s farmers

Vietnam’s technology innovators and farmers convened at a workshop to gain a deeper appreciation of the challenges in the field on the one hand, and existing technology solutions on the other.

Data cleaning, the unsexy but essential aspect of data science

Data scientists need to be more serious about standardization, to make data interoperable, says Daniel Jimenez.

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