The Enterprise Breeding System (EBS) is an open-source breeding informatics software being developed for crop breeding programs serving resource-poor farmers in Africa, Asia and Latin America.
The EBS connects, merges and builds upon existing breeding software and data solutions to offer a single powerful tool, so that breeders can focus on using data to create better varieties, faster.
CGIAR Breeding Resource Initiative (led and managed by Genetic Innovation’s Breeding Research Services unit) sends out periodic updates to inform partners about plans, progress, events and resources. These updates are collated on this page. For more information, please contact: a.hunt@cgiar.org
In March 2021, a new network of specialists, the Breeding Informatics Network (BrIN) was established to standardize and modernize breeding data storage, access, curation, analysis, visualization, and decision support. The ultimate goal of BrIN is to improve quality and efficiency in delivering improved varieties to farmers. On December 15, 2021, BrIN celebrated their 2021 successes, and recognized key contributors around the globe.
The Breeding Informatics Network (BrIN) convenes four teams of experts from across CGIAR breeding programs to assess the current state and requirements for analytics processes in the areas of genetic gains and experiment design, advancement decisions, genomic selection applications and marker-assisted breeding.
During the BrIN end-of-year celebration, 35 participants connected from around the globe to share their progress and successes in 2021, reflect on their impacts and recommend action plans for 2022.
On 15 December 2021, members of the Breeding Informatics Network (BrIN) came together to share their progress and experiences in the network.
A bold new data management platform, the Enterprise Breeding System (EBS), is helping breeders harness the power of data, save time, and make the best breeding decisions. EiB has just released a faster, more feature-filled version.
An expert presentation and discussion on how Bayer increased data fluency and analytics to make better decisions. What did they learn and what changes were adopted over the last 10 years? Join Bayer, Cornell, EiB and partners for an examination of how over the years of testing, Bayer has aimed to get increasing confidence for performance in farmers’ fields and conditions. This includes increasing the number of locations and size of experimental plots.
