DATABIO BIG DATA MANAGEMENT

PILOT : BIG DATA MANAGEMENT IN GREENHOUSE ECO-SYSTEM

DATA – DRIVEN BIOECONOMY

LOCATION    THESSALI

COUNTRY    GREECE

CONTACT NAME    EPHREM HABYARIMANA, ANAGNOSTIS ARGIRIOU

WEB    https://www.databio.eu/en/pilots/

EMAIL    ,

PILOT PRODUCT DESCRIPTION

The pilot will be run by a close partnership between CREA and CERTH, and will build upon ongoing greenhouse horticulture breeding works in the Thessali Region, Greece, where tomato materials are grown throughout the year in two greenhouses (2ha) and 2 walking growth chambers. CREA and CERTH will share complete complementary tasks with the former handling genomic predictions and selection, while the latter will be responsible for phenomics, metabolomics, genomics and environmental datasets acquisition. The end users of this pilot include farmers and farming cooperatives who currently grow crops following standard farming practices and selection based on phenotypes, which is time and resource consuming, with low resolution and efficiency. The end users therefore want cost-effective, high-resolution solutions capable of expediting breeding activities in order to simplify

breeding scheme, shorten the time to cultivar development; selecting for genetic merit estimated through genomic modelling in order to sustainably improve productivity and profits. Within the DataBio framework, the services that are expected to be provided include mainly farmer-customized estimates and selection for individual (plant, genotype) genetic merit for a trait of interest, or several traits of interest for the farmer aggregated in Index.

OBJETIVE OF THE PILOT

The main motivations for this pilot are

  • to predict the performance of unphenotyped tomato materials in grasshouse agroecosystems using molecular data information.
  • to empirically demonstrate the benefit of using genomic data in terms of increasing genetic gain by unit cost and time

KPIS AND METRICS

Three relevant KPIs have been identified so far:

  • Model accuracy: performance prediction accuracy showing an acceptable error rate including when the testing and training sets are genetically distant.
  • Revenue potential with alternative cropping strategy vs. what happened: Quantify increased revenue potential GS versus phenotypic selection.
  • GS take-up: number of vegetable growers adopting GS approach. This is technology transfer and takes time to establish; for this project, a baseline will be measured first, then followed-up by monitoring usage after system deployment.

PILOT PARTNERS