The DIVERSIFOOD database aims to provide an information basis on performances of genetic resources, as well as to stimulate participatory crop evaluation experiences. The DIVERSIFOOD database enables an interactive, analytic exploration of genetic resources performances in different trials, each with its pedo-climatic and socio-economic context and its objectives and resources, as resulting from a participatory multi-actor approach.
In this database it is possible to build and/or substantiate with evidence narratives about reintroduction and increase of crop genetic and species diversity, identify useful genetic resources, get in contact with teams having undertaken specific trials.
The database is currently a proof-of-concept including 16 integrated datasets from 55 individual experiments (one experiment = one location in one crop season) and conveys performance information on approximately 250 accessions, with more datasets currently in the pipeline that will be completed and integrated soon. As such, this tool is open to improvements and to host new datasets from past, current or future experiences.
We hope to see it developing in a common resource to enable communities engaged in testing and using a diversity of plant genetic resources to collect, share, and base their decisional processes on, structured evidence.
Download here the Excel Database
Download here the full Report on the trials, including a user guide to the Database
The data spreadsheets. Fixed variables (in this case, Year, Species, Type, Soil type, Preceding crop, Entry) are on the left-hand side. Performance variables are inserted as scores derived by relating actual values to a min-max range derived from literature and/or from local conditions and knowledge. A scoring 0 to 9, ‘0’ indicates the minimum acceptable value and ‘9’ indicates the maximum or best achievable value. This way, the charts on the right-hand side will visualise the performances as positioned in a meaningful range: even visualising the profile of a single accession, you will be able to understand if its yield and quality are “low”, “average” or “high”.