In 2005, a collaboration started between the French National Institute for Agricultural Research (INRA) and the farmer organization Réseau Semences Paysannes (RSP). The aim was: (1) to study on-farm management of crop diversity; (2) to develop population-varieties adapted to organic and low inputs agricultures in the context of a participatory plant breeding program involving farmers, NGOs' faciltators and researchers.
In this project, researchers needed to map the history of the population-varieties using the network formalism. In addition to the diffusion among farms, they wanted to document the other steps of life cycle of the seed lot like the reproduction, the selection, and the cross steps. Data characterizing the different seed lots were produced at each step like phenotyping and cultural practices data. All this information needed to be centralized and stored.
Thus, we developed SHiNeMaS (Seeds History and Network Management System) a database with its web interface, dedicated to the management of the history of seed lots and the associated data.

SHiNeMaS has been developed to be a flexible tool and to support multiple agronomic species. The schema of the database is organized around the seed lot. It manages several types of relations between seed lots : multiplication, cross, intra-varietal selection, seed lot mixture and diffusion. For each type of event the user can define the measured variables such as traits, practices, etc., and how they were measured. The stock information of the seed lots is also stored in the database.
SHiNeMaS provides interfaces to massively load data in tabulated format, or to individually load data through the web interface. A file format was designed for each type of event, and contains the minimum information to describe the concerned event. SHiNeMaS also provides tools to correct data already recorded.

A tool was developed in SHiNeMaS to provide a helpful assistant to the creation of the files used for massive data loading. SHiNeMaS also provides query interfaces to retrieve and extract data. The user can access to the profile of a seed lot or a population-variety.