Plant, space and time - linked together in an integrative and scalable data management system for phenomic approaches in agronomic field trials

Abstract

Background: To ensure further genetic gain, genomic approaches in plant breeding rely on precise phenotypic data, describing plant structure, function and performance. A more precise characterization of the environment will allow a better dealing with genotype-by-environment-by-management interactions. Therefore, space and time dependencies of the crop production processes have to be considered. The use of novel sensor technologies has drastically increased the amount and diversity of phenotypic data from agronomic field trials. Existing data management systems either do not consider space and time, are not customizable to individual needs such as field trial handling, or have restricted availability. Hence, we propose an integrative data management and information system (DMIS) for handling of traditional and novel sensor-based phenotypic, environmental and management data. The DMIS must be customizable, applicable and scalable from individual users to organizations. Results: Key element of the system is a dynamic PostgreSQL database with GIS-extension, capable of importing, storing and managing all types of data including images. The database references every structural database object and measurement in a threefold approach with semantic, spatial and temporal reference. Timestamps and geo-coordinates allow automated linking of all data. Traits can be precisely defined individually or uploaded as predefined lists. Filtering and selection routines allow compilation of all data for visualization via tables, charts or maps and for export and external statistical analysis. New possibilities of environmental information-based planning of field trials, weatherguided phenotyping and data analysis for outlier or hot-spot detection are demonstrated. Conclusions: The DMIS supports users in handling experimental field trials with crop plants and modern phenotyping methods. It focuses on linking all space and time dependent processes of plant production. Weather, soil and management, as well as growth and yield formation of the plants can be depicted, thus allowing a more precise interpretation of the results in relation to environment and management. Breeders, extension specialists, official testing agencies and agricultural scientists are assisted in all steps of a typical workflow with planning, designing, conducting, controlling and analyzing field trials to generate new information for decision support in the crop improvement process.

Empowered through our diversity: How to bring in a new age of plant science collaboration

“…We offer a theoretical infrastructure within which the creativity and feedback of surveyed researchers could potentially draw inspiration, deliver constructive criticism or offer new ideas. In this way, we also support the idea of sustainability, that is, building off of ideas of existing databases, whether mentioned here, proposed in other works or proposed in the questionnaire results (Assante et al, 2016; Cliffin, 2011; Honecker et al, 2020). The proposed questionnaire may in itself be a time investment of the community; however, it could go a long way for plant science research to ensure informed support for future work.…”

Effect of Planting Season in the Crop Production in Indian States

“…Autumn, kharif, rabi, summer, winter, and whole-year crops are all grown in India's 33 states. For high productivity, a crop needs specific weather and terrain conditions [3], [4]. If a farmer has access to knowledge about this over time, it may assist him in determining which crop to cultivate.…”