Enhancing Potato Breeding with Efficient Digital Data Collection and Management
by Jorge Luis Alonso with ChatGPT
The open-access journal, Heliyon, has published a compilation of information on data collection tools and breeding data management software, with a focus on potato breeding data management. Below is a summary of its content.
Introduction
Breeding research heavily relies on a large amount of phenotypic data. However, managing such data can be daunting, especially when multiple people are involved and working at different times and locations. To scale up breeding operations in the 21st century, it is critical to digitize breeding data. Although breeding informatics software has been developed worldwide, public sector breeding programs have been slow to adopt it. As a result, manual data collection and management processes using pen and paper are still prevalent. This practice limits access to historical data sets and resources, making it challenging to make informed breeding decisions. Digitizing collected data is essential to managing the vast amount of information collected through genotyping, greenhouse and field experiments, and the labor involved. In this review, researchers from ICAR-Central Potato Research Institute (CPRI), CGIAR Excellence in Breeding Platform (EiB), and Division of Horticultural Science (India) will explore various data management tools and applications that enable informed decisions and foster collaboration among multiple users.
Digital data recording tools
To assist breeders in collecting and managing data more efficiently, several digital data recording tools have been developed. One such tool is the open-source Integrated Breeding (IB) FieldBook, which enables breeders to collect and analyze phenotypic data across various crops with ease. Another option is the PhenoApps, which includes the Field Book for data collection and Intercross for cross-management. Additionally, Phenobook is a web-based software that enables breeders to design experiments, input data, visualize results, and export data. It also has a mobile app for remote data collection. Android users can take advantage of PhenoTyper for data management and FieldLab for image storage. Phenome App replaces the traditional pen-and-paper method for collecting phenotypic data, while GridScore is a plant phenotyping tool equipped with GPS referencing, image tagging, and speech recognition capabilities.
Handheld devices for data recording
Plant breeding programs can use various digital devices for data recording, including smartphones. However, rugged devices like Toughpads are better suited to breeders’ needs due to their specialized design and features. Developed by Panasonic, Toughpads are durable and provide access to breeding software apps like Field Book while withstanding harsh handling and environmental conditions. Meanwhile, the Digital Data Collector (DIB) simplifies data recording in the field with its intuitive interface, barcode reading, and on-site data recording. Both devices can seamlessly transfer data to other programs like BMS for analysis and storage.
Plant breeding data management platforms
Plant breeders have a wide variety of software programs at their disposal to help them manage their projects. These options include commercially available software such as AGROBASE/4, AGROBASE Generation II, and Genovix, as well as open-source software like the Enterprise Breeding System (EBS), Breeding Management System (BMS), and Plant Research Information Sharing Manager (PRISM). Another program, Progeno, is user-friendly and integrates both phenotypic and genotypic information. For high-throughput phenotyping, Phenome allows for the collection and management of plant phenotypic data through the use of a Personal Digital Assistant (PDA) equipped with an in-built barcode scanner. The CropSight platform, powered by the Internet of Things (IoT), is designed to automate data collection, storage, and management. Lastly, the Breeding Information Management System (BIMS) is an online, secure, and free database system that offers individual breeders access to a variety of tools, such as data import/export, analysis, and archiving.
Breeding interface/ontology tool
Plant breeders have access to several software tools that can help improve their work. One such tool is the Breeding Application Programming Interface (BrAPI), which facilitates data collection and transfer, as well as the visualization of geographic information and searching of parameter combinations across various systems and applications. Another tool is Crop Ontology (CO), which is a free online tool developed by CGIAR. The CO allows breeders to browse a vast database of crop terminology, classified into categories such as phenotypic, breeding, germplasm, and trait, thereby enhancing the consistency and quality of collected data. Finally, Protege is the most widely used tool for creating and maintaining ontologies. It provides a customizable user interface for building simple and complex ontology-based applications in a single workspace, while fully supporting RDF and OWL 2 Web Ontology Language specifications.
Data analytical support of breeding software
To improve breeding outcomes, it is essential to integrate different data types, such as phenotype, genotype, and meteorological information, and develop user-friendly informatics systems. While data management software can support data analysis, no widely-used system exists across different crops. These pipelines offer features such as data cleaning, basic statistics, and multi-site stability analysis. A fully operational and interconnected system would simplify trial design, utilize yield trial analysis, and provide predictions about variance components, among other benefits.
Digitalization of potato breeding program in India
BMS software is currently being utilized by ICAR-CPRI in partnership with BMGF to enhance genetic gain in Indian staple crops. To date, 4918 germplasm lines, including parent lines, breeding clones, and varieties, have been incorporated into BMS. Multi-location trials for all major AICRP potato trials have been devised using the BMS since 2020. Furthermore, past AICRP potato trials from the past decade have been updated and uploaded as an online repository. By implementing a single, unified platform from parent line selection to multilocational trials, the breeding programs will be simplified and errors will be reduced.
Work done at the International Potato Center (CIP)
The CGIAR Research Center, CIP, located in Lima, Peru, has developed an accessible online global trial data management system that comprises CIPCROSS, HIDAP, and the Field Book Registry. CIPCROSS enables the tracking of breeding materials, while HIDAP consolidates data collection, quality control, and analysis. The Field Book Registry allows researchers to share and update field books in real time via the HIDAP network. CIP’s Global Roots & Tubers Base stores data from the potato breeding program, including genotypic, phenotypic, pedigree, environmental, and geographical data. The database is structured to facilitate analysis of both phenotypic and genotypic data with the primary objective of developing effective models that can predict phenotypic traits and provide insights into their genetics.
Benefits of data management using digitalization
Breeding programs can benefit greatly from digitization. With streamlined data upload, improved accuracy, enhanced accessibility, greater privacy and security, efficient data analysis, and robust record-keeping, digitization has the potential to revolutionize breeding. It can facilitate prompt decision-making, bolster selection efficiency, increase genetic gain, hasten hybrid registration, and enable precision breeding. Digital record management can furnish up-to-date information, refine selection decisions, and reduce errors. Overall, digitization is essential to reducing the time and effort expended on data collection and analysis, ultimately leading to increased breeding program efficiency.
Future perspective
An effective data management system is necessary for managing thousands of experimental breeding lines located across various sites andtw years. In order to achieve optimal breeding, parallel computing, big data-capable infrastructure, and new algorithms are crucial. Accessing the necessary data demands community integration through global projects, while data harmonization and integration using appropriate rules and practices is of utmost importance. Artificial intelligence (AI) plays a significant role in data processing and mining, enabling the integration of multiple sensors and predictive phenotyping. Recent advancements in data analytics, image processing, and machine learning have the potential to automate rapid phenotyping and decision-making in plant research, speeding up genetic gain and enabling automated, real-time detection and quantification of stress traits.
Conclusion
In order to achieve successful breeding, reliable and effective data management is essential. The traditional methods of data management can be costly, time-consuming, and often result in transcription errors. Precise phenotyping is necessary for breeding, but the growing amount of data can make manual interpretation and decision-making difficult. The use of digital tools and breeding data management software allows for real-time tracking and monitoring of the breeding program, while voice recognition software provides audio recording capabilities. Adopting data management tools can enhance data quality, speed, and efficiency in breeding programs.
Source
Dipta, B., Sood, S., Devi, R., Bhardwaj, V., Mangal, V., Thakur, A. K., Kumar, V., Pandey, N., Rathore, A., & Singh, A. (2023). Digitalization of potato breeding program: Improving data collection and management. Heliyon, 9(1), e12974.
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