The number of traits in each trait class as recommended by the Crop Ontology website that were used in the strawberry crop ontology of 120 variables.
The number of methods in each method class as recommended by the Crop Ontology website used in the strawberry crop ontology of 120 variables.
The number of measurements per entity of the strawberry crop ontology of 120 variables.
The number of variables for each trait in the strawberry crop ontology of 120 variables.
Screenshots describing how to access the Strawberry Crop Ontology on the Genome Database for Rosaceae (GDR; www.rosaceae.org). (A) GDR home page with top toolbar showing the Search dropdown menu and Search Trait Descriptor option (orange outlines). (B) Selecting the Strawberry Crop Ontology from the Group dropdown menu. (C) Results of selecting the Strawberry Crop Ontology with no filters, the Crop Ontology Variable matches the variable name on the Crop Ontology website. (D) Descriptor Overview page of a selected descriptor. (E) Datasets associated with the selected descriptor. (F) Project Overview page of the selected dataset. (G) Trait Descriptors associated with the selected dataset. (H) Trait Overview page showing available associated data.
Screen captures of the strawberry ontology from the Crop Ontology database. (A) Home page for the Crop Ontology website and the link to the Strawberry Crop Ontology (alphabetical order) and the icons that can be used to load and view the ontology (orange box). (B) Strawberry Crop Ontology home page with information on the curators, the address for the Genome Database for Rosaceae (GDR) strawberry descriptors, and the search filter (orange box). (C) Strawberry Crop Ontology navigation page providing information on each trait, method and scale, and the variable name (orange box) that matches the Crop Ontology Variable on the GDR results display.
Screenshot of the Breeding Information Management System (BIMS) page in the Genome Database for Rosaceae (GDR) where users can download the Strawberry Crop Ontology.
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Strawberries are grown around the world in both northern and southern hemispheres. The U.S. has the second largest acreage in production after China (World Strawberry Production by Country—AtlasBig.com accessed 1 Jul 2024) with most of the acreage in California (43,100 acres) and Florida (14,200 acres) [US Department of Agriculture (USDA), National Agricultural Statistics Service 2024]. In 2023, the United States produced 1.250 billion kilograms of strawberries totaling USD $3.173 billion for fresh market or processing (including frozen products), and U-pick agrotourism ventures (USDA Small Fruit Crop Germplasm Committee 2023).
Private and public strawberry breeding programs around the world are breeding for specific requirements for their regions, although many have the same goals: extended growing season using cultivars of different bearing types (day-neutral or short-day); phenological traits (e.g., flowering date, harvest date, and growing degree days); improved yield, plant vigor, plant architecture, and ease of harvest; better disease and insect tolerance; improved fruit quality (e.g., size, color, firmness, and chemical composition including organic acids, sugars, anthocyanins); and adaptations to abiotic stressors (Mathey et al. 2013).
Generating phenotypic data is costly, and the raw data are often incompatible between programs and are rarely published (Zamir 2013). Breeding programs have different trait naming styles and corresponding trait scoring scales, creating inconsistent terminology and measurement scales that inhibit combining data among programs (Andrés-Hernández et al. 2021). Developing a community-supported set of common vocabulary and methods provides an opportunity for combining data for large-scale analyzes when used across multiple studies (Shrestha et al. 2010).
Crops in the Rosaceae (rose family), like strawberry, have many traits in common in addition to a set of crop-specific traits. The Genome Database for Rosaceae [GDR (rosaceae.org; Jung et al. 2019)] has compiled a searchable trait ontology (TO) for the Rosaceae to integrate quantitative trait loci (QTL) and genome-wide association studies (GWAS) from various sources. The TO in GDR was built based on the Plant Trait Ontology (https://www.planteome.org, version 5.0 Jul 2023; Cooper et al. 2024), adding terms as needed. Plant Trait Ontology is species-neutral and describes traits without specific methods and scales. To integrate phenotype data from various sources, however, more precise ontology with specific crop, method, and scale is needed.
The Crop Ontology (CO) website (https://cropontology.org; Matteis et al. 2013) is a repository for crop ontologies developed by each crop community and provides a template for creating crop ontologies with variables composed of three components: trait, method, and scale. Briefly, CO encourages the use of existing ontology sites, such as GDR and GDV, and supports compliance with the Open Biological and Biomedical (OBO) foundry to improve FAIR (findable, accessible, interoperable, and reusable) principles (Arnaud et al. 2020). Crop ontologies are available for grain, legume, root, and forage crops including maize, barley, castor bean, lentils, beet, potato, and others. Currently, the fruit crops represented are apple, banana, and coconut, with blueberry and strawberry being the first ontologies for specialty small fruit crops. Templates are provided by CO to standardize the entry of traits, methods, and scales and create a controlled vocabulary. This provides a tool to help organize, aggregate, and retrieve large amounts of genomic and phenomic data for discovery of commonalities within and among crops and to enable large scale analyzes from data repositories such as GDR (Pietragalla et al. 2024). The blueberry crop ontology (Hislop et al. 2024) creates a shared data language for researchers, including 178 traits and methods with associated datasets available through Genome Database for Vaccinium (GDV; www.vaccinium.org; Humann et al. 2023) with the CO deposited on the Crop Ontology website.
The need to develop the tools for and fund data curation was recognized by researchers and stakeholders. One of seven National Research Support Projects, the NRSP10 project (www.nrsp10.org) for tool development was funded and implemented in 2014, followed by the funding of this project in 2021. The objectives of the USDA-National Institute of Food and Agriculture (NIFA)-Specialty Crop Research Initiative (SCRI)-funded project “Advanced National Database Resources for Specialty Crop Research and Improvement” are to 1) collect, curate, and integrate all types of genomics, genetics, and breeding big data in easy-to-use and robust crop-specific databases; 2) develop and integrate tools to promote the collection, integration, and utilization of big data by scientists and breeders; and 3) provide scientists/breeders with personalized training for field data-collection software, big data management and analysis, and use of database resources; quantify the impact of databases on genomics-assisted breeding; and broadly disseminate project activities and outcomes. To address these goals, data curators are working on locating new and historic datasets for small fruits, formatting them, and loading them to the respective databases (strawberry, blackberry, raspberry, stone fruits, apple, and pear to GDR; citrus to Citrus Genome Database; blueberry, cranberry, and crop wild relatives to Genome Database for Vaccinium) and to the USDA’s GRIN-Global (Byrne et al. 2018; Postman et al. 2010). So far, the phenotyping data have been entered into these databases with the trait descriptors that are specific to these public datasets or publications.
Our objective was to develop a crop ontology that systematizes the traits measured in strawberry and provides a shared vocabulary and phenotyping methodology for each trait that is also computer-readable thus allowing integration of phenotypic data from the various strawberry breeding programs and collections. We use crop ontology to refer to a comprehensive collection of traits measured, quantified, or observed in strawberry research, with one ontological entry per method of measurement. Another objective was to associate strawberry CO to Rosaceae Plant Trait Ontology in GDR to integrate phenotyping data with other data types such as QTLs and GWAS.
This article describes the crop ontology that we developed for strawberry. Our strawberry crop ontology reflects a consensus understanding of traits being captured across strawberry breeding and research institutions representing major programs in the United States and Canada.
The traits included in this ontology are those recognized as priority traits for strawberry breeding in the 2023 Crop Vulnerability Statement (fruit size, firmness, and yield; fruit quality; phenology; resistance to fungal and bacterial diseases; resistance to insect and arthropod pests; USDA Small Fruit Crop Germplasm Committee). To develop this crop ontology, we compiled 281 traits from Mathey et al. (2013), Hummer et al. (2023), GDR and GRIN-Global (https://npgsweb.ars-grin.gov/gringlobal/descriptors, filtered for crop strawberry accessed 30 Oct 2023; Byrne et al. 2018; Postman et al. 2010). Phenotyping data in GDR included those from RosBREED (Iezzoni et al. 2020) and GRIN-Global, and each dataset had their unique trait descriptor sets. Strawberry phenotypic descriptors from the RosBREED project were downloaded from GDR and those from GRIN-Global datasets were directly downloaded from GRIN-Global. Strawberry phenotypic descriptors from the RosBREED project are composed of 37 trait descriptors used by 32 breeding programs from 12 states (Iezzoni et al. 2020). We began with these sources of trait names because they were easily accessible and in use by some of the institutions we contacted for feedback. The compiled trait list was scrutinized for duplication, and similar traits were combined (e.g., bloom date vs. flowering date). Multiple methods and/or scales were detailed for some traits (e.g., anthocyanin measurements). This resulted in a preliminary set of 69 traits that were sent to breeders and researchers representing major regions of the US and Canadian strawberry production and breeding regions. Responders were the Agriculture et Agroalimentaire Canada–Agriculture and Agri-Food Canada, Cornell University, University of California, Davis, University of Florida, North Carolina State University, and USDA-ARS Horticultural Crops Production and Genetic Improvement Research Unit. We also received input from the descriptor specialist of the International Treaty on Plant Genetic Resources for Food and Agriculture.
After a first round, a second round of feedback was requested on an additional 43 traits including new entries from the International Union for the Protection of New Varieties of Plants (UPOV) (2023), the Canadian Plant Breeders’ Rights (Canadian Food Inspection Agency 2012), and published disease traits (Feldmann et al. 2024; Jiménez et al. 2023; Knapp et al. 2024; Pincot et al. 2018, 2020; Zurn et al. 2020), for a total of 112 traits. The descriptor format of each set of traits that was retrieved from original sources and reported by responding researchers was different, therefore, we used the Crop Ontology standardized template with required components (Table 1, https://cropontology.org; Matteis et al. 2013). In brief, trait variables are composed of three components, trait, method, and scale, with various metadata for each component. During that process, new traits were compared with existing traits in the GDR and GRIN-Global databases and common abbreviations and ontology terms were used to facilitate future integration into GDR and GRIN-Global. Specifically, the trait name components in newly developed CO variables were matched exactly to the Rosaceae Plant Trait Ontology terms with similar terms added as aliases and incorporated into the GDR as well as the CO templates.
Supported by the feedback from six researchers in the United States and Canada, which captured traits of interest in modern U.S. and Canadian strawberry research at the time of the survey, 120 curated variables were made publicly accessible on GDR (https://www.rosaceae.org) and CO (https://cropontology.org) websites.
Traits were organized for GDR by classification into seven groups following the Crop Ontology website ‘Trait Class’, which specifies the type of trait. These classifications were agronomic, biochemical, biotic stress, morphological, phenological, physiological, and quality, with quality being the largest group with 29 traits (Fig. 1). Other choices for trait class are abiotic stress and fertility, although we did not have CO variables that belong to them in this first round. GRIN-Global, Plant Trait Ontology in GDR, and CO all have a few different category names or variations on a name, such as “morphology” vs. “morphological” or “chemical composition” vs. “biochemical.” GRIN-Global has the categories “molecular,” “cytological or cellular,” and “genetic stock” [Descriptors GRIN-Global (ars-grin.gov) search for crop strawberry] that neither of the other two databases have. Method classes that we used, so far, were calculation, counting, estimation, and measurement (Fig. 2) with prediction, description, or classification as other options. The crop ontology format also includes the scale or unit on which the trait is recorded (e.g., binary no/yes, date, percentage), the scale class (e.g., numerical, ordinal), and any relevant numerical limits (e.g., 0 to 100 for percentages, 0 to 14 for pH measurements). In this ontology, estimation was the highest used method class with 76 entries, followed by measurement with 42 entries (Fig. 2). The ontologies also specify the anatomical entity of the plant that is associated with each trait (flower, fruit, plant, etc.) (Fig. 3), trait attribute being measured, whether the trait is being used actively by a research program, method of collection, method class, and method description. Among the various anatomical entities, “plant” and “fruit” had the highest number of variables, with 44 and 33, respectively (Fig. 3).
Citation: HortScience 60, 5; 10.21273/HORTSCI18420-24
Citation: HortScience 60, 5; 10.21273/HORTSCI18420-24
Citation: HortScience 60, 5; 10.21273/HORTSCI18420-24
The GRIN-Global strawberry traits categorized as molecular descriptors were all related to flowering phenotype. These were consolidated into the trait “Plant bearing type” and classified as phenological. The traits categorized as cytological or cellular descriptors, “DNA ratio” and “Ploidy,” were classified as physiological, and the descriptor “Ploidy Equation” was excluded. The traits categorized as genetic stock descriptors, “Fragaria core subset” and “True to Type,” were excluded. Some traits had more than one classification. For example, pH can be classified as a quality and a biochemical trait. The CO website format does not allow for a trait to belong to multiple classes, although Plant Trait Ontology and GRIN-Global do.
The project started with existing entries with associated data from different data sources; entries that were measuring the same traits but using different methods or scales were given distinct variable names and distinct identifiers. One example of this is the Runner number trait where three methods from three sources exist, “Runner number count,” “Runner number 1 to 9 scale,” and “Runner number 1 to 7 scale.” All entries measured runner number, but “Runner number count” is a numerical value and was assigned the variable name in the CO format of “RUNNERNUM_RUNNERNUMCT_count” (Hummer et al. 2023), “Runner number 1 to 9 scale” uses categories of 1 = no runners and 9 = hundreds methodology (Mathey et al. 2013) and was assigned the variable name in the CO format of “RUNNERNUM_RUNNERNUM1-9M_1-9RUNNERNUMscale,” and “Runner number 1 to 7 scale” uses a more granular scale of 1 = absent or very weak, 3 = few, 5 = medium, 7 = many (Canadian Food Inspection Agency 2012) and was assigned the variable name “RUNNERNUM_RUNNERNUM1-7M_1-7RUNNERNUMscale.” The variable name serves as a unique, computer-readable name. Most trait names (61) had a single variable, 23 had two variables, three had three variables and one, fruit yield, had four variables, which was the most methods for any trait (Fig. 4).
Citation: HortScience 60, 5; 10.21273/HORTSCI18420-24
By creating a common vocabulary with aliases, methods and scales defined and deposited in a central location, researchers can find all the data associated with their trait of interest regardless of what it is called in a publication.
This curated strawberry crop ontology has been published via the GDR and the Crop Ontology website. Best practice would be to use the curated strawberry crop ontology published via the GDR for descriptors and associated data (Fig. 5). The Strawberry Crop Ontology can be accessed without a log in on GDR by going to the home page and selecting Search Trait Descriptor from the dropdown menu under Search in the top tool bar (Fig. 5A). On the Search Trait Descriptor page, users can select Strawberry Crop Ontology under Group (Fig. 5B). If no category or other filtering is done, the entire list of descriptors will show in the results (Fig. 5C). Descriptors have hyperlinks to the Descriptor Overview pages that display pertinent information including the associated datasets and germplasm (Fig. 5D). Datasets can be explored by selecting the Dataset link in the side panel (Fig. 5E). Dataset names have hyperlinks to the Project Overview page (Fig. 5F) with pertinent information on the dataset including links to the germplasm (Stocks) used and the entire set of Trait Descriptors (Fig. 5G). On the Trait Overview page (Fig. 5H), users can see all trait-related data such as QTLs, GWAS, and trait descriptors from other crops as well as associated Strawberry Crop Ontology terms.
Citation: HortScience 60, 5; 10.21273/HORTSCI18420-24
The Strawberry Crop Ontology can be accessed in the Crop Ontology website using the ontology ID “CO_372” (https://cropontology.org/term/CO_372); it can also be found by searching for “strawberry” (Fig. 6). The main strawberry crop page can also be accessed by scrolling through the alphabetical list of ontologies to find strawberry (Fig. 6A). The Crop Ontology database enables the downloading and visualization of the ontology through Ontology Explorer tools (orange outline Fig. 6A). Once the strawberry ontology page is accessed (Fig. 6B), users can search by trait using the filter (orange outline Fig. 6B). Alternatively, users can explore the traits associated with a particular Term category (Agronomic, Biochemical, etc.) by clicking on the arrow beside the Term. Each category has multiple Traits and clicking on each arrow takes users to the Method and the Scale (Fig. 6C) along with Concept details providing information about the selection (Trait, Method, or Scale). The Variables box (orange outline Fig. 6C) has the variable name that is identical to the GDR descriptor abbreviation.
Citation: HortScience 60, 5; 10.21273/HORTSCI18420-24
Strawberry Crop Ontology data are loaded into GDR as a trait descriptor set named “Strawberry Crop Ontology.” The CO variable label matches the GDR trait descriptor abbreviation and the metadata such as trait name, description, descriptor type, unit, and scale categories were added.
GDR CO will continue to grow as descriptors are added from curated journal articles. The Crop Ontology will be synced monthly if new traits are curated. Strawberry CO are also available from the Breeding Information Management System (BIMS) tool (https://www.rosaceae.org/bims; Jung et al. 2021), a secure, comprehensive, open-source online system for managing private or public breeding data. Strawberry CO are available for breeders to download to use in their program. To create a program in BIMS, users must request an account. Once logged in to BIMS, users can download the Strawberry CO (Fig. 7). BIMS users can also transfer the CO to the Field Book App (https://github.com/PhenoApps/Field-Book), an Android app that allows the replacement of hard-copy field notes and Excel spreadsheets, enhancing the speed and the accuracy of data collection (Morales et al. 2022; Rife & Poland, 2014). BIMS and the Field Book App are integrated with Breeding Application Programming Interface (BrAPI; https://brapi.org), which allow data transfer without files (Selby et al. 2019).
We have described how we developed a crop ontology for strawberry by surveying breeding programs around the United States and Canada and curating key recent articles (Hummer et al. 2023; Iezzoni et al. 2020; Mathey et al. 2013) and the GRIN-Global descriptor list shaped by the priority traits established by the 2023 Strawberry Crop Vulnerability Statement (https://www.ars-grin.gov/CGC, filter by Small Fruits). We encourage researchers and plant breeders of strawberry around the globe to use the ontology to allow integration and comparison of phenotypic data.
Citation: HortScience 60, 5; 10.21273/HORTSCI18420-24
GDR hosts many valuable resources for strawberry including multiple genome sequences and a pangenome, as well as linkage maps, QTL, genotype and phenotype data. Some of the sequence assemblies have been used as references that allowed identification of QTL for resistance to powdery mildew (Sargent et al. 2019); genetic analyzes of organic and biochemical compounds (Rey-Serra et al. 2022); comparative genomics within Rosaceae genera (Buti et al. 2018; Jung et al. 2012; Vilanova et al. 2008; Zhang et al. 2020), among others. The addition of this strawberry crop ontology and integration with the larger Trait Ontology in GDR improves resources by integrating not only strawberry phenotyping data from various sources but also various trait-related data, such as QTLs and GWAS, across Rosaceae crops. These integrated data can be used in further studies, such as comparative and synteny analyzes for disease resistance, agronomic traits, and other traits important to breeders and stakeholders.
This ontology intends to promote collaboration across programs and to provide researchers with tools to better analyze existing and future strawberry data for the same traits once the CO is implemented. Future expansion of this ontology would benefit from the inclusion of more of the international UPOV Fragaria trait list (UPOV 2023) because this CO construction mostly focused on the traits measured by breeders and researchers in the Unites States and Canada. However, 16 of the 44 traits are included. Many of the remaining traits found in the UPOV (i.e., stipule intensity of anthocyanin coloration, flower arrangement of petals, petal ratio length/width) appear to be standardized for the protection and classification of new cultivars. It would still be beneficial to consider both trait lists for standardized data collection in future cultivar releases. Additional improvements to the CO could include standardizing developmental stages and expanding resolution of phenological stages. In addition, the implementation of automatic curation or reasoning has been suggested to be an important factor for bridging biological levels, such as using the Ontology of Biological Attributes (OBA) computational traits for the life sciences (Laporte et al. 2016; Stefancsik et al. 2023). Although many improvements can be made, we encourage use of the CO across programs. The availability of strawberry CO in GDR-BIMS will encourage new BIMS users to choose strawberry CO as their trait descriptors instead of creating a new set of trait descriptors for their program. In this manner, we reduce the amount of redundancy for recording the same or similar phenotypes.
The current CO is only a starting point and should be maintained by the community and updated to reflect new and refined technologies, traits, and methods. GDR curators will add more CO terms as they continue to curate phenotyping data from publications. Other researchers can submit new traits, request changes be made to a trait, or suggest trait synonyms at the CO website at (https://cropontology.org/page/Submit). For the greater good of the community, we encourage all Fragaria breeders and researchers to evaluate the CO for missing traits and use standardized methods and scales.
The strawberry crop ontology provides an opportunity for standardization in phenotyping to improve communication and compare results and integrate multiple types of data for stronger and broader analyzes. The goal is to encourage national and international collaborations and work toward better strawberry breeding and production.
The number of traits in each trait class as recommended by the Crop Ontology website that were used in the strawberry crop ontology of 120 variables.
The number of methods in each method class as recommended by the Crop Ontology website used in the strawberry crop ontology of 120 variables.
The number of measurements per entity of the strawberry crop ontology of 120 variables.
The number of variables for each trait in the strawberry crop ontology of 120 variables.
Screenshots describing how to access the Strawberry Crop Ontology on the Genome Database for Rosaceae (GDR; www.rosaceae.org). (A) GDR home page with top toolbar showing the Search dropdown menu and Search Trait Descriptor option (orange outlines). (B) Selecting the Strawberry Crop Ontology from the Group dropdown menu. (C) Results of selecting the Strawberry Crop Ontology with no filters, the Crop Ontology Variable matches the variable name on the Crop Ontology website. (D) Descriptor Overview page of a selected descriptor. (E) Datasets associated with the selected descriptor. (F) Project Overview page of the selected dataset. (G) Trait Descriptors associated with the selected dataset. (H) Trait Overview page showing available associated data.
Screen captures of the strawberry ontology from the Crop Ontology database. (A) Home page for the Crop Ontology website and the link to the Strawberry Crop Ontology (alphabetical order) and the icons that can be used to load and view the ontology (orange box). (B) Strawberry Crop Ontology home page with information on the curators, the address for the Genome Database for Rosaceae (GDR) strawberry descriptors, and the search filter (orange box). (C) Strawberry Crop Ontology navigation page providing information on each trait, method and scale, and the variable name (orange box) that matches the Crop Ontology Variable on the GDR results display.
Screenshot of the Breeding Information Management System (BIMS) page in the Genome Database for Rosaceae (GDR) where users can download the Strawberry Crop Ontology.
Contributor Notes
This research was supported by US Department of Agriculture (USDA), Agricultural Research Service (ARS) Current Research Information System Project (CRIS) 2072-21000-059-00D, the USDA National Research Support Project (NRSP10), and the Specialty Crop Research Initiative (SCRI)-National Institute of Food and Agriculture (NIFA) Award 2022-51181-38449. J.M.B. collected inputs from the strawberry community, constructed the strawberry crop ontology, linked the strawberry crop ontology to the Rosaceae trait ontology, and drafted the article; S.J. designed the project, advised on the strawberry crop ontology construction, linked the strawberry crop ontology to Rosaceae trait ontology, and edited the article; N.V.B. supervised the different stages of ontology construction and edited the article; M.S. provided preliminary ontology construction; T.L. and C-H.C. provided technical support for loading and displaying the data on GDR; S.R., D.M. and J.L.H. advised on ontology construction and edited the article. Special thanks for input from Adriana Alercia, Beatrice Amyotte, Tom Davis, Mitchell Feldmann, Gina Fernandez, James Hancock, Peter Henry, Steven Knapp, Ted Mackey, Ian Mellon, Debora Menicos, Penny Perkins-Veazie, Hillary Thomas, Courtney Weber, and Vance Whitaker. All opinions expressed in this paper are the authors’ and do not necessarily reflect the policies and views of USDA.
J.M.B. and S.J. contributed equally to this work.
The number of traits in each trait class as recommended by the Crop Ontology website that were used in the strawberry crop ontology of 120 variables.
The number of methods in each method class as recommended by the Crop Ontology website used in the strawberry crop ontology of 120 variables.
The number of measurements per entity of the strawberry crop ontology of 120 variables.
The number of variables for each trait in the strawberry crop ontology of 120 variables.
Screenshots describing how to access the Strawberry Crop Ontology on the Genome Database for Rosaceae (GDR; www.rosaceae.org). (A) GDR home page with top toolbar showing the Search dropdown menu and Search Trait Descriptor option (orange outlines). (B) Selecting the Strawberry Crop Ontology from the Group dropdown menu. (C) Results of selecting the Strawberry Crop Ontology with no filters, the Crop Ontology Variable matches the variable name on the Crop Ontology website. (D) Descriptor Overview page of a selected descriptor. (E) Datasets associated with the selected descriptor. (F) Project Overview page of the selected dataset. (G) Trait Descriptors associated with the selected dataset. (H) Trait Overview page showing available associated data.
Screen captures of the strawberry ontology from the Crop Ontology database. (A) Home page for the Crop Ontology website and the link to the Strawberry Crop Ontology (alphabetical order) and the icons that can be used to load and view the ontology (orange box). (B) Strawberry Crop Ontology home page with information on the curators, the address for the Genome Database for Rosaceae (GDR) strawberry descriptors, and the search filter (orange box). (C) Strawberry Crop Ontology navigation page providing information on each trait, method and scale, and the variable name (orange box) that matches the Crop Ontology Variable on the GDR results display.
Screenshot of the Breeding Information Management System (BIMS) page in the Genome Database for Rosaceae (GDR) where users can download the Strawberry Crop Ontology.
