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The 5-year project 'Breeding roots, tubers and banana products for end user preferences' (RTBfoods) focused on collecting consumers' preferences on 12 food products to guide breeding programmes. It involved multidisciplinary teams from Africa, Latin America, and Europe. Diverse data types were generated on preferred qualities of users (farmers, family and entrepreneurial processors, traders or retailers, and consumers). Country-based target product profiles were produced with a comprehensive market analysis, disaggregating gender's role and preferences, providing prioritised lists of traits for the development of new plant varieties. We describe the approach taken to create, in the roots, tubers, and banana breeding databases, a centralised and meaningful open access to sensory information on food products and genotypes. Biochemical, instrumental textural, and sensory analysis data are then directly connected to the specific plant record while user survey data, bearing personal information, were analysed, anonymised, and uploaded in a repository. Names and descriptions of food quality traits were added into the Crop Ontology for labelling data in the databases, along with the various methods of measurement used by the project. The development and application of standard operating procedures, data templates, and adapted trait ontologies improved the data quality and its format, enabling the linking of these to the plant material studied when uploaded in the breeding databases or in repositories. Some modifications to the database model were necessary to accommodate the food sensory traits and sensory panel trials.

Plant genetic resources are source genetic material for conducting research and breeding. The use of this material is subject to international and national regulations on access and benefit-sharing (ABS). With modern genetic technologies generating desired trait and gene function improvement by replicating genetic signatures, ABS must adapt to the new technological reality. As the constituencies of international ABS conventions discuss if and how to extend the application of the conventions to digital sequence information (DSI) derived from source material, the genomics science community resists any incumbrance to continued free and unrestricted access to such information. Based on current ABS discussions and the likely future co-existence of diverse policy regimes, this paper proposes interoperability among data systems as an essential tool to implement legal solutions for benefit-sharing as well as advance science and innovation objectives. Two information technology tools are suggested for associating DSI to plant genetic resources and reciprocal citations with data exchange, namely digital object identifiers and digital genetic objects. This paper concludes that interoperability should be experimented with in both its technical and social dimensions, in order to support long-term alliances between policy and science through data archives, knowledge bases and live specimen collection resources.

The biomedical domain has shown that in silico analyses over vast data pools enhances the speed and scale of scientific innovation. This can hold true in agricultural research and guide similar multi-stakeholder action in service of global food security as well (Streich et al. Curr Opin Biotechnol 61:217–225. Retrieved from https://doi.org/10.1016/j.copbio.2020.01.010, 2020). However, entrenched research culture and data and standards governance issues to enable data interoperability and ease of reuse continue to be roadblocks in the agricultural research for development sector. Effective operationalization of the FAIR Data Principles towards Findable, Accessible, Interoperable, and Reusable data requires that agricultural researchers accept that their responsibilities in a digital age include the stewardship of data assets to assure long-term preservation, access and reuse. The development and adoption of common agricultural data standards are key to assuring good stewardship, but face several challenges, including limited awareness about standards compliance; lagging data science capacity; emphasis on data collection rather than reuse; and limited fund allocation for data and standards management. Community-based hurdles around the development and governance of standards and fostering their adoption also abound. This chapter discusses challenges and possible solutions to making FAIR agricultural data assets the norm rather than the exception to catalyze a much-needed revolution towards “translational agriculture”.

The CGIAR crop improvement (CI) programs, unlike commercial CI programs, which are mainly geared to profit though meeting farmers’ needs, are charged with meeting multiple objectives with target populations that include both farmers and the community at large. We compiled the opinions from >30 experts in the private and public sector on key strategies, methodologies, and activities that could the help CGIAR meet the challenges of providing farmers with improved varieties while simultaneously meeting the goals of: (i) nutrition, health, and food security; (ii) poverty reduction, livelihoods, and jobs; (iii) gender equality, youth, and inclusion; (iv) climate adaptation and mitigation; and (v) environmental health and biodiversity. We review the crop improvement processes starting with crop choice, moving through to breeding objectives, production of potential new varieties, selection, and finally adoption by farmers. The importance of multidisciplinary teams working towards common objectives is stressed as a key factor to success. The role of the distinct disciplines, actors, and their interactions throughout the process from crop choice through to adoption by farmers is discussed and illustrated., This document arose from the urgent need to join forces (across nations and research disciplines) and stand together to improve the prospects for the most vulnerable population on the globe whose well-being depends on agricultural production. Here, we learn from the past, and draft future strategic guidelines that will facilitate efforts of the CGIAR institutions (https://www.cgiar.org/) to enhance the livelihoods of the less privileged through crop improvement.

Summary User-oriented research is important in breeding improved genotypes, for developing and validating product concepts (mostly involving trait prioritisation), as well as evaluating breeding products in use situations (mostly involving participatory variety evaluation). This paper examines key aspects that enable cumulative learning in user-oriented research for root, tuber and banana (RTB) crop breeding. We reviewed empirical user-oriented studies on RTB crops published between 1996 and 2020. We examined the ability of user-oriented research to foster cumulative learning by examining four key aspects: spatial and temporal coverage; gender aspects; the range of traits considered and publishing practices as evident in reports and datasets. We conclude that user-oriented studies have received attention in RTB breeding but fall short of enabling cumulative learning. Substantial investments in methodology development and capacity are needed to bring greater coherence to this field and enable cumulative learning about user perspectives to iteratively increase the fit between improved genotypes and user preferences.

Modern breeding methods integrate next-generation sequencing and phenomics to identify plants with the best characteristics and greatest genetic merit for use as parents in subsequent breeding cycles to ultimately create improved cultivars able to sustain high adoption rates by farmers. This data-driven approach hinges on strong foundations in data management, quality control, and analytics. Of crucial importance is a central database able to (1) track breeding materials, (2) store experimental evaluations, (3) record phenotypic measurements using consistent ontologies, (4) store genotypic information, and (5) implement algorithms for analysis, prediction, and selection decisions. Because of the complexity of the breeding process, breeding databases also tend to be complex, difficult, and expensive to implement and maintain. Here, we present a breeding database system, Breedbase (https://breedbase.org/, last accessed 4/18/2022). Originally initiated as Cassavabase (https://cassavabase.org/, last accessed 4/18/2022) with the NextGen Cassava project (https://www.nextgencassava.org/, last accessed 4/18/2022), and later developed into a crop-agnostic system, it is presently used by dozens of different crops and projects. The system is web based and is available as open source software. It is available on GitHub (https://github.com/solgenomics/, last accessed 4/18/2022) and packaged in a Docker image for deployment (https://hub.docker.com/u/breedbase, last accessed 4/18/2022). The Breedbase system enables breeding programs to better manage and leverage their data for decision making within a fully integrated digital ecosystem. ispartof: G3-GENES GENOMES GENETICS vol:12 issue:7 ispartof: location:England status: published

The new phytologist 227(1), 260-273 (2020). doi:10.1111/nph.16544, Published by Wiley-Blackwell, Oxford [u.a.]

International audience; Many vocabularies and ontologies are produced to represent and annotate agronomic data. However, those ontologies are spread out, in different formats, of different size, with different structures and from overlapping domains. Therefore, there is need for a common platform to receive and host them, align them, and enabling their use in agro-informatics applications. By reusing the National Center for Biomedical Ontologies (NCBO) BioPortal technology, we have designed AgroPortal, an ontology repository for the agronomy domain. The AgroPortal project re-uses the biomedical domain's semantic tools and insights to serve agronomy, but also food, plant, and biodiversity sciences. We offer a portal that features ontology hosting, search, versioning, visualization, comment, and recommendation; enables semantic annotation; stores and exploits ontology alignments; and enables interoperation with the semantic web. The AgroPortal specifically satisfies requirements of the agronomy community in terms of ontology formats (e.g., SKOS vocabularies and trait dictionaries) and supported features (offering detailed metadata and advanced annotation capabilities). In this paper, we present our platform's content and features, including the additions to the original technology, as well as preliminary outputs of five driving agronomic use cases that participated in the design and orientation of the project to anchor it in the community. By building on the experience and existing technology acquired from the biomedical domain, we can present in AgroPortal a robust and feature-rich repository of great value for the agronomic domain.

Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labelling to be interoperable. As recommended by the FAIR principles, data, labels and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no agreed full set of ontologies recommended for data annotation across agricultural research disciplines, which may span genetics, environment, agroecology, biology and socioeconomics. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant ontology expertise. This CoP aims to stimulate knowledge sharing and directly support platform development teams by producing ontologies or contributing missing concepts, recommending best practices and identifying mitigation solutions when gold standard datasets are difficult to attain.

Scientific innovation is increasingly reliant on data and computational resources. Much of today’s life science research involves generating, processing, and reusing heterogeneous datasets that are growing exponentially in size. Demand for technical experts (data scientists and bioinformaticians) to process these data is at an all-time high, but these are not typically trained in good data management practices. That said, we have come a long way in the last decade, with funders, publishers, and researchers themselves making the case for open, interoperable data as a key component of an open science philosophy. In response, recognition of the FAIR Principles (that data should be Findable, Accessible, Interoperable and Reusable) has become commonplace. However, both technical and cultural challenges for the implementation of these principles still exist when storing, managing, analysing and disseminating both legacy and new data.COPO is a computational system that attempts to address some of these challenges by enabling scientists to describe their research objects (raw or processed data, publications, samples, images, etc.) using community-sanctioned metadata sets and vocabularies, and then use public or institutional repositories to share it with the wider scientific community. COPO encourages data generators to adhere to appropriate metadata standards when publishing research objects, using semantic terms to add meaning to them and specify relationships between them. This allows data consumers, be they people or machines, to find, aggregate, and analyse data which would otherwise be private or invisible. Building upon existing standards to push the state of the art in scientific data dissemination whilst minimising the burden of data publication and sharing.AvailabilityCOPO is entirely open source and freely available on GitHub at https://github.com/collaborative-open-plant-omics. A public instance of the platform for use by the community, as well as more information, can be found at copo-project.org.

GnpIS is a data repository for plant phenomics that stores whole field and greenhouse experimental data including environment measures. It allows long-term access to datasets following the FAIR principles: Findable, Accessible, Interoperable, and Reusable, by using a flexible and original approach. It is based on a generic and ontology driven data model and an innovative software architecture that uncouples data integration, storage, and querying. It takes advantage of international standards including the Crop Ontology, MIAPPE, and the Breeding API. GnpIS allows handling data for a wide range of species and experiment types, including multiannual perennial plants experimental network or annual plant trials with either raw data, i.e., direct measures, or computed traits. It also ensures the integration and the interoperability among phenotyping datasets and with genotyping data. This is achieved through a careful curation and annotation of the key resources conducted in close collaboration with the communities providing data. Our repository follows the Open Science data publication principles by ensuring citability of each dataset. Finally, GnpIS compliance with international standards enables its interoperability with other data repositories hence allowing data links between phenotype and other data types. GnpIS can therefore contribute to emerging international federations of information systems.

Today, global food production is the largest driver of environmental degradation and biodiversity loss (Willett et al. 2019). Rising global food demand and limited arable land are pushing us to expand agricultural frontiers and production. This often happens without regard to the environment, causing biodiversity loss, land and water degradation (Bioversity International 2017) Climate change is accelerating biodiversity loss. Higher temperatures disrupt pollination and natural pest control, affecting food quality (Food and Agriculture Organization of the UN 2017). Equally, the need to feed an additional 2 billion people by 2050 is pushing us to increase yields in a few staple foods, which erodes food and genetic diversity. Biodiversity loss in food systems leaves farmers with fewer options to deal with risks of crop failure, declining soil fertility, or increasingly variable weather (Bioversity International 2017), causing production losses, food insecurity and malnutrition(FAO, IFAD, UNICEF, WFP WHO 2018). The way we produce and consume our food is hurting both people and the planet. This calls upon all of us, from governments to producers to consumers, to put biodiversity back into food (World Economic Forum (WEF) 2017). Food and - more broadly - agricultural biodiversity are essential for sustainable food systems. Agrobiodiversity boosts productivity and nutrition quality, increases soil and water quality, and reduces the need for synthetic fertilizers. It makes farmers’ livelihoods more resilient, reducing yield losses due to climate change and pest damage. Broadening the types of cultivated plants also benefits the environment, increasing the abundance of pollinators and beneficial soil organisms, and reducing the risk of pest epidemics. To sustainably use and conserve agrobiodiversity, governments need dedicated, multi-sectoral and evidence-based policies and strategies. From smallholder farmers to multinational companies, food producers are becoming increasingly important in conserving genetic resources and adopting sustainable agricultural practices. Consumers need to become more aware of the impact of their food choices on the planet and their role in preserving the environment. What actions do we need to put in place to make change happen? To answer, we need to be able to measure biodiversity in food systems. While decades of effort have advanced our understanding of sustainable food systems, biodiversity data remain uneven and oftentimes information is analyzed from sectoral perspectives (i.e.: production, consumption or conservation). To transform food systems, we need to look at the broader picture and understand the systemic linkages between biodiversity, food security and nutrition, agricultural production, and the environment. Bioversity International has developed the Agrobiodiversity Index, an innovative tool that brings together existing data on diets and markets, production and genetic resources, analyzing them under the lens of agricultural biodiversity (Bioversity International 2018). Through open access to agricultural biodiversity data for science and society, the tool crosses disciplinary boundaries and allows users to monitor biodiversity trends in food systems. In particular, it helps food systems actors to measure agrobiodiversity in a selected area or value chain, and understand to what extent their commitments and actions are contributing to its sustainable use and conservation. This user-friendly tool equips food systems actors with the data needed to make informed decisions. For example, it helps governments to formulate evidence-based agricultural, health and food policies and strategies to address today’s global challenges, by providing information on how biological and geographical diversity influence food systems sustainability. Through the Index, companies can understand how to diversify their supply chain and production to reduce risks, and what are the best agricultural practices for their agro-ecological zone. The tool can thereby support best practices dissemination, and track progress towards global goals related to agrobiodiversity, including Sustainable Development Goals 3, 12, 13, 15 and Aichi targets 7.

© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research. The Planteome project (http://www.planteome.org) provides a suite of reference and species-specific ontologies for plants and annotations to genes and phenotypes. Ontologies serve as common standards for semantic integration of a large and growing corpus of plant genomics, phenomics and genetics data. The reference ontologies include the Plant Ontology, Plant Trait Ontology and the Plant Experimental Conditions Ontology developed by the Planteome project, along with the Gene Ontology, Chemical Entities of Biological Interest, Phenotype and Attribute Ontology, and others. The project also provides access to species-specific Crop Ontologies developed by various plant breeding and research communities from around the world. We provide integrated data on plant traits, phenotypes, and gene function and expression from 95 plant taxa, annotated with reference ontology terms. The Planteome project is developing a plant gene annotation platform; Planteome Noctua, to facilitate community engagement. All the Planteome ontologies are publicly available and are maintained at the Planteome GitHub site (https://github.com/Planteome) for sharing, tracking revisions and new requests. The annotated data are freely accessible from the ontology browser (http://browser.planteome.org/amigo) and our data repository.

© The Author(s) 2018. Published by Oxford University Press. The future of agricultural research depends on data. The sheer volume of agricultural biological data being produced today makes excellent data management essential. Governmental agencies, publishers and science funders require data management plans for publicly funded research. Furthermore, the value of data increases exponentially when they are properly stored, described, integrated and shared, so that they can be easily utilized in future analyses. AgBioData (https://www.agbiodata.org) is a consortium of people working at agricultural biological databases, data archives and knowledgbases who strive to identify common issues in database development, curation and management, with the goal of creating database products that are more Findable, Accessible, Interoperable and Reusable. We strive to promote authentic, detailed, accurate and explicit communication between all parties involved in scientific data. As a step toward this goal, we present the current state of biocuration, ontologies, metadata and persistence, database platforms, programmatic (machine) access to data, communication and sustainability with regard to data curation. Each section describes challenges and opportunities for these topics, along with recommendations and best practices.

Plant research produces data in a profusion of types and scales, and in ever-increasing volume. What are the challenges and opportunities presented by data management in contemporary plant science? And how can researchers make efficient and fruitful use of data management tools and strategies?

In this article, we present a joint effort of the wheat research community, along with data and ontology experts, to develop wheat data interoperability guidelines. Interoperability is the ability of two or more systems and devices to cooperate and exchange data, and interpret that shared information. Interoperability is a growing concern to the wheat scientific community, and agriculture in general, as the need to interpret the deluge of data obtained through high-throughput technologies grows. Agreeing on common data formats, metadata, and vocabulary standards is an important step to obtain the required data interoperability level in order to add value by encouraging data sharing, and subsequently facilitate the extraction of new information from existing and new datasets. During a period of more than 18 months, the RDA Wheat Data Interoperability Working Group (WDI-WG) surveyed the wheat research community about the use of data standards, then discussed and selected a set of recommendations based on consensual criteria. The recommendations promote standards for data types identified by the wheat research community as the most important for the coming years: nucleotide sequence variants, genome annotations, phenotypes, germplasm data, gene expression experiments, and physical maps. For each of these data types, the guidelines recommend best practices in terms of use of data formats, metadata standards and ontologies. In addition to the best practices, the guidelines provide examples of tools and implementations that are likely to facilitate the adoption of the recommendations. To maximize the adoption of the recommendations, the WDI-WG used a community-driven approach that involved the wheat research community from the start, took into account their needs and practices, and provided them with a framework to keep the recommendations up to date. We also report this approach’s potential to be generalizable to other (agricultural) domains.

Ex situ conservation in genebanks is the most important way of conserving plant genetic resources for food and agriculture (PGRFA) (FAO 2010). The use of germplasm conserved in genebanks depends to a large extent on the quality and quantity of data available about each accession. Initial selection of accessions for use in research or breeding is often made based on the available passport information, which describes the source of the material. Availability of collecting site description or geographic coordinates is considered a quality indicator in particular for accessions of wild species and landraces (Van Hintum et al. in Plant Genet Resour Charact Util 9(3):478–485, 2011). However lack or unavailability of accession specific data, including passport and location data, continues to represent a constraint to enhanced utilization of accessions (FAO 2010; Khoury et al. in Genet Resour Crop Evol 57(4):625–639, 2010). Collecting mission reports and collecting forms provide original data, including location data, about materials collected and distributed to genebanks. The International Board for Plant Genetic Resources and its successor, the International Plant Genetic Resources Institute (now Bioversity International) have supported the collection of over 225,000 samples of PGRFA during the last quarter of the past century. The documentation gathered at the time of their collection has recently been digitized, passport data extracted, and made available through the web ( http://www.central-repository.cgiar.org/ ; http://singer.cgiar.org/index.jsp?page=biomissions ), where it can be consulted to integrate and improve the quality of passport data. Collected samples can be linked to accessions in genebanks. The original collecting mission reports often include eco-geographic, environmental, biotic and climate data that can be used to improve knowledge about the accessions and facilitate their utilization.

Background and Aims The production of triploid banana and plantain (Musa spp.) cultivars with improved characteristics (e.g. greater disease resistance or higher yield), while still preserving the main features of current popular cultivars (e.g. taste and cooking quality), remains a major challenge for Musa breeders. In this regard, breeders require a sound knowledge of the lineage of the current sterile triploid cultivars, to select diploid parents that are able to transmit desirable traits, together with a breeding strategy ensuring final triploidization and sterility. Highly polymorphic single sequence repeats (SSRs) are valuable markers for investigating phylogenetic relationships. Methods Here, the allelic distribution of each of 22 SSR loci across 561 Musa accessions is analysed. Key Results and ConclusionsWe determine the closest diploid progenitors of the triploid 'Cavendish' and 'Gros Michel' subgroups, valuable information for breeding programmes. Nevertheless, in establishing the likely monoclonal origin of the main edible triploid banana subgroups (i.e. 'Cavendish', 'Plantain' and 'Mutika- Lujugira'), we postulated that the huge phenotypic diversity observed within these subgroups did not result from gamete recombination, but rather from epigenetic regulations. This emphasizes the need to investigate the regulatory mechanisms of genome expression on a unique model in the plant kingdom. We also propose experimental standards to compare additional and independent genotyping data for reference. (Resume d'auteur)

Plant methods 12(1), 44 (2016). doi:10.1186/s13007-016-0144-4, Published by BioMed Central, London

Background: Opportunities to use data and information to address challenges in international agricultural research and development are expanding rapidly. The use of agricultural trial and evaluation data has enormous potential to improve crops and management practices. However, for a number of reasons, this potential has yet to be realized. This paper reports on the experience of the AgTrials initiative, an effort to build an online database of agricultural trials applying principles of interoperability and open access. Methods: Our analysis evaluates what worked and what did not work in the development of the AgTrials information resource. We analyzed data on our users and their interaction with the platform. We also surveyed our users to gauge their perceptions of the utility of the online database. Results: The study revealed barriers to participation and impediments to interaction, opportunities for improving agricultural knowledge management and a large potential for the use of trial and evaluation data. Conclusions: Technical and logistical mechanisms for developing interoperable online databases are well advanced. More effort will be needed to advance organizational and institutional work for these types of databases to realize their potential.

Large ex situ collections require approaches for sampling manageable amounts of germplasm for in-depth characterization and use. We present here a large diversity survey in sorghum with 3367 accessions and 41 reference nuclear SSR markers. Of 19 alleles on average per locus, the largest numbers of alleles were concentrated in central and eastern Africa. Cultivated sorghum appeared structured according to geographic regions and race within region. A total of 13 groups of variable size were distinguished. The peripheral groups in western Africa, southern Africa and eastern Asia were the most homogeneous and clearly differentiated. Except for Kafir, there was little correspondence between races and marker-based groups. Bicolor, Caudatum, Durra and Guinea types were each dispersed in three groups or more. Races should therefore better be referred to as morphotypes. Wild and weedy accessions were very diverse and scattered among cultivated samples, reinforcing the idea that large gene-flow exists between the different compartments. Our study provides an entry to global sorghum germplasm collections. Our reference marker kit can serve to aggregate additional studies and enhance international collaboration. We propose a core reference set in order to facilitate integrated phenotyping experiments towards refined functional understanding of sorghum diversity.

The Global Biodiversity Informatics Outlook helps to focus effort and investment towards better understanding of life on Earth and our impacts upon it. It proposes a framework that will help harness the immense power of information technology and an open data culture, to gather unprecedented evidence about biodiversity and to inform better decisions. This document is accompanied by a website, www.biodiversityinformatics.org, that will report progress towards each part of the framework and provide a forum for ideas, projects and funding sources supporting the goals of the Outlook, JRC.H.5-Land Resources Management

The Crop Ontology (CO) of the Generation Challenge Program (GCP) (http://cropontology.org/ ) is developed for the Integrated Breeding Platform (https://www.integratedbreeding.net/ ) by several centers of The Consultative Group on International Agricultural Research (CGIAR): Bioversity, CIMMYT, CIP, ICRISAT, IITA, and IRRI. Integrated breeding necessitates that breeders access genotypic and phenotypic data related to a given trait. The Crop Ontology provides validated trait names used by the crop communities of practice for harmonizing the annotation of phenotypic and genotypic data and thus supporting data accessibility and discovery through web queries. The trait information is completed by the description of the measurement methods and scales, and images. The trait dictionaries used to produce the Integrated Breeding (IB) fieldbooks are synchronized with the Crop Ontology terms for an automatic annotation of the phenotypic data measured in the field. The IB fieldbook provides breeders with direct access to the CO to get additional descriptive information on the traits. Ontologies and trait dictionaries are online for cassava, chickpea, common bean, groundnut, maize, Musa, potato, rice, sorghum and wheat. Online curation and annotation tools facilitate (http://cropontology.org) direct maintenance of the trait information and production of trait dictionaries by the crop communities. An important feature is the cross referencing of CO terms with the Crop database trait ID and with their synonyms in Plant Ontology and Trait Ontology. Web links between cross referenced terms in CO provide online access to data annotated with similar ontological terms, particularly the genetic data in Gramene (University of Cornell) or the evaluation and climatic data in the Global Repository of evaluation trials of the Climate Change, Agriculture and Food Security programme (CCAFS). Cross-referencing and annotation will be further applied in the Integrated Breeding Platform.

The Consultative Group on International Agricultural Research (CGIAR; http://www.cgiar.org/) centres have developed the International Crop Information System (ICIS; http://www.icis.cgiar.org) for the management and integration of global information on genetic resources, and germplasm improvement for any crop. The Maize breeding programs at CIMMYT (http://beta.cimmyt.org/) have different software tools to manage phenotypic, genotypic, and environmental information for their experiments generated worldwide. These tools have the capacity of collecting information in the field, wet lab, and store it into different relational databases. The IMIS (http://imis.cimmyt.org/confluence/display/IMIS/Crop+Finder) is an implementation of the ICIS, which is a computerized database system for general, integrated management and utilization of genealogy, nomenclature, genetic, phenotypic and characterization data for maize. Data exchange within and between databases as well as retrieving information are often hampered by the variability of terms used to describe comparable objects. To overcome this problem, the Crop Ontology (CO) database (http://cropontology.org/) is developed. It provides controlled vocabulary sets for several economically important plant species and facilitates biocurators working in genebanks of plant genetic resources (PGR) and crop breeding data curation and annotation. The maize trait ontology is developed as one of subclasses of CO trait ontology providing standardized trait descriptions, scales and scale values implemented into the IMIS. This ontology-driven IMIS will allow researchers who wish to exploit comparative phenotypic and genotypic information of maize to elucidate functional aspects of each trait.

The ‘Crop Ontology’ database we describe provides a controlled vocabulary for several economically important crops. It facilitates data integration and discovery from global databases and digital literature. This allows researchers to exploit comparative phenotypic and genotypic information of crops to elucidate functional aspects of traits., Background and aims Agricultural crop databases maintained in gene banks of the Consultative Group on International Agricultural Research (CGIAR) are valuable sources of information for breeders. These databases provide comparative phenotypic and genotypic information that can help elucidate functional aspects of plant and agricultural biology. To facilitate data sharing within and between these databases and the retrieval of information, the crop ontology (CO) database was designed to provide controlled vocabulary sets for several economically important plant species. Methodology Existing public ontologies and equivalent catalogues of concepts covering the range of crop science information and descriptors for crops and crop-related traits were collected from breeders, physiologists, agronomists, and researchers in the CGIAR consortium. For each crop, relationships between terms were identified and crop-specific trait ontologies were constructed following the Open Biomedical Ontologies (OBO) format standard using the OBO-Edit tool. All terms within an ontology were assigned a globally unique CO term identifier. Principal results The CO currently comprises crop-specific traits for chickpea (Cicer arietinum), maize (Zea mays), potato (Solanum tuberosum), rice (Oryza sativa), sorghum (Sorghum spp.) and wheat (Triticum spp.). Several plant-structure and anatomy-related terms for banana (Musa spp.), wheat and maize are also included. In addition, multi-crop passport terms are included as controlled vocabularies for sharing information on germplasm. Two web-based online resources were built to make these COs available to the scientific community: the ‘CO Lookup Service’ for browsing the CO; and the ‘Crops Terminizer’, an ontology text mark-up tool. Conclusions The controlled vocabularies of the CO are being used to curate several CGIAR centres' agronomic databases. The use of ontology terms to describe agronomic phenotypes and the accurate mapping of these descriptions into databases will be important steps in comparative phenotypic and genotypic studies across species and gene-discovery experiments.