Building the Embrapa rice breeding dataset for efficient data reuse.

Embrapa has led breeding programs for irrigated and upland rice (Oryza sativa L.) since 1977, generating a large amount of pedigree and phenotypic data. However, there were no systematic standards for data recording nor long-term data preservation and reuse strategies. With the new aim of making data reuse practical, we recovered all data available and structured it into the Embrapa Rice Breeding Dataset (ERBD). In its current version, the ERBD includes 20,504 crosses involving 9,974 parents, the pedigrees of most of the 4,532 inbred lines that took part in advanced field trials, and phenotypic data from 2,711 field trials (1,118 irrigated, 1,593 upland trials), representing 226,458 field plots. Those trials were conducted over 38 years (1982-2019), in 247 locations, in latitudes ranging from 3°N to 33°S. Phenotypic traits included grain yield, days to flowering, plant height, canopy lodging, and five important fungal diseases: leaf blast, panicle blast, brown spot, leaf scald, and grain discoloration. The total number of data points surpasses 1.27 million. Descriptive statistics were computed over the dataset, split by cropping systems (irrigated or upland). The mean heritability of grain yield was high for both systems, at around .7, whereas the mean coefficient of variation was 13.9% for irrigated trials and 18.7% for upland trials. The ERBD offers the possibility of conducting studies on different aspects of rice breeding and genetics, including genetic gain, G×E analysis, genome-wide association studies and genomic prediction

Heat stress adaptation in elite lines derived from synthetic hexaploid wheat

The contribution of synthetic hexaploids in spring wheat (Triticum aestivum L.) breeding has been documented under drought stress, but not previously under heat stress. A set of six advanced wheat lines derived from synthetic hexaploid wheat (ASD) was compared to their conventional hexaploid (Conv‐Hex) and synthetic derivative (Syn‐Der) parents under three different temperature scenarios in the field (temperate or non‐stress, heat‐stress environment, and late‐ or extreme heat environment). The ASD lines showed a yield advantage under heat and extreme heat stress compared to the best parent (Syn‐Der) by on average 15 and 13%, respectively, while the average yield advantage under temperate conditions was just 5%. A similar pattern to yield was observed for grain number, while individual kernel weight of ASD lines was similar to the best parent (Syn‐Der) in all three environments. The ASD lines expressed on average 12% more final biomass than the best parent (Syn‐Der) under heat environment, but similar biomass to them at temperate and extreme heat environments, respectively. Physiological traits related to heat tolerance included higher crop growth rate, increased water‐soluble carbohydrates (WSC) storage in stems, cooler canopy temperature, and spectral indices which are related to pigment composition, photo‐protective mechanisms, and radiation use efficiency. These traits enabled a larger number of grains to be set, in addition to growth of taller stems with a greater WSC storage capacity that was significantly related to kernel weight. Results reinforce the positive impact of using synthetic wheat in plant breeding for climate change.C. Mariano Cossani and Matthew P. Reynold

The Crop Ontology: a source of standard traits and variables for breeding and agronomy

The Crop Ontology is a service of the Integrated Breeding Platform (www.integratedbreeding.net) in collaboration with the CGIAR and partners and under the leadership of Bioversity international. The Crop Ontology (www.cropontology.org) provides harmonized and validated breeders’ trait names, measurement methods, scales for currently 18 crops that are used by the Breeding Management System (BMS). The NextGeneration Breeding Databases developed by Boyce Thompson Institute also embed the Crop Ontology traits. The Crop Ontology contributes to the content of the reference ontologies of the Planteome project (http://www.planteome.org/). A new Trait Dictionary Template was released that now includes the ‘standard variable’. A standard variable is equal to 'one trait+one method+one variable' and a trait can be measured through different variables, according to the method or the scale used. These variables will accurately annotate the measurements stored in the BMS databases and also will support the creation of standard manual or electronic fieldbooks. Ten crop trait dictionaries have already been migrated into this new template and uploaded on the Crop ontology site. Using similar methodology, an Agronomy Ontology is being developed to support combining results of field management practices with crop traits which is important to fully understand the dynamic of varying factors within any cropping system. Curation is currently performed to secure the compliance between the Agronomy ontology and the variables of the International Consortium for Agricultural Systems Applications (ICASA)

Phase II Study on Weekly Bolus Topotecan in Advanced or Recurrent Cervical Cancer

It was the aim of our study to evaluate the efficacy and safety of weekly topotecan in patients with advanced or recurrent cervical disease

Crop ontology: integration of standard variables

The Crop Ontology (CO, http://www.cropontology.org/) is a resource of the Integrated Breeding Platform (IBP, http://integratedbreeding.net/) providing breeders with crop specific terms for fieldbook edition and data annotation. Until Mai 2015, a plant phenotype was annotated with 3 CO identifiers for the trait, the method and the scale, respectively. Yet, breeders’ fieldbook and most phenotypic databases are designed to annotate a datapoint with only one identifier. To meet the need of providing one single identifier to an observation variable, the CO and IBP teams have worked on integrating the notion of variable into the CO. This has led to a thorough revision of the structure of the Trait Dictionary (TD) template. The TD template is a user-friendly xls file that is used to submit terms to CO which are then stored in the IBP Breeding Management System and other information systems (NextGen, Agtrials…). The most notable changes to the TD template are the addition of the term type “variable” and the decomposition of a trait into an entity and an attribute so as to formalize the trait definition and to foster the mapping with external ontologies (TO, PO, PATO, CHEBI, EO, PDO, GO…). Guidelines document how to post-compose variables. Along with the partners, the CO and IBP team have been working on formatting and curating the TD of pigeonpea (ICRISAT), cowpea (IITA), wheat (CIMMYT), groundnut (ICRISAT/USDA), yam (IITA), chickpea (ICRISAT), lentil (ICARDA), cassava (IITA), soybean (IITA/USDA), common bean (CIAT), rice (IRRI), pearl millet (ICRISAT), sorghum (CIRAD/ICRISAT), and maize (CIMMYT)

The Ontologies Community of Practice: A CGIAR Initiative for Big Data in Agrifood Systems.

Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order 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 comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines. 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 expertise in ontology development and identifying innovative solutions that support quality data annotation. The Ontologies CoP stimulates knowledge sharing among stakeholders, such as researchers, data managers, domain experts, experts in ontology design, and platform development teams