AgroPortal: a vocabulary and ontology repository for agronomy

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. Keyword

Big Data Coordination Platform: Full Proposal 2017-2022

This proposal for a Big Data and ICT Platform therefore focuses on enhancing CGIAR and partner capacity to deliver big data management, analytics and ICT-focused solutions to CGIAR target geographies and communities. The ultimate goal of the platform is to harness the capabilities of Big Data to accelerate and enhance the impact of international agricultural research. It will support CGIAR’s mission by creating an enabling environment where data are expertly managed and used effectively to strengthen delivery on CGIAR SRF’s System Level Outcome (SLO) targets. Critical gaps were identified during the extensive scoping consultations with CGIAR researchers and partners (provided in Annex 8). The Platform will achieve this through ambitious partnerships with initiatives and organizations outside CGIAR, both upstream and downstream, public and private. It will focus on promoting CGIAR-wide collaboration across CRPs and Centers, in addition to developing new partnership models with big data leaders at the global level. As a result, CGIAR and partner capacity will be enhanced, external partnerships will be leveraged, and an institutional culture of collaborative data management and analytics will be established. Important international public goods such as new global and regional datasets will be developed, alongside new methods that support CGIAR to use the data revolution as an additional means of delivering on SLOs

Association mapping in tetraploid potato

The results of a four year project within the Centre for BioSystems Genomics (www.cbsg.nl), entitled “Association mapping and family genotyping in potato” are described in this thesis. This project was intended to investigate whether a recently emerged methodology, association mapping, could provide the means to improve potato breeding efficiency. In an attempt to answer this research question a set of potato cultivars representative for the commercial potato germplasm was selected. In total 240 cultivars and progenitor clones were chosen. In a later stage this set was expanded with 190 recent breeds contributed by five participating breeding companies which resulted in a total of 430 genotypes. In a pilot experiment, the results of which are reported in Chapter 2, a subset of 220 of the abovementioned 240 cultivars and progenitor clones was used. Phenotypic data was retrieved through contributions of the participating breeding companies and represented summary statistics of recent observations for a number of traits across years and locations, calculated following company specific procedures. With AFLP marker data, in the form of normalised log-transformed band intensities, obtained from five well-known primer combinations, the extent of linkage disequilibrium (LD), using the r2 statistic, was estimated. Population structure within the set of 220 cultivars was analysed by deploying a clustering approach. No apparent, nor statistically supported population structure was revealed and the LD seemed to decay below the threshold of 0.1 at a genetic distance of about 3cM with this set of marker data. Furthermore, marker-trait associations were investigated by fitting single marker regression models for phenotypic traits on marker band intensities with and without correction for population structure. Population structure correction was performed in a straightforward way by incorporating a design matrix into the model assuming that each breeding company represented a different breeding germplasm pool. The potential of association mapping in tetraploid potato has been demonstrated in this pilot experiment, because existing phenotypic data, a modest number of AFLP markers, and a relatively straightforward statistical analysis allowed identification of interesting associations for a number of agro-morphological and quality traits. These promising results encouraged us to engage into an encompassing genome-wide association mapping study in potato. Two association mapping panels were compiled. One panel comprising 205 genotypes, all of which were also present in the set used for the pilot experiment, and another panel containing in total 299 genotypes including the entire set of 190 recent breeds together with a series of standard cultivars, about 100 of which are in common with the first panel. Phenotypic data for the association panel with 205 genotypes were obtained in a field trial performed in 2006 in Wageningen at two locations with two replicates. We will refer to this set as the “2006 field trial”. Phenotypic data for the other panel with 299 genotypes was contributed by the five participating breeding companies and consisted of multi-year-multi-location data obtained during generations of clonal selection. The 2006 data were nicely balanced, because the trial was designed in that way. The historical breeding dataset was highly unbalanced. Analysis of these two differing phenotypic datasets was performed to deliver insight in variance components for the genotypic main effects and the genotype by environment interaction (GEI), besides estimated genotype main effects across environments. Both phenotypic datasets were analysed separately within a mixed model framework including terms for GEI. In Chapter 3 we describe both phenotypic datasets by comparing variance components, heritabilities (=repeatabilities), intra-dataset relationships and inter-dataset relationships. Broader aspects related to phenotypic datasets and their analysis are discussed as well. To retrieve information about hidden population structure and genetic relatedness, and to estimate the extent of LD in potato germplasm, we used marker information generated with 41 AFLP primer combinations and 53 microsatellite loci on a collection of 430 genotypes. These 430 genotypes contain all genotypes present in the two association mapping panels introduced before plus a few extra genotypes to increase potato germplasm coverage. Two methods were used: a Bayesian approach and a distance-based clustering approach. Chapter 4 describes the results of this exercise. Both strategies revealed a weak level of structure in our material. Groups were detected which complied with criteria such as their intended market segment, as well as groups differing in their year of first registration on a national list. Linkage disequilibrium, using the r2 statistic, appeared to decay below the threshold of 0.1 across linkage groups at a genetic distance of about 5cM on average. The results described in Chapter 4 are promising for association mapping research in potato. The odds are reasonable that useful marker-trait associations can be detected and that the potential mapping resolution will suffice for detection of QTL in an association mapping context. In Chapter 5 a comprehensive genome-wide association mapping study is presented. The adjusted genotypic means obtained from two association mapping panels as a result of phenotypic analysis performed in Chapter 3 were combined with marker information in two association mapping models. Marker information consisted of normalised log-transformed band intensities of 41 AFLP primer combinations and allele dosage information from 53 microsatellites. A baseline model without correction for population structure and a more advanced model with correction for population structure and genetic relatedness were applied. Population structure and genetic relatedness were estimated using available marker information. Interesting QTL could be identified for 19 agro-morphological and quality traits. The observed QTL partly confirm previous studies e.g. for tuber shape and frying colour, but also new QTL have been detected e.g. for after baking darkening and enzymatic browning. In the final chapter, the general discussion, results of preceding chapters are evaluated and their implications for research as well as breeding are discussed. <br/

Preliminary Work Towards Publishing Vocabularies for Germplasm and Soil Data as Linked Data

The agINFRA project focuses on the production of interoperable data in agriculture, starting from the vocabularies and KOS used to classify and an-notate them. In this paper we report on our first steps in the direction of con-tributing to a LOD of agricultural data. In particular we look at germplasm data and soil data, which are still widely missing from the LOD landscape, seeming-ly because information managers in this field are still not very familiar with LOD practices

Ecological genomics and adaptation of rosewoods Dalbergia cochinchinensis and D. oliveri for conservation and restoration

Global biodiversity, in particular tropical forests, is decreasing under both environmental change and anthropogenic disturbance. Environmental change alters species’ adaptability to their current habitat, leading to loss of fitness and range shift, while anthropogenic disturbance reduces their adaptive capacity. Conserving and restoring threatened species and ecosystems thus become a grand challenge for the 21st century. This thesis studies two threatened rosewood species, Dalbergia cochinchinensis and D. oliveri, which are illegally exploited for their valuable timber in the Greater Mekong Subregion. They became the world’s most trafficked wild product between 2005 and 2014, amounting to ~40% of the total global trade. Conservation efforts grew in the last decade to tackle the range-wide challenge, aiming to improve the species’ survival, amplify the production of genetic materials, and designate more conservation units. However, a sustainable supply of genetic materials can meet several challenges that compromise the effectiveness of a restoration programme, namely the genetic bottlenecks, maladaptation, and climate change. While knowledge of adaptation can predict and mitigate these risks, standard study approaches such as common garden experiments have become impractical due to the acute threats of illegal logging in these two species, which are lacking in a priori knowledge. This thesis aims to increase the knowledge of genetic and physiological underpinning of adaptation in the two Dalbergia species with relevance to application in conservation and restoration strategies. This thesis presents a rich body of genomic resources such as chromosome-scale genomes and reference transcriptomes, which advance the progress in less-represented angiosperm tree genomes and woody legume genomes and enable studies in genetic diversity. Comparative genomic studies revealed insight into the evolution and potential adaptive role of of certain gene families in tropical Dalbergia species. The landscape genomic study provides a comprehensive scan of adaptive signals and reports significant differences of the adaptive variation between the two species, where D. cochinchinensis is driven by temperature variability and D. oliveri by precipitation variability. The controlled stress experiment provides a physiological understanding of how the two species regulate their water relations and photosynthetic apparatus to respond to drought differently, where D. cochinchinensis has a more anisohydric behaviour than D. oliveri. These contrasting patterns of adaptation indicate how the two species may differentiate their niches, while co-occurring in some habitats. The knowledge of adaptive variation identifies hotspots of local adaptation and vulnerability towards climate change, and thus are expected to help conservation practitioners delineate conservation units, compare provenances for assisted germplasm transfer, and prioritise conservation actions. It also opens new avenues for future research, including combining common garden experiments and genomic approaches to more fully unravel genotype-phenotype-environment relationships

Sustainable Agricultural Productivity Growth and Bridging the Gap for Small-Family Farms: Interagency Report to the Mexican G20 Presidency

In 2011, G20 leaders committed to sustainably increase agricultural (production and) productivity (paragraph 43 of the Cannes Declaration). They "agree(d) to further invest in agriculture, in particular in the poorest countries, and bearing in mind the importance of smallholders, through responsible public and private investment," they "decide(d) to invest in research and development of agricultural productivity. Early in 2012 Mexico, as G20 President, invited international organisations to examine practical actions that could be undertaken to sustainably improve agricultural productivity growth, in particular on small family farms. The preparation of this report, co-ordinated by the FAO and the OECD, responds to this request. It is a collaborative undertaking by Bioversity, CGIAR Consortium, FAO, IFAD, IFPRI, IICA, OECD, UNCTAD, Coordination team of UN High Level Task Force on the Food Security Crisis, WFP, World Bank, and WTO. We, the international organisations, are pleased to provide you with this joint report and look forward to continuing collaboration within the G20 framework to further elaborate and, as appropriate, implement the recommendations that it contains

Data management support pack

This pack is designed to help you produce high quality, reusable and open data from your research activities. It consists of documents, templates and videos covering the different aspects of data management and ranging from the overarching concepts and strategies through to the day-to-day activities. For each of the videos in the pack we have included a transcript of the narrative. The Data Management Support Pack was created to support the implementation of the CCAFS Data Management strategy

Using Diversity : enhancing and maintaining genetic resources on-farm; proceedings of a workshop held on 19-21 June 1995, New Delhi, India

Meeting: Using Diversity Workshop, 19-21 June 1995, New Delhi, I

Beyond plant blindness: seeing the importance of plants for a sustainable world

In recent years an interdisciplinary nexus has been generated around what it means to experience life as a plant. From the science of plant behaviours, plant language and meaning-making to plant-based philosophy, plant enquiries are crossing disciplinary and conceptual boundaries. The everyday life of a plant can appear to be static and silent to human perception. And yet, as modern science narratives tell their stories, we are realising that plants live in complex, and often social worlds. Removing plants from the human view makes it easier for us to exploit them and appears accordingly to reduce our ability to see into their worlds. In this research study we ask how, by taking a different view through an interdisciplinary lens, might we improve our understanding and sensitivity to the lives of plants? Thus, our research contributes to policy contexts in which society cannot afford its citizens to be plant blind to contemporary conservation issues

Ontology-based knowledge representation and semantic search information retrieval: case study of the underutilized crops domain

The aim of using semantic technologies in domain knowledge modeling is to introduce the semantic meaning of concepts in knowledge bases, such that they are both human-readable as well as machine-understandable. Due to their powerful knowledge representation formalism and associated inference mechanisms, ontology-based approaches have been increasingly adopted to formally represent domain knowledge. The primary objective of this thesis work has been to use semantic technologies in advancing knowledge-sharing of Underutilized crops as a domain and investigate the integration of underlying ontologies developed in OWL (Web Ontology Language) with augmented SWRL (Semantic Web Rule Language) rules for added expressiveness. The work further investigated generating ontologies from existing data sources and proposed the reverse-engineering approach of generating domain specific conceptualization through competency questions posed from possible ontology users and domain experts. For utilization, a semantic search engine (the Onto-CropBase) has been developed to serve as a Web-based access point for the Underutilized crops ontology model. Relevant linked-data in Resource Description Framework Schema (RDFS) were added for comprehensiveness in generating federated queries. While the OWL/SWRL combination offers a highly expressive ontology language for modeling knowledge domains, the combination is found to be lacking supplementary descriptive constructs to model complex real-life scenarios, a necessary requirement for a successful Semantic Web application. To this end, the common logic programming formalisms for extending Description Logic (DL)-based ontologies were explored and the state of the art in SWRL expressiveness extensions determined with a view to extending the SWRL formalism. Subsequently, a novel fuzzy temporal extension to the Semantic Web Rule Language (FT-SWRL), which combines SWRL with fuzzy logic theories based on the valid-time temporal model, has been proposed to allow modeling imprecise temporal expressions in domain ontologies