AgroFIMS v.1.0 - User manual

The Agronomy Field Information Management System (AgroFIMS) has been developed on CGIAR’s HIDAP (Highly Interactive Data Analysis Platform) created by CGIAR’s International Potato Center, CIP. AgroFIMS draws fully on ontologies, particularly the Agronomy Ontology (AgrO)1. It consists of modules that represent the typical cycle of operations in agronomic trial management (seeding, weeding, fertilization, harvest, and more) and enables the creation of data collection sheets using the same ontology-based set of variables, terminology, units and protocols. AgroFIMS therefore enables a priori harmonization with metadata and data interoperability standards and adherence to the FAIR Data Principles essential for data reuse and increasingly, for compliance with funder mandates - without any extra work for researchers. AgroFIMS is therefore of value to anyone (scientist, researcher, agronomist, etc.) who wishes to easily design a standards-compliant agronomic research fieldbook following the FAIR Data Principles. AgroFIMS also allows users to collect data electronically in the field, thereby reducing errors. Currently this is restricted to the KDSmart Android platform, but we expect to enable this capability with other platforms such as the Open Data Kit (ODK) and Field Book in v.2.0. Once data is collected using KDSmart, the data can be uploaded back to AgroFIMS for data validation, statistical analysis, and the generation of statistical analysis reports. V.2.0 will allow easy upload of the data from AgroFIMS to an institutional or compliant repository of the user’s choice

Text mining national commitments towards agrobiodiversity conservation and use

Capturing countries' commitments for measuring and monitoring progress towards certain goals, including the Sustainable Development Goals (SDGs), remains underexplored. The Agrobiodiversity Index bridges this gap by using text mining techniques to quantify countries' commitments towards safeguarding and using agrobiodiversity for healthy diets, sustainable agriculture, and effective genetic resource management. The Index extracts potentially relevant sections of official documents, followed by manual sifting and scoring to identify agrobiodiversity-related commitments and assign scores. Our aim is to present the text mining methodology used in the Agrobiodiversity Index and the calculated commitments scores for nine countries while identifying methodological improvements to strengthen it. Our results reveal that levels of commitment towards using and protecting agrobiodiversity vary between countries, with most showing the strongest commitments to enhancing agrobiodiversity for genetic resource management followed by healthy diets. No commitments were found in any country related to some specific themes including varietal diversity, seed diversity, and functional diversity. The revised text mining methodology can be used for benchmarking, learning, and improving policies to enable conservation and sustainable use of agrobiodiversity. This low-cost, rapid, remotely applicable approach to capture and analyse policy commitments can be readily applied for tracking progress towards meeting other sustainability objectives.</p

Ontologies for increasing the FAIRness of plant research data

The importance of improving the FAIRness (findability, accessibility, interoperability, reusability) of research data is undeniable, especially in the face of large, complex datasets currently being produced by omics technologies. Facilitating the integration of a dataset with other types of data increases the likelihood of reuse, and the potential of answering novel research questions. Ontologies are a useful tool for semantically tagging datasets as adding relevant metadata increases the understanding of how data was produced and increases its interoperability. Ontologies provide concepts for a particular domain as well as the relationships between concepts. By tagging data with ontology terms, data becomes both human and machine interpretable, allowing for increased reuse and interoperability. However, the task of identifying ontologies relevant to a particular research domain or technology is challenging, especially within the diverse realm of fundamental plant research. In this review, we outline the ontologies most relevant to the fundamental plant sciences and how they can be used to annotate data related to plant-specific experiments within metadata frameworks, such as Investigation-Study-Assay (ISA). We also outline repositories and platforms most useful for identifying applicable ontologies or finding ontology terms.Comment: 34 pages, 4 figures, 1 table, 1 supplementary tabl

Guidelines for creating crop-specific ontologies to annotate phenotypic data: version 2.1

The Crop Ontology Guidelines version 2.1 provide detailed information and numerous examples for the use of the Trait Dictionary Template v.5.2 to develop a high quality Trait Dictionary with trait and variables used for the annotation of crop phenotypic data. The guidelines were developed in collaboration with the Integrated Breeding Platform and CIMMYT in the context of the CGIAR Big Data Platform

AgroFIMS v.2.0 - User manual.

This documentation provides instructions to help you get familiarized with the Agronomy Field Information Management System (AgroFIMS) and to produce a fieldbook that you can use to collect well-described, standards-compliant data in the field. AgroFIMS allows users to create fieldbooks to collect agronomic data. The fieldbook is already tied to a metadata standard (the CG Core Metadata Schema, aligned with the industry standard Dublin Core Metadata Schema and required by CGIAR and many other repositories). The data variables and protocol parameters in AgroFIMS fieldbooks align with semantic standards like the Agronomy Ontology (AgrO). This a priori compliance with data standards facilitates data to be Findable, Accessible, Interoperable, and Reusable (FAIR) at collection, making it easier to interpret and aggregate. Data collection is currently available via the Android-based KDSmart or Field Book applications, and the collected data can be imported back to AgroFIMS for statistical analysis and reports. By mid-2021 you will be able to easily upload this collected data through AgroFIMS to a Dublin Core or CG Core-compliant Dataverse repository. To enable access, exchange, and integration of agronomic data across systems and applications we have made available the Agronomy API or AgrAPI, which is a RESTful web service API specification. The AgrAPI blueprint can be implemented in different programming languages, but is currently implemented in the R statistical programming language, allowing you to analyze your data with the R packages and graphics of your choice

AgroFIMS: A tool to enable digital collection of standards-compliant FAIR data

Agricultural research has been traditionally driven by linear approaches dictated by hypothesis-testing. With the advent of powerful data science capabilities, predictive, empirical approaches are possible that operate over large data pools to discern patterns. Such data pools need to contain well-described, machine-interpretable, and openly available data (represented by high-scoring Findable, Accessible, Interoperable, and Reusable—or FAIR—resources). CGIAR's Platform for Big Data in Agriculture has developed several solutions to help researchers generate open and FAIR outputs, determine their FAIRness in quantitative terms1, and to create high-value data products drawing on these outputs. By accelerating the speed and efficiency of research, these approaches facilitate innovation, allowing the agricultural sector to respond agilely to farmer challenges. In this paper, we describe the Agronomy Field Information Management System or AgroFIMS, a web-based, open-source tool that helps generate data that is “born FAIRer” by addressing data interoperability to enable aggregation and easier value derivation from data. Although license choice to determine accessibility is at the discretion of the user, AgroFIMS provides consistent and rich metadata helping users more easily comply with institutional, founder and publisher FAIR mandates. The tool enables the creation of fieldbooks through a user-friendly interface that allows the entry of metadata tied to the Dublin Core standard schema, and trial details via picklists or autocomplete that are based on semantic standards like the Agronomy Ontology (AgrO). Choices are organized by field operations or measurements of relevance to an agronomist, with specific terms drawn from ontologies. Once the user has stepped through required fields and desired modules to describe their trial management practices and measurement parameters, they can download the fieldbook to use as a standalone Excel-driven file, or employ via free Android-based KDSmart, Fieldbook, or ODK applications for digital data collection. Collected data can be imported back to AgroFIMS for statistical analysis and reports. Development plans for 2021 include new features such ability to clone fieldbooks and the creation of agronomic questionnaires. AgroFIMS will also allow archiving of FAIR data after collection and analysis from a database and to repository platforms for wider sharing

Developing an ontology for a graph database in agriculture: Technical guidelines

These guidelines are aimed at users already familiar with an Ontology structure, and its definitions of classes, properties and axioms. This document guides the reader through the steps of an ontology modeling process to be integrated into a multi-source graph database such as NEO4J . The development of a Soil Ontology is used as an example. It covers the principles of modeling to include soil measurements, geocoordinates and units, so that the ontology can support data transformation and queries. These guidelines result from collaboration between the Varda platform development team and the CGIAR Ontology Team

Measuring agricultural biodiversity for sustainable food 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