Crop Ontology by | Jul 6, 2020 Edit digital intervention Digital Intervention Title Digital Intervention Excerpt The Crop Ontology (CO) is developed collaboratively with partners in response to the need of digital breeding tools to have access to valid lists of defined crop traits and variables. Description Visual Text File Edit View Insert Format Tools Table Paragraph HashBarTimes New Roman 12pt The Crop Ontology (CO) is developed collaboratively with partners in response to the need of digital breeding tools to have access to valid lists of defined crop traits and variables. By providing descriptions of agronomic, morphological, physiological, quality, and stress traits along with a standard nomenclature for composing the variables, the CO enables digital capture and aggregation of crop trait data, as well as comparison across multi-location varietal evaluation projects, including Participatory Varietal Selection (PVS) with farmers and surveys with citizen science tools ( e.g. ClimMob). As a whole, the aim is to describe traits that are important to communities and that simultaneously allows scientists to integrate the traits in a breeding product profile. Today the CO comprises 4,235 traits and 6,151 variables for 31 plant species (www.cropontology.org) and supports to generation of FAIR data. Featured Image Types of Digital Intervention Decision support agriculture Digital advisory Digital analytics and modelling Digital disease monitoring and control Digital Financial Services Digital mechanization Farm management software Market systems Remote sensing Robotics Food system components Aggregation Consumption Distribution Policy Processing Production Food system activities Agricultural extension Agricultural subsidy provision Animal disease management Climate change Crop diseases management Crop production Farm inputs Finance Genetic resources Labor force Livestock production Natural resource management Nutrition information Packaging Post-harvest management Regulation and standards Scientific knowledge Tractor services provision Trade and market information Transport Urbanization Waste management Primary users Extension agents Farmers Financial institutions Government ministries Researchers Traders Intervention inception number of users Intervention current number of users Country/ies of implementation Afghanistan Bangladesh Bolivia Burkina Faso Benin Cameroon China Colombia Costa Rica Côte d’Ivoire El Salvador Ethiopia Ghana Grenada Guatemala Haiti Honduras India Indonesia Italy Jamaica Kenya Malaysia Mali Malawi Mexico Mongolia Mozambique Myanmar Nepal Nicaragua Niger Nigeria Pakistan Peru Philippines Rwanda Senegal Sierra Leone South Africa South Sudan Sri Lanka Tanzania Thailand Trinidad and Tobago Tunisia Turkey Uganda United States of America Vietnam Zambia Zimbabwe Region(s) of implementation Africa Asia / Pacific Europe and Central Asia Latin America and the Caribbean Middle East North America Geolocation Address We could not connect to the Google Maps autocomplete service, but you can add an address manually. Add new Evidence of Impact Evidence narrative Visual Text File Edit View Insert Format Tools Table Paragraph HashBarTimes New Roman 12pt The Crop Ontology is largely used by data scientists and researchers who also contribute to its content. It is integrated into the Planteome database of crop genetic data (www.plarteome.org; NSF project), in comprehensive breeding management systems and analysis software like the CGIAR Integrated Breeding Platform and Breedbase of the Boyce Thompson Institute, and by national databases such as INRAe GnPIS in France or international projects like Emphasis (European Plant Phenotyping Infrastructures). Agrifood Industry uses the Crop Ontology (e.g. Bayer, KWS, Syngenta) . The Minimum Information About a Plant Phenotype Experiment metadata schema (MIAPPE) and the Breeding Application Programme Interface (BrAPI) are to global metadata standards compliant with the Crop Ontology format. Intervention evidence of economic impact Increased volume of sales Increased yield Increased market access Increased access to credit Increased transaction cost Increased production cost Decreased market access Reduced transaction cost Reduced production cost Received higher product prices Decreased volume of sales I do not know Decreased yield None of the above Intervention evidence of environmental impact Increased efficiency in agro chemical use Increased resilience to climate shocks Increased access to weather information Increased access to agricultural information services in real time Decreased efficiency of agro chemical use Decreased resilience to climate shocks Decreased access to weather information I do not know Decreased access to agricultural information services in real time None of the above Intervention evidence of social impact Increased women participation Decreased women participation Increased youth participation Decreased participation of minority group Improved social capital Increased economic mobility Enhanced social inclusion I do not know Improved social equity None of the above Enhanced social well being Intervention evidence of technical impact Increased technology adoption Improved information dissemination Increased labor demand Increased the need for agricultural extension agents Better support for extension agents Reduced the need for agricultural extension agents Decreased labor demand Displacement of on-farm labor I do not know None of the above Intervention quantitative impact Increased efficiency by 0-25% Increased efficiency by 26-50% Increased efficiency by 51-75% Increased efficiency by 76-100% Decreased efficiency by 0-25% Decreased efficiency by 26-50% Decreased efficiency by 51-75% Decreased efficiency by 76-100% Additional Information Enter URL Add new Submit Intervention File Add new Contact details First Name Last Name Email
