Grassland systems, both natural and seeded, cover more than 16 million hectares of land in Canada and are an incredibly valuable resource. Grasslands sequester a large amount of carbon and do so stably as it is stored in the soil. Unfortunately, many grasslands are lost every year, leading to large GHG fluxes, and many others are not well managed with respect to carbon storage or GHG mitigation. We seek to stem conversion of grassland systems by improving our understanding and valuation of the services they provide, and to use that understanding to enhance GHG mitigation in seeded grasslands. Increasing species and genetic diversity in grassland systems by including native species in seeded pasture can help maintain productivity in stressful years and mitigate greenhouse gases (GHGs) by enhancing carbon storage. Diversity can also enhance other ecosystem services (ES) including forage productivity, biodiversity conservation, pollination, and pest control, which can mitigate GHGs and reduce costs by reducing fertilizer and pesticide inputs. These services develop slowly, however, and more immediate indicators of future GHG-mitigating ES are required to encourage beneficial management activities, including native species use, which can be expensive. Microbial and insect communities change rapidly and can be used to predict future ES that will improve grazing system sustainability. Through co-development with producer groups, NGOs and First Nations, we propose using metabarcoding and metagenomics to quantify how microbial and insect biodiversity relate to native plant genetic and species diversity, and how these relationships influence ES in native grassland systems, particularly those reducing GHGs. This data will be used to develop diversity targets for mitigating GHGs in degraded and restored native grasslands and in pasture systems. Additionally, we will use machine learning models to identify genomic indicators that can predict GHG-mitigating ES. We will then test the validity of these indicators in working rangelands and pastures, and in experimental grassland systems varying in age. Further, we will conduct new field trials to determine how much native plant species or genetic diversity is required to maximize ES, thereby maximizing return on investment in terms of both forage production and GHG mitigation. We will use these trials to determine whether the indicators developed in this project can be detected in newly established grasslands, allowing us to develop new tools for rapidly forecasting the ES potential of forage trials. As native species seed is expensive, we will conduct producer and public surveys to better understand the perceived benefits and barriers to using native species, and the value associated with the ecosystem services they produce. These data will be used to improve economic models of ES to encourage native species adoption and thus reduce GHGs in Canadian grasslands. Ultimately, we will combine the data generated throughout the project into a single model that will predict the ES benefits associated with either different seeding strategies (producer focused) or based on tests for genomic indicators (consultant focused). Layered over these models will be the economic models, which we will use to estimate the costs and benefits of management strategies, or the value of the land being tested. These models will then be ported to web and mobile apps for industry use. Ultimately, this will facilitate adoption of GHG friendly grassland management practices, helping Canada meet its climate change goals.