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The amount of data that can be being collected and openly distributed is increasing daily, but only a fraction of this data is being used to constrain models of agricultural systems under global change and help land managers make informed land-use decisions. Until we can make models accessible to the greater public and bring models and data together, none of these resources can be used to their full potential.Here we present the (RETINA) project: Monitoring, Reporting and Verification (MRV) systems implemented at the farm level, used to quantify soil carbon change and greenhouse gasses (GHG) emissions combined with novel approaches in predictive modelling and stakeholder engagement, culminating in negative emission strategies in managed ecosystems.By developing a dynamic digital system that connects multi-scale sensors using AI to novel cloud-based soil carbon and GHG modelling approaches, we can detect changes in organic matter and GHG emissions across land uses. Additionally, individual user-based inputs through a mobile RETINA app capture changes in agriculture management. This data is used within the Predictive Ecosystem Analyser framework to produce forecasts of GHG emissions and carbon sequestration. Landowners are provided with decision tools to not only interpret the effects of current land management practices on future emissions and carbon sequestration, but also to explore alternative interventions that can help mitigate the effects of climate change. This study to demonstrates smart farming at a local scale, however, these approaches are applicable globally. The RETINA project provides an accessible, automated and repeatable framework that moves us towards realizing this goal. |
