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@prefix gcis: <> .
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   dcterms:identifier "key-message-18-2";
   gcis:findingNumber "18.2"^^xsd:string;
   gcis:findingStatement "Integrating data on human drivers of the carbon cycle into Earth system and ecosystem models improves representation of carbon-climate feedbacks and increases the usefulness of model output to decision makers (<em>high confidence</em>)."^^xsd:string;
   gcis:isFindingOf <>;
   gcis:isFindingOf <>;

## Properties of the finding:
   gcis:descriptionOfEvidenceBase "For Key Finding 2, the impacts of human management activities on carbon stocks have been analyzed and documented for entity-scale greenhouse gas estimation of agricultural activities (Eve et al., 2014). This information is being integrated into models for use by agricultural land managers. For U.S. forests, attribution of human and natural influences (e.g., harvesting, natural disturbance, and forest age) has been successfully disaggregated using field data and models (Woodall et al., 2015) to help inform decision makers. Finally, to better represent human drivers on climate, carbon stocks, and commodity production and consumption at the global scale, human drivers representing land management are being integrated into Earth System Models (ESMs); Drewniak et al., 2013), and the management of land, energy, and fossil fuels is included in Integrated Assessment Models (IAMs; Chaturvedi et al., 2013; Le Page et al., 2016). As human drivers continue to be included in scientific research models, these models will continue to better represent actual local and global dynamics, thereby becoming more useful for decision making."^^xsd:string;
   gcis:assessmentOfConfidenceBasedOnEvidence "Continued inclusion of human drivers within ecosystem models and ESMs will better represent the influence of human activities on the carbon cycle, thereby improving the usefulness of results to decision makers."^^xsd:string;
   gcis:newInformationAndRemainingUncertainties "While inclusion of human drivers in estimates of carbon cycle fluxes and stock changes often results in more useful information for decision making, it also can result in a higher number of model parameters, which can increase statistical uncertainty and variability of model results. However, this increased statistical uncertainty does not necessarily reduce the usefulness of findings for decision making, particularly if the uncertainty is a uniform bias or a broader confidence interval surrounding a stable trend."^^xsd:string;

   a gcis:Finding .

## This finding cites the following entities:

   prov:wasDerivedFrom <>.