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@prefix dcterms: <http://purl.org/dc/terms/> . @prefix xsd: <http://www.w3.org/2001/XMLSchema#> . @prefix gcis: <http://data.globalchange.gov/gcis.owl#> . @prefix cito: <http://purl.org/spar/cito/> . @prefix biro: <http://purl.org/spar/biro/> . <https://data.globalchange.gov/report/second-state-carbon-cycle-report-soccr2-sustained-assessment-report/chapter/forests/finding/key-message-9-2> dcterms:identifier "key-message-9-2"; gcis:findingNumber "9.2"^^xsd:string; gcis:findingStatement "Forest regrowth following historical clearing plays a substantial role in determining the size of the forest carbon sink, but studies also suggest sizeable contributions from growth enhancements such as carbon dioxide (CO<sub>2</sub>) fertilization, nitrogen deposition, or climate trends supporting accelerated growth (<em>medium confidence</em>). Resolving each factor’s contribution is a major challenge and critical for developing reliable predictions."^^xsd:string; gcis:isFindingOf <https://data.globalchange.gov/report/second-state-carbon-cycle-report-soccr2-sustained-assessment-report/chapter/forests>; gcis:isFindingOf <https://data.globalchange.gov/report/second-state-carbon-cycle-report-soccr2-sustained-assessment-report>; ## Properties of the finding: gcis:descriptionOfEvidenceBase "Although the use of remote sensing (e.g., Landsat) has led to major advances over the past decade in monitoring aspects of disturbance and land-use change (Bachelet et al., 2015; Hansen et al., 2013), critical research gaps remain. Disturbance histories at the stand scale and attribution to disturbance type and severity remain poorly characterized, as are rates of forest conversion."^^xsd:string; gcis:newInformationAndRemainingUncertainties "Improved estimates of the location, severity, and timing of natural disturbances are needed, particularly in Mexico. Degradation of forest stocks (e.g., from selective logging, low-severity disturbances, and stress) also remain poorly characterized at the scales needed for assessing carbon dynamics and managing forest carbon. Also needed are landscape-scale records of management practices such as replanting, selective harvesting, cyclical use, and agroforestry. Integration of a range of remote-sensing technologies, including light detection and ranging (LIDAR), with field plot data and carbon cycle modeling, promises to substantially improve the ability to measure and monitor forest carbon dynamics at large scales. Addressing these and other gaps ultimately will lead to spatially explicit estimates of carbon stocks and fluxes that comprehensively assess impacts of disturbance, management, and environmental changes on carbon fluxes."^^xsd:string; a gcis:Finding . ## This finding cites the following entities: <https://data.globalchange.gov/report/second-state-carbon-cycle-report-soccr2-sustained-assessment-report/chapter/forests/finding/key-message-9-2> prov:wasDerivedFrom <https://data.globalchange.gov/report/second-state-carbon-cycle-report-soccr2-sustained-assessment-report/chapter/preface/figure/figurep-4>.