Barnes & Noble Synthesis and Modeling of Greenhouse Gas Emissions and Carbon Storage in Agricultural and Forest Systems to Guide Mitigation and Adaptation at Hamilton Place in Chattanooga, TN
Synthesis and Modeling of Greenhouse Gas Emissions and Carbon Storage in Agricultural and Forest Systems to Guide Mitigation and Adaptation in Chattanooga, TN
Synthesis and Modeling of Greenhouse Gas Emissions and Carbon Storage in Agricultural and Forest Systems to Guide Mitigation and Adaptation in Chattanooga, TN
Crop, livestock, and forestry productions systems are important sources and sinks of greenhouses gases, but estimates of the magnitude of gas fluxes are more uncertain than those for other economic sectors such as transportation and electricity generation. Recent improvements in process-level un-derstanding, modeling software, and observational data used for model testing have increased the accuracy of model predictions, but substantial uncertainty remains, particularly regarding the potential for different management practices to mitigate emissions. The chapters in this volume demonstrate that both simple methods and complex models have strengths and limitations depending on stake-holder interest, scale of application, and other factors. Future improvement can be facilitated by or-ganizing model input and testing data into web-accessible databases and by making model algorithms more available and transparent.
Crop, livestock, and forestry productions systems are important sources and sinks of greenhouses gases, but estimates of the magnitude of gas fluxes are more uncertain than those for other economic sectors such as transportation and electricity generation. Recent improvements in process-level un-derstanding, modeling software, and observational data used for model testing have increased the accuracy of model predictions, but substantial uncertainty remains, particularly regarding the potential for different management practices to mitigate emissions. The chapters in this volume demonstrate that both simple methods and complex models have strengths and limitations depending on stake-holder interest, scale of application, and other factors. Future improvement can be facilitated by or-ganizing model input and testing data into web-accessible databases and by making model algorithms more available and transparent.
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