{"id":41093,"date":"2023-02-07T17:33:10","date_gmt":"2023-02-07T17:33:23","guid":{"rendered":"https:\/\/agsci.colostate.edu\/divi-borch\/?page_id=41093"},"modified":"2023-08-14T19:55:20","modified_gmt":"2023-08-14T19:55:20","slug":"developing-an-integrated-measurement-modeling-approach-to-improve-forecasting-of-soil-organic-matter-dynamics","status":"publish","type":"page","link":"https:\/\/agsci.colostate.edu\/soilinnovationlab\/current-research-themes\/developing-an-integrated-measurement-modeling-approach-to-improve-forecasting-of-soil-organic-matter-dynamics\/","title":{"rendered":"Developing an Integrated Measurement-modeling Approach to Improve Forecasting of Soil Organic Matter Dynamics: The MEMS Model."},"content":{"rendered":"

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Developing an Integrated Measurement-modeling Approach to Improve Forecasting of Soil Organic Matter Dynamics: The MEMS Model.<\/h1>\n

Predictive tools, i.e., ecosystem models, are required to inform policy and land management to support food production and to forecast carbon-climate feedback. These ecosystem models need to correctly represent the context-dependent effects of both management and climate on ecosystem biogeochemistry, from the farm to the global scale. Parametrization, calibration, and verification of these models are essential to their performance; thus, they need to represent quantifiable variables. At the SoIL, we work in close integration with modelers to develop integrated measurement-modeling approaches that can be used as tools for both scientific inquiry and decision support.<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ admin_label=”Developing an Integrated Measurement-modeling Approach to Improve Forecasting of Soil Organic Matter Dynamics: The MEMS Model.” module_id=”developing” _builder_version=”4.20.0″ _module_preset=”default” background_color=”#F1F1F1″ locked=”off” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ use_custom_gutter=”on” gutter_width=”2″ _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.20.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” header_2_font_size=”26.1px” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n

Major Contribution and Highlights<\/h2>\n
    \n
  1. The MEMS 1.0 soil carbon model<\/a><\/li>\n
  2. The MEMS 2.0 ecosystem model<\/a><\/li>\n<\/ol>\n

    [\/et_pb_text][et_pb_image src=”https:\/\/agsci.colostate.edu\/soilinnovationlab\/wp-content\/uploads\/sites\/155\/2023\/07\/soil-innovation-lab-figure.jpg” alt=”visual schematic of the MEMS model” title_text=”soil-innovation-lab-figure” _builder_version=”4.21.0″ _module_preset=”default” border_width_top=”4px” border_color_top=”#F15625″ global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.20.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.20.0″ _module_preset=”default” header_2_font_size=”26.1px” global_colors_info=”{}”]<\/p>\n

    Future Directions and Goals<\/h2>\n

    We contribute to the further development of the MEMS model to:<\/p>\n

      \n
    1. Represent C and N biogeochemistry of all major terrestrial ecosystems.<\/li>\n
    2. Predict responses of cropland and grassland C and N biogeochemistry to conventional and regenerative management practices.<\/li>\n
    3. We are excited to keep developing the model structure to incorporate the most relevant and novel understandings of SOM dynamics, such as effects of microbial traits, controls on stable and dynamic organo-mineral interactions, and more.<\/li>\n<\/ol>\n

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