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 Research Our projects Ph.D. Projects

Our projects

  • Projects we are participating in
  • Postdoctoral research
  • Ph.D. Projects
    • Can Isotopic Maps Reveal N2O Emissions?
    • Visualizing Heterogenous Microenvironments
    • Integrating machine learning with agroecosystem modelling
    • Spatial and temporal variability of soil GHG fluxes of urban greens
    • Integrating farm and farmer policy relevant typologies to tailor agri-environmental schemes
    • Investigating Gradients and Emissions of N2O in Soil Profiles and Across Landscapes
    • Isoscapes in a long-term agricultural experiment
    • Investigating Stakeholder Perspectives and Landscape Transitions through 3D Visualisations
    • Rooting N2O – Cropping strategies for mitigating GHG emissions in agricultural landscapes
    • Soil GHG emissions from Danish lowland organic rich agricultural soil and the landscape to be
    • Biogeochemistry of Agricultural Microtopographies: Unveiling the role of Microtopographic features in Agricultural GHG emissions
    • Process-guided machine learning with multi-source satellite data to quantify global soil moisture and N2O emissions
    • Modelling the livestock carrying capacity in sub-Saharan Africa

Ph.D. Projects

Here you can find our P.h.D students pojects.

Can Isotopic Maps Reveal N2O Emissions?
Visualizing Heterogenous Microenvironments
Integrating machine learning with agroecosystem modelling
Spatial and temporal variability of soil GHG fluxes of urban greens
Integrating farm and farmer policy relevant typologies to tailor agri-environmental schemes
Investigating Gradients and Emissions of N2O in Soil Profiles and Across Landscapes
Isoscapes in a long-term agricultural experiment
Investigating Stakeholder Perspectives and Landscape Transitions through 3D Visualisations
Rooting N2O – Cropping strategies for mitigating GHG emissions in agricultural landscapes
Soil GHG emissions from Danish lowland organic rich agricultural soil and the landscape to be
Unveiling the role of Microtopographic features in Agricultural GHG emissions
Process-guided machine learning with multi-source satellite data
Modelling the livestock carrying capacity in sub-Saharan Africa
Revised 24.03.2025
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Helle Marit Petersen
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