Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 13
Articles | Volume 19, issue 13
https://doi.org/10.5194/gmd-19-6335-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-6335-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 13
Development and technical paper
 | 
15 Jul 2026
Development and technical paper |  | 15 Jul 2026

mLDNDCv1.0: a machine learning-based surrogate of LandscapeDNDC for optimising cropping systems in Denmark

Meshach Ojo Aderele, Edwin Haas, Licheng Liu, João Serra, David Kraus, Klaus Butterbach-Bahl, and Jaber Rahimi

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Short summary
This study develops a fast, data‑driven tool to virtually test millions of ways to manage winter wheat fields in Denmark, without running slow process-based crop models each time. It finds fertilizer, residue, manure, catch crop and irrigation strategies that cut nitrogen pollution and greenhouse gases while increasing yields and soil carbon, all without using more fertilizer overall.
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