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

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

doi:10.5194/gmd-19-6335-2026

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

Supplement

https://doi.org/10.5194/gmd-19-6335-2026-supplement

Data sets

mLDNDCv1.0: A Machine Learning-based Surrogate of LandscapeDNDC for Optimising Cropping Systems in Denmark Meshach Ojo Aderele et al. https://doi.org/10.5281/zenodo.18573225

Model code and software

mLDNDCv1.0: A Machine Learning-based Surrogate of LandscapeDNDC for Optimising Cropping Systems in Denmark Meshach Ojo Aderele et al. https://doi.org/10.5281/zenodo.18278474

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.