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

Optimising Danish arable management is critical for reducing greenhouse-gas (GHG) emissions and ni- trogen (N) losses while maintaining or even improving crop productivity and soil health. Process-based models such as LandscapeDNDC can simulate the effects of management on agroecosystem functioning. However, their computational demand limits large-scale optimisation. Here we present mLDNDCv1.0, a tree-based machine-learning surrogate of LandscapeDNDC that allows for the rapid exploration of large decision spaces while maintaining high fidelity to the parent process-based model’s input-output behaviour. We generated a synthetic training set of > 45 million LandscapeDNDC simulations from a full factorial of soils, climate (2011–2020), and management options for winter wheat. We benchmarked gradient-boosted tree algorithms (LightGBM, XGBoost, CatBoost) on predictive performance. XGBoost and LightGBM outperformed CatBoost and achieved similar predictive performance for the core indicators in this study. XGBoost, selected as the final model for its much faster inference in our implementation, achieved: soil N$_2$O emissions (R$^2$ = 0.81), NO$^−_3$ leaching (R$^2$ = 0.84), yield (R$^2$ = 0.93), and for soil-organic-carbon stock changes (R$^2$ = 0.86). When evaluated on real field activity data from Denmark, the surrogate model closely reproduced the process-based model outputs, with particularly strong agreement for crop yield, which was further corroborated by independent observational data. Coupling mLDNDC with the multi-objective evolutionary algorithm NSGA-II allowed us to optimise millions of management combinations within a predefined decision boundary across all winter wheat fields in Denmark. Pareto-optimal solutions reduced N$_2$O emissions by 27.5 ± 4.5 %, NO$^−_3$ and leaching by 27 ± 3.0 %. These solutions also increased grain yield by 8.5 ± 1.5 % and soil-organic-carbon stocks by 1.2 ± 0.1 %, and improving nitrogen-use efficiency (NUE) by 10 ± 2 %, while turning the system into a net GHG sink (2200 ±400 Mg CO$_2$-eq yr$^{−1}$). These gains were achieved without increasing total fertiliser input. They arose from re-allocating mineral and organic fertliser N input, adjusting incorporation depth, and optimising residue, catch-crop, and irrigation practices. Thus, mLDNDC provides a scalable, transparent framework for country-wide scenario comparison and strategic planning at an annual time scale in climate-smart agriculture.