mLDNDCv1.0: A Machine Learning-based Surrogate of LandscapeDNDC for Optimising Cropping Systems in Denmark

Abstract. Optimising Danish arable management is critical for reducing greenhouse‐gas (GHG) emissions and nitrogen (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 without sacrificing mechanistic fidelity. 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 delivered the most accurate and stable predictions for the core indicators in this study: soil N₂O emissions (R² = 0.81), NO₃⁻ leaching (R² = 0.84), yield (R² = 0.93), and for soil-organic-carbon stock changes (R² = 0.86). The model maintained high accuracy when confronted with real management and environmental settings that reflected true operating conditions. Coupling mLDNDC with the multi-objective evolutionary algorithm NSGA-II allowed us to optimise millions of management combinations across all winter wheat fields in Denmark. Pareto-optimal solutions reduced N₂O emissions by 27.5 ± 4.5 %, NO₃⁻ 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 CO₂-eq ha⁻¹ 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 therefore provides a scalable, transparent framework for country-wide optimisation and real-time decision support in climate-smart agriculture.