Johannsen, LucasRamm, SebastianReckleben, YvesDoerfel, Stephan2024-04-082024-04-082024978-3-88579-738-82944-7682https://dl.gi.de/handle/20.500.12116/43925In the wake of eco-schemes introduced by the EU's Common Agricultural Policy, this study evaluates AI-based interpolation methods for generating yield maps as one component of a decision support system, aiding farmers in eco-scheme implementation. The research contrasts ordinary Kriging (OK) with AI techniques – Random Forest (RF) enhanced with spatial fea-tures (RFsp), covariates (RFspco) and DeepKriging (DK), utilizing combine harvester yield data. Performance metrics show AI, especially RF variants, surpassing OK. For a 0.7 split, R² were 0.6 (OK), 0.77 (RF), 0.81 (RFsp), 0.78 (DK); MSE were 0.6 (OK), 0.34 (RF), 0.28 (RFsp), 0.32 (DK). Spatial features boosted accuracy, while incorporating Terrain Models had no rele-vant impact on the results. These findings are crucial for an automated, accurate decision support system, facilitating eco-scheme adoption for farmers. The efficiency of AI methods underscores their potential in promoting sustainable, informed agricultural practices.enartificial intelligencemachine learningyield mapspatial predictioninterpolationText/Conference Paper10.18420/giljt2024_171617-5468