Quantifying Shelterbelt-Induced Edge Effects on Maize Yield Using LiDAR in a Desert Oasis Agroforestry System

In desert oasis regions, agroforestry systems play a crucial role in maintaining stable and high crop yields; however, they often face the “edge effect” caused by resource competition between shelterbelts and crops, resulting in yield reduction near forest edges. Despite extensive studies, quantifying the spatial extent and magnitude of shelterbelt-induced suppression at large scales remains challenging, and the key influencing factors and underlying processes are still unclear. In this study, we investigated 90 agroforestry plots (each composed of Populus alba var. pyramidalis and maize) at the Ulan Buh Desert Experimental Farm in China. By integrating airborne LiDAR and Sentinel-2 high-resolution remote sensing data, calibrated with in situ measurements, we examined the effects of shelterbelts on maize yield variation. Based on a leaf area index–yield model, we employed a generalized additive model (GAM) to derive the relative yield–distance curves under gradients of shelterbelt orientation, density, canopy density, and individual stand volume. Furthermore, a random forest model was applied to identify key shelterbelt characteristics influencing variations in maize absorbed photosynthetically active radiation, leaf chlorophyll content, canopy water content, canopy cover and gap fraction within 3H of the shelterbelt. Using a partial least squares path model (PLS-PM), we established the causal chain underlying the formation of shelterbelt-induced suppression effects. Finally, we proposed targeted strategies to mitigate these edge effects and enhance yield gains, providing both theoretical insights and practical guidance for optimizing agroforestry system configurations and maximizing productivity in desert oasis ecosystems.