Synthesis of Landscape Yield Metrics
(SLYM)
Project description
Rising global food demand, driven by population growth, challenges the sustainability of intensified agricultural practices, which often degrade biodiversity, soil health, and yield stability. To address this, we examined how landscape structure influences the effectiveness of sustainable agricultural practices at a global scale. We compiled 8,052 georeferenced yield data points from 13 meta-analyses and integrated them with high-resolution land cover, climate, and species richness data using Google Earth Engine. Focusing on the 10 most widespread pollinator-dependent and -independent crops, we analyzed landscape composition, heterogeneity, and scale effects within 1–5 km buffers. Using generalized linear mixed-effects models, we tested hypotheses linking land cover diversity, natural habitat proportion, and arable land share to treatment effectiveness. Our results reveal that landscape complexity can enhance yield responses, especially in diversified systems, supporting multifunctional agricultural landscapes as a pathway toward more resilient and productive food systems.