Spatiotemporal Land Equivalent Ratio (ST-LER) for Agroforestry Design

Abstract Achieving climate resilience in Mediterranean agriculture hinges on adaptive strategies like agroforestry, yet optimization is constrained by retrospective indices that are effectively spatially blind and temporally static. Consequently, their utility is insufficient for guiding spatial design or quantifying the operational trade-offs inherent to mechanized systems. We hypothesize that decomposing land-use efficiency into geometric occupancy and biological facilitation, via a Spatially and Temporally Explicit Land Equivalent Ratio (ST-LER), enables the evidence-based optimization of productive and climate-resilient designs. To empirically parameterize the ST-LER framework, we utilized a recently established experimental platform intended for long-term research monitoring in central Israel under a Mediterranean climate. A super-high-density olive orchard was intercropped with winter crops (clover, wheat) in a silvoarable arrangement across three alley widths (4, 8, and 14 m) alongside monoculture controls. Data from the initial establishment phase (over two seasons), included high-resolution monitoring of crop yield spatial gradients and tree. These data were used to calibrate a non-linear optimization model, projecting system efficiency as a continuous function of alley spacing. Despite prevailing drought conditions, all silvoarable maintained land-use parity (ST-LER ≥ 1.0), confirming resilience to climatic stress. Efficiency drivers shifted from density-dependent compensation in narrow spacings to microclimatic facilitation in wide alleys. The model identified a biological optimum at ~ 10.7 m, balancing geometric costs against facilitation decay. Crucially, this peak reveals an inherent conflict: it falls within an operational gap incompatible with standard machinery widths. Uniquely, the ST-LER framework repurposes LER from a passive retrospective metric into a prospective design engine. By rigorously quantifying the ‘biological penalty’ of forcing agroecological systems to fit mechanical constraints, it resolves the inherent tension between operational efficiency and biological optimization, offering a scalable blueprint for mechanized agroforestry advancing climate-resilient agriculture.