Adapting empirical soil erosion models to agrivoltaic systems: Limitations of RUSLE and factor-specific adaptations
Year: 2025
Type: article
Abstract: Integrating solar energy production with agriculture through agrivoltaic (APV) systems substantially alters rainfall distribution, runoff processes, and soil-erosion rates. The Revised Universal Soil Loss Equation (RUSLE) and other empirical soil erosion models were developed for traditional agricultural environments and do not account for the spatial complexities introduced by APV systems. This review evaluates these empirical models in the context of APV farms and highlights their key limitations in accurately capturing changes in rainfall distribution, runoff behavior, and erosion risk. Specifically, solar panels induce an uneven rainfall distribution, leading to concentrated runoff and an increased erosion risk. They also alter the microclimate beneath and between panels, affecting vegetation growth and soil susceptibility to erosion. As a result, empirical soil erosion models such as RUSLE require substantial modifications to predict soil loss accurately. Critical parameters—rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C), and support practices (P)—require systematic adaptation to APV-specific conditions. We identify critical knowledge gaps in applying these models to APV environments and underscore the imperative for high-resolution monitoring of soil moisture, runoff, and erosion rates. We further highlight the need to develop machine-learning-assisted models to improve soil erosion predictions under APV conditions. Such integrated modeling approaches will improve the capacity to balance renewable energy production with agricultural soil conservation. • APV systems alter rainfall distribution, runoff processes, and soil erosion rates. • Soil erosion models need modifications to predict soil loss in agrivoltaic systems. • APV needs imperative high-resolution monitoring of soil, runoff, and erosion. • Integrated modeling balances renewable energy production with soil conservation.
Source: Soil and Tillage Research
Authors: Eli Argaman, Wang Fei, Xiao Yan, Ilan Stavi
Language: English
Subfield: Soil Science
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $4,570
OpenAlex ID: W4416525003
