Comparison and Optimization of Light Use Efficiency-Based Gross Primary Productivity Models in an Agroforestry Orchard

The light-use efficiency-based gross primary productivity (LUE-GPP) model is widely utilized for simulating terrestrial ecosystem carbon exchanges owing to its perceived simplicity and reliability. Variations in cloud cover and aerosol concentrations can affect ecosystem LUE, thereby influencing the performance of the LUE-GPP model, particularly in humid regions. In this study, the performance of six big-leaf LUE-GPP models and one two-leaf LUE-GPP model were evaluated in a humid agroforestry ecosystem from 2018–2020. All big-leaf LUE-GPP models yielded GPP values consistent with that derived from the eddy covariance system (GPP_EC), with R² ranging from 0.66–0.73 and RMSE ranging from 1.81–3.04 g C m⁻² d⁻¹. Differences in model performance were attributed to the differences in the quantification of temperature (T_s) and moisture constraints (W_s) and their combination forms in the models. The T_s and W_s algorithms in the eddy covariance-light-use efficiency (EF-LUE) model well characterized the environmental constraints on LUE. Simulation accuracy under the common limitation of T_s and W_s (T_s × W_s) was higher than the maximum limitation of T_s or W_s (Min (T_s, W_s)), and the combination of the T_s algorithm in the Carnegie–Ames–Stanford Approach (CASA) and the W_s algorithm in the EF-LUE model was optimized in combination forms, thereby constraining LUE for GPP estimates (GPP_BLO, R² = 0.76). Various big-leaf LUE-GPP models overestimated or underestimated GPP on sunny or cloudy days, respectively, while the two-leaf LUE-GPP model, which considered the transmission of diffuse radiation and the difference in photosynthetic capacity of canopy leaves, performed well (R² = 0.72, p < 0.01). Nevertheless, the underestimation/overestimation for shaded/sunlit leaves remained under different weather conditions. Then, the clearness index (K_t) was introduced to calculate the dynamic LUE in the big-leaf and two-leaf LUE-GPP models in the form of exponential or power functions, resulting in consistent performance even in different weather conditions and an overall higher simulation accuracy. This study confirmed the potential applicability of different LUE-GPP models and emphasized the importance of dynamic LUE on model performance.