Assessing gross primary production dynamics in differently managed tallgrass prairies using Eddy covariance and...

Accurately quantifying gross primary production (GPP) is crucial for understanding grassland carbon dynamics. We examined spatiotemporal variations in eddy covariance-derived GPP (GPP_EC) across six native tallgrass prairie pastures (∼32 to 64 ha each) in central Oklahoma and compared them against GPP estimates from the global simulation of the Vegetation Photosynthesis Model (GPP_{VPM_global}) and the site-level simulation of the Vegetation Photosynthesis and Ecosystem Respiration Model (GPP_{VPERM_site}). These sites encompass varied burning frequencies (annual vs. 4-5-year rotation), grazing intensities (light rotational vs. intensive), and a hayed pasture, as well as diverse growing conditions, enabling us to thoroughly evaluate the models' capability to estimate GPP in tallgrass prairie. We observed a strong seasonal pattern in GPP_EC, though peak timing and magnitude varied greatly across pastures and years due to differences in management and weather. Variations in GPP_EC-enhanced vegetation index (EVI) linear regression slopes (16.78 to 25.82) among sites reflected management impacts on carbon uptake. Linear regression, stepwise ordinary least squares, and linear mixed-effects models consistently identified EVI as the dominant predictor of GPP_EC, while air temperature, solar radiation, and rainfall added little explanatory power. Narrow marginal (0.74-0.79) and conditional (0.76-0.79) R² ranges indicated minor spatial and interannual effects, suggesting a robust GPP_EC-EVI relationship across sites and years. Both GPP_{VPM_global} and GPP_{VPERM_site} closely tracked GPP_EC dynamics (R² = 0.72-0.85, P < 0.01). The seasonal peaks of GPP_{VPM_global} and GPP_{VPERM_site} matched those of GPP_EC in lightly grazed pastures (0-2% annual overestimation at Pasture 14), but models substantially overpredicted annual cumulative fluxes (15-30%) in intensively grazed and hayed pastures (RMSE = 2.08-2.66 g C m^-2 d^-1). These results underscore the general robustness of light use efficiency models while highlighting the need to incorporate dynamic C₃:C₄ functional composition and management disturbances to improve model accuracy in mixed species grasslands.