Assessing gross primary production dynamics in differently managed tallgrass prairies using eddy covariance and satellite-derived estimates

Authors: Wagle, Pradeep; PAN, LI - University Of Oklahoma; MENG, CHENG - University Of Oklahoma; XIAO, XIANGMING - University Of Oklahoma; Cibils, Andres; Gunter, Stacey

Submitted to: Science of the Total Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/21/2026
Publication Date: 8/28/2026
Citation: Wagle, P., Pan, L., Meng, C., Xiao, X., Cibils, A.F., Gunter, S.A. 2026. Assessing gross primary production dynamics in differently managed tallgrass prairies using eddy covariance and satellite-derived estimates. Science of the Total Environment. 1050. Article 182247. https://doi.org/10.1016/j.scitotenv.2026.182247.
DOI: https://doi.org/10.1016/j.scitotenv.2026.182247

Interpretive Summary: This study assessed the spatial and temporal dynamics of eddy covariance-derived Gross Primary Production (GPPEC, the rate of carbon uptake) across six ecologically managed native tallgrass prairies in central Oklahoma. The study also evaluated two remote sensing-based models: the global version of the Vegetation Photosynthesis Model (GPPVPM_global) and a new Vegetation Photosynthesis Ecosystem Respiration Model (GPPVPERM), which extends VPM by including ecosystem respiration against GPPEC. The results showed that the enhanced vegetation index (EVI) was the primary dominant predictor of GPPEC, significantly outperforming the influence of air temperature, rainfall, and solar radiation. Furthermore, the strong and consistent relationship between GPPEC and EVI across various sites and years, indicated by high R2 values, suggests a robust, consistent mechanism linking vegetation greenness to productivity, though management practices subtly altered the vegetation's light use efficiency. While both models successfully captured seasonal trends, they exhibited significant overestimation of GPP in pastures subjected to intensive management, such as heavy grazing or haying. Conversely, the standard GPPMOD17 product consistently underestimated GPP across all regimes. These findings underscore the fundamental reliability of remote sensing models in tracking carbon flux, but they also highlight the models’ inability to account for the impact of intensive management practices and temporal shifts in C3/C4 grass species composition. Therefore, improving GPP prediction accuracy in mixed-species prairie grasslands requires explicitly integrating these management and compositional factors into future model development.

Technical Abstract: Accurately quantifying gross primary production (GPP) across terrestrial ecosystems is crucial for a better understanding of the global carbon cycle. We examined the spatiotemporal variation of eddy covariance-derived GPP (GPPEC) across six native tallgrass prairie pastures in central Oklahoma and compared these with GPP estimates from the global-scale simulation of the Vegetation Photosynthesis Model (GPPVPM_global) and the site-level simulation of the Vegetation Photosynthesis and Ecosystem Respiration Model (GPPVPERM_site). These sites represent different burning and grazing regimes and diverse growing conditions, enabling us to thoroughly evaluate the models’ capability to estimate GPP in tallgrass prairie. We observed a strong seasonal pattern in GPPEC. However, the timing and magnitude of GPPEC peaks varied greatly within and across pastures and years due to differences in management and weather variability. Differences in linear regression slopes between GPPEC and EVI among sites reflected the influence of management practices on carbon uptake. Linear regression, stepwise ordinary least squares, and linear mixed-effects models consistently identified EVI as the dominant predictor of GPPEC across all pastures and years, while air temperature, solar radiation, and rainfall contributed little additional explanatory power. A small range of marginal (0.74–0.79) and conditional (0.76–0.79) R² values indicated that spatial and interannual effects were minor, suggesting a robust and consistent GPPEC–EVI relationship across sites and management regimes. Seasonal dynamics of GPPVPM_global and GPPVPERM_site closely followed GPPEC across the pastures. The seasonal peaks of GPPVPM_global and GPPVPERM_site matched those of GPPEC in lightly grazed pastures but were substantially higher in intensively grazed and hayed pastures, indicating models’ overestimation under intensive management. Strong correlations between GPPEC and satellite-derived GPP estimates underscore the robustness of light use efficiency models, while highlighting the need to incorporate temporal shifts in C3:C4 species composition and management effects to improve model accuracy in mixed species grasslands.