# Estimating Global Gross Primary Production Using an Improved MODIS Leaf Area Index Dataset
## 4 Discussion
### Drivers of Global GPP Changes from 2000 to 2022
The SEM model was used to construct the direct response of species richness to abiotic stress and disturbance and the intervention effect on community biomass [70]. We were able to quantify both the direct and indirect paths of atmospheric CO2 concentration, air temperature, and precipitation for increasing GPP based on the SEM model (Figure 9). The total effects on GPP change, including the effects of atmospheric CO2 concentration, air temperature, and precipitation change, were 0.853, 0.75, and \(-\)0.144, respectively, when the direct and indirect effects of the latent variables were aggregated (Figure 9A). The increasing concentration of CO2 altered the surface energy balance and led to climate change, which subsequently affected atmospheric circulation and the global water cycle, changing the temporal and spatial distribution of precipitation [93; 94]. The change in CO2 had not only a positive and direct effect, with an influence coefficient of 0.261, but also a positive and indirect effect, with an influence coefficient of 0.62, on the change in GPP through its positive effect on temperature, as well as a positive and indirect effect, with an influence coefficient of -0.028, on the GPP change through its negative effect on precipitation (Figure 9B). The EC-LUE model assessed the impact of atmospheric CO2 concentration on GPP trends, discovering that a 100 ppm\({}^{-1}\) rise in atmospheric CO2 concentration significantly boosted the global GPP by 12.31 \(\pm\) 0.61 PgC [4]. While precipitation made a minor but negative contribution to global GPP, the VPM, VI (Vegetation Indices), TG (Temperature-Greenness), and GR (Greenness-Radiation) models had respective mean values of  
Figure 8: Correlations between global GPP and (**A**) annual mean atmospheric CO2 concentration, (**B**) global annual mean temperature, and (**C**) global annual precipitation. \(-1.4949\) PgC year\({}^{-1}\) 100 mm\({}^{-1}\), \(-0.85859\) PgC year\({}^{-1}\) 100 mm\({}^{-1}\), \(-0.85859\) PgC year\({}^{-1}\) 100 mm\({}^{-1}\), and \(-0.36364\) PgC year\({}^{-1}\) 100 mm\({}^{-1}\)[82]. The VPM model focuses on radiation and temperature in GPP estimation and neglects precipitation, while the VI and TG models focus on vegetation indices and temperature and indirectly reflect the effects of precipitation. The GR model, which relies heavily on radiation, associates increased precipitation with lower radiation and GPP. The global GPP has a negative impact at the global level but not at the regional level, where the increase in precipitation depends on the climatic characteristics of the region, the water requirements of the ecosystems, and interaction with other relevant environmental variables. At the global scale, an increase in precipitation could lead to cloud thickening and reduced solar radiation, as well as waterlogged soils that limit the oxygen supply to roots, thereby inhibiting plant photosynthesis. At a regional scale, the effects of precipitation on GPP vary depending on the type of vegetation, topography, and soil conditions. GPP changes are influenced primarily by an increase in radiation and temperature at mid- and high latitudes, by changes in moisture conditions at low latitudes, and by fluctuations in both temperature and moisture conditions at high altitudes [95]. Changes in photosynthetically active radiation, relative humidity, and temperature were the primary drivers of the interannual variability in GPP, accounting for 80% in deciduous broadleaf forests and evergreen coniferous forests, 65% in crop plants, and 58% in shrubs [96]. Different plants have different light saturation points and radiation utilization rates, which means that the radiation intensity required to achieve maximum photosynthetic efficiency varies from plant to plant. Temperature affects the activity of key enzymes such as Rubisco, influencing the rate of photosynthesis. Humidity affects the transpiration and stomatal conductance of plants, which, in turn, affect CO\({}_{2}\) uptake and water use efficiency.