Photosynthetic rate is a key source of uncertainty in the modeling of the terrestrial carbon cycle. Recent studies have utilized leaf chlorophyll content (Chl) as a proxy for leaf photosynthetic capacity in croplands and deciduous forests, with little investigation into this relationship for other plant function types and for different leaf ages. In this study, we evaluated the relationship between the maximum rate of carboxylation (V_cmax25) and the maximum electron transport capacity (J_cmax25) at 25 °C with both leaf nitrogen and Chl from different leaf ages (current and previous year) in Masson's pine (Pinus massoniana Lamb.) and slash pine (Pinus elliottii Engelm.) species in a subtropical evergreen coniferous forest. Our results showed small changes in leaf nitrogen over the growing season. In contrast, V_cmax25, J_max25, and Chl displayed larger seasonal variations. V_cmax25 was more related to leaf Chl than leaf nitrogen in both previous year's and current year's leaves, likely due to the variable partitioning of leaf nitrogen between and within photosynthetic and nonphotosynthetic fractions. Leaf Chl and month after budding (MAB) were the main predictors for V_cmax25 based on the random forest regression analysis. These findings highlighted the problem in using leaf nitrogen as a proxy for V_cmax25 where there is a dynamic nitrogen investment (i.e., with leaf ontogenesis, or between different species) and illustrated the value of using leaf Chl (as retrievable from remotely sensing) and MAB to constrain V_cmax25 in process‐based models to improve the simulation of photosynthetic rates in evergreen coniferous forests.

Water use efficiency (WUE), the ratio of gross primary productivity (GPP) over evapotranspiration (ET), is a critical ecosystem function. However, it is difficult to distinguish the individual effects of climatic variables and leaf area index (LAI) on WUE, mainly due to the high collinearity among these factors. Here we proposed a partial least squares regression‐based sensitivity algorithm to confront the issue, which was first verified at seven ChinaFlux sites and then applied across China. The results showed that across all biomes in China, monthly GPP (0.42–0.65), ET (0.33–0.56), and WUE (0.01–0.31) showed positive sensitivities to air temperature, particularly in croplands in northeast China and forests in southwest China. Radiation exerted stronger effects on ET (0.55–0.78) than GPP (0.19–0.65), resulting in negative responses (−0.44 to 0.04) of WUE to increased radiation among most biomes. Increasing precipitation stimulated both GPP (0.06–0.17) and ET (0.05–0.12) at the biome level, but spatially negative effects of excessive precipitation were also found in some grasslands. Both monthly GPP (−0.01 to 0.29) and ET (0.02–0.12) showed weak or moderate responses to vapor pressure deficit among biomes, resulting in weak response of monthly WUE to vapor pressure deficit (−0.04 to 0.08). LAI showed positive effects on GPP (0.18–0.60), ET (0–0.23), and WUE (0.13–0.42) across biomes, particularly on WUE in grasslands (0.42 ± 0.30). Our results highlighted the importance of LAI in influencing WUE against climatic variables. Furthermore, the sensitivity algorithm can be used to inform the design of manipulative experiments and compare with factorial simulations for discerning effects of various variables on ecosystem functions.

Estimating gross primary productivity (GPP) is important for simulating the subsequent carbon cycle elements and assessing the capacity of terrestrial ecosystems to support the sustainable development of human society. Light use efficiency (LUE) models were widely used to estimate GPP due to their concise model structures. However, quantifying LUE_max (maximum light use efficiency) and representing the responses of photosynthesis to environmental factors are still subject to large uncertainties, which lead to substantial errors in GPP simulations. In this study, we developed a hybrid model based on machine learning and a LUE model for GPP estimates. This hybrid model was built by targeting LUE with a machine learning approach, namely multi-layer perceptron (MLP), and then, estimating GPP within a LUE model framework with the MLP-based LUE and other required inputs. We trained the hybrid LUE (H-LUE) model and then, compared it against two conventional LUE models, the vegetation photosynthesis model (VPM) and vegetation photosynthesis and respiration model (VPRM), regarding GPP estimation, using tower-based daily-scale observations from 180 flux sites that cover nine different plant function types (PFTs). The results revealed better performance (R² = 0.86 and RMSE = 1.79 gC m⁻² d⁻¹ on the test dataset) of the H-LUE model compared to the VPM and VPRM. Evaluations of the three models under four different extreme conditions consistently revealed better performance of the H-LUE model, indicating greater adaptability of the model to varied environments in the context of climate change. Furthermore, we also found that the H-LUE model can reasonably represent the responses of the LUE to meteorological variables. Our study revealed the reliable and robust performance of the developed hybrid LUE when simulating GPP across global biomes, providing references for developing better hybrid GPP models.

Photosynthesis is arguably the most important biochemical process on Earth, which is dramatically influenced by environmental conditions. How environmental factors drive stomatal conductance and biochemical processes of leaf photosynthetic rate has not been sufficiently investigated in subtropical China. In this study, we analysed the effects of stomatal and biochemical parameters on the photosynthetic rate of native Masson’s pine (Pinus massoniana Lamb.) and exotic slash pine (Pinus elliottii Engelm.) in response to leaf temperature and vapour pressure deficit (VPD) in subtropical China, based on leaf gas exchange measurements in 2016. Our results showed that there was no significant difference in the light-saturated photosynthetic rate (A_sat) between native Masson’s pine and exotic slash pine. The seasonal patterns of maximum rate of the carboxylation (V_cmax25) were basically consistent with seasonal patterns of A_sat for both species. The positive effect of leaf temperature on A_sat was mainly produced through its positive effect on V_cmax25. Leaf temperature had no significant effect on stomatal conductance. V_cmax25 and g_s simultaneously affected A_sat in response to VPD. Our results highlighted the importance of biochemical processes in limiting leaf photosynthetic rate in response to environmental conditions in subtropical evergreen coniferous plantations.