Photosynthesis Model Research Articles

Mid-season lodging modulates photosynthesis, evapotranspiration, and dry matter accumulation and distribution simulated by the optimized model in maize

IntroductionMid-season lodging in maize (Zea mays L.) often restricts grain yield potential and is a great hurdle in improving production efficiency. The aim of this research was to study the effects of lodging on photosynthesis, evapotranspiration, dry matter accumulation, and distribution in a maize population.MethodsWe examined the effects of lodging on photosynthesis, dry matter accumulation, and distribution of maize in Zhengzhou Agro-meteorological station in August 2016, following a strong wind lodging process. Based on observational data of crops, meteorology, and CO2/H2O flux from milk maturity to maturity of maize in a normal growth year (2017), a model of population photosynthesis and evapotranspiration of maize under normal growth conditions was constructed. The validated model was used to simulate the theoretical value of photosynthesis and evapotranspiration in lodging years (2016), then the measured value of population photosynthesis and evapotranspiration after lodging was calculated based on the measured data of CO2/H2O flux from milk maturity to maturity of maize, and the difference between the simulated value and the measured value of population photosynthesis and evapotranspiration in lodging years (2016) was compared and analyzed. The correlation between dry matter accumulation and population photosynthetic accumulation was examined in order to estimate the reduction of dry matter accumulation after lodging. The effect of lodging on dry matter accumulation, distribution, and yield was analyzed using field biomass data. The population photosynthesis model and evapotranspiration model could accurately simulate the characteristics of normal growth conditions of summer maize.ResultsThe population photosynthesis model absolute error between the simulated value and the measured values in 2017 was −0.43 mg m−2 s−1, and the relative error was −3.3%; the evapotranspiration model absolute error between the simulated value and the measured values in 2017 was −0.005 mm·30 min−1, and the relative error was −10.7%. In 2016, the measured value of photosynthesis after lodging was significantly lower than the simulated value, and the daily average population photosynthesis rate decreased by 13.99 mg m−2 s−1 or 53%. Under the same condition, the daily average evapotranspiration decreased by 1.03 mm d−1 or 28%. The lodging process altered the accumulation and distribution of dry matter in maize. The dry weight of the stem and sheath increased by 5.5% and the ear weight decreased by 10.9% after lodging, compared to without lodging but there was no significant effect on leaf dry weight. After lodging, the proportion of stem sheath distribution increased by 3.0%, while the proportion of ear distribution decreased by 3.0%. After lodging, 100-grain weight and plant grain weight decreased by 2.8 and 10.8%, respectively. According to the lodging rate and density theory of computation yield, the yield of the entire field was reduced by 5.0%.

Climate and Management Practices Jointly Control Vegetation Phenology in Native and Introduced Prairie Pastures

Climate, human disturbances, and management practices jointly control the spatial and temporal patterns of land surface phenology. However, most studies solely focus on analyzing the climatic controls on the inter-annual variability and trends in vegetation phenology. Investigating the main and interacting effects of management practices and climate might be crucial in determining vegetation phenology and productivity. This study examined the impacts of climate and management practices on vegetation phenology and productivity in adjacent native and introduced prairie pastures, which have detailed long-term management records, by combining climate, management, and satellite remote sensing data (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat). Modeled gross primary production (GPP) using vegetation photosynthesis model (VPM) was also included to investigate the dynamics of productivity. When comparing the impacts of the same management practices on different pastures, we used paired comparison, namely, comparing the native and introduced prairies side by side in the same year. The interactions of management practices and climate were investigated through comparing years with similar management but different climate (e.g., years with rainfall or not following baling events) in the same pasture. Results showed that air temperature (Ta) was an important factor in determining the start of the season (SOS) and the length of the season (LOS). Total rainfall (RF) during the annual growing season (AGS, derived from vegetation indices (VIs)) had the largest explanatory power (R2 = 0.53) in explaining the variations in the seasonal sums of VIs. The variations in GPP were better explained by RF (R2 = 0.43) than Ta (R2 = 0.14). Using the thermal growing season (March–October) or AGS climate factors did not show large differences in determining the relationships between phenology, GPP, and climate factors. Drought shortened the LOS and decreased GPP. In terms of management practices, grazing generally reduced the VIs and burning induced early greening-up and enhanced vegetation growth. Drought plus other management practices (e.g., grazing or baling) greatly affected vegetation phenology and suppressed GPP. The negative impacts (i.e., removal of biomass) of grazing on vegetation was compensated by enhanced vegetation growth after good RF. This study demonstrated that the interactions of climate and management practices could be positive (burning plus baling in a good RF year) or negative (grazing/baling plus drought), and can significantly affect vegetation phenology and production.

Improving the cotton simulation model, GOSSYM, for soil, photosynthesis, and transpiration processes

GOSSYM, a mechanistic, process-level cotton crop simulation model, has a two-dimensional (2D) gridded soil model called Rhizos that simulates the below-ground processes daily. Water movement is based on gradients of water content and not hydraulic heads. In GOSSYM, photosynthesis is calculated using a daily empirical light response function that requires calibration for response to elevated carbon dioxide (CO2). This report discusses improvements made to the GOSSYM model for soil, photosynthesis, and transpiration processes. GOSSYM’s predictions of below-ground processes using Rhizos are improved by replacing it with 2DSOIL, a mechanistic 2D finite element soil process model. The photosynthesis and transpiration model in GOSSYM is replaced with a Farquhar biochemical model and Ball-Berry leaf energy balance model. The newly developed model (modified GOSSYM) is evaluated using field-scale and experimental data from SPAR (soil–plant–atmosphere-research) chambers. Modified GOSSYM better predicted net photosynthesis (root mean square error (RMSE) 25.5 versus 45.2 g CO2 m−2 day−1; index of agreement (IA) 0.89 versus 0.76) and transpiration (RMSE 3.3 versus 13.7 L m−2 day−1; IA 0.92 versus 0.14) and improved the yield prediction by 6.0%. Modified GOSSYM improved the simulation of soil, photosynthesis, and transpiration processes, thereby improving the predictive ability of cotton crop growth and development.

Contribution of vegetation restoration to carbon sequestration driven by ex‐situ poverty alleviation and relocation in ecologically fragile areas—Taking Guizhou, China, as a case

AbstractVegetation restoration in ecologically fragile areas has a significant carbon sequestration (CS) effect. However, it is usually difficult to achieve quantitative assessment at the regional scale for this part of human activity intervention. The Chinese government's ex‐situ poverty alleviation and relocation (EPAR) project has relocated approximately 10 million people from areas with a fragile ecological environment to urban centralized resettlement, which is a typical case of weakened environmental intervention by human activities (Guizhou Province accounting for approximately 20% of the total relocated population in China). The CS model of vegetation photosynthesis and spatial analysis of geographic information were used to quantify the contribution of human activities to the natural restoration of vegetation CS, based on the data of net primary productivity (NPP) of vegetation from 2000 to 2020. The results show that the implementation of the EPAR project acts as an external force to drive vegetation restoration and CS, which increases the slope of the carbon density change trend (from k = 30.9 to k = 57.41), resulting in an overall carbon density increase of 26.51 tCkm−2. The regional spatial analysis showed that the correlation coefficients between carbon density and relocation intensity in the 5‐year and 10‐year change were r = 0.976 (p < 0.01) and r = 0.949 (p < 0.05), respectively, indicating a significant positive correlation. Based on this, the CS contribution of vegetation in 84 districts in Guizhou Province that implemented EPAR projects was calculated, showing that 79 districts contributed positively, accounting for 94%. The average contribution of CS by vegetation restoration in each district was 0.0556 Tg, and offset CO2 emissions were 0.2059 Tg. The other five districts with a negative contribution to CS were distributed in regions with relatively stable ecosystems and mature forests. This shows that human intervention in the environment changes more significantly in fragile ecological areas.

Determining Regression Models for Photosynthesis and Stomatal Resistance as Affected by Temperature and Light Intensity in Tomato (Lycopersicon Esculentum Mill.) and Eggplant (Solanum Melongena L.) Grown in Glasshouses

This study was carried out to examine the relationships between net leaf photosynthesis and temperature and light intensity, between stomatal resistance and temperature and light intensity in tomato and aubergine grown with a range temperature from 10 to 30 °C and different light intensities from 3 to 7 MJm-2 d-1. The study was carried out in a six-compartment greenhouse (size 4 m * 8 m), the temperature of which can be controlled by air conditioning, on tomato and eggplant plants. Each of the six greenhouse compartments was set to have maximum temperatures of 10, 12, 16, 18, 20 and 24 °C. Commercial varieties named "Counter" for tomato and "Bonica" for eggplant were used. "Fisons M2" commercial compost was used in all growing media and nutrient was applied equally. In the study, different sowing and planting dates were applied to benefit from natural light conditions (between 3 and 7 MJm-2d-1). Average temperature in each compartment was recorded using a 'Combine' data logger at 15 minute intervals. A porometer (Delta-T device, MT -3) was used to measure the stomatal resistance of tomato and eggplant leaves. The stomatal resistance measurements of the plants were made at the same time of the day (between 11.00-13.00) at 15-day intervals at the top, middle and lower levels of the crown of four different plants in different environmental conditions. In tomato, leaf photosynthesis increased curvilinearly with temperatures up to about 20.5 °C at low light intensity and declined at higher temperatures. The highest photosynthesis was obtained from the plants grown at a temperature of 22.5 °C and 7 MJm-2d-1 light intensity. The lowest photosynthesis was at 10 °C and 3 MJm-2d-1. In aubergine, at low light intensities, net photosynthesis increased curvilinearly up to 23 °C while it increased up to 20 °C at high light intensities and declined at higher temperatures. Maximum net leaf photosynthesis was found to be greater in tomato than aubergine.

Temporal and spatial variations and influencing factors of gross primary productivity in Changbai Mountain Nature Reserve, China.

Changbai Mountain Nature Reserve (CNR) is a typical temperate forest ecosystem, and gross primary production (GPP) of which is closely related to topography and climate change. Research on the spatio-temporal variations and influencing factors of GPP in the CNR is of great significance for assessing growth status of vegetation and the quality of ecological environment. We calculated GPP in CNR using the vegetation photosynthesis model (VPM), and analyzed the influences of slope, altitude, temperature, precipitation, and total radiation. The results showed that the range of annual average GPP in CNR was 63-1706 g C·m-2·a-1 from 2000 to 2020 and that GPP decreased with the increases of altitude. Temperature played the most important role in driving the spatial varia-tion of GPP, with a significant positive correlation with GPP. During the study period, the overall annual GPP showed a significant increase trend in CNR, with an average annual increase of 13 g C·m-2·a-1. The areas with increase of annual GPP accounted for 79.9% of the total area, and the area proportion of annual GPP increase differed in each plant functional type. Annual precipitation was significantly negatively correlated with GPP in 43.2% of CNR, while annual mean temperature and annual total radiation were significantly positively correlated with GPP in 47.2% and 82.4% of CNR. GPP would increase continuously in CNR under the scenario of future global warming.

The role of chloroplast movement in C4 photosynthesis: a theoretical analysis using a three-dimensional reaction-diffusion model for maize.

Chloroplasts movement within mesophyll cells in C4 plants is hypothesized to enhance the CO2 concentrating mechanism, but this is difficult to verify experimentally. A three-dimensional (3D) leaf model can help analyse how chloroplast movement influences the operation of the CO2 concentrating mechanism. The first volumetric reaction-diffusion model of C4 photosynthesis that incorporates detailed 3D leaf anatomy, light propagation, ATP and NADPH production, and CO2, O2 and bicarbonate concentration driven by diffusional and assimilation/emission processes was developed. It was implemented for maize leaves to simulate various chloroplast movement scenarios within mesophyll cells: the movement of all mesophyll chloroplasts towards bundle sheath cells (aggregative movement) and movement of only those of interveinal mesophyll cells towards bundle sheath cells (avoidance movement). Light absorbed by bundle sheath chloroplasts relative to mesophyll chloroplasts increased in both cases. Avoidance movement decreased light absorption by mesophyll chloroplasts considerably. Consequently, total ATP and NADPH production and net photosynthetic rate increased for aggregative movement and decreased for avoidance movement compared with the default case of no chloroplast movement at high light intensities. Leakiness increased in both chloroplast movement scenarios due to the imbalance in energy production and demand in mesophyll and bundle sheath cells. These results suggest the need to design strategies for coordinated increases in electron transport and Rubisco activities for an efficient CO2 concentrating mechanism at very high light intensities.

Improving the Estimation of Gross Primary Productivity across Global Biomes by Modeling Light Use Efficiency through Machine Learning

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 LUEmax (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.

Effect of meteorological data assimilation using 3DVAR on high-resolution simulations of atmospheric CO2 concentrations in East Asia

In this study, the effects of meteorological data assimilation (DA) on high-resolution CO2 concentration simulations in East Asia were investigated using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and three-dimensional variational data assimilation (3DVAR). Experiments with and without meteorological DA were performed for June and December 2018. To determine an appropriate DA cycling interval, two experiments were performed at cycling intervals of 6 and 12 h. Compared to the meteorological fields simulated from experiment without DA, those simulated from experiments with DA were more similar to atmospheric observations. The CO2 concentrations simulated from the experiment with a 6 h DA cycling interval were highly similar to the observed CO2 concentrations. Compared to the root mean square error (RMSE) of the experiment without DA, the RMSE of simulated CO2 concentrations in the experiment with a 6 h DA interval decreased by 8.19% in June and 1.43% in December with respect to surface CO2 concentration observations, and decreased by 2.52% in June and 0.47% in December against column-averaged CO2 concentration (XCO2) observations of the Orbiting Carbon Observatory 2 (OCO-2). The improved 2 m temperature forecasts by DA enhanced biogenic CO2 simulations using the Vegetation Photosynthesis and Respiration Model (VPRM) in WRF-Chem. The improved wind speed and direction forecasts by DA led to improved simulations of the horizontal transport of CO2 concentrations and planetary boundary layer height. Overall, enhanced meteorological forecasts by meteorological DA using 3DVAR improved the accuracy of high-resolution CO2 concentration simulations using WRF-Chem in East Asia.

Cover Image

The cover image is based on the Original Article An exploratory steady-state redox model of photosynthetic linear electron transport for use in complete modelling of photosynthesis for broad applications by Lianhong Gu et al., https://doi.org/10.1111/pce.14563.

The appropriate analytical solution for coupled leaf photosynthesis and stomatal conductance models for C3 plants

Models coupling photosynthesis (An) and stomatal conductance (Gs) have been widely used for their estimation since it was found that An and Gs are correlated. Although a cubic equation for An can be derived from the coupled An-Gs model for C3 plants and a method of obtaining analytical solutions has been proposed, these analytical solutions have several problems like no solutions, changes in appropriate solutions, and no physical and biological logic for selecting the appropriate solution. In this study, a method is proposed to solve these problems using a different expression of analytical solutions and imposing requirements. The proposed method was then numerically tested under a wide range of environmental conditions. By proving the existence and uniqueness of physically and biologically appropriate solutions, this study showed that the proposed method can solve all the above problems and can provide an appropriate analytical solution for each of environmental conditions tested. The proposed method can be used in various studies on photosynthesis and stomatal conductance in the future.

Parameter sensitivity analysis for a biochemically-based photosynthesis model

A challenge for the development of Land Surface Models (LSMs) is improving transpiration of water exchange and photosynthesis of carbon exchange between terrestrial plants and the atmosphere, both of which are governed by stoma in leaves. In the photosynthesis module of these LSMs, variations of parameters arising from diversity in plant functional types (PFTs) and climate remain unclear. Identifying sensitive parameters among all photosynthetic parameters before parameter estimation can not only reduce operation cost, but also improve the usability of photosynthesis models worldwide. Here, we analyzed 13 parameters of a biochemically-based photosynthesis model (FvCB), implemented in many LSMs, using two sensitivity analysis (SA) methods (i.e., the Sobol’ method and the Morris method) for setting up the parameter ensemble. Three different model performance metrics, i.e., Root Mean Squared Error (RMSE), Nash Sutcliffe efficiency (NSE), and Standard Deviation (STDEV) were introduced for model assessment and sensitive parameters identification. The results showed that among all photosynthetic parameters only a small portion of parameters were sensitive, and the sensitive parameters were different across plant functional types: maximum rate of Rubisco activity (Vcmax25), maximum electron transport rate (Jmax25), triose phosphate use rate (TPU) and dark respiration in light (Rd) were sensitive in broad leaf-evergreen trees (BET), broad leaf-deciduous trees (BDT) and needle leaf-evergreen trees (NET), while only Vcmax25 and TPU are sensitive in short vegetation (SV), dwarf trees and shrubs (DTS), and agriculture and grassland (AG). The two sensitivity analysis methods suggested a strong SA coherence; in contrast, different model performance metrics led to different SA results. This misfit suggests that more accurate values of sensitive parameters, specifically, species specific and seasonal variable parameters, are required to improve the performance of the FvCB model.

Forest Management Model based on Photosynthesis Model and Market Simulation

In order to mitigate the impact of climate change, it is necessary to make rational use of forest resources for carbon cycle. Firstly, based on the actual data of tomato single leaf light and rate, this paper establishes rectangular hyperbolic model, exponential model and non-rectangular hyperbolic model, and photosynthesis model based on light and effective radiation, temperature and carbon dioxide concentration of three environmental factors. Finally, it is analyzed through simulation experiments, The improved rectangular hyperbolic model with the best simulation effect is determined. The model based on photosynthesis designed in this paper is helpful to the decision-making of forest management.

Regional‐Scale Wilting Point Estimation Using Satellite SIF, Radiative‐Transfer Inversion, and Soil‐Vegetation‐Atmosphere Transfer Simulation: A Grassland Study

AbstractAlthough water availability strongly controls gross primary production (GPP), the impact of soil moisture content (SMC) (wilting point) is poorly quantified on regional and global scales. In this study, we used 10 years of observations of solar‐induced chlorophyll fluorescence (SIF) from the Greenhouse gases Observing Satellite (GOSAT) satellite to estimate the wilting point of a semiarid grassland on the Mongolian Plateau. Radiative‐transfer model inversion and soil‐vegetation‐atmosphere transfer simulation were sequentially conducted to distinguish the drought impacts on plant physiology from the changes in the leaf‐canopy optical properties. We modified an existing inversion algorithm and the widely used Soil‐Canopy Observation of Photosynthesis and Energy fluxes model to adequately evaluate dryland features, for example, sparse canopy and strong convection. The modified model, with retrieved parameters and calibration to GOSAT SIF, predicted realistic GPP values. We found that (a) the SIF yield estimated from GOSAT showed a clear sigmoidal pattern in relation to drought, and the estimated wilting point matched ground‐based observations in the literature within ∼0.01 m3 m−3 for the SMC, (b) tuning the maximum carboxylation rate improved the SIF prediction after considering the changes in the leaf‐canopy optical properties, implying that GOSAT detected drought stress in leaf‐level photosynthesis, and (c) the surface energy balance significantly impacted the grassland's SIF; the modified model reproduced observed SIF well (mean bias = 0.004 mW m−2 nm−1 sr−1 in summer), whereas the original model predicted substantially low values under weak horizontal wind conditions. Some model‐observation mismatches in the SIF suggest that more research is needed for fluorescence parametrization (e.g., photoinhibition) and for additional observation constraints.

Global patterns and drivers of leaf photosynthetic capacity: The relative importance of environmental factors and evolutionary history

AbstractAimUnderstanding the considerable variability and drivers of global leaf photosynthetic capacity [indicated by the maximum carboxylation rate standardized to 25°C (Vc,max25)] is an essential step for accurate modelling of terrestrial plant photosynthesis and carbon uptake under climate change. Although current environmental conditions have often been connected with empirical and theoretical models to explain global Vc,max25 variability through acclimatization and adaptation, long‐term evolutionary history has largely been neglected, but might also explicitly play a role in shaping the Vc,max25 variability.LocationGlobal.Time periodContemporary.Major taxa studiedTerrestrial plants.MethodsWe compiled a geographically comprehensive global dataset of Vc,max25 for C3 plants (n = 6917 observations from 2157 species and 425 sites covering all major biomes world‐wide), explored the biogeographical and phylogenetic patterns of Vc,max25, and quantified the relative importance of current environmental factors and evolutionary history in driving global Vc,max25 variability.ResultsWe found that Vc,max25 differed across different biomes, with higher mean values in relatively drier regions, and across different life‐forms, with higher mean values in non‐woody relative to woody plants and in legumes relative to non‐leguminous plants. The values of Vc,max25 displayed a significant phylogenetic signal and diverged in a contrasting manner across phylogenetic groups, with a significant trend along the evolutionary axis towards a higher Vc,max25 in more modern clades. A Bayesian phylogenetic linear mixed model revealed that evolutionary history (indicated by phylogeny and species) explained nearly 3‐fold more of the variation in global Vc,max25 than present‐day environment (53 vs. 18%).Main conclusionsThese findings contribute to a comprehensive assessment of the patterns and drivers of global Vc,max25 variability, highlighting the importance of evolutionary history in driving global Vc,max25 variability, hence terrestrial plant photosynthesis.

Very high-resolution Net Ecosystem Exchange over India using Vegetation Photosynthesis and Respiration Model (VPRM) simulations

The terrestrial biosphere has a crucial role in controlling the rate of CO2 accumulation in the atmosphere. The quantification of Net Ecosystem Exchange (NEE) is critical to determine whether the regional terrestrial ecosystem is a net sink or source of CO2. This study uses a satellite-data derived light-use-efficiency model (the Vegetation Photosynthesis and Respiration Model, VPRM) to compute the biospheric CO2 fluxes over India from 2011–2020. A very high-resolution Land Use-Land Cover (LULC) data from the IRS-P6 satellite and MODIS derived surface reflectance are utilised to compute the NEE over distinct vegetation types at ∼9km× 9 km. The VPRM model for the Indian region captures the spatial pattern and seasonal features of NEE over the country. The magnitude of annual mean NEE over India from the VPRM model is relatively high compared to that from Carbon Tracker, FLUXCOM, and Soil Moisture Active Passive Level-4 carbon data products. However, the VPRM model simulated NEE agrees with the NEE observations from the eddy covariance estimates, which are accepted as the ground truth. The fusion of high-resolution LULC and surface reflectance data in VPRM suggests that the average NEE over the Indian region during 2011–2020 is −0.16 ± 0.02 PgC yr−1. Among this, the contribution from Gross Ecosystem Exchange is about −0.47 ± 0.02 PgC yr−1, and Respiration is 0.31 ± 0.01 PgC yr−1. The maximum biospheric CO2 absorption is during the post-monsoon season, and the minimum is during the pre-monsoon season. The very high spatial resolution and heterogeneity resolved in the NEE data derived in this work offer the first data product of this kind for a variety of NEE analyses over India.

Terrestrial CO2 exchange diagnosis using a peatland-optimized vegetation photosynthesis and respiration model (VPRM) for the Hudson Bay Lowlands

Satellite-based light use efficiency (LUE) models have been widely used to estimate gross primary production in various terrestrial ecosystems such as forests and croplands, but northern peatlands have received less attention. In particular, the Hudson Bay Lowlands (HBL) which is a massive peatland-rich region in Canada has been largely ignored in previous LUE-based studies. These peatland ecosystems have accumulated large stocks of organic carbon over many millennia, and play a vital role in the global carbon cycle. In this study, we used the satellite data-driven Vegetation Photosynthesis and Respiration Model (VPRM) to examine the suitability of LUE models for carbon flux diagnosis in the HBL. VPRM was driven alternately with the satellite-derived enhanced vegetation index (EVI) and solar-induced chlorophyll fluorescence (SIF). The model parameter values were constrained by eddy covariance (EC) tower observations from the Churchill fen and Attawapiskat River bog sites. The main objectives of the study were to (i) investigate if site-specific parameter optimization improved NEE estimates, (ii) determine which satellite-based proxy of photosynthesis produced more reliable estimates of peatland net carbon exchange, and (iii) examine how LUE and other model parameters vary within and between the study sites. The results indicate that the VPRM mean diurnal and monthly estimates of NEE had significant strong agreements with EC tower fluxes at the two study sites. A comparison of the site-optimized VPRM against a generic peatland-optimized version of the model revealed that the site-optimized VPRM provided better estimates of NEE only during the calibration period at the Churchill fen. The diurnal and seasonal cycles of peatland carbon exchange were better captured by the SIF-driven VPRM, demonstrating that SIF is a more accurate proxy for photosynthesis compared to EVI. Our study suggests that satellite-based LUE models have the potential to be applied on a larger scale to the HBL region.

Disentangling the Heterosis in Biomass Production and Radiation Use Efficiency in Maize: A Phytomer-Based 3D Modelling Approach

Maize (Zea mays L.) benefits from heterosis in-yield formation and photosynthetic efficiency through optimizing canopy structure and improving leaf photosynthesis. However, the role of canopy structure and photosynthetic capacity in determining heterosis in biomass production and radiation use efficiency has not been separately clarified. We developed a quantitative framework based on a phytomer-based three-dimensional canopy photosynthesis model and simulated light capture and canopy photosynthetic production in scenarios with and without heterosis in either canopy structure or leaf photosynthetic capacity. The accumulated above-ground biomass of Jingnongke728 was 39% and 31% higher than its male parent, Jing2416, and female parent, JingMC01, while accumulated photosynthetically active radiation was 23% and 14% higher, correspondingly, leading to an increase of 13% and 17% in radiation use efficiency. The increasing post-silking radiation use efficiency was mainly attributed to leaf photosynthetic improvement, while the dominant contributing factor differs for male and female parents for heterosis in post-silking yield formation. This quantitative framework illustrates the potential to identify the key traits related to yield and radiation use efficiency and helps breeders to make selections for higher yield and photosynthetic efficiency.

An exploratory steady-state redox model of photosynthetic linear electron transport for use in complete modelling of photosynthesis for broad applications.

A photochemical model of photosynthetic electron transport (PET) is needed to integrate photophysics, photochemistry, and biochemistry to determine redox conditions of electron carriers and enzymes for plant stress assessment and mechanistically link sun-induced chlorophyll fluorescence to carbon assimilation for remotely sensing photosynthesis. Towards this goal, we derived photochemical equations governing the states and redox reactions of complexes and electron carriers along the PET chain. These equations allow the redox conditions of the mobile plastoquinone pool and the cytochrome b6 f complex (Cyt) to be inferred with typical fluorometry. The equations agreed well with fluorometry measurements from diverse C3 /C4 species across environments in the relationship between the PET rate and fraction of open photosystem II reaction centres. We found the oxidation of plastoquinol by Cyt is the bottleneck of PET, and genetically improving the oxidation of plastoquinol by Cyt may enhance the efficiency of PET and photosynthesis across species. Redox reactions and photochemical and biochemical interactions are highly redundant in their complex controls of PET. Although individual reaction rate constants cannot be resolved, they appear in parameter groups which can be collectively inferred with fluorometry measurements for broad applications. The new photochemical model developed enables advances in different fronts of photosynthesis research.

Estimating the gross primary productivity based on VPM correction model for Xishuangbanna tropical seasonal rainforest

ABSTRACT Due to the unique climatic characteristics and vegetation features of tropical regions, the correlation R2 of gross primary productivity (GPP) estimation in tropical regions using remote sensing models was generally lower than 0.3. Therefore, for the cloudy and rainy tropical regions, the influence brought by clouds on remote sensing images needed to be considered in GPP estimation. This paper developed a corrected vegetation photosynthesis model (VPM) for estimating GPP under cloudy conditions. It mainly corrected the two parameters, W scalar and Enhanced Vegetation Index (EVI), which were obtained from remote sensing images and were therefore greatly influenced by clouds in the model. First, the water stress factor W scalar was replaced by Evaporation Fraction (EF). Secondly, using the good correlation between near surface temperature and EVI, the conversion coefficient between near surface temperature and EVI was fitted to achieve the effective reconstruction of EVI contaminated by clouds. The correction of the two factors improved the estimation accuracy of the VPM model, and the comparison with the observed values of the GPP site in 4 years showed that the correction of EVI had a better improvement, with an increase of 0.22 in R2 compared with the pre-correction, and the correction of W scalar was increased by 0.11 in R2. To verify the proposed method, the in-situ observation data of Xishuangbanna flux site from 2007 to 2010 were used. The results showed that the proposed method effectively improved the accuracy of GPP estimation by VPM model, especially in 2007 it was strongly influenced by clouds, and the improvement was significant, with R2 increasing from 0.2 to 0.82. In general, the accuracy of GPP estimation by the proposed method had been significantly improved, with RMSE (gC·m−2·8 day−1) decreasing from 15,14.4, 18.1, 14.2 to 8.07, 6.56, 10.33, 11.44, respectively. Therefore, the proposed method can be used to estimate the GPP for tropical seasonal rain forests in Xishuangbanna.