Analysis of a Farquhar-von Caemmerer-Berry leaf-level photosynthetic rate model for Populus tremuloides in the context of modeling and measurement limitations

A photosynthetic rate prediction model using improved RBF neural network

A photosynthetic prediction rate model is a theoretical basis for light environmental regulation, and the existing photosynthetic rate prediction models are limited by low modeling speed and prediction accuracy. Therefore, this paper analyses effects of light quality on photosynthesis rate, and proposes a method based on Radial basis function (RBF) optimized by Quantum genetic algorithm (QGA) to establish photosynthetic rate prediction model. We selected "golden embryo² formula 98-1F1" cucumber seedlings as experimental material and used LI-6800 to record the photosynthetic rates under different temperatures, light intensities and light quality. Experimental data is used to train and test the proposed model. The determinant coefficient of the model between the predicted and the measured values is 0.996, the straight slope of linear fitting is 1.000, and the straight intercept of linear fitting is 0.061. Moreover, the proposed method is compared with 6 artificial intelligence algorithms. The comparison results also validate that the proposed model has the highest accuracy compared with other algorithms.

Leaf temperatures mediate alpine plant communities' response to a simulated extended summer

We use a quantitative model of photosynthesis to explore leaf-level limitations to plant growth in an alpine tundra ecosystem that is expected to have longer, warmer, and drier growing seasons. The model is parameterized with abiotic and leaf trait data that is characteristic of two dominant plant communities in the alpine tundra and specifically at the Niwot Ridge Long Term Ecological Research Site: the dry and wet meadows. Model results produce realistic estimates of photosynthesis, nitrogen-use efficiency, water-use efficiency, and other gas exchange processes in the alpine tundra. Model simulations suggest that dry and wet meadow plant species do not significantly respond to changes in the volumetric soil moisture content but are sensitive to variation in foliar nitrogen content. In addition, model simulations indicate that dry and wet meadow species have different maximum rates of assimilation (normalized for leaf nitrogen content) because of differences in leaf temperature. These differences arise from the interaction of plant height and the abiotic environment characteristic of each plant community. The leaf temperature of dry meadow species is higher than wet meadow species and close to the optimal temperature for photosynthesis under current conditions. As a result, 2°C higher air temperatures in the future will likely lead to declines in dry meadow species' carbon assimilation. On the other hand, a longer and warmer growing season could increase nitrogen availability and assimilation rates in both plant communities. Nonetheless, a temperature increase of 4°C may lower rates of assimilation in both dry and wet meadow plant communities because of higher, and suboptimal, leaf temperatures.

SCOPE model applied for rapeseed in Spain

The integrated SCOPE (Soil, Canopy Observation, Photochemistry and Energy balance) model, coupling radiative transfer theory and biochemistry, was applied to a biodiesel crop grown in a Spanish agricultural area. Energy fluxes and CO₂ exchange were simulated with this model for the period spanning January 2008 to October 2008. Results were compared to experimental measurements performed using eddy covariance and meteorological instrumentation. The reliability of the model was proven by simulating latent (LE) and sensible (H) heat fluxes, soil heat flux (G), and CO₂ exchanges (NEE and GPP). LAI data used as input in the model were retrieved from the MODIS and MERIS sensors. SCOPE was able to reproduce similar seasonal trends to those measured for NEE, GPP and LE. When considering H, the modelled values were underestimated for the period covering July 2008 to mid-September 2008. The modelled fluxes reproduced the observed seasonal evolution with determination coefficients of over 0.77 when LE and H were evaluated. The modelled results offered good agreement with observed data for NEE and GPP, regardless of whether LAI data belonged to MODIS or MERIS, showing slopes of 0.87 and 0.91 for NEE-MODIS and NEE-MERIS, and 0.91 and 0.94 for GPP-MODIS and GPP-MERIS, respectively. Moreover, SCOPE was able to reproduce similar seasonal behaviours to those observed for the experimental carbon fluxes, clearly showing the CO₂ sink/source behaviour for the whole period studied.

Responses to atmospheric CO2 concentrations in crop simulation models: a review of current simple and semicomplex representations and options for model development

Elevated atmospheric CO₂ concentrations ([CO₂ ]) cause direct changes in crop physiological processes (e.g. photosynthesis and stomatal conductance). To represent these CO₂ responses, commonly used crop simulation models have been amended, using simple and semicomplex representations of the processes involved. Yet, there is no standard approach to and often poor documentation of these developments. This study used a bottom-up approach (starting with the APSIM framework as case study) to evaluate modelled responses in a consortium of commonly used crop models and illuminate whether variation in responses reflects true uncertainty in our understanding compared to arbitrary choices of model developers. Diversity in simulated CO₂ responses and limited validation were common among models, both within the APSIM framework and more generally. Whereas production responses show some consistency up to moderately high [CO₂ ] (around 700 ppm), transpiration and stomatal responses vary more widely in nature and magnitude (e.g. a decrease in stomatal conductance varying between 35% and 90% among models was found for [CO₂ ] doubling to 700 ppm). Most notably, nitrogen responses were found to be included in few crop models despite being commonly observed and critical for the simulation of photosynthetic acclimation, crop nutritional quality and carbon allocation. We suggest harmonization and consideration of more mechanistic concepts in particular subroutines, for example, for the simulation of N dynamics, as a way to improve our predictive understanding of CO₂ responses and capture secondary processes. Intercomparison studies could assist in this aim, provided that they go beyond simple output comparison and explicitly identify the representations and assumptions that are causal for intermodel differences. Additionally, validation and proper documentation of the representation of CO₂ responses within models should be prioritized.

Modelling photosynthesis in flag leaves of winter wheat (Triticum aestivum) considering the variation in photosynthesis parameters during development

The Farquhar-von Caemmerer-Berry (FvCB) model of photosynthesis has been widely used to estimate the photosynthetic C flux of plants under different growth conditions. However, the seasonal fluctuation of some photosynthesis parameters (e.g. the maximum carboxylation rate of Rubisco (Vcmax), the maximum electron transport rate (Jmax) and internal mesophyll conductance to CO2 transport (gm)) is not considered in the FvCB model. In this study, we investigated the patterns of the FvCB parameters during flag leaf development based on measured photosynthesis-intercellular CO2 curves in two cultivars of winter wheat (Triticum aestivum L.). Parameterised seasonal patterns of photosynthesis parameters in the FvCB model have subsequently been applied in order to predict the photosynthesis of flag leaves. The results indicate that the Gaussian curve characterises the dynamic patterns of Vcmax, Jmax and gm well. Compared with the model with fixed photosynthesis parameter values, updating the FvCB model by considering seasonal changes in Vcmax and Jmax during flag leaf development slightly improved predictions of photosynthesis. However, if the updated FvCB model incorporated the seasonal patterns of Vcmax and Jmax, and also of gm, predictions of photosynthesis was improved a lot, matching well with the measurements (R2=0.87, P<0.0001). This suggests that the dynamics of photosynthesis parameters, particularly gm, play an important role in estimating the photosynthesis rate of winter wheat.

Developmental changes in mesophyll diffusion conductance and photosynthetic capacity under different light and water availabilities in Populus tremula: how structure constrains function

Finite mesophyll diffusion conductance (g(m) ) significantly constrains net assimilation rate (A(n) ), but g(m) variations and variation sources in response to environmental stresses during leaf development are imperfectly known. The combined effects of light and water limitations on g(m) and diffusion limitations of photosynthesis were studied in saplings of Populus tremula L. An one-dimensional diffusion model was used to gain insight into the importance of key anatomical traits in determining g(m) . Leaf development was associated with increases in dry mass per unit area, thickness, density, exposed mesophyll (S(mes) /S) and chloroplast (S(c) /S) to leaf area ratio, internal air space (f(ias) ), cell wall thickness and chloroplast dimensions. Development of S(mes) /S and S(c) /S was delayed under low light. Reduction in light availability was associated with lower S(c) /S, but with larger f(ias) and chloroplast thickness. Water stress reduced S(c) /S and increased cell wall thickness under high light. In all treatments, g(m) and A(n) increased and CO(2) drawdown because of g(m) , C(i) -C(c) , decreased with increasing leaf age. Low light and drought resulted in reduced g(m) and A(n) and increased C(i) -C(c) . These results emphasize the importance of g(m) and its components in determining A(n) variations during leaf development and in response to stress.