Photosynthesis Model Research Articles

Identification of photosynthetic parameters for superior yield of two super hybrid rice varieties: A cross-scale study from leaf to canopy.

Enhancing photosynthetic capacity is widely accepted as critical to advancing crop yield. Therefore, identifying photosynthetic parameters positively related to biomass accumulation in elite cultivars is the major focus of current rice research. In this work, we assessed leaf photosynthetic performance, canopy photosynthesis, and yield attributes of super hybrid rice cultivars Y-liangyou 3218 (YLY3218) and Y-liangyou 5867 (YLY5867) at tillering stage and flowering stage, using inbred super rice cultivars Zhendao11(ZD11) and Nanjing 9108 (NJ9108) as control. A diurnal canopy photosynthesis model was applied to estimate the influence of key environmental factors, canopy attributes, and canopy nitrogen status on daily aboveground biomass increment (AMDAY). Results showed that primarily the light-saturated photosynthetic rate at tillering stage contributed to the advancing yield and biomass of super hybrid rice in comparison to inbred super rice, and the light-saturated photosynthetic rate between them was similar at flowering stage. At tillering stage, the higher CO2 diffusion capacity, together with higher biochemical capacity (i.e., maximum carboxylation rate of Rubisco, maximum electron transport rate (J max), and triose phosphate utilization rate) favored leaf photosynthesis of super hybrid rice. Similarly, AMDAY in super hybrid rice was higher than inbred super rice at tillering stage, and comparable at flowering stage partially due to increased canopy nitrogen concentration (SLNave) of inbred super rice. At tillering stage, model simulation revealed that replacement of J max and g m in inbred super rice by super hybrid rice always had a positive effect on AMDAY, and the averaged AMDAY increment was 5.7% and 3.4%, respectively. Simultaneously, the 20% enhancement of total canopy nitrogen concentration through the improvement of SLNave (TNC-SLNave) resulted in the highest AMDAY across cultivars, with an average increase of 11.2%. In conclusion, the advancing yield performance of YLY3218 and YLY5867 was due to the higher J max and g m at tillering stage, and TCN-SLNave is a promising target for future super rice breeding programs.

Short-term effects of solar storms in phytoplankton photosynthesis

The potential short-term influence of a solar radiation storm on microalgal photosynthesis is investigated. We focus on muons from the secondary cosmic rays at sea level, given their high penetrating power. According to NOAA’s classification of solar radiation storms, two kinds of solar storms are considered: moderate/strong and extreme. An exponential decay of muon fluxes down the water column and a direct proportionality between their penetrating power and energy are assumed. This allows obtaining a function of ionizing radiation to be embedded in a physical-mathematical model for photosynthesis previously modified by some of us to include particulate ionizing radiation. It is shown that moderate/strong solar radiation storms can cause a short-term depletion of photosynthesis of up to 61%, while this figure scales to 75% for extreme storms.

Net Primary Productivity Estimation Using a Modified MOD17A3 Model in the Three-River Headwaters Region

Remote sensing (RS) models can easily estimate the net primary productivity (NPP) on a large scale. The majority of RS models try to couple the effects of temperature, water, stand age, and CO2 concentration to attenuate the maximum light use efficiency (LUE) in the NPP models. The water effect is considered the most unpredictable, significant, and challenging. Because the stomata of alpine plants are less sensitive to limiting water vapor loss, the typically employed atmospheric moisture deficit or canopy water content may be less sensitive in signaling water stress on plant photosynthesis. This study introduces a soil moisture (SM) content index and an alpine vegetation photosynthesis model (AVPM) to quantify the RS NPP for the alpine ecosystem over the Three-River Headwaters (TRH) region. The SM content index was based on the minimum relative humidity and maximum vapor pressure deficit during the noon, and the AVPM model was based on the framework of a moderate resolution imaging spectroradiometer NPP (MOD17) model. A case study was conducted in the TRH region, covering an area of approximately 36.3 × 104 km2. The results demonstrated that the AVPM NPP greatly outperformed the MOD17 and had superior accuracy. Compared with the MOD17, the average bias of the AVPM was −9.8 gCm−2yr−1, which was reduced by 91.8%. The average mean absolute percent error was 57.0%, which was reduced by 68.2%. The average Pearson’s correlation coefficient was 0.4809, which was improved by 30.0%. The improvements in the NPP estimation were mainly attributed to the decreasing estimation of the water stress coefficient on the NPP, which was considered the higher constraint of water impact on plant photosynthesis. Therefore, the AVPM model is more accurate in estimating the NPP for the alpine ecosystem. This is of great significance for accurately assessing the vegetation growth of alpine ecosystems across the entire Qinghai–Tibet Plateau in the context of grassland degradation and black soil beach management.

Energy Transfer between CdZnS Quantum Dots and Perylene Diimide Dyes

QD–dye systems are promising models for artificial photosynthesis and down-converter systems for LEDs and displays. To better understand the factors controlling energy transfer from the QDs to the dyes, we fabricated a series of CdxZn1–xS/ZnS quantum dot (QD)-perylene diimide (PDI) composite nanocrystals. The core–shell CdxZn1–xS/ZnS QDs were chosen for better control of surface chemistry and for the control of photophysical properties through core composition. The PDIs were designed with bulky substituents to reduce dye–dye aggregation effects. Energy transfer efficiency was found to depend upon both the length of the anchoring alkyl chain and the type of the terminal anchoring group. The maximum energy transfer efficiency of 91% from QDs to PDIs was achieved with composites containing PDIs with carboxylic acid anchoring groups and longer alkyl chains. It was found that composites with carboxylic acid anchors exhibited greater photostability than composites with amine anchors. Longer alkyl chains also led to greater photostability. Conversely, shorter chain alkanes promoted faster aggregation of the nanocrystal composites.

Do Tree Size and Tree Shade Tolerance Affect the Photosynthetic Capacity of Broad-Leaved Tree Species?

(1) Background: leaf structure traits are closely related to leaf photosynthesis, reflecting the ability of trees to obtain external resources in the process of growth. (2) Methods: We studied the morphological, chemical, anatomical, stomatal traits and maximum net photosynthetic rate of six broad-leaf species in northern temperate mixed broad-leaved Korean pine (Pinus koraiensis) forest. (3) Aim: To investigate whether there are differences in leaf structural traits of trees with different shade tolerances and different sizes and the effects of these differences on leaf photosynthetic capacity. (4) Results: the effects of leaf structure traits on leaf photosynthesis were different among trees with different shade tolerances or different sizes. Under the condition of light saturation, the net photosynthetic rate, nitrogen use efficiency, phosphorus use efficiency and stomatal conductance of shade-intolerant trees or small trees were higher than those of shade-tolerant trees or large trees. (5) Conclusions: the shade tolerance of tree species or the size of trees affect the traits of leaf structure and indirectly affect the photosynthetic ability of plants. When constructing the leaf trait-photosynthesis model, the shade tolerance and tree size of tree species should be taken into account.

Accelerated photosynthesis routine in LPJmL4

Abstract. The increasing impacts of climate change require strategies for climate adaptation. Dynamic global vegetation models (DGVMs) are one type of multi-sectorial impact model with which the effects of multiple interacting processes in the terrestrial biosphere under climate change can be studied. The complexity of DGVMs is increasing as more and more processes, especially for plant physiology, are implemented. Therefore, there is a growing demand for increasing the computational performance of the underlying algorithms as well as ensuring their numerical accuracy. One way to approach this issue is to analyse the routines which have the potential for improved computational efficiency and/or increased accuracy when applying sophisticated mathematical methods. In this paper, the Farquhar–Collatz photosynthesis model under water stress as implemented in the Lund–Potsdam–Jena managed Land DGVM (4.0.002) was examined. We additionally tested the uncertainty of most important parameter of photosynthesis as an additional approach to improve model quality. We found that the numerical solution of a nonlinear equation, so far solved with the bisection method, could be significantly improved by using Newton's method instead. The latter requires the computation of the derivative of the underlying function which is presented. Model simulations show a significantly lower number of iterations to solve the equation numerically and an overall run time reduction of the model of about 16 % depending on the chosen accuracy. Increasing the parameters θ and αC3 by 10 %, respectively, while keeping all other parameters at their original value, increased global gross primary production (GPP) by 2.384 and 9.542 GtC yr−1, respectively. The Farquhar–Collatz photosynthesis model forms the core component in many DGVMs and land surface models. An update in the numerical solution of the nonlinear equation in connection with adjusting globally important parameters to best known values can therefore be applied to similar photosynthesis models. Furthermore, this exercise can serve as an example for improving computationally costly routines while improving their mathematical accuracy.

Quantifying Contributions of Different Factors to Canopy Photosynthesis in 2 Maize Varieties: Development of a Novel 3D Canopy Modeling Pipeline.

Crop yield potential is intrinsically related to canopy photosynthesis; therefore, improving canopy photosynthetic efficiency is a major focus of current efforts to enhance crop yield. Canopy photosynthesis rate (Ac) is influenced by several factors, including plant architecture, leaf chlorophyll content, and leaf photosynthetic properties, which interact with each other. Identifying factors that restrict canopy photosynthesis and target adjustments to improve canopy photosynthesis in a specific crop cultivar pose an important challenge for the breeding community. To address this challenge, we developed a novel pipeline that utilizes factorial analysis, canopy photosynthesis modeling, and phenomics data collected using a 64-camera multi-view stereo system, enabling the dissection of the contributions of different factors to differences in canopy photosynthesis between maize cultivars. We applied this method to 2 maize varieties, W64A and A619, and found that leaf photosynthetic efficiency is the primary determinant (17.5% to 29.2%) of the difference in Ac between 2 maize varieties at all stages, and plant architecture at early stages also contribute to the difference in Ac (5.3% to 6.7%). Additionally, the contributions of each leaf photosynthetic parameter and plant architectural trait were dissected. We also found that the leaf photosynthetic parameters were linearly correlated with Ac and plant architecture traits were non-linearly related to Ac. This study developed a novel pipeline that provides a method for dissecting the relationship among individual phenotypes controlling the complex trait of canopy photosynthesis.

Evaluating biochemical and biophysical characteristics of tropical deciduous forests of eastern India using remote sensing and in-situ parameter estimation

Owing to such a crucial role that forests perform in an ecosystem, studying the vegetation dynamics in terms of its productivity becomes inevitable. The study aimed to assess forest productivity and biochemical variability of Dalma Wildlife Sanctuary (DWS), Eastern India coupling remote sensing and in-situ measurements of 51 samples comprising 17 different species. Vegetative Photosynthesis Model (VPM) was incorporated for measuring the gross primary productivity (GPP) using Sentinel-2A data. The result showed that microclimatic conditions that determine the nutrient content of the plant, as some plant species(Shorea robusta) present at different locations showed different concentrations of chlorophyll(25.4 mgg-1), N(2.2%), P(0.5%), and K(2.0%). The relationship between biophysical and biochemical properties of the forest was analyzed by coupling laboratory-based leaf samples analysis and satellite-based retrieval methods. High values of GPP was present in the dense forested (core) region dominated by species like Shorea robusta, Bauhinia tomentosa, Butea monosperma, Syzygium cumini, Cleistanthus collinus, Diospyros melanoxylon, etc. as compared to buffer regions. The central region of DWS dominated by dense vegetation contributed higher GPP value of 21 gCm-2day-1 to 24 gCm-2day-1. The degraded peripheral area exhibited GPP value of 8 gCm-2day-1. Shorea robusta being the most dominant species in this region, exhibited total chlorophyll of 25.4 mgg-1 (lab estimates) with a standard error of 2.0 mgg-1. Correlation analysis between Sentinel-2 satellite-based biophysical parameters and laboratory-based biochemical parameters showed a close relationship between the variables. The correlation between GPP and FAPAR was found to be more profound (R2 = 0.625), whereas GPP and LAI showed a moderate correlation of R2 = 0.58. Nitrogen and Phosphorous content in leaf samples showed a negative relationship (R2 = 0.414) indicating their individual concentration do not actuate to the coefficient of correlation. With rational limitations, the study finds wide application for studying carbon cycle and biochemical assessment in the region with remote sensing driven techniques backed by in-situ parameter estimation.

Differences in diffuse photosynthetically active radiation effects on cropland light use efficiency calculated via contemporary remote sensing and crop production models

Diffuse photosynthetically active radiation (PARdiff) is instrumental to the light use efficiency (LUE) of vegetation. Accurately assessing the impact of PARdiff on crop LUE can better our understanding of the carbon cycle in cropland ecosystems. LUE estimates from six remote sensing models (including four big-leaf models and two two-leaf models) and two crop production models were compared with measured FLUXNET LUE data from cropland sites under different PARdiff fraction (FDIFFPAR) intervals. Compared with the FLUXNET observations, the Eddy Covariance-Light Use Efficiency (EC-LUEa) model exhibited the best LUE estimation (R2 = 0.250, RMSE = 0.868 gC·MJ−1, and Bias = −0.005 gC·MJ−1) owing to the use of more accurate calculation scheme of environmental stress factors. LUEs calculated from FLUXNET observational data were positively correlated with FDIFFPAR, but only LUEs simulated by the Moderate Resolution Imaging Spectroradiometer Photosynthesis (MOD17) and Two-Leaf Light Use Efficiency (TL-LUE) models increased with increasing FDIFFPAR. This is attributed to the fact that the MOD17 model divides the crop growth types into cereal and broadleaf, while the TL-LUE model considers the change of light interception with increased FDIFFPAR. Furthermore, the maximum LUE (LUEmax) increased with FDIFFPAR at FLUXNET observational sites, but the eight models could not capture the effects of PARdiff on the crop LUEmax. Among the eight models, the LUEmax–FDIFFPAR relationship simulated by the two-leaf models fluctuated because the crops were divided into sunlit and shaded leaves, while the big-leaf and crop production models used a constant LUEmax and showed a constant LUEmax–FDIFFPAR relationship. Additionally, big-leaf models performed better than two-leaf models for gross primary production (GPP) simulation in the cropland ecosystem, which is related to the planting density and vegetation structure. These results demonstrate the importance of considering the impact of FDIFFPAR on LUEmax in LUE modeling.

Simulated Analysis of Influence of Changes in H+-ATPase Activity and Membrane CO2 Conductance on Parameters of Photosynthetic Assimilation in Leaves.

Photosynthesis is an important process in plants which influences their development and productivity. Many factors can control the efficiency of photosynthesis, including CO2 conductance of leaf mesophyll, which affects the CO2 availability for Rubisco. It is known that electrical stress signals can decrease this conductance, and the response is probably caused by inactivation of H+-ATPase in the plasma membrane. In the current work, we analyzed the influence of both CO2 conductance in the plasma membrane, and chloroplast envelopes and H+-ATPase activity on photosynthetic CO2 assimilation, using a two-dimensional mathematical model of photosynthesis in leaves. The model included a description of assimilation on the basis of the Farquhar-von Caemmerer-Berry model, ion transport through the plasma membrane, diffusion of CO2 in the apoplast, and transport of CO2 through the plasma membrane and chloroplast envelope. The model showed that the photosynthetic CO2 assimilation rate was mainly dependent on the plasma membrane and chloroplast envelope conductance; direct influence of the H+-ATPase activity (through changes in pH and CO2/HCO3- concentration ratio) on this rate was weak. In contrast, both changes in CO2 conductance of the plasma membrane and chloroplast envelopes and changes in the H+-ATPase activity influenced spatial heterogeneity of the CO2 assimilation on the leaf surface in the simulated two-dimensional system. These effects were also observed under simultaneous changes in the CO2 conductance of the plasma membrane and H+-ATPase activity. Qualitatively similar influence of changes in the CO2 conductance of the plasma membrane and chloroplast envelopes, and changes in the H+-ATPase activity on photosynthesis were shown for two different densities of stomata in the simulated leaf; however, lowering the density of stomata decreased the assimilation rate and increased the heterogeneity of assimilation. The results of the model analysis clarify the potential influence of H+-ATPase inactivation on photosynthesis, and can be the basis for development of new methods for remote sensing of the influence of electrical signals.

Application of feedback control to stomatal optimisation in a global land surface model

Accurate representations of stomatal conductance are required to predict the effects of climate change on terrestrial ecosystems. Stomatal optimisation theory, the idea that plants have evolved to maximise carbon gain under certain constraints, such as minimising water loss or preventing hydraulic damage, is a powerful approach to representing stomatal behaviour that bypasses the need to represent complex physiological processes. However, while their ability to replicate observed stomatal responses is promising, optimisation models often present practical problems for those trying to simulate the land surface. In particular, when realistic models of photosynthesis and more complex cost functions are used, closed-form solutions for the optimal stomatal conductance are often very difficult to find. As a result, implementing stomatal optimisation in land surface models currently relies either on simplifying approximations, that allow closed-form solutions to be found, or on numerical iteration which can be computationally expensive. Here we propose an alternative approach, using a method motivated by control theory that is computationally efficient and does not require simplifying approximations to be made to the underlying optimisation. Stomatal conductance is treated as the control variable in a simple closed-loop system and we use the Newton-Raphson method to track the time-varying maximum of the objective function. We compare the method to both numerical iteration and a semi-analytical approach by applying the methods to the SOX stomatal optimisation model at multiple sites across the Amazon rainforest. The feedback approach is able to more accurately replicate the results found by numerical iteration than the semi-analytical approach while maintaining improved computational efficiency.

Global modeling diurnal gross primary production from OCO-3 solar-induced chlorophyll fluorescence

Photosynthesis plays a crucial role in regulating the global carbon cycle and mitigating climate change. The diurnal variation in photosynthesis provides key information on the responses of ecosystems to environmental drivers, but there is a critical gap in the large-scale estimation of diurnal photosynthesis. In the last decade, satellite estimates of solar-induced chlorophyll fluorescence (SIF) have been found to mimic the seasonality of photosynthesis. Recently, the deployment of the Orbiting Carbon Observatory-3 (OCO-3) on the International Space Station has provided the opportunity to retrieve SIF at different times of the day. Here we utilized OCO-3 measurements to estimate and analyze diurnal cycles of SIF and gross primary production (GPP) at the global scale. We first mitigated the sun-sensor geometry effects on nadir-mode OCO-3 SIF (SIFnadir) at the sub-diurnal scale (hourly) by deriving the total canopy SIF emission (SIFtotal) using radiative transfer theory. Next, we generated the spatially and temporally continuous hourly SIFnadir and SIFtotal using artificial neural networks under clear-sky conditions, whose extrapolation ability was evaluated using the data from independent years. Compared with SIFnadir, the diurnal relationship between clear-sky SIFtotal and GPP from 38 homogeneous flux sites had smaller variations in the slope (the coefficient of variation was 0.07 vs 0.19). In addition, a correction to account for the bias between clear-sky and overcast conditions was used to estimate all-sky GPP from clear-sky SIFtotal and the resulting GPP was strongly correlated with tower GPP (R2 = 0.75; RMSE = 3.53 μmol/m2/s). Our results demonstrated that the new OCO-3 SIF trained GPP product (GPPSIF) was able to depict the diurnal pattern of photosynthesis globally, capturing also the physiologically hysteresis or afternoon depression of photosynthesis. By doing so, hourly GPPSIF has the potential to improve the modeling of terrestrial photosynthesis and the projection of the global carbon cycle under climate change.

Not All Light Spectra Were Created Equal: Can We Harvest Light for Optimum Food‐Energy Co‐Generation?

AbstractHumanity's growing appetites for food and energy are placing unprecedented yield targets on our lands. Chasing those ever‐expanding land intensification targets gave rise to monocultures and sharpened the divide between food and energy production groups. Here, we argue that this does not have to be a zero‐sum game if food and energy can be co‐generated in the same land. Co‐generation can lead to sustainable intensification but requires a paradigm shift in the way we manage our resources, particularly light. Using an extended model of plant photosynthesis and transpiration, we demonstrate how plants react to different incident light spectra and show that manipulating light could be effective for boosting land and water efficiencies, thus potentially improving soil health. This knowledge can possibly unlock the real potential of promising modern agricultural technologies that target optimization of light allocations such as agrivoltaics. This study suggests that the blue part of the light spectrum is less efficient in terms of carbon assimilation and water use and could be more effectively used to produce solar energy, while the red part could efficiently produce biomass. A sensitivity analysis to the most important crop and environmental variables (irradiance, air temperature, humidity, and CO2 concentration) shows that plant response to different light treatments is sensitive to environmental boundary conditions and is species‐specific. Therefore, further research is necessary to assess which crops and climates are more suitable to optimize the proposed food‐water‐energy nexus.

Development of Two-Dimensional Model of Photosynthesis in Plant Leaves and Analysis of Induction of Spatial Heterogeneity of CO2 Assimilation Rate under Action of Excess Light and Drought.

Photosynthesis is a key process in plants that can be strongly affected by the actions of environmental stressors. The stressor-induced photosynthetic responses are based on numerous and interacted processes that can restrict their experimental investigation. The development of mathematical models of photosynthetic processes is an important way of investigating these responses. Our work was devoted to the development of a two-dimensional model of photosynthesis in plant leaves that was based on the Farquhar-von Caemmerer-Berry model of CO2 assimilation and descriptions of other processes including the stomatal and transmembrane CO2 fluxes, lateral CO2 and HCO3- fluxes, transmembrane and lateral transport of H+ and K+, interaction of these ions with buffers in the apoplast and cytoplasm, light-dependent regulation of H+-ATPase in the plasma membrane, etc. Verification of the model showed that the simulated light dependences of the CO2 assimilation rate were similar to the experimental ones and dependences of the CO2 assimilation rate of an average leaf CO2 conductance were also similar to the experimental dependences. An analysis of the model showed that a spatial heterogeneity of the CO2 assimilation rate on a leaf surface should be stimulated under an increase in light intensity and a decrease in the stomatal CO2 conductance or quantity of the open stomata; this prediction was supported by the experimental verification. Results of the work can be the basis of the development of new methods of the remote sensing of the influence of abiotic stressors (at least, excess light and drought) on plants.

Defining the scope for altering rice leaf anatomy to improve photosynthesis: a modelling approach.

Leaf structure plays an important role in photosynthesis. However, the causal relationship and the quantitative importance of any single structural parameter to the overall photosynthetic performance of a leaf remains open to debate. In this paper, we report on a mechanistic model, eLeaf, which successfully captures rice leaf photosynthetic performance under varying environmental conditions of light and CO2 . We developed a 3D reaction-diffusion model for leaf photosynthesis parameterised using a range of imaging data and biochemical measurements from plants grown under ambient and elevated CO2 and then interrogated the model to quantify the importance of these elements. The model successfully captured leaf-level photosynthetic performance in rice. Photosynthetic metabolism underpinned the majority of the increased carbon assimilation rate observed under elevated CO2 levels, with a range of structural elements making positive and negative contributions. Mesophyll porosity could be varied without any major outcome on photosynthetic performance, providing a theoretical underpinning for experimental data. eLeaf allows quantitative analysis of the influence of morphological and biochemical properties on leaf photosynthesis. The analysis highlights a degree of leaf structural plasticity with respect to photosynthesis of significance in the context of attempts to improve crop photosynthesis.

Environmental controls on the light use efficiency of terrestrial gross primary production.

Gross primary production (GPP) by terrestrial ecosystems is a key quantity in the global carbon cycle. The instantaneous controls of leaf-level photosynthesis are well established, but there is still no consensus on the mechanisms by which canopy-level GPP depends on spatial and temporal variation in the environment. The standard model of photosynthesis provides a robust mechanistic representation for C3 species; however, additional assumptions are required to "scale up" from leaf to canopy. As a consequence, competing models make inconsistent predictions about how GPP will respond to continuing environmental change. This problem is addressed here by means of an empirical analysis of the light use efficiency (LUE) of GPP inferred from eddy covariance carbon dioxide flux measurements, in situ measurements of photosynthetically active radiation (PAR), and remotely sensed estimates of the fraction of PAR (fAPAR) absorbed by the vegetation canopy. Focusing on LUE allows potential drivers of GPP to be separated from its overriding dependence on light. GPP data from over 100 sites, collated over 20 years and located in a range of biomes and climate zones, were extracted from the FLUXNET2015 database and combined with remotely sensed fAPAR data to estimate daily LUE. Daytime air temperature, vapor pressure deficit, diffuse fraction of solar radiation, and soil moisture were shown to be salient predictors of LUE in a generalized linear mixed-effects model. The same model design was fitted to site-based LUE estimates generated by 16 terrestrial ecosystem models. The published models showed wide variation in the shape, the strength, and even the sign of the environmental effects on modeled LUE. These findings highlight important model deficiencies and suggest a need to progress beyond simple "goodness of fit" comparisons of inferred and predicted carbon fluxes toward an approach focused on the functional responses of the underlying dependencies.

A Simple Model for Algal Photosynthesis for Better Light Utilization with Flashing Light

A Simple Model for Algal Photosynthesis for Better Light Utilization with Flashing Light

Expansion of Known Rhodobacter capsulatus Bacteriophage Diversity with 24 Additional Genomes.

We report the genome sequences of 24 newly discovered bacteriophages that infect Rhodobacter capsulatus, a model for photosynthesis and horizontal gene transfer studies. All have substantial relatedness to previously reported siphovirus bacteriophages. Most are categorized in known clusters (RcB, RcC, RcD, and RcF), with one forming a new cluster, RcG.

Hybrid AI model for estimating the canopy photosynthesis of eggplants.

Modern models for estimating canopy photosynthetic rates (Ac) can be broadly classified into two categories, namely, process-based mechanistic models and artificial intelligence (AI) models, each category having unique strengths (i.e., process-based models have generalizability to a wide range of situations, and AI models can reproduce a complex process using data without prior knowledge about the underlying mechanism). To exploit the strengths of both categories of models, a novel "hybrid" canopy photosynthesis model that combines process-based models with an AI model was proposed. In the proposed hybrid model, process-based models for single-leaf photosynthesis and image analysis first transform raw inputs (environmental data and canopy images) into the single-leaf photosynthetic rate (AL) and effective leaf area index (Lc)), after which AL and Lc are fed into an artificial neural network (ANN) model to predict Ac. The hybrid model successfully predicted the diurnal cycles of Ac of an eggplant canopy even with a small training dataset and successfully reproduced a typical Ac response to changes in the CO2 concentration outside the range of the training data. The proposed hybrid AI model can provide an effective means to estimate Ac in actual crop fields, where obtaining a large amount of training data is difficult.

Neglecting acclimation of photosynthesis under drought can cause significant errors in predicting leaf photosynthesis in wheat.

Extreme climatic events, such as heat waves, cold snaps and drought spells, related to global climate change, have become more frequent and intense in recent years. Acclimation of plant physiological processes to changes in environmental conditions is a key component of plant adaptation to climate change. We assessed the temperature response of leaf photosynthetic parameters in wheat grown under contrasting water regimes and growth temperatures (Tgrowth ). Two independent experiments were conducted under controlled conditions. In Experiment 1, two wheat genotypes were subjected to well-watered or drought-stressed treatments; in Experiment 2, the two water regimes combined with high, medium and low Tgrowth were imposed on one genotype. Parameters of a biochemical C3 -photosynthesis model were estimated at six leaf temperatures for each factor combination. Photosynthesis acclimated more to drought than to Tgrowth . Drought affected photosynthesis by lowering its optimum temperature (Topt ) and the values at Topt of light-saturated net photosynthesis, stomatal conductance, mesophyll conductance, the maximum rate of electron transport (Jmax ) and the maximum rate of carboxylation by Rubisco (Vcmax ). Topt for Vcmax was up to 40°C under well-watered conditions but 24-34°C under drought. The decrease in photosynthesis under drought varied among Tgrowth but was similar between genotypes. The temperature response of photosynthetic quantum yield under drought was partly attributed to photorespiration but more to alternative electron transport. All these changes in biochemical parameters could not be fully explained by the changed leaf nitrogen content. Further model analysis showed that both diffusional and biochemical parameters of photosynthesis and their thermal sensitivity acclimate little to Tgrowth , but acclimate considerably to drought and the combination of drought and Tgrowth . The commonly used modelling approaches, which typically consider the response of diffusional parameters, but ignore acclimation responses of biochemical parameters to drought and Tgrowth , strongly overestimate leaf photosynthesis under variable temperature and drought.