Biochemical model of C3 photosynthesis applied to wheat at different temperatures

Contrasting leaf-scale photosynthetic low-light response and its temperature dependency are key to differences in crop-scale radiation use efficiency

Radiation use efficiency (RUE) is a key crop adaptation trait that quantifies the potential amount of aboveground biomass produced by the crop per unit of solar energy intercepted. But it is unclear why elite maize and grain sorghum hybrids differ in their RUE at the crop level. Here, we used a non-traditional top-down approach via canopy photosynthesis modelling to identify leaf-level photosynthetic traits that are key to differences in crop-level RUE. A novel photosynthetic response measurement was developed and coupled with use of a Bayesian model fitting procedure, incorporating a C4 leaf photosynthesis model, to infer cohesive sets of photosynthetic parameters by simultaneously fitting responses to CO2 , light, and temperature. Statistically significant differences between leaf photosynthetic parameters of elite maize and grain sorghum hybrids were found across a range of leaf temperatures, in particular for effects on the quantum yield of photosynthesis, but also for the maximum enzymatic activity of Rubisco and PEPc. Simulation of diurnal canopy photosynthesis predicted that the leaf-level photosynthetic low-light response and its temperature dependency are key drivers of the performance of crop-level RUE, generating testable hypotheses for further physiological analysis and bioengineering applications.

Photosynthesis in newly developed leaves of heat-tolerant wheat acclimates to long-term nocturnal warming

We examined photosynthetic traits of pre-existing and newly developed flag leaves of four wheat genotypes grown in controlled-environment experiments. In newly developed leaves, acclimation of the maximum rate of net CO2 assimilation (An) to warm nights (i.e. increased An) was associated with increased capacity of Rubisco carboxylation and photosynthetic electron transport, with Rubisco activation state probably contributing to increased Rubisco activity. Metabolite profiling linked acclimation of An to greater accumulation of monosaccharides and saturated fatty acids in leaves; these changes suggest roles for osmotic adjustment of leaf turgor pressure and maintenance of cell membrane integrity. By contrast, where An decreased under warm nights, the decline was related to lower stomatal conductance and rates of photosynthetic electron transport. Decreases in An occurred despite higher basal PSII thermal stability in all genotypes exposed to warm nights: Tcrit of 45-46.5 °C in non-acclimated versus 43.8-45 °C in acclimated leaves. Pre-existing leaves showed no change in An-temperature response curves, except for an elite heat-tolerant genotype. These findings illustrate the impact of night-time warming on the ability of wheat plants to photosynthesize during the day, thereby contributing to explain the impact of global warming on crop productivity.

Modelling plants across scales of biological organisation for guiding crop improvement

Grain yield improvement in globally important staple crops is critical in the coming decades if production is to keep pace with growing demand; so there is increasing interest in understanding and manipulating plant growth and developmental traits for better crop productivity. However, this is confounded by complex cross-scale feedback regulations and a limited ability to evaluate the consequences of manipulation on crop production. Plant/crop modelling could hold the key to deepening our understanding of dynamic trait-crop-environment interactions and predictive capabilities for supporting genetic manipulation. Using photosynthesis and crop growth as an example, this review summarises past and present experimental and modelling work, bringing about a model-guided crop improvement thrust, encompassing research into: (1) advancing cross-scale plant/crop modelling that connects across biological scales of organisation using a trait dissection-integration modelling principle; (2) improving the reliability of predicted molecular-trait-crop-environment system dynamics with experimental validation; and (3) innovative model application in synergy with cross-scale experimentation to evaluate G×M×E and predict yield outcomes of genetic intervention (or lack of it) for strategising further molecular and breeding efforts. The possible future roles of cross-scale plant/crop modelling in maximising crop improvement are discussed.

A cross-scale analysis to understand and quantify the effects of photosynthetic enhancement on crop growth and yield across environments

Photosynthetic manipulation provides new opportunities for enhancing crop yield. However, understanding and quantifying the importance of individual and multiple manipulations on the seasonal biomass growth and yield performance of target crops across variable production environments is limited. Using a state-of-the-art cross-scale model in the APSIM platform we predicted the impact of altering photosynthesis on the enzyme-limited (A_c ) and electron transport-limited (A_j ) rates, seasonal dynamics in canopy photosynthesis, biomass growth, and yield formation via large multiyear-by-location crop growth simulations. A broad list of promising strategies to improve photosynthesis for C₃ wheat and C₄ sorghum were simulated. In the top decile of seasonal outcomes, yield gains were predicted to be modest, ranging between 0% and 8%, depending on the manipulation and crop type. We report how photosynthetic enhancement can affect the timing and severity of water and nitrogen stress on the growing crop, resulting in nonintuitive seasonal crop dynamics and yield outcomes. We predicted that strategies enhancing A_c alone generate more consistent but smaller yield gains across all water and nitrogen environments, A_j enhancement alone generates larger gains but is undesirable in more marginal environments. Large increases in both A_c and A_j generate the highest gains across all environments. Yield outcomes of the tested manipulation strategies were predicted and compared for realistic Australian wheat and sorghum production. This study uniquely unpacks complex cross-scale interactions between photosynthesis and seasonal crop dynamics and improves understanding and quantification of the potential impact of photosynthesis traits (or lack of it) for crop improvement research.

Interspecific variation in the temperature response of mesophyll conductance is related to leaf anatomy

Although mesophyll conductance (g_m ) is known to be sensitive to temperature (T), the mechanisms underlying the temperature response of g_m are not fully understood. In particular, it has yet to be established whether interspecific variation in g_m -T relationships is associated with mesophyll anatomy and vein traits. In the present study, we measured the short-term response of g_m in eight crop species, and leaf water potential (Ψ_leaf ) in five crop species over a temperature range of 15-35°C. The considered structural parameters are surface areas of mesophyll cells and chloroplasts facing intercellular airspaces per unit leaf area (S_m and S_c ), cell wall thickness (T_cw ), and vein length per area (VLA). We detected large interspecific variations in the temperature responses of g_m and Ψ_leaf . The activation energy for g_m (E_a,gm ) was found to be positively correlated with S_c , although it showed no correlation with T_cw . In contrast, VLA was positively correlated with the slope of the linear model of Ψ_leaf -T (a), whereas E_a,gm was marginally correlated with VLA and a. A two-component model was subsequently used to model g_m -T relationships, and the mechanisms underlying the temperature response of g_m are discussed. The data presented here indicate that leaf anatomy is a major determinant of the interspecific variation in g_m -T relationships.

Mining for allelic gold: finding genetic variation in photosynthetic traits in crops and wild relatives

Improvement of photosynthetic traits in crops to increase yield potential and crop resilience has recently become a major breeding target. Synthetic biology and genetic technologies offer unparalleled opportunities to create new genetics for photosynthetic traits driven by existing fundamental knowledge. However, large 'gene bank' collections of germplasm comprising historical collections of crop species and their relatives offer a wealth of opportunities to find novel allelic variation in the key steps of photosynthesis, to identify new mechanisms and to accelerate genetic progress in crop breeding programmes. Here we explore the available genetic resources in food and fibre crops, strategies to selectively target allelic variation in genes underpinning key photosynthetic processes, and deployment of this variation via gene editing in modern elite material.

Phenotypic variation in photosynthetic traits in wheat grown under field versus glasshouse conditions

Recognition of the untapped potential of photosynthesis to improve crop yields has spurred research to identify targets for breeding. The CO2-fixing enzyme Rubisco is characterized by a number of inefficiencies, and frequently limits carbon assimilation at the top of the canopy, representing a clear target for wheat improvement. Two bread wheat lines with similar genetic backgrounds and contrasting in vivo maximum carboxylation activity of Rubisco per unit leaf nitrogen (Vc,max,25/Narea) determined using high-throughput phenotyping methods were selected for detailed study from a panel of 80 spring wheat lines. Detailed phenotyping of photosynthetic traits in the two lines using glasshouse-grown plants showed no difference in Vc,max,25/Narea determined directly via in vivo and in vitro methods. Detailed phenotyping of glasshouse-grown plants of the 80 wheat lines also showed no correlation between photosynthetic traits measured via high-throughput phenotyping of field-grown plants. Our findings suggest that the complex interplay between traits determining crop productivity and the dynamic environments experienced by field-grown plants needs to be considered in designing strategies for effective wheat crop yield improvement when breeding for particular environments.

Rapid stomatal closure contributes to higher water use efficiency in major C4 compared to C3 Poaceae crops

Understanding water use characteristics of C3 and C4 crops is important for food security under climate change. Here, we aimed to clarify how stomatal dynamics and water use efficiency (WUE) differ in fluctuating environments in major C3 and C4 crops. Under high and low nitrogen conditions, we evaluated stomatal morphology and kinetics of stomatal conductance (gs) at leaf and whole-plant levels in controlled fluctuating light environments in four C3 and five C4 Poaceae species. We developed a dynamic photosynthesis model, which incorporates C3 and C4 photosynthesis models that consider stomatal dynamics, to evaluate the contribution of rapid stomatal opening and closing to photosynthesis and WUE. C4 crops showed more rapid stomatal opening and closure than C3 crops, which could be explained by smaller stomatal size and higher stomatal density in plants grown at high nitrogen conditions. Our model analysis indicated that accelerating the speed of stomatal closure in C3 crops to the level of C4 crops could enhance WUE up to 16% by reducing unnecessary water loss during low light periods, whereas accelerating stomatal opening only minimally enhanced photosynthesis. The present results suggest that accelerating the speed of stomatal closure in major C3 crops to the level of major C4 crops is a potential breeding target for the realization of water-saving agriculture.

Estimating peanut and soybean photosynthetic traits using leaf spectral reflectance and advance regression models

MAIN CONCLUSION

By combining hyperspectral signatures of peanut and soybean, we predicted Vcmax and Jmax with 70 and 50% accuracy. The PLS was the model that better predicted these photosynthetic parameters. One proposed key strategy for increasing potential crop stability and yield centers on exploitation of genotypic variability in photosynthetic capacity through precise high-throughput phenotyping techniques. Photosynthetic parameters, such as the maximum rate of Rubisco catalyzed carboxylation (Vc,max) and maximum electron transport rate supporting RuBP regeneration (Jmax), have been identified as key targets for improvement. The primary techniques for measuring these physiological parameters are very time-consuming. However, these parameters could be estimated using rapid and non-destructive leaf spectroscopy techniques. This study compared four different advanced regression models (PLS, BR, ARDR, and LASSO) to estimate Vc,max and Jmax based on leaf reflectance spectra measured with an ASD FieldSpec4. Two leguminous species were tested under different controlled environmental conditions: (1) peanut under different water regimes at normal atmospheric conditions and (2) soybean under high [CO2] and high night temperature. Model sensitivities were assessed for each crop and treatment separately and in combination to identify strengths and weaknesses of each modeling approach. Regardless of regression model, robust predictions were achieved for Vc,max (R2 = 0.70) and Jmax (R2 = 0.50). Field spectroscopy shows promising results for estimating spatial and temporal variations in photosynthetic capacity based on leaf and canopy spectral properties.

Wheat physiology predictor: predicting physiological traits in wheat from hyperspectral reflectance measurements using deep learning

BACKGROUND

The need for rapid in-field measurement of key traits contributing to yield over many thousands of genotypes is a major roadblock in crop breeding. Recently, leaf hyperspectral reflectance data has been used to train machine learning models using partial least squares regression (PLSR) to rapidly predict genetic variation in photosynthetic and leaf traits across wheat populations, among other species. However, the application of published PLSR spectral models is limited by a fixed spectral wavelength range as input and the requirement of separate custom-built models for each trait and wavelength range. In addition, the use of reflectance spectra from the short-wave infrared region requires expensive multiple detector spectrometers. The ability to train a model that can accommodate input from different spectral ranges would potentially make such models extensible to more affordable sensors. Here we compare the accuracy of prediction of PLSR with various deep learning approaches and an ensemble model, each trained and tested using previously published data sets.

RESULTS

We demonstrate that the accuracy of PLSR to predict photosynthetic and related leaf traits in wheat can be improved with deep learning-based and ensemble models without overfitting. Additionally, these models can be flexibly applied across spectral ranges without significantly compromising accuracy.

CONCLUSION

The method reported provides an improved prediction of wheat leaf and photosynthetic traits from leaf hyperspectral reflectance and do not require a full range, high cost leaf spectrometer. We provide a web service for deploying these algorithms to predict physiological traits in wheat from a variety of spectral data sets, with important implications for wheat yield prediction and crop breeding.

Effect of leaf temperature on the estimation of photosynthetic and other traits of wheat leaves from hyperspectral reflectance

A growing number of leaf traits can be estimated from hyperspectral reflectance data. These include structural and compositional traits, such as leaf mass per area (LMA) and nitrogen and chlorophyll content, but also physiological traits such a Rubisco carboxylation activity, electron transport rate, and respiration rate. Since physiological traits vary with leaf temperature, how does this impact on predictions made from reflectance measurements? We investigated this with two wheat varieties, by repeatedly measuring each leaf through a sequence of temperatures imposed by varying the air temperature in a growth room. Leaf temperatures ranging from 20 °C to 35 °C did not alter the estimated Rubisco capacity normalized to 25 °C (Vcmax25), or chlorophyll or nitrogen contents per unit leaf area. Models estimating LMA and Vcmax25/N were both slightly influenced by leaf temperature: estimated LMA increased by 0.27% °C-1 and Vcmax25/N increased by 0.46% °C-1. A model estimating Rubisco activity closely followed variation associated with leaf temperature. Reflectance spectra change with leaf temperature and therefore contain a temperature signal.

Heat-induced changes in the abundance of wheat Rubisco activase isoforms

The Triticum aestivum (wheat) genome encodes three isoforms of Rubisco activase (Rca) differing in thermostability, which could be exploited to improve the resilience of this crop to global warming. We hypothesized that elevated temperatures would cause an increase in the relative abundance of heat-stable Rca1β. Wheat plants were grown at 25° C : 18°C (day : night) and exposed to heat stress (38° C : 22°C) for up to 5 d at pre-anthesis. Carbon (C) assimilation, Rubisco activity, CA1Pase activity, transcripts of Rca1β, Rca2β, and Rca2α, and the quantities of the corresponding protein products were measured during and after heat stress. The transcript of Rca1β increased 40-fold in 4 h at elevated temperatures and returned to the original level after 4 h upon return of plants to control temperatures. Rca1β comprised up to 2% of the total Rca protein in unstressed leaves but increased three-fold in leaves exposed to elevated temperatures for 5 d and remained high at 4 h after heat stress. These results show that elevated temperatures cause rapid changes in Rca gene expression and adaptive changes in Rca isoform abundance. The improved understanding of the regulation of C assimilation under heat stress will inform efforts to improve wheat productivity and climate resilience.

Measuring Rubisco activity: challenges and opportunities of NADH-linked microtiter plate-based and 14C-based assays

Rubisco is central to carbon assimilation, and efforts to improve the efficiency and sustainability of crop production have spurred interest in phenotyping Rubisco activity. We tested the hypothesis that microtiter plate-based methods provide comparable results to those obtained with the radiometric assay that measures the incorporation of 14CO2 into 3-phosphoglycerate (3-PGA). Three NADH-linked assays were tested that use alternative coupling enzymes: glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and glycerolphosphate dehydrogenase (GlyPDH); phosphoenolpyruvate carboxylase (PEPC) and malate dehydrogenase (MDH); and pyruvate kinase (PK) and lactate dehydrogenase (LDH). To date there has been no thorough evaluation of their reliability by comparison with the 14C-based method. The three NADH-linked assays were used in parallel to estimate (i) the 3-PGA concentration-response curve of NADH oxidation, (ii) the Michaelis-Menten constant for ribulose-1,5-bisphosphate, (iii) fully active and inhibited Rubisco activities, and (iv) Rubisco initial and total activities in fully illuminated and shaded leaves. All three methods correlated strongly with the 14C-based method, and the PK-LDH method showed a strong correlation and was the cheapest method. PEPC-MDH would be a suitable option for situations in which ADP/ATP might interfere with the assay. GAPDH-GlyPDH proved more laborious than the other methods. Thus, we recommend the PK-LDH method as a reliable, cheaper, and higher throughput method to phenotype Rubisco activity for crop improvement efforts.

An isoleucine residue acts as a thermal and regulatory switch in wheat Rubisco activase

The regulation of Rubisco, the gatekeeper of carbon fixation into the biosphere, by its molecular chaperone Rubisco activase (Rca) is essential for photosynthesis and plant growth. Using energy from ATP hydrolysis, Rca promotes the release of inhibitors and restores catalytic competence to Rubisco-active sites. Rca is sensitive to moderate heat stress, however, and becomes progressively inhibited as the temperature increases above the optimum for photosynthesis. Here, we identify a single amino acid substitution (M159I) that fundamentally alters the thermal and regulatory properties of Rca in bread wheat (Triticum aestivum L.). Using site-directed mutagenesis, we demonstrate that the M159I substitution extends the temperature optimum of the most abundant Rca isoform by 5°C in vitro, while maintaining the efficiency of Rubisco activation by Rca. The results suggest that this single amino acid substitution acts as a thermal and regulatory switch in wheat Rca that can be exploited to improve the climate resilience and efficiency of carbon assimilation of this cereal crop as temperatures become warmer and more volatile.

Temperature responses of photosynthesis and leaf hydraulic conductance in rice and wheat

Studies on the temperature (T) responses of photosynthesis and leaf hydraulic conductance (K_leaf ) are important to plant gas exchange. In this study, the temperature responses of photosynthesis and K_leaf were studied in Shanyou 63 (Oryza sativa) and Yannong 19 (Triticum aestivum). Leaf water potential (Ψ_leaf ) was insensitive to T in Shanyou 63, while it significantly decreased with T in Yannong 19. The differential Ψ_leaf  - T relationship partially accounted for the differing g_m -T relationships, where g_m was less sensitive to T in Yannong 19 than in Shanyou 63. With different g_m -T and Ψ_leaf -T relationships, the temperature responses of photosynthetic limitations were surprisingly similar between the two lines, and the photosynthetic rate was highly correlated with g_m . With the increasing T, K_leaf increased in Shanyou 63 while it decreased in Yannong 19. The different K_leaf -T relationships were related to different Ψ_leaf -T relationships. When excluding the effects of water viscosity and Ψ_leaf , K_leaf was insensitive to T in both lines. g_m and K_leaf were generally not coordinated across different temperatures. This study highlights the importance of Ψ_leaf on leaf carbon and water exchanges, and the mechanisms for the g_m -T and K_leaf -T relationships were discussed.

Genetic variation for photosynthetic capacity and efficiency in spring wheat

One way to increase yield potential in wheat is screening for natural variation in photosynthesis. This study uses measured and modelled physiological parameters to explore genotypic diversity in photosynthetic capacity (Pc, Rubisco carboxylation capacity per unit leaf area at 25 °C) and efficiency (Peff, Pc per unit of leaf nitrogen) in wheat in relation to fertilizer, plant stage, and environment. Four experiments (Aus1, Aus2, Aus3, and Mex1) were carried out with diverse wheat collections to investigate genetic variation for Rubisco capacity (Vcmax25), electron transport rate (J), CO2 assimilation rate, stomatal conductance, and complementary plant functional traits: leaf nitrogen, leaf dry mass per unit area, and SPAD. Genotypes for Aus1 and Aus2 were grown in the glasshouse with two fertilizer levels. Genotypes for Aus3 and Mex1 experiments were grown in the field in Australia and Mexico, respectively. Results showed that Vcmax25 derived from gas exchange measurements is a robust parameter that does not depend on stomatal conductance and was positively correlated with Rubisco content measured in vitro. There was significant genotypic variation in most of the experiments for Pc and Peff. Heritability of Pc reached 0.7 and 0.9 for SPAD. Genotypic variation and heritability of traits show that there is scope for these traits to be used in pre-breeding programmes to improve photosynthesis with the ultimate objective of raising yield potential.

The role of leaf water potential in the temperature response of mesophyll conductance

Variation in temperature (T) is usually accompanied by changes in leaf water potential (Ψ_leaf ), which may influence mesophyll conductance (g_m ). However, the effects of Ψ_leaf on g_m have not yet been considered in models of the g_m response to temperature. Temperature responses of g_m and Ψ_leaf and the response of g_m to Ψ_leaf were studied in rice (Oryza sativa) and wheat (Triticum aestivum), and then an empirical model of Ψ_leaf was incorporated into an existing g_m -T model. In wheat, Ψ_leaf was dramatically decreased with increasing T, whereas in rice Ψ_leaf was less sensitive or insensitive to T. Without taking Ψ_leaf into account, g_m for wheat showed no response to T. However, at a given Ψ_leaf , g_m was significantly higher at high temperature compared with low. After incorporating the function of Ψ_leaf into the g_m -T model, we suggest that the g_m -T relationship can be influenced by the activation and deactivation energy for membrane permeability, Ψ_leaf gradient between temperatures, and the sensitivity of g_m to Ψ_leaf , below a threshold (Ψ_leaf,0 ). The data presented here suggest that Ψ_leaf plays an important role in the g_m -T relationship and should be considered in future studies related to the temperature response of g_m and photosynthesis.

Elevated CO2 alleviates the negative impact of heat stress on wheat physiology but not on grain yield

Hot days are becoming hotter and more frequent, threatening wheat yields worldwide. Developing wheat varieties ready for future climates calls for improved understanding of how elevated CO2 (eCO2) and heat stress (HS) interactively impact wheat yields. We grew a modern, high-yielding wheat cultivar (Scout) at ambient CO2 (aCO2, 419 μl l -1) or eCO2 (654 μl l-1) in a glasshouse maintained at 22/15 °C (day/night). Half of the plants were exposed to HS (40/24 °C) for 5 d at anthesis. In non-HS plants, eCO2 enhanced (+36%) CO2 assimilation rates (Asat) measured at growth CO2 despite down-regulation of photosynthetic capacity. HS reduced Asat (-42%) in aCO2- but not in eCO2-grown plants because eCO2 protected photosynthesis by increasing ribulose bisphosphate regeneration capacity and reducing photochemical damage under HS. eCO2 stimulated biomass (+35%) of all plants and grain yield (+30%) of non-HS plants only. Plant biomass initially decreased following HS but recovered at maturity due to late tillering. HS equally reduced grain yield (-40%) in aCO2- and eCO2-grown plants due to grain abortion and reduced grain filling. While eCO2 mitigated the negative impacts of HS at anthesis on wheat photosynthesis and biomass, grain yield was reduced by HS in both CO2 treatments.

Variation in Responses of Photosynthesis and Apparent Rubisco Kinetics to Temperature in Three Soybean Cultivars

Recent in vivo assays of the responses of Rubisco to temperature in C₃ plants have revealed substantial diversity. Three cultivars of soybean (Glycine max L. Merr.), Holt, Fiskeby V, and Spencer, were grown in indoor chambers at 15, 20, and 25 °C. Leaf photosynthesis was measured over the range of 15 to 30 °C, deliberately avoiding higher temperatures which may cause deactivation of Rubisco, in order to test for differences in temperature responses of photosynthesis, and to investigate in vivo Rubisco kinetic characteristics responsible for any differences observed. The three cultivars differed in the optimum temperature for photosynthesis (from 15 to 30 °C) at 400 μmol mol⁻¹ external CO₂ concentration when grown at 15 °C, and in the shapes of the response curves when grown at 25 °C. The apparent activation energy of the maximum carboxylation rate of Rubisco differed substantially between cultivars at all growth temperatures, as well as changing with growth temperature in two of the cultivars. The activation energy ranged from 58 to 84 kJ mol⁻¹, compared with the value of 64 kJ mol⁻¹ used in many photosynthesis models. Much less variation in temperature responses occurred in photosynthesis measured at nearly saturating CO₂ levels, suggesting more diversity in Rubisco than in electron transport thermal properties among these soybean cultivars.

Diverse photosynthetic capacity of global ecosystems mapped by satellite chlorophyll fluorescence measurements

Photosynthetic capacity is often quantified by the Rubisco-limited photosynthetic capacity (i.e. maximum carboxylation rate, V_cmax). It is a key plant functional trait that is widely used in Earth System Models for simulation of the global carbon and water cycles. Measuring V_cmax is time-consuming and laborious; therefore, the spatiotemporal distribution of V_cmax is still poorly understood due to limited measurements of V_cmax. In this study, we used a data assimilation approach to map the spatial variation of V_cmax for global terrestrial ecosystems from a 11-year-long satellite-observed solar-induced chlorophyll fluorescence (SIF) record. In this SIF-derived V_cmax map, the mean V_cmax value for each plant function type (PFT) is found to be comparable to a widely used N-derived V_cmax dataset by Kattge et al. (2009). The gradient of V_cmax along PFTs is clearly revealed even without land cover information as an input. Large seasonal and spatial variations of V_cmax are found within each PFT, especially for diverse crop rotation systems. The distribution of major crop belts, characterized with high V_cmax values, is highlighted in this V_cmax map. Legume plants are characterized with high V_cmax values. This V_cmax map also clearly illustrates the emerging soybean revolution in South America where V_cmax is the highest among the world. The gradient of V_cmax in Amazon is found to follow the transition of soil types with different soil N and P contents. This study suggests that satellite-observed SIF is powerful in deriving the important plant functional trait, i.e. V_cmax, for global climate change studies.

Modelling the seasonal changes in the gas exchange response to CO2 in relation to short-term leaf temperature changes in Vitis vinifera cv. Shiraz grapevines grown in outdoor conditions

Effects of temperature on the photosynthetic response of Vitis vinifera cv. Shiraz leaves to CO₂ were investigated across the growing season and modelling was used to determine relationships between photosynthesis and seasonal climate. Results indicated that photosynthetic rates declined from spring to summer, conforming to the deciduous habit of grapevines. Rates of ribulose 1,5-bisphosphate (RuBP) carboxylation and regeneration increased in a temperature dependent pattern throughout the season. However, the maximum rates decreased as the season progressed. There were also marked decreases in temperature sensitivity for each of these processes, consistent with the decreases occurring faster at high compared to low temperatures. There were no correlations between the seasonal climate and each of these photosynthetic processes but the effect of day was significant in all cases. CO₂ saturated rates of photosynthesis (A_max) across the season were highly correlated with the maximum rates of RuBP carboxylation and regeneration. The transition temperature between RuBP regeneration and RuBP carboxylation-limited assimilation varied across the growing season, from 23 °C in spring, 35 °C in mid-summer and 30 °C at harvest and were highly correlated with mean day temperature. This suggested dynamic control of assimilation by carboxylation and regeneration processes occurred in these grapevines in tune with the seasonal climate.

Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops

Plant phenotyping forms the core of crop breeding, allowing breeders to build on physiological traits and mechanistic science to inform their selection of material for crossing and genetic gain. Recent rapid progress in high-throughput techniques based on machine vision, robotics, and computing (plant phenomics) enables crop physiologists and breeders to quantitatively measure complex and previously intractable traits. By combining these techniques with affordable genomic sequencing and genotyping, machine learning, and genome selection approaches, breeders have an opportunity to make rapid genetic progress. This review focuses on how field-based plant phenomics can enable next-generation physiological breeding in cereal crops for traits related to radiation use efficiency, photosynthesis, and crop biomass. These traits have previously been regarded as difficult and laborious to measure but have recently become a focus as cereal breeders find genetic progress from 'Green Revolution' traits such as harvest index become exhausted. Application of LiDAR, thermal imaging, leaf and canopy spectral reflectance, Chl fluorescence, and machine learning are discussed using wheat and sorghum phenotyping as case studies. A vision of how crop genomics and high-throughput phenotyping could enable the next generation of crop research and breeding is presented.

Exploring the optimum nitrogen partitioning to predict the acclimation of C3 leaf photosynthesis to varying growth conditions

The distribution of leaf nitrogen among photosynthetic proteins (i.e. chlorophyll, the electron transport system, Rubisco, and other soluble proteins) responds to environmental changes. We hypothesize that this response may underlie the biochemical aspect of leaf acclimation to the growth environment, and describe an analytical method to solve optimum nitrogen partitioning for maximized photosynthesis in C3 leaves. The method predicts a high investment of nitrogen in Rubisco under conditions leading to excessive energy supply relative to metabolic demand (e.g. low temperature, high light, low nitrogen, or low CO2). Conversely, more nitrogen is invested in chlorophyll when the energy supply is limiting. Overall, our optimization results are qualitatively consistent with literature reports. Commonly reported changes in photosynthetic parameters with growth temperature were emergent properties of the optimum nitrogen partitioning. The method was used to simulate dynamic acclimation under varying environmental conditions, using first-order kinetics. Simulated diurnal patterns of leaf photosynthetic rates as a result of acclimation differed greatly from those without acclimation (Awithout). However, differences in predicted photosynthesis integrated over a day or over the growing season from Awithout depended on the value of the kinetic time constant (τ), suggesting that τ is a critical parameter determining the overall impact of nitrogen distribution on acclimated photosynthesis.

Manganese binding to Rubisco could drive a photorespiratory pathway that increases the energy efficiency of photosynthesis

Most plants, contrary to popular belief, do not waste over 30% of their photosynthate in a futile cycle called photorespiration. Rather, the photorespiratory pathway generates additional malate in the chloroplast that empowers many energy-intensive chemical reactions, such as those involved in nitrate assimilation. Thus, the balance between carbon fixation and photorespiration determines the plant carbon-nitrogen balance and protein concentrations. Plant protein concentrations, in turn, depend not only on the relative concentrations of carbon dioxide and oxygen in the chloroplast but also on the relative activities of magnesium and manganese, which are metals that associate with several key enzymes in the photorespiratory pathway and alter their function. Understanding the regulation of these processes is critical for sustaining food quality under rising CO2 atmospheres.

Hyperspectral reflectance as a tool to measure biochemical and physiological traits in wheat

Improving photosynthesis to raise wheat yield potential has emerged as a major target for wheat physiologists. Photosynthesis-related traits, such as nitrogen per unit leaf area (Narea) and leaf dry mass per area (LMA), require laborious, destructive, laboratory-based methods, while physiological traits underpinning photosynthetic capacity, such as maximum Rubisco activity normalized to 25 °C (Vcmax25) and electron transport rate (J), require time-consuming gas exchange measurements. The aim of this study was to assess whether hyperspectral reflectance (350-2500 nm) can be used to rapidly estimate these traits on intact wheat leaves. Predictive models were constructed using gas exchange and hyperspectral reflectance data from 76 genotypes grown in glasshouses with different nitrogen levels and/or in the field under yield potential conditions. Models were developed using half of the observed data with the remainder used for validation, yielding correlation coefficients (R2 values) of 0.62 for Vcmax25, 0.7 for J, 0.81 for SPAD, 0.89 for LMA, and 0.93 for Narea, with bias <0.7%. The models were tested on elite lines and landraces that had not been used to create the models. The bias varied between -2.3% and -5.5% while relative error of prediction was similar for SPAD but slightly greater for LMA and Narea.