Similar photosynthetic but different yield responses of C3 and C4 crops to elevated O3.

The deleterious effects of ozone (O3) pollution on crop physiology, yield, and productivity are widely acknowledged. It has also been assumed that C4 crops with a carbon concentrating mechanism and greater water use efficiency are less sensitive to O3 pollution than C3 crops. This assumption has not been widely tested. Therefore, we compiled 46 journal articles and unpublished datasets that reported leaf photosynthetic and biochemical traits, plant biomass, and yield in five C3 crops (chickpea, rice, snap bean, soybean, and wheat) and four C4 crops (sorghum, maize, Miscanthus × giganteus, and switchgrass) grown under ambient and elevated O3 concentration ([O3]) in the field at free-air O3 concentration enrichment (O3-FACE) facilities over the past 20 y. When normalized by O3 exposure, C3 and C4 crops showed a similar response of leaf photosynthesis, but the reduction in chlorophyll content, fluorescence, and yield was greater in C3 crops compared with C4 crops. Additionally, inbred and hybrid lines of rice and maize showed different sensitivities to O3 exposure. This study quantitatively demonstrates that C4 crops respond less to elevated [O3] than C3 crops. This understanding could help maintain cropland productivity in an increasingly polluted atmosphere.

Approaches to investigate crop responses to ozone pollution: from O3 -FACE to satellite-enabled modeling.

Ozone (O3 ) is a damaging air pollutant to crops. As one of the most reactive oxidants known, O3 rapidly forms other reactive oxygen species (ROS) once it enters leaves through stomata. Those ROS in turn can cause oxidative stress, reduce photosynthesis, accelerate senescence, and decrease crop yield. To improve and adapt our feed, fuel, and food supply to rising O3 pollution, a number of Free Air Concentration Enrichment (O3 -FACE) facilities have been developed around the world and have studied key staple crops. In this review, we provide an overview of the FACE facilities and highlight some of the lessons learned from the last two decades of research. We discuss the differences between C3 and C4 crop responses to elevated O3 , the possible trade-off between productivity and protection, genetic variation in O3 response within and across species, and how we might leverage this observed variation for crop improvement. We also highlight the need to improve understanding of the interaction between rising O3 pollution and other aspects of climate change, notably drought. Finally, we propose the use of globally modeled O3 data that are available at increasing spatial and temporal resolutions to expand upon the research conducted at the limited number of global O3 -FACE facilities.

Reductions in leaf area index, pod production, seed size, and harvest index drive yield loss to high temperatures in soybean.

Improvements in genetics, technology, and agricultural intensification have increased soybean yields; however, adverse climate conditions may prevent these gains from being fully realized in the future. Higher growing season temperatures reduce soybean yields in key production regions including the US Midwest, and better understanding of the developmental and physiological mechanisms that constrain soybean yield under high temperature conditions is needed. This study tested the response of two soybean cultivars to four elevated temperature treatments (+1.7, +2.6, +3.6, and +4.8 °C) in the field over three growing seasons and identified threshold temperatures for response and linear versus non-linear trait responses to temperature. Yield declined non-linearly to temperature, with decreases apparent when canopy temperature exceeded 20.9 °C for the locally adapted cultivar and 22.7°C for a cultivar adapted to more southern locations. While stem node number increased with increasing temperature, leaf area index decreased substantially. Pod production, seed size, and harvest index significantly decreased with increasing temperature. The seasonal average temperature of even the mildest treatment exceeded the threshold temperatures for yield loss, emphasizing the importance of improving temperature tolerance in soybean germplasm with intensifying climate change.

Predicting biochemical acclimation of leaf photosynthesis in soybean under in-field canopy warming using hyperspectral reflectance.

Traditional gas exchange measurements are cumbersome, which makes it difficult to capture variation in biochemical parameters, namely the maximum rate of carboxylation measured at a reference temperature (Vcmax25 ) and the maximum electron transport at a reference temperature (Jmax25 ), in response to growth temperature over time from days to weeks. Hyperspectral reflectance provides reliable measures of Vcmax25 and Jmax25 ; however, the capability of this method to capture biochemical acclimations of the two parameters to high growth temperature over time has not been demonstrated. In this study, Vcmax25 and Jmax25 were measured over multiple growth stages during two growing seasons for field-grown soybeans using both gas exchange techniques and leaf spectral reflectance under ambient and four elevated canopy temperature treatments (ambient+1.5, +3, +4.5, and +6°C). Spectral vegetation indices and machine learning methods were used to build predictive models for Vcmax25 and Jmax25 , based on the leaf reflectance. Results showed that these models yielded an R2 of 0.57-0.65 and 0.48-0.58 for Vcmax25 and Jmax25 , respectively. Hyperspectral reflectance captured biochemical acclimation of leaf photosynthesis to high temperature in the field, improving spatial and temporal resolution in the ability to assess the impact of future warming on crop productivity.

Advances in field-based high-throughput photosynthetic phenotyping.

Gas exchange techniques revolutionized plant research and advanced understanding, including associated fluxes and efficiencies, of photosynthesis, photorespiration, and respiration of plants from cellular to ecosystem scales. These techniques remain the gold standard for inferring photosynthetic rates and underlying physiology/biochemistry, although their utility for high-throughput phenotyping (HTP) of photosynthesis is limited both by the number of gas exchange systems available and the number of personnel available to operate the equipment. Remote sensing techniques have long been used to assess ecosystem productivity at coarse spatial and temporal resolutions, and advances in sensor technology coupled with advanced statistical techniques are expanding remote sensing tools to finer spatial scales and increasing the number and complexity of phenotypes that can be extracted. In this review, we outline the photosynthetic phenotypes of interest to the plant science community and describe the advances in high-throughput techniques to characterize photosynthesis at spatial scales useful to infer treatment or genotypic variation in field-based experiments or breeding trials. We will accomplish this objective by presenting six lessons learned thus far through the development and application of proximal/remote sensing-based measurements and the accompanying statistical analyses. We will conclude by outlining what we perceive as the current limitations, bottlenecks, and opportunities facing HTP of photosynthesis.

Plot-level rapid screening for photosynthetic parameters using proximal hyperspectral imaging.

Photosynthesis is currently measured using time-laborious and/or destructive methods which slows research and breeding efforts to identify crop germplasm with higher photosynthetic capacities. We present a plot-level screening tool for quantification of photosynthetic parameters and pigment contents that utilizes hyperspectral reflectance from sunlit leaf pixels collected from a plot (~2 m×2 m) in <1 min. Using field-grown Nicotiana tabacum with genetically altered photosynthetic pathways over two growing seasons (2017 and 2018), we built predictive models for eight photosynthetic parameters and pigment traits. Using partial least squares regression (PLSR) analysis of plot-level sunlit vegetative reflectance pixels from a single visible near infra-red (VNIR) (400-900 nm) hyperspectral camera, we predict maximum carboxylation rate of Rubisco (Vc,max, R2=0.79) maximum electron transport rate in given conditions (J1800, R2=0.59), maximal light-saturated photosynthesis (Pmax, R2=0.54), chlorophyll content (R2=0.87), the Chl a/b ratio (R2=0.63), carbon content (R2=0.47), and nitrogen content (R2=0.49). Model predictions did not improve when using two cameras spanning 400-1800 nm, suggesting a robust, widely applicable and more 'cost-effective' pipeline requiring only a single VNIR camera. The analysis pipeline and methods can be used in any cropping system with modified species-specific PLSR analysis to offer a high-throughput field phenotyping screening for germplasm with improved photosynthetic performance in field trials.

Uncovering hidden genetic variation in photosynthesis of field-grown maize under ozone pollution.

Ozone is the most damaging air pollutant to crops, currently reducing Midwest US maize production by up to 10%, yet there has been very little effort to adapt germplasm for ozone tolerance. Ozone enters plants through stomata, reacts to form reactive oxygen species in the apoplast and ultimately decreases photosynthetic C gain. In this study, 10 diverse inbred parents were crossed in a half-diallel design to create 45 F1 hybrids, which were tested for ozone response in the field using free air concentration enrichment (FACE). Ozone stress increased the heritability of photosynthetic traits and altered genetic correlations among traits. Hybrids from parents Hp301 and NC338 showed greater sensitivity to ozone stress, and disrupted relationships among photosynthetic traits. The physiological responses underlying sensitivity to ozone differed in hybrids from the two parents, suggesting multiple mechanisms of response to oxidative stress. FACE technology was essential to this evaluation because genetic variation in photosynthesis under elevated ozone was not predictable based on performance at ambient ozone. These findings suggest that selection under elevated ozone is needed to identify deleterious alleles in the world's largest commodity crop.

High-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR) reveals genetic modifications to photosynthetic capacity.

Spectroscopy is becoming an increasingly powerful tool to alleviate the challenges of traditional measurements of key plant traits at the leaf, canopy, and ecosystem scales. Spectroscopic methods often rely on statistical approaches to reduce data redundancy and enhance useful prediction of physiological traits. Given the mechanistic uncertainty of spectroscopic techniques, genetic modification of plant biochemical pathways may affect reflectance spectra causing predictive models to lose power. The objectives of this research were to assess over two separate years, whether a predictive model can represent natural and imposed variation in leaf photosynthetic potential for different crop cultivars and genetically modified plants, to assess the interannual capabilities of a partial least square regression (PLSR) model, and to determine whether leaf N is a dominant driver of photosynthesis in PLSR models. In 2016, a PLSR analysis of reflectance spectra coupled with gas exchange data was used to build predictive models for photosynthetic parameters including maximum carboxylation rate of Rubisco (V c,max ), maximum electron transport rate (J max ) and percentage leaf nitrogen ([N]). The model was developed for wild type and genetically modified plants that represent a wide range of photosynthetic capacities. Results show that hyperspectral reflectance accurately predicted V c,max, J max and [N] for all plants measured in 2016. Applying these PLSR models to plants grown in 2017 resulted in a strong predictive ability relative to gas exchange measurements for V c,max, but not for J max, and not for genotypes unique to 2017. Building a new model including data collected in 2017 resulted in more robust predictions, with R2 increases of 17% for V c,max . and 13% J max . Plants generally have a positive correlation between leaf nitrogen and photosynthesis, however, tobacco with reduced Rubisco (SSuD) had significantly higher [N] despite much lower V c,max. The PLSR model was able to accurately predict both lower V c,max and higher leaf [N] for this genotype suggesting that the spectral based estimates of V c,max and leaf nitrogen [N] are independent. These results suggest that the PLSR model can be applied across years, but only to genotypes used to build the model and that the actual mechanism measured with the PLSR technique is not directly related to leaf [N]. The success of the leaf-scale analysis suggests that similar approaches may be successful at the canopy and ecosystem scales but to use these methods across years and between genotypes at any scale, application of accurately populated physical based models based on radiative transfer principles may be required.

High-Throughput Phenotyping of Maize Leaf Physiological and Biochemical Traits Using Hyperspectral Reflectance.

High-throughput, noninvasive field phenotyping has revealed genetic variation in crop morphological, developmental, and agronomic traits, but rapid measurements of the underlying physiological and biochemical traits are needed to fully understand genetic variation in plant-environment interactions. This study tested the application of leaf hyperspectral reflectance (λ = 500-2,400 nm) as a high-throughput phenotyping approach for rapid and accurate assessment of leaf photosynthetic and biochemical traits in maize (Zea mays). Leaf traits were measured with standard wet-laboratory and gas-exchange approaches alongside measurements of leaf reflectance. Partial least-squares regression was used to develop a measure of leaf chlorophyll content, nitrogen content, sucrose content, specific leaf area, maximum rate of phosphoenolpyruvate carboxylation, [CO2]-saturated rate of photosynthesis, and leaf oxygen radical absorbance capacity from leaf reflectance spectra. Partial least-squares regression models accurately predicted five out of seven traits and were more accurate than previously used simple spectral indices for leaf chlorophyll, nitrogen content, and specific leaf area. Correlations among leaf traits and statistical inferences about differences among genotypes and treatments were similar for measured and modeled data. The hyperspectral reflectance approach to phenotyping was dramatically faster than traditional measurements, enabling over 1,000 rows to be phenotyped during midday hours over just 2 to 4 d, and offers a nondestructive method to accurately assess physiological and biochemical trait responses to environmental stress.

Similar photosynthetic response to elevated carbon dioxide concentration in species with different phloem loading strategies.

Species have different strategies for loading sugars into the phloem, which vary in the route that sugars take to enter the phloem and the energetics of sugar accumulation. Species with passive phloem loading are hypothesized to have less flexibility in response to changes in some environmental conditions because sucrose export from mesophyll cells is dependent on fixed anatomical plasmodesmatal connections. Passive phloem loaders also have high mesophyll sugar content, and may be less likely to exhibit sugar-mediated down-regulation of photosynthetic capacity at elevated CO2 concentrations. To date, the effect of phloem loading strategy on the response of plant carbon metabolism to rising atmospheric CO2 concentrations is unclear, despite the widespread impacts of rising CO2 on plants. Over three field seasons, five species with apoplastic loading, passive loading, or polymer-trapping were grown at ambient and elevated CO2 concentration in free air concentration enrichment plots. Light-saturated rate of photosynthesis, photosynthetic capacity, leaf carbohydrate content, and anatomy were measured and compared among the species. All five species showed significant stimulation in midday photosynthetic CO2 uptake by elevated CO2 even though the two passive loading species showed significant down-regulation of maximum Rubisco carboxylation capacity at elevated CO2. There was a trend toward greater starch accumulation at elevated CO2 in all species, and was most pronounced in passive loaders. From this study, we cannot conclude that phloem loading strategy is a key determinant of plant response to elevated CO2, but compelling differences in response counter to our hypothesis were observed. A phylogenetically controlled experiment with more species may be needed to fully test the hypothesis.

Elevated ozone reduces photosynthetic carbon gain by accelerating leaf senescence of inbred and hybrid maize in a genotype-specific manner.

Exposure to elevated tropospheric ozone concentration ([O3 ]) accelerates leaf senescence in many C3 crops. However, the effects of elevated [O3 ] on C4 crops including maize (Zea mays L.) are poorly understood in terms of physiological mechanism and genetic variation in sensitivity. Using free air gas concentration enrichment, we investigated the photosynthetic response of 18 diverse maize inbred and hybrid lines to season-long exposure to elevated [O3 ] (~100 nl L-1 ) in the field. Gas exchange was measured on the leaf subtending the ear throughout the grain filling period. On average over the lifetime of the leaf, elevated [O3 ] led to reductions in photosynthetic CO2 assimilation of both inbred (-22%) and hybrid (-33%) genotypes. There was significant variation among both inbred and hybrid lines in the sensitivity of photosynthesis to elevated [O3 ], with some lines showing no change in photosynthesis at elevated [O3 ]. Based on analysis of inbred line B73, the reduced CO2 assimilation at elevated [O3 ] was associated with accelerated senescence decreasing photosynthetic capacity and not altered stomatal limitation. These findings across diverse maize genotypes could advance the development of more O3 tolerant maize and provide experimental data for parameterization and validation of studies modeling how O3 impacts crop performance.

Leaf and canopy scale drivers of genotypic variation in soybean response to elevated carbon dioxide concentration.

The atmospheric [CO2 ] in which crops grow today is greater than at any point in their domestication history and represents an opportunity for positive effects on seed yield that can counteract the negative effects of greater heat and drought this century. In order to maximize yields under future atmospheric [CO2 ], we need to identify and study crop cultivars that respond most favorably to elevated [CO2 ] and understand the mechanisms contributing to their responsiveness. Soybean (Glycine max Merr.) is a widely grown oilseed crop and shows genetic variation in response to elevated [CO2 ]. However, few studies have studied the physiological basis for this variation. Here, we examined canopy light interception, photosynthesis, respiration and radiation use efficiency along with yield and yield parameters in two cultivars of soybean (Loda and HS93-4118) previously reported to have similar seed yield at ambient [CO2 ], but contrasting responses to elevated [CO2 ]. Seed yield increased by 26% at elevated [CO2 ] (600 µmol/mol) in the responsive cultivar Loda, but only by 11% in HS93-4118. Canopy light interception and leaf area index were greater in HS93-4118 in ambient [CO2 ], but increased more in response to elevated [CO2 ] in Loda. Radiation use efficiency and harvest index were also greater in Loda than HS93-4118 at both ambient and elevated [CO2 ]. Daily C assimilation was greater at elevated [CO2 ] in both cultivars, while stomatal conductance was lower. Electron transport capacity was also greater in Loda than HS93-4118, but there was no difference in the response of photosynthetic traits to elevated [CO2 ] in the two cultivars. Overall, this greater understanding of leaf- and canopy-level photosynthetic traits provides a strong conceptual basis for modeling genotypic variation in response to elevated [CO2 ].

Two decades of fumigation data from the Soybean Free Air Concentration Enrichment facility.

The Soybean Free Air Concentration Enrichment (SoyFACE) facility is the longest running open-air carbon dioxide and ozone enrichment facility in the world. For over two decades, soybean, maize, and other crops have been exposed to the elevated carbon dioxide and ozone concentrations anticipated for late this century. The facility, located in East Central Illinois, USA, exposes crops to different atmospheric concentrations in replicated octagonal ~280 m2 Free Air Concentration Enrichment (FACE) treatment plots. Each FACE plot is paired with an untreated control (ambient) plot. The experiment provides important ground truth data for predicting future crop productivity. Fumigation data from SoyFACE were collected every four seconds throughout each growing season for over two decades. Here, we organize, quality control, and collate 20 years of data to facilitate trend analysis and crop modeling efforts. This paper provides the rationale for and a description of the SoyFACE experiments, along with a summary of the fumigation data and collation process, weather and ambient data collection procedures, and explanations of air pollution metrics and calculations.

High-throughput characterization, correlation, and mapping of leaf photosynthetic and functional traits in the soybean (Glycine max) nested association mapping population.

Photosynthesis is a key target to improve crop production in many species including soybean [Glycine max (L.) Merr.]. A challenge is that phenotyping photosynthetic traits by traditional approaches is slow and destructive. There is proof-of-concept for leaf hyperspectral reflectance as a rapid method to model photosynthetic traits. However, the crucial step of demonstrating that hyperspectral approaches can be used to advance understanding of the genetic architecture of photosynthetic traits is untested. To address this challenge, we used full-range (500-2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits, including the rate-limiting processes of photosynthesis, maximum Rubisco carboxylation rate, and maximum electron transport. In total, 11 models were produced from a diverse population of soybean sampled over multiple field seasons to estimate photosynthetic parameters, chlorophyll content, leaf carbon and leaf nitrogen percentage, and specific leaf area (with R2 from 0.56 to 0.96 and root mean square error approximately <10% of the range of calibration data). We explore the utility of these models by applying them to the soybean nested association mapping population, which showed variability in photosynthetic and leaf traits. Genetic mapping provided insights into the underlying genetic architecture of photosynthetic traits and potential improvement in soybean. Notably, the maximum Rubisco carboxylation rate mapped to a region of chromosome 19 containing genes encoding multiple small subunits of Rubisco. We also mapped the maximum electron transport rate to a region of chromosome 10 containing a fructose 1,6-bisphosphatase gene, encoding an important enzyme in the regeneration of ribulose 1,5-bisphosphate and the sucrose biosynthetic pathway. The estimated rate-limiting steps of photosynthesis were low or negatively correlated with yield suggesting that these traits are not influenced by the same genetic mechanisms and are not limiting yield in the soybean NAM population. Leaf carbon percentage, leaf nitrogen percentage, and specific leaf area showed strong correlations with yield and may be of interest in breeding programs as a proxy for yield. This work is among the first to use hyperspectral reflectance to model and map the genetic architecture of the rate-limiting steps of photosynthesis.

Assessing diversity in canopy architecture, photosynthesis, and water-use efficiency in a cowpea magic population.

Optimizing crops to improve light absorption and CO2 assimilation throughout the canopy is a proposed strategy to increase yield and meet the needs of a growing population by 2050. Globally, the greatest population increase is expected to occur in Sub-Saharan Africa where large yield gaps currently persist; therefore, it is crucial to develop high-yielding crops adapted to this region. In this study, we screened 50 cowpea (Vigna unguiculata (L.) Walp) genotypes from the multi-parent advanced generation inter-cross (MAGIC) population for canopy architectural traits, canopy photosynthesis, and water-use efficiency using a canopy gas exchange chamber in order to improve our understanding of the relationships among those traits. Canopy architecture contributed to 38.6% of the variance observed in canopy photosynthesis. The results suggest that the light environment within the canopy was a limiting factor for canopy CO2 assimilation. Traits favoring greater exposure of leaf area to light such as the width of the canopy relative to the total leaf area were associated with greater canopy photosynthesis, especially in canopies with high biomass. Canopy water-use efficiency was highly determined by canopy photosynthetic activity and therefore canopy architecture, which indicates that optimizing the canopy will also contribute to improving canopy water-use efficiency. We discuss different breeding strategies for future programs aimed at the improvement of cowpea yield for the Sub-Saharan African region. We show that breeding for high biomass will not optimize canopy CO2 assimilation and suggest that selection should include multiple canopy traits to improve light penetration.