Photorespiration is essential for C3 plants but operates at the massive expense of fixed carbon dioxide and energy. Photorespiration is initiated when the initial enzyme of photosynthesis, ...ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco), reacts with oxygen instead of carbon dioxide and produces a toxic compound that is then recycled by photorespiration. Photorespiration can be modeled at the canopy and regional scales to determine its cost under current and future atmospheres. A regional-scale model reveals that photorespiration currently decreases US soybean and wheat yields by 36% and 20%, respectively, and a 5% decrease in the losses due to photorespiration would be worth approximately $500 million annually in the United States. Furthermore, photorespiration will continue to impact yield under future climates despite increases in carbon dioxide, with models suggesting a 12-55% improvement in gross photosynthesis in the absence of photorespiration, even under climate change scenarios predicting the largest increases in atmospheric carbon dioxide concentration. Although photorespiration is tied to other important metabolic functions, the benefit of improving its efficiency appears to outweigh any potential secondary disadvantages.
Global agriculture production is challenged by increasing demands from rising population and a changing climate, which may be alleviated through development of genetically improved crop cultivars. ...Research into increasing photosynthetic energy conversion efficiency has proposed many strategies to improve production but have yet to yield real-world solutions, largely because of a phenotyping bottleneck. Partial least squares regression (PLSR) is a statistical technique that is increasingly used to relate hyperspectral reflectance to key photosynthetic capacities associated with carbon uptake (maximum carboxylation rate of Rubisco,
) and conversion of light energy (maximum electron transport rate supporting RuBP regeneration,
) to alleviate this bottleneck. However, its performance varies significantly across different plant species, regions, and growth environments. Thus, to cope with the heterogeneous performances of PLSR, this study aims to develop a new approach to estimate photosynthetic capacities. A framework was developed that combines six machine learning algorithms, including artificial neural network (ANN), support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), random forest (RF), Gaussian process (GP), and PLSR to optimize high-throughput analysis of the two photosynthetic variables. Six tobacco genotypes, including both transgenic and wild-type lines, with a range of photosynthetic capacities were used to test the framework. Leaf reflectance spectra were measured from 400 to 2500 nm using a high-spectral-resolution spectroradiometer. Corresponding photosynthesis vs. intercellular CO
concentration response curves were measured for each leaf using a leaf gas-exchange system. Results suggested that the mean
value of the six regression techniques for predicting
(
) ranged from 0.60 (0.45) to 0.65 (0.56) with the mean
value varying from 47.1 (40.1) to 54.0 (44.7) μmol m
s
. Regression stacking for
(
) performed better than the individual regression techniques with increases in
of 0.1 (0.08) and decreases in
by 4.1 (6.6) μmol m
s
, equal to 8% (15%) reduction in
. Better predictive performance of the regression stacking is likely attributed to the varying coefficients (or weights) in the level-2 model (the LASSO model) and the diverse ability of each individual regression technique to utilize spectral information for the best modeling performance. Further refinements can be made to apply this stacked regression technique to other plant phenotypic traits.
Transpiration--the movement of water from the soil, through plants, and into the atmosphere--is the dominant water flux from the earth's terrestrial surface. The evolution of vascular plants, while ...increasing terrestrial primary productivity, led to higher transpiration rates and widespread alterations in the global climate system. Similarly, anthropogenic influences on transpiration rates are already influencing terrestrial hydrologic cycles, with an even greater potential for changes lying ahead. Intricate linkages among anthropogenic activities, terrestrial productivity, the hydrologic cycle, and global demand for ecosystem services will lead to increased pressures on ecosystem water demands. Here, we focus on identifying the key drivers of ecosystem water use as they relate to plant physiological function, the role of predicted global changes in ecosystem water uses, trade-offs between ecosystem water use and carbon uptake, and knowledge gaps.
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 (Vc,max), maximum electron transport rate (Jmax) 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 Vc,max, Jmax 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 Vc,max, but not for Jmax, 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 Vc,max. and 13% Jmax. Plants generally have a positive correlation between leaf nitrogen and photosynthesis, however, tobacco with reduced Rubisco (SSuD) had significantly higher N despite much lower Vc,max. The PLSR model was able to accurately predict both lower Vc,max and higher leaf N for this genotype suggesting that the spectral based estimates of Vc,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.
•We built PLS models to predict photosynthesis from hyperspectral reflectance.•The models work for plants with genetic modification to photosynthesis.•Predicted photosynthetic capacity is uncoupled from leaf nitrogen.•PLS photosynthesis models must be built with representation of all genetic material.
This review explores current understanding in crop photosynthesis and temperature across scales, linking enzyme processes within the leaf to stomata, plant transport systems, individual plants, and ...ecosystem-scale responses.
Abstract
As global land surface temperature continues to rise and heatwave events increase in frequency, duration, and/or intensity, our key food and fuel cropping systems will likely face increased heat-related stress. A large volume of literature exists on exploring measured and modelled impacts of rising temperature on crop photosynthesis, from enzymatic responses within the leaf up to larger ecosystem-scale responses that reflect seasonal and interannual crop responses to heat. This review discusses (i) how crop photosynthesis changes with temperature at the enzymatic scale within the leaf; (ii) how stomata and plant transport systems are affected by temperature; (iii) what features make a plant susceptible or tolerant to elevated temperature and heat stress; and (iv) how these temperature and heat effects compound at the ecosystem scale to affect crop yields. Throughout the review, we identify current advancements and future research trajectories that are needed to make our cropping systems more resilient to rising temperature and heat stress, which are both projected to occur due to current global fossil fuel emissions.
Plant responses to the projected future levels of CO2 were first characterized in short-term experiments lasting days to weeks. However, longer term acclimation responses to elevated CO2 were ...subsequently discovered to be very important in determining plant and ecosystem function. Free-Air CO2 Enrichment (FACE) experiments are the culmination of efforts to assess the impact of elevated CO2 on plants over multiple seasons and, in the case of crops, over their entire lifetime. FACE has been used to expose vegetation to elevated concentrations of atmospheric CO2 under completely open-air conditions for nearly two decades. This review describes some of the lessons learned from the long-term investment in these experiments. First, elevated CO2 stimulates photosynthetic carbon gain and net primary production over the long term despite down-regulation of Rubisco activity. Second, elevated CO2 improves nitrogen use efficiency and, third, decreases water use at both the leaf and canopy scale. Fourth, elevated CO2 stimulates dark respiration via a transcriptional reprogramming of metabolism. Fifth, elevated CO2 does not directly stimulate C4 photosynthesis, but can indirectly stimulate carbon gain in times and places of drought. Finally, the stimulation of yield by elevated CO2 in crop species is much smaller than expected. While many of these lessons have been most clearly demonstrated in crop systems, all of the lessons have important implications for natural systems.
Photosynthetic responses to carbon dioxide concentration can provide data on a number of important parameters related to leaf physiology. Methods for fitting a model to such data are briefly ...described. The method will fit the following parameters: Vcmax, J, TPU, Rd and gmmaximum carboxylation rate allowed by ribulose 1·5-bisphosphate carboxylase/oxygenase (Rubisco), rate of photosynthetic electron transport (based on NADPH requirement), triose phosphate use, day respiration and mesophyll conductance, respectively. The method requires at least five data pairs of net CO₂ assimilation (A) and CO₂ in the intercellular airspaces of the leaf (Ci) and requires users to indicate the presumed limiting factor. The output is (1) calculated CO₂ partial pressure at the sites of carboxylation, Cc, (2) values for the five parameters at the measurement temperature and (3) values adjusted to 25 °C to facilitate comparisons. Fitting this model is a way of exploring leaf level photosynthesis. However, interpreting leaf level photosynthesis in terms of underlying biochemistry and biophysics is subject to assumptions that hold to a greater or lesser degree, a major assumption being that all parts of the leaf are behaving in the same way at each instant.
Summary
Adapting crops to warmer growing season temperatures is a major challenge in mitigating the impacts of climate change on crop production. Warming temperatures drive greater evaporative demand ...and can directly interfere with both reproductive and vegetative physiological processes. Most of the world’s crop species have C3 photosynthetic metabolism for which increasing temperature means higher rates of photorespiration, wherein the enzyme responsible for fixing CO2 fixes O2 instead followed by an energetically costly recycling pathway that spans several cell compartments. In C3 crops like wheat, rice and soybean, photorespiration translates into large yield losses that are predicted to increase as global temperature warms. Engineering less energy‐intensive alternative photorespiratory pathways into crop chloroplasts drives increases in C3 biomass production under agricultural field conditions, but the efficacy of these pathways in mitigating the impact of warmer growing temperatures has not been tested. We grew tobacco plants expressing an alternative photorespiratory pathway under current and elevated temperatures (+5 °C) in agricultural field conditions. Engineered plants exhibited higher photosynthetic quantum efficiency under heated conditions than the control plants, and produced 26% (between 16% and 37%) more total biomass than WT plants under heated conditions, compared to 11% (between 5% and 17%) under ambient conditions. That is, engineered plants sustained 19% (between 11% and 21%) less yield loss under heated conditions compared to non‐engineered plants. These results support the theoretical predictions of temperature impacts on photorespiratory losses and provide insight toward the optimisation strategies required to help sustain or improve C3 crop yields in a warming climate.
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.
Leaf area index (LAI) is a key variable for characterizing crop growth conditions and estimating crop productivity. Despite continuing efforts to develop LAI estimation algorithms, LAI datasets still ...need improvement at spatial and temporal resolutions to meet the requirements of agricultural applications. Advancements in data fusion technique and the emergence of new satellite data provide opportunities for LAI data at higher resolutions in both space and time. In this study, we derived new LAI estimations by leveraging novel satellite remote sensing datasets, STAIR fusion (MODIS-Landsat fusion) and Planet Labs' CubeSat data (through a reprocessed pipeline) for a typical agricultural landscape in the U.S. Corn Belt. The STAIR fused data and our reprocessed CubeSat data have both fine spatial resolutions (30 m and 3.125 m, respectively) and high frequencies (daily for both). To reliably estimate LAI from these advanced satellite datasets, we used two methods: inversion of a radiative transfer model (RTM), and empirical relationship with vegetation index (VI) calibrated from field measured LAI. Compared to the ground-truth LAI collected at 36 sites across the study region, reliable approximations were achieved by both LAI estimations based on PROSAIL RTM (STAIR: R2 = 0.69 and root mean squared error (RMSE) = 1.12 (m2 m−2), CubeSat: R2 = 0.76 and RMSE = 1.09 (m2 m−2)), and LAI estimations based on Green Wide Dynamic Range Vegetation Index (GrWDRVI) (STAIR: R2 = 0.75, RMSE = 1.10 (m2 m−2), CubeSat: R2 = 0.76, RMSE = 1.08 (m2 m−2), where validation ground-truth is independent from calibration data). Newly estimated high-resolution LAI data were aggregated at 500 m resolution and compared with MODIS and VIIRS LAI products, revealing substantial uncertainties and biases in these two products. We also demonstrated phenology stage estimation at fine spatial resolutions based on our high-frequency LAI data. The proposed LAI estimation methods at both high spatial resolution and temporal frequency can be applied to the entire U.S. Corn Belt and provide significant advancement to crop monitoring and precision agriculture.
•High-spatiotemporal-resolution LAI is achieved from MODIS-Landsat Fusion and CubeSat.•Both empirical and process-based LAI estimations achieve high performance.•The MODIS and VIIRS LAI products show large bias for cropland.
PDF
