Spectral reflectance from a soybean canopy exposed to elevated CO2 and O3

Solar-induced chlorophyll fluorescence captures the effects of elevated ozone on canopy structure and acceleration of senescence in soybean

Solar-induced chlorophyll fluorescence (SIF) provides an opportunity to rapidly and non-destructively investigate how plants respond to stress. Here, we explored the potential of SIF to detect the effects of elevated O3 on soybean in the field where soybean was subjected to ambient and elevated O3 throughout the growing season in 2021. Exposure to elevated O3 resulted in a significant decrease in canopy SIF at 760 nm (SIF760), with a larger decrease in the late growing season (36%) compared with the middle growing season (13%). Elevated O3 significantly decreased the fraction of absorbed photosynthetically active radiation by 8-15% in the middle growing season and by 35% in the late growing stage. SIF760 escape ratio (fesc) was significantly increased under elevated O3 by 5-12% in the late growth stage due to a decrease of leaf chlorophyll content and leaf area index. Fluorescence yield of the canopy was reduced by 5-11% in the late growing season depending on the fesc estimation method, during which leaf maximum carboxylation rate and maximum electron transport were significantly reduced by 29% and 20% under elevated O3. These results demonstrated that SIF could capture the elevated O3 effect on canopy structure and acceleration of senescence in soybean and provide empirical support for using SIF for soybean stress detection and phenotyping.

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.

Physiological and Multi-Omics Approaches for Explaining Drought Stress Tolerance and Supporting Sustainable Production of Rice

Drought differs from other natural disasters in several respects, largely because of the complexity of a crop's response to it and also because we have the least understanding of a crop's inductive mechanism for addressing drought tolerance among all abiotic stressors. Overall, the growth and productivity of crops at a global level is now thought to be an issue that is more severe and arises more frequently due to climatic change-induced drought stress. Among the major crops, rice is a frontline staple cereal crop of the developing world and is critical to sustaining populations on a daily basis. Worldwide, studies have reported a reduction in rice productivity over the years as a consequence of drought. Plants are evolutionarily primed to withstand a substantial number of environmental cues by undergoing a wide range of changes at the molecular level, involving gene, protein and metabolite interactions to protect the growing plant. Currently, an in-depth, precise and systemic understanding of fundamental biological and cellular mechanisms activated by crop plants during stress is accomplished by an umbrella of -omics technologies, such as transcriptomics, metabolomics and proteomics. This combination of multi-omics approaches provides a comprehensive understanding of cellular dynamics during drought or other stress conditions in comparison to a single -omics approach. Thus a greater need to utilize information (big-omics data) from various molecular pathways to develop drought-resilient crop varieties for cultivation in ever-changing climatic conditions. This review article is focused on assembling current peer-reviewed published knowledge on the use of multi-omics approaches toward expediting the development of drought-tolerant rice plants for sustainable rice production and realizing global food security.

Hyperspectral Estimation Models of Winter Wheat Chlorophyll Content Under Elevated CO2

Chlorophyll content is an important indicator of winter wheat health status. It is valuable to investigate whether the relationship between spectral reflectance and the chlorophyll content differs under elevated CO₂ condition. In this open-top chamber experiment, the CO₂ treatments were categorized into ambient (aCO₂; about 400 μmol⋅mol^-1) or elevated (eCO₂; ambient + 200 μmol⋅mol^-1) levels. The correlation between the spectral reflectance and the chlorophyll content of the winter wheat were analyzed by constructing the estimation model based on red edge position, sensitive band and spectral index methods, respectively. The results showed that there was a close relationship between chlorophyll content and the canopy spectral curve characteristics of winter wheat. Chlorophyll content was better estimated based on sensitive spectral bands and difference vegetation index (DVI) under both aCO₂ and eCO₂ conditions, though the accuracy of the models varied under different CO₂ conditions. The results suggested that the hyperspectral measurement can be effectively used to estimate the chlorophyll content under both aCO₂ and eCO₂ conditionsand could provide a useful tool for monitoring plants physiology and growth.

Reflectance spectroscopy: a novel approach to better understand and monitor the impact of air pollution on Mediterranean plants

The Mediterranean basin can be considered a hot spot not only in terms of climate change (CC) but also for air quality. Assessing the impact of CC and air pollution on ecosystem functions is a challenging task, and adequate monitoring techniques are needed. This paper summarizes the present knowledge on the use of reflectance spectroscopy for the evaluation of the effects of air pollution on plants. First, the history of this technique is outlined. Next, we describe the vegetation reflectance spectrum, how it can be scaled from leaf to landscape levels, what information it contains, and how it can be exploited to understand plant and ecosystem functions. Finally, we review the literature concerning this topic, with special attention to Mediterranean air pollutants, showing the increasing interest in this technique. The ability of spectroscopy to detect the influence of air pollution on plant function of all major and minor Mediterranean pollutants has been evaluated, and ozone and its interaction with other gases (carbon dioxide, nitrogen oxides, and sulfur dioxide) have been the most studied. In the recent years, novel air pollutants, such as particulate matter, nitrogen deposition, and heavy metals, have drawn attention. Although various vegetation types have been studied, few of these species are representative of the Mediterranean environment. Thus, major emphasis should be placed on using vegetation spectroscopy for better understanding and monitoring the impact of air pollution on Mediterranean plants in the CC era.

Beyond greenness: Detecting temporal changes in photosynthetic capacity with hyperspectral reflectance data

Earth's future carbon balance and regional carbon exchange dynamics are inextricably linked to plant photosynthesis. Spectral vegetation indices are widely used as proxies for vegetation greenness and to estimate state variables such as vegetation cover and leaf area index. However, the capacity of green leaves to take up carbon can change throughout the season. We quantify photosynthetic capacity as the maximum rate of RuBP carboxylation (Vcmax) and regeneration (Jmax). Vcmax and Jmax vary within-season due to interactions between ontogenetic processes and meteorological variables. Remote sensing-based estimation of Vcmax and Jmax using leaf reflectance spectra is promising, but temporal variation in relationships between these key determinants of photosynthetic capacity, leaf reflectance spectra, and the models that link these variables has not been evaluated. To address this issue, we studied hybrid poplar (Populus spp.) during a 7-week mid-summer period to quantify seasonally-dynamic relationships between Vcmax, Jmax, and leaf spectra. We compared in situ estimates of Vcmax and Jmax from gas exchange measurements to estimates of Vcmax and Jmax derived from partial least squares regression (PLSR) and fresh-leaf reflectance spectroscopy. PLSR models were robust despite dynamic temporal variation in Vcmax and Jmax throughout the study period. Within-population variation in plant stress modestly reduced PLSR model predictive capacity. Hyperspectral vegetation indices were well-correlated to Vcmax and Jmax, including the widely-used Normalized Difference Vegetation Index. Our results show that hyperspectral estimation of plant physiological traits using PLSR may be robust to temporal variation. Additionally, hyperspectral vegetation indices may be sufficient to detect temporal changes in photosynthetic capacity in contexts similar to those studied here. Overall, our results highlight the potential for hyperspectral remote sensing to estimate determinants of photosynthetic capacity during periods with dynamic temporal variations related to seasonality and plant stress, thereby improving estimates of plant productivity and characterization of the associated carbon budget.

Intensifying drought eliminates the expected benefits of elevated carbon dioxide for soybean

Stimulation of C3 crop yield by rising concentrations of atmospheric carbon dioxide ([CO2]) is widely expected to counteract crop losses that are due to greater drought this century. But these expectations come from sparse field trials that have been biased towards mesic growth conditions. This eight-year study used precipitation manipulation and year-to-year variation in weather conditions at a unique open-air field facility to show that the stimulation of soybean yield by elevated [CO2] diminished to zero as drought intensified. Contrary to the prevalent expectation in the literature, rising [CO2] did not counteract the effect of strong drought on photosynthesis and yield because elevated [CO2] interacted with drought to modify stomatal function and canopy energy balance. This new insight from field experimentation under hot and dry conditions, which will become increasingly prevalent in the coming decades, highlights the likelihood of negative impacts from interacting global change factors on a key global commodity crop in its primary region of production.

Genome-assisted Breeding For Drought Resistance

Drought stress caused by unpredictable precipitation poses a major threat to food production worldwide, and its impact is only expected to increase with the further onset of climate change. Understanding the effect of drought stress on crops and plants' response is critical for developing improved varieties with stable high yield to fill a growing food gap from an increasing population depending on decreasing land and water resources. When a plant encounters drought stress, it may use multiple response types, depending on environmental conditions, drought stress intensity and duration, and the physiological stage of the plant. Drought stress responses can be divided into four broad types: drought escape, drought avoidance, drought tolerance, and drought recovery, each characterized by interacting mechanisms, which may together be referred to as drought resistance mechanisms. The complex nature of drought resistance requires a multi-pronged approach to breed new varieties with stable and enhanced yield under drought stress conditions. High throughput genomics and phenomics allow marker-assisted selection (MAS) and genomic selection (GS), which offer rapid and targeted improvement of populations and identification of parents for rapid genetic gains and improved drought-resistant varieties. Using these approaches together with appropriate genetic diversity, databases, analytical tools, and well-characterized drought stress scenarios, weather and soil data, new varieties with improved drought resistance corresponding to grower preferences can be introduced into target regions rapidly.

Ozone impacts on vegetation in a nitrogen enriched and changing climate

This paper provides a process-oriented perspective on the combined effects of ozone (O3), climate change and/or nitrogen (N) on vegetation. Whereas increasing CO2 in controlled environments or open-top chambers often ameliorates effects of O3 on leaf physiology, growth and C allocation, this is less likely in the field. Combined responses to elevated temperature and O3 have rarely been studied even though some critical growth stages such as seed initiation are sensitive to both. Under O3 exposure, many species have smaller roots, thereby enhancing drought sensitivity. Of the 68 species assessed for stomatal responses to ozone, 22.5% were unaffected, 33.5% had sluggish or increased opening and 44% stomatal closure. The beneficial effect of N on root development was lost at higher O3 treatments whilst the effects of increasing O3 on root biomass became more pronounced as N increased. Both responses to gradual changes in pollutants and climate and those under extreme weather events require further study.

Using leaf optical properties to detect ozone effects on foliar biochemistry

Efficient methods for accurate and meaningful high-throughput plant phenotyping are limiting the development and breeding of stress-tolerant crops. A number of emerging techniques, specifically remote sensing methods, have been identified as promising tools for plant phenotyping. These remote sensing methods can be used to accurately and rapidly relate variations in leaf optical properties with important plant characteristics, such as chemistry, morphology, and photosynthetic properties at the leaf and canopy scales. In this study, we explored the potential to utilize optical (λ = 500-2,400 nm) near-surface remote sensing reflectance spectroscopy to evaluate the effects of ozone pollution on photosynthetic capacity of soybean (Glycine max Merr.). The research was conducted at the Soybean Free Air Concentration Enrichment (SoyFACE) facility where we subjected plants to ambient (44 nL L(-1)) and elevated ozone (79-82 nL L(-1) target) concentrations throughout the growing season. Exposure to elevated ozone resulted in a significant loss of productivity, with the ozone-treated plants displaying a ~30 % average decrease in seed yield. From leaf reflectance data, it was also clear that elevated ozone decreased leaf nitrogen and chlorophyll content as well as the photochemical reflectance index (PRI), an optical indicator of the epoxidation state of xanthophyll cycle pigments and thus physiological status. We assessed the potential to use leaf reflectance properties and partial least-squares regression (PLSR) modeling as an alternative, rapid approach to standard gas exchange for the estimation of the maximum rates of RuBP carboxylation (V c,max), an important parameter describing plant photosynthetic capacity. While we did not find a significant impact of ozone fumigation on V c,max, standardized to a reference temperature of 25 °C, the PLSR approach provided accurate and precise estimates of V c,max across ambient plots and ozone treatments (r (2) = 0.88 and RMSE = 13.4 μmol m(-2) s(-1)) based only on the variation in leaf optical properties and despite significant variability in leaf nutritional status. The results of this study illustrate the potential for combining the phenotyping methods used here with high-throughput genotyping methods as a promising approach for elucidating the basis for ozone tolerance in sensitive crops.

Phenotyping for drought tolerance of crops in the genomics era

Improving crops yield under water-limited conditions is the most daunting challenge faced by breeders. To this end, accurate, relevant phenotyping plays an increasingly pivotal role for the selection of drought-resilient genotypes and, more in general, for a meaningful dissection of the quantitative genetic landscape that underscores the adaptive response of crops to drought. A major and universally recognized obstacle to a more effective translation of the results produced by drought-related studies into improved cultivars is the difficulty in properly phenotyping in a high-throughput fashion in order to identify the quantitative trait loci that govern yield and related traits across different water regimes. This review provides basic principles and a broad set of references useful for the management of phenotyping practices for the study and genetic dissection of drought tolerance and, ultimately, for the release of drought-tolerant cultivars.

Integrated genomics, physiology and breeding approaches for improving drought tolerance in crops

Drought is one of the most serious production constraint for world agriculture and is projected to worsen with anticipated climate change. Inter-disciplinary scientists have been trying to understand and dissect the mechanisms of plant tolerance to drought stress using a variety of approaches; however, success has been limited. Modern genomics and genetic approaches coupled with advances in precise phenotyping and breeding methodologies are expected to more effectively unravel the genes and metabolic pathways that confer drought tolerance in crops. This article discusses the most recent advances in plant physiology for precision phenotyping of drought response, a vital step before implementing the genetic and molecular-physiological strategies to unravel the complex multilayered drought tolerance mechanism and further exploration using molecular breeding approaches for crop improvement. Emphasis has been given to molecular dissection of drought tolerance by QTL or gene discovery through linkage and association mapping, QTL cloning, candidate gene identification, transcriptomics and functional genomics. Molecular breeding approaches such as marker-assisted backcrossing, marker-assisted recurrent selection and genome-wide selection have been suggested to be integrated in crop improvement strategies to develop drought-tolerant cultivars that will enhance food security in the context of a changing and more variable climate.

Leaf optical properties reflect variation in photosynthetic metabolism and its sensitivity to temperature

Researchers from a number of disciplines have long sought the ability to estimate the functional attributes of plant canopies, such as photosynthetic capacity, using remotely sensed data. To date, however, this goal has not been fully realized. In this study, fresh-leaf reflectance spectroscopy (λ=450-2500 nm) and a partial least-squares regression (PLSR) analysis were used to estimate key determinants of photosynthetic capacity-namely the maximum rates of RuBP carboxylation (V(cmax)) and regeneration (J(max))-measured with standard gas exchange techniques on leaves of trembling aspen and eastern cottonwood trees. The trees were grown across an array of glasshouse temperature regimes. The PLSR models yielded accurate and precise estimates of V(cmax) and J(max) within and across species and glasshouse temperatures. These predictions were developed using unique contributions from different spectral regions. Most of the wavelengths selected were correlated with known absorption features related to leaf water content, nitrogen concentration, internal structure, and/or photosynthetic enzymes. In a field application of our PLSR models, spectral reflectance data effectively captured the short-term temperature sensitivities of V(cmax) and J(max) in aspen foliage. These findings highlight a promising strategy for developing remote sensing methods to characterize dynamic, environmentally sensitive aspects of canopy photosynthetic metabolism at broad scales.