Converging phenomics and genomics to study natural variation in plant photosynthetic efficiency

Exploring natural genetic variation in photosynthesis-related traits of barley in the field

Optimizing photosynthesis is considered an important strategy for improving crop yields to ensure food security. To evaluate the potential of using photosynthesis-related parameters in crop breeding programs, we measured chlorophyll fluorescence along with growth-related and morphological traits of 23 barley inbred lines across different developmental stages in field conditions. The photosynthesis-related parameters were highly variable, changing with light intensity and developmental progression of plants. Yet, the variation in photosystem II quantum yield observed among the inbred lines in the field largely reflected the variation in CO2 assimilation properties in controlled climate chamber conditions, confirming that the chlorophyll fluorescence-based technique can provide proxy parameters of photosynthesis to explore genetic variation under field conditions. Heritability (H2) of the photosynthesis-related parameters in the field ranged from 0.16 for the quantum yield of non-photochemical quenching to 0.78 for the fraction of open photosystem II center. Two parameters, the maximum photosystem II efficiency in the light-adapted state (H2=0.58) and the total non-photochemical quenching (H2=0.53), showed significant positive and negative correlations, respectively, with yield-related traits (dry weight per plant and net straw weight) in the barley inbred lines. These results indicate the possibility of improving crop yield through optimizing photosynthetic light use efficiency by conventional breeding programs.

Modern phenomics to empower holistic crop science, agronomy, and breeding research

Crop phenomics enables the collection of diverse plant traits for a large number of samples along different time scales, representing a greater data collection throughput compared with traditional measurements. Most modern crop phenomics use different sensors to collect reflective, emitted, and fluorescence signals, etc., from plant organs at different spatial and temporal resolutions. Such multi-modal, high-dimensional data not only accelerates basic research on crop physiology, genetics, and whole plant systems modeling, but also supports the optimization of field agronomic practices, internal environments of plant factories, and ultimately crop breeding. Major challenges and opportunities facing the current crop phenomics research community include developing community consensus or standards for data collection, management, sharing, and processing, developing capabilities to measure physiological parameters, and enabling farmers and breeders to effectively use phenomics in the field to directly support agricultural production.

Genome-wide association study of leaf photosynthesis using a high-throughput gas exchange system in rice

Enhancing leaf photosynthetic capacity is essential for improving the yield of rice (Oryza sativa L.). Although the exploitation of natural genetic resources is considered a promising approach to enhance photosynthetic capacity, genomic factors related to the genetic diversity of leaf photosynthetic capacity have yet to be fully elucidated due to the limitation of measurement efficiency. In this study, we aimed to identify novel genomic regions for the net CO2 assimilation rate (A) by combining genome-wide association study (GWAS) and the newly developed rapid closed gas exchange system MIC-100. Using three MIC-100 systems in the field at the vegetative stage, we measured A of 168 temperate japonica rice varieties with six replicates for three years. We found that the modern varieties exhibited higher A than the landraces, while there was no significant relationship between the release year and A among the modern varieties. Our GWAS scan revealed two major peaks located on chromosomes 4 and 8, which were repeatedly detected in the different experiments and in the generalized linear modelling approach. We suggest that high-throughput gas exchange measurements combined with GWAS is a reliable approach for understanding the genetic mechanisms underlying photosynthetic diversities in crop species.

Recent advances in artificial intelligence, mechanistic models, and speed breeding offer exciting opportunities for precise and accelerated genomics-assisted breeding

Given the challenges of population growth and climate change, there is an urgent need to expedite the development of high-yielding stress-tolerant crop cultivars. While traditional breeding methods have been instrumental in ensuring global food security, their efficiency, precision, and labour intensiveness have become increasingly inadequate to address present and future challenges. Fortunately, recent advances in high-throughput phenomics and genomics-assisted breeding (GAB) provide a promising platform for enhancing crop cultivars with greater efficiency. However, several obstacles must be overcome to optimize the use of these techniques in crop improvement, such as the complexity of phenotypic analysis of big image data. In addition, the prevalent use of linear models in genome-wide association studies (GWAS) and genomic selection (GS) fails to capture the nonlinear interactions of complex traits, limiting their applicability for GAB and impeding crop improvement. Recent advances in artificial intelligence (AI) techniques have opened doors to nonlinear modelling approaches in crop breeding, enabling the capture of nonlinear and epistatic interactions in GWAS and GS and thus making this variation available for GAB. While statistical and software challenges persist in AI-based models, they are expected to be resolved soon. Furthermore, recent advances in speed breeding have significantly reduced the time (3-5-fold) required for conventional breeding. Thus, integrating speed breeding with AI and GAB could improve crop cultivar development within a considerably shorter timeframe while ensuring greater accuracy and efficiency. In conclusion, this integrated approach could revolutionize crop breeding paradigms and safeguard food production in the face of population growth and climate change.

Multi-Omics Pipeline and Omics-Integration Approach to Decipher Plant's Abiotic Stress Tolerance Responses

The present day's ongoing global warming and climate change adversely affect plants through imposing environmental (abiotic) stresses and disease pressure. The major abiotic factors such as drought, heat, cold, salinity, etc., hamper a plant's innate growth and development, resulting in reduced yield and quality, with the possibility of undesired traits. In the 21st century, the advent of high-throughput sequencing tools, state-of-the-art biotechnological techniques and bioinformatic analyzing pipelines led to the easy characterization of plant traits for abiotic stress response and tolerance mechanisms by applying the 'omics' toolbox. Panomics pipeline including genomics, transcriptomics, proteomics, metabolomics, epigenomics, proteogenomics, interactomics, ionomics, phenomics, etc., have become very handy nowadays. This is important to produce climate-smart future crops with a proper understanding of the molecular mechanisms of abiotic stress responses by the plant's genes, transcripts, proteins, epigenome, cellular metabolic circuits and resultant phenotype. Instead of mono-omics, two or more (hence 'multi-omics') integrated-omics approaches can decipher the plant's abiotic stress tolerance response very well. Multi-omics-characterized plants can be used as potent genetic resources to incorporate into the future breeding program. For the practical utility of crop improvement, multi-omics approaches for particular abiotic stress tolerance can be combined with genome-assisted breeding (GAB) by being pyramided with improved crop yield, food quality and associated agronomic traits and can open a new era of omics-assisted breeding. Thus, multi-omics pipelines together are able to decipher molecular processes, biomarkers, targets for genetic engineering, regulatory networks and precision agriculture solutions for a crop's variable abiotic stress tolerance to ensure food security under changing environmental circumstances.

PhytoOracle: Scalable, modular phenomics data processing pipelines

As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to (i) improve data processing efficiency; (ii) provide an extensible, reproducible computing framework; and (iii) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area).

Long-Term Alleviation of the Functional Phenotype in Chlorophyll-Deficient Wheat and Impact on Productivity: A Semi-Field Phenotyping Experiment

Wheat mutants with a reduced chlorophyll synthesis are affected by a defective control of the photosynthetic electron flow, but tend to recover a wild-type phenotype. The sensitivity of some mutants to light fluctuations suggested that cultivation outdoors could significantly impact productivity. Six mutant lines of Triticum durum or Triticum aestivum with their respective wild-type cultivars were cultivated with a regular seasonal cycle (October-May) in a semi-field experiment. Leaf chlorophyll content and fluorescence parameters were analysed at the early (November) and late (May) developmental stages, and checked for correlation with morphometric and grain-production parameters. The alleviation of the phenotype severity concerned primarily the recovery of the photosynthetic-membrane functionality, but not the leaf chlorophyll content. Photosystem II (PSII) was less photoprotected in the mutants, but a moderate PSII photoinhibition could help control the electron flow into the chain. The accumulation of interchain electron carriers was a primary acclimative response towards the naturally fluctuating environment, maximally exploited by the mature durum-wheat mutants. The mutation itself and/or the energy-consuming compensatory mechanisms markedly influenced the plant morphogenesis, leading especially to reduced tillering, which in turn resulted in lower grain production per plant. Consistently with the interrelation between early photosynthetic phenotype and grain-yield per plant, chlorophyll-fluorescence indexes related to the level of photoprotective thermal dissipation (pNPQ), photosystem II antenna size (ABS/RC), and pool of electron carriers (Sm) are proposed as good candidates for the in-field phenotyping of chlorophyll-deficient wheat.

The genome sequence of Hirschfeldia incana, a new Brassicaceae model to improve photosynthetic light-use efficiency

Photosynthesis is a key process in sustaining plant and human life. Improving the photosynthetic capacity of agricultural crops is an attractive means to increase their yields. While the core mechanisms of photosynthesis are highly conserved in C₃ plants, these mechanisms are very flexible, allowing considerable diversity in photosynthetic properties. Among this diversity is the maintenance of high photosynthetic light-use efficiency at high irradiance as identified in a small number of exceptional C₃ species. Hirschfeldia incana, a member of the Brassicaceae family, is such an exceptional species, and because it is easy to grow, it is an excellent model for studying the genetic and physiological basis of this trait. Here, we present a reference genome of H. incana and confirm its high photosynthetic light-use efficiency. While H. incana has the highest photosynthetic rates found so far in the Brassicaceae, the light-saturated assimilation rates of closely related Brassica rapa and Brassica nigra are also high. The H. incana genome has extensively diversified from that of B. rapa and B. nigra through large chromosomal rearrangements, species-specific transposon activity, and differential retention of duplicated genes. Duplicated genes in H. incana, B. rapa, and B. nigra that are involved in photosynthesis and/or photoprotection show a positive correlation between copy number and gene expression, providing leads into the mechanisms underlying the high photosynthetic efficiency of these species. Our work demonstrates that the H. incana genome serves as a valuable resource for studying the evolution of high photosynthetic light-use efficiency and enhancing photosynthetic rates in crop species.

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.

Genetics as a key to improving crop photosynthesis

Since the basic biochemical mechanisms of photosynthesis are remarkably conserved among plant species, genetic modification approaches have so far been the main route to improve the photosynthetic performance of crops. Yet, phenotypic variation observed in wild species and between varieties of crop species implies there is standing natural genetic variation for photosynthesis, offering a largely unexplored resource to use for breeding crops with improved photosynthesis and higher yields. The reason this has not yet been explored is that the variation probably involves thousands of genes, each contributing only a little to photosynthesis, making them hard to identify without proper phenotyping and genetic tools. This is changing, though, and increasingly studies report on quantitative trait loci for photosynthetic phenotypes. So far, hardly any of these quantitative trait loci have been used in marker assisted breeding or genomic selection approaches to improve crop photosynthesis and yield, and hardly ever have the underlying causal genes been identified. We propose to take the genetics of photosynthesis to a higher level, and identify the genes and alleles nature has used for millions of years to tune photosynthesis to be in line with local environmental conditions. We will need to determine the physiological function of the genes and alleles, and design novel strategies to use this knowledge to improve crop photosynthesis through conventional plant breeding, based on readily available crop plant germplasm. In this work, we present and discuss the genetic methods needed to reveal natural genetic variation, and elaborate on how to apply this to improve crop photosynthesis.

Using breeding and quantitative genetics to understand the C4 pathway

Reducing photorespiration in C3 crops could significantly increase rates of photosynthesis and yield. One method to achieve this would be to integrate C4 photosynthesis into C3 species. This objective is challenging as it involves engineering incompletely understood traits into C3 leaves, including complex changes to their biochemistry, cell biology, and anatomy. Quantitative genetics and selective breeding offer underexplored routes to identify regulators of these processes. We first review examples of natural intraspecific variation in C4 photosynthesis as well as the potential for hybridization between C3 and C4 species. We then discuss how quantitative genetic approaches including artificial selection and genome-wide association could be used to better understand the C4 syndrome and in so doing guide the engineering of the C4 pathway into C3 crops.

Natural variation in stomatal dynamics drives divergence in heat stress tolerance and contributes to seasonal intrinsic water-use efficiency in Vitis vinifera (subsp. sativa and sylvestris)

Stomata control CO2 uptake for photosynthesis and water loss through transpiration, thus playing a key role in leaf thermoregulation, water-use efficiency (iWUE), and plant productivity. In this work, we investigated the relationship between several leaf traits and hypothesized that stomatal behavior to fast (i.e. minutes) environmental changes co-determines, along with steady-state traits, the physiological response of grapevine to the surrounding fluctuating environment over the growing season. No relationship between iWUE, heat stress tolerance, and stomatal traits was observed in field-grown grapevine, suggesting that other physiological mechanisms are involved in determining leaf evaporative cooling capacity and the seasonal ratio of CO2 uptake (A) to stomatal conductance (gs). Indeed, cultivars that in the field had an unexpected combination of high iWUE but low sensitivity to thermal stress displayed a quick stomatal closure to light, but a sluggish closure to increased vapor pressure deficit (VPD) levels. This strategy, aiming both at conserving water under a high to low light transition and in prioritizing evaporative cooling under a low to high VPD transition, was mainly observed in the cultivars Regina and Syrah. Moreover, cultivars with different known responses to soil moisture deficit or high air VPD (isohydric versus anisohydric) had opposite behavior under fluctuating environments, with the isohydric cultivar showing slow stomatal closure to reduced light intensity but quick temporal responses to VPD manipulation. We propose that stomatal behavior to fast environmental fluctuations can play a critical role in leaf thermoregulation and water conservation under natural field conditions in grapevine.

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.

MIC-100, a new system for high-throughput phenotyping of instantaneous leaf photosynthetic rate in the field

Photosynthesis occurs mainly in plant leaves and is a fundamental process in the global carbon cycle and in crop production. The exploitation of natural genetic variation in leaf photosynthetic capacity is a promising strategy to meet the increasing demand for crops. The present study reports the newly developed photosynthesis measurement system 'MIC-100,' with a higher throughput for measuring instantaneous photosynthetic rate in the field. MIC-100 is established based on the closed system and directly detects the CO2 absorption in the leaf chamber. The reproducibility, accuracy, and measurement throughput of MIC-100 were tested using soybean (Glycine max L. (Merr.)) and rice (Oryza sativa L.) grown under field conditions. In most cases, the coefficient of variance (CV) for repeated-measurements of the same leaf was less than 0.1. The photosynthetic rates measured with the MIC-100 model showed a significant correlation (R2 = 0.93-0.95) with rates measured by a widely used gas-exchange system. The measurement throughput of the MIC-100 is significantly greater than that of conventional open gas-exchange systems under field conditions. Although MIC-100 solely detects the instantaneous photosynthetic rate under a given environment, this study demonstrated that the MIC-100 enables the rough evaluation of leaf photosynthesis within the large-scale plant populations grown in the field.

Applications of hyperspectral imaging in plant phenotyping

Our ability to interrogate and manipulate the genome far exceeds our capacity to measure the effects of genetic changes on plant traits. Much effort has been made recently by the plant science research community to address this imbalance. The responses of plants to environmental conditions can now be defined using a variety of imaging approaches. Hyperspectral imaging (HSI) has emerged as a promising approach to measure traits using a wide range of wavebands simultaneously in 3D to capture information in lab, glasshouse, or field settings. HSI has been applied to define abiotic, biotic, and quality traits for optimisation of crop management.

Site-specific, genotypic and temporal variation in photosynthesis and its related biochemistry in wheat (Triticum aestivum)

Photosynthesis in wheat (Triticum aestivum L.) pericarps may contribute appreciably to wheat grain yield. Consequently, we investigated the temporal variation of traits related to photosynthesis and sucrose metabolism in the pericarps and flag leaves of three wheat genotypes, Huandoy, Amurskaja 75 and Greece 25, which are reported to differ in expression of genes related to the C4 pathway in wheat grain. Significant site-specific, genotypic and temporal variation in the maximum carboxylation rate (Vc max ) and maximum rates of electron transport (J max ) (biological capacity of carbon assimilation) were observed early in ontogeny that dissipated by late grain filling. Although the transcript abundance of rbcS and rbcL in flag leaves was significantly higher than in the pericarps, in line with their photosynthetic prominence, both organ types displayed similar expression patterns among growth stages. The higher N concentrations in the pericarps during grain enlargement suggest increased Rubisco; however, expression of rbcS and rbcL indicated the contrary. From heading to 14days post-anthesis, wheat pericarps exhibited a strong, positive correlation between biological capacity for carbon assimilation and expression of key genes related to sucrose metabolism (SPS1 , SUS1 and SPP1 ). The strong correlation between spike dry weight and the biological capacity for carbon assimilation along with other findings of this study suggest that metabolic processes in wheat spikes may play a major role in grain filling, total yield and quality.

Genome-wide association analysis and pathway enrichment provide insights into the genetic basis of photosynthetic responses to drought stress in Persian walnut

Uncovering the genetic basis of photosynthetic trait variation under drought stress is essential for breeding climate-resilient walnut cultivars. To this end, we examined photosynthetic capacity in a diverse panel of 150 walnut families (1500 seedlings) from various agro-climatic zones in their habitats and grown in a common garden experiment. Photosynthetic traits were measured under well-watered (WW), water-stressed (WS) and recovery (WR) conditions. We performed genome-wide association studies (GWAS) using three genomic datasets: genotyping by sequencing data (∼43 K SNPs) on both mother trees (MGBS) and progeny (PGBS) and the Axiom™ Juglans regia 700 K SNP array data (∼295 K SNPs) on mother trees (MArray). We identified 578 unique genomic regions linked with at least one trait in a specific treatment, 874 predicted genes that fell within 20 kb of a significant or suggestive SNP in at least two of the three GWAS datasets (MArray, MGBS, and PGBS), and 67 genes that fell within 20 kb of a significant SNP in all three GWAS datasets. Functional annotation identified several candidate pathways and genes that play crucial roles in photosynthesis, amino acid and carbohydrate metabolism, and signal transduction. Further network analysis identified 15 hub genes under WW, WS and WR conditions including GAPB, PSAN, CRR1, NTRC, DGD1, CYP38, and PETC which are involved in the photosynthetic responses. These findings shed light on possible strategies for improving walnut productivity under drought stress.

Nighttime Chlorophyll Fluorescence Imaging of Dark-Adapted Plants Using a Robotic Field Phenotyping Platform

Photosynthetic efficiency is increasingly recognized as an integration of plant responses to dynamic environments, establishing the need for data sets from both field trials and controlled environments. A robotic field scanner phenotyping platform at the University of Arizona is equipped with a high-throughput chlorophyll fluorescence imaging system capable of collecting data on field trials for genetic studies of a photosynthetic trait (Fv/Fm). A description of the fluorescence imaging system is provided in addition to methods for measurements across experimental field plots and a test to determine the impact of variable plant heights. The overall focus is on aspects of field applications of a chlorophyll fluorescence imaging system that differ from analogous systems in controlled environments.

Analysis of chlorophyll fluorescence parameters as predictors of biomass accumulation and tolerance to heat and drought stress of wheat (Triticum aestivum) plants

Agricultural technologies aimed at increasing yields require the development of highly productive and stress-tolerant cultivars. Phenotyping can significantly accelerate breeding; however, no reliable markers have been identified to select the most promising cultivars at an early stage. In this work, we determined the light-induced dynamic of chlorophyll fluorescence (ChlF) parameters in young seedlings of 10 wheat (Triticum aestivum L.) cultivars and evaluated potency of these parameters as predictors of biomass accumulation and stress tolerance. Dry matter accumulation positively correlated with the effective quantum efficiency of photosystem II (Φ PSIIef ) and negatively correlated with the half-time of Φ PSIIef reaching (t 1/2 (Φ PSIIef )). There was a highly significant correlation between t 1/2 (Φ PSIIef ) and dry matter accumulation with increasing prediction period. Short-term heating and drought caused an inhibition of biomass accumulation and photosynthetic activity depending on the stressor intensity. The positive correlation between the Φ PSII dark level (Φ PSIId ) in young seedlings and tolerance to a rapidly increasing short-term stressor (heating) was shown. In the case of a long-term stressor (drought), we revealed a strong negative relationship between tolerance and the level of non-photochemical fluorescence quenching (NPQ). In general, the results show the potency of the ChlF parameters of young seedlings as predictors of biomass accumulation and stress tolerance.

Novel target sites for soybean yield enhancement by photosynthesis

Photosynthesis plays an important role in plant growth and development. Increasing photosynthetic rate is a main objective of improving crop productivity. Chlorophyll fluorescence is an effective method for quickly evaluating photosynthesis. In this study, four representative chlorophyll fluorescence parameters, that is, maximum quantum efficiency of photosystem II, quantum efficiency of PSII, photochemical quenching, and non-photochemical quenching, of 219 diverse soybean accessions were measured across three environments. The underlying genetic architecture was analyzed by genome-wide association study. Forty-eight SNPs were detected to associate with the four traits and explained 10.43-20.41% of the phenotypic variation. Nine candidate genes in the stable QTLs were predicted. Great differences in the expression levels of the candidate genes existed between the high photosynthetic efficiency accessions and low photosynthetic efficiency accessions. In all, we uncover 17 QTLs associated with photosynthesis-related traits and nine genes that may participate in the regulation of photosynthesis, which can provide references for revealing the genetic mechanism of photosynthesis. These QTLs and candidate genes will provide new targets for crop yield improvement through increasing photosynthesis.

Omics-Facilitated Crop Improvement for Climate Resilience and Superior Nutritive Value

Novel crop improvement approaches, including those that facilitate for the exploitation of crop wild relatives and underutilized species harboring the much-needed natural allelic variation are indispensable if we are to develop climate-smart crops with enhanced abiotic and biotic stress tolerance, higher nutritive value, and superior traits of agronomic importance. Top among these approaches are the "omics" technologies, including genomics, transcriptomics, proteomics, metabolomics, phenomics, and their integration, whose deployment has been vital in revealing several key genes, proteins and metabolic pathways underlying numerous traits of agronomic importance, and aiding marker-assisted breeding in major crop species. Here, citing several relevant examples, we appraise our understanding on the recent developments in omics technologies and how they are driving our quest to breed climate resilient crops. Large-scale genome resequencing, pan-genomes and genome-wide association studies are aiding the identification and analysis of species-level genome variations, whilst RNA-sequencing driven transcriptomics has provided unprecedented opportunities for conducting crop abiotic and biotic stress response studies. Meanwhile, single cell transcriptomics is slowly becoming an indispensable tool for decoding cell-specific stress responses, although several technical and experimental design challenges still need to be resolved. Additionally, the refinement of the conventional techniques and advent of modern, high-resolution proteomics technologies necessitated a gradual shift from the general descriptive studies of plant protein abundances to large scale analysis of protein-metabolite interactions. Especially, metabolomics is currently receiving special attention, owing to the role metabolites play as metabolic intermediates and close links to the phenotypic expression. Further, high throughput phenomics applications are driving the targeting of new research domains such as root system architecture analysis, and exploration of plant root-associated microbes for improved crop health and climate resilience. Overall, coupling these multi-omics technologies to modern plant breeding and genetic engineering methods ensures an all-encompassing approach to developing nutritionally-rich and climate-smart crops whose productivity can sustainably and sufficiently meet the current and future food, nutrition and energy demands.

High photosynthesis rate in two wild rice species is driven by leaf anatomy mediating high Rubisco activity and electron transport rate

The importance of increasing photosynthetic efficiency for sustainable crop yield increases to feed the growing world population is well recognized. The natural genetic variation in leaf photosynthesis in crop plants is largely unexploited for increasing yield potential. The genus Oryza, including cultivated rice and wild relatives, offers tremendous genetic variability to explore photosynthetic differences and underlying biochemical, photochemical, and developmental traits. We quantified leaf photosynthesis and related physiological parameters for six cultivated and three wild rice genotypes, and identified photosynthetically efficient wild rice accessions. Fitting A/Ci curves and biochemical analyses showed that leaf photosynthesis in cultivated rice varieties IR 64 and Nipponbare was limited due to leaf nitrogen content, Rubisco activity, and electron transport rate compared with photosynthetically efficient wild rice accessions Oryza australiensis and Oryza latifolia. The selected wild rice accessions with high leaf photosynthesis per unit area had anatomical features such as larger mesophyll cells with more chloroplasts, fewer mesophyll cells between two adjacent veins, and higher mesophyll cell and chloroplast surface area exposed to intercellular space. Our results show the existence of desirable variations in Rubisco activity, electron transport rate, and leaf anatomical features that could be targeted for increasing the photosynthetic efficiency of cultivated rice varieties.

Next-Generation Breeding Strategies for Climate-Ready Crops

Climate change is a threat to global food security due to the reduction of crop productivity around the globe. Food security is a matter of concern for stakeholders and policymakers as the global population is predicted to bypass 10 billion in the coming years. Crop improvement via modern breeding techniques along with efficient agronomic practices innovations in microbiome applications, and exploiting the natural variations in underutilized crops is an excellent way forward to fulfill future food requirements. In this review, we describe the next-generation breeding tools that can be used to increase crop production by developing climate-resilient superior genotypes to cope with the future challenges of global food security. Recent innovations in genomic-assisted breeding (GAB) strategies allow the construction of highly annotated crop pan-genomes to give a snapshot of the full landscape of genetic diversity (GD) and recapture the lost gene repertoire of a species. Pan-genomes provide new platforms to exploit these unique genes or genetic variation for optimizing breeding programs. The advent of next-generation clustered regularly interspaced short palindromic repeat/CRISPR-associated (CRISPR/Cas) systems, such as prime editing, base editing, and de nova domestication, has institutionalized the idea that genome editing is revamped for crop improvement. Also, the availability of versatile Cas orthologs, including Cas9, Cas12, Cas13, and Cas14, improved the editing efficiency. Now, the CRISPR/Cas systems have numerous applications in crop research and successfully edit the major crop to develop resistance against abiotic and biotic stress. By adopting high-throughput phenotyping approaches and big data analytics tools like artificial intelligence (AI) and machine learning (ML), agriculture is heading toward automation or digitalization. The integration of speed breeding with genomic and phenomic tools can allow rapid gene identifications and ultimately accelerate crop improvement programs. In addition, the integration of next-generation multidisciplinary breeding platforms can open exciting avenues to develop climate-ready crops toward global food security.

Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges

Many biochemical and physiological properties of plants that are of interest to breeders and geneticists have extremely low throughput and/or can only be measured destructively. This has limited the use of information on natural variation in nutrient and metabolite abundance, as well as photosynthetic capacity in quantitative genetic contexts where it is necessary to collect data from hundreds or thousands of plants. A number of recent studies have demonstrated the potential to estimate many of these traits from hyperspectral reflectance data, primarily in ecophysiological contexts. Here, we summarize recent advances in the use of hyperspectral reflectance data for plant phenotyping, and discuss both the potential benefits and remaining challenges to its application in plant genetics contexts. The performances of previously published models in estimating six traits from hyperspectral reflectance data in maize were evaluated on new sample datasets, and the resulting predicted trait values shown to be heritable (e.g., explained by genetic factors) were estimated. The adoption of hyperspectral reflectance-based phenotyping beyond its current uses may accelerate the study of genes controlling natural variation in biochemical and physiological traits.

Undisclosed, unmet and neglected challenges in multi-omics studies

Multi-omics approaches have become a reality in both large genomics projects and small laboratories. However, the multi-omics research community still faces a number of issues that have either not been sufficiently discussed or for which current solutions are still limited. In this Perspective, we elaborate on these limitations and suggest points of attention for future research. We finally discuss new opportunities and challenges brought to the field by the rapid development of single-cell high-throughput molecular technologies.

The carbon isotopic signature of C4 crops and its applicability in breeding for climate resilience

KEY MESSAGE

Carbon isotope discrimination is a promising trait for indirect screening for improved water use efficiency of C4 crops. In the context of a changing climate, drought is one of the major factors limiting plant growth and yield. Hence, breeding efforts are directed toward improving water use efficiency (WUE) as a key factor in climate resilience and sustainability of crop production. As WUE is a complex trait and its evaluation is rather resource consuming, proxy traits, which are easier to screen and reliably reflect variation in WUE, are needed. In C3 crops, a trait established to be indicative for WUE is the carbon isotopic composition (δ13C) of plant material, which reflects the preferential assimilation of the lighter carbon isotope 12C over 13C during photosynthesis. In C4 crops, carbon fixation is more complex and δ13C thus depends on many more factors than in C3 crops. Recent physiological and genetic studies indicate a correlation between δ13C and WUE also in C4 crops, as well as a colocalization of quantitative trait loci for the two traits. Moreover, significant intraspecific variation as well as a medium to high heritability of δ13C has been shown in some of the main C4 crops, such as maize, sorghum and sugarcane, indicating its potential for indirect selection and breeding. Further research on physiological, genetic and environmental components influencing δ13C is needed to support its application in improving WUE and making C4 crops resilient to climate change.

UAV-Based Thermal, RGB Imaging and Gene Expression Analysis Allowed Detection of Fusarium Head Blight and Gave New Insights Into the Physiological Responses to the Disease in Durum Wheat

Wheat is one of the world's most economically important cereal crop, grown on 220 million hectares. Fusarium head blight (FHB) disease is considered a major threat to durum (Triticum turgidum subsp. durum (Desfontaines) Husnache) and bread wheat (T. aestivum L.) cultivars and is mainly managed by the application of fungicides at anthesis. However, fungicides are applied when FHB symptoms are clearly visible and the spikes are almost entirely bleached (% of diseased spikelets > 80%), by when it is too late to control FHB disease. For this reason, farmers often react by performing repeated fungicide treatments that, however, due to the advanced state of the infection, cause a waste of money and pose significant risks to the environment and non-target organisms. In the present study, we used unmanned aerial vehicle (UAV)-based thermal infrared (TIR) and red-green-blue (RGB) imaging for FHB detection in T. turgidum (cv. Marco Aurelio) under natural field conditions. TIR and RGB data coupled with ground-based measurements such as spike's temperature, photosynthetic efficiency and molecular identification of FHB pathogens, detected FHB at anthesis half-way (Zadoks stage 65, ZS 65), when the percentage (%) of diseased spikelets ranged between 20% and 60%. Moreover, in greenhouse experiments the transcripts of the key genes involved in stomatal closure were mostly up-regulated in F. graminearum-inoculated plants, demonstrating that the physiological mechanism behind the spike's temperature increase and photosynthetic efficiency decrease could be attributed to the closure of the guard cells in response to F. graminearum. In addition, preliminary analysis revealed that there is differential regulation of genes between drought-stressed and F. graminearum-inoculated plants, suggesting that there might be a possibility to discriminate between water stress and FHB infection. This study shows the potential of UAV-based TIR and RGB imaging for field phenotyping of wheat and other cereal crop species in response to environmental stresses. This is anticipated to have enormous promise for the detection of FHB disease and tremendous implications for optimizing the application of fungicides, since global food crop demand is to be met with minimal environmental impacts.

Integrating multi-omics data for crop improvement

Our agricultural systems are now in urgent need to secure food for a growing world population. To meet this challenge, we need a better characterization of plant genetic and phenotypic diversity. The combination of genomics, transcriptomics and metabolomics enables a deeper understanding of the mechanisms underlying the complex architecture of many phenotypic traits of agricultural relevance. We review the recent advances in plant genomics to see how these can be integrated with broad molecular profiling approaches to improve our understanding of plant phenotypic variation and inform crop breeding strategies.

Prior Biological Knowledge Improves Genomic Prediction of Growth-Related Traits in Arabidopsis thaliana

Prediction of growth-related complex traits is highly important for crop breeding. Photosynthesis efficiency and biomass are direct indicators of overall plant performance and therefore even minor improvements in these traits can result in significant breeding gains. Crop breeding for complex traits has been revolutionized by technological developments in genomics and phenomics. Capitalizing on the growing availability of genomics data, genome-wide marker-based prediction models allow for efficient selection of the best parents for the next generation without the need for phenotypic information. Until now such models mostly predict the phenotype directly from the genotype and fail to make use of relevant biological knowledge. It is an open question to what extent the use of such biological knowledge is beneficial for improving genomic prediction accuracy and reliability. In this study, we explored the use of publicly available biological information for genomic prediction of photosynthetic light use efficiency (Φ PSII ) and projected leaf area (PLA) in Arabidopsis thaliana. To explore the use of various types of knowledge, we mapped genomic polymorphisms to Gene Ontology (GO) terms and transcriptomics-based gene clusters, and applied these in a Genomic Feature Best Linear Unbiased Predictor (GFBLUP) model, which is an extension to the traditional Genomic BLUP (GBLUP) benchmark. Our results suggest that incorporation of prior biological knowledge can improve genomic prediction accuracy for both Φ PSII and PLA. The improvement achieved depends on the trait, type of knowledge and trait heritability. Moreover, transcriptomics offers complementary evidence to the Gene Ontology for improvement when used to define functional groups of genes. In conclusion, prior knowledge about trait-specific groups of genes can be directly translated into improved genomic prediction.

Rice Cultivar Takanari Has Higher Photosynthetic Performance Under Fluctuating Light Than Koshihikari, Especially Under Limited Nitrogen Supply and Elevated CO2

Plants in the field experience dynamic changes of sunlight rather than steady-state irradiation. Therefore, increasing the photosynthetic rate of an individual leaf under fluctuating light is essential for improving crop productivity. The high-yielding indica rice (Oryza sativa L.) cultivar Takanari is considered a potential donor of photosynthesis genes because of its higher steady-state photosynthesis at both atmospheric and elevated CO₂ concentrations than those of several Japanese commercial cultivars, including Koshihikari. Photosynthetic induction after a sudden increase in light intensity is faster in Takanari than in Koshihikari, but whether the daily carbon gain of Takanari outperforms that of Koshihikari under fluctuating light in the field is unclear. Here we report that Takanari has higher non-steady-state photosynthesis, especially under low nitrogen (N) supply, than Koshihikari. In a pot experiment, Takanari had greater leaf carbon gain during the initial 10 min after a sudden increase in irradiation and higher daily CO₂ assimilation under simulated natural fluctuating light, at both atmospheric (400 ppm) and elevated (800 ppm) CO₂ concentrations. The electron transport rate during a day under field conditions with low N supply was also higher in Takanari than in Koshihikari. Although the advantages of Takanari were diminished under high N supply, photosynthetic N use efficiency was consistently higher in Takanari than in Koshihikari, under both low and high N supply. This study demonstrates that Takanari is a promising donor parent to use in breeding programs aimed at increasing CO₂ assimilation in a wide range of environments, including future higher CO₂ concentrations.

Decoding Plant-Environment Interactions That Influence Crop Agronomic Traits

To ensure food security in the face of increasing global demand due to population growth and progressive urbanization, it will be crucial to integrate emerging technologies in multiple disciplines to accelerate overall throughput of gene discovery and crop breeding. Plant agronomic traits often appear during the plants' later growth stages due to the cumulative effects of their lifetime interactions with the environment. Therefore, decoding plant-environment interactions by elucidating plants' temporal physiological responses to environmental changes throughout their lifespans will facilitate the identification of genetic and environmental factors, timing and pathways that influence complex end-point agronomic traits, such as yield. Here, we discuss the expected role of the life-course approach to monitoring plant and crop health status in improving crop productivity by enhancing the understanding of plant-environment interactions. We review recent advances in analytical technologies for monitoring health status in plants based on multi-omics analyses and strategies for integrating heterogeneous datasets from multiple omics areas to identify informative factors associated with traits of interest. In addition, we showcase emerging phenomics techniques that enable the noninvasive and continuous monitoring of plant growth by various means, including three-dimensional phenotyping, plant root phenotyping, implantable/injectable sensors and affordable phenotyping devices. Finally, we present an integrated review of analytical technologies and applications for monitoring plant growth, developed across disciplines, such as plant science, data science and sensors and Internet-of-things technologies, to improve plant productivity.

Natural variation of photosynthetic efficiency in Arabidopsis thaliana accessions under low temperature conditions

Low, but non-freezing, temperatures have negative effects on plant growth and development. Despite some molecular signalling pathways being known, the mechanisms causing different responses among genotypes are still poorly understood. Photosynthesis is one of the processes that are affected by low temperatures. Using an automated phenotyping platform for chlorophyll fluorescence imaging the steady state quantum yield of photosystem II (PSII) electron transport (Φ_PSII ) was measured and used to quantify the effect of moderately low temperature on a population of Arabidopsis thaliana natural accessions. Observations were made over the course of several weeks in standard and low temperature conditions and a strong decrease in Φ_PSII upon the cold treatment was found. A genome wide association study identified several quantitative trait loci (QTLs) that are associated with changes in Φ_PSII in low temperature. One candidate for a cold specific QTL was validated with a mutant analysis to be one of the genes that is likely involved in the PSII response to the cold treatment. The gene encodes the PSII associated protein PSB27 which has already been implicated in the adaptation to fluctuating light.

Photosynthesis in a Changing Global Climate: Scaling Up and Scaling Down in Crops

Photosynthesis is the major process leading to primary production in the Biosphere. There is a total of 7000bn tons of CO2 in the atmosphere and photosynthesis fixes more than 100bn tons annually. The CO2 assimilated by the photosynthetic apparatus is the basis of crop production and, therefore, of animal and human food. This has led to a renewed interest in photosynthesis as a target to increase plant production and there is now increasing evidence showing that the strategy of improving photosynthetic traits can increase plant yield. However, photosynthesis and the photosynthetic apparatus are both conditioned by environmental variables such as water availability, temperature, [CO2], salinity, and ozone. The "omics" revolution has allowed a better understanding of the genetic mechanisms regulating stress responses including the identification of genes and proteins involved in the regulation, acclimation, and adaptation of processes that impact photosynthesis. The development of novel non-destructive high-throughput phenotyping techniques has been important to monitor crop photosynthetic responses to changing environmental conditions. This wealth of data is being incorporated into new modeling algorithms to predict plant growth and development under specific environmental constraints. This review gives a multi-perspective description of the impact of changing environmental conditions on photosynthetic performance and consequently plant growth by briefly highlighting how major technological advances including omics, high-throughput photosynthetic measurements, metabolic engineering, and whole plant photosynthetic modeling have helped to improve our understanding of how the photosynthetic machinery can be modified by different abiotic stresses and thus impact crop production.

GWAS reveals two novel loci for photosynthesis-related traits in soybean

Photosynthesis plays an extremely important role throughout the life cycle of plants. Improving the photosynthetic rate is a major target for increasing crop productivity. This study was conducted to identify single nucleotide polymorphisms (SNPs) associated with the net photosynthetic rate (Pn), stomatal conductance (Cond), intercellular carbon dioxide concentration (Ci) and transpiration rate (Trmmol) through genome-wide association study (GWAS) and to inspect the relationships among these traits in soybean (Glycine max (L.) Merr.). A population of 219 soybean accessions was used in this research. A total of 12 quantitative trait loci (QTLs) associated with Pn, Cond, Ci and Trmmol were detected and distributed on chromosomes 1, 2, 6, 7, 9, 11, 12, 13, 15, 16, 18, and 19, and some of these QTL overlapped with previously reported QTLs. Furthermore, four candidate genes were identified, and there were significantly different expression levels between the high-light-efficiency accessions and low-light-efficiency accessions. These putative genes may participate in the regulation of photosynthesis through different metabolic pathways. Therefore, the associated novel QTLs and candidate genes detected in this study will provide a theoretical basis for genetic studies of photosynthesis and provide new avenues for crop improvement.

Emerging Technologies in Algal Biotechnology: Toward the Establishment of a Sustainable, Algae-Based Bioeconomy

Mankind has recognized the value of land plants as renewable sources of food, medicine, and materials for millennia. Throughout human history, agricultural methods were continuously modified and improved to meet the changing needs of civilization. Today, our rapidly growing population requires further innovation to address the practical limitations and serious environmental concerns associated with current industrial and agricultural practices. Microalgae are a diverse group of unicellular photosynthetic organisms that are emerging as next-generation resources with the potential to address urgent industrial and agricultural demands. The extensive biological diversity of algae can be leveraged to produce a wealth of valuable bioproducts, either naturally or via genetic manipulation. Microalgae additionally possess a set of intrinsic advantages, such as low production costs, no requirement for arable land, and the capacity to grow rapidly in both large-scale outdoor systems and scalable, fully contained photobioreactors. Here, we review technical advancements, novel fields of application, and products in the field of algal biotechnology to illustrate how algae could present high-tech, low-cost, and environmentally friendly solutions to many current and future needs of our society. We discuss how emerging technologies such as synthetic biology, high-throughput phenomics, and the application of internet of things (IoT) automation to algal manufacturing technology can advance the understanding of algal biology and, ultimately, drive the establishment of an algal-based bioeconomy.

Growth under Fluctuating Light Reveals Large Trait Variation in a Panel of Arabidopsis Accessions

The capacity of photoautotrophs to fix carbon depends on the efficiency of the conversion of light energy into chemical potential by photosynthesis. In nature, light input into photosynthesis can change very rapidly and dramatically. To analyze how genetic variation in Arabidopsis thaliana affects photosynthesis and growth under dynamic light conditions, 36 randomly chosen natural accessions were grown under uniform and fluctuating light intensities. After 14 days of growth under uniform or fluctuating light regimes, maximum photosystem II quantum efficiency (Fv/Fm) was determined, photosystem II operating efficiency (ΦPSII) and non-photochemical quenching (NPQ) were measured in low light, and projected leaf area (PLA) as well as the number of visible leaves were estimated. Our data show that ΦPSII and PLA were decreased and NPQ was increased, while Fv/Fm and number of visible leaves were unaffected, in most accessions grown under fluctuating compared to uniform light. There were large changes between accessions for most of these parameters, which, however, were not correlated with genomic variation. Fast growing accessions under uniform light showed the largest growth reductions under fluctuating light, which correlated strongly with a reduction in ΦPSII, suggesting that, under fluctuating light, photosynthesis controls growth and not vice versa.

Natural genetic variation in photosynthesis: an untapped resource to increase crop yield potential?

Raising crop yield potential is a major goal to ensure food security for the growing global population. Photosynthesis is the primary determinant of crop productivity and any gain in photosynthetic CO2 assimilation per unit of leaf area (A) has the potential to increase yield. Significant intraspecific variation in A is known to exist in various autotrophic organs that represent an unexploited target for crop improvement. However, the large number of factors that influence photosynthetic rates often makes it difficult to measure or estimate A under dynamic field conditions (i.e. fluctuating light intensities or temperatures). This complexity often results in photosynthetic capacity, rather than realized photosynthetic rates being used to assess natural variation in photosynthesis. Here we review the work on natural variation in A, the different factors determining A and their interaction in yield formation. A series of drawbacks and perspectives are presented for the most common analyses generally used to estimate A. The different yield components and their determination based on different photosynthetic organs are discussed with a major focus on potential exploitation of various traits for crop improvement. To conclude, an example of different possibilities to increase yield in wheat through enhancing A is illustrated.

Synchronization of developmental, molecular and metabolic aspects of source-sink interactions

Plants have evolved a multitude of strategies to adjust their growth according to external and internal signals. Interconnected metabolic and phytohormonal signalling networks allow adaption to changing environmental and developmental conditions and ensure the survival of species in fluctuating environments. In agricultural ecosystems, many of these adaptive responses are not required or may even limit crop yield, as they prevent plants from realizing their fullest potential. By lifting source and sink activities to their maximum, massive yield increases can be foreseen, potentially closing the future yield gap resulting from an increasing world population and the transition to a carbon-neutral economy. To do so, a better understanding of the interplay between metabolic and developmental processes is required. In the past, these processes have been tackled independently from each other, but coordinated efforts are required to understand the fine mechanics of source-sink relations and thus optimize crop yield. Here, we describe approaches to design high-yielding crop plants utilizing strategies derived from current metabolic concepts and our understanding of the molecular processes determining sink development.

Genotype Specific Photosynthesis x Environment Interactions Captured by Automated Fluorescence Canopy Scans Over Two Fluctuating Growing Seasons

Photosynthesis reacts dynamic and in different time scales to changing conditions. Light and temperature acclimation balance photosynthetic processes in a complex interplay with the fluctuating environment. However, due to limitations in the measurements techniques, these acclimations are often described under steady-state conditions leading to inaccurate photosynthesis estimates in the field. Here we analyze the photosynthetic interaction with the fluctuating environment and canopy architecture over two seasons using a fully automated phenotyping system. We acquired over 700,000 chlorophyll fluorescence transients and spectral measurements under semi-field conditions in four crop species including 28 genotypes. As expected, the quantum efficiency of the photosystem II (Fv/Fm in the dark and Fq'/Fm' in the light) was determined by light intensity. It was further significantly affected by spectral indices representing canopy structure effects. In contrast, a newly established parameter, monitoring the efficiency of electron transport (Fr2/Fv in the dark respective Fr2'/Fq' in the light), was highly responsive to temperature (R2 up to 0.75). This parameter decreased with temperature and enabled the detection of cold tolerant species and genotypes. We demonstrated the ability to capture and model the dynamic photosynthesis response to the environment over entire growth seasons. The improved linkage of photosynthetic performance to canopy structure, temperature and cold tolerance offers great potential for plant breeding and crop growth modeling.

High-yielding rice Takanari has superior photosynthetic response to a commercial rice Koshihikari under fluctuating light

Leaves within crop canopies experience variable light over the course of a day, which greatly affects photosynthesis and crop productivity. Little is known about the mechanisms of the photosynthetic response to fluctuating light and their genetic control. Here, we examined gas exchange, metabolite levels, and chlorophyll fluorescence during the photosynthetic induction response in an Oryza sativa indica cultivar with high yield (Takanari) and a japonica cultivar with lower yield (Koshihikari). Takanari had a faster induction response to sudden increases in light intensity than Koshihikari, as demonstrated by faster increases in net CO2 assimilation rate, stomatal conductance, and electron transport rate. In a simulated light regime that mimicked a typical summer day, the faster induction response in Takanari increased daily CO2 assimilation by 10%. The faster response of Takanari was explained in part by its maintenance of a larger pool of Calvin-Benson cycle metabolites. Together, the rapid responses of electron transport rate, metabolic flux, and stomatal conductance in Takanari contributed to the greater daily carbon gain under fluctuating light typical of natural environments.

Genetic architecture of leaf photosynthesis in rice revealed by different types of reciprocal mapping populations

The improvement of leaf net photosynthetic rate (An) is a major challenge in enhancing crop productivity. However, the genetic control of An among natural genetic accessions is still poorly understood. The high-yielding indica cultivar Takanari has the highest An of all rice cultivars, 20-30% higher than that of the high-quality japonica cultivar Koshihikari. By using reciprocal backcross inbred lines and chromosome segment substitution lines derived from a cross between Takanari and Koshihikari, we identified three quantitative trait loci (QTLs) where the Takanari alleles enhanced An in plants with a Koshihikari genetic background and five QTLs where the Koshihikari alleles enhanced An in plants with a Takanari genetic background. Two QTLs were expressed in plants with both backgrounds (type I QTL). The expression of other QTLs depended strongly on genetic background (type II QTL). These beneficial alleles increased stomatal conductance, the initial slope of An versus intercellular CO2 concentration, or An at CO2 saturation. Pyramiding of these alleles consistently increased An. Some alleles positively affected biomass production and grain yield. These alleles associated with photosynthesis and yield can be a valuable tool in rice breeding programs via DNA marker-assisted selection.

High throughput procedure utilising chlorophyll fluorescence imaging to phenotype dynamic photosynthesis and photoprotection in leaves under controlled gaseous conditions

BACKGROUND

As yields of major crops such as wheat (T. aestivum) have begun to plateau in recent years, there is growing pressure to efficiently phenotype large populations for traits associated with genetic advancement in yield. Photosynthesis encompasses a range of steady state and dynamic traits that are key targets for raising Radiation Use Efficiency (RUE), biomass production and grain yield in crops. Traditional methodologies to assess the full range of responses of photosynthesis, such a leaf gas exchange, are slow and limited to one leaf (or part of a leaf) per instrument. Due to constraints imposed by time, equipment and plant size, photosynthetic data is often collected at one or two phenological stages and in response to limited environmental conditions.

RESULTS

Here we describe a high throughput procedure utilising chlorophyll fluorescence imaging to phenotype dynamic photosynthesis and photoprotection in excised leaves under controlled gaseous conditions. When measured throughout the day, no significant differences (P > 0.081) were observed between the responses of excised and intact leaves. Using excised leaves, the response of three cultivars of T. aestivum to a user-defined dynamic lighting regime was examined. Cultivar specific differences were observed for maximum PSII efficiency (F v'/F m'-P < 0.01) and PSII operating efficiency (F q'/F m'-P = 0.04) under both low and high light. In addition, the rate of induction and relaxation of non-photochemical quenching (NPQ) was also cultivar specific. A specialised imaging chamber was designed and built in-house to maintain gaseous conditions around excised leaf sections. The purpose of this is to manipulate electron sinks such as photorespiration. The stability of carbon dioxide (CO2) and oxygen (O2) was monitored inside the chambers and found to be within ± 4.5% and ± 1% of the mean respectively. To test the chamber, T. aestivum 'Pavon76' leaf sections were measured under at 20 and 200 mmol mol-1 O2 and ambient [CO2] during a light response curve. The F v'/F m'was significantly higher (P < 0.05) under low [O2] for the majority of light intensities while values of NPQ and the proportion of open PSII reaction centers (qP) were significantly lower under > 130 μmol m-2 s-1 photosynthetic photon flux density (PPFD).

CONCLUSIONS

Here we demonstrate the development of a high-throughput (> 500 samples day-1) method for phenotyping photosynthetic and photo-protective parameters in a dynamic light environment. The technique exploits chlorophyll fluorescence imaging in a specifically designed chamber, enabling controlled gaseous environment around leaf sections. In addition, we have demonstrated that leaf sections do not different from intact plant material even > 3 h after sampling, thus enabling transportation of material of interest from the field to this laboratory based platform. The methodologies described here allow rapid, custom screening of field material for variation in photosynthetic processes.

Chlorophyll fluorescence analysis in diverse rice varieties reveals the positive correlation between the seedlings salt tolerance and photosynthetic efficiency

BACKGROUND

Photosynthetic efficiency might be a key factor determining plant resistance to abiotic stresses. Plants can sense when growing conditions are not favorable and trigger an internal response at an early stage before showing external symptoms. When a high amount of salt enters the plant cell, the membrane system and function of thylakoids in chloroplasts could be destroyed and affect photosynthetic performance if the salt concentration is not regulated to optimal values. Oryza species have salt-tolerant and salt-sensitive genotypes; however, very few studies have investigated the genetic architecture responsible for photosynthetic efficiency under salinity stress in cultivated rice.

RESULTS

We used an imaging-based chlorophyll fluorometer to monitor eight rice varieties that showed different salt tolerance levels for four consecutive days under control and salt conditions. An analysis of the changes in chlorophyll fluorescence parameters clearly showed the maximum quantum efficiency of PSII in sensitive varieties was significantly reduced after NaCl treatment when compared to tolerant varieties. A panel of 232 diverse rice accessions was then analyzed for chlorophyll fluorescence under salt conditions, the results showed that chlorophyll fluorescence parameters such as F0 and NPQ were higher in Japonica subspecies, ΦPSII of Indica varieties was higher than that in other subgroups, which suggested that the variation in photosynthetic efficiency was extensively regulated under salt treatment in diverse cultivated rice. Two significant regions on chromosome 5 were identified to associate with the fraction of open PSII centers (qL) and the minimum chlorophyll fluorescence (F0). These regions harbored genes related to senescence, chloroplast biogenesis and response to salt stress are of interest for future functional characterization to determine their roles in regulating photosynthesis.

CONCLUSIONS

Rice plant is very sensitive to salinity stress, especially at young seedling stage. Our work identified the distribution pattern of chlorophyll fluorescence parameters in seedlings leaf and their correlations with salt tolerance level in a diverse gene pool. We also revealed the complexity of the genetic architecture regulating rice seedling photosynthetic performance under salinity stress, the germplasm analyzed in this study and the associated genetic information could be utilized in rice breeding program.

Using Plant Phenomics to Exploit the Gains of Genomics

Agricultural scientists face the dual challenge of breeding input-responsive, widely adoptable and climate-resilient varieties of crop plants and developing such varieties at a faster pace. Integrating the gains of genomics with modern-day phenomics will lead to increased breeding efficiency which in turn offers great promise to develop such varieties rapidly. Plant phenotyping techniques have impressively evolved during the last two decades. The low-cost, automated and semi-automated methods for data acquisition, storage and analysis are now available which allow precise quantitative analysis of plant structure and function; and genetic dissection of complex traits. Appropriate plant types can now be quickly developed that respond favorably to low input and resource-limited environments and address the challenges of subsistence agriculture. The present review focuses on the need of systematic, rapid, minimal invasive and low-cost plant phenotyping. It also discusses its evolution to modern day high throughput phenotyping (HTP), traits amenable to HTP, integration of HTP with genomics and the scope of utilizing these tools for crop improvement.