Phenomics allows identification of genomic regions affecting maize stomatal conductance with conditional effects of water deficit and evaporative demand

Genetic variability of aquaporin expression in maize: From eQTLs to a MITE insertion regulating PIP2;5 expression

Plant aquaporins are involved in numerous physiological processes, such as cellular homeostasis, tissue hydraulics, transpiration, and nutrient supply, and are key players of the response to environmental cues. While varying expression patterns of aquaporin genes have been described across organs, developmental stages, and stress conditions, the underlying regulation mechanisms remain elusive. Hence, this work aimed to shed light on the expression variability of 4 plasma membrane intrinsic protein (PIP) genes in maize (Zea mays) leaves, and its genetic causes, through expression quantitative trait locus (eQTL) mapping across a 252-hybrid diversity panel. Significant genetic variability in PIP transcript abundance was observed to different extents depending on the isoforms. The genome-wide association study mapped numerous eQTLs, both local and distant, thus emphasizing the existing natural diversity of PIP gene expression across the studied panel and the potential to reveal regulatory actors and mechanisms. One eQTL associated with PIP2;5 expression variation was characterized. Genomic sequence comparison and in vivo reporter assay attributed, at least partly, the local eQTL to a transposon-containing polymorphism in the PIP2;5 promoter. This work paves the way to the molecular understanding of PIP gene regulation and its possible integration into larger networks regulating physiological and stress adaptation processes.

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.

High-dimensional multi-omics measured in controlled conditions are useful for maize platform and field trait predictions

KEY MESSAGE

Transcriptomics and proteomics information collected on a platform can predict additive and non-additive effects for platform traits and additive effects for field traits. The effects of climate change in the form of drought, heat stress, and irregular seasonal changes threaten global crop production. The ability of multi-omics data, such as transcripts and proteins, to reflect a plant's response to such climatic factors can be capitalized in prediction models to maximize crop improvement. Implementing multi-omics characterization in field evaluations is challenging due to high costs. It is, however, possible to do it on reference genotypes in controlled conditions. Using omics measured on a platform, we tested different multi-omics-based prediction approaches, using a high dimensional linear mixed model (MegaLMM) to predict genotypes for platform traits and agronomic field traits in a panel of 244 maize hybrids. We considered two prediction scenarios: in the first one, new hybrids are predicted (CV-NH), and in the second one, partially observed hybrids are predicted (CV-POH). For both scenarios, all hybrids were characterized for omics on the platform. We observed that omics can predict both additive and non-additive genetic effects for the platform traits, resulting in much higher predictive abilities than GBLUP. It highlights their efficiency in capturing regulatory processes in relation to growth conditions. For the field traits, we observed that the additive components of omics only slightly improved predictive abilities for predicting new hybrids (CV-NH, model MegaGAO) and for predicting partially observed hybrids (CV-POH, model GAOxW-BLUP) in comparison to GBLUP. We conclude that measuring the omics in the fields would be of considerable interest in predicting productivity if the costs of omics drop significantly.

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.

Plasticity QTLs specifically contribute to the genotype × water availability interaction in maize

KEY MESSAGE

Multi-trial genome wide association study of plasticity indices allow to detect QTLs specifically involved in the genotype x water availability interaction. Concerns regarding high maize yield losses due to increasing occurrences of drought events are growing, and breeders are still looking for molecular markers for drought tolerance. However, the genetic determinism of traits in response to drought is highly complex and identification of causal regions is a tremendous task. Here, we exploit the phenotypic data obtained from four trials carried out on a phenotyping platform, where a diversity panel of 254 maize hybrids was grown under well-watered and water deficit conditions, to investigate the genetic bases of the drought response in maize. To dissociate drought effect from other environmental factors, we performed multi-trial genome-wide association study on well-watered and water deficit phenotypic means, and on phenotypic plasticity indices computed from measurements made for six ecophysiological traits. We identify 102 QTLs and 40 plasticity QTLs. Most of them were new compared to those obtained from a previous study on the same dataset. Our results show that plasticity QTLs cover genetic regions not identified by QTLs. Furthermore, for all ecophysiological traits, except one, plasticity QTLs are specifically involved in the genotype by water availability interaction, for which they explain between 60 and 100% of the variance. Altogether, QTLs and plasticity QTLs captured more than 75% of the genotype by water availability interaction variance, and allowed to find new genetic regions. Overall, our results demonstrate the importance of considering phenotypic plasticity to decipher the genetic architecture of trait response to stress.

Robotized indoor phenotyping allows genomic prediction of adaptive traits in the field

Breeding for resilience to climate change requires considering adaptive traits such as plant architecture, stomatal conductance and growth, beyond the current selection for yield. Robotized indoor phenotyping allows measuring such traits at high throughput for speed breeding, but is often considered as non-relevant for field conditions. Here, we show that maize adaptive traits can be inferred in different fields, based on genotypic values obtained indoor and on environmental conditions in each considered field. The modelling of environmental effects allows translation from indoor to fields, but also from one field to another field. Furthermore, genotypic values of considered traits match between indoor and field conditions. Genomic prediction results in adequate ranking of genotypes for the tested traits, although with lesser precision for elite varieties presenting reduced phenotypic variability. Hence, it distinguishes genotypes with high or low values for adaptive traits, conferring either spender or conservative strategies for water use under future climates.

Phenomics: conceptualization and importance for plant physiology

Phenomics is a relatively new discipline of biology that has been widely applied in several fields, mainly in crop sciences. We reviewed the concepts used in this discipline (particularly for plants) and found a lack of consensus on what defines a phenomic study. Furthermore, phenomics has been primarily developed around its technical aspects (operationalization), while the conceptual framework of the actual research lags behind. Each research group has given its own interpretation of this 'omic' and thus unwittingly created a 'conceptual controversy'. Addressing this issue is of particular importance, as the experimental designs and concepts of phenomics are so diverse that it is difficult to compare studies. In this opinion article, we evaluate the conceptual framework of phenomics.

Opportunity and challenges of phenotyping plant salt tolerance

Salinity is a key factor limiting agricultural production worldwide. Recent advances in field phenotyping have enabled the recording of the environmental history and dynamic response of plants by considering both genotype × environment (G×E) interactions and envirotyping. However, only a few studies have focused on plant salt tolerance phenotyping. Therefore, we analyzed the potential opportunities and major challenges in improving plant salt tolerance using advanced field phenotyping technologies. RGB imaging and spectral and thermal sensors are the most useful and important sensing techniques for assessing key morphological and physiological traits of plant salt tolerance. However, field phenotyping faces challenges owing to its practical applications and high costs, limiting its use in early generation breeding and in developing countries.

A simple system for phenotyping of plant transpiration and stomatal conductance response to drought

Plant breeding for increased crop water use efficiency or drought stress resistance requires methods to quickly assess the transpiration rate (E) and stomatal conductance (g_s) of a large number of individual plants. Several methods to measure E and g_s exist, each of which has its own advantages and shortcomings. To add to this toolbox, we developed a method that uses whole-plant thermal imaging in a controlled environment, where aerial humidity is changed rapidly to induce changes in E that are reflected in changes in leaf temperature. This approach is based on a simplified energy balance equation, without the need for a reference material or complicated calculations. To test this concept, we built a double-sided, perforated, open-top plexiglass chamber that was supplied with air at a high flow rate (35 L min^-1) and whose relative humidity (RH) could be switched rapidly. Measurements included air and leaf temperature as well as RH. Using several well-watered and drought stressed genotypes of Arabidopsis thaliana that were exposed to multiple cycles in RH (30-50 % and back), we showed that leaf temperature as measured in our system correlated well with E and g_s measured in a commercial gas exchange system. Our results demonstrate that, at least within a given species, the differences in leaf temperature under several RH can be used as a proxy for E and g_s. Given that this method is fairly quick, noninvasive and remote, we envision that it could be upscaled for work within rapid plant phenotyping systems.

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).

Physiological and genetic control of transpiration efficiency in African rice, Oryza glaberrima Steud

Improving crop water use efficiency, the amount of carbon assimilated as biomass per unit of water used by a plant, is of major importance as water for agriculture becomes scarcer. In rice, the genetic bases of transpiration efficiency, the derivation of water use efficiency at the whole-plant scale, and its putative component trait transpiration restriction under high evaporative demand remain unknown. These traits were measured in 2019 in a panel of 147 African rice (Oryza glaberrima) genotypes known to be potential sources of tolerance genes to biotic and abiotic stresses. Our results reveal that higher transpiration efficiency is associated with transpiration restriction in African rice. Detailed measurements in a subset of highly contrasted genotypes in terms of biomass accumulation and transpiration confirmed these associations and suggested that root to shoot ratio played an important role in transpiration restriction. Genome wide association studies identified marker-trait associations for transpiration response to evaporative demand, transpiration efficiency, and its residuals, with links to genes involved in water transport and cell wall patterning. Our data suggest that root-shoot partitioning is an important component of transpiration restriction that has a positive effect on transpiration efficiency in African rice. Both traits are heritable and define targets for breeding rice with improved water use strategies.

Physiological adaptive traits are a potential allele reservoir for maize genetic progress under challenging conditions

Combined phenomic and genomic approaches are required to evaluate the margin of progress of breeding strategies. Here, we analyze 65 years of genetic progress in maize yield, which was similar (101 kg ha-1 year-1) across most frequent environmental scenarios in the European growing area. Yield gains were linked to physiologically simple traits (plant phenology and architecture) which indirectly affected reproductive development and light interception in all studied environments, marked by significant genomic signatures of selection. Conversely, studied physiological processes involved in stress adaptation remained phenotypically unchanged (e.g. stomatal conductance and growth sensitivity to drought) and showed no signatures of selection. By selecting for yield, breeders indirectly selected traits with stable effects on yield, but not physiological traits whose effects on yield can be positive or negative depending on environmental conditions. Because yield stability under climate change is desirable, novel breeding strategies may be needed for exploiting alleles governing physiological adaptive traits.

High-throughput plant phenotyping: a role for metabolomics?

High-throughput (HTP) plant phenotyping approaches are developing rapidly and are already helping to bridge the genotype-phenotype gap. However, technologies should be developed beyond current physico-spectral evaluations to extend our analytical capacities to the subcellular level. Metabolites define and determine many key physiological and agronomic features in plants and an ability to integrate a metabolomics approach within current HTP phenotyping platforms has huge potential for added value. While key challenges remain on several fronts, novel technological innovations are upcoming yet under-exploited in a phenotyping context. In this review, we present an overview of the state of the art and how current limitations might be overcome to enable full integration of metabolomics approaches into a generic phenotyping pipeline in the near future.

Identifying Developmental Patterns in Structured Plant Phenotyping Data

Technological breakthroughs concerning both sensors and robotized plant phenotyping platforms have totally renewed the plant phenotyping paradigm in the last two decades. This has impacted both the nature and the throughput of data with the availability of data at high-throughput from the tissular to the whole plant scale. Sensor outputs often take the form of 2D or 3D images or time series of such images from which traits are extracted while organ shapes, shoot or root system architectures can be deduced. Despite this change of paradigm, many phenotyping studies often ignore the structure of the plant and therefore loose the information conveyed by the temporal and spatial patterns emerging from this structure. The developmental patterns of plants often take the form of succession of well-differentiated phases, stages or zones depending on the temporal, spatial or topological indexing of data. This entails the use of hierarchical statistical models for their identification.The objective here is to show potential approaches for analyzing structured plant phenotyping data using state-of-the-art methods combining probabilistic modeling, statistical inference and pattern recognition. This approach is illustrated using five different examples at various scales that combine temporal and topological index parameters, and development and growth variables obtained using prospective or retrospective measurements.

Adaptive Agricultural Strategies for Facing Water Deficit in Sweet Maize Production: A Case Study of a Semi-Arid Mediterranean Region

Maize is a crucial global commodity, which is used not only for food, but also as an alternative crop in biogas production and as a major energy-supply ingredient in animal diets. However, climate change is jeopardizing current maize production due to its direct impact on weather instability and water availability or its indirect effects on regional climate suitability loss. Hence, new areas for sweet maize cultivation should be considered in the future. Therefore, this study focuses on the possibility of producing maize in a challenging environment in Southern Italy considering rainfed cultivation and two irrigation regimes (full and deficit). The experiment was conducted during two subsequent growing seasons under semi-arid Mediterranean climate conditions. The overall results indicated a significant difference in biomass and yield between irrigated and non-irrigated treatments, and between full and deficit irrigation. Sweet maize cultivated under deficit irrigation gained less biomass than under full irrigation and its development and fruit maturation were delayed. Under deficit irrigation, the plants gave lower yields and a higher percentage of the panicle weight consisted of kernels. Irrigation water productivity was higher for deficit than for full irrigated treatment. These findings indicate the feasibility of sweet maize production in semi-arid areas of Southern Italy using adaptive agricultural strategies including deficit irrigation and controlled water stress. Given the importance of maize production, understanding of maize growth and productivity in a challenging environment may support future agricultural programming and thereby contribute e to mitigation of the direct and indirect effects of climate change.

Correlation and co-localization of QTL for stomatal density, canopy temperature, and productivity with and without drought stress in Setaria

Mechanistic modeling indicates that stomatal conductance could be reduced to improve water use efficiency (WUE) in C4 crops. Genetic variation in stomatal density and canopy temperature was evaluated in the model C4 genus, Setaria. Recombinant inbred lines (RILs) derived from a Setaria italica×Setaria viridis cross were grown with ample or limiting water supply under field conditions in Illinois. An optical profilometer was used to rapidly assess stomatal patterning, and canopy temperature was measured using infrared imaging. Stomatal density and canopy temperature were positively correlated but both were negatively correlated with total above-ground biomass. These trait relationships suggest a likely interaction between stomatal density and the other drivers of water use such as stomatal size and aperture. Multiple quantitative trait loci (QTL) were identified for stomatal density and canopy temperature, including co-located QTL on chromosomes 5 and 9. The direction of the additive effect of these QTL on chromosome 5 and 9 was in accordance with the positive phenotypic relationship between these two traits. This, along with prior experiments, suggests a common genetic architecture between stomatal patterning and WUE in controlled environments with canopy transpiration and productivity in the field, while highlighting the potential of Setaria as a model to understand the physiology and genetics of WUE in C4 species.

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.

High-Throughput Phenotyping: A Platform to Accelerate Crop Improvement

Development of high-throughput phenotyping technologies has progressed considerably in the last 10 years. These technologies provide precise measurements of desired traits among thousands of field-grown plants under diversified environments; this is a critical step towards selection of better performing lines as to yield, disease resistance, and stress tolerance to accelerate crop improvement programs. High-throughput phenotyping techniques and platforms help unraveling the genetic basis of complex traits associated with plant growth and development and targeted traits. This review focuses on the advancements in technologies involved in high-throughput, field-based, aerial, and unmanned platforms. Development of user-friendly data management tools and softwares to better understand phenotyping will increase the use of field-based high-throughput techniques, which have potential to revolutionize breeding strategies and meet the future needs of stakeholders.

Proteome profiling of repeated drought stress reveals genotype-specific responses and memory effects in maize

Drought has become a major stress for agricultural productivity in temperate regions, such as central Europe. Thus, information on how crop plants respond to drought is important to develop tolerant hybrids and to ensure yield stability. Posttranscriptional regulation through changed protein abundances is an important mechanism of short-term response to stress events that has not yet been widely exploited in breeding strategies. Here, we investigated the response to repeated drought exposure of a tolerant and a sensitive maize hybrid in order to understand general protein abundance changes induced by singular drought or repeated drought events. In general, drought affected protein abundance of multiple pathways in the plant. We identified starch metabolism, aquaporin abundance, PSII proteins and histones as strongly associated with typical drought-induced phenotypes such as increased membrane leakage, osmolality or effects on stomatal conductance and assimilation rate. In addition, we found a strong effect of drought on nutrient assimilation, especially the sulfur metabolism. In general, pre-experience of mild drought before exposure to a more severe drought resulted in visible adaptations resulting in dampened phenotypes as well as lower magnitude of protein abundance changes.

Phenomics-based GWAS analysis reveals the genetic architecture for drought resistance in cotton

Drought resistance (DR) is a complex trait that is regulated by a variety of genes. Without comprehensive profiling of DR-related traits, the knowledge of the genetic architecture for DR in cotton remains limited. Thus, there is a need to bridge the gap between genomics and phenomics. In this study, an automatic phenotyping platform (APP) was systematically applied to examine 119 image-based digital traits (i-traits) during drought stress at the seedling stage, across a natural population of 200 representative upland cotton accessions. Some novel i-traits, as well as some traditional i-traits, were used to evaluate the DR in cotton. The phenomics data allowed us to identify 390 genetic loci by genome-wide association study (GWAS) using 56 morphological and 63 texture i-traits. DR-related genes, including GhRD2, GhNAC4, GhHAT22 and GhDREB2, were identified as candidate genes by some digital traits. Further analysis of candidate genes showed that Gh_A04G0377 and Gh_A04G0378 functioned as negative regulators for cotton drought response. Based on the combined digital phenotyping, GWAS analysis and transcriptome data, we conclude that the phenomics dataset provides an excellent resource to characterize key genetic loci with an unprecedented resolution which can inform future genome-based breeding for improved DR in cotton.

A systems genetics approach reveals environment-dependent associations between SNPs, protein coexpression, and drought-related traits in maize

The effect of drought on maize yield is of particular concern in the context of climate change and human population growth. However, the complexity of drought-response mechanisms makes the design of new drought-tolerant varieties a difficult task that would greatly benefit from a better understanding of the genotype–phenotype relationship. To provide novel insight into this relationship, we applied a systems genetics approach integrating high-throughput phenotypic, proteomic, and genomic data acquired from 254 maize hybrids grown under two watering conditions. Using association genetics and protein coexpression analysis, we detected more than 22,000 pQTLs across the two conditions and confidently identified 15 loci with potential pleiotropic effects on the proteome. We showed that even mild water deficit induced a profound remodeling of the proteome, which affected the structure of the protein coexpression network, and a reprogramming of the genetic control of the abundance of many proteins, including those involved in stress response. Colocalizations between pQTLs and QTLs for ecophysiological traits, found mostly in the water deficit condition, indicated that this reprogramming may also affect the phenotypic level. Finally, we identified several candidate genes that are potentially responsible for both the coexpression of stress response proteins and the variations of ecophysiological traits under water deficit. Taken together, our findings provide novel insights into the molecular mechanisms of drought tolerance and suggest some pathways for further research and breeding.

Simulating the effect of flowering time on maize individual leaf area in contrasting environmental scenarios

The quality of yield prediction is linked to that of leaf area. We first analysed the consequences of flowering time and environmental conditions on the area of individual leaves in 127 genotypes presenting contrasting flowering times in fields of Europe, Mexico, and Kenya. Flowering time was the strongest determinant of leaf area. Combined with a detailed field experiment, this experiment showed a large effect of flowering time on the final leaf number and on the distribution of leaf growth rate and growth duration along leaf ranks, in terms of both length and width. Equations with a limited number of genetic parameters predicted the beginning, end, and maximum growth rate (length and width) for each leaf rank. The genotype-specific environmental effects were analysed with datasets in phenotyping platforms that assessed the effects (i) of the amount of intercepted light on leaf width, and (ii) of temperature, evaporative demand, and soil water potential on leaf elongation rate. The resulting model was successfully tested for 31 hybrids in 15 European and Mexican fields. It potentially allows prediction of the vertical distribution of leaf area of a large number of genotypes in contrasting field conditions, based on phenomics and on sensor networks.

Leaf application of chitosan and physiological evaluation of maize hybrids contrasting for drought tolerance under water restriction

Abstract It is a fact that the regions that cultivate the most maize crop do not have fully adequate technologies to measure productivity losses caused by irregularities in water availability. The objective of this study was to evaluate the physiological characteristics of maize hybrids tolerant (DKB 390) and sensitive (BRS 1030) to drought, at V5 growth stage and under water restriction, in order to understand the mechanisms involved in the induction of tolerance to drought by chitosan in contrasting maize genotypes. Plants were cultivated in pots at a greenhouse, and chitosan 100 ppm was applied by leaf spraying. The water restriction was imposed for 10 days and then leaf gaseous exchange and chlorophyll fluorescence were evaluated. The tolerant hybrid (DKB 390) showed higher photosynthesis, stomatal conductance, carboxylation efficiency, electron transport rate, and non-photochemical quenching when chitosan was used. Plants from tolerant genotype treated with chitosan were more tolerant to water stress because there were more responsive to the biopolymer.

A Brassica napus Reductase Gene Dissected by Associative Transcriptomics Enhances Plant Adaption to Freezing Stress

Cold treatment (vernalization) is required for winter crops such as rapeseed (Brassica napus L.). However, excessive exposure to low temperature (LT) in winter is also a stress for the semi-winter, early-flowering rapeseed varieties widely cultivated in China. Photosynthetic efficiency is one of the key determinants, and thus a good indicator for LT tolerance in plants. So far, the genetic basis underlying photosynthetic efficiency is poorly understood in rapeseed. Here the current study used Associative Transcriptomics to identify genetic loci controlling photosynthetic gas exchange parameters in a diversity panel comprising 123 accessions. A total of 201 significant Single Nucleotide Polymorphisms (SNPs) and 147 Gene Expression Markers (GEMs) were detected, leading to the identification of 22 candidate genes. Of these, Cab026133.1, an ortholog of the Arabidopsis gene AT2G29300.2 encoding a tropinone reductase (BnTR1), was further confirmed to be closely linked to transpiration rate. Ectopic expressing BnTR1 in Arabidopsis plants significantly increased the transpiration rate and enhanced LT tolerance under freezing conditions. Also, a much higher level of alkaloids content was observed in the transgenic Arabidopsis plants, which could help protect against LT stress. Together, the current study showed that AT is an effective approach for dissecting LT tolerance trait in rapeseed and that BnTR1 is a good target gene for the genetic improvement of LT tolerance in plant.

Thermography methods to assess stomatal behaviour in a dynamic environment

Although thermography allows rapid, non-invasive measurements of large numbers of plants, it has not been used extensively due to the difficulty in deriving biologically relevant information such as leaf transpiration (E) and stomatal conductance (gsw) from thermograms. Methods normalizing leaf temperature using temperatures from reference materials (e.g. with and without evaporative flux) to generate stress indices are generally preferred due to their ease of use to assess plant water status. Here, a simplified method to solve dynamic energy balance equations is presented, which enables the calculation of 'wet' and 'dry' leaf temperatures in order to derive stress indices, whilst providing accurate estimates of E and gsw. Comparing stress indices and gas exchange parameters highlights the limitation of stress indices in a dynamic environment and how this problem can be overcome using artificial leaf references with known conductance. Additionally, applying the equations for each pixel of a thermogram to derive the rapidity of stomatal response over the leaf lamina in wheat revealed the spatial heterogeneity of stomatal behaviour. Rapidity of stomatal movements is an important determinant of water use efficiency, and our results showed 'patchy' responses that were linked to both the spatial and temporal response of gsw.

We aren't good at picking candidate genes, and it's slowing us down

In order to gain a molecular understanding of the genetic basis for plant traits, we need to be able to identify the underlying gene and the causal allele for genetic loci. This process usually involves a step where a researcher selects likely candidate genes from a list. The process of picking candidate genes is inherently prone to distortion due to human bias, and this is slowing down our research enterprise.

Review: Climate change impacts on food security- focus on perennial cropping systems and nutritional value

Anthropogenic increases in fossil fuel emissions have been a primary driver of increased concentrations of atmospheric carbon dioxide ([CO₂]) and other greenhouse gases resulting in warmer temperatures, alterations in precipitation patterns, and increased occurrence of extreme weather events in terrestrial areas across the globe. In agricultural growing regions, alterations in climate can challenge plant productivity in ways that impact the ability of the world to sustain adequate food production for a growing and increasingly affluent population with shifting access to affordable and nutritious food. While the knowledge gap that exists regarding potential climate change impacts is large across agriculture, it is especially large in specialty cropping systems. This includes fruit and vegetable crops, and perennial cropping systems which also contribute (along with row crops) to our global diet. In order to obtain a comprehensive view of the true impact of climate change on our global food supply, we must expand our narrow focus from improving yield and plant productivity to include the impact of climate change on the nutritional value of these crops. In order to address these questions, we need a multi-faceted approach that integrates physiology and genomics tools and conducts comprehensive experiments under realistic depictions of future projected climate. This review describes gaps in our knowledge in relation to these responses, and future questions and actions that are needed to develop a sustainable future food supply in light of global climate change.

Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives

Since whole-genome sequencing of many crops has been achieved, crop functional genomics studies have stepped into the big-data and high-throughput era. However, acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies. Nevertheless, recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years. In this article, we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades. We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies. Finally, we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap. It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.

Strengthened antioxidant capacity improves photosynthesis by regulating stomatal aperture and ribulose-1,5-bisphosphate carboxylase/oxygenase activity

ABA is important for plant growth and development; however, it also inhibits photosynthesis by regulating the stomatal aperture and ribulose-1,5-bisphosphate carboxylase/oxygenase activity. Noteworthy, this negative effect can be alleviated by antioxidants including ascorbic acid (AsA) and catalase (CAT), but the underlying mechanism remains unclear. Two rice cultivars, Zhefu802 (recurrent parent) and its near-isogenic line, fgl were selected and planted in a greenhouse with 30/24 °C (day/night) under natural sunlight conditions. Compared to fgl, Zhefu802 had significantly lower net photosynthetic rate (PN) and stomatal conductance (Cond) as well as significantly higher ABA and H₂O₂ contents. However, AsA and CAT increased PN, Cond, and stomatal aperture, which decreased H₂O₂ and malondialdehyde (MDA) levels. In this process, AsA and CAT significantly increased the ribulose-1,5-bisphosphate carboxylase activity, while they strongly decreased the ribulose-1,5-bisphosphate oxygenase activity, and finally caused an obvious decrease in the ratio of photorespiration (Pr) to PN. Additionally, AsA and CAT significantly increased the expression levels of RbcS and RbcL genes of leaves, while H₂O₂ significantly decreased them, especially the RbcS gene. In summary, the removal of H₂O₂ by AsA and CAT can improve the leaf photosynthesis by alleviating the inhibition on the stomatal conductance and ribulose-1,5-bisphosphate carboxylase capacity caused by ABA.

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

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

To clean or not to clean phenotypic datasets for outlier plants in genetic analyses?

Based on case studies, we discuss the extent to which genome-wide association studies (GWAS) are affected by outlier plants, i.e. those deviating from the expected distribution on a multi-criteria basis. Using a raw dataset consisting of daily measurements of leaf area, biomass, and plant height for thousands of plants, we tested three different cleaning methods for their effects on genetic analyses. No-cleaning resulted in the highest number of dubious quantitative trait loci, especially at loci with highly unbalanced allelic frequencies. A trade-off was identified between the risk of false-positives (with no-cleaning and/or a low threshold for minor allele frequency) and the risk of missing interesting rare alleles. Cleaning can lower the risk of the latter by making it possible to choose a higher threshold in GWAS.

Changes in the vertical distribution of leaf area enhanced light interception efficiency in maize over generations of selection

Breeders select for yield, thereby indirectly selecting for traits that contribute to it. We tested if breeding has affected a range of traits involved in plant architecture and light interception, via the analysis of a panel of 60 maize hybrids released from 1950 to 2015. This was based on novel traits calculated from reconstructions derived from a phenotyping platform. The contribution of these traits to light interception was assessed in virtual field canopies composed of 3D plant reconstructions, with a model tested in a real field. Two categories of traits had different contributions to genetic progress. (a) The vertical distribution of leaf area had a high heritability and showed a marked trend over generations of selection. Leaf area tended to be located at lower positions in the canopy, thereby improving light penetration and distribution in the canopy. This potentially increased the carbon availability to ears, via the amount of light absorbed by the intermediate canopy layer. (b) Neither the horizontal distribution of leaves in the relation to plant rows nor the response of light interception to plant density showed appreciable trends with generations. Hence, among many architectural traits, the vertical distribution of leaf area was the main indirect target of selection.

The foliar application of a mixture of semisynthetic chitosan derivatives induces tolerance to water deficit in maize, improving the antioxidant system and increasing photosynthesis and grain yield

Research has shown that chitosan induces plant stress tolerance and protection, but few studies have explored chemical modifications of chitosan and their effects on plants under water stress. Chitosan and its derivatives were applied (isolated or in mixture) to maize hybrids sensitive to water deficit under greenhouse conditions through foliar spraying at the pre-flowering stage. After the application, water deficit was induced for 15 days. Analyses of leaves and biochemical gas exchange in the ear leaf were performed on the first and fifteenth days of the stress period. Production attributes were also analysed at the end of the experiment. In general, the application of the two chitosan derivatives or their mixture potentiated the activities of the antioxidant enzymes superoxide dismutase, catalase, ascorbate peroxidase, glutathione reductase and guaiacol peroxidase at the beginning of the stress period, in addition to reducing lipid peroxidation (malonaldehyde content) and increasing gas exchange and proline contents at the end of the stress period. The derivatives also increased the content of phenolic compounds and the activity of enzymes involved in their production (phenylalanine ammonia lyase and tyrosine ammonia lyase). Dehydroascorbate reductase and compounds such as total soluble sugars, total amino acids, starch, grain yield and harvest index increased for both the derivatives and chitosan. However, the mixture of derivatives was the treatment that led to the higher increase in grain yield and harvest index compared to the other treatments. The application of semisynthetic molecules derived from chitosan yielded greater leaf gas exchange and a higher incidence of the biochemical conditions that relieve plant stress.

Genomic prediction of maize yield across European environmental conditions

The development of germplasm adapted to changing climate is required to ensure food security^1,2. Genomic prediction is a powerful tool to evaluate many genotypes but performs poorly in contrasting environmental scenarios^3-7 (genotype × environment interaction), in spite of promising results for flowering time⁸. New avenues are opened by the development of sensor networks for environmental characterization in thousands of fields^9,10. We present a new strategy for germplasm evaluation under genotype × environment interaction. Yield was dissected in grain weight and number and genotype × environment interaction in these components was modeled as genotypic sensitivity to environmental drivers. Environments were characterized using genotype-specific indices computed from sensor data in each field and the progression of phenology calibrated for each genotype on a phenotyping platform. A whole-genome regression approach for the genotypic sensitivities led to accurate prediction of yield under genotype × environment interaction in a wide range of environmental scenarios, outperforming a benchmark approach.

Dynamic leaf energy balance: deriving stomatal conductance from thermal imaging in a dynamic environment

In spite of the significant progress made in recent years, the use of thermography to derive biologically relevant traits remains a challenge under fluctuating conditions. The aim of this study was to rethink the current method to process thermograms and derive temporal responses of stomatal conductance (gsw) using dynamic energy balance equations. Time-series thermograms provided the basis for a spatial and temporal characterization of gsw responses in wheat (Triticum aestivum). A leaf replica with a known conductance was used to validate the approach and to test the ability of our model to be used with any material and under any environmental conditions. The results highlighted the importance of the co-ordinated stomatal responses that run parallel to the leaf blade despite their patchy distribution. The diversity and asymmetry of the temporal response of gsw observed after a step increase and step decrease in light intensity can be interpreted as a strategy to maximize photosynthesis per unit of water loss and avoid heat stress in response to light flecks in a natural environment. This study removes a major bottleneck for plant phenotyping platforms and will pave the way to further developments in our understanding of stomatal behaviour.

Genetic and environmental dissection of biomass accumulation in multi-genotype maize canopies

Multi-genotype canopies are frequent in phenotyping experiments and are of increasing interest in agriculture. Radiation interception efficiency (RIE) and radiation use efficiency (RUE) have low heritabilities in such canopies. We propose a revised Monteith equation that identifies environmental and genetic components of RIE and RUE. An environmental term, a component of RIE, characterizes the effect of the presence or absence of neighbours on light interception. The ability of a given plant to compete with its neighbours is then identified, which accounts for the genetic variability of RIE of plants having similar leaf areas. This method was used in three experiments in a phenotyping platform with 765 plants of 255 maize hybrids. As expected, the heritability of the environmental term was near zero, whereas that of the competitiveness term increased with phenological stage, resulting in the identification of quantitative trait loci. In the same way, RUE was dissected as an effect of intercepted light and a genetic term. This approach was used for predicting the behaviour of individual genotypes in virtual multi-genotype canopies. A large effect of competitiveness was observed in multi-genotype but not in single-genotype canopies, resulting in a bias for genotype comparisons in breeding fields.

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

In recent years developments in plant phenomic approaches and facilities have gradually caught up with genomic approaches. An opportunity lies ahead to dissect complex, quantitative traits when both genotype and phenotype can be assessed at a high level of detail. This is especially true for the study of natural variation in photosynthetic efficiency, for which forward genetics studies have yielded only a little progress in our understanding of the genetic layout of the trait. High-throughput phenotyping, primarily from chlorophyll fluorescence imaging, should help to dissect the genetics of photosynthesis at the different levels of both plant physiology and development. Specific emphasis should be directed towards understanding the acclimation of the photosynthetic machinery in fluctuating environments, which may be crucial for the identification of genetic variation for relevant traits in food crops. Facilities should preferably be designed to accommodate phenotyping of photosynthesis-related traits in such environments. The use of forward genetics to study the genetic architecture of photosynthesis is likely to lead to the discovery of novel traits and/or genes that may be targeted in breeding or bio-engineering approaches to improve crop photosynthetic efficiency. In the near future, big data approaches will play a pivotal role in data processing and streamlining the phenotype-to-gene identification pipeline.

Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective

Employing computer vision to extract useful information from images and videos is becoming a key technique for identifying phenotypic changes in plants. Here, we review the emerging aspects of computer vision for automated plant phenotyping. Recent advances in image analysis empowered by machine learning-based techniques, including convolutional neural network-based modeling, have expanded their application to assist high-throughput plant phenotyping. Combinatorial use of multiple sensors to acquire various spectra has allowed us to noninvasively obtain a series of datasets, including those related to the development and physiological responses of plants throughout their life. Automated phenotyping platforms accelerate the elucidation of gene functions associated with traits in model plants under controlled conditions. Remote sensing techniques with image collection platforms, such as unmanned vehicles and tractors, are also emerging for large-scale field phenotyping for crop breeding and precision agriculture. Computer vision-based phenotyping will play significant roles in both the nowcasting and forecasting of plant traits through modeling of genotype/phenotype relationships.

Dealing with multi-source and multi-scale information in plant phenomics: the ontology-driven Phenotyping Hybrid Information System

Phenomic datasets need to be accessible to the scientific community. Their reanalysis requires tracing relevant information on thousands of plants, sensors and events. The open-source Phenotyping Hybrid Information System (PHIS) is proposed for plant phenotyping experiments in various categories of installations (field, glasshouse). It unambiguously identifies all objects and traits in an experiment and establishes their relations via ontologies and semantics that apply to both field and controlled conditions. For instance, the genotype is declared for a plant or plot and is associated with all objects related to it. Events such as successive plant positions, anomalies and annotations are associated with objects so they can be easily retrieved. Its ontology-driven architecture is a powerful tool for integrating and managing data from multiple experiments and platforms, for creating relationships between objects and enriching datasets with knowledge and metadata. It interoperates with external resources via web services, thereby allowing data integration into other systems; for example, modelling platforms or external databases. It has the potential for rapid diffusion because of its ability to integrate, manage and visualize multi-source and multi-scale data, but also because it is based on 10 yr of trial and error in our groups.

Carbon isotope composition, water use efficiency, and drought sensitivity are controlled by a common genomic segment in maize

KEY MESSAGE

A genomic segment on maize chromosome 7 influences carbon isotope composition, water use efficiency, and leaf growth sensitivity to drought, possibly by affecting stomatal properties. Climate change is expected to decrease water availability in many agricultural production areas around the globe. Therefore, plants with improved ability to grow under water deficit are urgently needed. We combined genetic, phenomic, and physiological approaches to understand the relationship between growth, stomatal conductance, water use efficiency, and carbon isotope composition in maize (Zea mays L.). Using near-isogenic lines derived from a maize introgression library, we analysed the effects of a genomic region previously identified as affecting carbon isotope composition. We show stability of trait expression over several years of field trials and demonstrate in the phenotyping platform Phenodyn that the same genomic region also influences the sensitivity of leaf growth to evaporative demand and soil water potential. Our results suggest that the studied genomic region affecting carbon isotope discrimination also harbours quantitative trait loci playing a role in maize drought sensitivity possibly via stomatal behaviour and development. We propose that the observed phenotypes collectively originate from altered stomatal conductance, presumably via abscisic acid.

Integrating modelling and phenotyping approaches to identify and screen complex traits: transpiration efficiency in cereals

Following advances in genetics, genomics, and phenotyping, trait selection in breeding is limited by our ability to understand interactions within the plant and with the environment, and to identify traits of most relevance to the target population of environments. We propose an integrated approach that combines insights from crop modelling, physiology, genetics, and breeding to characterize traits valuable for yield gain in the target population of environments, develop relevant high-throughput phenotyping platforms, and identify genetic controls and their value in production environments. This paper uses transpiration efficiency (biomass produced per unit of water used) as an example of a complex trait of interest to illustrate how the approach can guide modelling, phenotyping, and selection in a breeding programme. We believe that this approach, by integrating insights from diverse disciplines, can increase the resource use efficiency of breeding programmes for improving yield gains in target populations of environments.

The Physiological Basis of Drought Tolerance in Crop Plants: A Scenario-Dependent Probabilistic Approach

Drought tolerance involves mechanisms operating at different spatial and temporal scales, from rapid stomatal closure to maintenance of crop yield. We review how short-term mechanisms are controlled for stabilizing shoot water potential and how long-term processes have been constrained by evolution or breeding to fit into acclimation strategies for specific drought scenarios. These short- or long-term feedback processes participate in trade-offs between carbon accumulation and the risk of deleterious soil water depletion. Corresponding traits and alleles may therefore have positive or negative effects on crop yield depending on drought scenarios. We propose an approach that analyzes the genetic architecture of traits in phenotyping platforms and of yield in tens of field experiments. A combination of modeling and genomic prediction is then used to estimate the comparative interests of combinations of alleles depending on drought scenarios. Hence, drought tolerance is understood probabilistically by estimating the benefit and risk of each combination of alleles.