High-throughput estimation of incident light, light interception and radiation-use efficiency of thousands of plants in a phenotyping platform

Greenhouse Spatial Effects Detected in the Barley (Hordeum vulgare L.) Epigenome Underlie Stochasticity of DNA Methylation

Environmental cues are known to alter the methylation profile of genomic DNA, and thereby change the expression of some genes. A proportion of such modifications may become adaptive by adjusting expression of stress response genes but others have been shown to be highly stochastic, even under controlled conditions. The influence of environmental flux on plants adds an additional layer of complexity that has potential to confound attempts to interpret interactions between environment, methylome, and plant form. We therefore adopt a positional and longitudinal approach to study progressive changes to barley DNA methylation patterns in response to salt exposure during development under greenhouse conditions. Methylation-sensitive amplified polymorphism (MSAP) and phenotypic analyses of nine diverse barley varieties were grown in a randomized plot design, under two salt treatments (0 and 75 mM NaCl). Combining environmental, phenotypic and epigenetic data analyses, we show that at least part of the epigenetic variability, previously described as stochastic, is linked to environmental micro-variations during plant growth. Additionally, we show that differences in methylation increase with time of exposure to micro-variations in environment. We propose that subsequent epigenetic studies take into account microclimate-induced epigenetic variability.

Blocking and re-arrangement of pots in greenhouse experiments: which approach is more effective?

BACKGROUND

Observations measured in field and greenhouse experiments always contain errors. These errors can arise from measurement error, local or positional conditions of the experimental units, or from the randomization of experimental units. In statistical analysis errors can be modelled as independent effects or as spatially correlated effects with an appropriate variance-covariance structure. Using a suitable experimental design, a part of the variance can be captured through blocking of the experimental units. If experimental units (e.g. pots within a greenhouse) are mobile, they can be re-arranged during the experiment. This re-arrangement enables a separation of variation due to time-invariant position effects and variation due to the experimental units. If re-arrangement is successful, the time-invariant positional effect can average out for experimental units moved between different positions during the experiment. While re-arrangement is commonly done in greenhouse experiments, data to quantify its usefulness is limited.

RESULTS

A uniformity greenhouse experiment with barley (Hordeum vulgare L.) to compare re-arrangement of pots with a range of designs under fixed-position arrangement showed that both methods can reduce the residual variance and the average standard error of a difference. All designs with fixed-position arrangement, which accounted for the known north-south gradient in the greenhouse, outperformed re-arrangement. An α-design with block size four performed best across seven plant growth traits.

CONCLUSION

Blocking with a fixed-position arrangement was more efficient in improving precision of greenhouse experiments than re-arrangement of pots and hence can be recommended for comparable greenhouse experiments.

Modelling tiller growth and mortality as a sink-driven process using Ecomeristem: implications for biomass sorghum ideotyping

BACKGROUND AND AIMS

Plant modelling can efficiently support ideotype conception, particularly in multi-criteria selection contexts. This is the case for biomass sorghum, implying the need to consider traits related to biomass production and quality. This study evaluated three modelling approaches for their ability to predict tiller growth, mortality and their impact, together with other morphological and physiological traits, on biomass sorghum ideotype prediction.

METHODS

Three Ecomeristem model versions were compared to evaluate whether tillering cessation and mortality were source (access to light) or sink (age-based hierarchical access to C supply) driven. They were tested using a field data set considering two biomass sorghum genotypes at two planting densities. An additional data set comparing eight genotypes was used to validate the best approach for its ability to predict the genotypic and environmental control of biomass production. A sensitivity analysis was performed to explore the impact of key genotypic parameters and define optimal parameter combinations depending on planting density and targeted production (sugar and fibre).

KEY RESULTS

The sink-driven control of tillering cessation and mortality was the most accurate, and represented the phenotypic variability of studied sorghum genotypes in terms of biomass production and partitioning between structural and non-structural carbohydrates. Model sensitivity analysis revealed that light conversion efficiency and stem diameter are key traits to target for improving sorghum biomass within existing genetic diversity. Tillering contribution to biomass production appeared highly genotype and environment dependent, making it a challenging trait for designing ideotypes.

CONCLUSIONS

By modelling tiller growth and mortality as sink-driven processes, Ecomeristem could predict and explore the genotypic and environmental variability of biomass sorghum production. Its application to larger sorghum genetic diversity considering water deficit regulations and its coupling to a genetic model will make it a powerful tool to assist ideotyping for current and future climatic scenario.

A Novel Image-Based Screening Method to Study Water-Deficit Response and Recovery of Barley Populations Using Canopy Dynamics Phenotyping and Simple Metabolite Profiling

Plant phenotyping platforms offer automated, fast scoring of traits that simplify the selection of varieties that are more competitive under stress conditions. However, indoor phenotyping methods are frequently based on the analysis of plant growth in individual pots. We present a reproducible indoor phenotyping method for screening young barley populations under water stress conditions and after subsequent rewatering. The method is based on a simple read-out of data using RGB imaging, projected canopy height, as a useful feature for indirectly following the kinetics of growth and water loss in a population of barley. A total of 47 variables including 15 traits and 32 biochemical metabolites measured (morphometric parameters, chlorophyll fluorescence imaging, quantification of stress-related metabolites; amino acids and polyamines, and enzymatic activities) were used to validate the method. The study allowed the identification of metabolites related to water stress response and recovery. Specifically, we found that cadaverine (Cad), 1,3-aminopropane (DAP), tryptamine (Tryp), and tyramine (Tyra) were the major contributors to the water stress response, whereas Cad, DAP, and Tyra, but not Tryp, remained at higher levels in the stressed plants even after rewatering. In this work, we designed, optimized and validated a non-invasive image-based method for automated screening of potential water stress tolerance genotypes in barley populations. We demonstrated the applicability of the method using transgenic barley lines with different sensitivity to drought stress showing that combining canopy height and the metabolite profile we can discriminate tolerant from sensitive genotypes. We showed that the projected canopy height a sensitive trait that truly reflects other invasively studied morphological, physiological, and metabolic traits and that our presented methodological setup can be easily applicable for large-scale screenings in low-cost systems equipped with a simple RGB camera. We believe that our approach will contribute to accelerate the study and understanding of the plant water stress response and recovery capacity in crops, such as barley.

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.

A High-Throughput Model-Assisted Method for Phenotyping Maize Green Leaf Area Index Dynamics Using Unmanned Aerial Vehicle Imagery

The dynamics of the Green Leaf Area Index (GLAI) is of great interest for numerous applications such as yield prediction and plant breeding. We present a high-throughput model-assisted method for characterizing GLAI dynamics in maize (Zea mays subsp. mays) using multispectral imagery acquired from an Unmanned Aerial Vehicle (UAV). Two trials were conducted with a high diversity panel of 400 lines under well-watered and water-deficient treatments in 2016 and 2017. For each UAV flight, we first derived GLAI estimates from empirical relationships between the multispectral reflectance and ground level measurements of GLAI achieved over a small sample of microplots. We then fitted a simple but physiologically sound GLAI dynamics model over the GLAI values estimated previously. Results show that GLAI dynamics was estimated accurately throughout the cycle (R2 > 0.9). Two parameters of the model, biggest leaf area and leaf longevity, were also estimated successfully. We showed that GLAI dynamics and the parameters of the fitted model are highly heritable (0.65 ≤ H2 ≤ 0.98), responsive to environmental conditions, and linked to yield and drought tolerance. This method, combining growth modeling, UAV imagery and simple non-destructive field measurements, provides new high-throughput tools for understanding the adaptation of GLAI dynamics and its interaction with the environment. GLAI dynamics is also a promising trait for crop breeding, and paves the way for future genetic studies.

Genomic Bayesian Confirmatory Factor Analysis and Bayesian Network To Characterize a Wide Spectrum of Rice Phenotypes

With the advent of high-throughput phenotyping platforms, plant breeders have a means to assess many traits for large breeding populations. However, understanding the genetic interdependencies among high-dimensional traits in a statistically robust manner remains a major challenge. Since multiple phenotypes likely share mutual relationships, elucidating the interdependencies among economically important traits can better inform breeding decisions and accelerate the genetic improvement of plants. The objective of this study was to leverage confirmatory factor analysis and graphical modeling to elucidate the genetic interdependencies among a diverse agronomic traits in rice. We used a Bayesian network to depict conditional dependencies among phenotypes, which can not be obtained by standard multi-trait analysis. We utilized Bayesian confirmatory factor analysis which hypothesized that 48 observed phenotypes resulted from six latent variables including grain morphology, morphology, flowering time, physiology, yield, and morphological salt response. This was followed by studying the genetics of each latent variable, which is also known as factor, using single nucleotide polymorphisms. Bayesian network structures involving the genomic component of six latent variables were established by fitting four algorithms (i.e., Hill Climbing, Tabu, Max-Min Hill Climbing, and General 2-Phase Restricted Maximization algorithms). Physiological components influenced the flowering time and grain morphology, and morphology and grain morphology influenced yield. In summary, we show the Bayesian network coupled with factor analysis can provide an effective approach to understand the interdependence patterns among phenotypes and to predict the potential influence of external interventions or selection related to target traits in the interrelated complex traits systems.

Crop Phenomics: Current Status and Perspectives

Reliable, automatic, multifunctional, and high-throughput phenotypic technologies are increasingly considered important tools for rapid advancement of genetic gain in breeding programs. With the rapid development in high-throughput phenotyping technologies, research in this area is entering a new era called 'phenomics.' The crop phenotyping community not only needs to build a multi-domain, multi-level, and multi-scale crop phenotyping big database, but also to research technical systems for phenotypic traits identification and develop bioinformatics technologies for information extraction from the overwhelming amounts of omics data. Here, we provide an overview of crop phenomics research, focusing on two parts, from phenotypic data collection through various sensors to phenomics analysis. Finally, we discussed the challenges and prospective of crop phenomics in order to provide suggestions to develop new methods of mining genes associated with important agronomic traits, and propose new intelligent solutions for precision breeding.

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.

High-throughput 3D modelling to dissect the genetic control of leaf elongation in barley (Hordeum vulgare)

To optimize shoot growth and structure of cereals, we need to understand the genetic components controlling initiation and elongation. While measuring total shoot growth at high throughput using 2D imaging has progressed, recovering the 3D shoot structure of small grain cereals at a large scale is still challenging. Here, we present a method for measuring defined individual leaves of cereals, such as wheat and barley, using few images. Plant shoot modelling over time was used to measure the initiation and elongation of leaves in a bi-parental barley mapping population under low and high soil salinity. We detected quantitative trait loci (QTL) related to shoot growth per se, using both simple 2D total shoot measurements and our approach of measuring individual leaves. In addition, we detected QTL specific to leaf elongation and not to total shoot size. Of particular importance was the detection of a QTL on chromosome 3H specific to the early responses of leaf elongation to salt stress, a locus that could not be detected without the computer vision tools developed in this study.

What is cost-efficient phenotyping? Optimizing costs for different scenarios

Progress in remote sensing and robotic technologies decreases the hardware costs of phenotyping. Here, we first review cost-effective imaging devices and environmental sensors, and present a trade-off between investment and manpower costs. We then discuss the structure of costs in various real-world scenarios. Hand-held low-cost sensors are suitable for quick and infrequent plant diagnostic measurements. In experiments for genetic or agronomic analyses, (i) major costs arise from plant handling and manpower; (ii) the total costs per plant/microplot are similar in robotized platform or field experiments with drones, hand-held or robotized ground vehicles; (iii) the cost of vehicles carrying sensors represents only 5-26% of the total costs. These conclusions depend on the context, in particular for labor cost, the quantitative demand of phenotyping and the number of days available for phenotypic measurements due to climatic constraints. Data analysis represents 10-20% of total cost if pipelines have already been developed. A trade-off exists between the initial high cost of pipeline development and labor cost of manual operations. Overall, depending on the context and objsectives, "cost-effective" phenotyping may involve either low investment ("affordable phenotyping"), or initial high investments in sensors, vehicles and pipelines that result in higher quality and lower operational costs.

Modelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breeding

New types of phenotyping tools generate large amounts of data on many aspects of plant physiology and morphology with high spatial and temporal resolution. These new phenotyping data are potentially useful to improve understanding and prediction of complex traits, like yield, that are characterized by strong environmental context dependencies, i.e., genotype by environment interactions. For an evaluation of the utility of new phenotyping information, we will look at how this information can be incorporated in different classes of genotype-to-phenotype (G2P) models. G2P models predict phenotypic traits as functions of genotypic and environmental inputs. In the last decade, access to high-density single nucleotide polymorphism markers (SNPs) and sequence information has boosted the development of a class of G2P models called genomic prediction models that predict phenotypes from genome wide marker profiles. The challenge now is to build G2P models that incorporate simultaneously extensive genomic information alongside with new phenotypic information. Beyond the modification of existing G2P models, new G2P paradigms are required. We present candidate G2P models for the integration of genomic and new phenotyping information and illustrate their use in examples. Special attention will be given to the modelling of genotype by environment interactions. The G2P models provide a framework for model based phenotyping and the evaluation of the utility of phenotyping information in the context of breeding programs.

Multi-scale high-throughput phenotyping of apple architectural and functional traits in orchard reveals genotypic variability under contrasted watering regimes

Despite previous reports on the genotypic variation of architectural and functional traits in fruit trees, phenotyping large populations in the field remains challenging. In this study, we used high-throughput phenotyping methods on an apple tree core-collection (1000 individuals) grown under contrasted watering regimes. First, architectural phenotyping was achieved using T-LiDAR scans for estimating convex and alpha hull volumes and the silhouette to total leaf area ratio (STAR). Second, a semi-empirical index (I PL) was computed from chlorophyll fluorescence measurements, as a proxy for leaf photosynthesis. Last, thermal infrared and multispectral airborne imaging was used for computing canopy temperature variations, water deficit, and vegetation indices. All traits estimated by these methods were compared to low-throughput in planta measurements. Vegetation indices and alpha hull volumes were significantly correlated with tree leaf area and trunk cross sectional area, while I PL values showed strong correlations with photosynthesis measurements collected on an independent leaf dataset. By contrast, correlations between stomatal conductance and canopy temperature estimated from airborne images were lower, emphasizing discrepancies across measurement scales. High heritability values were obtained for almost all the traits except leaf photosynthesis, likely due to large intra-tree variation. Genotypic means were used in a clustering procedure that defined six classes of architectural and functional combinations. Differences between groups showed several combinations between architectural and functional traits, suggesting independent genetic controls. This study demonstrates the feasibility and relevance of combining multi-scale high-throughput methods and paves the way to explore the genetic bases of architectural and functional variations in woody crops in field conditions.

Review: New sensors and data-driven approaches-A path to next generation phenomics

At the 4th International Plant Phenotyping Symposium meeting of the International Plant Phenotyping Network (IPPN) in 2016 at CIMMYT in Mexico, a workshop was convened to consider ways forward with sensors for phenotyping. The increasing number of field applications provides new challenges and requires specialised solutions. There are many traits vital to plant growth and development that demand phenotyping approaches that are still at early stages of development or elude current capabilities. Further, there is growing interest in low-cost sensor solutions, and mobile platforms that can be transported to the experiments, rather than the experiment coming to the platform. Various types of sensors are required to address diverse needs with respect to targets, precision and ease of operation and readout. Converting data into knowledge, and ensuring that those data (and the appropriate metadata) are stored in such a way that they will be sensible and available to others now and for future analysis is also vital. Here we are proposing mechanisms for "next generation phenomics" based on our learning in the past decade, current practice and discussions at the IPPN Symposium, to encourage further thinking and collaboration by plant scientists, physicists and engineering experts.

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.

A generic individual-based model can predict yield, nitrogen content, and species abundance in experimental grassland communities

Functional-structural plant models are increasingly being used to analyse relationships between plant functioning and the topological and spatial organisation of their modular structure. In this study, the performance of an individual-based model accounting for the the architecture and population dynamics of forage legumes in multi-species grasslands was assessed. Morphogenetic shoot and root parameters were calibrated for seven widely used species. Other model parameters concerning C and N metabolism were obtained from the literature. The model was evaluated using a series of independent experiments combining the seven species in binary mixtures that were subject to regular defoliation. For all the species, the model could accurately simulate phytomer demography, leaf area dynamics, and root growth under conditions of weak competition. In addition, the plastic changes induced by competition for light and N in terms of plant development, leaf area, N uptake, and total plant biomass were correctly predicted. The different species displayed contrasting sensitivities to defoliation, and the model was able to predict the superior ability of creeping species to sustain regular defoliation. As a result of competition and management, the balance between species changed over time and was strongly dependent on the pair of species used. The model proved able to capture these differences in community dynamics. Overall, the results demonstrate that integrating the individual components of population dynamics in a process-based model can provide good predictive capacity regarding mixtures of cultivated species.

An Accurate Skeleton Extraction Approach From 3D Point Clouds of Maize Plants

Accurate and high-throughput determination of plant morphological traits is essential for phenotyping studies. Nowadays, there are many approaches to acquire high-quality three-dimensional (3D) point clouds of plants. However, it is difficult to estimate phenotyping parameters accurately of the whole growth stages of maize plants using these 3D point clouds. In this paper, an accurate skeleton extraction approach was proposed to bridge the gap between 3D point cloud and phenotyping traits estimation of maize plants. The algorithm first uses point cloud clustering and color difference denoising to reduce the noise of the input point clouds. Next, the Laplacian contraction algorithm is applied to shrink the points. Then the key points representing the skeleton of the plant are selected through adaptive sampling, and neighboring points are connected to form a plant skeleton composed of semantic organs. Finally, deviation skeleton points to the input point cloud are calibrated by building a step forward local coordinate along the tangent direction of the original points. The proposed approach successfully generates accurately extracted skeleton from 3D point cloud and helps to estimate phenotyping parameters with high precision of maize plants. Experimental verification of the skeleton extraction process, tested using three cultivars and different growth stages maize, demonstrates that the extracted matches the input point cloud well. Compared with 3D digitizing data-derived morphological parameters, the NRMSE of leaf length, leaf inclination angle, leaf top length, leaf azimuthal angle, leaf growth height, and plant height, estimated using the extracted plant skeleton, are 5.27, 8.37, 5.12, 4.42, 1.53, and 0.83%, respectively, which could meet the needs of phenotyping analysis. The time required to process a single maize plant is below 100 s. The proposed approach may play an important role in further maize research and applications, such as genotype-to-phenotype study, geometric reconstruction, functional structural maize modeling, and dynamic growth animation.

Heliaphen, an Outdoor High-Throughput Phenotyping Platform for Genetic Studies and Crop Modeling

Heliaphen is an outdoor platform designed for high-throughput phenotyping. It allows the automated management of drought scenarios and monitoring of plants throughout their lifecycles. A robot moving between plants growing in 15-L pots monitors the plant water status and phenotypes the leaf or whole-plant morphology. From these measurements, we can compute more complex traits, such as leaf expansion (LE) or transpiration rate (TR) in response to water deficit. Here, we illustrate the capabilities of the platform with two practical cases in sunflower (Helianthus annuus): a genetic and genomic study of the response of yield-related traits to drought, and a modeling study using measured parameters as inputs for a crop simulation. For the genetic study, classical measurements of thousand-kernel weight (TKW) were performed on a biparental population under automatically managed drought stress and control conditions. These data were used for an association study, which identified five genetic markers of the TKW drought response. A complementary transcriptomic analysis identified candidate genes associated with these markers that were differentially expressed in the parental backgrounds in drought conditions. For the simulation study, we used a crop simulation model to predict the impact on crop yield of two traits measured on the platform (LE and TR) for a large number of environments. We conducted simulations in 42 contrasting locations across Europe using 21 years of climate data. We defined the pattern of abiotic stresses occurring at the continental scale and identified ideotypes (i.e., genotypes with specific trait values) that are more adapted to specific environment types. This study exemplifies how phenotyping platforms can assist the identification of the genetic architecture controlling complex response traits and facilitate the estimation of ecophysiological model parameters to define ideotypes adapted to different environmental conditions.

Functional phenomics: an emerging field integrating high-throughput phenotyping, physiology, and bioinformatics

The emergence of functional phenomics signifies the rebirth of physiology as a 21st century science through the use of advanced sensing technologies and big data analytics. Functional phenomics seeks to fill the significant knowledge gaps that still exist in the relationship of plant phenotype to function. Here, a general approach for the theory and practice of functional phenomics is outlined. The functional phenomics pipeline is proposed as a general method for conceptualizing, measuring, and validating utility of plant phenes, or elemental units of phenotype. The functional phenomics pipeline begins with ideotype development. Second, a phenotyping platform is developed to maximize the throughput of phene measurements. Target phenes and indicators of plant function, or performance, are measured in a mapping population. Forward genetics allows genetic mapping, while functional phenomics links phenes to plant performance. Based on these data, genotypes with contrasting phenotypes can be selected for smaller yet more intensive experiments to understand phene-environment interactions in depth. Simulation modeling is used to further understand the phenotypes, and all stages of the pipeline feed back to ideotype and phenotyping platform development. In total, functional phenomics represents an evolution of pre-existing disciplines, but the goals and unique methodologies constitute a novel research program.

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.

Maize Plant Phenotyping: Comparing 3D Laser Scanning, Multi-View Stereo Reconstruction, and 3D Digitizing Estimates

High-throughput phenotyping technologies have become an increasingly important topic of crop science in recent years. Various sensors and data acquisition approaches have been applied to acquire the phenotyping traits. It is quite confusing for crop phenotyping researchers to determine an appropriate way for their application. In this study, three representative three-dimensional (3D) data acquisition approaches, including 3D laser scanning, multi-view stereo (MVS) reconstruction, and 3D digitizing, were evaluated for maize plant phenotyping in multi growth stages. Phenotyping traits accuracy, post-processing difficulty, device cost, data acquisition efficiency, and automation were considered during the evaluation process. 3D scanning provided satisfactory point clouds for medium and high maize plants with acceptable efficiency, while the results were not satisfactory for small maize plants. The equipment used in 3D scanning is expensive, but is highly automatic. MVS reconstruction provided satisfactory point clouds for small and medium plants, and point deviations were observed in upper parts of higher plants. MVS data acquisition, using low-cost cameras, exhibited the highest efficiency among the three evaluated approaches. The one-by-one pipeline data acquisition pattern allows the use of MVS high-throughput in further phenotyping platforms. Undoubtedly, enhancement of point cloud processing technologies is required to improve the extracted phenotyping traits accuracy for both 3D scanning and MVS reconstruction. Finally, 3D digitizing was time-consuming and labor intensive. However, it does not depend on any post-processing algorithms to extract phenotyping parameters and reliable phenotyping traits could be derived. The promising accuracy of 3D digitizing is a better verification choice for other 3D phenotyping approaches. Our study provides clear reference about phenotyping data acquisition of maize plants, especially for the affordable and portable field phenotyping platforms to be developed.

Phenotyping Whole Forests Will Help to Track Genetic Performance

Phenotyping is the accurate and precise physical description of organisms. Accurate and quantitative phenotyping underpins the delivery of benefits from genetic improvement programs in agriculture. In forest trees, phenotyping at an equivalent precision has been impossible because trees and forests are large, long-lived, and highly variable. These facts have restricted the delivery of genetic gains in forestry compared to other agricultural sectors. We describe a landscape-scale phenotyping platform that integrates remote sensing, spatial information systems, and genomics to facilitate the delivery of greater gains enabling forestry to catch up with other sectors. Combining remote sensing at a range of spatial and temporal scales with genomics will ultimately impact on tree breeding globally.

Breeding to adapt agriculture to climate change: affordable phenotyping solutions

Breeding is one of the central pillars of adaptation of crops to climate change. However, phenotyping is a key bottleneck that is limiting breeding efficiency. The awareness of phenotyping as a breeding limitation is not only sustained by the lack of adequate approaches, but also by the perception that phenotyping is an expensive activity. Phenotyping is not just dependent on the choice of appropriate traits and tools (e.g. sensors) but relies on how these tools are deployed on their carrying platforms, the speed and volume of data extraction and analysis (throughput), the handling of spatial variability and characterization of environmental conditions, and finally how all the information is integrated and processed. Affordable high throughput phenotyping aims to achieve reasonably priced solutions for all the components comprising the phenotyping pipeline. This mini-review will cover current and imminent solutions for all these components, from the increasing use of conventional digital RGB cameras, within the category of sensors, to open-access cloud-structured data processing and the use of smartphones. Emphasis will be placed on field phenotyping, which is really the main application for day-to-day phenotyping.

Measuring the dynamic photosynthome

Background

Photosynthesis underpins plant productivity and yet is notoriously sensitive to small changes in environmental conditions, meaning that quantitation in nature across different time scales is not straightforward. The 'dynamic' changes in photosynthesis (i.e. the kinetics of the various reactions of photosynthesis in response to environmental shifts) are now known to be important in driving crop yield.

Scope

It is known that photosynthesis does not respond in a timely manner, and even a small temporal 'mismatch' between a change in the environment and the appropriate response of photosynthesis toward optimality can result in a fall in productivity. Yet the most commonly measured parameters are still made at steady state or a temporary steady state (including those for crop breeding purposes), meaning that new photosynthetic traits remain undiscovered.

Conclusions

There is a great need to understand photosynthesis dynamics from a mechanistic and biological viewpoint especially when applied to the field of 'phenomics' which typically uses large genetically diverse populations of plants. Despite huge advances in measurement technology in recent years, it is still unclear whether we possess the capability of capturing and describing the physiologically relevant dynamic features of field photosynthesis in sufficient detail. Such traits are highly complex, hence we dub this the 'photosynthome'. This review sets out the state of play and describes some approaches that could be made to address this challenge with reference to the relevant biological processes involved.

Image-based methods for phenotyping growth dynamics and fitness components in Arabidopsis thaliana

BACKGROUND

The model species Arabidopsis thaliana has extensive resources to investigate intraspecific trait variability and the genetic bases of ecologically relevant traits. However, the cost of equipment and software required for high-throughput phenotyping is often a bottleneck for large-scale studies, such as mutant screening or quantitative genetics analyses. Simple tools are needed for the measurement of fitness-related traits, like relative growth rate and fruit production, without investment in expensive infrastructures. Here, we describe methods that enable the estimation of biomass accumulation and fruit number from the analysis of rosette and inflorescence images taken with a regular camera.

RESULTS

We developed two models to predict plant dry mass and fruit number from the parameters extracted with the analysis of rosette and inflorescence images. Predictive models were trained by sacrificing growing individuals for dry mass estimation, and manually measuring a fraction of individuals for fruit number at maturity. Using a cross-validation approach, we showed that quantitative parameters extracted from image analysis predicts more 90% of both plant dry mass and fruit number. When used on 451 natural accessions, the method allowed modeling growth dynamics, including relative growth rate, throughout the life cycle of various ecotypes. Estimated growth-related traits had high heritability (0.65 < H2 < 0.93), as well as estimated fruit number (H2 = 0.68). In addition, we validated the method for estimating fruit number with rev5, a mutant with increased flower abortion.

CONCLUSIONS

The method we propose here is an application of automated computerization of plant images with ImageJ, and subsequent statistical modeling in R. It allows plant biologists to measure growth dynamics and fruit number in hundreds of individuals with simple computing steps that can be repeated and adjusted to a wide range of laboratory conditions. It is thus a flexible toolkit for the measurement of fitness-related traits in large populations of a model species.

Plant Phenomics, From Sensors to Knowledge

Major improvements in crop yield are needed to keep pace with population growth and climate change. While plant breeding efforts have greatly benefited from advances in genomics, profiling the crop phenome (i.e., the structure and function of plants) associated with allelic variants and environments remains a major technical bottleneck. Here, we review the conceptual and technical challenges facing plant phenomics. We first discuss how, given plants' high levels of morphological plasticity, crop phenomics presents distinct challenges compared with studies in animals. Next, we present strategies for multi-scale phenomics, and describe how major improvements in imaging, sensor technologies and data analysis are now making high-throughput root, shoot, whole-plant and canopy phenomic studies possible. We then suggest that research in this area is entering a new stage of development, in which phenomic pipelines can help researchers transform large numbers of images and sensor data into knowledge, necessitating novel methods of data handling and modelling. Collectively, these innovations are helping accelerate the selection of the next generation of crops more sustainable and resilient to climate change, and whose benefits promise to scale from physiology to breeding and to deliver real world impact for ongoing global food security efforts.

Accelerating genetic gains in legumes for the development of prosperous smallholder agriculture: integrating genomics, phenotyping, systems modelling and agronomy

Grain legumes form an important component of the human diet, provide feed for livestock, and replenish soil fertility through biological nitrogen fixation. Globally, the demand for food legumes is increasing as they complement cereals in protein requirements and possess a high percentage of digestible protein. Climate change has enhanced the frequency and intensity of drought stress, posing serious production constraints, especially in rainfed regions where most legumes are produced. Genetic improvement of legumes, like other crops, is mostly based on pedigree and performance-based selection over the past half century. To achieve faster genetic gains in legumes in rainfed conditions, this review proposes the integration of modern genomics approaches, high throughput phenomics, and simulation modelling in support of crop improvement that leads to improved varieties that perform with appropriate agronomy. Selection intensity, generation interval, and improved operational efficiencies in breeding are expected to further enhance the genetic gain in experimental plots. Improved seed access to farmers, combined with appropriate agronomic packages in farmers' fields, will deliver higher genetic gains. Enhanced genetic gains, including not only productivity but also nutritional and market traits, will increase the profitability of farming and the availability of affordable nutritious food especially in developing countries.

Holistic and component plant phenotyping using temporal image sequence

BACKGROUND

Image-based plant phenotyping facilitates the extraction of traits noninvasively by analyzing large number of plants in a relatively short period of time. It has the potential to compute advanced phenotypes by considering the whole plant as a single object (holistic phenotypes) or as individual components, i.e., leaves and the stem (component phenotypes), to investigate the biophysical characteristics of the plants. The emergence timing, total number of leaves present at any point of time and the growth of individual leaves during vegetative stage life cycle of the maize plants are significant phenotypic expressions that best contribute to assess the plant vigor. However, image-based automated solution to this novel problem is yet to be explored.

RESULTS

A set of new holistic and component phenotypes are introduced in this paper. To compute the component phenotypes, it is essential to detect the individual leaves and the stem. Thus, the paper introduces a novel method to reliably detect the leaves and the stem of the maize plants by analyzing 2-dimensional visible light image sequences captured from the side using a graph based approach. The total number of leaves are counted and the length of each leaf is measured for all images in the sequence to monitor leaf growth. To evaluate the performance of the proposed algorithm, we introduce University of Nebraska-Lincoln Component Plant Phenotyping Dataset (UNL-CPPD) and provide ground truth to facilitate new algorithm development and uniform comparison. The temporal variation of the component phenotypes regulated by genotypes and environment (i.e., greenhouse) are experimentally demonstrated for the maize plants on UNL-CPPD. Statistical models are applied to analyze the greenhouse environment impact and demonstrate the genetic regulation of the temporal variation of the holistic phenotypes on the public dataset called Panicoid Phenomap-1.

CONCLUSION

The central contribution of the paper is a novel computer vision based algorithm for automated detection of individual leaves and the stem to compute new component phenotypes along with a public release of a benchmark dataset, i.e., UNL-CPPD. Detailed experimental analyses are performed to demonstrate the temporal variation of the holistic and component phenotypes in maize regulated by environment and genetic variation with a discussion on their significance in the context of plant science.

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.

A generic individual-based model to simulate morphogenesis, C-N acquisition and population dynamics in contrasting forage legumes

Background and Aims

Individual-based models (IBMs) are promising tools to disentangle plant interactions in multi-species grasslands and foster innovative species mixtures. This study describes an IBM dealing with the morphogenesis, growth and C-N acquisition of forage legumes that integrates plastic responses from functional-structural plant models.

Methods

A generic model was developed to account for herbaceous legume species with contrasting above- and below-ground morphogenetic syndromes and to integrate the responses of plants to light, water and N. Through coupling with a radiative transfer model and a three-dimensional virtual soil, the model allows dynamic resolution of competition for multiple resources at individual plant level within a plant community. The behaviour of the model was assessed on a range of monospecific stands grown along gradients of light, water and N availability.

Key Results

The model proved able to capture the diversity of morphologies encountered among the forage legumes. The main density-dependent features known about even-age plant populations were correctly anticipated. The model predicted (1) the 'reciprocal yield' law relating average plant mass to density, (2) a self-thinning pattern close to that measured for herbaceous species and (3) consistent changes in the size structure of plant populations with time and pedo-climatic conditions. In addition, plastic changes in the partitioning of dry matter, the N acquisition mode and in the architecture of shoots and roots emerged from the integration of plant responses to their local environment. This resulted in taller plants and thinner roots when competition was dominated by light, and shorter plants with relatively more developed root systems when competition was dominated by soil resources.

Conclusions

A population dynamic model considering growth and morphogenesis responses to multiple resources heterogeneously distributed in the environment was presented. It should allow scaling plant-plant interactions from individual to community levels without the inconvenience of average plant models.

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

Stomatal conductance is central for the trades-off between hydraulics and photosynthesis. We aimed at deciphering its genetic control and that of its responses to evaporative demand and water deficit, a nearly impossible task with gas exchanges measurements. Whole-plant stomatal conductance was estimated via inversion of the Penman-Monteith equation from data of transpiration and plant architecture collected in a phenotyping platform. We have analysed jointly 4 experiments with contrasting environmental conditions imposed to a panel of 254 maize hybrids. Estimated whole-plant stomatal conductance closely correlated with gas-exchange measurements and biomass accumulation rate. Sixteen robust quantitative trait loci (QTLs) were identified by genome wide association studies and co-located with QTLs of transpiration and biomass. Light, vapour pressure deficit, or soil water potential largely accounted for the differences in allelic effects between experiments, thereby providing strong hypotheses for mechanisms of stomatal control and a way to select relevant candidate genes among the 1-19 genes harboured by QTLs. The combination of allelic effects, as affected by environmental conditions, accounted for the variability of stomatal conductance across a range of hybrids and environmental conditions. This approach may therefore contribute to genetic analysis and prediction of stomatal control in diverse environments.

Untargeted Analysis of Semipolar Compounds by LC-MS and Targeted Analysis of Fatty Acids by GC-MS/GC-FID: From Plant Cultivation to Extract Preparation

The way plants are grown and samples are harvested, prepared, and extracted has a profound impact on the output of a metabolomics experiment. In this chapter, we detail the experimental procedures from plant cultivation to extract preparation, in order to avoid difficulties that could result in contamination or undesired changes of the analytes. Two plant organs are mentioned as examples: tomato fruits (Solanum lycopersicum) and flax seeds (Linum usitatissimum). Extractions designed for the untargeted analysis of semipolar compounds by liquid chromatography-mass spectrometry (LC-MS) and targeted analysis of fatty acids by gas chromatography-mass spectrometry (GC-MS) or gas chromatography with flame ionization detector (GC-FID) are described.

Image-based methods for phenotyping growth dynamics and fitness components in Arabidopsis thaliana

BACKGROUND

The model species Arabidopsis thaliana has extensive resources to investigate intraspecific trait variability and the genetic bases of ecologically relevant traits. However, the cost of equipment and software required for high-throughput phenotyping is often a bottleneck for large-scale studies, such as mutant screening or quantitative genetics analyses. Simple tools are needed for the measurement of fitness-related traits, like relative growth rate and fruit production, without investment in expensive infrastructures. Here, we describe methods that enable the estimation of biomass accumulation and fruit number from the analysis of rosette and inflorescence images taken with a regular camera.

RESULTS

We developed two models to predict plant dry mass and fruit number from the parameters extracted with the analysis of rosette and inflorescence images. Predictive models were trained by sacrificing growing individuals for dry mass estimation, and manually measuring a fraction of individuals for fruit number at maturity. Using a cross-validation approach, we showed that quantitative parameters extracted from image analysis predicts more 90% of both plant dry mass and fruit number. When used on 451 natural accessions, the method allowed modeling growth dynamics, including relative growth rate, throughout the life cycle of various ecotypes. Estimated growth-related traits had high heritability (0.65 < H² < 0.93), as well as estimated fruit number (H² = 0.68). In addition, we validated the method for estimating fruit number with rev5, a mutant with increased flower abortion.

CONCLUSIONS

The method we propose here is an application of automated computerization of plant images with ImageJ, and subsequent statistical modeling in R. It allows plant biologists to measure growth dynamics and fruit number in hundreds of individuals with simple computing steps that can be repeated and adjusted to a wide range of laboratory conditions. It is thus a flexible toolkit for the measurement of fitness-related traits in large populations of a model species.

Differential manipulation of leaf angle throughout the canopy: current status and prospects

Leaf angle is defined as the inclination between the midrib of the leaf blade and the vertical stem of a plant. This trait has been identified as a key component in the development of high-yielding varieties of cereal species, particularly maize, rice, wheat, and sorghum. The effect of leaf angle on light interception efficiency, photosynthetic rate, and yield has been investigated since the 1960s, yet, significant knowledge gaps remain in understanding the genetic control of this complex trait. Recent advances in physiology and modeling have proposed a plant ideotype with varying leaf angles throughout the canopy. In this context, we present historical and recent evidence of: (i) the effect of leaf angle on photosynthetic efficiency and yield; (ii) the hormonal regulation of this trait; (iii) the current knowledge on its quantitative genetic control; and (iv) the opportunity to utilize high-throughput phenotyping methods to characterize leaf angle at multiple canopy levels. We focus on research conducted on grass species of economic importance, with similar plant architecture and growth patterns. Finally, we present the challenges and strategies plant breeders will need to embrace in order to manipulate leaf angle differentially throughout the canopy and develop superior crops for food, feed, and fuel production.

A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform

BACKGROUND

In maize, silks are hundreds of filaments that simultaneously emerge from the ear for collecting pollen over a period of 1-7 days, which largely determines grain number especially under water deficit. Silk growth is a major trait for drought tolerance in maize, but its phenotyping is difficult at throughputs needed for genetic analyses.

RESULTS

We have developed a reproducible pipeline that follows ear and silk growths every day for hundreds of plants, based on an ear detection algorithm that drives a robotized camera for obtaining detailed images of ears and silks. We first select, among 12 whole-plant side views, those best suited for detecting ear position. Images are segmented, the stem pixels are labelled and the ear position is identified based on changes in width along the stem. A mobile camera is then automatically positioned in real time at 30 cm from the ear, for a detailed picture in which silks are identified based on texture and colour. This allows analysis of the time course of ear and silk growths of thousands of plants. The pipeline was tested on a panel of 60 maize hybrids in the PHENOARCH phenotyping platform. Over 360 plants, ear position was correctly estimated in 86% of cases, before it could be visually assessed. Silk growth rate, estimated on all plants, decreased with time consistent with literature. The pipeline allowed clear identification of the effects of genotypes and water deficit on the rate and duration of silk growth.

CONCLUSIONS

The pipeline presented here, which combines computer vision, machine learning and robotics, provides a powerful tool for large-scale genetic analyses of the control of reproductive growth to changes in environmental conditions in a non-invasive and automatized way. It is available as Open Source software in the OpenAlea platform.

Contribution of Crop Models to Adaptation in Wheat

With world population growing quickly, agriculture needs to produce more with fewer inputs while being environmentally friendly. In a context of changing environments, crop models are useful tools to simulate crop yields. Wheat (Triticum spp.) crop models have been evolving since the 1960s to translate processes related to crop growth and development into mathematical equations. These have been used over decades for agronomic purposes, and have more recently incorporated advances in the modeling of environmental footprints, biotic constraints, trait and gene effects, climate change impact, and the upscaling of global change impacts. This review outlines the potential and limitations of modern wheat crop models in assisting agronomists, breeders, and policymakers to address the current and future challenges facing agriculture.

Fluxomics links cellular functional analyses to whole-plant phenotyping

Fluxes through metabolic pathways reflect the integration of genetic and metabolic regulations. While it is attractive to measure all the mRNAs (transcriptome), all the proteins (proteome), and a large number of the metabolites (metabolome) in a given cellular system, linking and integrating this information remains difficult. Measurement of metabolome-wide fluxes (termed the fluxome) provides an integrated functional output of the cell machinery and a better tool to link functional analyses to plant phenotyping. This review presents and discusses sets of methodologies that have been developed to measure the fluxome. First, the principles of metabolic flux analysis (MFA), its 'short time interval' version Inst-MFA, and of constraints-based methods, such as flux balance analysis and kinetic analysis, are briefly described. The use of these powerful methods for flux characterization at the cellular scale up to the organ (fruits, seeds) and whole-plant level is illustrated. The added value given by fluxomics methods for unravelling how the abiotic environment affects flux, the process, and key metabolic steps are also described. Challenges associated with the development of fluxomics and its integration with 'omics' for thorough plant and organ functional phenotyping are discussed. Taken together, these will ultimately provide crucial clues for identifying appropriate target plant phenotypes for breeding.

Rooting for cassava: insights into photosynthesis and associated physiology as a route to improve yield potential

Contents 50 I. 50 II. 52 III. 54 IV. 55 V. 57 VI. 57 VII. 59 60 References 61 SUMMARY: As a consequence of an increase in world population, food demand is expected to grow by up to 110% in the next 30-35 yr. The population of sub-Saharan Africa is projected to increase by > 120%. In this region, cassava (Manihot esculenta) is the second most important source of calories and contributes c. 30% of the daily calorie requirements per person. Despite its importance, the average yield of cassava in Africa has not increased significantly since 1961. An evaluation of modern cultivars of cassava showed that the interception efficiency (ɛ_i ) of photosynthetically active radiation (PAR) and the efficiency of conversion of that intercepted PAR (ɛ_c ) are major opportunities for genetic improvement of the yield potential. This review examines what is known of the physiological processes underlying productivity in cassava and seeks to provide some strategies and directions toward yield improvement through genetic alterations to physiology to increase ɛ_i and ɛ_c . Possible physiological limitations, as well as environmental constraints, are discussed.

High-Throughput and Computational Study of Leaf Senescence through a Phenomic Approach

Leaf senescence is influenced by its life history, comprising a series of developmental and physiological experiences. Exploration of the biological principles underlying leaf lifespan and senescence requires a schema to trace leaf phenotypes, based on the interaction of genetic and environmental factors. We developed a new approach and concept that will facilitate systemic biological understanding of leaf lifespan and senescence, utilizing the phenome high-throughput investigator (PHI) with a single-leaf-basis phenotyping platform. Our pilot tests showed empirical evidence for the feasibility of PHI for quantitative measurement of leaf senescence responses and improved performance in order to dissect the progression of senescence triggered by different senescence-inducing factors as well as genetic mutations. Such an establishment enables new perspectives to be proposed, which will be challenged for enhancing our fundamental understanding on the complex process of leaf senescence. We further envision that integration of phenomic data with other multi-omics data obtained from transcriptomic, proteomic, and metabolic studies will enable us to address the underlying principles of senescence, passing through different layers of information from molecule to organism.

Pampered inside, pestered outside? Differences and similarities between plants growing in controlled conditions and in the field

I. 839 II. 839 III. 841 IV. 845 V. 847 VI. 848 VII. 849 VIII. 851 851 852 References 852 Appendix A1 854 SUMMARY: Plant biologists often grow plants in growth chambers or glasshouses with the ultimate aim to understand or improve plant performance in the field. What is often overlooked is how results from controlled conditions translate back to field situations. A meta-analysis showed that lab-grown plants had faster growth rates, higher nitrogen concentrations and different morphology. They remained smaller, however, because the lab plants had grown for a much shorter time. We compared glasshouse and growth chamber conditions with those in the field and found that the ratio between the daily amount of light and daily temperature (photothermal ratio) was consistently lower under controlled conditions. This may strongly affect a plant's source : sink ratio and hence its overall morphology and physiology. Plants in the field also grow at higher plant densities. A second meta-analysis showed that a doubling in density leads on average to 34% smaller plants with strong negative effects on tiller or side-shoot formation but little effect on plant height. We found the r² between lab and field phenotypic data to be rather modest (0.26). Based on these insights, we discuss various alternatives to facilitate the translation from lab results to the field, including several options to apply growth regimes closer to field conditions.

Fortune telling: metabolic markers of plant performance

BACKGROUND

In the last decade, metabolomics has emerged as a powerful diagnostic and predictive tool in many branches of science. Researchers in microbes, animal, food, medical and plant science have generated a large number of targeted or non-targeted metabolic profiles by using a vast array of analytical methods (GC-MS, LC-MS, 1H-NMR….). Comprehensive analysis of such profiles using adapted statistical methods and modeling has opened up the possibility of using single or combinations of metabolites as markers. Metabolic markers have been proposed as proxy, diagnostic or predictors of key traits in a range of model species and accurate predictions of disease outbreak frequency, developmental stages, food sensory evaluation and crop yield have been obtained.

AIM OF REVIEW

(i) To provide a definition of plant performance and metabolic markers, (ii) to highlight recent key applications involving metabolic markers as tools for monitoring or predicting plant performance, and (iii) to propose a workable and cost-efficient pipeline to generate and use metabolic markers with a special focus on plant breeding.

KEY MESSAGE

Using examples in other models and domains, the review proposes that metabolic markers are tending to complement and possibly replace traditional molecular markers in plant science as efficient estimators of performance.

Reduced nighttime transpiration is a relevant breeding target for high water-use efficiency in grapevine

Increasing water scarcity challenges crop sustainability in many regions. As a consequence, the enhancement of transpiration efficiency (TE)-that is, the biomass produced per unit of water transpired-has become crucial in breeding programs. This could be achieved by reducing plant transpiration through a better closure of the stomatal pores at the leaf surface. However, this strategy generally also lowers growth, as stomatal opening is necessary for the capture of atmospheric CO2 that feeds daytime photosynthesis. Here, we considered the reduction in transpiration rate at night (En) as a possible strategy to limit water use without altering growth. For this purpose, we carried out a genetic analysis for En and TE in grapevine, a major crop in drought-prone areas. Using recently developed phenotyping facilities, potted plants of a cross between Syrah and Grenache cultivars were screened for 2 y under well-watered and moderate soil water deficit scenarios. High genetic variability was found for En under both scenarios and was primarily associated with residual diffusion through the stomata. Five quantitative trait loci (QTLs) were detected that underlay genetic variability in En Interestingly, four of them colocalized with QTLs for TE. Moreover, genotypes with favorable alleles on these common QTLs exhibited reduced En without altered growth. These results demonstrate the interest of breeding grapevine for lower water loss at night and pave the way to breeding other crops with this underexploited trait for higher TE.