Identifying, naming and interoperating data in a Phenotyping platform network : the good, the bad and the ugly

The EPPN2020 is a research project funded by Horizon 2020 Programme of the EU that will provide European public and private scientific sectors with access to a wide range of state-of-the-art plant phenotyping installations, techniques and methods. Specifically, EPPN2020 includes access to 31 plant phenotyping installations, and joint research activities to develop: novel technologies and methods for environmental and plant measurements.Here we present the results of the discussions of the 2019 annual project meeting to adopt community-approved architectural choices. It focuses on persistent identification of data and real objects, the naming of variables and the priorities for increasing interoperability among phenotyping installations. We describe the main elements to prioritize (the good) in order to enhance Findable, Accessible, Interoperable and Reusable (FAIR) quality for each data management system with a pragmatic concern for all partners. The plant phenotyping community gathers different actors with various means and practices. Among all the recommendations (including the bad: avoiding bad practices), the community requests identification methods (including the use of ontologies) compatible with the ‘local’ pre-existing ones. The identification scheme being adopted is based on Uniform Resource Identifiers (URIs) with independant left and right parts for each identifier. It focuses on the associated objects and variables common to all EPPN2020 members, namely the experimental units (which can be a plant in a pot or a plot), sensors and variables. A common architecture for identifiers and variable names is presented in order to enable a first level of interoperation between information systems.In conclusion, we present some of the next challenges (the ugly) that need to be addressed by the EPPN2020 community related with i) the partial reuse of pre-existing ontologies, ii) the persistence of long-term access to data iii) interoperation between all potential users of the phenotyping data

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

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

International audienceWe developed a non-invasive method to measure light interception and radiation-use efficiency (RUE) in thousands of maize (Zea mays) plants at the PHENOARCH phenotyping platform.Different models were interfaced to estimate (i) the amount of light reaching each plant from hemispherical images, (ii) light intercepted by each plant via a functional-structural plant model, (iii) RUE, as the ratio of plant biomass to intercepted light. The inputs of these models were leaf area, biomass and architecture estimated from plant images and environmental data collected with a precise spatial and temporal resolution. We have tested this method by comparing two experiments performed in autumn and winter/spring.Biomass and leaf area differed between experiments showing a high G×E interaction. Difference in biomass between experiments was entirely accounted for by the difference in intercepted light. Hence, the mean RUE was common to both experiments and genotypes ranked similarly.The methods presented here allowed dissecting the differences between experiments into (i) genotypic traits that did not differ between experiments but had a high genetic variability, namely plant architecture and RUE (ii) environmental differences, essentially incident light, that affected both biomass and leaf area, (iii) plant traits that differed between experiments due to environmental variables, in particular leaf growth

Do metabolic changes underpin physiological responses to water limitation in alfalfa (Medicago sativa) plants during a regrowth period?

Drought is one of the most limiting factors on crop productivity under Mediterranean conditions, where the leguminous species alfalfa (Medicago sativa L.) is extensively cultivated. Whereas the effect of drought on plant performance has been widely described at leaf and nodule levels, less attention has been given to plant-nodule interactions and their implication on metabolites exchange during a regrowth period, when water is limiting. For this purpose, physiological characterization and metabolite profiles in different plant organs and nodules were undertaken under water deficit, including regrowth after removal of aerial parts. In order to study in more detail how nitrogen (N) metabolism was affected by water stress, plants were labelled with Nenriched isotopic air (15N2) using especially designed chambers. Water stress affected negatively water status and photosynthetic machinery. Metabolite profile and isotopic composition analyses revealed that, water deficit induced major changes in the accumulation of amino acids (proline, asparagine, histidine, lysine and cysteine), carbohydrates (sucrose, xylose and pinitol) and organic acids (fumarate, succinate and maleic acid) in the nodules in comparison with other organs. The lower 15N-labeling observed in serine, compared with other amino acids, was related with its high turnover rate, which in turn, indicates its potential implication in photorespiration. Isotopic analysis of amino acids also revealed that proline synthesis in the nodule was a local response to water stress and not associated with a feedback inhibition from the leaves.. Water deficit induced extensive reprogramming of wholeplant C and N metabolism, including when the aerial part was removed to trigger regrowth

A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data

High throughput phenotyping (HTP) platforms and devices are increasingly used for the characterization of growth and developmental processes for large sets of plant genotypes. Such HTP data require challenging statistical analyses in which longitudinal genetic signals need to be estimated against a background of spatio-temporal noise processes. We propose a two-stage approach for the analysis of such longitudinal HTP data. In a first stage, we correct for design features and spatial trends per time point. In a second stage, we focus on the longitudinal modelling of the spatially corrected data, thereby taking advantage of shared longitudinal features between genotypes and plants within genotypes. We propose a flexible hierarchical three-level P-spline growth curve model, with plants/plots nested in genotypes, and genotypes nested in populations. For selection of genotypes in a plant breeding context, we show how to extract new phenotypes, like growth rates, from the estimated genotypic growth curves and their first-order derivatives. We illustrate our approach on HTP data from the PhenoArch greenhouse platform at INRAE Montpellier and the outdoor Field Phenotyping platform at ETH Zürich.Ministerio de Ciencia, Innovación y Universidades | Ref. BCAM Severo Ochoa accreditation SEV-2017-0718Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung | Ref. project PhenoCOOL (project no. 169542)Horizon 2020 Framework Programme | Ref. grant agreement ID 731013 (EPPN2020)Ministerio de Ciencia, Innovación y Universidades | Ref. MTM2017-82379-

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

International audienceMulti-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

Water and nitrogen conditions affect the relationships of Δ13C and Δ18O to gas exchange and growth in durum wheat

Whereas the effects of water and nitrogen (N) on plant Δ13C have been reported previously, these factors have scarcely been studied for Δ18O. Here the combined effect of different water and N regimes on Δ13C, Δ18O, gas exchange, water-use efficiency (WUE), and growth of four genotypes of durum wheat [Triticum turgidum L. ssp. durum (Desf.) Husn.] cultured in pots was studied. Water and N supply significantly increased plant growth. However, a reduction in water supply did not lead to a significant decrease in gas exchange parameters, and consequently Δ13C was only slightly modified by water input. Conversely, N fertilizer significantly decreased Δ13C. On the other hand, water supply decreased Δ18O values, whereas N did not affect this parameter. Δ18O variation was mainly determined by the amount of transpired water throughout plant growth (Tcum), whereas Δ13C variation was explained in part by a combination of leaf N and stomatal conductance (gs). Even though the four genotypes showed significant differences in cumulative transpiration rates and biomass, this was not translated into significant differences in Δ18Os. However, genotypic differences in Δ13C were observed. Moreover, ∼80% of the variation in biomass across growing conditions and genotypes was explained by a combination of both isotopes, with Δ18O alone accounting for ∼50%. This illustrates the usefulness of combining Δ18O and Δ13C in order to assess differences in plant growth and total transpiration, and also to provide a time-integrated record of the photosynthetic and evaporative performance of the plant during the course of crop growth

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