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A large FACE x rainout shelter system (Technology). Worldwide CO2 increases and little is known about its interaction with abiotic/biotic constraints. We developed a large FACE system with the help of the world leading team in FACE installation (F. Miglietta, CNR, Italy).

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Large scale rainout shelters for dought simulation with gantry imaging (technology). Simulating different drought scenarios is essential for disentangling environmental effects that are often correlated. The PhénoField® platform involves eight automatic rainout shelters, covering 384 microplots. Gantries are equipped with LiDARs, spectroradiometers and high resolution cameras.

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Innovative root phenotyping (Technology). Roots play an essential role in the adaptation of plants to environmental constraints. With an industrial partner (Inoviaflow), innovative plant culture containers (RhizoTube) and dedicated imaging cabins were developed and patented for automated dynamic and non-destructive in situ visualization of root systems. This gave rise to industrialization and international distribution (QUBIT, ViewPoint, Phenotrait). These tools allow genetic analysis of growth as affected by beneficial relationships with soil micro-organisms.

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A fully automatic Phenomobile for high-throughput field phenotyping (technology). It was developed for accurate estimation of, e.g. vertical distribution of leaf area or spike number. The measurement head attached at the extremity of a telescopic boom of 12m length can move in all directions with height from 1.0 m to 4.5 m. The system is guided by high precision GPS and allows scanning a wide range of species with 100-200 plots/hour throughput. It is equipped with LiDARs, multispectral cameras and high spatial resolution cameras working with a built-in illumination system making data independent from natural illumination. This innovative and unique ground robot was built by Meca3D (mechanics) and Robopec (automatics) based on Phenome-Emphasis specifications.

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An ontology-driven multi source and multi scale information system (PHIS) (data sciences). It non-ambiguously identifies all objects and traits in an experiment and establishes their relations via ontologies and semantics that apply to both field and controlled conditions. 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, e.g. modelling platforms or genetic databases. It is progressively deployed in Phenome--Emphasis local infrastructures, but also over Europe through European projects

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Combining high throughput phenotyping in controlled conditions and a network of field experiments: Genomic prediction of maize yield across European environmental scenarios (output example). Genomic prediction is a powerful tool to evaluate large number of genotypes but performs poorly in contrasting environmental scenarios. New avenues are opened by high throughput estimation of hundreds of genotypes and by the development of sensor networks in hundreds of fields. The yield of 250 maize hybrids was dissected in grain weight and number, and modelled as genotypic sensitivity to environmental drivers. Environmental indices were computed from sensor data and the progression of phenology calibrated for each genotype in the Montpellier-controlled installation. A whole genome regression for genotypic sensitivities led to accurate prediction of yield in a wide range of environmental scenarios, including 10 new fields and 40 new new hybrids. Data collected by the FP7 DROPS and ANR Amaizing projects.

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Combining high throughput metabolomics and phenotyping in controlled conditions: prediction of maize yield across Europe (output example). The Bordeaux installation can analyse the metabolism in large numbers of samples with targeted (major metabolites) and untargeted (LC-Orbitrap) methods. 250 maize hybrids were sampled in the Montpellier-controlled installation for 1,400 metabolic variables measured in Bordeaux. A model, based on these variables, predicted the yield in the network of fields presented in a6, with good accuracy. This is the first time that fully independent metabolomic datasets are used to predict yield. Data collected by the FP7 DROPS and ANR Amaizing projects.

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Use of chlorophyll fluorescence imaging for objective phenotyping of the impact of acquisition of virulence genes by environmental non-pathogenic bacterial strains (new group in Phenome Emphasis). Bacterial strains periodically emerge that are responsible for novel diseases on plants. The Angers group tested whether the acquisition of virulence genes by non-pathogenic environmental strains of Xanthomonas could lead to leaf damage by novel pathogenic strains. They estimated photosynthesis via two fluorescence parameters (Fo and Fm). Results provide functional data that support a model of evolution of bacterial strains in the genus Xanthomonas.