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24, chemin de Borde Rouge –Auzeville – CS52627
31326 Castanet Tolosan CEDEX - France

Dernière mise à jour : Mai 2018

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Commented representative publications

Commented representative publications citing the infrastructure since 2014

  1. Millet EJ, Kruijer W, Coupel-Ledru A, Alvarez Prado S, Cabrera-Bosquet L, Lacube S, Charcosset A, Welcker C, van Eeuwijk F, Tardieu F (2019) Genomic prediction of maize yield across European environmental conditions. Nature Genetics, DOI  10.1038/s41588-019-0414-y.  ('Phenome' in acknowledgements). Experiments in the Montpellier-controlled installation are combined with 30 field experiments across Europe and outputs of a sensor network for performing genomic prediction of the response of yield to environmental variables, allowing prediction of maize yield across Europe.
  2. Reynolds D, Baret F, Welcker C, Bostrom A, Ball J, Cellini F, Lorence A, Chawade A, Khafif M, Noshita K, Mueller-Linow M, Zhou J, Tardieu F (2019) What is cost-efficient phenotyping? Optimizing cost for different scenarios. Plant Science 282, 14-22.  Techniques for low cost phenotyping result in trade-offs with labor costs. Plant/plot handling and labor costs represent the major proportion of costs in phenotyping experiments The costs of high-throughput experiments in the field and in automated platforms is similar regardless of vehicles. The development of software applications is a major part of costs.
  3. Rincent R, Charpentier J-P, Faivre-Rampant P, Paux E, Le Gouis J, Bastien C, Segura V (2018) Phenomic selection: a low-cost and high-throughput method based on indirect predictions. Proof of concept on wheat and poplar. G3 8:3961-3972. (‘Phenome’ in acknowledgements).Based on a wheat Phéno3C experiment, a new approach named phenomic selection is proposed using near-infrared spectroscopy (NIRS) as a high-throughput, non-destructive tool to compute relationship matrices for predicting complex traits.
  4. Avramova V, Meziane A, Bauer E, Blankenagel S, Eggels S, Gresset S, Grill E, Niculaes C, Ouzunova M, Poppenberger B, Presterl T, Rozhon W, Welcker C, Yang ZY, Tardieu F, Schon CC. (2019) Carbon isotope composition, water use efficiency, and drought sensitivity are controlled by a common genomic segment in maize. Theoretical and Applied Genetics 132, 53-63. ('Phenodyn' in M&M). An EPPN access to the Montpellier-controlled installation (Phenodyn) shows a genetic variability of the discrimination of 13C in a C4 species, and colocation of effects with drought sensitivity.
  5. Neveu P., Tireau A., Hilgert N., Nègre V., Mineau-Cesari J., Brichet N., Chapuis R., Sanchez I., Pommier C., Charnomordic B., Tardieu F., Cabrera-Bosquet L. (2019) Dealing with multi-source and multi-scale information in plant phenomics: the ontology-driven Phenotyping Hybrid Information System. New Phytologist 221:588–601. ('Phenome' in acknowledgements). This paper presents, to a biological readership, why new generation information systems are required for phenomics, together with the technical solutions of PHIS, the information system developed by the MCP2 of Phenome-Emphasis and a case-use in field and greenhouse.
  6. Meline V, Delage W, Brin C, Li-Marchetti C, Sochard D, et al. 2019. Role of the acquisition of a type 3 secretion system in the emergence of novel pathogenic strains of Xanthomonas. Molecular Plant Pathology 20:33-50 ('Phenotics' in M&M). This paper presents developments in the Angers installation for the use of multiparametric chlorophyll fluorescence imaging for an objective phenotyping of the impact of acquisition of virulence genes by environmental non-pathogenic bacterial strains. See a8 in highlights.
  7. Rasti, P., Ahmad, A., Samiei, S., Belin, E., & Rousseau, D. (2019). Supervised Image Classification by Scattering Transform with Application to Weed Detection in Culture Crops of High Density. Remote Sensing, 11(3), 249. This paper assesses the interest of multiscale scattering transform for texture classification applied for the first time in plant science. Scattering transform is shown to outperform monoscale approaches (gray-level co-occurrence matrix, local binary patterns) but also multiscale approaches (wavelet decomposition) which do not include combinatory steps.  
  8. Parent B, Leclere M, Lacube M, Semenov MA, Welcker C, Martre P, Tardieu F (2018). Maize yields over Europe may increase in spite of climate change, with an appropriate use of the genetic variability of flowering time. Proc Natl Acad Sci USA 115, 10642-10647. ('Phenome' in acknowledgements). Combining phenomic experiments and modelling developed in Phenome-Emphasis, this paper shows that the genetic variability of flowering time may mitigate the effects of climate change provided that farmers continue using the same rules as today for choosing genotypes.
  9. Tardieu F, Cabrera Bosquet L, Pridmore T, Bennett M (2017) Plant Phenomics, From Sensors to Knowledge. Current biology 27: R770-R783 ('Phenome' in acknowledgements). This paper presents how we envisage the future of Phenomics in terms of multi-scale phenotyping and role of data sciences and modelling. The programme of Phenome-Emphasis derives from this view.
  10. Salon C, Avice JC., Colombie S., Dieuaide-Noubhani M, Gallardo-Guerrero K, Jeudy C, Ourry A., Prudent M, Voisin AS, Rolin D (2017). Fluxomics links cellular functional analyses to whole-plant phenotyping. Darwin Review. Journal of Experimental Botany 68, 2083-2098. This paper presents an interaction between a phenotyping installation, Dijon controlled, and a metabolomics installation, Bordeaux. It presents methods to measure the fluxome, describes how this complements ‘omics’ and high throughput plant phenotyping for identifying appropriate target plant phenotypes for breeding plant better adapted to biotic or abiotic constraints.
  11. Roy J, Tardieu F, Tixier-Boichard M, Schurr U (2017) European infrastructures for sustainable agriculture. Nature Plants 3: 756-758 ('Phenome' in acknowledgements.). This paper presents the respective domains and complementarities between EMPHASIS (European counterpart of Phenome-Emphasis) and the ESFRI infrastructure AnAEE.
  12. Madec, S., F. Baret, B. de Solan, S. Thomas, D. Dutartre, S. Jezequel, M. Hemmerlé, G. Colombeau and A. Comar (2017). High-Throughput Phenotyping of Plant Height: Comparing Unmanned Aerial Vehicles and Ground LiDAR Estimates. Frontiers in Plant Science 8:2002. See a4 in highlights . ('Phenome' in acknowledgements). This paper demonstrates that plant height can be accessed with a centimetre accuracy both from the LiDARs equipping the phenomobile, or from cameras aboard drones using advanced photogrammetric techniques. Monitoring plant height provides a good proxy of the flowering time for wheat.
  13. Liu, S., F. Baret, F. Boudon, S. Thomas, K. Zhao, C. Fournier, B. Andrieu, I. Kamran and B. de Solan (2017). Estimating wheat Green area index from ground-based LiDAR measurement through 3D ADEL-Wheat model. Agricultural and Forest Meteorology 247, 12-20 ('Phenome' acknowledgements). This paper presents a novel approach that exploits the knowledge embedded in the 3D structural plant model to get more accurate estimates of the green area index by combining the measurements of a high resolution camera and a LiDAR, both instruments operated by the phenomobile.
  14. Lancelot, E., Bertrand, D., Hanafi, M. & Jaillais, B., (2017) Near-infrared hyperspectral imaging for following imbibition of single wheat kernel sections. Vibrational Spectroscopy 92, 46-53. ('Phenome' in acknowledgements). This paper presents a method for measuring the water diffusion in single wheat grains over time using NIR imaging. Imbibition conditions germination, so monitoring the water diffusion in the different tissues of the grain allows analysis of the genetic variability of plant initial growth.
  15. Colombié S, Beauvoit B, Nazaret C, Bénard C, Vercambre G, Le Gall S, Biais B, Cabasson C, Maucourt M, Bernillon S, Moing A, Dieuaide-Noubhani M, Mazat J-P and Gibon Y (2017) Respiration climacteric in tomato fruits elucidated by constraint-based modelling. New Phytologist 213: 1726-1739. ('Phenome' in acknowledgements). An example of joint work of the omic installations of Bordeaux and Nantes, for an ERANET-supported research. A stoichiometric model of tomato fruit central metabolism, parameterised with data in our installations, revealed that the respiration enables dissipation of excessive energy resulting from the degradation of starch and cell wall.
  16. Fernandez O, Urrutia M, Bernillon S, Giauffret C, Tardieu F, Le Gouis J, Langlade N, Charcosset A, Moing A, Gibon Y (2016) Fortune telling : metabolic markers of plant performance. Metabolomics 12: #158. ('Phenome' in acknowledgements). 17 cit. This paper evaluates the use of metabolomic markers of agronomic performance and proposes a strategy to predict agronomic traits from metabolomic data obtained from plants grown under controlled conditions. 
  17. Jeudy C, Adrian M, Baussard C, Bernard C, Bernaud E, Bourion V, Busset H, Cabrera L, Cointault F, Han S, Moreau D, Pivato B, Prudent M, Truong HT, Vernoud V, Voisin AS, Wipf D, Salon C. 2016. High throughput image acquisition of plant roots with RhizoTubes. Plant Methods 12:31. ('Phenome' in acknowledgements) 12 cit. This paper describes novels tools developed in Dijon-controlled for high throughput imaging of roots of diverse plant species. Dynamic, non-destructive high resolution images of root system architecture assess interactions with biotic partners, neighboring plants or soil microbiome.
  18. Cabrera-Bosquet L, Fournier C, Brichet N, Welcker C, Suard B, Tardieu F. 2016. High-throughput estimation of incident light, light interception and radiation-use efficiency of thousands of plants in a phenotyping platform. New Phytologist 212:269-81 ('Phenome' in acknowledgements) 37 cit. This paper presents an indirect method for measuring canopy photosynthesis of 1000s of plants, by in situ evaluation of local incident light, estimation of intercepted light based on a 3D model and measurement of biomass.
  19. Coupel-Ledru A., Lebon E., Christophe A., Gallo A., Gago P., Pantin F., Doligez A., Simonneau T. (2016) Reduced night time transpiration is a relevant breeding target for high water-use efficiency in grapevine. Proc Natl Acad Sci USA 113: 8963-8968. ('PhenoArch' in M&M). 21 cit. Based on an access to the Montpellier-controlled installation (PhenoArch), this paper shows a high genetic variability of night transpiration in vine, with common QTLs as for transpiration efficiency. This paves the way for using this novel trait in breeding.
  20. Dapp M., Reinders J., Bédiée A., Balsera C., Bucher E., Theiler G., Granier G. & Paszkowski J. (2015) Heterosis and inbreeding depression of epigenetic Arabidopsis hybrids. Nature Plants 1: 15092 ('Phenopsis' in M&M and results). 22 cit. Based on an EPPN access to the Montpellier-controlled installation (Phenopsis), this paper report a genetic analysis of recombinant inbred lines with varying levels of DNA methylation, revealing an epigenetic diversity and regulation that participates to hybrid vigour.
  21. Caldeira CF, Jeanguenin L, Chaumont F, Tardieu F (2014) Circadian rhythms of hydraulic conductance and growth are enhanced by drought and improve plant performance. Nature Communications 5:5365 ('Phenodyn' in M&M). 49 cit. Based on an installation in the Montpellier controlled condition site, the first demonstration of a hydraulic origin of circadian oscillations of growth in maize, and of the role of entrainment for high evaporative demands. A model shows the advantage of this entrainment effect.