Search

Root system scientists strive to understand how a single root, emerging from a plant’s seed, can form a complex, dynamic and plastic network of thousands of individual roots. They investigate how such a network is ideally suited to perform a number of functions required for the harmonious development of the whole plant. Everyone in the community also knows how complicated it can be to study root systems, with tasks ranging from digging plants out of the soil, creating experimental set-ups that allow the observation of the roots, to quantifying the root network itself or the processes underlying its formation. Within the community, there is one person, Dr Loïc Pagès, who has been working on all these tasks for many years, and who has moved the field forward numerous times. On the occasion of his soon-to-be retirement, we would like to express our appreciation to him via this editorial.

The complete lists of terms identified in each cluster of the plant phenotyping, image analysis tools, crop models and plant models.

Bibliographic coupling maps created on retrieved publications, using the VOSviewer software version 1.6.13 (freely available at https://www.vosviewer.com/)., {"references":["van Eck, N. J., and Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84, 523–538. doi:10.1007/s11192-009-0146-3."]}

International audience

Guttation is the exudation of xylem sap from vascular plant leaves. This process is particularly interesting because in its configuration root water uptake occurs against the hydrostatic pressure driving force. Hence, it emphasizes the contribution of another driving force that lifts water in plants: the osmotic potential gradient.

Executive Summary Objectives Phenomic datasets are at the crossroad of several disciplines, in particular plant physiology, genetics, ecology and crop modelling. All these communities have developed their own solutions to organise and store resulting datasets in dedicated repositories or information systems, so it is crucial that the effort of EMPHASIS to organize phenomic datasets results in information systems that are interoperable with those of other infrastructures such as ELIXIR (genomics and genetics), ANaEE (Ecology) and AgMIP (crop modelling). This is in a context in which data sciences are receiving an increasing interest, in particular with the EOSC initiative of the European Commission. Main results The information systems developed by different communities for phenotypic data differ in their organization and content. Phenomic information systems need to track all elements for re-analysis of individual experiments, in particular the time courses and spatial variability of environmental variables, the time course of traits and the link between samples, organs, genotypes, plants,events, x-y positions of each plant or plot. This requires the use of semantic web to automatically generate complex metadata from a few indications. This level of detail is not necessary in Genetic-Genomic or in Ecology, so corresponding information systems relate integrative plant traits with integrative indicators of environmental conditions, and/or with genomic information. The crop modelling community developed its own standards for traits, environmental variables and managing practices. EMPHASIS has launched common working groups with each of these communities, resulting in a clarification of the role of each information system, in common tasks for mapping ontologies and in a list of tasks that will facilitate interoperability between information systems.

Plus de la moitié des pertes de bois dans les forêts européennes sont dues aux tempêtes. Une connaissance des mécanismes impliqués dans la stabilité mécanique des arbres est alors capitale. L’ancrage de l’arbre dans le sol constitue l’une des composantes principales du maintien mécanique de l’arbre. Il est principalement déterminé par l’architecture du système racinaire et son interaction mécanique avec le sol. Au cours de son développement, l’arbre modifie ses dimensions et se complexifie. Plus particulièrement, le système racinaire semble s’acclimater (ex : croissance en diamètre plus importante) aux déformations engendrées par le vent. L’ensemble de ces modifications conduit à une évolution des mécanismes à l’origine de l’ancrage au cours du développement de l’arbre. L’étude expérimentale de cette fonction est compliquée car les racines sont difficilement mesurables en continu dans le sol. Nous avons alors mis au point une approche numérique pour décrire la croissance du système racinaire et la distribution des déformations dues au vent. Une grande base de données structurée en chronoséquence de systèmes racinaires numérisés (Pinus pinaster) a été mobilisée. Comme l’étude de la structure et des fonctions des racines est plus efficiente quand la différentiation entre racines est prise en compte, nous avons d’abord formalisé les types racinaires du système racinaire du pin maritime à partir d’une technique de classification (« k-means clustering ») réalisée avec quatre variables. La classification des racines latérales du pin maritime nous a permis d’identifier 5 types racinaires au cours du développement du pin maritime. Ce regroupement explique 70% de la variabilité de notre base de données. Chaque système racinaire est caractérisé par trois grosses racines horizontales émises par la souche. Les racines montrent une forte différentiation pour leur tropisme, avec une direction de croissance soit horizontale soit verticale. La structure de la partie centrale du système racinaire est pratiquement complète dès l'âge de 4 ans. Sur la base des types racinaires identifiés, nous avons calibré un modèle architectural (RootTyp ; Pagès et al. 2004) pour le pin maritime. Treize paramètres pour chaque type racinaire ont été estimés par l’intermédiaire de la base de données, d’informations issues de la littérature et d’une procédure d'optimisation. Une modélisation réaliste du système racinaire jusqu'à 50 ans n’a pu être obtenue qu'en implémentant au modèle RootTyp de nouveaux processus biologiques : la diminution de la ramification avec la croissance de la racine et la diminution de la vigueur des racines avec l'ordre de ramification. Malgré ces améliorations, les systèmes racinaires de la base de données présentent des diamètres plus importants à proximité de la souche par rapport aux systèmes racinaires simulés. Ce biais systématique est principalement attribué à l’acclimatation des racines au vent dominant. Les altérations de croissance dues aux contraintes pédologiques ont également été implémentées grâce à l’amélioration du module de sol du modèle architectural.Enfin, pour comprendre les mécanismes à l’origine de l’acclimatation des racines nous avons combiné plusieurs modèles pour prédire la distribution spatiale des déformations dans des maquettes simplifiées de systèmes racinaires à 4, 6 et 13 ans, pour trois régimes de vent spécifiques à la région étudiée. D’après les simulations, les déformations des racines sous l'effet du vent diminuent avec l’âge, en raison de l’augmentation de la rigidité des racines. Cela suggère une plus forte réponse thigmomorphogénétique aux stades jeunes. Les modifications structurelles et anatomiques du système racinaire par acclimatation au vent s’expliquent principalement par les distributions des déformations et des contraintes dans les racines., Storms cause more than 50% of the timber loss in European forests. However, forest tree anchorage mechanisms throughout their lifespan are not fully understood, especially the strong acclimation of root systems to common winds. This lack of knowledge is mainly due to technical difficulties: neither the root structure nor the mechanical contribution of the roots could be characterized continually. Thus we set up a numerical approach to model the development of the root system and to describe the strains resulting from common winds. This generic approach has been developed using Pinus pinaster grown in sandy soils as model species.Seven datasets of excavated root systems from 0 to 50 years were employed. The assessment of root structure and functions is more powerful if the differentiation of root system in several root types is considered. We first proposed an automatic classification of roots with the k-means clustering algorithm. Four root traits were chosen as classifiers, including three geometric architectural traits, which can be precisely assessed whatever the tree/root age. Clustering yielded similar five groups of laterals roots at all ages, explaining 70% of the variability. The three largest lateral roots per tree were all horizontal roots branching from stump and the other lateral roots show a large differentiation for tropism: nearly all the roots were horizontal or vertical roots. The framework of the central part of the root system can be almost completed in 4-year-old trees (3.5 cm collar diameter). We then calibrated the existing RootTyp (Pagès et al. 2004) architectural model for P. pinaster for each of the root types defined by the cluster analysis. We used the database combined with a literature review and an optimization method to get accurate values for 13 parameters by root types. We devoted effort to validate our model calibration. In order to model architecture of the root system, damping properties had to be implemented to yield realistic outputs up to the mature stage. Branching varied as a function of distance from the root base, and growth capacity decreased with branching order. Nevertheless, the root diameters of simulated root systems were generally underestimated. This was certainly due to root growth plasticity to the prevailing wind, an acclimation facet not taken into account at this calibration step. Growth alterations due to a cemented horizon were reproduced using the new calibrated soil module. Then, the wind acclimation of roots was numerically investigated by examining the root mechanical stimuli due to wind. A chain of biomechanical models was used to predict the spatial distribution of stress and strain in simplified root systems at 4, 6 and 13-year-old as a result of three levels of usual winds. According to simulations, the strain amplitude decreased with tree growth due to the increasing root system stiffness. This suggests larger thigmomorphogenetic responses at young stages. The modifications of the structural and wood root properties related to wind acclimation were largely explained by the stress and strain distribution in the root system.

National audience

International audience

Root architecture and anchorage: what we learned from maritime pine on sandy soils in the Landes forest. Mathematical Modelling of Wind Damage Risk to Forests

The complete lists of terms identified in each cluster of the plant phenotyping, image analysis tools, crop models and plant models., {"references":["van Eck, N. J., and Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84, 523–538. doi:10.1007/s11192-009-0146-3."]}

Executive Summary Objectives Phenomic datasets are at the crossroad of several disciplines, in particular plant physiology, genetics, ecology and crop modelling. All these communities have developed their own solutions to organise and store resulting datasets in dedicated repositories or information systems, so it is crucial that the effort of EMPHASIS to organize phenomic datasets results in information systems that are interoperable with those of other infrastructures such as ELIXIR (genomics and genetics), ANaEE (Ecology) and AgMIP (crop modelling). This is in a context in which data sciences are receiving an increasing interest, in particular with the EOSC initiative of the European Commission. Main results The information systems developed by different communities for phenotypic data differ in their organization and content. Phenomic information systems need to track all elements for re-analysis of individual experiments, in particular the time courses and spatial variability of environmental variables, the time course of traits and the link between samples, organs, genotypes, plants,events, x-y positions of each plant or plot. This requires the use of semantic web to automatically generate complex metadata from a few indications. This level of detail is not necessary in Genetic-Genomic or in Ecology, so corresponding information systems relate integrative plant traits with integrative indicators of environmental conditions, and/or with genomic information. The crop modelling community developed its own standards for traits, environmental variables and managing practices. EMPHASIS has launched common working groups with each of these communities, resulting in a clarification of the role of each information system, in common tasks for mapping ontologies and in a list of tasks that will facilitate interoperability between information systems.