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Plant phenotyping platforms generate large amounts of high dimensional data at different scales of plant organization. The possibility to use this information as inputs of models is an opportunity to develop models that integrate new processes and genetic inputs. We assessed to what extent the phenomics and modelling communities can address the issues of interoperability and data exchange, using a science mapping approach (i.e. visualization and analysis of a broad range of scientific and technological activities as a whole). In this paper, we (i) evaluate connections, (ii) identify compatible and connectable research topics, and (iii) propose strategies to facilitate connection across communities. We applied a science mapping approach based on reference and term analyses to a set of 4332 scientific papers published by the plant phenomics and modelling communities from 1980 to 2019, retrieved using the Elsevier's Scopus database and the quantitative-plant.org website. The number of papers on phenotyping and modelling dramatically increased during the past decade, boosted by progress in phenotyping technologies and by key developments at hard- and software levels. The science mapping approach indicated a large diversity of research topics studied in each community. Despite compatibilities of research topics, the level of connection between the phenomics and modelling communities was low. Although phenomics and modelling crucially need to exchange data, the two communities appeared to be weakly connected. We encourage these communities to work on ontologies, harmonized formats, translators and connectors to facilitate transparent data exchange.

Water potential explains water transport in the Soil-Plant-Atmosphere Continuum (SPAC), and is gaining interest as connecting variable between ‘pedo-, bio- and atmosphere’. It is primarily used to simulate hydraulics in the SPAC, and is thus essential for studying drought effects. Recent implementations of hydraulics in large-scale Terrestrial Biosphere Models (TBMs) improved their performance under water-limited conditions, while hydraulic features of recent detailed FunctionalStructural Plant Models (FSPMs) open new possibilities for dissecting complex traits for drought tolerance. These developments in models across scales deserve a critical appraisal to evaluate its potential for wider use in FSPMs, but also in crop systems models (CSMs), where hydraulics are currently still absent. After refreshing the physical basis, we first address models where water potential is primarily used for describing water transport along the transpiration pathway from the soil to the leaves, through the roots, the xylem and the leaf mesophyll. Then, we highlight models for three ecophysiological processes, which have well-recognised links to water potential: phloem transport, stomatal conductance and organ growth. We identify water potential as the bridge between soil, root and shoot models, as the physiological variable integrating below- and aboveground abiotic drivers, but also as the link between water status and growth. Models making these connections enable identifying crucial traits for ecosystem resilience to drought and for breeding towards improved drought tolerance in crops. Including hydraulics often increases model complexity, and thus requires experimental data on soil and plant hydraulics. Nevertheless, modelling hydraulics is insightful at different scales (FSPMs, CSMs and TBMs).

Plant phenotyping platforms generate large amounts of high-dimensional data at diferent scales of plant organization. Te possibility to use this information as inputs of models is an opportunity to develop models that integrate new processes and genetic inputs. We assessed to what extent the phenomics and modelling communities can address the issues of interoperability and data exchange, using a science mapping approach (i.e. visualization and analysis of a broad range of scientifc and technological activities as a whole). In this paper, we (i) evaluate connections, (ii) identify compatible and connectable research topics and (iii) propose strategies to facilitate connection across communities. We applied a science mapping approach based on reference and term analyses to a set of 4332 scientifc papers published by the plant phenomics and modelling communities from 1980 to 2019, retrieved using the Elsevier’s Scopus database and the quantitative-plant.org website. Te number of papers on phenotyping and modelling dramatically increased during the past decade, boosted by progress in phenotyping technologies and by key developments at hardware and sofware levels. Te science mapping approach indicated a large diversity of research topics studied in each community. Despite compatibilities of research topics, the level of connection between the phenomics and modelling communities was low. Although phenomics and modelling crucially need to exchange data, the two communities appeared to be weakly connected. We encourage these communities to work on ontologies, harmonized formats, translators and connectors to facilitate transparent data exchange.

Plant phenotyping platforms generate large amounts of high dimensional data at different scales of plant organization. The possibility to use this information as inputs of models is an opportunity to develop models that integrate new processes and genetic inputs. We assessed to what extent the phenomics and modelling communities can address the issues of interoperability and data exchange, using a science mapping approach (i.e. visualization and analysis of a broad range of scientific and technological activities as a whole). In this paper, we (i) evaluate connections, (ii) identify compatible and connectable research topics, and (iii) propose strategies to facilitate connection across communities. We applied a science mapping approach based on reference and term analyses to a set of 4332 scientific papers published by the plant phenomics and modelling communities from 1980 to 2019, retrieved using the Elsevier's Scopus database and the quantitative-plant.org website. The number of papers on phenotyping and modelling dramatically increased during the past decade, boosted by progress in phenotyping technologies and by key developments at hard- and software levels. The science mapping approach indicated a large diversity of research topics studied in each community. Despite compatibilities of research topics, the level of connection between the phenomics and modelling communities was low. Although phenomics and modelling crucially need to exchange data, the two communities appeared to be weakly connected. We encourage these communities to work on ontologies, harmonized formats, translators and connectors to facilitate transparent data exchange.

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.

In recent years, plant phenomics has produced massive datasets involving millions of images in experiments performed in the field and in controlled conditions, concerning hundreds of genotypes at different phenological stages and scales (Tardieu et al., 2017). In the future, information extracted from these datasets will be used increasingly as variables or parameters of mathematical and computational models, thereby broadening the scope of information extracted from phenomics data (Muller and Martre, 2019). Feeding such data to structural plant models (SPMs), functional plant models (FPMs), functional-structural plant models (FSPMs) and process-based crop simulation models (CSMs) in ad hoc pipelines has the potential to derive high-throughput predictions of integrated (e.g. yield) or functional traits (e.g. root system architecture) across a wide range of target environments or management practices (Chen et al., 2019). Unfortunately, the connectivity between these two communities is greatly limited by the absence of a common semantic framework and harmonized vocabulary. Currently, the terminology (e.g. objects, variables) used by these communities can be quite heterogeneous depending on the research discipline, scale and objective and even can differ between research groups. This limits the ability to accurately relate information within and across communities. A solution to facilitate the connection and the exchange of information is the use of a controlled and standardized vocabulary of common and internationally recognized descriptive terminology that can be connected and shared uniformly among the communities. The phenomics community has tackled these issues by reusing existing ontologies (e.g. Plant Ontology – PO or Plant Trait Ontology – TO), developing new standards (e.g. MIAPPE) as well as ontology-driven information systems (e.g. PHIS). Similarly, some initiatives have been developed within the crop modelling community such as the ICASA Master Variables Li

In recent years, plant phenomics has produced massive datasets involving millions of images in experiments performed in the field and in controlled conditions, concerning hundreds of genotypes at different phenological stages and scales (Tardieu et al., 2017). In the future, information extracted from these datasets will be used increasingly as variables or parameters of mathematical and computational models, thereby broadening the scope of information extracted from phenomics data (Muller and Martre, 2019). Feeding such data to structural plant models (SPMs), functional plant models (FPMs), functional-structural plant models (FSPMs) and process-based crop simulation models (CSMs) in ad hoc pipelines has the potential to derive high-throughput predictions of integrated (e.g. yield) or functional traits (e.g. root system architecture) across a wide range of target environments or management practices (Chen et al., 2019). Unfortunately, the connectivity between these two communities is greatly limited by the absence of a common semantic framework and harmonized vocabulary.

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.

In recent years, plant phenomics has produced massive datasets involving millions of images in experiments performed in the field and in controlled conditions, concerning hundreds of genotypes at different phenological stages and scales (Tardieu et al., 2017). In the future, information extracted from these datasets will be used increasingly as variables or parameters of mathematical and computational models, thereby broadening the scope of information extracted from phenomics data (Muller and Martre, 2019). Feeding such data to structural plant models (SPMs), functional plant models (FPMs), functional-structural plant models (FSPMs) and process-based crop simulation models (CSMs) in ad hoc pipelines has the potential to derive high-throughput predictions of integrated (e.g. yield) or functional traits (e.g. root system architecture) across a wide range of target environments or management practices (Chen et al., 2019). Unfortunately, the connectivity between these two communities is greatly limited by the absence of a common semantic framework and harmonized vocabulary. Currently, the terminology (e.g. objects, variables) used by these communities can be quite heterogeneous depending on the research discipline, scale and objective and even can differ between research groups. This limits the ability to accurately relate information within and across communities. A solution to facilitate the connection and the exchange of information is the use of a controlled and standardized vocabulary of common and internationally recognized descriptive terminology that can be connected and shared uniformly among the communities. The phenomics community has tackled these issues by reusing existing ontologies (e.g. Plant Ontology – PO or Plant Trait Ontology – TO), developing new standards (e.g. MIAPPE) as well as ontology-driven information systems (e.g. PHIS). Similarly, some initiatives have been developed within the crop modelling community such as the ICASA Master Variables Li