It is generally believed that an osmotically generated pressure gradient drives the phloem mass flow. So far, this widely accepted Münch theory has required remarkably few adaptations, but the debate on alternative and additional hypotheses is still ongoing. Recently, a possible shortcoming of the Münch theory has been pointed out, suggesting that the Münch pressure flow is more suitable for herbs than for trees. Estimation of the phloem resistance indicates that a point might be reached in long sieve tubes where the pressure required to drive the Münch flow cannot be generated. Therefore, the relay hypothesis regained belief as it implies that the sieve tubes are shorter then the plant’s axial axis. In the source phloem, three different loading strategies exist which probably result from evolutionary advantages. Passive diffusion seems to be the most primitive one, whereas active loading strategies substantially increase the growth potential. Along the transport phloem, a leakage-retrieval mechanism is observed. Appreciable amounts of carbohydrates are lost from the sieve tubes to feed the lateral sinks, while a part of these lost carbohydrates is subsequently reloaded into the sieve tubes. This mechanism is probably involved to buffer short-term irregularities in phloem turgor and gradient. In the long term, the mechanism controls the replenishment and remobilization of lateral stem storage tissues. As phloem of higher plants has multiple functions in plant development, reproduction, signalling, and growth, the fundamental understanding of the mechanisms behind phloem transport should be elucidated to increase our ability to influence plant growth and development.

High-resolution stem diameter variations (SDV) are widely recognized as a useful drought stress indicator and have therefore been used in many irrigation scheduling studies. More recently, SDV have been used in combination with other plant measurements and biophysical modelling to study fundamental mechanisms underlying whole-plant functioning and growth. The present review aims to scrutinize the important insights emerging from these more recent SDV applications to identify trends in ongoing fundamental research. The main mechanism underlying SDV is variation in water content in stem tissues, originating from reversible shrinkage and swelling of dead and living tissues, and irreversible growth. The contribution of different stem tissues to the overall SDV signal is currently under debate and shows variation with species and plant age, but can be investigated by combining SDV with state-of-the-art technology like magnetic resonance imaging. Various physiological mechanisms, such as water and carbon transport, and mechanical properties influence the SDV pattern, making it an extensive source of information on dynamic plant behaviour. To unravel these dynamics and to extract information on plant physiology or plant biophysics from SDV, mechanistic modelling has proved to be valuable. Biophysical models integrate different mechanisms underlying SDV, and help us to explain the resulting SDV signal. Using an elementary modelling approach, we demonstrate the application of SDV as a tool to examine plant water relations, plant hydraulics, plant carbon relations, plant nutrition, freezing effects, plant phenology and dendroclimatology. In the ever-expanding SDV knowledge base we identified two principal research tracks. First, in detailed short-term experiments, SDV measurements are combined with other plant measurements and modelling to discover patterns in phloem turgor, phloem osmotic concentrations, root pressure and plant endogenous control. Second, long-term SDV time series covering many different species, regions and climates provide an expanding amount of phenotypic data of growth, phenology and survival in relation to microclimate, soil water availability, species or genotype, which can be coupled with genetic information to support ecological and breeding research under on-going global change. This under-exploited source of information has now encouraged research groups to set up coordinated initiatives to explore this data pool via global analysis techniques and data-mining.

The persistence and productivity of forage grasses, important sources for feed production, are threatened by climate change-induced drought. Breeding programs are in search of new drought tolerant forage grass varieties, but those programs still rely on time-consuming and less consistent visual scoring by breeders. In this study, we evaluate whether Unmanned Aerial Vehicle (UAV) based remote sensing can complement or replace this visual breeder score. A field experiment was set up to test the drought tolerance of genotypes from three common forage types of two different species: Festuca arundinacea, diploid Lolium perenne and tetraploid Lolium perenne. Drought stress was imposed by using mobile rainout shelters. UAV flights with RGB and thermal sensors were conducted at five time points during the experiment. Visual-based indices from different colour spaces were selected that were closely correlated to the breeder score. Furthermore, several indices, in particular H and NDLab, from the HSV (Hue Saturation Value) and CIELab (Commission Internationale de l’éclairage) colour space, respectively, displayed a broad-sense heritability that was as high or higher than the visual breeder score, making these indices highly suited for high-throughput field phenotyping applications that can complement or even replace the breeder score. The thermal-based Crop Water Stress Index CWSI provided complementary information to visual-based indices, enabling the analysis of differences in ecophysiological mechanisms for coping with reduced water availability between species and ploidy levels. All species/types displayed variation in drought stress tolerance, which confirms that there is sufficient variation for selection within these groups of grasses. Our results confirmed the better drought tolerance potential of Festuca arundinacea, but also showed which Lolium perenne genotypes are more tolerant.

Close remote sensing approaches can be used for high throughput on-field phenotyping in the context of plant breeding and biological research. Data on canopy cover (CC) and canopy height (CH) and their temporal changes throughout the growing season can yield information about crop growth and performance. In the present study, sigmoid models were fitted to multi-temporal CC and CH data obtained using RGB imagery captured with a drone for a broad set of soybean genotypes. The Gompertz and Beta functions were used to fit CC and CH data, respectively. Overall, 90.4% fits for CC and 99.4% fits for CH reached an adjusted R² > 0.70, demonstrating good performance of the models chosen. Using these growth curves, parameters including maximum absolute growth rate, early vigor, maximum height, and senescence were calculated for a collection of soybean genotypes. This information was also used to estimate seed yield and maturity (R8 stage) (adjusted R² = 0.51 and 0.82). Combinations of parameter values were tested to identify genotypes with interesting traits. An integrative approach of fitting a curve to a multi-temporal dataset resulted in biologically interpretable parameters that were informative for relevant traits.

High-throughput field phenotyping using close remote sensing platforms and sensors for non-destructive assessment of plant traits can support the objective evaluation of yield predictions of large breeding trials. The main objective of this study was to examine the potential of unmanned aerial vehicle (UAV)-based structural and spectral features and their combination in herbage yield predictions across diploid and tetraploid varieties and breeding populations of perennial ryegrass (Lolium perenne L.). Canopy structural (i.e., canopy height) and spectral (i.e., vegetation indices) information were derived from data gathered with two sensors: a consumer-grade RGB and a 10-band multispectral (MS) camera system, which were compared in the analysis. A total of 468 field plots comprising 115 diploid and 112 tetraploid varieties and populations were considered in this study. A modelling framework established to predict dry matter yield (DMY), was used to test three machine learning algorithms, including Partial Least Squares Regression (PLSR), Random Forest (RF), and Support Vector Machines (SVM). The results of the nested cross-validation revealed: (a) the fusion of structural and spectral features achieved better DMY estimates as compared to models fitted with structural or spectral data only, irrespective of the sensor, ploidy level or machine learning algorithm applied; (b) models built with MS-based predictor variables, despite their lower spatial resolution, slightly outperformed the RGB-based models, as lower mean relative root mean square error (rRMSE) values were delivered; and (c) on average, the RF technique reported the best model performances among tested algorithms, regardless of the dataset used. The approach introduced in this study can provide accurate yield estimates (up to an RMSE = 308 kg ha⁻¹) and useful information for breeders and practical farm-scale applications.

Grassland-based ruminant production systems are integral to sustainable food production in Europe, converting plant materials indigestible to humans into nutritious food, while providing a range of environmental and cultural benefits. Climate change poses significant challenges for such systems, their productivity and the wider benefits they supply. In this context, grassland models have an important role in predicting and understanding the impacts of climate change on grassland systems, and assessing the efficacy of potential adaptation and mitigation strategies. In order to identify the key challenges for European grassland modelling under climate change, modellers and researchers from across Europe were consulted via workshop and questionnaire. Participants identified fifteen challenges and considered the current state of modelling and priorities for future research in relation to each. A review of literature was undertaken to corroborate and enrich the information provided during the horizon scanning activities. Challenges were in four categories relating to: 1) the direct and indirect effects of climate change on the sward 2) climate change effects on grassland systems outputs 3) mediation of climate change impacts by site, system and management and 4) cross-cutting methodological issues. While research priorities differed between challenges, an underlying theme was the need for accessible, shared inventories of models, approaches and data, as a resource for stakeholders and to stimulate new research. Developing grassland models to effectively support efforts to tackle climate change impacts, while increasing productivity and enhancing ecosystem services, will require engagement with stakeholders and policy-makers, as well as modellers and experimental researchers across many disciplines. The challenges and priorities identified are intended to be a resource 1) for grassland modellers and experimental researchers, to stimulate the development of new research directions and collaborative opportunities, and 2) for policy-makers involved in shaping the research agenda for European grassland modelling under climate change.