A flexible geometric model for leaf shape descriptions with high accuracy

Accurate assessment of canopy structure is crucial in studying plant-environment interactions. The advancement of functional-structural plant models (FSPM), which incorporate the 3D structure of individual plants, increases the need for a method for accurate mathematical descriptions of leaf shape. A model was developed as an improvement of an existing leaf shape algorithm to describe a large variety of leaf shapes. Modelling accuracy was evaluated using a spatial segmentation method and shape differences were assessed using principal component analysis (PCA) on the optimised parameters. Furthermore, a method is presented to calculate the mean shape of a dataset, intended for obtaining a representative shape for modelling purposes. The presented model is able to accurately capture a large range of single, entire leaf shapes. PCA illustrated the interpretability of the parameter values and allowed evaluation of shape differences. The model parameters allow straightforward digital reconstruction of leaf shapes for modelling purposes such as FSPMs

LEAF-E: a tool to analyze grass leaf growth using function fitting

In grasses, leaf growth is often monitored to gain insights in growth processes, biomass accumulation, regrowth after cutting, etc. To study the growth dynamics of the grass leaf, its length is measured at regular time intervals to derive the leaf elongation rate (LER) profile over time. From the LER profile, parameters such as maximal LER and leaf elongation duration (LED), which are essential for detecting inter-genotype growth differences and/or quantifying plant growth responses to changing environmental conditions, can be determined. As growth is influenced by the circadian clock and, especially in grasses, changes in environmental conditions such as temperature and evaporative demand, the LER profiles show considerable experimental variation and thus often do not follow a smooth curve. Hence it is difficult to quantify the duration and timing of growth. For these reasons, the measured data points should be fitted using a suitable mathematical function, such as the beta sigmoid function for leaf elongation. In the context of high-throughput phenotyping, we implemented the fitting of leaf growth measurements into a user-friendly Microsoft Excel-based macro, a tool called LEAF-E. LEAF-E allows to perform non-linear regression modeling of leaf length measurements suitable for robust and automated extraction of leaf growth parameters such as LER and LED from large datasets. LEAF-E is particularly useful to quantify the timing of leaf growth, which forms an important added value for detecting differences in leaf growth development. We illustrate the broad application range of LEAF-E using published and unpublished data sets of maize, Miscanthus spp. and Brachypodium distachyon, generated in independent experiments and for different purposes. In addition, we show that LEAF-E could also be used to fit datasets of other growth-related processes that follow the sigmoidal profile, such as cell length measurements along the leaf axis. Given its user-friendliness, ability to quantify duration and timing of leaf growth and broad application range, LEAF-E is a tool that could be routinely used to study growth processes following the sigmoidal profile

Gloxinia—an open-source sensing platform to monitor the dynamic responses of plants

The study of the dynamic responses of plants to short-term environmental changes is becoming increasingly important in basic plant science, phenotyping, breeding, crop management, and modelling. These short-term variations are crucial in plant adaptation to new environments and, consequently, in plant fitness and productivity. Scalable, versatile, accurate, and low-cost data-logging solutions are necessary to advance these fields and complement existing sensing platforms such as high-throughput phenotyping. However, current data logging and sensing platforms do not meet the requirements to monitor these responses. Therefore, a new modular data logging platform was designed, named Gloxinia. Different sensor boards are interconnected depending upon the needs, with the potential to scale to hundreds of sensors in a distributed sensor system. To demonstrate the architecture, two sensor boards were designed—one for single-ended measurements and one for lock-in amplifier based measurements, named Sylvatica and Planalta, respectively. To evaluate the performance of the system in small setups, a small-scale trial was conducted in a growth chamber. Expected plant dynamics were successfully captured, indicating proper operation of the system. Though a large scale trial was not performed, we expect the system to scale very well to larger setups. Additionally, the platform is open-source, enabling other users to easily build upon our work and perform application-specific optimisations

Limitations of snapshot hyperspectral cameras to monitor plant response dynamics in stress-free conditions

Plants' dynamic eco-physiological responses are vital to their productivity in continuously fluctuating conditions, such as those in agricultural fields. However, it is currently still very difficult to capture these responses at the field scale for phenotyping purposes. Advanced hyperspectral imaging tools are increasingly used in phenotyping, and have been applied to detect changes in plants in response to a specific treatment, phenological state or monitor its growth and development. Phenotyping has to evolve towards capturing dynamic behaviour under more subtle fluctuations in environmental conditions, without the presence of clear treatments or stresses. Therefore, we investigated the potential of hyperspectral imaging to capture dynamic behaviour of plants in stress-free conditions at a temporal resolution of seconds. Two growth chamber experiments were set up, in which strawberry plants and four different background materials, serving as controls, were monitored by a snapshot hyperspectral camera in variable conditions of light, temperature and relative humidity. The sampling period was set to three seconds, triggering image acquisition and gas exchange measurements. Different background materials were used to assess the influence of the environment and the camera in both experiments. To separate the plant and background data, static masks were determined. Two datasets were created, which encompass both experiments. One dataset was constructed after averaging over the entire mask to acquire one value per spectral band. These values were then used to calculate a set of vegetation indices. The other dataset used spatial subsampling to retain spatial information. From both datasets, linear models were constructed using ridge regression, which estimated the measured eco-physiological and environmental data. Leaf temperature and vapour pressure deficit based on leaf temperature are the two main eco-physiological characteristics that could be predicted successfully. Stomatal conductance, photosynthesis and transpiration rate show less promising results. We suspect that limited variation, and low spectral resolution and range are the main causes of the inability of the models to extract meaningful predictions. Furthermore, the models that were only trained on background data also showed good predictive performance. This is probably because the main drivers for good performing eco-physiological variables are temperature and incident light intensity. Environmental characteristics that have good performance are photosynthetically active radiation and air temperature. Current hyperspectral sensing technologies are not yet able to uncover most plant dynamic eco-physiological responses when plants are cultivated in stress-free conditions

The variable effect of polyploidization on the phenotype in Escallonia

To induce new variation within the Escallonia genus, chromosome doubling was performed in E. rubra, E. rosea, and E. illinita, three important species within this genus of mainly evergreen woody ornamental species. Obtained tetraploids and diploid controls were analyzed for rooting capacity, leaf and flower characteristics, and plant architecture using image analysis and cold tolerance. In the present study, a breeders' collection of 23 accessions was characterized cytogenetically and described morphologically. All analyzed species and cultivars were diploid (2n =2x =24), with exception of E. pendula, a tetraploid. Today, breeding in Escallonia is limited to lucky finds in seedling populations and few efforts in interspecific hybridization. Three selected Escallonia species underwent an in vitro chromosome doubling with both oryzalin and trifluralin applied as either a continuous or shock treatment. The treatments successfully induced polyploids in all three species. Image analysis revealed that tetraploid E. rosea had decreased shoot length (from 3.8 to 1.3 cm), higher circularity and more dense growth habit compared to diploids. No significant changes in cold tolerance were seen. Tetraploid E. illinita did not differ in shoot length, but an increased outgrowth of axillary buds on the main axis led to denser plants. For tetraploid E. rubra, an increase in plant height (from 4.9 to 5.5 cm) was observed together with a large decrease in circularity and density due to a more polar outgrowth of branches on the main axis. E. rubra tetraploids bore larger flowers than diploids and had an increased cold tolerance (from 7.7 to 11.8-C). Leaf width and area of tetraploids increased for both E. illinita and E. rubra, while a decrease was seen in E. rosea genotypes. For all three species, the rooting capacity of the tetraploids did not differ from the diploids. We conclude that the effect of polyploidization on Escallonia was highly variable and species dependent

Applying RGB- and thermal-based vegetation indices from UAVs for high-throughput field phenotyping of drought tolerance in forage grasses

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

Physiological basis of chilling tolerance and early-season growth in miscanthus

Background and Aims: The high productivity of Miscanthus x giganteus has been at least partly ascribed to its high chilling tolerance compared with related C-4 crops, allowing for a longer productive growing season in temperate climates. However, the chilling tolerance of M. x giganteus has been predominantly studied under controlled environmental conditions. The understanding of the underlying mechanisms contributing to chilling tolerance in the field and their variation in different miscanthus genotypes is largely unexplored. Methods: Five miscanthus genotypes with different sensitivities to chilling were grown in the field and scored for a comprehensive set of physiological traits throughout the spring season. Chlorophyll fluorescence was measured as an indication of photosynthesis, and leaf samples were analysed for biochemical traits related to photosynthetic activity (chlorophyll content and pyruvate, Pi dikinase activity), redox homeostasis (malondialdehyde, glutathione and ascorbate contents, and catalase activity) and water-soluble carbohydrate content. Key Results: Chilling-tolerant genotypes were characterized by higher levels of malondialdehyde, raffinose and sucrose, and higher catalase activity, while the chilling-sensitive genotypes were characterized by higher concentrations of glucose and fructose, and higher pyruvate, Pi dikinase activity later in the growing season. On the early sampling dates, the biochemical responses of M. x giganteus were similar to those of the chilling-tolerant genotypes, but later in the season they became more similar to those of the chilling-sensitive genotypes. Conclusions: The overall physiological response of chilling-tolerant genotypes was distinguishable from that of chilling-sensitive genotypes, while M. x giganteus was intermediate between the two. There appears to be a trade-off between high and efficient photosynthesis and chilling stress tolerance. Miscanthus x giganteus is able to overcome this trade-off and, while it is more similar to the chilling-sensitive genotypes in early spring, its photosynthetic capacity is similar to that of the chilling-tolerant genotypes later on

Roller-Crimping As An Alternative To Incorporation Of Agro-Ecological Service Crops Changes Nitrogen Dynamics In Organic Cabbage Production Under Northern And Western European Conditions

Agro-ecological service crops (ASCs) are used to improve organic vegetable production in terms of weed suppression, nitrogen (N) recycling, or addition of N through symbiotic N2 fixation by legumes. Full incorporation (FI) of ASCs is commonly conducted to terminate ASCs, but alternative termination can be obtained by roller-crimping (RC) in reduced tillage systems. Field experiments were conducted in Estonia, Denmark, and at three locations in Belgium during two growing seasons (autumn 2015-2017) to investigate the effect of ASC termination method (FI and RC) and ASC species (pea, pea/cereal mixtures and cereals), compared with a bare soil control, on soil mineral N content, cabbage yield and N accumulation. Cabbage yield and N accumulation were reduced under RC compared to BS and FI in a majority of cases mainly due to reduced soil mineral N availability, in some cases owing to a later ASC termination time. Furthermore, slower mineralisation of soil organic matter and ASCs at the soil surface contributed to the yield reduction under RC as compared with FI. Cabbage yield could be maintained under RC at standard fertilisation rate following pea ASC in Denmark. The RC system needs further investigation to improve N availability to the succeeding crop before it can be implemented in organic vegetable production

Progress on optimizing miscanthus biomass production for the European bioeconomy:Results of the EU FP7 project OPTIMISC

This paper describes the complete findings of the EU-funded research project OPTIMISC, which investigated methods to optimize the production and use of miscanthus biomass. Miscanthus bioenergy and bioproduct chains were investigated by trialing 15 diverse germplasm types in a range of climatic and soil environments across central Europe, Ukraine, Russia, and China. The abiotic stress tolerances of a wider panel of 100 germplasm types to drought, salinity, and low temperatures were measured in the laboratory and a field trial in Belgium. A small selection of germplasm types was evaluated for performance in grasslands on marginal sites in Germany and the UK. The growth traits underlying biomass yield and quality were measured to improve regional estimates of feedstock availability. Several potential high-value bioproducts were identified. The combined results provide recommendations to policymakers, growers and industry. The major technical advances in miscanthus production achieved by OPTIMISC include: (1) demonstration that novel hybrids can out-yield the standard commercially grown genotype Miscanthus x giganteus; (2) characterization of the interactions of physiological growth responses with environmental variation within and between sites; (3) quantification of biomass-quality-relevant traits; (4) abiotic stress tolerances of miscanthus genotypes; (5) selections suitable for production on marginal land; (6) field establishment methods for seeds using plugs; (7) evaluation of harvesting methods; and (8) quantification of energy used in densification (pellet) technologies with a range of hybrids with differences in stem wall properties. End-user needs were addressed by demonstrating the potential of optimizing miscanthus biomass composition for the production of ethanol and biogas as well as for combustion. The costs and life-cycle assessment of seven miscanthusbased value chains, including small- and large-scale heat and power, ethanol, biogas, and insulation material production, revealed GHG-emission- and fossil-energy-saving potentials of up to 30.6 t CO2eqC ha(-1) y(-1) and 429 GJ ha(-1)y(-1), respectively. Transport distance was identified as an important cost factor. Negative carbon mitigation costs of-78 epsilon t(-1) CO2eq C were recorded for local biomass use. The OPTIMISC results demonstrate the potential of miscanthus as a crop for marginal sites and provide information and technologies for the commercial implementation of miscanthus-based value chains