Functional annotation of proteins for signaling network inference in non-model species.

Molecular biology aims to understand cellular responses and regulatory dynamics in complex biological systems. However, these studies remain challenging in non-model species due to poor functional annotation of regulatory proteins. To overcome this limitation, we develop a multi-layer neural network that determines protein functionality directly from the protein sequence. We annotate kinases and phosphatases in Glycine max. We use the functional annotations from our neural network, Bayesian inference principles, and high resolution phosphoproteomics to infer phosphorylation signaling cascades in soybean exposed to cold, and identify Glyma.10G173000 (TOI5) and Glyma.19G007300 (TOT3) as key temperature regulators. Importantly, the signaling cascade inference does not rely upon known kinase motifs or interaction data, enabling de novo identification of kinase-substrate interactions. Conclusively, our neural network shows generalization and scalability, as such we extend our predictions to Oryza sativa, Zea mays, Sorghum bicolor, and Triticum aestivum. Taken together, we develop a signaling inference approach for non-model species leveraging our predicted kinases and phosphatases.

Predicting yield of individual field-grown rapeseed plants from rosette-stage leaf gene expression.

In the plant sciences, results of laboratory studies often do not translate well to the field. To help close this lab-field gap, we developed a strategy for studying the wiring of plant traits directly in the field, based on molecular profiling and phenotyping of individual plants. Here, we use this single-plant omics strategy on winter-type Brassica napus (rapeseed). We investigate to what extent early and late phenotypes of field-grown rapeseed plants can be predicted from their autumnal leaf gene expression, and find that autumnal leaf gene expression not only has substantial predictive power for autumnal leaf phenotypes but also for final yield phenotypes in spring. Many of the top predictor genes are linked to developmental processes known to occur in autumn in winter-type B. napus accessions, such as the juvenile-to-adult and vegetative-to-reproductive phase transitions, indicating that the yield potential of winter-type B. napus is influenced by autumnal development. Our results show that single-plant omics can be used to identify genes and processes influencing crop yield in the field.

Usefulness of cultivar-level calibration of AquaCrop for vegetables depends on the crop and data availability.

As a result of climate change, climatic extremes are expected to increase. For high-value crops like vegetables, irrigation is a potentially economically viable adaptation measure in western Europe. To optimally schedule irrigation, decision support systems based on crop models like AquaCrop are increasingly used by farmers. High value vegetable crops like cauliflower or spinach are grown in two distinct growth cycles per year and, additionally, have a high turnover rate of new varieties. To successfully deploy the AquaCrop model in a decision support system, it requires a robust calibration. However, it is not known whether parameters can be conserved over both growth periods, nor whether a cultivar dependent model calibration is always required. Furthermore, when data are collected from farmers' fields, there are constraints in data availability and uncertainty. We collected data from commercial cauliflower and spinach fields in Belgium in 2019, 2020 and 2021 during different growing periods and of different cultivars. With the use of a Bayesian calibration, we confirmed the need for a condition or cultivar specific calibration for cauliflower, while for spinach, splitting the data per cultivar or pooling the data together did not improve uncertainty on the model simulations. However, due to uncertainties arising from field specific soil and weather conditions, or measurement errors from calibration data, real time field specific adjustments are advised to simulations when using AquaCrop as decision support tool. Remotely sensed or in situ ground data may be invaluable information to reduce uncertainty on model simulations.

Phenotypic characterization of drought responses in red clover (Trifolium pratense L.).

Introduction: Red clover (Trifolium pratense) is a protein-rich, short-lived perennial forage crop that can achieve high yields, but suffers increasingly from drought in different cultivation areas. Breeding for increased adaptation to drought is becoming essential, but at this stage it is unclear which traits breeders should target to phenotype responses to drought that allow them to identify the most promising red clover genotypes. In this study, we assessed how prolonged periods of drought affected plant growth in field conditions, and which traits could be used to distinguish better adapted plant material. Methods: A diverse panel of 395 red clover accessions was evaluated during two growing seasons. We simulated 6-to-8-week drought periods during two consecutive summers, using mobile rain-out shelters, while an irrigated control field was established in an adjacent parcel. Plant growth was monitored throughout both growing seasons using multiple flights with a drone equipped with RGB and thermal sensors. At various observation moments throughout both growing seasons, we measured canopy cover (CC) and canopy height (CH). The crop water stress index (CWSI) was determined at two moments, during or shortly after the drought event. Results: Manual and UAV-derived measurements for CH were well correlated, indicating that UAV-derived measurements can be reliably used in red clover. In both years, CC, CH and CWSI were affected by drought, with measurable growth reductions by the end of the drought periods, and during the recovery phase. We found that the end of the drought treatment and the recovery phase of approximately 20 days after drought were suitable periods to phenotype drought responses and to distinguish among genotypes. Discussion: Multifactorial analysis of accession responses revealed interactions of the maturity type with drought responses, which suggests the presence of two independent strategies in red clover: 'drought tolerance' and 'drought recovery'. We further found that a large proportion of the accessions able to perform well under well-watered conditions were also the ones that were less affected by drought. The results of this investigation are interpreted in view of the development of breeding for adaptation to drought in red clover.

Response of a Diverse European Soybean Collection to "Short Duration" and "Long Duration" Drought Stress.

Drought causes significant damage to a high value crop of soybean. Europe has an increasing demand for soybean and its own production is insufficient. Selection and breeding of cultivars adapted to European growth conditions is therefore urgently needed. These new cultivars must have a shorter growing cycle (specifically for adaptation to North-West Europe), high yield potential under European growing conditions, and sufficient drought resistance. We have evaluated the performance of a diverse collection of 359 soybean accessions under drought stress using rain-out shelters for 2 years. The contrasting weather conditions between years and correspondingly the varying plant responses demonstrated that the consequences of drought for an individual accession can vary strongly depending on the characteristics (e.g., duration and intensity) of the drought period. Short duration drought stress, for a period of four to 7 weeks, caused an average reduction of 11% in maximum canopy height (CH), a reduction of 17% in seed number per plant (SN) and a reduction of 16% in seed weight per plant (SW). Long duration drought stress caused an average reduction of 29% in CH, a reduction of 38% in SN and a reduction of 43% in SW. Drought accelerated plant development and caused an earlier cessation of flowering and pod formation. This seemed to help some accessions to better protect the seed yield, under short duration drought stress. Drought resistance for yield-related traits was associated with the maintenance of growth under long duration drought stress. The collection displayed a broad range of variation for canopy wilting and leaf senescence but a very narrow range of variation for crop water stress index (CWSI; derived from canopy temperature data). To the best of our knowledge this is the first study reporting a detailed investigation of the response to drought within a diverse soybean collection relevant for breeding in Europe.

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.

Automatic wheat ear counting using machine learning based on RGB UAV imagery.

In wheat (Triticum aestivum L) and other cereals, the number of ears per unit area is one of the main yield-determining components. An automatic evaluation of this parameter may contribute to the advance of wheat phenotyping and monitoring. There is no standard protocol for wheat ear counting in the field, and moreover it is time consuming. An automatic ear-counting system is proposed using machine learning techniques based on RGB (red, green, blue) images acquired from an unmanned aerial vehicle (UAV). Evaluation was performed on a set of 12 winter wheat cultivars with three nitrogen treatments during the 2017-2018 crop season. The automatic system uses a frequency filter, segmentation and feature extraction, with different classification techniques, to discriminate wheat ears in micro-plot images. The relationship between the image-based manual counting and the algorithm counting exhibited high levels of accuracy and efficiency. In addition, manual ear counting was conducted in the field for secondary validation. The correlations between the automatic and the manual in-situ ear counting with grain yield were also compared. Correlations between the automatic ear counting and grain yield were stronger than those between manual in-situ counting and GY, particularly for the lower nitrogen treatment. Methodological requirements and limitations are discussed.

Turgor-driven plant growth applied in a soybean functional-structural plant model.

BACKGROUND AND AIMS: Turgor pressure within a plant cell represents the key to the mechanistical descriptiion of plant growth, combining the effects of both water and carbon availability. The high level of spatio-temporal variation and diurnal dynamics in turgor pressure within a single plant make it a challenge to model these on the fine spatial scale required for functional-structural plant models (FSPMs). A conceptual model for turgor-driven growth in FSPMs has been established previously, but its practical use has not yet been explored. METHODS: A turgor-driven growth model was incorporated in a newly established FSPM for soybean. The FSPM simulates dynamics in photosynthesis, transpiration and turgor pressure in direct relation to plant growth. Comparisons of simulations with field data were used to evaluate the potential and shortcomings of the modelling approach. KEY RESULTS: Model simulations revealed the need to include an initial seed carbon contribution, a more realistic sink function, an estimation of respiration, and the distinction between osmotic and structural sugars, in order to achieve a realistic model of plant growth. However, differences between simulations and observations remained in individual organ growth patterns and under different environmental conditions. This exposed the need to further investigate the assumptions of developmental and environmental (in)sensitivity of the parameters, which represent physiological and biophysical organ properties in the model, in future research. CONCLUSIONS: The model in its current form is primarily a diagnostic tool, to better understand and model the behaviour of water relations on the scale of individual plant organs throughout the plant life cycle. Potential future applications include its use as a phenotyping tool to capture differences in plant performance between genotypes and growing environments in terms of specific plant characteristics. Additionally, focused experiments can be used to further improve the model mechanisms to lead to better predictive FSPMs, including scenarios of water deficit.

Use of identifiability analysis in designing phenotyping experiments for modelling forage production and quality.

Agricultural systems models are complex and tend to be over-parameterized with respect to observational datasets. Practical identifiability analysis based on local sensitivity analysis has proved effective in investigating identifiable parameter sets in environmental models, but has not been applied to agricultural systems models. Here, we demonstrate that identifiability analysis improves experimental design to ensure independent parameter estimation for yield and quality outputs of a complex grassland model. The Pasture Simulation model (PaSim) was used to demonstrate the effectiveness of practical identifiability analysis in designing experiments and measurement protocols within phenotyping experiments with perennial ryegrass. Virtual experiments were designed combining three factors: frequency of measurements, duration of the experiment. and location of trials. Our results demonstrate that (i) PaSim provides sufficient detail in terms of simulating biomass yield and quality of perennial ryegrass for use in breeding, (ii) typical breeding trials are insufficient to parameterize all influential parameters, (iii) the frequency of measurements is more important than the number of growing seasons to improve the identifiability of PaSim parameters, and (iv) identifiability analysis provides a sound approach for optimizing the design of multi-location trials. Practical identifiability analysis can play an important role in ensuring proper exploitation of phenotypic data and cost-effective multi-location experimental designs. Considering the growing importance of simulation models, this study supports the design of experiments and measurement protocols in the phenotyping networks that have recently been organized.

Modelling leaf spectral properties in a soybean functional-structural plant model by integrating the prospect radiative transfer model.

Background and Aims: Currently, functional-structural plant models (FSPMs) mostly resort to static descriptions of leaf spectral characteristics, which disregard the influence of leaf physiological changes over time. In many crop species, including soybean, these time-dependent physiological changes are of particular importance as leaf chlorophyll content changes with leaf age and vegetative nitrogen is remobilized to the developing fruit during pod filling. Methods: PROSPECT, a model developed to estimate leaf biochemical composition from remote sensing data, is well suited to allow a dynamic approximation of leaf spectral characteristics in terms of leaf composition. In this study, measurements of the chlorophyll content index (CCI) were linked to leaf spectral characteristics within the 400-800 nm range by integrating the PROSPECT model into a soybean FSPM alongside a wavelength-specific light model. Key Results: Straightforward links between the CCI and the parameters of the PROSPECT model allowed us to estimate leaf spectral characteristics with high accuracy using only the CCI as an input. After integration with an FSPM, this allowed digital reconstruction of leaf spectral characteristics on the scale of both individual leaves and the whole canopy. As a result, accurate simulations of light conditions within the canopy were obtained. Conclusions: The proposed approach resulted in a very accurate representation of leaf spectral properties, based on fast and simple measurements of the CCI. Integration of accurate leaf spectral characteristics into a soybean FSPM leads to a better, dynamic understanding of the actual perceived light within the canopy in terms of both light quantity and quality.

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.

Introducing turgor-driven growth dynamics into functional-structural plant models.

Background and Aims: In many scenarios the availability of assimilated carbon is not the constraining factor of plant growth. Rather, organ growth appears driven by sink activity in which water availability plays a determinant role. Current functional-structural plant models (FSPMs) mainly focus on plant-carbon relations and largely disregard the importance of plant water status in organogenesis. Consequently, incorporating a turgor-driven growth concept, coupling carbon and water dynamics in an FSPM, presents a significant improvement towards capturing plant development in a more mechanistic manner. Methods: An existing process-based water flow and storage model served as a basis for implementing water control in FSPMs. Its concepts were adjusted to the scale of individual plant organs and interwoven with the basic principles of modelling carbon dynamics to allow evaluation of turgor pressure across the entire plant. This was then linked to plant organ growth by applying the principles of the widely used Lockhart equation. Key results: This model successfully integrates a mechanistic understanding of plant water transport dynamics coupled with simple carbon dynamics within a dynamically developing plant architecture. It allows evaluation of turgor pressure on the scale of plant organs, resulting in clear diel and long-term patterns, directly linked to plant organ growth. Conclusions: A conceptual sap flow and turgor-driven growth model was introduced for functional-structural plant modelling. It is applicable to any plant architecture and allows visual exploration of the diel patterns of organ water content and growth. Integrated in existing FSPMs, this new concept fosters an array of possibilities for FSPMs, as it presents a different formulation of growth in terms of local processes, influenced by local and external conditions.

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

Background and Aims: The high productivity of Miscanthus × giganteus has been at least partly ascribed to its high chilling tolerance compared with related C4 crops, allowing for a longer productive growing season in temperate climates. However, the chilling tolerance of M. × 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. × 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. × giganteus was intermediate between the two. There appears to be a trade-off between high and efficient photosynthesis and chilling stress tolerance. Miscanthus × 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.

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 miscanthus-based 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 CO2eq C ha-1y-1 and 429 GJ ha-1y-1, respectively. Transport distance was identified as an important cost factor. Negative carbon mitigation costs of -78€ 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.

High-throughput phenotyping of lateral expansion and regrowth of spaced Lolium perenne plants using on-field image analysis.

BACKGROUND: Genetic studies and breeding of agricultural crops frequently involve phenotypic characterization of large collections of genotypes grown in field conditions. These evaluations are typically based on visual observations and manual (destructive) measurements. Robust image capture and analysis procedures that allow phenotyping large collections of genotypes in time series during developmental phases represent a clear advantage as they allow non-destructive monitoring of plant growth and performance. A L. perenne germplasm panel including wild accessions, breeding material and commercial varieties has been used to develop a low-cost, high-throughput phenotyping tool for determining plant growth based on images of individual plants during two consecutive growing seasons. Further we have determined the correlation between image analysis-based estimates of the plant's base area and the capacity to regrow after cutting, with manual counts of tiller number and measurements of leaf growth 2 weeks after cutting, respectively. When working with field-grown plants, image acquisition and image segmentation are particularly challenging as outdoor light conditions vary throughout the day and the season, and variable soil colours hamper the delineation of the object of interest in the image. Therefore we have used several segmentation methods including colour-, texture- and edge-based approaches, and factors derived after a fast Fourier transformation. The performance of the procedure developed has been analysed in terms of effectiveness across different environmental conditions and time points in the season. RESULTS: The procedure developed was able to analyse correctly 77.2 % of the 24,048 top view images processed. High correlations were found between plant's base area (image analysis-based) and tiller number (manual measurement) and between regrowth after cutting (image analysis-based) and leaf growth 2 weeks after cutting (manual measurement), with r values up to 0.792 and 0.824, respectively. Nevertheless, these relations depend on the origin of the plant material (forage breeding lines, current forage varieties, current turf varieties, and wild accessions) and the period in the season. CONCLUSIONS: The image-derived parameters presented here deliver reliable, objective data, complementary to the breeders' scores, and are useful for genetic studies. Furthermore, large variation was shown among genotypes for the parameters investigated.

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.

Low night temperature acclimation of Phalaenopsis.

The capability of Phalaenopsis to acclimate its photosynthetic capacity and metabolic activity to cool night temperature conditions is crucial for improving orchid production in terms of efficient greenhouse heating. The extent to which Phalaenopsis possesses acclimation potential and the mechanistic background of the metabolic processes involved, have, however, not been studied before. Plants were subjected to a direct and gradual shift from a day to night temperature regime of 28/28-28/16°C, the cold stress and cold acclimation treatment, respectively. In comparison with the cold stress treatment, the cold acclimation treatment led to a higher malate accumulation and a reduction in leaf net CO(2) uptake. Consistently, the contribution of respiratory CO(2) recycling to nocturnal malate synthesis was calculated to be 23.5 and 47.0% for the cold stress and cold acclimation treatment, respectively. Moreover, the lower levels of starch measured in the cold acclimated leaves confirmed the suggested enhanced respiratory CO(2) recycling, implying that Phalaenopsis CAM operation evolved towards CAM idling. It is, however, plausible that this adjustment was not an effect of the low night temperature per se but a consequence of cool-root induced drought stress. Apart from that, at the start of the photoperiod, membrane stability showed a depression which was directly counteracted by an increased generation of glucose, fructose and sucrose. From these observations, it can be concluded that the observed plasticity in CAM operation and metabolic flexibility may be recognized as important steps in the low night temperature acclimation of Phalaenopsis.

A chlorophyll a fluorescence-based Lemna minor bioassay to monitor microbial degradation of nanomolar to micromolar concentrations of linuron.

A plant-microbial bioassay, based on the aquatic macrophyte Lemna minor L. (duckweed), was used to monitor biodegradation of nano- and micromolar concentrations of the phenylurea herbicide linuron. After 7 days of exposure to linuron, log-logistic-based dose-response analysis revealed significant growth inhibition on the total frond area of L. minor when linuron concentrations > or = 80 nM were added to the bioassay. A plant-protective effect was obtained for all concentrations > 80 nM by inoculation with either a bacterial consortium or Variovorax paradoxus WDL1, which is probably the main actor in this consortium. The outcome of the plant-microbe-toxicant interaction was also assessed using pulse amplitude-modulated chlorophyll a fluorescence and chlorophyll a fluorescence imaging. Linuron toxicity to L. minor became apparent as a significant decrease in the effective quantum yield (Delta F/Fm') within 90 min after exposure of the plants to linuron concentrations > or = 160 nM. Inoculation of the bioassay with the linuron-degrading bacteria neutralized the effect on the effective quantum yield at concentrations > or = 160 nM, indicating microbial degradation of these concentrations. The chlorophyll a fluorescence-based Lemna bioassay described here offers a sensitive, fast and cost-effective approach to study the potential of biodegrading microorganisms to break down minute concentrations of photosynthesis-inhibiting xenobiotics.