Plant Methods

Chlorophyll fluorescence imaging is a powerful tool used to characterize abiotic and biotic stress response of photosynthesis in plants. However, extracting metrics such as Fv/Fm (maximum quantum yield of photosystem II) from chlorophyll fluorescence images remains a substantial bottleneck in large measurement campaigns, often requiring significant manual effort. To resolve this issue, we developed fvfmPy, a user-friendly program which enables rapid analysis of chlorophyll fluorescence images of arrays of leaf discs. fvfmPy uses algorithmic image segmentation to identify regions of interest and extract fluorescence metrics from tens or hundreds of leaf samples per image with little to no manual input. fvfmPy is developed in Python and includes a graphical user interface enabling intuitive parameter adjustment and real-time quality control checking. We additionally developed fvfmR, an R wrapper package for fvfmPy. Fv/Fm values extracted with fvfmPy were indistinguishable from those obtained using manual methods (r2 > 0.99) but required 94% less time to complete (13 s vs. 200 s per 100 measurements). fvfmPy and fvfmR were built to directly integrate with existing fluorescence software packages and can greatly increase throughput of fluorescence measurements to help address key challenges in agriculture and ecology.

Efficient yield prediction under field conditions remains a key challenge for crop breeders and agronomists. YieldLearn is an interpretable machine-learning pipeline developed to estimate yield-related traits in durum wheat (Triticum durum) using physiological indicators measured by the Hansatech Instruments Pocket PEA. The workflow integrates fluorescence parameters (Area, Fo, Fm, Fv/Fm, PI Inst.) with field-measured agronomic traits (Booting, Height, Maturity, Lodging, Yield kg/plot, and Adjusted Yield kg/ha). Five supervised learning algorithms multinomial logistic regression, random forest, support vector machine (radial kernel), k-nearest neighbors, and decision tree were evaluated using five-fold cross-validation. Models achieved average classification accuracy around 0.57 with AUC values exceeding 0.58. SHAP (SHapley Additive exPlanations) visualizations revealed that high Fv/Fm and PI Inst. values were positively associated with yield potential, whereas elevated Fo indicated stress-induced yield reduction. An interactive Shiny App enables real-time prediction and feature-importance exploration. Given the small sample size (165 plots) and absence of environmental covariates such as temperature or soil moisture, YieldLearn should be regarded as a reproducible exploratory framework rather than a production-ready predictor. Nonetheless, it offers a valuable foundation for guiding experimental design, validating physiological indicators, and supporting the next generation of scalable, interpretable crop-yield models.

The apoplast is a primary location for interactions between plants and invasive pathogens and is the site where many pathogen-secreted effectors are perceived by cell surface immune receptors to activate defence responses. However, our understanding of apoplastic interactions remains limited because protein interactions are difficult to investigate in this harsh extracellular environment. TurboID-based proximity labelling (PL) has emerged as a powerful approach for studying protein interactions in plants, though its application has to date been restricted to intracellular proteins. Here, we designed and validated a TurboID-based PL strategy for investigating protein interactions in the leaf apoplast using the well-characterised interaction between the Phytophthora infestans elicitor INF1 and the receptor-like protein (RLP) REL in Nicotiana benthamiana. Transient expression of SP-INF1-TurboID (INF1-T) induced a cell death (CD) response comparable to that triggered by native INF1, demonstrating that fusion of the TurboID tag did not impair INF1 recognition by REL. Apoplastic localisation of both INF1-T and the control construct SP-eGFP-TurboID (eGFP-T) was confirmed, validating their suitability for PL experiments. Efficient TurboID-mediated biotinylation was achieved in the apoplast using co-infiltration of biotin, ATP and magnesium acetate. Streptavidin-HRP immunoblotting revealed distinct biotinylation profiles for INF1-T and eGFP-T. Furthermore, co-immunoprecipitation demonstrated specific biotinylation of REL by INF1-T, but not by eGFP-T, in wild-type, bak1 and sobir1/sobir1-like N. benthamiana. These findings demonstrate that TurboID-based PL is functional in the apoplast and provides a proof-of-concept for investigating elicitor-receptor interactions in this compartment.

An increasing number of researchers, educators, and at-home growers are turning to hydroponics as an alternative method of food production, diversifying and strengthening the food supply chain. This has led to a substantial increase in the amount of academic work involving hydroponic production of plants for a variety of purposes, including but not limited to a more controlled and customizable environment than conventional production methods, utilizing hydroponics as a method of wastewater treatment, and investigating the vertical production of plants on a much smaller footprint than traditional commercial-scale production. Because so many unique stakeholders are interested in hydroponics for a variety of reasons, academic studies use a wide variety of hydroponic systems with varying levels of technology and automation, making comparisons across studies and systems challenging. A few institutions have sophisticated plant growth and research facilities, like those used by commercial companies, but these systems are often prohibitively expensive, costing upwards of $10,000 USD for a very limited amount of growing space for experimental trials. To enable more researchers, educators, and citizen scientists to conduct experiments and easily compare their results, this study details three pilot-scale hydroponic production systems (Kratky, NFT, and DWC), utilizing entirely off-the-shelf components, for under $5,000 USD each, that are capable of maintaining the desired consistent temperature and relative humidity levels, and have space for 216, 108 (6 irrigation loops), and 64 (16 irrigation bins) plants, respectively. The material lists and prices are provided along with assembly instructions and general guidelines for each hydroponic system. The testing and optimization process of environmental conditions within each growth system is also detailed, with suggested customizations for potential experimental setups. The authors developed these systems during their Masters and Doctoral degree programs, enabling improved hydroponic research at the University of Illinois Urbana-Champaign. They hope that other aspiring hydroponic researchers can benefit from this work in the development and operation of their hydroponic systems, increasing research efficiency and collaboration opportunities in this field of study.

Breedbase ( is a comprehensive, web-based, open-source breeding management and analysis software for plant breeding that was introduced to address gaps with existing software for plant breeding data. Breedbase is continually improved and developed, with new features being added as required by the breeding community. Since its inception, the capabilities of Breedbase were improved to better serve the diverse data modeling needs of additional crops beyond our original ensemble of cassava, banana, yam, sweet potato, tomato, rice, and wheat. We describe here major updates that have been made in the past 4 years, including (1) improved handling of repetitive measurements; (2) a redesigned experimental treatments interface; (3) a more fine-grained user permission system; (4) improved trial quality control workflows; (5) intercropping support; (6) the addition of a decision meeting feature that helps decision makers make the best selection with the available data; (7) transgenics and tracking tools and finally, (8) an improved field layout tool which consolidates several trials in one field. These new features integrate seamlessly with the previous feature set of Breedbase - making it one of the most feature rich breeding management softwares available.

Drought stress significantly reduces tomato (Solanum lycopersicum L.) productivity, and early detection is critical to minimize yield losses through timely interventions. In this study, we developed Drought-Spec-Net, a hybrid 1D convolutional neural network that integrates local and global spectral feature extraction to detect early drought stress from visible and near infrared (Vis NIR) spectra data of tomato seedlings. The model was trained on 378 samples using an 80:20 train test split, with 20% of the training set reserved for validation. DroughtSpecNet outperformed the evaluated baseline and state of the art models, achieving 97% accuracy, 95% precision, 98% recall, and an F1 score of 97%. To improve the agronomic interpretation of the model outputs, predicted drought probabilities were converted into a literature-informed potential yield impact indicator using a maximum impact level of 60%. On the test set (76 samples), mapped potential yield-impact values ranged from 0% to 60%, with an average reduction of 12.97%. We also conducted an initial experiment using our greenhouse RGB dataset, collected daily from drought treated and well-watered tomato plants at West Virginia State University (WVSU). From this dataset, 44 images were selected for ilastik-based canopy segmentation, producing plant-level drought severity indices (DSI) with a mean of 0.28, median of 0.14, and range of 0.01 to 0.91. Additionally, we trained and fine-tuned a large language model (LLM) based on PLLaMA7BInstruct, called AgriLLaMA, for automated agronomic report generation from Drought-Spec-Net outputs. The generated reports summarize predicted stress levels, mapped potential yield impacts, and preliminary management considerations. This integrated approach not only improves early drought stress detection but also delivers quantitative and interpretable estimates of potential productivity losses, providing a complete framework connecting physiological stress detection to actionable agricultural outcomes.

Background: Long non protein coding (lnc)RNAs are ubiquitous transcripts known to be major regulators of cellular processes in eukaryotes. In diatoms, specific lncRNA expression is tightly correlated to nutrient fluctuations, which suggests function, but their biological relevance remains to be established. In order to fill this knowledge gap, we sought to design a protocol to generate diatom lncRNA knockout mutants by CRISPR-Cas9 enabling subsequent functional studies to shed light into their mechanistic roles. Since lncRNAs do not contain open reading frames that can be targeted by point mutations for the generation of functional knockouts, we designed a multiplexed CRISPR-Cas9 plasmid harboring two independent gRNA for the specific ablation of lncRNA loci in P. tricornutum. As proof of concept, we used our plasmid-based system to target three phosphate depletion responsive lncRNAs. Results: Using the golden gate based uLoop system, we assembled a single plasmid containing all the necessary components to generate CRISPR-Cas9 large genomic ablations of lncRNAs in P. tricornutum using bacterial conjugation. Three intergenic lncRNAs (Linc91, Linc77 and Linc24) responsive to phosphate fluctuations were selected and independently targeted. Around 32% of the exconjugants presented a monoallelic ablation of the targeted loci and 11% presented a biallelic ablation on the first PCR-screening round. Mutated lines, harboring biallelic lncRNAs ablations for each lncRNA targeted, were successfully generated for each of the targeted lncRNAs. Additionally, a first line of phenotypic analysis revealed significant growth rate differences for one of the mutants ({Delta}Lnc24) under both full and phosphate depleted conditions, suggesting its involvement in cell division regulation under nutrient fluctuations. Conclusions: This work provides a simple and efficient method for the generation of lncRNA knock-out mutants in P. tricornutum. Furthermore, this approach can be easily adapted to other microalgae amenable to transformation, enabling the enhancement of our understanding of these important cellular regulatory transcripts in the marine environment.

Understanding floral visitor dynamics along elevational gradients is essential for assessing potential implications for pollen limitation and population genetic processes in fragmented populations of cliff-dwelling alpine endemics. Physoplexis comosa (Campanulaceae) is a chasmophytic paleoendemic restricted to vertical rock faces on dolomitic and limestone bedrock from 200 to 2,800 m a.s.l. The species exhibits a specific floral morphology with fused corolla apices, basal openings for nectar access, and secondary pollen presentation, yet its functional floral visitor assemblage has remained poorly characterized across the species' elevational range. In this pilot study, we tested a two-tier camera-monitoring setup combining action cameras for diurnal monitoring with Raspberry Pi-based systems equipped with IR illumination for combined diurnal and nocturnal recording. We conducted monitoring across five elevational sites (1,480 to 2,400 m a.s.l.) in the Eastern Alps. Recorded video material was downsampled and annotated using the Addax platform alongside manual cross-validation to record physical contact events. Preliminary results confirm the technical feasibility of continuous monitoring in steep terrain and suggest possible elevational variation in diurnal visitors, alongside nocturnal activity by Geometridae moths. Camera-based observations provided higher functional resolution and revealed a different visitor assemblage than previous eDNA metabarcoding trials. This proof-of-concept establishes a methodological foundation for forthcoming multi-day monitoring campaigns and complements ongoing studies of the mating system and population-genetic analyses.

Gray leaf spot can cause severe turfgrass damage, particularly in intensively managed sports fields, while disease management remains challenging due to the rapid development of symptoms under favorable environmental conditions. Early detection of Pyricularia oryzae could therefore provide valuable information for anticipating disease development and supporting management decisions. This study aimed to develop a digital PCR (dPCR) molecular tool to support the monitoring and management of gray leaf spot disease in stadium turfgrass. The assay showed high sensitivity, consistently detecting P. oryzae when infected tissue represented only 0.05% of a mixture with healthy turfgrass, and enabled pathogen detection before symptom appearance under both controlled and field conditions. Different matrices were also investigated across 15 stadiums to identify the most relevant material for disease monitoring. Roots showed the highest prevalence of P. oryzae detection, including during asymptomatic periods, highlighting their potential as a relevant matrix for pathogen surveillance. In a stadium monitored several months after a gray leaf spot outbreak, P. oryzae DNA was still detected in roots from 27 of the 32 sampled zones despite the absence of visible symptoms. Overall, these results demonstrate the potential of dPCR for the early detection and monitoring of P. oryzae in stadium turfgrass, particularly through root sampling during periods when environmental conditions are not favorable for gray leaf spot development, and a spatio-temporal surveillance at stadium scale.

Agrobacterium-mediated transformation is a cornerstone of plant genetic engineering, yet many crops remain recalcitrant to transformation and its efficiency is highly species- and genotype-dependent. The limited diversity of disarmed agrobacterial strains available for biotechnology, and the technically challenging workflows typically required for strain modification, constrain efforts to develop improved plant transformation platforms. Here, we describe the development of AGROtools-a versatile, expandable, and user-friendly modular cloning plasmid collection comprising more than 75 individual components, that is designed to simplify genetic engineering of agrobacteria. Constructs assembled with this toolkit couple visual reporters with selectable and counterselectable markers to facilitate identification of recombinant clones, thereby reducing reliance on PCR-based screening. We constructed and tested reporter cassettes that are functional across diverse agrobacterial strains and genomic contexts. By linking a visual reporter combinatorially with various antibiotic resistance markers and orthogonal site-specific recombinase recognition sequences, AGROtools can enable marker removal and recycling for up to four successive genomic modifications. We demonstrate the utility of AGROtools by disarming 10 novel wild-type strains, and accomplishing serial modifications within individual strain backgrounds. In addition to conventional recA-mediated homologous recombination, the collection incorporates components for applying contemporary strain engineering approaches, including CRISPR/dCas9 base editing, INTEGRATE-mediated targeted insertion, and phage-derived recombineering systems. Constructs can be assembled and deployed rapidly, enabling isolation of strains carrying an intended modification in less than two weeks. AGROtools provides a broadly applicable resource for accelerating strain development and genetic studies in agrobacteria, with potential to advance plant biotechnology by aiding improvement of disarmed strains.

The mechanisation of agriculture has achieved major increases in labour productivity but has also led to an erosion of agrobiodiversity. Neglected and underutilized crops such as common buckwheat (Fagopyrum esculentum Moench) offer the potential to diversify food production systems but require major genetic improvement to compete with established crops. Although target traits for breeding such as more homogeneous ripening, reduced height, larger seeds and higher and more stable yields are well known, large-scale and field-based phenotyping data of diverse common buckwheat accessions across multiple environments are still scarce. Here, we tested the agronomic performance of a common buckwheat germplasm collection consisting of 170 accessions of diverse origin. The collection was tested at seven year-sites, and 19 agronomic traits related to phenology, growth habit and yield were assessed. Most traits showed high repeatability (0.31 < R2 < 0.98) and heritability (0.45 < H2 < 0.97), indicating genetic control of these traits. Coefficients of variation ranged between 6.19% and 46.15%, showing the high phenotypic diversity within the collection. By combining multiple traits, distinct ideotypes for different roles in the crop rotation were investigated, and accessions with promising agronomic profiles identified. Genome-wide association studies revealed marker-trait associations for 18 traits, allowing the identification of putative candidate genes influencing these traits such as a putative GRF-INTERACTING FACTOR associated with flowering time and biomass production. Our results show that the collection offers vast phenotypic diversity that can be exploited for the development of improved varieties tailored to meet specific agronomic purposes. The efficient utilization of these genetic resources should be supported by the application of genomics-based breeding approaches. The development of genetic markers and search for putative candidate genes mark important steps towards the establishment of efficient breeding schemes. Thus, these genomic resources provide a foundation for accelerating the genetic improvement of common buckwheat and developing agronomically competitive varieties.

RNA-guided nucleases (RGNs), such as Cas9 from Streptococcus pyogenes (SpCas9), are widely used for plant genome editing. Previous surveys for off-targeting, the modification of unintended targets with similarity to the intended target, indicate high specificity of SpCas9 in plant cells. However, off-targeting has not been assessed for efficiency-optimized editing systems combined with extensive multiplexing, which might increase the likelihood of cleavage at unintended sites. We therefore analyzed Arabidopsis thaliana lines that had been extensively mutagenized using zCas9i and up to 29 gRNAs addressing >45 target sites over several rounds of editing. Genomes were sequenced by short- and long-read technologies, and genome-wide variants were catalogued. Our pipeline for variant calling reliably detected RGN-induced mutations at on-targets. When excluding these on-target modifications, variants were detected in edited lines at frequencies similar to those previously reported for spontaneous mutations. In further analyses, we did not find any evidence for an origin of these variants from RGN activity. Our data are thus consistent with high specificity of SpCas9. In contrast, we detected genomic reorganization events upon editing at two complex loci, RPP1 and RPP7, encompassing multiple homologous genes, and also identified an allele by WGS that had escaped detection by amplicon sequencing. We conclude that, while off-targets may efficiently be avoided by selection of specific gRNAs, on-target modifications may be more extensive than intended, especially at complex loci and/or during multiplexing.

Non-destructive methods for real-time phenotyping of root growth and development are essential to accelerate the breeding of climate-resilient crops. We present DIRTNet (Digital Investigations of Root Traits Net), a hybrid deep learning model that interprets in-soil signals collected by Fiber Bragg Grating (FBG) sensors. We first evaluated DIRTNet in a controlled simulation study in which metal rods of varying diameters were inserted into soil to emulate root growth. Using augmented training data with an 80:20 train-test split, DIRTNet surpassed baseline models, achieving 0.97 accuracy for root depth prediction and 0.94 for root diameter prediction. We then applied the best-performing models to maize (Zea mays) roots monitored by six FBG sensors placed at different soil location and depths. A control trial and a drought-stress trial yielded 192 samples, split 80:20 into training and holdout sets; the hold-out portion was divided equally into validation and test sets. Augmentation was applied only to the training data. On real maize root data, DIRTNet achieved 0.92 accuracy for both root depth and root width prediction across variable growth periods, covering root mass lengths up to 20 cm and diameters up to 10 cm. The prediction accuracy is independent of the soil moisture and drought condition. In a fixed-interval analysis, accuracy reached 0.97 for days 25-42 after planting and 0.93 for days 25-48, demonstrating robust performance as root complexity increased.

Red crown rot (RCR), caused by Calonectria ilicicola, is an emerging soybean disease in the U.S. Midwest for which scalable approaches to characterize within-field disease distribution are lacking. This study evaluated high-resolution PlanetScope satellite imagery for mapping RCR-affected soybean canopies across 15 commercial fields in Illinois surveyed during the 2024 and 2025 growing seasons. A total of 2,921 georeferenced canopy plots were classified as asymptomatic or RCR-affected and paired with six multispectral bands and seven vegetation indices. Spectral differences between classes were evaluated using linear mixed-effects models, and seven machine-learning classifiers representing linear, tree-based, neural-network, kernel, and probabilistic approaches were compared using spatially independent leave-one-field-out cross-validation. RCR-affected canopies exhibited increased reflectance in the visible and red-edge regions, reduced near-infrared reflectance, and lower vegetation-index values relative to asymptomatic canopies. All classifiers showed strong discrimination, with ROC-AUC values ranging from 0.963 to 0.982. Regularized logistic regression achieved the highest overall performance, with an accuracy of 0.945, balanced accuracy of 0.945, F1-score of 0.948, and ROC-AUC of 0.982 at the optimized decision threshold. Permutation analysis identified EVI, NDVI, and red reflectance as the most influential predictors across representative model architectures. Satellite-derived probability and classification maps generally corresponded with symptomatic canopy patterns observed in high-resolution UAV imagery, although mixed pixels reduced precision near disease-patch boundaries. These results demonstrate the potential of high-resolution satellite imagery for within-field mapping of RCR-associated canopy symptoms across independent commercial soybean fields.

Traditional cell tracking methods focus on fully automated tracking. The quality of the generated tracks, however, is heavily influenced by factors such as cell speed and density. In complex scenarios where track correctness is essential, an efficient and reliable tool for manual track correction remains a key component of the cell tracking process. Most tracking tools that allow for such manual correction do so only as a post-processing step, using human feedback to correct one track error at a time -- without improving the quality of other tracks in the dataset. This highlights the need for a more efficient track correction method that iteratively integrates human feedback into the automated tracking process. In this paper we propose a human-in-the-loop approach to cell tracking and investigate the extent to which a tracking algorithm can learn from human input during the tracking process. We combine a graph representation of cell detections, Dijkstra's shortest path algorithm, and a neural network that predicts cell displacement to generate initial track suggestions. We introduce a tool that enables human feedback on individual tracks suggested by the algorithm; this feedback is then propagated to improve subsequent track generation. Compared to the traditional approach of fully automated tracking with correction as a post-processing step, our human-in-the-loop tracking framework reduces the number of corrections required. Finally, we show how this approach can be integrated with existing state-of-the-art tracking algorithms by using pre-generated tracks as a prior, improving the quality of tracks suggested to the human annotator.

Pseudomonas species include important plant pathogens responsible for substantial agricultural losses. Although CRISPR-INTEGRATE has been applied for genome engineering in medically relevant Pseudomonas, its utility in plant-pathogenic Pseudomonas has not been established. Here, we demonstrate CRISPR-INTEGRATE, an RNA-guided Tn7-like transposition system that enables targeted chromosomal integration without double-strand breaks or host-dependent homologous recombination, across three plant-pathogenic Pseudomonas backgrounds. Targeted disruption of hrpJ, hrcC, and gacA was confirmed by PCR and Sanger sequencing and resulted in distinct phenotypes, including impaired type III secretion-associated virulence and hypersensitive response, altered colony morphology and biofilm formation, and reduced motility. We further coupled CRISPR-INTEGRATE with Cre-lox recombination to excise a ~40-kb prophage-associated region from the P. syringae pv. tomato DC3000 chromosome. Genomic characterization identified a 33.45-kb predicted prophage within this region, putative attL/attR sites sharing an identical 14-bp core sequence, an orthologous empty locus containing a candidate attB site, and conserved prophage-associated gene organization across P. syringae genomes. Deletion of the region altered bacterial growth and increased susceptibility to phage infection, with the strongest effect observed for phage Plaza, while having no detectable effect on the tested plant virulence phenotypes. Together, these findings establish CRISPR-INTEGRATE as a portable genome-engineering platform for plant-pathogenic Pseudomonas and demonstrate its utility for functional interrogation of individual genes and large accessory genomic elements.

We performed a screening to evaluate the responses to water deficit before the flowering stage in a panel of 31 genotypes composed of one commercial hybrid, one wild species, multiple ex-PVP (vintage) lines, several commercial heirlooms from the Northeast USA, Mexican landraces from hot and humid environments and genome edited lines with altered flowering and inflorescence traits. We characterized 18 different morphological, physiological and biochemical traits to assess the response to 40% water deficit. We observed diverse morphological, physiological and biochemical responses across the 31 genotypes screened, suggesting the presence of useful variation for breeding for drought tolerance in cultivated tomato. Interestingly, some heirlooms exhibited superior performance under drought, suggesting they can be used as donors to discover novel genes involved in drought responses. Unexpectedly, we found mutants in SlCLE9, a paralog of the stem cell regulator SlCLV3 known to co-regulate meristem proliferation in tomato, exhibited enhanced drought tolerance. Notably, the slcle9 mutant also conferred drought tolerance when used as rootstock with grafted scions from our reference cultivar M82, suggesting SlCLE9 influence root architecture and drought response. Consistent with this, we found SlCLE9 is transcriptionally upregulated in roots and leaves during drought stress, and exogenous application of SlCLE9 modifies root growth and response to drought. Altogether, our screening uncovered new potential trait donors for breeding against drought stress and describes a novel role for SlCLE9 in drought tolerance.

Drought and waterlogging are prominent climate change challenges that, alone or in sequence, pronouncedly constrain agricultural crop production. A better understanding of relationships between water availability and nutrition regarding plant functioning is needed to optimise fertilisation under these challenges. We investigated impacts of drought, waterlogging and drought followed by waterlogging under two fertilisation regimes (low, normal) on various traits of wheat (Triticum aestivum). Next to biomass and photosynthesis measurements, cross-platform metabolomics was applied for in-depth metabolic analyses of flag leaves. We found detrimental effects of drought and drought-waterlogging on shoot and root biomass, especially at normal fertilisation, while photosynthetic traits were not or only slightly affected. Foliar nitrogen contents were higher in plants exposed to drought or to drought-waterlogging. Numerous primary and specialised metabolites were modulated by fertilisation and/ or watering. In particular, amino acids, benzoxazinoids and flavonoids were affected by fertilisation. Due to watering especially several amino acids changed in concentrations, with the induction pattern partly depending on fertilisation. Our study deepens the understanding of the interplay between water and nutrient availabilities as well as nutritional demands, growth and metabolism of wheat. This is highly relevant to balance sustainable agricultural nutrient management under the challenges of ongoing climate change.

Extensive green roofs are an important component of urban green infrastructure. However, harsh rooftop conditions, including shallow substrates, high temperatures and limited water availability, considerably restrict plant selection. Although extensive green roofs are traditionally dominated by succulent species, increasing attention has been directed towards annual plants because of their rapid establishment, developmental plasticity and potential contribution to vegetation diversity. This review aimed to identify annual plant species introduced on extensive green roofs and to characterize the functional traits most frequently associated with their establishment under rooftop conditions. In addition, the review examined the geographical distribution and methodological characteristics of existing studies and identified species with potential for application under temperate climatic conditions. A total of 22 studies published between 2015 and 2025 were analyzed, comprising 100 annual species representing 29 plant families. The reviewed studies showed a clear geographical bias towards Mediterranean and arid regions, whereas temperate climates were considerably underrepresented. Among species occurring in Central Europe, ruderal life strategies, drought avoidance, efficient dispersal mechanisms, low plant height and prolonged flowering were the most frequently observed functional characteristics, while seed mass and specific leaf area (SLA) exhibited substantial interspecific variation. These findings suggest that functional traits, rather than taxonomic affiliation alone, provide a useful framework for selecting annual species for extensive green roofs. The review also highlights the need for long-term studies conducted under temperate climatic conditions to improve evidence-based plant selection and support the development of resilient and multifunctional green roof vegetation.

Cannabis sativa L. cultivation in tropical regions or under indoor conditions requires supplemental lighting to maintain plant yields. Night-break (NB) photoperiods offer a cost-effective alternative to conventional day extension, although genotypic responses have not been clearly described. This study evaluated the effects of continuous and pulsed NB treatments on the growth, phenology, physiology, productivity, and cannabinoid accumulation of three Cannabis sativa genotypes representing two chemotypes. Plants were exposed to a commercial 18-h photoperiod (T1), a continuous 1-h NB (T2), or four 15-min NB pulses (T3) with equal total duration. Continuous NB produced responses comparable to the commercial photoperiod. In contrast, pulsed NB accelerated phenological progression by two to three weeks and reduced final plant height by 15-28%, depending on genotype, without impairing photosynthetic performance or decreasing floral dry biomass. Harvest index increased under both NB treatments, indicating enhanced biomass allocation to reproductive structures, while cannabinoid accumulation remained primarily genotype-dependent with only minor treatment-specific modulation. These findings demonstrate that NB efficacy depends not only on the duration of nocturnal light but also on its temporal distribution, highlighting temporal organization of nocturnal light as a key regulator of photoperiodic responses and a promising strategy for optimizing cannabis cultivation.